Earth Science Applications and Decision Support Tools

J. W. Skiles* Biospheric Science Branch, Earth Science Division, NASA Ames Research Center, Moffett Field, CA 94040

Cindy Schmidt+

San Jose State University and Biospheric Science Branch, Earth Science Division, NASA Ames Research Center, Moffett Field, CA 94040

[Abstract] Resource managers at all levels can benefit by using NASA remotely sensed imagery, models and data. NASA products provide information for decision support tools (DSTs) used by local, county, state, federal and tribal governments in order to help them manage resources and mitigate ecosystem perturbations. Data used in DSTs can be derived from Applications of National Priority as assigned by NASA. For the past four summers, a student internship program, called DEVELOP, a portion of which is run out of NASA Ames Research Center (ARC), has tasked students to use NASA data, field work and computer analysis to derive information for federal, state, local and tribal DSTs. The discussion below describes a West Nile Virus risk detection study in a California county, a carbon sequestration study in a national forest, several invasive species studies, and a waste tire detection project. All of the projects make use of remotely sensed imagery and all yield data and information that can be used in DSTs. The use or non-use of these data and information involves a certain amount of risk by those who make decisions regarding natural resources. A brief discussion is given of resource managers using or not using the results of these studies.

I. Introduction Remotely sensed (RS) imagery and the data derived therefrom coupled with a geographical information system (GIS) provide powerful tools for resource managers at all levels of government. These tools enable the managers to make informed decisions about how to manage a resource for the greatest benefit to the resource user. Providing managers with RS and GIS results involves working with the managers to ascertain their requirements and then designing studies and data analysis to meet those requirements. Producing results without the collaboration of the end user results in the circumstance “Build It and They Will Come”, meaning that once data and analyses are known, users will appear and make use of the results. This actually rarely happens, and it is prudent to determine user needs before offering data and analysis results. An example of how this should work is the NASA internship program called DEVELOP1. DEVELOP is a Human Capital Development Program funded by NASA's Applied Sciences Program of the Earth Science Division in the Science Mission Directorate at NASA Headquarters. Student run and student led projects are conducted intensively during the summer. Students are responsible for designing projects in conjunction with the customers and collaborators, collecting the necessary data (some of which are satellite data and some are from field work), analyzing those data, presenting the results from the studies to the customers and collaborators, and writing papers on the study and the study results. Students benefit from this program by receiving intensive training in the use of remote sensing, GIS, __________________________ * Mail Stop 239-20, Biospheric Science Branch, Earth Science Division, NASA Ames Research Center, Moffett Field, CA 94040, Member +

Mail Stop 242-4, San Jose State University and Biospheric Science Branch, Earth Science Division, NASA Ames Research Center, Moffett Field, CA 94040, non-Member

Space 200619 - 21 September 2006, San Jose, California

AIAA 2006-7356

This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States.

and visualization software. Students also benefit by learning about the obstacles and the rewards of working on a “real world” problem. The tribal or government agencies, the customers and collaborators, learn a new approach to solve a particular long-term or short-term issue. NASA benefits by using students as “ambassadors” to extend the benefits of NASA Earth science research to government agencies. We discus in some detail these projects below. Information about the ten projects undertaken to date by the NASA Ames Research Center (ARC) DEVELOP student interns is shown in Table I. Table I. Information for the Ten ARC DEVELOP Projects Undertaken to Date Year State Short Title Brief Description 2003 NV Tall Whitetop Map and project cover of an invasive plant species along the Truckee River in

Nevada CA WNV County Map Map sources and habitat for West Nile Virus carrying mosquitoes and show

concentrations of human populations near those locations in Monterey County, California 2004 NV Salt Cedar Using ground truth and satellite imagery, find the extent of the invasive

species tamarisk in northwestern Nevada OR Forest Carbon Sequestration In the Fremont-Winema National Forest map tree species and

project the affect on the sequestration of carbon by the harvest of these trees 2005 CA Yosemite Fire Recovery Ascertain the type and extent of vegetation re-growth after wildfire in

Yosemite National Park UT Cheatgrass Validate existing range maps of the invasive species cheatgrass and

predictions using satellite imagery and statistical techniques CA Waste Tires Using high-resolution satellite imagery, locate waste tire piles in two

climatically different regions of California 2006 CA Yosemite Vegetation Anomalies Use satellite LAI values to determine where areas within Yosemite

National Park show vegetation anomalies and classify them according to possible causes NV Red Brome Determine extent of the invasive species red brome infestation in a fire scar

and show how it interacts with seeded native grasses AK Sea Ice/Walrus Use satellite radar images to determine walrus preferences for thin, medium

thickness, and high thickness sea ice off the western coast of Alaska ______________________________________________________________________ The DEVELOP program has activities at six NASA Centers and engages in activities all through the year, with the summer term being the most intensive and involving the greatest number of interns. Further information about DEVELOP may be found at:

http://develop.larc.nasa.gov

II. Decision Support Tools Decision support tools support policy makers, investors, resource managers, developers and other consumers. Moreover, whether we realize it or not, all of us use decision support tools (DSTs) or decision support systems (DSSs) every day. These may be as simple as deciding to wear a coat after viewing the weather forecast on television to the more complex decisions about large-scale purchases such as a vehicle or a home. These personal DDTs are rarely written down and are kept as a series of rules or thresholds in ones mind. They are, however, important for us to function in modern society. For resource management and use, more formal DSSs and DSTs exist. Examples abound and are quite disparate, but a few are mentioned here. There are DSTs for determining grazing pressure on rangelands2;3; for determining air quality and particulate concentrations4,5; for use in epidemiology6; for the prediction of soil water concentrations7; for forecasting hydroelectric energy generation8; and for determining when to salt winter roads9. The point is that DSSs are ubiquitous in resource management. Each DEVELOP project mentioned here provided input to DSSs and DSTs, thus aiding government and tribal resource managers. Further, DEVELOP projects

support DSSs that fall within twelve specific application areas. These areas have been identified by the Applied Science Program in the Earth Science Division at NASA Headquarters and are shown in Table II. Table II. The Twelve Applications of National Priority (ANP) as Identified by the Applied Science Program in the Earth Science Division of the Science Mission Directorate at NASA Headquarters.

Carbon Management Coastal Management Public Health Disaster management Energy Management Agricultural Efficiency Aviation Invasive Species Water Management Ecological Forecasting Security Air Quality

For each of the DEVELOP projects described within the body of this paper, we took one or more of the priorities shown in Table II as a starting point for each project. The interns who worked on each project were told how their project aligned with a national priority and they were directed to keep that priority in mind as they completed their project. They were further instructed to deliver products to their customers and collaborators that would help in decision making and/or supply information for a decision support tool.

III. Ten Projects A brief description of ten DEVELOP Projects follows. Noted in each description are the remotely sensed data used, the product(s) of the project, and the DSS/DST for which the project provided support. A. West Nile Virus (WNV), Monterey County, California This project spatially identified potential vector (mosquito) habitat, and spatially correlated these data to high-risk human populations in Monterey County, California. The resulting data and model became a part of the county vector monitoring and surveillance decision system, and provides critical support to community decision makers for a making swift and effective response to the spread of WNV in Monterey County. The project addressed the ANPs of Homeland Security, Community Growth and Public Health. Remotely sensed data came from Landsat 7 ETM+ and Shuttle Radar Topography Mission (SRTM). Data used also included Digital Ortho Quads (DOQs), GIS data layers, census data, and fieldwork for ground-truthing. The resulting vector map for WNV in the county is shown in Fig. 1. More information about this study may be found in reference 1. B. Nevada Tall Whitetop The ARC DEVELOP students incorporated remote sensing and ground-based methods in mapping and monitoring an invasive and noxious plant species, tall whitetop, that is rapidly encroaching upon the Northern Nevada territory of the Pyramid Lake Tribe Reservation. The study focused on mapping tall whitetop in upland, riparian, and wetland areas, all of which are a concern as invasive vegetation often displaces native populations. The study also included modeling the potential future spread of tall whitetop under various management scenarios. The remotely sensed imagery and data came from the Thematic Mapper (TM) onboard Landsats 4 and 5 and the enhanced Thematic Mapper (ETM+) on Landsat 7. Also used were Aster data from the Terra satellite. Census data and DOQs were also employed. Information gathered was displayed in a database, modeled, and incorporated into the tribe’s environmental database, which in turn will be used in a DSS to determine where to direct control measures for tall whitetop. This project aligned with the ANPs of Water Management, Invasive Species, and Agricultural Competitiveness. More information about this project may be found in reference 1. Figure 2 shows output from the predictive model developed for this study.

Fig. 1 Monterey County, California West Nile Virus Mosquito Vector Map. C. Nevada Tamarisk (Salt Cedar) Tamarisk is an invasive plant that is rapidly spreading through many of the western states. It supplants native vegetation by using large amounts of groundwater and making the surrounding ground area too saline for other plants to survive. Federal, state and local government agencies in Nevada did not know the extent of the plant, nor its spread rate. The DEVELOP students designed and implemented an invasive species field sampling protocol to collect field data in the state of Nevada that was integrated in a GIS with environmental variables including elevation, groundwater and percent coverage of Tamarisk, water areas, MODIS data, and Landsat TM imagery. The results of this project were a map summarizing an estimate of Tamarisk presence throughout the study area of northwestern Nevada. These results were given to the Natural Resource Conservation Service in Nevada and sent to the Invasive Species Forecasting System, maintained by the USGS. Remote sensing was also able to show the decrease of leaf area index (LAI) in one part of the study area. This was caused by the use of a biological control agent, a beetle from the part of the world to which tamarisk is native. Information from this project will be used to help the government agencies target areas to control. Figure 3 shows a student in the field taking geographical positioning system (GPS) points that make up a polygon overlain on a Landsat image. The ANPs addressed by this project include Water Management and Invasive Species. More about this project may be found in references 10 and 11. D. Oregon Forest Carbon Management Forest managers in the Fremont-Winema National Forest have recently begun to look at carbon sequestration for their long term forest planning. The DEVELOP team used satellite imagery and carbon models to assist the Fremont-Winema National Forest with data and analysis to assist them in their decision making process for their 2005 forest management plan. DEVELOP students provided managers of the Fremont-Winema National Forest in Oregon with data and analysis for carbon management. The goal of this project was to demonstrate how different fuel load reduction treatments affect forest carbon budgets and to provide fire modeling outputs to assist managers in the mitigation of disturbances that could potentially impact the forest’s carbon budget. The major components of the project were a vegetation map, model outputs from a NASA developed carbon simulation model, CASA (Carnegie-Ames-Stanford Approach), and fire behavior characteristic model outputs from a generic fire model.

These products were given to the Forest Service as an aid for managing the forest resource, particularly carbon and carbon sequestration. In Fig. 4 the Fremont-Winema National Forest is shown extracted from all non-National Forest land. The figure shows the extent of species-specific land cover and land use. The ANPs addressed by this study are Carbon Management and Ecological Forecasting.

Fig. 2. Showing predictive model out put. The Truckee River channel runs from the lower right to the upper left in each portion of the figure. The right portion of the figure shows unchecked tall whitetop growth. Areas in red are pure whitetop stands. Yellow areas are a mixture of whitetop and other vegetation. The left portion of the figure shows the same locations, but here a mixture of mechanical and chemical treatments have been applied to the invasive plant in the years 2003, 2004 and 2005 on the S-S Ranch.

Fig. 3 This figure shows a blue polygon, the corners of which were determined using GPS points taken in the field (shown). From field work it was determined that the area enclosed by the polygon is mostly tamarisk or bare soil. The polygon has been laid over a Landsat image.

Fig. 4. Dominant vegetation species and land-use map for the Fremont-Winema National Forest. The California-Oregon border is coincident with the bottom of the figure.

E. Yosemite Fire Recovery Developing methods to assess ecological change in vegetation is increasingly important to Yosemite National Park managers. Alterations in vegetation result in changes of wildfire frequency and intensity. Park managers are interested in monitoring catastrophic changes, due primarily to large fires, and subtle vegetation changes, such as an overall increase in vegetation density, over the long-term. The DEVELOP students assisted in these efforts by conducting a change detection study utilizing Landsat imagery to analyze catastrophic and subtle vegetation change. in areas that had been burned. The student interns studied three areas previously burned by fire: the Ackerson fire area (burned in 1997), the Walker fire area (burned in 1989), and the A-Rock/Steamboat fire area (burned in 1991). Fig. 5 shows vegetation change dynamics at each of the fire locations. These maps will be used in a model to predict burn severity levels within the Park. See reference 12 for the details of this study. The study addresses the ANPs of Ecological Forecasting and Carbon F. Utah Cheatgrass Cheatgrass (Bromus tectorum) is a widespread invasive species that has invaded the semi-arid, shrub-steppe region of the Great Basin in the western US because of its hardiness, phenology and its ability to recover from fire. Areas heavily infested with cheatgrass create large fire dangers and reduce animal habitat. Cheatgrass infested areas possess a distinctive appearance in satellite imagery which can be seen by comparing images across the course of a year. The DEVELOP student team created a predictive percent coverage maps for cheatgrass using field data points combined with NDVI (Normalized Difference Vegetation Index) MODIS values, and a customized regression model (CART). The maps and regression model can be used by managers to control cheatgrass spread and reduce large grassfires. Figure 6 is and example of one such prediction map. The ANP addressed by this study is Invasive species.

Fig. 5. Vegetation change at fire areas. Note the yellow north and west of the Walker fire scar. This area burned in 2001.

Fig. 6. Outline of the study area in northern Utah. Results show likelihood of cheatgrass infestation using one particular regression tree. G. California Waste Tire Detection DEVELOP student interns worked with the California Integrated Waste Management Board’s (CIWMB) Special Waste Division to create a proof-of-concept project investigating the use of high-resolution satellite imagery for locating and mapping waste tire disposal sites in the Sonoma and San Bernardino counties of California. Previous methods for locating waste tire disposal sites in California included contracting California Highway Patrol to fly over suspected sites and take photographs, a method that was costly, time consuming and not accurate. The methodology and outputs generated by this study were used to reduce the time and capital necessary to manage waste tire sites, which left unkempt, pose threats of combustion and run the risk of becoming breeding grounds for mosquitoes. This project addresses the ANP of Public Health. See reference 13 for further information about this study.

Fig. 7. In this high-resolution satellite image vegetation appears red and buildings appear blue. Computer analysis techniques were used to test the separability of tires from other dark features such as water and shadow.

H. Walrus and Sea Ice Long term changes in climate are causing changes in sea ice formation resulting in changing habitat for walrus. Students from NASA's DEVELOP program worked with the Department of Fish and Wildlife Service in Alaska to determine the usefulness of satellite imagery for studying wildlife habitat on sea ice. Few studies use sea ice image processing methods to observe marine mammal habitats in polar regions because of the difficulty in obtaining multispectral imagery and georeferenced data points of species location for the same time period. The dynamic nature of sea ice poses a challenge to remote sensing studies and matters are further complicated when additional data are incorporated. This study used a method for sea ice image analysis that incorporated remote sensing segmentation and classification techniques with RADARSAT1 SAR (Synthetic Aperture Radar) imagery. Results were associated with ground point data to determine the relationships of sea ice features to walrus' preferred habitat. MODIS data were utilized, where possible, to verify the classifications of sea ice surfaces obtained by RADARSAT1. The challenge and goal was to capture, display, and relate geophysical information from radar images that correlate with georeferenced species data points for the same time period. The students were able to determine that walrus do not use thick ice and actually prefer thin to medium ice thicknesses. This determination will mean that aircraft census of walrus populations will not need to be done over areas of thick ice, saving flight time and allowing FWS (Fish and Wildlife Service) personnel to concentrate their efforts on locations where walrus populations are expected to be found. Figure 8 is an example of the processing done on raw radar ice imagery. This study addresses the ANPs of Coastal Management and Ecological Forecasting. A manuscript detailing this study has been submitted for a conference proceedings publication.

Fig. 8. Scanline swaths at 12 km for April 1, 2005 showing a (a) raw unclassified scanline swath; (b) unsupervised classification of an April 1, 2005 scanline swath; (c) despeckled and unsupervised classification of a scanline swath; (d) a segmented scanline swath categorized into 4 classes of thick, medium, thin ice, and water (supervised classification).

I. Post-fire Landscape and the Invasive Species Red Brome Bromus rubens (Red brome), native to the arid deserts of Europe, Africa, and Asia, was first identified in the southwestern United States in the mid 1800s. Proliferation after fires has caused this annual to be regarded as a severe ecological threat. This plant suppresses the growth of native species by depleting the soil moisture and potentially degrades the habitat of the threatened Gopherus agassizii (desert tortoise). The purpose of this study was to assess the utility of using satellite imagery to detect B. rubens. Students worked with the Bureau of Land Management (BLM) and the National Park Service (NPS) to map the areal extent of red brome and predict its density within the region. The locations of B. rubens were visually identified and recorded using GPS in two study areas within the 2005 Goodsprings burn scar in southern Nevada. MODIS Enhanced Vegetation Index (EVI) was used to identify optimal Landsat and ASTER scenes, which were then used to evaluate the density of B. rubens. Statistical analysis was used to determine the relationship between the percent cover of the plant and predictive variables. This information will be valuable to the BLM, the NPS and other land managers as they make decisions on how to control this invasive plant. This study addresses the ANP of Invasive Species. A manuscript detailing this study has been submitted for a conference proceedings publication. J. Utilizing MODIS LAI to Identify Vegetative Anomalies in Yosemite National Park Students examined vegetation anomalies in Yosemite National Park using remotely sensed data. These anomalies result primarily from beetle infestation and fires but many anomalies have not been clearly explained by the Park. Such vegetation disturbances can result in forest fragmentation and subsequent loss of wildlife habitat and increased fire hazard. The project utilized the MODIS Leaf Area Index (LAI) product in the analysis of vegetation in Yosemite. LAI provides a ratio of leaf area to total ground area. The LAI data for each month were averaged from 2001 to 2005. Data for the summer months of 2005 were compared with the monthly averages to produce a map of LAI anomalies. These maps were overlaid with known areas of insect infestation, snow cover or recent wild fire, all variables apt to cause lower-than-expected LAI values. Field work was conducted to verify the known causes disturbance and ascertain the causes of unexplained anomalies. Using MODIS LAI and ancillary data, locations of unknown anomalies for further investigation were developed for use by research partners. Continuation of the project will result in the creation of an automated site selection and anomaly detecting utility which will allow park managers to quickly view files that provide locations of vegetation anomalies which subsequently should be examined on site. This methodology is of interest to managers of national parks and forests because of the accessibility of MODIS data, its high temporal resolution and the speed with which large areas of land can be analyzed. This study addresses the ANP of Ecological Forecasting. Figure 9 shows the MODIS pixels in blue that had LAI values below the five-year LAI average. A manuscript detailing this study has been submitted for a conference proceedings publication.

IV. Conclusion The studies reported here are mostly done in an intensive ten-week period during the summer months and are consequently done rapidly, producing examples of how the resource managers can use the techniques and technology in their work. The projects are designed to support Decision Support Systems and Decision Support Tools used by resource managers. Each project has had an associated Application of National Priority. Each project was done with the expectation that the customer and/or collaborators would gain by using the study results and conclusions. Once a DEVELOP project is completed a meeting is scheduled with the customers and the collaborators (CCs) to hand-off the study results, analysis techniques, and a student produced paper. In some instances, a workshop is presented to interested persons so that the analysis of the remotely sensed imagery can be shown in detail. Once the hand-off is accomplished, the ARC DEVELOP students generally have no further contact with the CCs other than to hear how using the results helped in making decisions. In some years, television reporters have interviewed the interns during the course of their projects, informing the students how the video footage will be used. In other instances, reporters from newspapers in the area of a particular project interviewed the students and articles subsequently appeared in the local press. We also hear from CCs via telephone and e-mail that a

visualization has been used or that data points in a vegetation survey were used by a resource manager or agency, To some degree, every study reported above has been used by decision makers in DSTs or DSSs. Of course, after the completion of the study, DEVELOP and NASA have no control over how the study data are used by resource managers and decision makers. That is because it is the job of the DEVELOP Program to attempt and report on studies that can be used by the CCs, not to insure that data, posters, and remarks in student papers are used as DEVELOP and NASA might desire. To our knowledge, use of DEVELOP project results by a resource manager has never caused a decrease in quality or abundance of the managed resource.

Fig. 9. Yosemite National Park outline with sites (in blue) for further investigation by on-site personnel. Sample sites shown as colored circles are locations visited by the student team. The program has been successful in exposing federal, state, local and tribal government agencies to new data and technologies that may help them in their decision making processes. The adoption of new technologies is often difficult for agencies for several reasons. One is that there is a learning curve involved in incorporating new technologies. Shrinking budgets usually mean that there are fewer and fewer individuals doing more work. Agency employees simply do not have enough time to fully learn about how to use the technologies and incorporate them into their operations. Second, the agency may not have the budget to invest in the necessary software and data required to complete similar projects. Third, there may be a discrepancy between what the agency wants and what the DEVELOP program can provide. Specifically, many natural resource management agencies would, ideally, like to work with high resolution imagery (1 to 4 meters). DEVELOP projects, because they are using NASA data, work with much coarser datasets (30m to 1km). Whether there difficulties in using DEVELOP products or not, one of program’s future objectives will be to follow up on past projects to determine the usefulness of the information provided. Future projects, then, could include summarizing efforts needed to ensure that the data are used. This information will aid DEVELOP in working with new collaborators. Further, there is no doubt that DEVELOP interns achieve a better understanding of the Earth system through their work in the DEVELOP Program. Some interns have returned for an additional summer and worked on entirely

different projects. Additionally, students have indicated that they are considering a change or will change their college major to more closely align their course work with the skills they learned in DEVELOP. While it is not obvious from the above text, the DEVELOP students range in educational level from those who have completed their junior year in high school, to undergraduates, to graduate students at the masters level. The project teams consist of a mix of student ages and skills and backgrounds. The program gives many interns the feel of working outside of the school environment, encountering many of the situations they will face upon graduation. Probably none of these students will don a space suit and explore another world, but their DEVELOP experience of using remotely sensed data, working as a team, interacting with customers and collaborators prepares them for exploring our home planet.

Acknowledgments The authors make note with thanks the efforts of the NASA Ames Research Center DEVELOP Interns for the summers of 2003, 2004, 2005, and 2006. We also acknowledge the help and consideration of the DEVELOP National Program Office staff at NASA Langley Research Center and of the help offered by ANP Program Managers at NASA Headquarters in Washington, DC. Comments from J. A. Brass and H. L. D’Antoni improved an early version this paper.

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11

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