Existing Vegetation Mapping Summary: Bridger-Teton National Forest US Department of Agriculture Forest Service Engineering Remote Sensing Applications Center January 2007 RSAC-0091-TECH1 Bridger-Teton National Forest Intermountain Regional Office Remote Sensing Applications Center For more information contact Liz Davy, Forest Silviculturist Bridger-Teton National Forest 307-739-5562 Bridger-Teton National Forest Intermountain Regional Office Technical Report RSAC—Integration of Remote Sensing
Transcript
Existing Vegetation Mapping Summary: Bridger-Teton National Forest
US Department of Agriculture Forest Service Engineering Remote Sensing Applications Center January 2007 RSAC-0091-TECH1
Bridger-Teton National Forest Intermountain Regional Office Remote Sensing Applications Center For more information contact Liz Davy, Forest Silviculturist Bridger-Teton National Forest 307-739-5562
Bridger-Teton National Forest Intermountain Regional Office
Technical Report
RSAC—Integration of Remote Sensing
The Forest Service, United States Department of Agriculture (USDA), has developed this information for the guidance of its employees, its contractors, and its cooperating Federal and State agencies and is not responsible for the interpretation or use of this information by anyone except its own employees. The use of trade, firm, or corporation names in this document is for the information and convenience of the reader. Such use does not constitute an official evaluation, conclusion, recommendation, endorsement, or approval by the Department of any product or service to the exclusion of others that may be suitable.
The USDA prohibits discrimination in all its programs and activities on the basis of race, color, national origin, sex, religion, age, disability, political beliefs, sexual orientation, or marital or family status (Not all prohibited bases apply to all programs). Persons with disabilities who require alternative means for communication of program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at 202-720-2600 (voice and TDD).
To file a complaint of discrimination, write USDA, Director, Office of Civil Rights, Room 326-W, Whitten Building, 1400 Independence Avenue, SW, Washington, D.C. 20250-9410 or call 202-720-5964 (voice and TDD). USDA is an equal opportunity provider and employer.
For additional information, contact Henry Lachowski, Remote Sensing Applications Center, 2222 West 2300 South, Salt Lake City, UT 84119; phone: 801-975-3750; e-mail: [email protected]. This publication can be downloaded from the RSAC Web site: http://fsweb.rsac.fs.fed.us
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TABLE OF CONTENTS Executive Summary ........................................................................................................v Introduction .......................................................................................................................1
Overview .....................................................................................................................1 Partnership .................................................................................................................1 The Bridger-Teton National Forest ............................................................................2 Background .................................................................................................................2
Methods ............................................................................................................................3 Project Planning ..........................................................................................................3 Geospatial Data Acquisition ........................................................................................5 Image Pre-Processing ................................................................................................7 Field Data Collection ...................................................................................................7 Segmentation and Mapping ......................................................................................10 Draft Map Review and Revision ...............................................................................11 Accuracy Assessment Design ..................................................................................12
Map Products .................................................................................................................12 Existing Vegetation Map ...........................................................................................12 Map Groups and Map Units ......................................................................................12 Canopy Cover Class .................................................................................................13 Tree Size Class ........................................................................................................13 Value-Added Products ..............................................................................................13
Accuracy Assessment Results .......................................................................................15 User’s Class Accuracy (Errors of Commission) ........................................................15 Producer’s Map Accuracy (Errors of Omission) ........................................................19
Conclusion ......................................................................................................................22 References .....................................................................................................................23 Appendix A: Project Planning .........................................................................................25 Appendix B: Geospatial Data Acquisition and Pre-Processing..................................... 51 Appendix C: Field Data Collection...................................................................................61 Appendix D: Segmentation and Mapping ........................................................................83 Appendix E: Draft Map Review and Revision..................................................................93 Appendix F: Accuracy Assessment Design...................................................................101 Appendix G: Accuracy Assessment Results..................................................................113 Appendix H: Map Products............................................................................................121
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EXECUTIVE SUMMARY The Bridger-Teton National Forest (BTNF) required continuous vegetation information across the forest (over 3.4 million acres) to support their Forest Plan Revision effort. A mid-level existing vegetation map and other products were developed through a partnership between the BTNF, Intermountain Regional Office (RO), and the Remote Sensing Applications Center (RSAC). The forest was mapped in four regions: Gros Ventre, Wyoming, Wind River, and Kemmerer areas. All map products were designed according to the Forest Service mid-level vegetation mapping standards for consistency with the Forest GIS and National databases. This report documents techniques used to produce a vegetation type, canopy cover, and tree size class map on the BTNF.
Existing vegetation maps provide consistent baseline information about current vegetation composition, structure and patterns. They can be used to assist with a variety of resource planning and monitoring activities. Some appropriate applications include: ecosystem and wildlife habitat assessments, rangeland and watershed assessments, fuel load assessments, benchmark analysis, updating range allotment management plans, threatened and endangered species modeling, and recreation activity management.
The vegetation maps were prepared over a 21-month period for around 15 cents per acre. The map designs were driven by the requirements of the BTNF Forest Leadership team. This team assisted with reviewing known vegetation types, preparing a hierarchical classification system, and establishing the map legends. The BTNF, RO, and RSAC all participated in the process of map unit design. Field crews from the BTNF implemented a field sampling strategy in June 2005 and visited sites and recorded ground-level information. The data was entered into a database and evaluated for consistency and accuracy in order to be used as training samples.
An advanced map-making process that incorporated new data-mining technology, was used to create the existing vegetation map. This entailed processing geospatial data, segmenting imagery, producing an image cube and a data cube, generating decision trees, and creating and evaluating the map products. Geospatial data processing involved collecting, assembling, and deriving new geospatial data layers from 13 Landsat satellite images and approximately 165 Digital Orthophoto Quads (DOQ). Image segmentation was performed on high-resolution imagery, which divided the landscape into homogeneous units. Spectral, topographic, and climatic layers were summarized for each homogeneous unit producing an image cube of 62 different layers. A data cube, which was produced by intersecting the training data with the image cube, was analyzed with data-mining software to generate a series of complex decision trees. The decision trees were applied to the image cube that resulted in the existing vegetation map product. Draft maps were distributed to local field resource specialists for comment and review in the summer of 2006. Recommended changes and manual edits were incorporated into the map and a field based accuracy assessment was conducted on the final product.
Existing vegetation map products were delivered to the BTNF in January, 2007. The map product was a digital vector layer (coverage) compatible with Forest Service corporate software like ArcGIS, ArcView, and Erdas Imagine. Thematic vegetation categories included: 28 map units (vegetation type), 7 forest canopy covers, two shrubland canopy covers, and 6 tree size classes. Other materials prepared for the Forest include: A management summary report, a technical report, powerpoint presentations, digital and hardcopy maps, databases of field information, digital photographs (ground & aerial); ancillary GIS and imagery layers; enhanced image products (high-resolution maps); and virtual fly-thrus for each ranger district.
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INTRODUCTION
Overview Existing vegetation was mapped on over 3.4 million acres of the Bridger-Teton National Forest in western Wyoming (figure 1). This mid-level map and associated map products will be used for the Bridger-Teton (BTNF) forest plan revision and for other forest needs. Initially, the forest completed a needs assessment and determined that vegetation structure maps were required for conducting analysis to support the plan revision effort. The BTNF formed a partnership with the Intermountain Regional Office (RO) and the Remote Sensing Applications Center (RSAC) to produce a seamless, forest-wide vegetation layer. Multiple sources and scales of remotely sensed imagery and geospatial data layers were used along with image segmentation and data-mining technologies to develop the maps. Vegetation maps were characterized by dominant cover-type, canopy cover class, tree size class, and were designed to meet a minimum polygon size of 5 acres. The final vegetation map product can be entered into the Forest Service corporate database, National Resource Information System (NRIS).
Partnership The mapping team was comprised of BTNF, RO, and RSAC. RSAC provided project management and vegetation mapping support in the form of geospatial data acquisition, data preparation, image processing, aerial photo-interpretation, rule-based decision-tree modeling, GIS modeling, and accuracy assessment analysis. The RO was instrumental in designing a sound field-based classification system, field keys, applying classifications to existing datasets, and developing map unit descriptions. The BTNF provided expert ground knowledge in the form of field support for training and accuracy assessment data collection. They also coordinated efforts for the entire project, provided key ancillary data, and access to existing field data. In addition, the BTNF also provided feedback and timely review of draft and final map products. Primary team members included:
Figure 1—Location of mapping regions on the BT mapped by RSAC.
Bridger-Teton National Forest staff: • Liz Davy, Forest Silviculturist
• Sarah Canham, Field Crew Leader
• Joette Katzer, Forest GIS Coordinator
• Brian Goldberg, Forest GIS Specialist
• Seasonal field crew personnel Intermountain Regional Office staff: • Dave Tart, Regional Ecologist
• Roberta Quigley, Regional Geometronics Group Leader
RSAC Staff: • Henry Lachowski, IRS Program Leader
• Haans Fisk, IRS Program Assistant
• John Gillham, Project Manager
• Wendy Goetz, RS/GIS Analyst
• Mark Beaty, RS/GIS Technician
• Steven Dale, RS/GIS Technician
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The Bridger-Teton National Forest The project area encompassed over 3.4 million acres of Forest Service and private lands and encompassed 165 7.5-minute quadrangles. The districts covered were Kemmerer, Big Piney, Buffalo, Jackson, Black Rock, and Greys River. The mapped area followed the Forest Service administrative boundary (figure 2).
The Bridger-Teton National Forest is part of the central Rockies and is characterized by younger glaciated mountain ranges, deeply carved river drainages, and bordering on its eastern edge, the continental divide. Climate in the area is characterized as alpine and mountain steppe. Weather and topography in the forest region is best described as contrasting just as it is across the entire state (http://www.wyomingtourism.org/cms/d/wyomings_weather_climate.php)
Background Vegetation structure is a term used to describe certain elements of forest-dominated landscapes. Individual components of forest structure usually pertain to tree size, distribution, and spacing. In the forest plan revision process, these factors are used to define current conditions, describe actions and time frames for achieving desired conditions, calculate allowable timber sale quantity, and assess wildlife habitat. Since vegetation structure factors are relatively fine scale, the success of many operational mapping techniques using satellite imagery are somewhat limited, and therefore, tend to infer this type of information through statistical analyses.
Figure 2—Bridger-Teton National Forest Administrative Boundary in green.
Wyoming
Idaho
Montana
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In 1999, RSAC evaluated techniques integrating multispectral satellite imagery with image-derived texture from high spatial resolution imagery like Digital Orthophoto Quads (Finco et. al. 2002). This approach was pilot tested on the Tongass National Forest and used to map standard map categories like cover type, canopy cover, and size class. An accuracy assessment was conducted on the final map products and demonstrated significant improvements as compared to traditional spectral classification of Landsat Thematic Mapper (TM).
RSAC has tested a software called See5 to develop rule-based decision trees from geospatial data sets and site-specific attributes. RSAC worked with Forest Inventory & Analysis (FIA) to generate a variety of pixel-based map products such as national timber estimates of basal area, tree height, biomass and tree volume. This process uses numerous survey-wide geospatial data sets (including texture) to determine relationships between the geospatial data layers and a limited number of site-specific field measurements. The relationships are converted to algorithms, which are tested, ranked, and finally used to predict, or map, ground-based information for the entire study area.
Using these new mapping concepts, RSAC conducted a feasibility study for the Dixie and Fishlake National Forests to investigate and propose a reasonable solution for mapping vegetation structure and support the forest plan revision process. Mapping procedures were tested on a four-quadrangle study area over the Monroe Mountain area in central Utah. This study area was selected because of its diversity of rangeland and forested vegetation communities. This feasibility study allowed RSAC to refine and automate data processing and thus apply the methodology over larger areas. This process was successfully implemented on a forest-wide level in 2003 when RSAC mapped existing vegetation on 6 million acres of the Humboldt-Toiyabe NF in Nevada (Gillham 2003). These projects laid the foundation for mapping existing vegetation on the BTNF .
METHODS Mapping techniques used in this project were based partially on methods currently in use by USGS EROS Data Center, Northern Region (R1), and Southwester Region (R5) (Brewer 2003; Homer 2002). The methods include using photo-interpreted training sites, high resolution DOQ imagery, vegetation indices from imagery, and other ancillary data sets to map existing vegetation. The mapping phases for this project included: project planning, geospatial data acquisition, image pre-processing, field data collection, segmentation and classification, draft map review and revision, and accuracy assessment (figure 3). Over the course of this project, new operational mapping procedures and tools were built enabling the process to be used as a template for implementing on other Forests to assist with their mapping needs. Each phase of the project is discussed in more detail in the sections that follow.
Project Planning In April 2005, BTNF, RO, and RSAC met to discuss map unit design and prepare a project plan. Since one of the goals for the project was to provide a template for other forests in the region, efforts were made to ensure that spatial and thematic characteristics of the maps as well as processes used would fulfill regional requirements. Vegetation classes were reviewed and a hierarchical classification system of mapping units was proposed that balanced budget and time constraints. The final map units, canopy cover, and tree size classes conformed to the mid-level mapping standards referenced in the Existing Vegetation Classification and Mapping Technical Guide (Brohman and Bryant, 2005). To minimize variation in ecological and vegetation characteristics, and to ease computer processing constraints, the study area was divided into four mapping regions: Gros Ventre, Wind River, Wyoming, and Kemmerer. (figure 4). For more information about the project planning, map unit design, and classification system, see Appendix A.
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Figure 3—Methods flowchart for existing vegetation mapping project.
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Geospatial Data Acquisition Geospatial data acquisition is a major activity in most vegetation mapping efforts using digital image processing methods. This project involved acquiring, loading, and assembling multiple sources and scales of remotely sensed imagery and geospatial data layers. See Appendix B for more details about these activities. A requirement of the mapping process was that any data layer used must be available across the entire mapping area to ensure consistency and efficiently. The complete set of Forest-wide geospatial data layers included Landsat Thematic Mapper (TM) satellite imagery, Color Infrared Digital Orthophoto Quadrangle (CIR DOQs), Digital Elevation Models (DEMs), Cartographic Feature Files (CFFs), Daymet climate data, roads, streams, and the BTNF administrative boundary. These spatial datasets were assembled for the entire mapping area and projected to UTM/Zone 12, Clarke 1866, NAD27. Data loading and image processing tasks were completed with the use of Forest Service Image Processing Systems—ERDAS Imagine and ArcGIS. A discussion of these data sources follows.
Landsat satellite imagery has been used to map vegetation for almost thirty years. This sensor records moderate spatial resolution information (30m pixels) every 16 days. Landsat has long been used to map existing land-cover, conduct change detection analysis, and other resource specific assessments. Three seasonal dates of Landsat satellite imagery were purchased from EROS Data Center for each path/row covering the BTNF (figure 5). Multi-temporal Landsat images captured spring, summer, and fall vegetation conditions providing for “leaf on” and “leaf off” periods. Such multi-temporal image sets have proven useful for mapping vegetation (Spies 1999, Vanderzanden 1999, Ruefenacht, et. al. 2000). A total of 13 ortho-rectified Landsat scenes were acquired, reprojected, clipped to the survey area, and filtered for clouds. The images were then mosaicked into a continuous area using ERDAS Imagine.
Figure 5—Multiple dates of Landsat imagery capture phenological changes in vegetation.
Leaf-Off
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Color infrared (CIR) DOQs are digital images derived from aerial photographs. DOQs depict ground features in their 'true' position by having the vertical displacement removed. Most DOQs have a spatial resolution of one meter and provide sufficient detail for mapping fine-scale features at 1:24,000-scale. These digital images are becoming extensively used throughout the Forest Service and other land management agencies. For this project, CIR DOQs were ordered through the Wyoming State GIS Clearinghouse (URL:http//wygia2.state.wy.us/htmaboutdoqq2002.asp). DOQs were imported, reprojected, resampled to 10 meters, mosaicked into a continuous data layer, and subset to the mapping region boundaries using ERDAS Imagine software (figure 6).
Digital Elevation Models (DEMs) consist of a regular array of elevation values cast on a designated coordinate projection system. They are commonly used to conduct a variety of earth science analyses. In this project, 10 meter DEMs were downloaded from the Forest Service Geospatial Data Clearinghouse (GDC) website (URL:http://fsweb.clearinghouse.fs.fed.us), imported, mosaicked into a continuous layer, and reprojected and subset to the mapping region boundaries using ArcInfo (figure 7).
Cartographic Feature Files (CFFs) provide information about geographic features, terrain, and political and administrative units. CFFs are used to produce 1:24,000- scale 7.5-minute topographic quadrangle maps. They depict water bodies, wetlands, streams, transportation, constructed features, and many other cartographic themes (figure 7). CFFs play an important role in the process of stratifying landscapes and mapping vegetation. For this project, CFFs were downloaded from the Forest Service GDC website, imported, mosaicked into a continuous layer, reprojected to the correct projection system, and subset to the mapping region boundaries using ArcInfo.
Resource aerial photographs have been the foundation of Forest Service remote sensing for almost 75 years, and continue to be a primary source of imagery (figure 8). Interpreting aerial photographs and/or high-resolution imagery are reliable alternatives to field data collection for increasing the number of training samples needed for digital image mapping. In this project, field-level information for several map unit types and canopy cover was lacking and time was not available for additional field data collection. To fill this gap, additional training samples were produced by interpreting aerial photographs and Figure 8—Natural color aerial photography.
Figure 7—CFF roads and streams overlaid on a 10-meter DEM.
Figure 6—CIR DOQ image (1-meter).
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Figure 9—Left is the original Landsat TM image with clouds and shadows (summer). Center is the reference Landsat image (fall). Right is the predicted cloud-free image.
Eliminating Clouds and Shadows from Landsat ETM Satellite Imagery
DOQ imagery. Knowledge of site-specific information gained through field observations, plot data, and existing datasets helped for interpreting the additional training samples. For more information about the geospatial data acquisition see Appendix B.
Image Pre-Processing The geospatial data sets were assembled for the entire mapping area, projected to the UTM/Zone 12, Clarke 1866, NAD27 coordinate system and clipped to each of the mapping region boundaries. All data sets were co-registered to DOQs and checked for missing data, haze and clouds. If clouds, cloud shadows or haze were found in the imagery, an automated cloud removal procedure was used to replace these areas. This procedure substitutes areas of unusable imagery with predicted values using new imagery of the same area (Maiersperger, 2001) (figure 9).
Once the geospatial data layers were co-registered and any anomalies were replaced, several spectral, topographic, and textural derived products were produced (see Appendix C). The spring, summer, and fall Landsat TM images were used to produce three standard spectral indices: Normalized Difference Vegetation Index (NDVI), Tasseled Cap, and Principle Component Analysis (PCA) (figure 10). Such indices are useful in discriminating between vegetated and non-vegetated as well as between vegetation cover-types. DOQ s were also used for deriving texture and a ratio of texture and tone. These were resampled to 10 meters to be spatially consistent with the 10-meter DEM (figure 11). DEMs were used to create topographic derivatives including elevation, slope, curvature, compound topographic wetness index, potential floodplain, distance to drainages, and a fully illuminated shaded relief image (figure 12). Such topographic models depict environmental parameters that help predict land cover-types in the mapping process. For more information about the image pre-processing see Appendix B.
Field Data Collection During the summer of 2005, BTNF field crews collected information such as dominant vegetation type, canopy cover, and tree size class for 225 field plots (figure 13). The number of training samples was designed to be proportional to the total acres of each mapping region and map unit type. The process for selecting training samples was based on an unsupervised classification of the leaf-on Landsat imagery, creating 20-60 spectral classes for each mapping region depending on landscape. Within a 1/4 mile buffer of roads, field sites were placed in homogenous areas of each spectral class
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Figure 11—DOQs are used to generate texture images and ratio bands of texture and tone.
DOQ Tone (10m)
DOQ Ratio Texture/Tone (10m) DOQ Texture (10m)
Image Stack of Texture and Tone (10m)
DOQ Tone and Texture Derivatives
Figure 10—Two dates of Landsat TM are used to generate a suite of standard band transformations.
Tasseled Cap
Leaf
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Land
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Leaf
-on
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Original Landsat ETM NDVI
Tasseled Cap
Principle Component Analysis
Original Landsat ETM
NDVI
Principle Component Analysis
Landsat Enhanced Thematic Mapper Satellite Imagery And Derived Band Transformations
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with a minimum size of 90 square meters. Approximately 10 accessible sites were located in each spectral class for each mapping region. A small number of the assigned training samples did not get visited by field crews due to limited time and difficult access (figure 14). RSAC provided different scales of maps to field crews to aid in navigation (figure 15). For all of the sites visited, a local field key was used to classify dominant vegetation type, and digital photos and GPS coordinates were collected. In addition to visiting field sites, nearly 1300 supplemental photo-interpreted sites were also collected in the field using supplied 8.5”x11” DOQ-based plot maps. All field data was reviewed for accuracy and consistency and entered into a database. These data were used as training samples for development of classification models in the mapping process. Additionally, by applying this project’s vegetation classification scheme to existing field data available from the BTNF, over 3,000 more training samples were also used. For more information about field keys and field data collection forms, refer to Appendix C.
Figure 12—DEMs are used to generate a variety of topographic models and indices.
Elevation
Percent slope
Aspect
Shade reliefIlluminated shade relief with contours
DEM Derived Topographic Models
Figure 13—BT field crews visited ground sites and recorded field information.
Figure 14—Field site that was visited and recorded.
Elevation
Aspect
Percent Slope
Shade relief Illuminated shade relief with contours
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Segmentation and Mapping Image segmentation was performed on the 10-meter CIR DOQ imagery and various indices (mostly NDVI & texture), dividing the landscape into homogeneous segments (figure 16). Segments were made using an image processing software package called eCognition, which creates stand-level segments from the CIR DOQ imagery. Using 10-meter imagery significantly increased the processing efficiently of image segmentation and greatly reduced the resulting polygon file size as compared with the original one-meter imagery. Generating homogeneous units reduces the spectral, topographic, and spatial variation within segments and improves the precision of assigning dominance types, canopy cover, and tree size class labels.
Once segmented, 62 image data layers representing climatic, spectral, and topographic information were summarized, using zonal mean and standard deviation, within each of the polygon segments. This process yielded an image cube of 115 different layers for each mapping region. Training sample points were then intersected with the image cube to produce a data cube — a data file containing all of the summarized information as it relates to each training sample location.
Using an iterative process, the data cube was analyzed with See5 data-mining software to generate a series of decision trees to model the various land cover attributes (Quinlan 1993). Data-mining is defined as the automated extraction of hidden predictive information from databases. These customized algorithms are used to predict outcomes for future or unknown situations. Rule-based decision trees are generated from the data-mining information. This process uses the geospatial data layers (i.e. Landsat TM imagery, DEMs, soil data, daily temperature, etc.) and site-specific interpreted measurements (training samples) to generate the decision trees. See5 determines which variables are statistically significant for each mapping attribute. The relationships are converted to a decision tree,
Figure 15—Several different scales of maps were provided to field crews to aid in
Field Maps Products Suite
Scale 1:50,000
Scale 1:24,000 Scale 1:9,000
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which is tested, ranked, and used to predict, or map, ground-based information across the entire study area. Each polygon is attributed with map unit, canopy cover, and tree size class. A summary of the classification process is shown in figure 17. For more information on the segmentation process, image cube creation, data cube construction and classification process, refer to Appendix D.
Draft Map Review/Revision In the spring of 2006, draft hardcopy maps were distributed to local field resource specialists for comment and review, providing an opportunity for local experts to assess the map and provide additional input they thought was needed to improve the final maps. The district personnel also had the opportunity, over six weeks, to further review the maps. The hardcopy maps were returned to RSAC with comments and notes. Recommended changes and manual edits were incorporated into the final map. For more information about the review and revision process involving local resource personnel, refer to Appendix E.
Figure 16—Polygon segments developed from eCognition.
Figure 17—Image cube combined with training data to develop decision trees and applied to create a classified map.
Input image cube Field Samples See5 decision-trees Applied to image-cube
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Accuracy Assessment Design In the summer of 2006, a field-based assessment was performed to determine the accuracy of the final vegetation maps. A stratified random, double-blind sampling method was used. Accuracy assessment points were distributed within predetermined constraints, such as accessibility. All points had to be accessible within one-quarter mile of a road and located within mapping polygons of a minimum size of two acres. Over 1,300 point locations were identified across the Forest. Field crews were instructed to implement protocols consistent with the initial field data collection effort. Field crews were provided polygon boundaries for each location, but had no indication what each polygon had been mapped as. Polygons were assessed using field keys to determine the map unit. Crews also collected canopy cover and size class information and took digital photos of the site. Nearly 1,200 assigned accuracy assessment points were visited in 2006 and used in the accuracy assessment. Accuracy was assessed using a traditional error matrix, discussed later in this report. For more information about how the accuracy assessment was designed and carried out, see Appendix F.
MAP PRODUCTS A suite of map products were completed in January 2007. The package included the existing vegetation map and numerous value-added products. The team also prepared technology transfer materials including a management summary, technical report, PowerPoint presentations, poster displays, hundreds of digital and hardcopy maps, hotlinked field photos & databases, and fly-thu visualizations. For more information about the existing vegetation map product and suite of deliverables, refer to Appendix H.
Existing Vegetation Map The final map product provides for continuous land cover information for the entire 3.4 million acres of the BTNF. This map shows existing vegetation types, and their structural characteristics and is formatted as a digital vector layer (coverage) compatible with Forest Service corporate GIS software. The mapped area encompasses the entire BTNF at 3.4 million acres. Categories mapped included Map Group (MG), Map Unit (MU), Canopy Cover (CC), and Tree Size Class (SC) (figure 18). The vegetation map is consistent with mid-level mapping standards set forth in the Existing Vegetation Classification and Mapping Technical Guide (Brohman and Bryant, 2004). In conformance with these standards, small polygons were aggregated up to five acres with the exception of riparian, deciduous, agriculture, water, and urban areas, which were aggregated to 2 acres. Map units are mutually exclusive and clearly defined by local dominance types.
Map Groups & Map Units A total of twenty-eight map units were mapped. Map unit descriptions were comprised from several sources including the Society of American Foresters (SAF), Society for Range Management (SRM) cover types, and alliances. These dominance type classes ranged from specific vegetation species (i.e. Mountain big sagebrush) to general land cover type (i.e. agriculture). Map Groups were made up of logical aggregations of the map units and consisted of 6 classes: conifer, deciduous, shrubland, herbaceous, riparian, and non-vegetated.
Figure 18—Existing vegetation map and tabular attributes.
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Canopy Cover Class A canopy cover map was generated by independently processing the following cover types: conifer, hardwood, conifer/hardwood mix, and shrubland. Each canopy cover category was assembled into a wall-to-wall map for each mapping region and mosaicked for the entire Forest. Also note that unique breaks were present between cover type categories.
Tree Size Class A tree size map was generated by independently processing conifer, hardwood and riparian forest cover types. Once classified into one of the six size classes: (<5”, 5-9.9”, 10-19.9”, 20-29.9”, 30”+, and non-tree), each size class map was assembled into a complete coverage for each mapping region.
Value-Added Products The value-added products developed as part of the vegetation mapping project include field-collected information, aerial photo flight line indexes, hot-linked field photographs, virtual fly-thru, mosaics of standard geospatial data sources, as well as numerous image derivatives and indices. Listed below are some of the products.
• Field-collected information: ∗ 1,300 field visited samples – stored in a database and point shapefile ∗ 10,670 digital ground photographs linked to field visited samples ∗ 1,300 field observations – stored in a database ∗ 1,200 aerial oblique photos linked to ground location
• Standard image product mosaics: ∗ Digital Elevation Models (10m) ∗ Color Infrared Digital Orthophoto Quads (1m & 10m resamples) ∗ Multiple dates of Landsat ETM imagery (30m & 10m resamples)
• Additional products: ∗ 1991 aerial resource photo index - (GIS coverages & hardcopy maps) ∗ 6 fly-through visualizations ∗ External hard drive containing all geospatial & field datasets, maps, etc…
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MAP APPLICATIONS A GIS land-cover map is a useful product for addressing specific resource and land management issues. The existing vegetation maps provide consistent baseline information about current vegetation composition, structure and landscape patterns that support many Forest Plan Revision needs. Vegetation maps are used to address a variety of important land management issues involving fuel loads, watersheds, rangelands, and wildlife habitat. They are also useful for modeling threatened and endangered species habitat, conducting benchmark analysis, and monitoring the sustainability of resource management practices. However, both resource specialists and land managers should acknowledge that there are appropriate and inappropriate uses for a mid-level existing vegetation map product.
Appropriate Uses Using this map product in an appropriate manner to assist with resource planning and monitoring activities will increase efficiency, accuracy, and defensibility of management practices. Appropriate uses of mid-scale existing vegetation maps include:
• Inventory assessments--summarizing acres of existing vegetation for an area of interest (e.g. a forest, district, range allotment, or watershed).
• Visual quality assessments-- evaluating scenic integrity for recreational land uses.
• Crosswalk development-- incorporating local resource knowledge with the vegetation map for deriving new meaningful interpretation layers (e.g. rangeland production estimates).
• Biophysical modeling-- combining other geospatial data layers with the existing vegetation map product to predict fuel loads, potential natural vegetation, and wildlife habitat.
• Fragmentation analysis-- analyzing landscape patterns using a mid-scale existing vegetation map product; however, this requires much ecological knowledge and technical expertise.
• Stratification-- assisting with the design of multi-staged sampling to assess condition and trend for items such as aspen decline/spread.
Inappropriate Uses Inappropriate uses of mid-scale existing vegetation maps usually involve assessing fine-scale resource issues, such as project-level activities or features not captured in the map. Although an existing vegetation map may provide supplemental information to these activities, this map should not be used alone to determine such things as:
• Inventorying noxious weed infestations
• Assessing riparian area condition
• Designing timber-harvest units
• Monitoring range utilization
• Determining historical conditions
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ACCURACY ASSESSMENT RESULTS The final vegetation map was assessed using field-visited information. Traditional (deterministic) error matrices were developed for map unit, canopy cover and tree size categories. The error matrix is a square array of numbers set out in rows and columns that expresses the number of polygons assigned to a particular category in a map relative to the reference sites. For this study the columns represent the reference or field data while the rows represent the map classification. An error matrix address individual accuracies for each category in terms of errors of inclusion (commission errors) and errors of exclusion (omission errors) (Congalton and Green 1999). Errors of commission are often referred to as ‘User’s Accuracy’. A commission error occurs when an area is included in a category where it doesn’t belong. It answers questions such as: “If I pick a willow polygon on the map, what are the chances it is actually going to be a willow polygon on the ground?” Errors of omission are often referred to as ‘Producer’s Accuracy’. An omission error is excluding that area from the category in which it does belong. It answers questions such as: “I’m on the ground standing in a willow area; what are the chances that it is going to be correctly identified as a willow polygon on the map?”. In addition to user’s and producer’s accuracy, an overall map accuracy was also calculated. The overall map accuracy answers the question: “If I throw a dart at the map, what are the chances the polygon I hit is correct compared to what is on the ground?”. All assessments of accuracy are important, but the one most commonly reported for this project is overall map accuracy.
One assumption of the traditional error matrix is that an accuracy assessment sample site can have only one label. Classification scheme rules often impose discrete boundaries on continuous conditions in nature, such as vegetation communities in this case. In some situations it is difficult to place one label on a field polygon because the vegetation composition does not fit neatly into single map class. Therefore, for our study the field crews were allowed to make a second call at a site if it was appropriate. For example, a second call of shrubland might be given to a very low canopy woodland site. Receiving only a single class label from field crews gives a bare minimum accuracy, while allowing for a first and second call where appropriate gives a maximum accuracy.
Error matrices are presented in the main body of this report for the map unit classification only. Summary tables are shown for canopy cover and tree size maps. Error matrices for all maps are found in Appendix G. Due to time constraints, cost, and travel limitations, it was not possible to get adequate field samples in some of the map classes. Although they were included in the error matrices, only those classes which had 15 or more field sites were individually calculated for class accuracies.
User’s Class Accuracy (Errors of Commission) The user’s class accuracy was calculated by dividing the number of correctly classified sites by the total number of mapped sites for a particular class. User’s accuracy reflects the likelihood of each mapped category being correctly identified. This type of accuracy does not take into account the total mapped area covered by a particular vegetation class — only the number of field sites in that class.
Map Groups All of the six map group categories assessed. The minimum user’s class accuracy ranged from a low of 63% in the deciduous category to a high of 88% in the conifer category (table 1). When looking at the maximum accuracy, the low changed to 69% in the riparian category and the high to 89% in the conifer category (table 2).
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Table 1 —Map Group Minimum User’s Class Accuracy.
Table 2 —Map Group Maximum User’s Class Accuracy.
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Table 3 —Map Group Minimum and Maximum User’s Class Accuracy.
Map Unit Twenty seven dominance type categories were assessed for the map unit layer. Several map units such as low/alkali sagebrush, spiked sagebrush, mountain mahogany, cottonwood, Rocky Mountain juniper, and limber pine had less than 15 samples and therefore were not assessed. The lowest minimum user’s accuracy occurred in the sagebrush/bitterbrush category and the highest vegetated accuracy occurred in willow (table 3). The percentages increased for many of the classes when the maximum accuracy was calculated. While all classes which had 15 or more samples sites were assessed, those map class accuracies with a small number of field sites must be viewed with caution.
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Table 5 —Map Group Maximum User’s Class Accuracy.
Table 4 —Canopy Cover Minimum and Maximum User’s Class Accuracy
Canopy Cover Six canopy cover categories were evaluated according to the existing vegetation mapping standards. The tree categories assessed for canopy cover were conifer, aspen, mixed conifer/aspen and cottonwood map units. Shrub categories included both upland and riparian shrub map units. The lowest accuracies occurred in the high canopy tree sites while the medium tree canopy classes had the highest accuracies (table 4), excluding non-tree classes.
Tree Size Class Six tree size categories were assessed. Conifer, aspen, mixed conifer/aspen, and cottonwood map units were all included. The minimum user’s individual class accuracies ranged from a low of 25% in the smallest size class to a high of 50% in the 10-19” DBH class (table 5). It should be noted that some of these categories had a very limited number of field sites (classes 5 & 6) therefore the accuracy results should be viewed with caution. As expected, accuracy percentages increased when the maximum accuracy was computed.
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Table 7 —Map Group Maximum Producer’s Accuracy.
Table 6 —Map Group Maximum Producer’s Accuracy.
Producer’s Map Accuracy (Errors of Omission) A weighted error matrix was develop in order to get a more representative producer’s accuracy. This was done because there were a disproportionate number of field sites in some of the map categories. The producer’s map accuracy was calculated using a sample weighting factor based on the number of acres and the number of field sites assigned to each map category. The first step in generating this weighted accuracy was to calculate the percentage of each map class in the final maps. This was the percent area mapped value. The sample weight value was derived by dividing the area mapped percentages by the total number of map data sites (field sites) for each category. This value was multiplied by the number of sites found in a particular cell, producing the percentages which populate the error matrix. These percentages add up to 100%. Each class producer’s accuracy was calculated by dividing the percent number of correctly classified sites by the percent number of reference (field) sites. This produced a weighted producer’s map accuracy which provides a more useful tool for map assessment.
Map Group Six map group categories assessed using the same weighting system. The minimum producer’s accuracy ranged from 54% in the deciduous category to 96% in the conifer category (table 6). The maximum call accuracy changed the deciduous to 61% and the conifer to 97% (table 7).
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Map Unit Twenty-eight map unit categories were assessed the results of which are shown in Table 8. The lowest producer’s accuracies (errors of ommision) occurred in the sagebrush/bitterbrush and mountain shrubland classes while the highest producer’s accuracies were in some of the non-vegetated classes (water, snow, barren). This indicates that nearly all the existing water and snow/ice present on the ground were identified in the map. Of the vegetated classes, alpine, willow, aspen, Douglas fir, and lodgepole had the highest producer’s accuracies, indicating these types were well mapped. Again, many of the map units had very few accuracy assessment sites and their accuracies may not be reliable and have not been listed in the table below.
Table 8 —Map Unit Minimum and Maximum Producer’s Accuracy.
21
Canopy Cover Six canopy cover categories were evaluated according to the existing vegetation mapping standards. The tree category included conifer, aspen, mixed conifer/aspen and cottonwood map units. The shrub categories assessed for canopy cover included both upland and riparian shrub map units. The lowest producer’s accuracy occurred in the low tree category while the highest occurred in the medium canopy tree category (table 9), excluding non-tree classes.
Tree Size Class Six tree size categories were assessed. Conifer, aspen, mixed conifer/aspen, and cottonwood map units were all included in these tree size assessment. The lowest producer’s class accuracy was in the smallest tree size class (<5” DBH), while the highest was in size class 4 (10-19” DBH), excluding non-tree classes. It should be noted that some of these categories had a very limited number of field sites (size classes 5 & 6), so the accuracy results may not be dependable and have not been listed. Most of the accuracy percentages increased when the maximum accuracy was computed.
Table 10 —Tree Size Class Minimum and Maximum Producer’s Class Accuracy.
Table 9 —Canopy Cover Minimum and Maximum Producer’s Accuracy.
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Overall Map Accuracy The overall map accuracy was calculated by adding up the diagonal values from the producer’s map error matrix (table 11). Again, this overall map accuracy reflects the weighted accuracies for each of the classes, thus producing a more useful map evaluation. It answers the question: “If I throw a dart at the map, what are the chances the polygon I hit is correct?”. The lowest accuracies occurred in the map unit map while the highest occurred in the shrub canopy cover cover map. These percentages reflect the generalized nature of some map types and the very specific nature of others.
Table 11—Map Group Maximum User’s Class Accuracy.
CONCLUSION A mid-level existing vegetation map product will support the Forest Plan Revision process and other assessments and analyses. The map is compatible with the Forest Service Existing Vegetation Mapping Standards, and provides the Forest with knowledge about current vegetation composition, structure, and patterns that will allow them to implement Forest Service policies and regulations.
Over the course of this project, numerous innovative techniques were developed and refined into an operational mapping procedure. These can now be implemented on other Forests to assist with their mapping needs. For example, using 10-meter imagery significantly increased the processing efficiency of image segmentation, greatly reduced the resulting polygon file size when compared to using 3-meter imagery, and resulted in only minor loss of delineative detail. In addition, generating homogeneous units reduced the spectral, topographic, and spatial variation within segments and improved the precision of assigning map units, canopy cover, and tree size class labels.
Resource specialists need to remember that there are appropriate and inappropriate uses for this mid-level existing vegetation map product. Appropriate uses include describing vegetation diversity, assessing resource conditions, modeling species habitat, conducting benchmark analysis, designing monitoring procedures, or addressing a variety of other important land management issues. Additionally, this map may provide a preliminary assessment to determine where more detailed studies are needed. Since this baseline product represents a single point in time, land managers should develop a strategy for maintaining their initial investment into the future. Scheduled updates, coordinating related work activities, and/or tracking changes would help keep the vegetation map current and applicable to future monitoring.
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REFERENCES Brewer, C.K.; Barber, J.A.; Willhauck, G.; Benz, U.C. 2003. Multi-source and multi-classifier system for regional landcover mapping. In Proceedings of the IEEE workshop on advances in techniques for analysis of remotely sensed data. NASA Goddard Space Flight Center, Greenbelt, Maryland: Institute of Electrical and Electronic Engineers; Geospatial and Remote Sensing Society.
Brohman, R.; Bryant, L. editors. 2005. Existing Vegetation Classification and Mapping Technical Guide – Review Draft, April 2003. USDA Forest Service, Washington Office, Ecosystem Management Coordination Staff.
Congalton, R.G.; Green, K. 1999. Assessing the accuracy of remotely sensed data, principles and practices. Boca Raton, FL: CRC/Lewis Publishers.
Erdas Imagine—Forest Service Image Processing System
Finco, M.; Fisk, H.; Vanderzanden, D.; Lachowski, H.; Gegayner, E.; Nowaki, G.; Caouette, J. 1999. Image texture information applied to forest structure mapping on the Tongass National Forest. RSAC-0014-TIP1. Salt Lake City, UT: U.S. Forest Service, Remote Sensing Applications Center. 4 p.
Gillham, J.; Fisk, H.; Goetz, W.; Lachowski, H. 2004. Existing Vegetation Mapping: Humboldt-Toiyabe National Forest. RSAC-0065-RPT1. Salt Lake City, UT: U.S. Department of Agriculture Forest Service, Remote Sensing Applications Center. 110 p.
Homer, C.G.; Huang, C.; Yang, L.; Wylie B. 2002. Development of circa 2000 landcover database for the United States. In Proceedings of ASPRS, April, 2002. Washington D.C.
Maiersperger, T.; Finco, M.; Helmer, E. 2004. Eliminating Cloud Contamination from satellite imagery: A review in support of FIA remote sensing initiatives.RSAC-4016-RPT1. Salt Lake City, UT: U.S. Forest Service, Remote Sensing Applications Center. 11 p.
Pirkle, E.C.; Yoho, W.H.;Henry, J.A. 1985. Natural Landscapes of the United States Fourth Edition, Kendall/Hunt Publishing Company, Dubuque, Iowa, 417 p.
Quinlan, J.R., 1993, C4.5 programs for machine learning (SanMateo, California: Morgan Kaufmann Publishers).
Ruefenacht, B.; Fisk, H.; Lachowski, H. 2001. Using remote sensing to map sagebrush steppe ecosystems: implications for modeling sage grouse habitat for brood rearing, breeding, and nesting.. RSAC-0033-TIP1. Salt Lake City, UT: U.S. Department of Agriculture Forest Service, Remote Sensing Applications Center. 4 p.
See5 Software. rulequest.com
Vanderzanden D. 1999. Mapping vegetation in the Southern Appalachians with multidate satellite imagery: a wilderness case study. RSAC-0009-RPT1. Salt Lake City, UT: U.S. Department of Agriculture Forest Service, Remote Sensing Applications Center. 31 p.
Wolf, P.R. 1983. Vertical photographs. In Elements of photogrammetry with air photo interpretation and remote sensing (2nd Ed. pp. 119—138). New York: McGraw-Hill Inc.
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25 І Appendix A: Project Planning
Key to Dominance Types for the Bridger-Teton NF The following vegetation key is intended for the identification of existing vegetation types, not for identification of potential natural vegetation (PNV) types. This key is intended for use only within the boundaries of the Bridger-Teton National Forest. This key identifies forest and shrubland vegetation to the dominance type level. It identifies herbaceous vegetation to the dominance type or community type level, depending on how much formal classification work has been done for particular communities or vegetation zones. A dominance type is “a recurring plant community defined by the dominance of one or more species that are usually the most important ones in the uppermost or dominant layer of the community…” (see Tart et al. 2005a). In contrast, a community type is based on plant species abundances in all layers. In herbaceous communities, where only one layer in present, there may often be no difference between a dominance type and a community type. A dominant species is defined as either “an organism exerting considerable influence on a community by its size, abundance, or coverage” (Lincoln et al. 1998), or as “the species with the highest percentage of cover, usually in the uppermost layer” (Kimmins 1997 as cited in Jennings et al. 2004). Both concepts are employed in this key. As a result, the dominance types are distinguished on the basis of either absolute or relative composition.
Absolute composition is “a list of the absolute amounts of each plant species present in a given area or stand” (Tart et al. 2005a). Relative composition is “a list of the proportions of each plant species relative to the total amount of all species present in a given area or stand” (Tart et al. 2005a).
When describing species amounts using canopy cover, absolute composition is expressed as absolute cover and relative composition is expressed as relative cover. Table 1 provides a simple example of each and compares them. This key uses both absolute and relative cover. Distinct phrases are used to indicate whether a given couplet refers to absolute or relative cover. Couplets referring to absolute cover are of the general form, [species or life form] [ > or < ] X% canopy cover while couplets referring to relative cover are of the form, [species or life form] provides [ > or < ] X% of the [life form or layer] canopy cover. In other words, the phrase “provides…of the” indicates relative cover and absence of this phrase indicates absolute cover.
Appendix A: Project Planning
26 І Appendix A: Project Planning
Instructions for Using Keys
1. Complete a vegetation plot following the procedures in section 2, and corresponding appendices, of the Existing Vegetation Classification and Mapping Technical Guide (Brohman and Bryant 2005).
2. Begin with the formation key and then proceed to the indicated dominance type key.
3. Work through the keys step by step, do not skip couplets, to determine the indicated dominance
type.
4. Compare your plot data to descriptions in the appropriate community type guide to verify the iden-tification arrived at with the key.
5. If the description and plot agree, then record the dominance type code in the appropriate field. If
the description is not a reasonable fit, then key out the plot again.
6. Once the dominance type is identified, identify the community type using the appropriate guide and record it on the filed form. This will provide further validation of the dominance type and pro-vide more detailed information about the sampled stand.
Trees species Absolute Cover Relative Cover Lodgepole pine Engelmann spruce Subalpine fir
42 21 7
60 30 10
Total Tree Cover 70 100
Table 1. Comparison of Absolute and Relative Cover Values.
27 І Appendix A: Project Planning
Key to Vegetation Formations This key does not apply to lands used for agriculture or urban/residential development. It applies only to natural and semi-natural vegetation. The latter includes planted vegetation that is not actively managed or cultivated. The criterion of 10% canopy cover for trees, shrubs, and herbs used in this key (couplets 1, 2, and 4) is based on Forest Service guidelines for mapping existing vegetation (Brewer et al. 2005). The 5% canopy cover criterion for sagebrush shrublands (couplet 3) is based on the needs of local resource managers and literature on synecology of sagebrush (Daubenmire 1970, Winward 1970, Bramble-Brodahl 1978, Hopkins 1979, Mueggler and Stewart 1980, Hironaka et al. 1983, Volland 1985, Tiedeman et al. 1987, Jensen et al. 1988, Johnson and Clausnitzer 1992, Tart 1995, Tart1996, Cooper et al. 1999). 1a. All vascular plants total < 1% canopy cover . . . . . . . . . . . . . . . . . . . . . . . . Non-Vegetated Key (p.__) 1b. All vascular plants total > 1% canopy cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2a. All vascular plants total < 10% canopy cover . . . . . . . . . . . . . . . . . . . . . . . Sparse Vegetation 2b. All vascular plants total > 10% canopy cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3a. Trees total > 10% canopy cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Forest Key (p. 4) 3b. Trees total < 10% canopy cover. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 4a. Shrubs total > 10% canopy cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shrubland Key (p. 7) 4b. Shrubs total < 10% canopy cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 5a. Upland sagebrush (Artemisia arbuscula, A. nova, A. tridentata, or A. tripartita) species total > 5% canopy cover . . . . . . . . . . . . . . . . . . . . . . . . . Shrubland Key (p. 7) 5b. Upland sagebrush species total < 5% canopy cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Key to Forest Dominance Types This key assigns plots or stands to dominance types based primarily on relative cover of tree species in the tallest tree layer present. This is typically the overstory layer, but in young stands it may be the regenera-tion layer (see Tart et al. 2005b). Stands where the overstory canopy cover is less than 10% may be keyed out on the basis of combined species cover in the overstory and regeneration layers. Single-species dominance types are based on relative cover of 60% or more (couplets 1-14). This value was suggested by vegetation specialists from the Northern Region (Brewer et al. 2004) and empirically tested on ecological plot data from the Bridger East and Teton divisions of the Forest. The use of two tree species to define the narrowleaf cottonwood d.t. is based on the riparian classification of Youngblood et al. (1985). Mixed species types were kept to a minimum (couplets 15-18) and were defined to reflect meaningful man-agement opportunities. Couplets 15 and 16 use absolute cover to define mixed types with significant amounts of aspen or whitebark pine, respectively. The remaining mixed types are based on relative cover of Douglas-fir and Lodgepole pine. 1a. A single tree species provides > 60% of the tree overstory canopy cover 1. . . . . . . . . . . . . . . . . . . . . 2 1b. No single tree species provides > 60% of the tree overstory canopy cover . . . . . . . . . . . . . . . . . . . . 14 2a. Aspen (Populus tremuloides) provides > 60% of the tree overstory
Key to Shrubland Dominance Types This key combines criteria from several vegetation classifications as done by Tart (1995) for PNV. In order to cite the appropriate sources, the key is subdivided by the headings Riparian, Alpine, and Upland. Do not use these headings to jump ahead in the key. Begin with couplet 1 and follow the key until your plot is as-signed to a dominance type. The riparian dominance types are based on the community type classifications of Youngblood et al. (1985) and Padgett et al. (1989). The alpine types are based the Potkin’s (1991) classification of alpine community types in the Wind River Mountains and additional plot data collected by Svalberg et al. (1997). The upland types are based on rangeland cover types for the Great Basin (Shiflet 1994) and sagebrush habitat type classifications by Hironaka et al. (1983), Bramble-Brodahl (1978), and Tart (1996). Riparian 1a. Geyer’s willow (Salix geyeriana) > 25% canopy cover . . . . . . . . . . . . . . . . . . Geyer’s willow (SAGE2) 1b. Geyer’s willow provides < 25% canopy cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2a. Booth’s willow (Salix boothii) and/or Drummond’s willow (S. drummondiana), together or separately, > 25% canopy cover . . Booth’s Willow (SABO2) 2b. Booth’s and Drummond’s willow total < 25% canopy cover . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3a. Wolf’s willow (Salix wolfii) and/or dwarf birch (Betula nana), together or separately, > 25% canopy cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wolf’s willow (SAWO) 3b. Wolf’s willow and dwarf birch total < 25% canopy cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Key to Herbland Dominance Types and Community Types This key is divided into riparian, alpine, and upland sections. More formal vegetation classification has been done in riparian habitats and that section of the key identifies community types. Alpine and tall-forb vegetation also have been classified to the community type level. Less formal classification has been done in other herbaceous uplands, so these are considered to be dominance types. The riparian section is based primarily on community type guides by Youngblood et al. (1985) and Padgett et al. (1989), and a study of peatland vegetation by Cooper and Andrus (1994). Type descriptions, diagnostic criteria, and key sequences for similar community types in northeastern Oregon (Crowe and Clausnitzer 1997) and Montana (Hansen et al. 1995) were also consulted in developing this key. The alpine section is based mostly on a Potkin’s (1991) study of alpine vegetation in the Wind River Mountains and plot data from Svalberg et al. (1997). Alpine vegetation studies in Montana (Cooper et al. 1997), Idaho (Caicco 1983), and Utah (Lewis 1970) were also compared to Potkin’s classification. The upland section is based in part on Gregory’s (1983) study of tall forb communities. The rest of the upland types are pulled out of our…uh…collective professional experience, with some validation from plot data collected by Svalberg et al. (1997). It is likely that some dominance types occur in more than one of these settings. If your plot does not key out successfully in one setting, then try another setting and compare the results as noted in the instructions to the keys (p.2). 1a. Stand is located in a riparian or wetland setting . . . . . . . . . . . . . . . . . . . . . . Riparian Section (below) 1b. Stand is located in an upland setting, not riparian or wetland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2a. Stand is located in an alpine setting above the continuous forest line . . Alpine Section (p.14) 2b. Stand is located below the continuous forest line . . . . . . . . . . . . . . . . . Upland Section (p.15) Riparian Section 1a. Common cattail (Typha latifolia) provides > 50% of the herbaceous canopy cover, typically on a ponded site . . . . . . . . . . . . . . . . . . . . . . . . . . common cattail c.t. (TYLA) 1b. Common cattail provides < 50% of the herbaceous canopy cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2a. Northern mannagrass (Glyceria borealis) > 25% canopy
most abundant forb species . . . . . . . . . . . . . . . . . . . . . . . western brackenfern d.t. (PTAQ) 28b. Western brackenfern is not the most abundant forb species . . . . . . . . . . . . . . . . . . . . . . . . 29
29a. Canada thistle (Cirsium arvense) is the most abundant forb species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Canada thistle d.t. (CIAR4) 29b. Canada thistle is not the most abundant forb species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 30a. Mountain tarweed (Madia glomerata) is the
most abundant forb species . . . . . . . . . . . . . . . . . . . . . . . . mountain tarweed d.t. (MAGL2) 30b. Mountain tarweed is not the most abundant forb species . . . . . . . . . . . . . . . . . Unclassified
42 І Appendix A: Project Planning
Literature Cited Bramble-Brodahl, Mary Kay. 1978. Classification of Artemisia Vegetation in the Gros Ventre Area, Wyoming. M.S. Thesis, University of Idaho. Moscow, ID. 126 p. Brewer, C. K., D. Berglund, J.A. Barber, R. Bush. 2004. Northern Region Vegetation Mapping Project Summary Report and Spatial Datasets. Unpublished project documentation USDA Forest Service, Northern Region. Missolua, MT. Brewer, C.; Schwind, B.; Warbington, R.; Clerke, W.; Krosse, P.; Suring, L.; and Schanta, M. 2005. Existing Vegetation Mapping Protocol. Section 3 in Brohman, R. and Bryant, L. editors. 2005. Existing Vegetation Classification and Mapping Technical Guide—March 2005. USDA Forest Service, Washington Office, Ecosystem Management Coordination Staff. Brohman, R. and Bryant, L. editors. 2005. Existing Vegetation Classification and Mapping Technical Guide—March 2005. USDA Forest Service, Washington Office, Ecosystem Management Coordination Staff. Caicco, S. L. 1983. Alpine Vegetation of the Copper Basin Area, South-Central Idaho. M.S. Thesis, University of Idaho. Moscow, ID. 99p. Cooper, D. J. and R. E. Andrus. 1994. Patterns of vegetation and water chemistry in peatlands of the west-central Wind River Range, Wyoming, U.S.A. Ca. J. Bot. 72:1586-1597. Cooper, S. V., C. Jean, and B. L. Heidel. 1999. Plant associations and related botanical inventory of the Beaverhead Mountains Section, Montana. Unpublished report to Bureau of Land Management. Montana Natural Heritage Program, Helena. 235p. Crowe, E. A. and R. R. Clausnitzer. 1997. Mid-Montane Wetland Plant associations of the Malheur, Umatilla and Wallowa-Whitman National Forests. R6-NR-ECOL-TP-22-97, USDA Forest Service, Pacific Northwest Region. Portland, OR. 299p. Daubenmire, R. 1970. Steppe Vegetation of Washingtion. Washington Agricultural Experiment Station Technical Bulletin 62. Washington State University. Pullman, WA. 131 p. Gregory, S. 1983. Subalpine Forb Community Types of the Bridger-Teton National Forest, Wyoming. USDA Forest Service, Intermountain Region, Bridger-Teton National Forest. Jackson, WY. Hansen, P. L., R. D. Pfister, K. Boggs, B. J. Cook, J. Joy, and D. K. Hinckley. 1995. Classification and Management of Monatana’s Riparian and Wetland Sites. Misc. Pub. No. 54, Montana Forest and Conservation Exp. Sta., School of Forestry, Univ. of Montana. Missoula, MT. 646p. Hironaka, M., M. A. Fosberg, and A. H. Winward. 1983. Sagebrush-Grass Habitat Types of Southern Idaho. University of Idaho Forest, Wildlife and Range Experiment Station, Bulletin Number 35, Moscow, ID. 44p. Hopkins, W. E. 1979. Plant Association of the Fremont National Forest. R6-ECOL-79-004, USDA Forest Service, Pacific Northwest Region. Portland, OR. 106p. Jennings, M.; Faber-Iangendoen, D.; Peet, R.; et al. 2004. Guidelines for describing associations and alliances of the U.S. national vegetation classification. Version 4.0. Vegetation Classification Panel. Ecological Society of America. Washington, DC. Jensen, M. E., L. S. Peck, and M. V. Wilson. 1988. Vegetation characteristics of mountainous northeastern Nevada sagebrush community types. Great Basin Naturalist 48(4):403-421.
43 І Appendix A: Project Planning
Johnson, C. G. and R. R. Clausnitzer. 1992. Plant Associations of the Blue and Ochoco Mountains. R6-ERW-TP-036-92, USDA Forest Service, Pacific Northwest Region. Portland, OR. 164p. Kimmins, J.P. 1997. Forest ecology: a foundation for sustainable management. 2nd ed. Upper Saddle River, NJ: Prentice Hall. Lewis, M. E. 1970. Alpine Rangelands of the Uinta Mountains, Ashley and Wasatch National Forests. Unpublished report. USDA Forest Service, Intermountain Region, Ogden, UT. 75p. Lincoln, R.; Boxshall, G.; Clark, P. 1998. A dictionary of ecology, evolution and systematics. 2nd ed. New York: Cambridge University Press. Mueggler, W. F. and W. L. Stewart. 1980. Grassland and Shrubland Habitat Types of Western Montana.
USDA Forest Service, Intermountain Research Station, General Technical Report INT-66, Ogden, UT. 154p.
Padgett, W. G., A. P. Youngblood, and A. H. Winward. 1989. Riparian Community Type Classification of Utah and Southeastern Idaho. R4-ECOL-89-01. USDA Forest Service, Intermountain Region, Ogden, UT. 191p. Potkin, M. A. 1991. Soil-Vegetation Relationships of Subalpine and Alpine Environments, Wind River Range, Wyoming. M.S. Thesis, University of Wyoming. Laramie, WY. 290p. Shiflet, T.N., ed. 1994. Rangeland cover types of the United States. Denver, CO: Society for Range Management. 152p. Svalberg, T.; Tart, D.; Fallon, D.; Ferwerda, M.; Lindquist, E.; Fisk, H. 1997. Bridger-East ecological unit inventory, Bridger-Teton National Forest. Final draft. U.S. Department of Agriculture, Forest Service, Bridger-Teton National Forest. Jackson, WY. 1868p. Tart, D. 1995. Vegetation Keys for the Bridger-Teton National Forest (DRAFT). USDA Forest Service, Intermountain Region, Bridger-Teton National Forest. Jackson, WY. 58p. Tart, D.L. 1996. Big sagebrush plant associations of the Pinedale Ranger District. Final review draft. Jackson, WY: U.S. Department of Agriculture, Forest Service, Bridger-Teton National Forest. 97p. Tart, D.; Williams, C.; Brewer, C.; Schwind, B.; DiBenedetto, J. and Schwind, B. 2005a. Existing Vegetation Classification and Mapping Framework. Section 1 in Brohman, R. and Bryant, L. editors. 2005. Existing Vegetation Classification and Mapping Technical Guide—March 2005. USDA Forest Service, Washington Office, Ecosystem Management Coordination Staff. Tart, D.; Williams, C.; DiBenedetto, J.; Crowe, E.; Girard, M.; Gordon, H.; Sleavin, K.; Manning, M.; Haglund, J.; Short, B.; and Wheeler, D. 2005b. Existing Vegetation Classification Protocol. Section 2 in Brohman, R. and Bryant, L. editors. 2005. Existing Vegetation Classification and Mapping Technical Guide—March 2005. USDA Forest Service, Washington Office, Ecosystem Management Coordination Staff. Tiedeman, J. A., R. E. Francis, C. Terwilliger Jr., and L. H. Carpenter. 1987. Shrub-Steppe Habitat
Types of Middle Park, Colorado. USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, Research Paper RM-273. Fort Collins, CO. 20p.
Volland, L. A. 1985. Plant Associations of the Central Oregon Pumice Zone. R6-ECOL-104-1985. USDA Forest Service, Pacific Northwest Region. Portland, OR. 138p.
44 І Appendix A: Project Planning
Winward, A. H. 1970. Taxonomic and ecological relationships of the big sagebrush complex in Idaho. Ph.D. Dissertation, University of Idaho. Moscow, ID. 80p. Youngblood, A. P., W. G. Padgett, and A. H. Winward. 1985. Riparian Community Type Classification of Eastern Idaho – Western Wyoming. R4-ECOL-85-01. USDA Forest Service, Intermountain Region, Ogden, UT. 78p.
<5” TS2** 5”-9.9” TS3 10”-19.9” TS4 20”-29.9” TS5 30”+ TS6 *—During this project, data collected as SC1 code was defined as 5-9%, but eliminated from mapping later on and the Shrub Canopy Classes were renamed SC1 for 10-24% and 25-100% in the final maps & documen-tation. **—During this project, data was collected as TS1 (1-3”DBH) and TS2 (3-5”DBH), but these classes were later combined during the map production to TS2 (<5”DBH) & final documentation.
51 І Appendix B: Geospatial Data Acquisition and Pre-Processing
DATA LOADING—LANDSAT TM AND DEMS Import TM
1. Acquired TM (30m multi-spectral and 15m pan) images are in NLAPS format, the scenes were: p38r30, p38r31,p37r30, p38r29, p37r30 (path and row)
2. Import TM images using ERDAS Imagine NLAPS import tool 3. Default Import projection is Albers Conical Equal Area 4. Top of the atmosphere reflectance correction was completed for each image iteratively
using standard methods 5. Scanline corrections were made on one region (Gros Ventre) 6. Clouds were eliminated from some scenes using RSAC cloud removal tools
Import DEM 1. All DEMs were downloaded from the geospatial data clearinghouse (URL:http://fsweb.
clearinghouse.fs.fed.us/cffdata/page_43110.html) using the TEUI tool kit for ERDAS imagine and were 10 meter resolution. 2. DEMs were then put together using the mosaic tool in ERDAS Imagine 8.7
Re-project TM
1. TM imagery was reprojected one image at a time – misalignment occurred in batch mode
2. Reproject TM images using Data Preparation → Reproject Images in ERDAS Imagine 3. Reproject to UTM Zone 12, NAD27, with pixel size set to 30m and 15m (multispectral an
panchromatic, respectively) Re-project DEMs
1. DEMs must be reprojected one file at a time – misalignment occurred when reprojection performed in batch mode
2. Reproject DEMs using Data Preparation → Reproject Images in ERDAS Imagine 3. Reproject to UTM Zone 12, NAD27, with pixel size set to 10m
Mapping Regions Total Acres
Wyoming 1,028,940 Wind River 594,089 Gros Ventre 1,527,167 Kemmerer 314,882
Total Acres: 3,465,079
Table 1—Total number of acres in each mapping region.
Mapping Regions of Bridger-Teton NF
Figure 1—Mapping region boundaries on Bridger-Teton National Forest mapped by Remote Sensing Applications Center.
Appendix B: Geospatial Data Acquisition and Pre-Processing
52 І Appendix B: Geospatial Data Acquisition and Pre-Processing
Subset TM and DEM data Subset TM images and DEM using study area shapefile boundaries converted to .AOI
format 1. Open image in ERDAS Imagine Viewer 2. Open boundary shapefile and save as AOI from ERDAS viewer 3. Open AOI boundary of study area 4. Select AOI boundary (AOI is highlighted) 5. In Viewer select Utility → Inquire Box → check the Snap to Raster box and select Apply 6. Click ‘Fit to AOI’ button in Inquire Box Coordinates window 7. Subset by selecting Data Preparation → Subset Image 8. Click ‘AOI’ and select from viewer button to create a complete subset definition
Data Processing Procedures Leaf On, Leaf Off and 1993 Landsat Imagery 1) Clipped out study areas using the mapping region boundaries (region_boundary.aoi’s). For
the regions which included more than one ETM scene, each scene was clipped out to the region boundary and mosaicked together using the image matching option in Imagine. The final images were registered to the CIR DOQ imagery.
Regions which contained more than 1 scene: Leaf ON - Kemmerer, Gros Ventre Leaf OFF - Kemmerer, Gros Ventre 2) 6-Band Leaf OFF and Leaf On Imagery. Leaf ON images: 3730_12aug04_multi_utm.img 3730_27july01_multi_utm.img 3829_1aug03_multi_utm.img 3830_1aug03_multi_utm.img 3831_29june00_multi_utm.img Leaf OFF images: 3730_10oct99_multi_utm.img 3730_8sept99_multi_utm.img 3829_15sept99_multi_utm.img 3829_23sept02_multi_utm.img 3830_15sept99_multi_utm.img 3830_3oct00_multi_utm.img 3831_15sept99_multi_utm.img 3831_3oct00_multi_utm.img
Figure 2—Landsat TM Path/Row boundaries for Bridger-Teton.
53 І Appendix B: Geospatial Data Acquisition and Pre-Processing
Color Infrared Digital Orthophoto Quadrangle (CIR DOQ) Imagery 1) CIR DOQs 2002 were ordered through the Wyoming state GIS clearinghouse, (URL:http//
wygia2.state.wy.us/htmaboutdoqq2002.asp) and downloaded as .zip (compressed) files. DOQs at 1 meter resolution were available for all areas. The 1m DOQs were mosaicked for an entire mapping region using Erdas Imagine and specifying the image matching option.
bt_wyoming_mosaic_nad27.img bt_windriver_mosaic_nad27_linear.img km_mosaic_nad27.img bt_gv_mosaic_nad27_linear.img 2) The full scene DOQs were clipped out to the mosaicked region mapping boundaries.
3) Used “The Neighborhood Function” to fill in the “0” pixels (holes in the DOQs) in Image (had to run several times). Only applied it to the “0” values and used all values in the computation except for “0”. A final model was developed to fill in any remaining “0” values with the lowest value in the imagery. The “0” value replacement files were iterative for each region and temporary files only.
Tone, Texture and Tone/Texture Ratio Images 1) Contrast and Clumps: Forest and shrub Tone, Texture, and Tone/Texture ratio products
were produced for each region. Tone image products are an image enhancement that produce backscatter information incident in an image. Texture products are another manner to demark forest and shrub texture from high resolution imagery, in this case CIR DOQs. CIR DOQs were used as an input into the Tone and Texture Modules in Erdas Imagine to enhance forest versus shrub tones and textures. Then used image modules to create a ratio of tone to texture which is the “tnt” image product. The tone, texture, and tnt image outputs were resampled to 10 meter resolution and subset to each region which were used in the vegetation classification.
region_10m_forest-texture_clipped.img region_10m_shrub-texture_clipped.img region_10m_forest-tone_clipped.img region_10m_shrub-tone_clipped.img region_10m_forest-tnt_clipped.img region_10m_shrub-tnt_clipped.img RGB Clustering 1) Used the “RGB Clustering” module in Imagine to generate 4, 5, 3 band clustering for each
region. (Options used: no. of layers 1, 2, 3; total bins 7, 6, 6; standard deviation of 2). bt_gv_mosaic_nad27_rgb_3m.img bt_km_mosaic_nad27_rgb_3m.img bt_wy_mosaic_nad27_rgb_3m.img bt_wr_mosaic_nad27_rgb_3m.img
54 І Appendix B: Geospatial Data Acquisition and Pre-Processing
Landsat Derivatives (NDVI, PC and Tassel Cap) 1) Imagine modules “Indices, Tasseled Cap, and Principal Components” were used to
generate NDVI, PCA and Tasseled Cap layers for Leaf On, Leaf Off imagery. (Options used on each: stretch to unsigned 8-bit, ignore zero in stats and the region aoi boundary). The output images shifted slightly (< 30m) from the original input imagery – so each image was re-checked for proper registration of pixels against the original imagery.
Digital Elevation Models (DEMs)
1) Downloaded DEM quads from the GSTC website at 10 meter resolution. Used the TEUI Geospatial Toolkit to uncompress grid files (in both feet and meters) and mosaic into a single file for each study area. These grid files were then imported to .img files and geographic subsets were made for each region boundary.
2) Full scene in meters (image files): Full scenes in feet (image files):
bt_dem_10m.img bt_dem_10m_ft.img (Each mapping region also contained full 10 meter DEM images) 3) Clipped scenes in meters (image files): Clipped scenes in feet (image files): wy_elev10m_m_clipped.img wy_elev10m_ft_clipped.img wr_elev_10m_m_clipped.img wr_elev_10m_ft_clipped.img km_elev10m_m_clipped.img km_elev10m_ft_clipped.img gv_elev_10m_m_clipped.img gv_elev_10m_ft_clipped.img DEM Derivatives (Slope, Tri-shade and Curvature) 1) Used TEUI toolkit to generate the tri-angular shaded relief, slope and curvature images.
These images were derived from the 10 meter (Wyoming, Wind river, Gros Ventre and Kemmerer) image DEMs.
Wyoming
· Slope Computed slope continuous data and ramp (using TEUI Geospatial Toolkit) for the 1 0m -m et e r DE M. C l i p p ed t o t h e r eg i o n bo u n da r y = wy_elev10m_m_clipped_slope_cont.img and wy_elev10m_m_clipped_slope_ramp.img.
· Tri-Shade (hillshade-120; hillshade-240; hillshade-360) Computed tri-shade (using TEUI Geospatial Toolkit) for the 10m-meter DEM. Clipped to region boundary = wy_trishade_elev10m_m_clipped.img.
· Slope Computed slope continuous data and ramp (using TEUI Geospatial Toolkit) for the 1 0m -m et e r DE M. C l i p p ed t o t h e r eg i o n bo u n da r y = wr_elev10m_m_clipped_slope_cont.img and wr_elev10m_m_clipped_slope_ramp.img.
55 І Appendix B: Geospatial Data Acquisition and Pre-Processing
· Tri-Shade (hillshade-120; hillshade-240; hillshade-360) Computed tri-shade (using TEUI Geospatial Toolkit) for the 10m-meter DEM. Clipped to region boundary = wr_trishade_elev10m_m_clipped.img.
Computed slope continuous data and ramp (using TEUI Geospatial Toolkit) for the 10m-meter DEM. Clipped to the region boundary = gv_elev10m_m_clipped_slope_cont.img and gv_elev10m_m_clipped_slope_ramp.img.
· Tri-Shade (hillshade-120; hillshade-240; hillshade-360) Computed tri-shade (using TEUI Geospatial Toolkit) for the 10m-meter DEM. Clipped to region boundary = gv_trishade_elev10m_m_clipped.img.
Computed slope continuous data and ramp (using TEUI Geospatial Toolkit) for the 10m-meter DEM. Clipped to the region boundary = km_elev10m_m_clipped_slope_cont.img and km_elev10m_m_clipped_slope_ramp.img.
· Tri-Shade (hillshade-120; hillshade-240; hillshade-360) Computed tri-shade (using TEUI Geospatial Toolkit) for the 10m-meter DEM. Clipped to region boundary = km_trishade_elev10m_m_clipped.img. Curvature Image (Identifying concave and convex surfaces from image) Computed curvature (used TEUI Geospatial Toolkit) = km_elev10m_m_curvature_.img.
Wetness (Compound Topographic Index- A compound of hill slope and water drainage) 1) Used the 10m-feet DEMs to derive a final wetness image (WY, WR, GV and KM used the
10-meter DEM). Process required two steps: 1. 02-dem10.cti.aml Generates a wetness images
2. topofilter_citi.gmd Filters the wetness image & generates the final wetness image
Wyoming
Used wy_elev_10m_ft.img to compute wetness = wy_cti_topowetness_10m.img. Clipped to the region boundary = wy_cti_topowetness_10m_clipped.img.
Wind River Used wr_elev_10m_ft.img to compute wetness = wr_cti_topowetness_10m.img. Clipped to the region boundary = wr_cti_topowetness_10m_clipped.img.
Gros Ventre Used gv_elev_10m_ft.img to compute wetness = gv_cti_topowetness_10m.img. Clipped to the region boundary = gv_cti_topowetness_10m_clipped.img.
56 І Appendix B: Geospatial Data Acquisition and Pre-Processing
Kemmerer Used km_elev_10m_ft.img to compute wetness = km_cti_topowetness_10m.img. Clipped to the region boundary = km_cti_topowetness_10m_clipped.img.
Climate 1) The TEUI Geospatial Toolkit was used to acquire the following Daymet climatic data layers:
annual temperature; annual precipitation; solar radiation; growing days. Each layer was subset by the mapping region boundary. Climatic data were 1-km pixel resolution.
Riparian
A Drainage Networks (Stream formations from associated hill slope low point)
1) Drainage networks were generated from the unclipped 10-meter DEMs in feet (Wyoming, Wind River, Gros Ventre, and Kemmerer). These files were of a temporary nature and were used as inputs to create distance to stream files. Regions were clipped to the region boundaries.
B Distance to Stream (Number of pixels from the nearest stream)
1) Used Erdas Module “Search” to calculate number of pixels to the nearest stream. Set search parameters. Input the clipped drainage networks and the aoi boundary and output a raster layer.
Used the following parameters:
Vector Type: Line Use Attribute As Value: Strahler Order Output Cell Size: 10
Units: Meters Classes: 1, 2, 3, 4, 5, 6 Distance to search: 300 (in pixels)
57 І Appendix B: Geospatial Data Acquisition and Pre-Processing
Continuous data layers
Strahler Order Maximum elevational dif-ference in meters
Maximum search distance from stream network (pixels)
1 1 3
2 3 6 3 7 9
4 10 15
5 12 21
6 14 30
C. Potential Riparian Zones (Floodplain)
1) Riparian buffers (or floodplains) were generated from the 10-meter clipped DEMs
using the floodplain.aml. The AML called up a “flood.exe” program and needed a text file that contained the x, y and z parameters for each stream network.
2) Used the following parameters for the 10-meter DEMs (Wyoming, Wind River, Gros
Ventre, Kemmerer): 3) Two models filtered out single pixels from the floodplain output. 04-
flood10m_filter01 & 02.gmd were used for the regions which used 10meter DEMs (Wyoming, Wind River, Gros Ventre, Kemmerer).
Thematic data layers wy_elev10m_m_aspect_ramp.img wy_elev10m_m_aspect_ramp_zonalmaj.img wy_geol_lvl_1_v1.img wy_geol_lvl_1_v1_zonalmaj.img wy_geol_lvl_2_v1.img wy_geol_lvl_2_v1_zonalmaj.img wy_geol_lvl_3_v1.img
59 І Appendix B: Geospatial Data Acquisition and Pre-Processing
Names of Erdas Imagine Models & ArcInfo Models Used to Process Imagery: 02-dem10_cti.aml Step 1 for generating a wetness image (Output =
region_wet10m_1). topofilter_citi.gmd Step 2 for generating wetness image. Filters output
from step 1. 03-dem10_process.aml Generates stream networks from 10meter DEMs
(values in feet). floodplain.aml Generates a “potential riparian” buffer around stream
networks (Uses flood.exe and param10m.txt). flood10m_filter01.gmd Step 1 for filtering the output from the floodplain model
which was generated from 10m DEMs. flood10m_filter02.gmd Step 2 for filtering the output from the floodplain model
which was generated from 10m DEMs. flood30m_filter01.gmd
60 І Appendix B: Geospatial Data Acquisition and Pre-Processing
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61 І Appendix C: Field Data Collection
Bridger-Teton National Forest Vegetation Map Data Collection Protocols 2005
This document will serve as a guide to data collection in the field for the Bridger-Teton Vegetation Mapping Project. Detailed instructions on how to fill out datasheets and plot maps follow in this document. [Other aids, such as coding information, are found at the end of the document in the Appendices.] These protocols have been established following the USFS Existing Vegetation Classification Mapping Technical Guide protocols as well as guidelines from the Remote Sensing Applications Center (RSAC).
Overview The data that you will be collecting will be used in an effort to create a 1:100000 Mid-Level scale map of vegetation patterns across the Bridger-Teton National Forest. This vegetation layer will identify current (existing) conditions to be used in Forest planning, watershed analysis, and project planning. Developing an up-to-date vegetation layer is important because it will provide information that is applicable to multiple resources and will be used to guide decisions at the local, regional, and national levels. The applications for this vegetation information are many and far-reaching. The data collected through this project will be used to assess fuel characteristics, describe timber stands, analyze wildlife habitat requirements, assess rangeland conditions and identify management opportunities. It will also be used to document species of concern, noxious weeds, and potential vegetation types. All of this information will help planners establish appropriate goals and make decisions regarding the Forest. The attributes to this vegetation layer will be as follows:
• Species Composition – current cover type (existing vegetation), emphasizing dominant and co-dominant species.
• Percent canopy cover of existing vegetation. • Structural stage for forested vegetation - size classes, canopy cover, growth stage.
The Minimum Map Unit (MMU) for the map is 2 acres for Riparian, Deciduous, Agricultural, Urban & Water classes and 5 acres for all others, with means to step it up to coarser scales.
Development So, how will this vegetation layer be developed? It is done in stages with cooperation by several people and agencies. 1. Describe finished project. To begin, managers describe what information will be required in the vegetation layer to make it usable for a broad range of management applications. This directs data collection, map development, and ultimately of what the final product will consist. Once these criteria are agreed upon, the next stage is to gather pre-existing data. 2. Gather existing data. Pre-existing data supply an outline of what information has already been collected, what has been useful in the past, what will be useful in the future, and what areas of the Forest are well documented or where more information is needed. These data also help to develop a vegetation classification, by providing a sketch of what community types may be found on the Forest. 3. Develop vegetation classification. Next a coarse classification is developed based on existing data sources and existing classifications. This step includes reviewing existing data and classifications as well as conducting reconnaissance of the Forest to ensure that an appropriate system is being developed. The purpose and taxonomic level of the classification determines what criteria and descriptive attributes will be used.
APPENDIX C: Field Data Collection
62 І Appendix C: Field Data Collection
4. Define map legend. A map legend is then created, which defines such map attributes as lifeform, community type, cover, and structure. This step is important for both the remote sensing work and field sampling to begin. 5. Field Sampling The field coordinator develops sampling methods for collection of plot data. These data will be used for both aspects of the project, the vegetation map and the coinciding vegetation classification. Sites are sampled to help ‘train the satellite’ to different vegetation types. The information collected in the field helps define trouble areas in the map, as well as verify what the mappers could observe from the satellite imagery. It also goes into the development and refinement of the vegetation classification system and description of vegetation communities on the Forest. Detailed instructions for field sampling follow in this document. 6. Assemble database(s) and analyze data After sufficient field data has been collected and checked for errors, it is then processed. From this information, the coarse classification and key are edited and refined. Definitions and ecological interpretations of vegetation types are compiled from plot information, and descriptions are written or adapted to better fit our Forest. This data also goes to the GIS contractors, in our case RSAC, where they conduct the GIS modeling. 7. Assess accuracy. Once the field data has been processed, and a more detailed version of the map has been created, the next step is to check the accuracy of that map. This begins with verification in the field, in our case the field season following the initial data collection season. The information from sampling polygons in the field then goes into computing the accuracy of the map. The details of the map accuracy are included in the final published product and are usually displayed in an error matrix. 8. Review and editing. An in-depth review process is conducted to ensure quality control for both the classification and vegetation guide as well as the map. 9. Publish technical guide and classification. After publication, the Forest will choose how to monitor and update the map for future use.
63 І Appendix C: Field Data Collection
Bridger-Teton National Forest Vegetation Mapping Project Protocols for Training Data Collection
2005 Field Season
I. Before leaving the office Before leaving the office, each crew should know where they are going, what information to collect, and have appropriate gear to complete their tasks.
A. Where: predetermined points from RSAC, plus planning from supervisor and lead. B. What: check with your supervisor and/or crew lead before leaving. This document will give you details on the information to collect. C. What to take: see gear checklist. Things to double check:
- Batteries (GPS and Camera) – make sure they are fully charged. - Storage space (GPS and Camera) – make sure files have been downloaded and deleted. - Make sure correct waypoints are loaded onto each GPS - Make sure you have packed all GPS cords and accessories - Check on each GPS that PDOP = 6, Zone = 12, and NAD= 27 - Datasheets – make sure you have plenty with you, on both regular paper and Rite in the Rain - Check that you have correct aerial photos, plot maps, travel maps, nav maps - Check with district managers regarding road conditions
II. Navigating to a plot
A. Selecting waypoint on GPS unit The waypoints should be pre-loaded. Select the waypoint you are going to in the navigation screen on the unit. (Nav→Option→Select Target→scroll to find plot number). (If not pre-loaded, the coordinates may be found on the front of the plot maps, in the upper left corner. You may key these in by hand if necessary.)
B. Travel Notes Along the way, look around. Digital data layers are great, but they do not replace human perception. On the way to the plot or observation point, take travel notes on the travel map about the representative vegetation types through which you walk or drive. Delineate the polygons you are describing, and take notes as you would for an observation point. (See Section IV. E. for directions on observation points.) The difference is that you will be delineating the areas you are describing—they will not be marked for you. Use a dashed line if you are not sure of the exact boundary of the area you want to represent. Try to look at areas that are 5 acres or bigger unless they are riparian, whitebark pine, or aspen. Use the same criteria as for an observation point: Lifeform, Map Dominance Type, DBH Class Code, Canopy Closure (+/-5%). Record any additional comments.
III. Establishing a plot BE CAREFUL NOT TO TRAMPLE THE ENTIRE PLOT! A. Setting up the plot
1. Walk-through assessment Once you arrive at the waypoint, make sure it is representative of the polygon. If it is not, consider moving the point (plot center) to a more representative location within that polygon. This option should be used with caution and good judgment. If the polygon is very heterogeneous, sample the most representative vegetation community type (i.e. of which type the polygon is mostly comprised). In the Remarks section, include rationale for moving the plot, and details of dominance composition within the segment.
2. Plot Location The plot center should be established from the given UTMs/waypoint. If you are unable to obtain a GPS reading (due to cover, weather, etc.), try to estimate the plot center’s location relative to where you could obtain a reading, and note information (such as distance and/or direction from the waypoint) in the Remarks section. If you moved plot center altogether, note in Remarks any new information
64 І Appendix C: Field Data Collection
information regarding a change in plot location, and be sure to mark the Plot Moved? box on the datasheet.
3. Plot Size The standard plot will be a 1/10th acre circle, with a radius of 37.4 feet.
4. Plot Setup One crew member will run the tape out 37.4 feet from plot center in a few directions, and mark the plot perimeter with flagging. This should be marked clearly enough so that the plot boundaries are obvious.
IV. Collection of data This section will give you detailed information on how to fill out the plot datasheets, as well as collect GPS data for a full plot. Instructions are listed in order of how the sections appear on the datasheet, not necessarily in order of how the sheet should be filled out. See Section IV. E. for directions on observation points.
A. GPS The first thing to do once the plot has been established is to start collecting points with the GPS. This is especially important if the plot center has been relocated. This is good to get started because, at times, satellite coverage may be low and thus take a while to collect. You should collect a minimum of 30-60 positions for non-forested plots, and 60-90 positions for forested plots (or as many as possible if experiencing difficulty). It is impor- tant to collect positions from the plot center, so be at the center to start collection. Every plot should use a PDOP mask of 6 and elevation mask of 15.
**IF the GPS is not working (low satellites, etc.), then raise the PDOP, using the highest accuracy (i.e. the lowest number) possible. Record changes to PDOP and elevation masks on the data sheet. Remember to set your PDOP back to 6 afterward!
To record data points: 1. Press Power to turn on the GPS unit, then press Data.
2. If it’s the first plot of the day, create a new rover file by pressing Enter. 3. Open up the feature labeled ‘Veg_Plot’ by pressing Enter, then press Enter again to
arrow/type in the unique number for the current plot. 4. Arrow down to ‘Close’ to return to the Veg_Plot screen. 5. Once the GPS unit has collected the recommended number of positions (see above),
press Close to close the feature, but not the rover file. 5. Record the UTMs off of the GPS unit onto the datasheet.
6. Turn off the GPS unit. To record a new point on the same rover file, choose “Open Selected File,” which will bring you to the Update Feature screen. Press Data, which will bring up the New Feature screen. Press Enter, and proceed from #3 above. After the last plot of the day, close the rover file.
To change the PDOP (Position Dilution of Precision): 1. Press SYS until you get to the Setup screen. 2. Scroll to Configurations and press Enter. 3. Scroll to GPS and press Enter. 4. Arrow Down to highlight the PDOP mask line. 5. Press Enter, arrow Up or Down to arrive at the desired number, and press Enter to close. 6. Press Close twice to return to the Setup screen. 7. Don’t forget to reset the PDOP to 6 before the next plot!
65 І Appendix C: Field Data Collection
B. Plot Metadata (Datasheet Page 1) 1. Plot ID – This will be a unique name, and naming guidelines are as follows:
[Region code] + [Forest Code] + [Year] + [Examiner’s initials*] + [RSAC ID] 04 - BT - 2005 - LD - XXXX *Examiner initials will be the crew supervisor, currently Liz Davy. The first number of the RSAC ID represents the geographic region, as designated by RSAC: 1 = Wyoming Range, 2 = Gros Ventre, 3 = Kemmerer, 4 = Wind River
2. Examiners – First initial and last name (ex: L. Davy) 3. Sample Type – standard at OCMA (Ocular Macroplot) 4. Plot Size – standard at 1/10th acre circle, 37.4′ radius 5. Species List Type – standard at Complete 6. Region – standard at 04 7. District – discern from the nav maps, and fill in 01 = Kemmerer, 02 = Big Piney,
03 = Greys River, 04 = Jackson, 06 = Buffalo, 07 = Pinedale 8. Plot Moved – check box only if plot moved to a more representative area. 9. UTMs, GPS Rover File, & PDOP – read off of the GPS. UTMs written on the sheet are not differentially corrected. 10. Zone 12 and NAD 27 are standard for this project. 11. Elevation – record exact number off of the GPS. 12. Declination – printed in the upper left corner of the plot maps. If not given on the plot maps, look up declination for the plot area online at the NOAA website: http://www.ngdc.noaa.gov/seg/geomag/jsp/Declination.jsp 13. Slope – Slope is determined by sighting the clinometer along a line parallel to the average incline (or decline). Take the slope from the center of the plot. To measure slope, Observer 1 should stand at plot center and sight Observer 2, who stands at the downhill edge of the plot. Sight Observer 2 at the same height as the eye-level of Observer 1. Read the slope directly from the percent scale of the clinometer. Next Observer 1 should turn 180º and sight Observer 2 on the uphill edge of the plot. Average the two numbers to obtain a slope value. Slope is null if less than 5%.
A few rules: · If slope changes gradually across the plot, record an average slope. · If slope changes across the plot, but the slope is predominantly one direction, record predominant
slope percentage rather than the average. Note in Remarks. · If the slope falls directly between two side hills, record the average slope of the side hill(s). · If the slope falls on a canyon bottom or on a narrow ridge top, but most of the area lies on one side hill, most likely you should move the plot center to an area with less heterogeneity (as the side hill may possess different vegetation).
14. Aspect – Aspect is measured with a hand compass along the same direction used to determine slope. Record the predominant plot aspect in degrees, 1° to 360°. Aspect is determined along the direction of slope for land surfaces with at least 5% slope in a generally uniform direction. Aspect is null if plot is flat.
15. Slope Position – Record the two-dimensional position of the plot using the following codes: SU = Summit, SH = Shoulder, BS = Backslope, FS = Footslope, TS = Toeslope, VB = Valley bottom, TE = Terrace. See cheatsheets on the tatums for a diagram of these terms.
16. Slope Position Modifier – Record the modifier which best describes the primary slope position using the following codes: LR = lower, MD = middle, UP = upper. 17-19. Dominance Type, DBH, & Map Dominance Type See Section D – fill out these fields after canopy cover has been collected
20. Plot Photos *Before taking pictures: · Doublecheck that all compasses are set to the correct declination! · Make sure the resolution on the camera is set to M1. Do this by pressing the menu button when in shooting mode, scrolling down to resolution, and selecting M1. · Make sure the compression is on the middle setting.
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a. Aerial Photo ID – Record the date, the roll number, and the exposure number, in the following format: 09/19/91-489-119
- First six numbers are the date the photo was taken - Second set of numbers (489) represents the roll number - Last set of numbers (119) represents the exposure number
▪ RSAC provides at least the roll & exposure numbers for the photo corresponding to each plot, but the crew will need to look up the dates on the actual photos, filed in Liz’s office. However, for this project, aerial photo information already has been compiled onto Excel spreadsheets, which should be found in the folders for each geographic region, on the K: drive. b. Photos A total of either 7 (in non-forested plots) or 9 (in forested plots) photos will be taken at each plot: 1 of the whole plot, 1 in each cardinal direction (N,E,S,W), and either 2 or 4 fuels photos, depending on whether the plot is non-forested or forested. (Sometimes the amount of photos will differ from 7 or 9; if so note the actual number on the datasheet, and make a note in the Remarks field as well.)
You may need to angle the whiteboard at any time to minimize glare. i. Photo #1 = Plot photo Take the plot photo from the perimeter of the plot towards plot center, perpendicular to the slope. This photo will be used as a representation of the plot, and should encompass as much of the plot as possible. If there is no slope, choose a view that is most representative of the plot. The whiteboard should have the Plot #, Date, and a “P” indicating Plot photo. (The Plot # is the RSAC 4-digit number for each plot.) Ex. 1001 6/16/05 P
ii. Photos #2, 3, 4, 5 = Cardinal Directions From the plot center, take photos of the view out 30′ in the cardinal directions in the following order: #2 = North, #3 = East, #4 = South, #5 = West. (Taking them in this order makes it easier to name the files back in the office.) If in a dense stand such as willow, move away from the plot center to obtain a representative view of the plot, and note the move in the Remarks section. The whiteboard may appear anywhere in the photos— provided it does not block the view of the plot—and may be held by a crew member or attached to the survey pole. The whiteboard should have the Plot #, Date, and the corresponding first letter of each direction. Ex. 1001 6/16/05 W
iii. Fuels Photos (#6, 7, sometimes 8 & 9) These photos will be taken from plot center, and should have a view 30 feet out. In special cases, such as really dense plots, photos can be taken at 15 feet. Any deviation from 30’ should be noted on the datasheet. If there is a slope to the plot, take photos at a direction that is perpendicular to the slope. Otherwise, choose a direction that is a good representative of the understory and overstory fuels for the plot. Facing one way perpendicular to the slope will be called Line 1. Facing the opposite way (180º from Line 1) will be called Line 2. For the Fuels photos, label the whiteboard like this: Plot #, Date, Line #, Azimuth. You may place the whiteboard on the survey pole at 30 feet, or choose to place it in a corner of the photo frame to minimize blockage of the plot. Take the photos, and remember to review the photos on the camera before moving on. Check that the whiteboard is legible and that the photo captures the composition of both the fuels and the vegetation along that line. For each photo, remember to change the Line # and Azimuth on the whiteboard! Non-forested plots: For Lines 1 & 2, only ‘down’ photos are needed to assess fuels in the understory. (Thus, Photo #6 will be the ‘down’ photo in one direction perpendicular to the slope, and Photo #7 will be the ‘down’ photo in the opposite direction perpendicular to the slope.) The ‘down’ photos should be taken with the camera angled down so the horizon is at the
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upper third of the photo. In plots with little or no slope, look around for representative fuel areas and photograph ‘down’ photos accordingly. For example, you may want to include a stand of conifers in the background, instead of just sky.
Forested plots: For Lines 1 & 2, both ‘down’ and ‘up’ photos are needed to assess fuels in the over-story as well as on the ground. Photos 6 & 7 will be the ‘down’ and ‘up’ photos for Line 1, while Photos 8 & 9 will be the ‘down’ and ‘up’ photos for Line 2. The ‘down’ photos should be taken with the camera angled down so the horizon is at the upper third of the photo. The ‘up’ photos should be angled so the horizon is at the bottom third of the photo, to capture the overstory fuels. This will capture the surface fuels.
A few notes on photos: · Make sure that the camera is set to take pictures, not videos, by making sure the mode is set
all the way to the right under the little camera diagram. · Make sure the camera is not zoomed in at all. · Try not to have people or items such as gear in the photo. · Keeping the display off saves batteries, and a better picture is taken if you look through the
view finder. 21. Ground Surface Cover – It is best to summarize ground surface cover as you are finishing the
plot, as you have had a chance to walk around the entire area. Record the estimated percent ground cover at the soil surface plane for each ground surface cover type. Cover is defined as that portion of the horizontal surface layer intersected by ground surface features. Select ground cover categories that are visible when looking down, as if the plot has been mowed—vegetation canopy cover above the soil surface plane is not considered to be ground surface cover. At times items will overlay each other, and when this occurs, the portions of each item that are viewed from above are what will be selected and recorded. Total ground surface cover of all features must equal 100%. Enter the 4-letter ground surface cover code, if not printed.
Brief descriptions provided here are from p. 2A-4 of the Vegetation Technical Guide: BAVE = basal vegetation; soil surface occupied by vascular plants, usually 3-7% (Dave Tart suggested dividing the total vegetation cover by 20 to arrive at this #.) NONV = nonvascular plant (moss, lichen, algae, etc.) cover WOOD = dead woody material >0.25 inch in diameter inc. bases of dead trees/shrubs BARE = soil particles <2mm not covered by rock or organic materials; does not include roads but does include foot trails LITT = plant litter and duff not yet decomposed, includes small twigs, dung, etc. BEDR = rock underlying soil or other superficial material BOUL = rock >600mm (>24”) in diameter or length STON = rock fragments 250-600mm (10-24”) in diameter COBB = rock fragments 75-250mm (3-10”) in diameter GRAV = rock fragments 2-75mm (<3”) in diameter Other fill-in categories: PEIS = permanent ice and snow covering plot TRIS = transient ice and snow covering plot WATE = permanent and transient water obscuring other cover types
22. Cover from Above – It is best to summarize canopy cover from above as you are finishing the plot, as you have had a chance to walk around the entire area. This is where you will record the % total canopy cover of each listed major lifeform as it would be seen from the air, as if unmoved. This is the bird’s eye view of total cover, thus should be determined imagining a top-down perspective. Record the actual cover (ex. if it’s 54%, write 54%, not 50% or 55%).
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For Total Tree Canopy Cover, consider both coniferous and deciduous tree cover, incorporating regeneration and overlap. For the individual layers in the next five fields, consider the cover of lifeforms and exclude any overlap of canopy. This total should add up to 100%.
Some helpful hints: · canopy cover from above should not equal more than what was assigned for cover by layer
· aerial photos are helpful in forested types
23. Remarks – This field can be used to make any comments about the setting of the plot, overall composition of the vegetation, and description of the segment as a whole. It is often good to save this field to do last, since you’ve been walking around the plot and have a good understanding of what is going on floristically. Keep in mind the goal of the map and the plots, and include such comments as:
· how representative is the plot (i.e. does the plot capture the general composition of the area/polygon?) · dominance type (whether it fits the community description very well) · vegetation description · variation in the landform · variation in fuels composition · any problems with keying, etc.
· any notes on photos · any information on road conditions, access, parking · any information that will help you to distinguish this plot (views, anecdotes) C. Vegetation Composition Form (Datasheet Page 2) This should be filled out after the species list has been compiled. Canopy cover of vegetation will be collected using the ocular macroplot method. When collecting this data, start with the uppermost layer and record species in that order. One percent canopy cover would fill a 6.5 foot square. 1. Vegetation Cover by Layer a. Layer Data – This field gives a sketch of the area. It is useful both for remote sensing and fuels information. Not all plots will have all layers. Assign a canopy cover for each layer that is present—this cover is a total for all species within that layer. Record exact percentages. List any major or dominant species within that layer, and assign a predominant height for trees and shrubs. For forested areas, give a predominant DBH for the tree layers. The following codes describe each layer: Tree Overstory (TO) layers: TO – Total tree overstory—trees greater than or equal to 16′ in height. Estimate the total canopy cover of single-stemmed woody plants greater than 16 feet in height. This cover estimate should account for overlap between sublayer or species. Total tree overstory cover cannot be greater than the sum of the sublayers. This cover estimate helps the examiner determine dominance type. Summarize cover, predominant DBH, and predominant height of the following sublayers, keeping in mind that they may add up to more than but not less than the total overstory cover. Sublayers are not separated out by species. TOSP – Supercanopy: scattered overstory trees that clearly rise above the main canopy. TOMC – Main canopy: dominant and co-dominant overstory trees that receive direct sunlight from above. TOSB – Overstory trees clearly overtopped by, and separate from, the main canopy, but taller than the regeneration layer. Tree Regeneration (TR) layer: TR – Total tree regeneration—trees less than 16′ in height. This cover estimate should account for overlap between seedlings (trees <4.5′ in height) and saplings (trees 4.5-16′) for the total regeneration. Shrub (S) layers: S – Total shrubs—defined as multiple-stemmed woody plants less than 16 feet in height at
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maturity. Summarize cover and predominant height of the following shrub sublayers, keeping in mind that they may add up to more than but not less than the total overstory cover. Sublayers are not separated out by species. ST – Tall Shrubs: shrubs > 6.5′ in height. SM – Medium Shrubs: shrubs 1.6′ to 6.5′ in height. SL – Low Shrubs: shrubs < 1.6′ in height. Herbaceous (H) layers: H – Total herbaceous—defined as vascular plants without significant woody tissue above ground. Summarize cover and height of the following sublayers, keeping in mind that due to overlap, this total may be less than the sum of the sublayers.
FB – Forbs (nongraminoid flowering plants) GR – Graminoids (grasses, sedges, rushes) b. Canopy Cover – record an exact percentage. c. Dominant Species – write the full name of the one dominant species occurring in each layer observed. If no one species is obviously dominant, leave the field blank. d. Predominant DBH – record the prevailing tree diameter at breast height (4.5 feet) to the nearest inch, for the overstory layer on forested plots only. Predominant diameter is the prevailing diameter of the most abundant tree species in the layer or sublayer. To determine this diameter, select a representative tree and measure it with a diameter tape, or “dbh tape,” using the following method. Tree diameter is a measure of the circumference of a tree. Breast height is defined as the average stem diameter at a point 4.5′ above ground as measured from the uphill side of the stem. Place the sharp pin at the end of the dbh tape in the tree stem at breast height, keeping the tape level, wrap it around the rest of the stem until the black line on the opposite end overlaps a number. Be sure to read the number representing the diameter of the tree at breast height in inches, and record to the nearest inch.
e. Predominant Plant Height – record the prevailing tree height for the overstory and regeneration layers to the nearest foot. To determine this height, select a representative tree for the layer or sublayer and estimate its height using a clinometer and measuring tape. For shrubs, a prevailing height must be recorded for each layer to the nearest foot.
To measure tree height using the clinometer, apply the Pythagorean Theorem (a²+ b² = c²). Use the tape to measure a distance on the ground from the base of the tree, note that distance and stand at that point with the clinometer. Look through the clinometer to the top of the tree, noting the number on the right-hand side. Note that number, and using the theorem, you should be able to calculate a height for the tree.
2. Vegetation Cover by Species a. Lifeform – Note whether a species is a Tree, Shrub, or Herb (T, S, or H). Try to group your species list in order by lifeform, as this will aid in determining cover by layer, and later data entry. a. Layer Code – Next, record the two- or four-letter code as listed above in Section C.1.a. (TOMC = Tree Overstory Main Canopy, FB = Forb, etc.) b. Species – Record all species found within the plot boundaries, and assign canopy cover (not foliar cover) to each, using exact percentages; this does account for overlap between sublayers for woody species. Record the full scientific name of each plant species. If you are unsure of the species name, write the genus, then “cf.”, then the supposed species name (cf. is a Latin a abbreviation for compare). If you can only be sure of family or genus, note “sp.” (as in Asteraceae sp. See also the note below on naming.) As you begin to collect the species, DBH, height, and cover information, keep these rules in mind—they will speed your data collection considerably: · Do not list species that do not occur in the plot, even if they are present in the stand.
· When recording species cover by sublayer , it is most efficient to first estimate canopy cover by sublayer and then estimate the overlap, if any, between sublayers to derive canopy
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cover for the overstory and regeneration layers. · Except in very diverse plots, do not spend more than 20 minutes looking for new and different species to record. Remember that the plot data is used to classify the overall vegetation of the forest, not to make a complete species list for it. If you have to spend much more than 20 minutes to find a species, it probably isn’t going to be important in characterizing the vegetation type. For plots with >25 taxa, you may take up to 30 minutes on the listing process.
· If you cannot identify a plant to species, record the species with a provisionary name such as “Carex unknown sp. 1,” “Brassicaceae yellow fls #2”, “Aster Fred”, or as a last resort “unknown species #1,” etc. The more information you record about the unknowns, the easier it is to keep track of them. Record associated cover estimate and other data for the unknown as you would for any other species. Then collect a specimen:
Take a sample of the species with as much of the plant as possible, using a trowel or pick. It is very important to get a good sample of the root system and several
samples of the sexual parts. These will be essential in later plant identification. Knock away as much dirt/mud/gravel as possible then place the sample in a baggie. Label the baggie with the plot code and the name you assigned it on the datasheet. Record the initials of the person who made the collection next to the provisionary name on the vegetation composition form. c. Total Overstory Canopy Cover – For each species in the overstory layer (trees >16′), record canopy cover in exact percentages. Remember, this is canopy cover, not foliar cover. Canopy cover includes interspaces ≤ 6″. d. Total Regeneration Canopy Cover – For each species in the regeneration layer (trees <16′) record canopy cover in exact percentages. Remember, this is canopy cover, not foliar cover. Canopy cover includes interspaces ≤ 6″. e. Total Canopy Cover – Record canopy cover in exact percentages. Total Canopy Cover
may be less than the sum of TO and TR canopy covers for aparticular species, due to overlap. When recording species cover by sublayer, it is most efficient to first estimate canopy cover by sublayer and then estimate the overlap, if any, between sublayers to derive canopy cover for the overstory and regeneration layers.
f. Tallest Height – This is the tallest plant of each species in the tree and shrub layers. Estimate to the nearest foot.
g. Lowest Crown Base Height – This will only be collected on plots with an overstory structure. For our purposes, crown base height for a stand is defined as the height of the lowest branches, live or dead, that can effectively carry a fire into the crown. Record this height to the nearest foot.
h. Cheatgrass – Note if cheatgrass (Bromus tectorum L.) is present, as it is a species of concern for managers.
D. Vegetation Classification: Dominance Type, DBH, and Map Dominance Type Now that the canopy cover has been collected, you have the information to use the Key to Dominance
Types in order to identify the existing vegetation type on the plot. Carefully go through the dichotomous key, following the instructions until you achieve a dominance type. Record the dominance type code in the Dominance Type field on the front page of the datasheet.
Now determine the dominant DBH (in inches) of the plot, which will indicate the dominant overstory. Record this value in the DBH field on the front page of the datasheet. Next crosswalk the dominance type to the code for the Map Dominance Type (see cheatsheet). Record this in the Map Dominance Type field on the front page of the datasheet. If it is not a clear call, or a second option needs to be mentioned, do so in that same field, but clearly mark which would be the first call. Note any difficulties with using the key or map codes in the Remarks field.
E. Observation Points Observation points are used to collect information when a full plot is not being surveyed. Observation points can be extremely useful when creating a vegetation map. These should be done using the plot maps
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and the segments that RSAC has already delineated. A maximum of five observation points should be done on each plot map. Criteria for selecting Observation Points:
1. Do not pick segments that have multiple lifeforms in them. 2. Try to select 5 different segments, based on the order of Lifeform, Map Dominance Type, DBH Class Code, Canopy Cover (%). 3. Do not select heterogeneous segments. If surrounding segments are heterogeneous or difficult to describe, note that in the Remarks field on the plot datasheets. However, it should be fairly easy to find at least 5 areas you can designate as observation points.
Once you have chosen a segment to describe, label it on the plot map with a letter that corresponds to the information recorded on the back. (ex: A, B, C, D, E). Make sure the labels are legible. Fill in the corresponding map codes for the following Observation Point fields:
- Lifeform (see legendcodes.xls cheat sheet - Map Dominance Type (see cheat sheet). - DBH Class Code (see cheat sheet and/or table →). - Canopy Cover (use exact percents) - Record any additional comments.
*Fill out as much as you can. Only record what you are completely sure of—if you are not confident on a call, DO NOT RECORD IT. Every field that you can fill out will be helpful, regardless of the order that it shows up on the form. F. Travel Notes Travel Notes are taken when traveling to and from the plots following these steps:
· Use the “travel map” supplied by RSAC that does not have segments drawn on it. · Delineate a polygon the location of which you are sure (from looking on the map and observing the viewscape firsthand) · Fill in the corresponding map codes for the following Travel Notes fields: - Lifeform (see cheat sheet) - Map Dominance Type (see cheat sheet). - DBH Class Code (see cheat sheet and/or DBH table above). - Canopy Cover (use exact percents) - Record any additional comments.
*Fill out as much as you can. Only record what you are completely sure of—if you are not confident on a call, DO NOT RECORD IT. Every field that you can fill out will be helpful, regardless of the order that it shows up on the form. G. Before packing up and leaving the plot
· Carefully doublecheck the datasheets and make sure every field has been filled out. If there is a field intentionally left blank, make sure that you put n/a, 0, —, etc. so it is clear that it is intentional.
· Have the crew member that did not fill out the sheet look it over for mistakes. Some things to look for: - missing information (empty fields) - succinct vegetation description and remarks - legibility! - correct spelling! · Make sure all unidentified plant specimens have been collected and labeled appropriately. · Double check that you have all equipment that was brought to the plot.
V. Back at the Ranch – What to do at the end of the day All of the following should be done daily, unless the crew is camping out for the week.
Tree DBH Class Map Code Less Than 1" TS0 1" - 2.9" TS1 3" - 4.9" TS2 5" - 9.9" TS3 10" - 19.9" TS4 20" - 29.9" TS5 30" + TS6
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Check in with Liz (via cell or home phone)!
A. Download GPS 1. Put the GPS unit onto the charger cradle. 2. Open the Pathfinder program on the computer. 3. Go to Utilities and click on Data Transfer. There should be a green check mark icon showing that the GPS unit is plugged into the computer. 4. Click on the ADD button. On the drop down list, click on the first item (Data File). 5. Highlight the file(s) that you would like to download and hit OPEN. You will be placed back at the Data Transfer screen. Check that the destination for the file is correct. 6. Click on Transfer All. A screen should pop up saying that the transfer is taking place. If a box appears that says, “Transfer Complete,” click on CLOSE.
7. Close out of Pathfinder. Go to My Computer → K:\btnf\forest_vegetation_layer\GPS (or whichever path is appropriate for your own crew) and make sure that the file(s) transferred successfully. (If the .ssf files you transferred are indeed there, you may delete the associated DTL files, as they say when the transfer took place and are not necessary to keep.)
8. Leave the GPS in a cradle overnight to recharge the battery.
B. Download Photos 1. While you download photos, it’s a good idea to charge the camera battery. Often it fully recharges in the time you spend downloading and renaming the photo files. 2. Remove the memory card from the camera. 3. Insert the memory card into the card reader and open the drive (usually F:) to see the stored photos. 4. Check to make sure you are looking at the correct photos for that day! 5. Rename the photos on the memory card. The naming convention will be:
· For the plot center photo: Plot number – P (for Plot). (ex: 1001-P.jpg.) · For cardinal directional photos: Plot number – Direction. (ex: 1001-N.jpg, 1001-E.jpg) · For fuels photos: Plot number – F for Fuels - Line # - U or D (for Up or Down view)
(ex: 1001-F1D, 1001-F1U, 1001-F2D, 1001-F2U) 6. While in the memory card files, go to the toolbar → Edit → Select All, then right click to copy the files. Go to the destination folder K:\btnf\forest_vegetation_layer\Photos and choose Edit → Paste. 7. Check that all the photos have made it into the destination file, THEN delete them off of the memory card. C. Press specimens 1. Make sure you have the necessary supplies:
2. Place the specimen in the newspaper. DO NOT put specimens of two different species in the same newspaper. Take care when placing the specimens in the paper. Make sure that the inflorescences will be easy to examine after pressing. Make sure that most of the leaves are uncurled, or unfolded, and the root system is clearly displayed so that they may be examined. Turn one leave over to expose the underside, which may aid in identification. In some cases, it is helpful to take a razor blade and split one of the inflorescences in half to make the inner parts of the flower easier to look at. This is particularly helpful with members of the Asteraceae family, and some bilaterally symmetrical flowers. You may need to trim the specimen so it fits in the newspaper. Basically, remember that this 3-dimensional plant will be flattened into 2 dimensions and will need to be keyed out to identify it, so care taken now will benefit later. 3. Label the newspaper with the following information: Plot # Provisionary name % Cover Date Initials (who pressed the plant) It is very important to label the specimens clearly and correctly, in order to track them—be sure to use the name assigned to the collected plant on the datasheet. Incomplete labels may result in an inability to identify a species for a plot, negating its worth to the project. 4. Layer the pressed specimens, making a sandwich in the following order:
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one side of plant press – cardboard – blotter – specimen (in newspaper) – blotter – cardboard – blotter – next specimen – blotter – cardboard...etc. until you are done. End with the other side of the plant press. Place straps around the press and flatten it as much as possible. This usually involves asking someone to stand on the press as the straps are tightened! 5. Place the press in a dry, well-ventilated area, preferably in front of a fan. 6. Empty the press regularly. Usually about a week is long enough, unless relative humidity has been high, or the specimens were unusually wet when collected. Bundle the removed specimens with cardboard and twine, and label them with the collection dates and their status (i.e., if they need identification, if they’ve been viewed by the botanist but not identified, etc.) 7. If a specimen has been identified, you may decide to keep it for further reference. If so, file it in the big blue bin, with associated taxa (Asteraceae, Bromus, Salicaceae, etc.) Be sure to include the necessary identification information (id, dates) on the Collected Plant List.docon the K: drive to ensure we have the most up-to-date list. D. Datasheets 1. Fill in any information missing on the datasheets, i.e. Remarks, aerial photo information, dominant species. Check spelling! 2. Let the lead crew member know of any new (previously unseen) species that need to be added to the project species list, so s/he can add them and ensure an up-to-date list. 3. Once you are satisfied that the datasheet(s) are complete and legible, copy them on the copier (so we have backups).
4. File the originals in the appropriate folder in the filing cabinet, and the copies in their appropriatefolder as well. 5. Periodically, file the copies in the box in the trailer out back (obtain keys from Sandra in the SO), and send the originals to RSAC.
E. Plot Maps/Observation Point Datasheets 1. Fill in any missing information on the plot maps, i.e. Remarks or labels. Check spelling! 2. One you are satisfied that the plot maps are complete and legible, copy them on the copier (so we have backups). In order to ensure that the observation point labels can be seen on the copies, use the following settings: - On the copier, press tab 2 to navigate to the Image Adjustment screen. Press Image Quality, and then choose the Photo setting. Also lighten the image by two clicks toward the “lighter” setting. - Once you have made the copies and checked to see if the observation point labels appear clearly, use a yellow highlighter to denote the letters on the copied plot map. This way we can be sure that we are applying the observation information to the correct corresponding segment. 3. File the originals in numerical order in the appropriate three-ring binder, and the copies in their appropriate folder as well.
4. Periodically, file the copies in the box in the trailer out back (obtain keys from Sandra in the SO), and send the originals to RSAC.
F. Other Maps 1. Refile the Travel Maps in the appropriate folders to ensure anyone can locate the correct map before next heading into the field. 2. Note on the Nav Maps (those on the wall, and all those used in the field that day) which plots you visited that day: - For plots successfully read, color in the yellow waypoint dot with a red or orange marker. - For plots with only observation points, no observation points, access issues, etc., make a black box around the plot number. - Be sure to note on each map any road closures, gates, greasy conditions to aid future travel. G. Documents 1. As stated above, update the species list if any new species were encountered on a plot that day. 2. On the hard-copy PLOTS DONE.xls document that corresponds to the geographic region in which you are
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working (hanging next to the nav maps on the wall): - Note the date your particular plots were read. - Place a check mark in the appropriate columns: if observation points were obtained, and then if/when datasheets and plot maps were copied and filed. - For those plots with issues, note in the Notes column the reason why they were not read, or why only observation points were obtained, etc. The font color should be changed to red. → The crew lead is responsible for updating this document online. VI. Data Entry A. Background The database for our project is located at K:\btnf\forest_vegetation_layer\2005 SEASON \Database_2005. It was designed by Randy Frazier, seasonal B-T Fire/GIS Tech, with assistance from Crew Lead Sarah Canham and guidance from the Humboldt-Toiyabe mapping project’s fields. It is an MS Access-based database formatted to match the datasheets used for the project. B. Naming Scheme The naming scheme follows this format: plotS28Sept2005D10Oct2005.mdb, which means that the last time the structure of the database was amended was September 28, and the last time data was entered was October 10. This is to avoid data being entered onto a previous version of the database design. (Eventually, notation on quality control, beginning with a Q and then date, will be added on to show when the data has been checked for accuracy.) Whenever you add data, be sure to save your work with the date changed. C. Notes to remember: **Always keep notes on separate paper about what plots you’ve entered, the date, and any issues, so that we can keep records to minimize errors and extra work. **Always CHECK YOUR SPELLING, as well as grammar and formatting. Our time and effort spent collecting mean nothing if the records are full of errors and look sloppy. About data from datasheets: - DO add plots that were both not read and are lacking observation points. - Capitalize letters in the rover file name - Trace cover values (listed as “Tr” or “Trace” in the cover fields on the datasheets) should be noted in the database as 0.1. - In the Remarks section of the datasheet and observation points, put a dash, space, then capitalize first letter of each sentence, as follows: - Plot was located in an open forest. - Substantial graminoid litter. For plots that have NOT been read, with or without observation points, enter Remarks in the following format: (For plots not read, but with obs pts:) NOT DONE; obs pts only - explanation. (For plots not read, without obs pts:) NOT DONE; explanation, no obs pts - In the Dominant Species fields, only add one species. If nothing has been written, do not enter anything. If more than one species is listed, you can discern the dominant species per layer by looking at the species list, to see which species has the highest cover. (For example, if both Mahonia repens and Paxistima myrsinites are listed as dominant for the small shrub layer, and Mahonia has a cover of 5 while Paxistima has cover of 3, then Mahonia should be entered onto the database as the dominant small shrub.) → Sometimes an abbreviation appears, and not the full species epithet—this is shorthand for the two first letters of the genus and the first two letters of the species. Look on the species list for that plot and match the abbreviation to the species, and enter that species
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onto the database. Example: PICO = Pinus contorta. A cheatsheet listing common abbreviations and their associated species is in the folder listed in Section A above. - For the Species List: - we ARE currently entering any species not identified—i.e., that do not have a complete scientific name. These include species with an “sp.” or “cf.” Simply note in the Species column the family or genus of the unidentified plant, and write any notes, such as “cf.”, in the Notes column. For example, cf. Aster engelmannii would appear as Species Notes Aster cf. Aster engelmannii. Try to get as detailed as possible—for example, if possible, put Aster in the Species column instead of Asteraceae. If family is as far as you feel comfortable in your id, that is okay. - If your plant is not identified to family or genus, but instead has a descriptor like “fuzzy head forb” or “tall gram,” then you need to consult the TERRA codes sent by Dave Tart. They begin with a number 2, and assist Dave when he transfers data into TERRA and performs searches. For forbs, the entry may look like this: Species Notes 2FORB Fuzzy head forb Consult with Dave if you are unsure what code to assign. - DO NOT add species to the species drop-down list—make a note and tell the crew lead, who will add it to the design, or see that it gets added to the drop-down menu. - Often you may need to discern the sublayer of tree and/or shrub species on the datasheet’s species list. Simply see what the tallest height was for each species, and then assign it the appropriate sublayer. For trees with overstory (OCC) and regeneration (RCC) cover, list the tree species twice on the database, first assigning the correct overstory sublayer—for example: On datasheet: Abies lasiocarpa OCC: 10 RCC: 3 TCC: 13 On database: TOMC (if applicable) T Abies lasiocarpa OCC: 10 TCC: 10 TR T Abies lasiocarpa RCC: 3 TCC: 3 Be sure that the OCC and RCC appear in the correct columns. About data from observation points: - Beware of the need to change the following lifeform codes in observation points: C → M for mixed conifer stands, and D → Q for aspen/conifer mixed stands.
V C F f p L V
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II. Comparison Chart of Visual Estimation of Foliage Cover
Printed from the Integrated Land Management Bureau of British Columbia website:
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81 І Appendix C: Field Data Collection
IV. Veg Crew Gear Checklist – 2005 Field Season
• Datasheets • GPS unit (plus antenna, battery, cords), with waypoints downloaded onto it • Maps – Navigation, Travel, Plot, and USFS Road/Visitor • Radio, plus extra batteries • Digital Camera (battery charged) • Aerial Photos • Whiteboard • Whiteboard Markers • Pencils • Compass (declinated) • Clinometer • 100m Tape • DBH Tape • Flags/flagging • Trowel • Ziploc Bags (for collecting plants) • Survey Pole (NOT IN BIN!)
• Dominance Key • Collection Protocols • Radio Instructions • Rite in the Rain Datasheets
Other: • Water • Food • Sunblock • First Aid Kit • Bear Spray • Clothing for Rain/Cold • Plant Books!
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83 І Appendix D: Segmentation and Mapping
Image Segmentation – Using eCognition Generating Segments: Image segmentation techniques were used to generate stand-level delineations that eventually formed the perimeters of final vegetation polygons. A software package called eCognition was used to create these fine-scale segments (Figure 1). The segments capture homogeneous areas in the imagery, minimizing spectral and topographic variation within units. Three input layers were used in eCognition to derive the segments: 1-meter CIR DOQs, 30-meter NDVI layer from LANDSAT TM satellite imagery, and in forested zones, an additional 10-meter texture layer derived from the DOQs. Below are examples of segments in rangeland, forest and riparian areas.
Initially the parameters in eCognition were set to constrain the maximum seg-ment size to 1 acre (image at left). This created files too large for economical data analyses in subsequent vegetation mapping steps. Segment size was in-creased between 1 and 2 acres speeding up the analyses time.
Rangeland Example
Average Polygon Size:
1.0 acres
Figure 1—Segmentation levels produced using eCognition software for various landcovers.
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Exporting Segments: Once the segments/polygons were created in eCognition, they were exported to a shapefiles (Figure 2). First, segments were converted to polygons by using the ‘Segmentation—Create Polygons’ tool. In this process one selects the correct segmentation level to convert. (Settings: Base threshold to 0.0 w/ remove slivers option checked; Shape threshold = 1). Use the ‘Export—Image Objects’ tool to generate ArcGIS shapefiles. (Settings: Vector File-raster, uncheck classification and class color options). Note: During the image segmentation process, eCognition segments the entire image, including the background (no data values). This exported vector files, include erroneous background polygons, which must be removed. Use ArcMap Selection and > select by location to select polygons using the study area boundary and subset the polygon segments.
Figure 2: Arcmap Selection tool and subsetting segments
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Image Cube Creation – Custom ArcInfo AMLs An image cube was developed from 54 different geospatial data layers representing topographic, spectral, textural, climatic, and other ancillary information. These base layers were summarized against stand-level delineations (segments) generated earlier, to produce an image cube containing 115 different data layers comprised of zonal means or zonal standard deviations of the original base layers. For each polygon segment, a mean value of all pixel values within the segment boundary was calculated and recoded to the zonal mean value. For many layers, a zonal standard deviation was generated as well. The base data layers that were used as summary layers in the mapping process are shown below.
Base Geospatial Data Layers Included in the Image Cube
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Mapping Interface:
The mapping process uses three programs in three software packages. The following sections will note highlight which program each step was completed in at the top of the page. • ArcMap Vegetation Mapping Tool
• ERDAS Imagine DataPrep & Apply See5 Tool
• SEE5 and the various dialogue interfaces
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Manual editing of the point locations was done in order to use the see5 modeling process. Points were sometimes edited to representative their most appropriate location on the imagery. The num-bered steps in the vegtool menu (below left) outlines this process. The graphic (below right) is the in-terface menu for editing the shapefile (QC) for the particular vegetation level being edited. The bot-toms graphic shows the dialogue box that pops-up when selecting step 4. The edited point shapefile is exported in this step through the vegetation tool into a text format that outputs the location of the point and the datacube information spatially associated in the segment in which the point was placed.
Use ArcMap Vegetation Mapping Tool – Export training data for See5 datacube • Input = shapefile of training sites • Output = .txt
1. Start Mapping—recode and export training data
Number of classes–you decide
Number of classes – you select the attributes you want in each class
Names of the classes – you decide
Column heading from training point shapefile
Attributes from training point shapefiles
Step 4 Dialogue Box
Step 3 Interface VegTool Dropdown Interface
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Use Erdas Imagine See5 tool – Prepare Data for See5 • Input = zonal means (& standard deviations if you have them) .txt file of training sites (From STEP 1) • Output = .data
2. Prepare data for See5
The first time you do this you will need to select all of your zonal means individually (and standard devia-tions if you have them). Once selected, save the list use it in future iterations. Alternative is to open a com-mand prompt and create a directory (cd) in the folder that all your .img files are contained in and use “dir > list.txt”, to output a text file list of the ,img inputs into See5
This is where you save the zonal mean list
This is where you load the zonal mean list you saved
Text file from STEP 1 Name of output file – you decide
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Use See5 to generate the decision trees – See5 Tool • Input = .data (from STEP2) • Output = .tree
3. Develop models in See5
Try different boost # - maybe start with 10
Use a sample of 90% when testing different boost numbers. When you decide on the best boost# click off this box. (cause you want to use all of the training points.
Leave these on the default
Input your .data file from STEP2
Customized algorithms were developed using a software program called See5. This program uses data mining to generate rule-based decision trees. Survey-wide geospatial data layers and site-specific field-based measurements are analyzed for predictable relationships. The relationships are converted to an regression-tree, which is tested, ranked, and finally used to predict, or map, ground-based information across the entire study area. Below are step-by-step instructions on how to de-velop the decision tree using See5.
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A result report pops up after each iteration of the model (see below). The simple error matrix in the graphic is a measure of how well the model, and the parameters, fit the data that was included in the model. The fit percentage is based on the sample size and the sample withheld from the model.
3. Develop models in See5 (cont.)
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Use Erdas See5 tool – Apply See5 Models- this step • Input = .tree (from STEP3) • Output = .img
4. Apply See5 models
Input .tree from STEP3 Output will be a classified image with your classes from STEP1
Output will be a classified image with your classes from STEP1
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93 І Appendix E: Draft Map Review and Revision
APPENDIX E: Draft Map Review and Revision
Figure 1—BT Vegetation Draft Map Quad sent to reviewers.
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Bridger-Teton Draft Map Review June-July 2006
To begin the process of draft map review we met with Bridger –Teton district personnel to setup the initial draft map review process. Decisions were made that allowed each district a particular area in which to edit and review vegetation maps for which they were most familiar with. A process for what the review included was decide upon and instructions came out of the process that each region would follow in a step-by-step manner while undergoing the review. Draft maps used for the review were a single quad (Figures 1 & 2) and the review steps and processes are outlined below.
Figure 2—BT Vegetation Draft Map Re-view Items.
This review is an opportunity for local experts to assess the draft vegetation maps and provide any addi-tional input you think is needed to improve the final maps. You have been provided with a draft maps covering your entire district. We encourage you to write notes, circle areas of concern, and document any other information on the maps. You have also been provided with a Draft Map Review Form. If you don’t have enough space on the draft maps to make notes you can circle a particular area on the map, label it with an unique site #, and write your notes on the draft map review form provided. It is important to follow the “Key to Vegetation Map Units for the Bridger-Teton NF” when determining the map class. The key ensures that everyone is assigning map classes based on same rules and descrip-tions. Some of the classes occurred infrequently or not at all, we need to make sure that these classes have not been under-sampled. Across all districts we need additional data on where (or if) these communities occur:
· Cottonwood · Blue Spruce · Black Sagebrush · Low/Alkali Sagebrush · Basin Big Sagebrush · Wyoming Big Sagebrush · Mountain Mahogany · Non-Native Herb
Overall we need as much information as you can provide. This includes feedback on what is correct and what is incorrect. Focus your attention on the general vegetation distribution. Our map revisions will be based almost solely on the information you provide to us and the final maps will directly reflect your comments. Schedule: We need to receive the draft maps & comments by July 31st. Please contact either John or Wendy if you have any questions.
Sample letter given to draft reviewers on each district
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Wind River Mapping Region June 2006
The purpose of this paper is to provide information to forest resource specialists concerning draft maps devel-oped for the Bridger-Teton National Forest. These vegetation types were not mapped because none or only a few training sites were collected (<5 sites). Do these communities need to be represented on the map? If so we need specific locations of these communities, otherwise the final map for the Wind River mapping region will not include the following classes: Blue Spruce Low/Alkali Sagebrush Black Sagebrush Basin Big Sagebrush Wyoming Big Sagebrush Sagebrush/Grasslands Spike Sagebrush Mountain Shrublands Mountain Mahogany Cottonwoods Tall Forbs Non-Native Herbland These vegetation types were mapped, but because very few ground samples were collected (<14) we do not have a lot of confidence in the draft map product: Aspen/Conifer Mix Douglas-fir Mix Rocky Mtn Juniper Subalpine Fir/Spruce Mix Sagebrush/Bitterbrush Mix Silver Sagebrush/Shrubby Cinquefoil Particular concerns: ▪ Mapped a lot of Whitebark and Lodgepole Pine – how do these communities look. ▪ Has the Aspen/Conifer Mix and Douglas-fir communities been under mapped? ▪ Have the alpine areas been over/under mapped? Did not have many training sites. ▪ No Wyoming Big Sagebrush was mapped on this district – does it exist in any significant numbers? Is Juniper over mapped?
Area Specific Letters Examples
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Bridger-Teton Draft Map Review Wyoming Mapping Region
June 2006 The purpose of this paper is to provide information to forest resource specialists concerning draft maps developed for the Bridger-Teton National Forest. These vegetation types were not mapped because none or only a few training sites were collected (<5 sites). Do these communities need to be represented on the map? If so we need specific locations of these communities, other-wise the final map for the Wyoming mapping region will not include the following classes: Blue Spruce Limber Pine Rocky Mountain Juniper Black Sage. Low/Alkali Sage Basin Big Sage Wyoming Big Sage Spike Sage Mountain Mahogany Non-Native Herbland Cottonwood
These vegetation types were mapped, but because very few ground samples were collected (<10) we do not have a lot of confidence in the draft map product: Mixed Whitebark Pine Riparian Herbland Aspen/Conifer Mix
Particular concerns: ▪ Aspen/Conifer Mix – has this been under mapped? ▪ How is the grassland/shrubland split? ▪ Is Silver Sagebrush/Shrubby Cinquefoil and Mountain shrublands mapped accurately ▪ Has Engelmann Spruce and White Bark Pine been under mapped? Are the Doug-fir and Engelmann Spruce areas OK?
Bridger-Teton Draft Map Review
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Gros Ventre Mapping Region June 2006
The purpose of this paper is to provide information to forest resource specialists concerning draft maps devel-oped for the Bridger-Teton National Forest. These vegetation types were not mapped because none or only a few training sites were collected (<5 sites). Do these communities need to be represented on the map? If so we need specific locations of these communities, otherwise the final map for the Gros Ventre mapping region will not include the following classes: Blue Spruce Black Sagebrush Basin Big Sagebrush Wyoming Sagebrush Mountain Mahogany Non-Native Herbaceous These vegetation types were mapped, but because very few ground samples were collected (<15) we do not have a lot of confidence in the draft map product: Aspen/Conifer Mix Douglas-fir Mix Limber Pine Rocky Mtn Juniper Low/Alkali Sagebrush Spike Sagebrush Mountain Shrublands Riparian Herbaceous Particular concerns: Burn areas in the northern part of the study area – not sure what to classify these as. The forested areas were heavily mapped as Lodgepole Pine. The alpine areas were mapped using soils and elevation information – do these areas look reasonable – have they been over or under mapped? Is Aspen/Conifer Mix under mapped? How do the riparian areas look – over or under mapped?
Bridger-Teton Draft Map Review Kemmerer Mapping Region
98 І Appendix E: Draft Map Review and Revision
June 2006 The purpose of this paper is to provide information to forest resource specialists concerning draft maps developed for the Bridger-Teton National Forest. These vegetation types were not mapped because none or only a few training sites were collected (<5 sites). Do these communities need to be represented on the map? If so we need specific locations of these communities, other-wise the final map for the Kemmerer mapping region will not include the following classes: Cottonwood Blue Spruce Limber Pine Rocky Mountain Juniper Mixed Doug Fir Black Sagebrush Wyoming Big Sagebrush Spike Sagebrush Sagebrush/Bitterbrush Mix Mountain Mahogany Non-Native Herbland
These vegetation types were mapped, but because very few ground samples were collected (<10) we do not have a lot of confidence in the draft map product: Low/Alkali Sagebrush Tall Forb Engelmann Spruce Mixed Whitebark Pine Riparian Herbland Particular concerns: ▪ Is there a lot of Whitebark pine at the higher elevations of the Kemmerer region? Check for any confusion between sagebrush and grassland communities.
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101 І Appendix F: Accuracy Assessment Design
Bridger-Teton National Forest Vegetation Mapping Project Protocols for Accuracy Assessment
2006 Field Season
Introduction During the 2006 summer field season, the Bridger-Teton Vegetation Mapping Crew collected ground information that was used to evaluate and assess the overall accuracy of the Bridger-Teton National Forest Vegetation Map. The data collection process for this project is very similar to the procedures used to collect the original data for the draft maps (in 2005), however only basic land cover information (species, size class, and canopy cover) was collected at each site for this phase of the project. The purpose of these protocols is to explain the procedures for collecting the ground information, so it is necessary to read through the entire document before starting data collection. Tools Dominance Key You will use the same dominance key provided to you for the 2005 field season, though abridged. The key contains the vegetation classification system for dominance types found on the Bridger-Teton National Forest. Please review these classification keys before you begin any fieldwork and have them available during the data collection. The field data collection will allow you to use the dominance key to determine the correct dominance type. Field Maps You have been provided with three sets of maps: navigation, travel and plot. The navigation maps are an overview of all the field sites on each mapping region. The travel maps are 1:36,000 scale covering approximately four quads. These maps display the center point of the segment polygon overlayed on the CIR DOQs (Color Infrared Digital Orthophoto Quads). They are provided for more detailed navigation. The plot maps were produced at a scale of 1:9,000. They also are CIR DOQs overlayed with road & hydrological data, as well as the segment polygon. The plots are approximately 1/10th acre circular sites. Field Equipment - GPS unit, plus antenna, battery, cords
Set the projection information on your GPS unit to: Projection: UTM Datum: NAD27
Spheroid: Clarke 1866 Zone: 12
- Datasheets/Plot Maps - Dominance Key - Maps – Navigation, FS - Collection Protocols - Radio, plus extra batteries & instructions - Plant identification books - Digital Camera (with charged battery) - Whiteboard & Whiteboard Markers - Water - Pencils/Markers (for sheets & collection bags) - Food - Compass (declinated!) - Sunblock - 100m Tape - Bear spray - DBH Tape - First Aid kit
Appendix F: Accuracy Assessment Design
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- Trowel (for collecting plants) - Clothing for rain/cold - Ziploc Bags (for collecting plants) - Insect repellent - Survey Pole - Binoculars Field Data Collection Procedures The field data collection process has been simplified for the accuracy assessment. Unlike the first field season of data collection (where plots took up to an hour to read), the actual time needed to collect all data at each field site should be approximately 10-15 minutes. Step 1 – Plan Review the plot maps and the geographic distribution of the plots that have been assigned. The plots are marked with a unique ID number on the map. A Forest Service visitor map, RSAC navigation map, RSAC travel map, the plot map, and/or GPS unit may be used to navigate to the waypoint shown on the map. A directory of plot ID numbers and UTMs (in meters) will be provided to you. If conditions allow, you should be able to collect data on 10-15 plots per day. Therefore, it is important that you plan your day to be efficient. Choose sites that are grouped together to minimize travel time. Often it is prudent to start at the furthest plot and work back—you can check out other plots on the way. Step 2 – Access The pre-delineated plots have been chosen to be spectrally homogeneous. It was also attempted to locate plots so that they are close (~1/8 mile) to roads, as deemed from the most current road layer. You may want to check with local districts for information on road conditions or covers. There is no guarantee that the plots will be accessible, due to weather, road conditions, private property, etc. Use your judgment when deciding whether to climb a slope, cross a stream, etc.—if you feel it is unsafe, do not attempt. *If a waypoint is located in a non-vegetated area such as a cliff or former gravel pit, you are required to read the plot as usual as these data are necessary for assessing non-vegetated map units. (You may need to view these plots if deemed unsafe, but still read them—see below.) If you cannot directly access the plot, but can clearly and accurately view it from some vantage point, you will still be able to fill out the field form; you will just have to note that the plot/segment was “Viewed from a Distance.” (See 4. Level of Observation below.) You are still required to take photographs, obtain GPS points and record as much information as possible for that plot. *You must be able to complete Box #7 Cover from Above, or else the plot is considered “Not Observable.” If you find that the plot is completely inaccessible and cannot be viewed—thus “Not Observable”—you can still fill out what you can on the field form, take photographs and GPS points, and record pertinent information as to the impasse. (See 4. Level of Observation below.) A shapefile of waypoints will be provided to you to upload onto your GPS units. A list of all plot waypoints (UTM coordinates) is available if desired, in case you need to manually enter them onto the GPS unit. Use this information to locate the exact center of the plot. Step 3 – Site Overview Identify the center of the plot and mark with the survey pole. Walk through the entire plot to get a sense of the vegetation. Notice the distribution of vegetation. While the vegetation on the plot may be heterogeneous by layer (e.g. trees, shrubs, herbaceous), the plot should be generally representative of one vegetation class. Step 4 – Fill Out Field Form You are now ready to enter information into the field form. You will see some shaded boxes on the form; these are especially important data from a mapping standpoint. Information requested includes:
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1. Plot ID Plot #: Enter the unique plot number designated on the plot map. Wyoming Range = AA-1xxx, Kemmerer = AA-2xxx, Gros Ventre = AA-3xxx, Wind River = AA-4xxx. 2. Observers Record the name(s) of observer(s) reading the plot. 3. Date Note the date the field data collection was taken. An mm/dd/yyyy format is preferable. 4. Level of Observation If you are able to directly access the plot then circle “Site visited.” If you are unable to access the plot, but are able to view it well enough to generally determine the vegetative cover, circle “Viewed from a Distance.” If the plot is inaccessible and unobservable, circle “Not observable.” If it is necessary to move the plot, say if the waypoint is in an area not representative of the segment, check the box next to Plot Moved and note the reason for the move, how far away the plot is/may be, and in what direction. 5. GPS Information (UTMs, Elevation, PDOP) Whether or not you can access the plot, always record GPS points. If you can access the plot, set up the survey pole at plot center and begin to take GPS readings. The GPS unit will need a minimum of 3 satellites to begin recording points, and the PDOP should be set to 6 (see below). For non-forested plots, collect 30-60 points; for forested plots, 60-90 points. Go on to other tasks while points are being collected. When you are finished, record the Northing and Easting UTM coordinates, as well as Elevation, as recorded at plot center. If you are viewing the plot from a distance, take GPS readings from your observation point. If the plot is inaccessible, take GPS points from the spot where you meet the impasse, be it a gate or flooded road, etc.
PDOP (Position Dilution of Precision) should always be set at 6, though it may need to be raised due to heavy canopy cover, time of day, etc. If so, note the new PDOP value on the line provided, and be sure to change it back to 6 before reading the next plot. To record data points:
1. Press Power to turn on the GPS unit, then press Data. 2. If it’s the first plot of the day, create a new rover file by pressing Enter. 3. Open up the feature labeled ‘Veg_Plot’ by pressing Enter, then press Enter again to arrow/
type in the unique number for the current plot. 4. Arrow down to ‘Close’ to return to the Veg_Plot screen. 5. Once the GPS unit has collected the recommended number of positions (see above), press
Close to close the feature, but not the rover file. 5. Record the UTMs off of the GPS unit onto the datasheet.
6. Turn off the GPS unit. To record a new point on the same rover file, choose “Open Selected File,” which will bring you to the Update Feature screen. Press Data, which will bring up the New Feature screen. Press Enter, and proceed from #3 above. After the last plot of the day, close the rover file.
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To change the PDOP: 1. Press SYS until you get to the Setup screen. 2. Scroll to Configurations and press Enter. 3. Scroll to GPS and press Enter. 4. Arrow Down to highlight the PDOP mask line. 5. Press Enter, arrow Up or Down to arrive at the desired number, and press Enter to close. 6. Press Close twice to return to the Setup screen. 7. Don’t forget to reset the PDOP to 6 before the next plot!
6. Photographs Photos are invaluable to all aspects of our project (ecology, mapping, and fuels). We will take several photos of/from our plot. Plot photo - Take the plot photo from the perimeter of the plot towards plot center, perpendicular to the slope. If there is no slope, choose a view that is most representative of the plot. This photo will be used as a representation of the plot, and should encompass as much of the plot as possible. Make sure the view of the plot is not obscured by crew, crew gear, or the whiteboard. - On the whiteboard, with whiteboard markers only, write the Plot ID, Date, and a “P” (for “plot”).
Ex. 1001 6/16/05 P The whiteboard can be attached to the survey pole with Velcro, or held by a fellow crew member. - The photographer should step out to the border of the plot, and take a representative photo. *Remember, if the plot is on a slope, take the photo across slope (not facing downslope or upslope). - Try to avoid glare, in order to facilitate photo renaming back in the office. - After taking the photo, you may check the display to see if it was captured successfully. - Record the “P” photo file number on the line provided on the datasheet. Cardinal direction photos - Next are cardinal direction photographs (North, East, South, West). The photographer should stand at plot center, use a compass to find North, and take a photo of the view 30 feet out. Then turn clockwise for East, then South, then West. Taking them in this order allows us to save time by not using the whiteboard—thus, following the N, E, S, W setup is very important. - If in a dense stand such as willow, move away from the plot center to obtain a representative view of the plot, and note the move in the Remarks section. Fuels photos - These photos will be taken from plot center, and should have a view 30 feet out. In special cases, such as really dense plots, photos can be taken at 15 feet. - If there is a slope to the plot, take photos at a direction that is perpendicular to the slope. Otherwise, choose a direction that is a good representative of the understory and overstory fuels for the plot. Facing one way perpendicular to the slope will be called Line 1. Facing the opposite way (180º from Line 1) will be called Line 2. Depending on whether or not the plot is forested or non-forested determines how many photos will be taken (see below), though every plot will have at least a Fuels Line 1 Down and Fuels Line 2 Down photo.
- On non-forested plots, for both Lines 1 & 2, only ‘down’ photos are needed to assess fuels in the understory. The ‘down’ photos should be taken with the camera angled down so that the horizon is at the upper third of the photo. In plots with little or no slope, look around for representative fuel areas and photograph ‘down’ photos accordingly. This will capture the surface fuels. For example, you may want to include a stand of conifers in the background, instead of just sky.
105 І Appendix F: Accuracy Assessment Design
- On forested plots, for both Lines 1 & 2, both ‘down’ and ‘up’ photos are needed to assess fuels in the overstory as well as understory. The ‘down’ photos should be taken with the camera angled down so that the horizon is at the upper third of the photo. The ‘up’ photos should be angled so that the horizon is at the bottom third of the photo, to capture the overstory fuels. - Be sure to note the Fuels Photo Line 1 Down file number on the datasheet, as well as the azimuths (1º to 360º) for Lines 1 & 2.
- A few notes on photos: · Make sure that the camera is set to take pictures, not videos, by making sure the mode is set all the way to the right under the little camera diagram. · Make sure the camera is not zoomed in at all. · Try not to have people or items such as gear in the photo. · Keeping the display off saves batteries and a better picture is taken if you look through the view finder.
Viewed from a Distance photos Take a photo or two of the plot (or whole polygon if you are unsure of the exact plot location) from where you are observing it, and note the azimuth (direction to the plot) and distance (if possible) in the Remarks section. Not Observable photos When a plot has been deemed Not Observable, you should photograph the reason why as best as possible. For example, if a road is flooded or gated, photograph the flood or gate. Note the issue in the Remarks section. 7. Cover from Above This is where you will record the % total cover of each lifeform layer (Trees, Shrubs, Herbaceous, Non-Vegetated) as it would be seen from above the plot, looking down. This is the bird’s eye view of total cover, thus should be determined imagining a top-down perspective. Record the actual covervalue (i.e. if it’s 54%, write 54%, not 50% or 55%). Do not count the parts of lifeforms (Trees, Shrubs, Herbs, Non-Veg) that are overlapped from above—consider only what you would see looking straight down onto the plot. It is easiest to start with the uppermost layer and work downward, ending with Non-Veg. This total should add up to 100%. It is important to consistently calibrate all crew members’ estimates of cover throughout the field season, to maintain accuracy of data collection. 8. Canopy Cover This is where you will record the canopy cover percentages of dominant species in each layer present on the plot. (For those layers not present, simply skip the box.) This is different than Cover From Above in that here you include those parts of species that are overlapped by other species, to arrive at a % canopy cover. Record canopy cover in exact percentages. *Sometimes it is helpful to think of the % canopy cover of each species, then add in your estimate of overlap (if any), to arrive at a % canopy cover value. 8a. Trees List all tree species seen on the plot, if any. Record the value from the Trees row in Box #7 in the All Trees row, % Canopy Cover column, in Box #8a. Divide that number into % tree overstory (TO - those trees ≥16 feet in height) and % tree regeneration layer (TR - those trees <16 feet in height.) DO NOT account for overlap—all percentages should add up: %TO + %TR = % Canopy Cover. Next, go through the tree species list you have recorded and assign %TO and %TR cover values to each, and DO include overlap, as some species may be overlapping other species in either the overstory or regeneration layers, or may be overlapping themselves in the regeneration layer.
106 І Appendix F: Accuracy Assessment Design
Total the %TO column and go through the key. If the total %TO value is ≥10% , use these TO values for each species, as the tree overstory percentage to carry you through the Forest key. However, if the total %TO value is <10%, add %TO & %TR to get % Canopy Cover, then total the % Canopy Cover column and use these % Canopy Cover values to run through the forest key. Example:
Here we have a Forested plot, as determined by the “10” in the % Canopy Cover column, the value from Box #7. We have found both Populus tremuloides (Aspen) and Abies lasiocarpa (Subalpine fir) on the plot. We determine that of that 10% canopy cover of all trees, 6% is in the overstory layer and 4% in the regeneration layer. Looking at each species, we find that there is an overlap of 2% between the two tree species in the overstory (TO) layer, as 5+3=8, which is 2 more than 6. Since 8% is less than the 10% needed to run through the key, we need to consider the regeneration covers for each species (3% aspen, 1% subalpine fir). We add the Total %TO and Total %TR to get our Total % Canopy Cover: 8+4=12%. Using the key, we see that we need 60% cover of aspen to achieve an Aspen map unit. 8/12=67%, so this plot is an Aspen plot. This gets recorded in sections #9 and #10 on the field form. Now you will fill in additional information in Box #8a: Tallest Height Estimate and note the height of the tallest tree on the plot, in feet. A clinometer can be used to estimate tree height. Lowest Crown Base Height This is information for Fuels interests, and will only be collected on forested plots. For our purposes, crown base height for a stand is defined as the height of the lowest branches that can effectively carry a fire into the crown. Record this height to the nearest foot.
8b. Shrubs Shrubs are defined as multiple-stemmed woody plants less than 16 feet in height at maturity. As with Trees, list the dominant shrubs on the plot, if any shrubs are present. Note the % canopy cover of all shrubs as a layer, then list % canopy cover by species. The sum of % cover by species may be more than % cover of the shrub layer as a whole due to overlap. As a guide for this project, you will see on the field form rows for Riparian Shrubs, POFR4 + ARCAV2, and Alpine Shrubs. These serve as reminders to the crew to look over their species list for those shrubs that fall into those categories, as deemed by the species lists in the Shrubland key. Those categories correspond to map units—if overlooked, the plot may be keyed to an incorrect map unit.
8a. Trees tallest ht: ______ % TO % TR % Canopy
Cover All Trees (use Cover From Above value) 6 4 10
Populus tremuloides 5 + 3 = 8
Abies lasiocarpa 3 + 1 = 4
Total 8 4 12
107 І Appendix F: Accuracy Assessment Design
Now you will fill in additional information in Box #8b:
Tallest Height Estimate and note the height of the tallest shrub on the plot, in feet.
8c. Herbs Herbs are defined as vascular plants without significant woody tissue above ground. These include forbs (non-graminoid flowering plants), and graminoids (grasses, sedges, rushes). As with Trees and Shrubs, list the dominant species on the plot, if any forbs or graminoids are present. Note the % canopy cover of all herbs as a layer, then list % canopy cover by species. The sum of % cover by species may be more than % cover of the herb layer as a whole due to overlap. As a guide for this project, you will see on the field form rows for Native Riparian Herbs, Native Alpine Herbs, and Non-Native Herbs. These serve as reminders to the crew to look over their species list for those herbs that fall into those categories, as deemed by the species lists in the Herbland key. Those categories correspond to map units—if overlooked, the plot may be keyed to an incorrect map unit. 8d. Non-Vegetated Record the value from the Non-Vegetated row in Box #7 as your Total. Look through the plot and
assign % cover for Barren (rock, soil), Downed Wood (*include standing dead if deemed to have been dead before this field season), Litter (pine needles, scat), and Permanent Snow/Ice (snow/ice that appears to be permanent on the plot, as opposed to transient snow/ice). You may choose to note other non-vegetated categories like Water or Transient Snow/Ice; these may be added in the rows provided.
Plot Summary – 1st Call check all
9. Map Group Map Group is the project’s most coarse distinction between vegetation types. Based on your totals in Box #7 Cover from Above, use the key to determine whether the plot is Forest, Shrubland, Herbland, or Non-Vegetated. (The Forest Key goes on to distinguish between Conifer and Deciduous forest types, while both the Shrubland and Herbland keys go on to discern Riparian or Alpine communities.) 10. Map Unit Once you have arrived at a vegetation type for your plot, and still using the key, find the page of the associated key for that map group. Based on what you determined as the dominant lifeform in Box #7 (either Trees, Shrubs, Herbaceous, or Non-Vegetated), focus on the appropriate #8 box for this section. For example, if you have determined the Herbaceous layer to be dominant, look at the cover percentages in Box #8c to run through the Herbland Key. Run through the key, using your cover values for the species you listed to arrive at the appropriate map unit. On the field form, circle the map unit code. 11. Tree DBH Class
For Forest plots only (those plots where the ‘Trees’ lifeform layer is dominant), note the relative percentages of trees in each DBH class and round to the nearest 5-10%. *Note: Relative percentages should be based on cover from above, not the number of individual trees, so once again consider the plot from a birds-eye perspective. There should be one clear dominant class— no ties. The total of all classes should equal 100%. 12. Tree/Shrub Cover from Above For Forest or Shrubland plots only (those plots where ‘Trees’ or ‘Shrubs’ is the dominant lifeform), use the corresponding value from Box #7 to arrive at the cover class. For example, If in Box #7 the dominant lifeform was determined to be Trees, with 23% Cover from Above, the cover class would be TC2. Circle only one cover class—TC_ for a Trees plot, SC_ for a Shrubs plot.
108 І Appendix F: Accuracy Assessment Design
12.5 Red Squirrel Presence To assist with a lynx study on the forest, we are recording the presence of red squirrels on forested plots with trees over 15 feet tall only. When on a plot that fits this description, look around and/or listen for the chatter and check either the Yes or No box. This should take only seconds, not minutes! Plot Summary – 2nd Call The “second call” procedure supports the mapping process by improving the accuracy of data collection. It does so in several ways: it accounts for expected ocular errors in the field, considers “borderline” plots where a call could go either way, and generates minimum and maximum accuracy ranges. For example, sometimes there are close calls in Boxes #7 or #8, and crews have the opportunity to give either the same or different “call” on what Map Group or Map Unit is dominant on the plot. *Note: Crews should always make a second call, even if it is the same as the first call.
13.Map Group See 9. Map Group above. 14. Map Unit See 10. Map Unit above. 15. Tree DBH Class See 11. Tree DBH Class above. If there is a narrow difference between the percentages assigned to the two top classes in Box #11, you may choose to select the class that was NOT chosen for 1st call. For example, if the classes were as follows: TS1=20%, TS2=35%, TS3=40%, and TS4=5%, then 1st call would be TS3, and you’d probably select TS2 for 2nd call.
16. Cover from Above
See 12. Cover from Above above. If the value in Box #7 is at the low or high end of a cover class range, you may want to choose a different cover class for your second call. For example, if the value in Box#7 is 20 for Trees, then 1st call in Box #12 would be TC2, as it’s range is 20-29% cover. However, for 2nd call, you would probably select TC1 (with a range of 10-19% cover) as 20 is on the low end of TC2’s range.
17. Remarks
Use this space to enter any additional information that you think will help characterize the plot, with suggestions offered below. It is often good to save this field to do last, since you’ve been walking around the plot and have a good understanding of what is going on floristically. Keep in mind the goal of the map and the plots, and include such comments as:
· how representative is the plot (i.e. does the plot capture the general composition of the area/polygon?)
· overall composition of the vegetation · dominance type (whether it fits the community description very well) · description of the segment as a whole · variation in the landform · variation in fuels composition · evidence of fire or other disturbance · any problems with keying, etc.
· any notes on photos · any information on/problems with road conditions, access, parking · any information that will help you to distinguish this plot (views, anecdotes) Step 5 – Back in the Office All of the following should be done daily, even if the crew is camping out for the week.
Check in with your supervisor (via phone, or via radio to Dispatch)!
109 І Appendix F: Accuracy Assessment Design
A. Download GPS 1. Put the GPS unit onto the charger cradle.
2. Open the Pathfinder program on the computer. 3. Go to Utilities and click on Data Transfer. There should be a green check mark icon showing that the GPS unit is plugged into the computer. 4. Click on the ADD button. On the drop down list, click on the first item (Data File). 5. Highlight the file(s) that you would like to download and hit OPEN. You will be placed back at the Data Transfer screen. Check that the destination for the file is correct. 6. Click on Transfer All. A screen should pop up saying that the transfer is taking place. If a box appears that says, “Transfer Complete,” click on CLOSE.
7. Close out of Pathfinder. Go to My Computer → K:\btnf\forest_vegetation_layer\GPS (or whichever path is appropriate for your own crew) and make sure that the file(s) transferred successfully. (If the .ssf files you transferred are indeed there, you may delete the associated DTL files, as they say when the transfer took place and are not necessary to keep.)
8. Delete the rover files from the GPS unit. 9. Leave the GPS in a cradle overnight to recharge the battery.
B. Download & Label Photos While you download photos, it’s a good idea to charge the camera battery. Often it fully recharges in the time you spend downloading and renaming the photo files, or you may leave it overnight.
1. Remove the memory card from the camera. 2. Insert the memory card into the card reader and open the drive (usually F:) to see the stored photos. 3. Check to make sure you are looking at the correct photos for that day! 4. Rename the photos on the memory card. Use “AA” to begin each name, for “Accuracy Assessment,” to distinguish from the 2005 photos. The naming convention will be as follows: · For the Plot center photo: AA–Plot number–P (for Plot). (ex: AA-1001-P.jpg.)
· For Cardinal Direction photos: AA–Plot number–Direction. (ex: AA-1001-N.jpg, AA-1001-E.jpg) · For Fuels photos: AA–Plot number–F (for Fuels)-Line #-U or D (for Up or Down view)
(ex: AA-1001-F1D, AA-1001-F1U, AA-1001-F2D, AA-1001-F2U) · For Viewed from a Distance photos: AA– Plot number – VFD (for Viewed From a Distance).
(ex: AA-1001-VFD.jpg) · For Not Observable photos: AA–Plot number–NotObs or whatever label you’d like, for example Road or Private. (ex: AA-1001-gate.jpg). 5. While in the memory card files, go to the toolbar → Edit → Select All, then right click to copy the files. Go to the destination folder K:\btnf\forest_vegetation_layer\Photos and choose Edit → Paste. 6. Check that all the photos have made it into the destination file, THEN delete them off of the memory card. C. Datasheets 1. Fill in any information missing on the datasheets, i.e. Remarks, date, etc. Check spelling! 2. Once you are satisfied that the datasheet(s) are complete and legible, copy them double-sided (so we have backups).
3. File the originals in the appropriate folder in the file cabinet, and the copies in their appropriate folder as well. 4. Periodically, send the originals to RSAC.
D. Maps 1. Refile all maps in the appropriate folders to ensure anyone can locate the correct map before
next heading into the field. 2. Note on the Nav Maps (those on the wall, and all those used in the field that day) which plots
110 І Appendix F: Accuracy Assessment Design
were visited that day: - For plots successfully read, color in the yellow waypoint dot with a red or orange marker. - For plots with only observation points, no observation points, access issues, etc., make a black box around the plot number. - Be sure to note on each map any road covers, gates, greasy conditions to aid future travel. E. Documents 1. On the hard-copy PLOTS DONE.xls document that corresponds to the geographic region in which you are working (hanging next to the nav maps on the wall): - Note the date your particular plots were read. - Place a check mark in the appropriate columns: if observation points were obtained, and then if/when datasheets and plot maps were copied and filed. - For those plots with issues, note in the Notes column the reason why they were not read,
or why only observation points were obtained, etc. The font color should be changed to red.
→ The Crew Lead is responsible for updating this document online.
V C F f p L V
111 І Appendix F: Accuracy Assessment Design
I. Datasheet – 2006 Field Season
B-T VMP Accuracy Assessment Datasheet 1. Plot ID #: ______________ 2. Observers: ______________________________________________ 3. Date: _____________ 4. Level of Observation: Site Visited Viewed from Distance Not Observable If moved, explain why:
5. UTMs N _________________ E _________________ Elevation ____________ Make sure PDOP = 6; if > 6, note value here: _______ 6. PHOTOGRAPHS: Plot Photo (“P”) Taken: Yes No “P” Photo File #: _________ TOTAL # OF PHOTOS: 7 or 9 Azimuth Azimuth
Fuels Line 1: ________ Fuels 1 Down (F1D) Photo File #: ________ Fuels Line 2: ________ 7. COVER FROM ABOVE 8. CANOPY COVER
If TO total is >10%, disregard TR & go thru key. If <10%, give TR for each species & then key. 8. (con’t)
PLOT SUMMARY—1st Call 9. Map Group Conifer (C) Deciduous (D) Shrubland (S) Herbland (H) Riparian (R) Alpine (A) Non-Veg (N)
BLS DGF EMS LBP ASP BB BK LA GF CTW AL AG UB
10. Map Unit LPP RMJ SAF WBP MB MM MS SB TF WI SI BA
MDF MLP MSF MWB MAS SS SK WY NN RH SV
11. Tree DBH Class: Note relative %s TS0 TS1 TS2 TS3 TS4 TS5 TS6 (based on cover from above, not # of trees) (<1”) (1-2.9”) (3-4.9”) (5-9.9”) (10-19.9”) (20-29.9”) (30”+) & round to the nearest 5-10% : ____ ____ ____ ____ ____ ____ ____ =100% Trees: Shrubs:
12. Tree/Shrub Cover From Above: TC1 TC2 TC3 TC4 TC5 TC6 TC7 SC1 SC2 SC3 (10-19%) (20-29%) (30-39%) (40-49%) (50-59%) (60-69%) (70-100%) (5-9%) (10-25%) (25-100%) Grass/shrub PLOT SUMMARY—2nd Call (ALWAYS make a 2nd call—see above for codes) 12.5 Red Squirrel Presence? Yes No 13. Map Group: __________ 14. Map Unit: _________ 15. Tree DBH Class: _________ 16. Cover From Above: __________
17. REMARKS:
Plot Moved? ?
8a. Trees tallest ht: ______ % TO % TR % Canopy
Cover All Trees (use Cover From Above value) + = + = + = + =
LIFEFORM: COVER: Trees Shrubs Herbaceous Non-Vegetated = 100%
Lowest Crown to Base Height: _______
8b. Shrubs tallest ht: ______ % Canopy
Cover 8c. Herbs % Canopy
Cover 8d. Non-Vegetated %
Cover All Shrubs All Herbs Barren (Rock, Soil) Riparian Shrubs Native Riparian Herbs Downed Wood POFR4 + ARCAV2 Native Alpine Herbs Litter Alpine Shrubs Non-Native Herbs Permanent Snow/Ice (Other:) Total (Cover from Above=)
112 І Appendix F: Accuracy Assessment Design
II. Veg Crew Gear Checklist – 2006 Field Season
• Datasheets • GPS unit (plus antenna, battery, cords), with waypoints
downloaded onto it • Maps – Navigation, Travel, Plot, and USFS Road/Visitor • Radio, plus extra batteries • Digital Camera (battery charged) • Aerial Photos • Whiteboard • Whiteboard Markers • Pencils • Compass (declinated) • Clinometer • 100m Tape • DBH Tape • Flags/flagging • Trowel • Ziploc Bags (for collecting plants) • Survey Pole (NOT IN BIN!)
• Dominance Key • Collection Protocols • Radio Instructions • Rite in the Rain Datasheets
Other: • Water • Food • Sunblock • First Aid Kit • Bear Spray • Clothing for Rain/Cold • Plant Books!
APPENDIX G: Accuracy Assessment Results
113 І Appendix G: Map Group Results
114 І Appendix G: Map Unit Results-Standard Error Matrix
115 І Appendix G: Map Unit Results-Proportional Error Matrix
116 І Appendix G: Veg Mapping Standards Tree Canopy Closure Results
