Applied Remote Sensing, FRWS 5750 December 8, 2003
1 Undergraduate Student, Department of Forestry Range and Wildlife Sciences, Utah State University, Logan, UT, 84321. Corresponding author’s e-mail address: [email protected]
ABSTRACT Cheatgrass (Bromus tectorum) has become one of the most dominant and competitive weeds in the intermountain West. It can germinate in fall or spring, and completes its life cycle in early spring. Its time-efficient life cycle and affinity for producing monocultures cause it to generate considerable amounts of extremely flammable, dry fuels, effectively advancing natural fire regime. For fire forecasting and rehabilitation alone, it is imperative that effective techniques be developed for monitoring the location and spread of cheatgrass. A comparative method using Normalized Difference Vegetation Index (NDVI) has been developed for monitoring the presence and spread of cheatgrass. This method was applied on Landsat-7 ETM data for Skull Valley, UT. NDVI values of the area from June 4, were subtracted from NDVI values from May 3 of the same year in order to indicate the presence and area of cheatgrass infestations in the valley. It is proposed that such values could be compared annually to determine spread and abundance over time for a given area. Due to the alkaline soils and vegetative dynamics of the area, quantitative statistics could not be accurately generated. Qualitative assumptions had to be made based on visual observation alone. Correlations between the NDVI and Fire Finder outputs of the area, which were both generated in ERDAS Imagine’s Model Maker, are visually apparent resulting from the mutualistic relationship between cheatgreass and wildfire.
2
TABLE OF CONTENTS, FIGURES, TABLES Title Page…………………………………………………………………….……1 Abstract……………………………………………………………………...…….2 Introduction…………………………………………………………………..……3 Figure 1 – NDVI equation……….………………………….…………….4
Figure 2 – ETM image of Skull Valley, UT. Taken on May 3, …………..5 Figure 3 – ETM image of Skull Valley, UT. Taken on June 4, ……..……6
Literature Review…………………………………………………………...……..6 Study Area……………………………………………………………...…………7 Figure 4 – Map showing the location of Skull Valley, UT………….....….7 Methods…………………………………………………………...……………….8
Figure 5 – Equation used by the COST Atmospheric Correction………...8 Figure 6 – NDVI model………………………………………………...…9
Figure 7 – May 3, NDVI output…………………..………………..…….10 Figure 8 – June 4, NDVI output……………….….……………………..11 Figure 9 – NDVI “subtraction” model…………………………….…….12
Figure 10 – Map of Skull Valley cheatgrass infestation……….………...13
Results……………………………………………………………………………13
Conclusion/Discussion…………………………………………..……………….14 Figure 11 – Fire Finder model…………………………………….……..15 Figure 12 – Fire Finder output/cheatgrass output comparison…….…….16
References………………………………………………………………………..17
3
INTRODUCTION Since its introduction from Eurasia in the late 1800’s, downy brome or cheatgrass (Bromus tectorum), has become one of the most dominant and competitive weeds in the intermountain West (DiTomaso 2000). Though it can germinate in fall or spring, it is primarily a winter annual, demonstrating vigorous root growth in winter months and completing its life cycle in early spring (Kennedy 1992). This time-efficient life cycle allows it to outcompete more desirable herbaceous species, especially after instances of disturbance such as fire or overgrazing. When cheatgrass infected rangelands are allowed to develop into monocultures following disturbance, its late spring senescence generates considerable amounts of extremely flammable, dry fuels. The presence of these fine fuels in late spring and early summer effectively advance the natural fire regime of an area. In areas where cheatgrass has already become dominant, fire regimes have been altered significantly enough to obstruct the regeneration of more desirable native and introduced species. The destructiveness of this effect has been particularly evident in Artemisia spp. rangelands, which typically require a fire return interval of 60 to 110 years rather than the one to five, which is prevalent in cheatgrass monocultures (DiTomaso 2000). For rangelands unaccustomed to such a high frequency of fire return, the net result has been losses in species richness, ecosystem stability, forage value, wildlife habitat, and aesthetics. In spite of these negative impacts, cheatgrass is capable of being grazed for a short time in early spring while providing livestock with moderate nutritional value. Consequently, intensive short-term grazing systems are continually being researched as management alternatives in areas containing high densities of cheatgrass. Such practices are performed before seed set in order to deplete the existing seedbed and provide an opportunity for desirable plants to compete with the weed. In cultivation agriculture, no registered herbicides have been found to control cheatgrass economically or consistently (Anderson 1996). Efforts at biological control using naturally occurring soil bacteria have also been attempted (Kennedy 1992). However, the feasibility of incorporating such bacteria into the soil is unlikely given the enormous acreage of cheatgrass infested rangelands in the west. These and other efforts have been met with varied success, while cheatgrass continues to degrade and alter the ecology of public and private wildlands. It is imperative that effective techniques be developed for monitoring the location of cheatgrass. Practices that provide land managers with information on the annual distribution of the weed, for the purpose of detecting its area of increase are desperately needed, especially for use in fire forecasting and rehabilitation (Burgan 1993). The Normalized Difference Vegetation Index (NDVI) shows patterns of vegetative growth from green-up to senescence by indicating the quantity of actively
4
photosynthesizing biomass on a landscape (Burgan 1996). Such images allow for the production of maps, which indicate visual greenness and can be extremely valuable to land managers and researchers in determining changes in vegetation over time. The NDVI is the difference of near-infrared and visible red reflectance values normalized over reflectance (Burgan 1993). Specifically,
NDVI = (NIR - RED)/(NIR + RED)
Figure 1 – NDVI equation. The equation produces values ranging from –1 to 1. Negative values are indicative of clouds, snow, water and other nonvegetated, non-reflective surfaces, while positive values denote vegetated or reflective surfaces (Burgan 1993).
Figure 2 – Landsat-7 Enhanced Thematic Mapper (ETM) image of Skull Valley, UT. Taken on May 3,
5
For this experiment, NDVI was used to predict the location of cheatgrass in Skull Valley, UT. By performing an NDVI on a Skull Valley image taken on May 3, (when cheatgrass has greened up – earlier than most other plants), and comparing it to an NDVI image of the same area from June 4 of the same year (when most cheatgrass will have senesced), areas can be detected in which cheatgrass is present.
Figure 3 – Landsat-7 Enhanced Thematic Mapper (ETM) image of Skull Valley, UT. Taken on Jun ,
ITERATURE REVIEW
n article by the USGS discusses using NDVI generated from Landsat-7 ETM data in the
Infestations of Cheatgrass on the Colorado Plateau).
e 4
L Amonths of April and July in order to indicate cheatgrass (Bromus tectorum) infestations on the Colorado Plateau. The article proposes subtracting the July NDVI values from the April NDVI values, in order to generate an output of NDVI values that indicate areas that are covered by plants that are green in April and non-green in July. (USGS. 2003. Monitoring Changes in Vegetation and Land Surfaces by Remote Sensing-Detecting
6
The exact technique was mentioned in an article by Menakis et. al. in a USDA Forest
ervice article, which discussed the technique as a method for creating a cheatgrass layer
ime series using AVHRR data for apping cheatgass infestations and spread during the growing season. Although this
TUDY AREA
ted directly south of the Great Salt Lake, UT (fig. 1) and its borders re formed by the Cedar, Stansbury, Onaqui, Sheeprock and Simpson mountain ranges.
Sin a GIS driven mapping system. (Menankis, 2002) A third paper discussed using biweekly NDVI tmarticle did not in fact propose to use NDVI comparison values in its detection methods, it further demonstrates the need and support for effective monitoring methods for the invasive weed (Mustard 2003). S Skull Valley is locaaThe floor of the valley consists mostly of flats with some intervening dunes. The area serves as habitat for greasewood, saltgrass, pickleweed, utah juniper, douglas-fir, maple, some cultivated land and considerable cheatgrass, which in many areas of the valley is the major vegetative component. The vegetation in the valley provides habitat and forage for deer, pronghorn antelope, sage grouse, chukar and raptors (Tooele County General Plan). A weather station located at Dugway, UT records a 50-years' average annual precipitation of 194 mm (Sperry 2002).
7
Figure 4 – Map showing the location of Skull Valley in relation to the Great Salt Lake and other areas of interest. Taken from www.kued.org/skullvalley/ road/ Historically, any development in the valley has been primarily for agricultural uses and livestock production. This continues today since the soils in the area have not proven suitable for much development. The northern portion of the valley bottom is covered by extensive mud flats (Tooele County General Plan). The surface water in the valley consists of several small streams that drain into the Great Salt Lake. The primary water sources are springs located on the west side of the Stansbury Mountains and the southeast sides of the Cedar Mountain Range (Tooele County General Plan). METHODS Landsat-7 Enhanced Thematic Mapper (ETM) images were obtained from the RS/GIS Laboratory at Utah State University. Before the NDVI was performed, the images were standardized. The process of standardization normalizes the image’s pixel values by accounting for differences in sun illumination geometry, atmospheric effects and instrument calibration. This process increases the ability to compare imagery over time, thus helping to better determine vegetation change (www.gis.usu.edu).
Standardization was done using an online tool provided by the RS/GIS Laboratory at Utah State University. The standardization procedures used by this website are image-based, creating ERDAS Imagine spatial models (.gmd format), and therefore require no additional information other than that provided by the imagery (www.gis.usu.edu). The COST Atmospheric Correction tool creates a spatial model by converting the images digital numbers to reflectance and performs an image-based atmospheric correction using the Chavez (1996) COST method (www.gis.usu.edu). The equation is thus:
τπθπρ
*))180/*)90(((**))*()*(( 2
−+−+
=COSE
DBiasGainHBiasGainLBandN
BandNBandNBandNBandNBandNBandNBandN
Where, ρBandN = Reflectance for Band N; LbandN = Digital Number for Band N; HbandN = Digital Number representing Dark Object for Band N; D = Normalized Earth-Sun Distance; EbandN = Solar Irradiance for Band N; τ = Atmospheric Transmittance expressed as
))180/*)90((( πθ−COS Figure 5 – Equation used by the COST Atmospheric Correction tool along with explanation of variables. Once the atmospheric correction was complete, an NDVI model was built in ERDAS Imagine’s Model Maker. This was designed to subject images to the NDVI equation (see fig. 1) and produce a resulting image. After the NDVI model was built, each image was “ran-through” the model. The output from each image being ran-through the model, is the desired NDVI image.
9
Figure 6 – NDVI model built in ERDAS Imagine’s Model Maker.
The higher the NDVI value, the more green, or photosyntheticly active, is the vegetative cover (Burgan 1993). Note the visually apparent reduction in brightness from the May 3 image to the June 4 image (fig. 7, fig. 8).
10
Figure 7 – NDVI output from the image taken on May 3, Skull Valley, UT.
11
Figure 8 – NDVI output from the image taken on June 4, Skull Valley, UT.
Once both images were ran-through the NDVI model, a second model was built using ERDAS Imagine’s Model Maker (fig 9). The purpose of the second model was to subtract the NDVI values from the June 4 image, from the NDVI values obtained from the May 3 image. It was expected that the remaining values would demonstrate which vegetation that had been green in May, was dead, or had senesced by June – a physiological growth cycle common only to cheatgrass on this large of a scale in Skull Valley.
12
Figure 9 – Model which subtracts NDVI values of June 4 image from NDVI values of May 3 image.
In this final image, high NDVI values, or the white, reflective areas, indicate land which was covered by green, photosynthesizing vegetation in April, and senesced, non- photosynthesizing vegetation in June. The accuracy of the predictive map was not evaluated in field observations.
13
Figure 10 – Map of Skull Valley indicating areas of cheatgrass infestation.
RESULTS A visual assessment of this image shows high densities of cheatgrass in the Skull Valley portion of this image. Although the accuracy of this map has not been evaluated in field observations, it is a known fact that cheatgrass is a prominent component of the vegetation in this valley. A decision was made not to perform a quantitative analysis of the results of this experiment. This was done because any statistical analysis would be highly flawed and biased as a result of the high reflectance values picked up from the large salt flat located in the top and center of the image. While cheatgrass can do well in alkaline soils, it is not halophytic, so it is extremely unlikely that cheatgrass is thriving in these areas. Due to the ongoing drought that was occurring when this image was taken, it is likely that vegetation that would not normally have senesced by June, may have under the
14
compounding effects of the previous years of drought conditions. Therefore any vegetation in these areas that had senesced by June, and were picked up in this image had to be drought stressed halophytes. Greasewood, halogeton, shadscale, or pickleweed are possibilities for this hypothesis. CONCLUSION/DISCUSSION A visual assessment of this image further validates the fact that high densities of cheatgrass exist in Skull Valley. Although the accuracy of this map was not been evaluated in field observations, the techniques used for making it demonstrate some promise of being a valuable tool in the monitoring and mapping of cheatgrass infested systems. However, during drought years, vegetation that would not normally have senesced by June, may drop their leaves by this time due to the compounding effects of the previous years of drought conditions. If this is the case, the result would be a visual detection of areas which are larger than known cheatgrass infestations, or in places other than where actual cheatgrass infestations exist. It is interesting to note a high degree of correlation between the areas on the “cheatgrass image” and on the output of the June 4th image after it had been run through a Fire Finder model built in ERDAS Imagine’s Model Maker (fig. 11). Statistical analysis was not performed on the Fire Finder output in comparison to the cheatgrass output, for the same reasons that it was not performed on the cheatgrass output. Nevertheless, there is an obvious visual correlation between the locations of fires in the Fire Finder output, and the locations and areas of cheatgrass detected in the Skull Valley portion of the cheatgrass output (fig. 12).
15
Figure 11 – Fire finder model in which the original June 4 Skull Valley image was ran.
This is an ecologically sound correlation since cheatgrass infested wildlands which are allowed to develop into monocultures, generate considerable amounts of flammable, dry fuels. These fine fuels in late spring and early summer effectively advance the natural fire regime of an area, often significantly enough to obstruct the regeneration of more desirable native and introduced species. In other words, cheatgrass = fire.
16
Figure 12 – A noticeable correlation exists between the cheatgrass and Fire Finder images in Skull Valley.
17
REFERENCES Anderson, R. L. 1996. Downy brome (Bromus tectorum) emergence variability in a semiarid region. Weed Technology 10:750-753. Burgan, R. E.; Hartford R. A. 1993. Monitoring vegetation greenness with satellite data. Gen. Tech. Rep. INT-297. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Research Station. 13 p. Burgan, R. E.; Hartford R. A.; Eidenshink J. C. 1996. Using NDVI to assess departure from average greenness and its relation to fire business. Gen. Tech. Rep. INT-GTR-333. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Research Station. 8 p. DiTomaso J. M. 2000. Invasive weeds in rangelands: species, impacts, and management. Weed Science 48:255-265. Kennedy, A. C. 1992. Biological control of annual grass weeds. Symposium on Ecology, Management, and Restoration of Intermountain Annual Rangelands, Boise, ID, May 18-22, 1992. Menakis, J. P., Osborne, D., Miller, M. 2002. Mapping the cheatgrass-caused departure from historical natural fire regimes in the Great Basin, USA. USDA Forest Service Proceedings RMRS-P-29. (http://216.239.57.104/search?q=cache:VNGoYPo4mpEJ:www.fs.fed.us/rm/pubs/rmrs_p029/rmrs_p029_281_288.pdf+cheatgrass+NDVI&hl=en&ie=UTF-8) Mustard J. F.; Hamburg S.; Young J. A.; Tausch R. 2003. Landscape Dynamics and Land-Use Land-Cover Change in the Great Basin-Mojave Desert Region (http://216.239.57.104/search?q=cache:IaH8pd5YQ80J:lcluc.gsfc.nasa.gov/products/pdfs/2003AnPrgRp/AnPrgRp_Mustard2003.doc+cheatgrass+NDVI&hl=en&ie=UTF-8) Sperry, J. S.; Hacke, U. G. 2002. Desert shrub water relations with respect to soil characteristics and plant functional type. Functional Ecology 16 (3), 367-378. (http://www.blackwell-synergy.com/links/doi/10.1046/j.1365-2435.2002.00628.x/full/) Tooele County General Plan, Skull Valley Planning District. 2003. http://www.co.tooele.ut.us/PDF/General%20Plan/Chapter%206.pdf
USGS. 2003. Monitoring Changes in Vegetation and Land Surfaces by Remote Sensing-Detecting Infestations of Cheatgrass on the Colorado Plateau (http://climchange.cr.usgs.gov/info/sw/monitor/remote1.html) USGS Southwest GAP Analysis. 2003. http://www.gis.usu.edu/docs/projects/swgap/ImageStandardization.htm
