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Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands
Remote Sensing of Environment 79 (2002) 266 – 278 www.elsevier.com/locate/rse
Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands A case study B.K. Wyliea,*, D.J. Meyera, L.L. Tieszenb, S. Mannelc a
Raytheon Systems Company, EROS Data Center, Sioux Falls, SD 57198, USA b U.S. Geological Survey, EROS Data Center, Sioux Falls, SD 57198, USA c Department of Geo-Ecology, University of Potsdam, Potsdam Germany
Received 25 August 1999; received in revised form 21 June 2000; accepted 21 July 2000
Abstract Quantification of biophysical parameters is needed by terrestrial process modeling and other applications. A study testing the role of multispectral data for monitoring biophysical parameters was conducted over a network of grassland field sites in the Great Plains of North America. Grassland biophysical parameters [leaf area index (LAI), fraction of absorbed photosynthetically active radiation (f PAR), and biomass] and their relationships with ground radiometer normalized difference vegetation index (NDVI) were established in this study (r2 = .66 – .85) from data collected across the central and northern Great Plains in 1995. These spectral/biophysical relationships were compared to 1996 field data from the Tallgrass Prairie Preserve in northeastern Oklahoma and showed no consistent biases, with most regression estimates falling within the respective 95% confidence intervals. Biophysical parameters were estimated for 21 ‘‘ground pixels’’ (grids) at the Tallgrass Prairie Preserve in 1996, representing three grazing/burning treatments. Each grid was 30 30 m in size and was systematically sampled with ground radiometer readings. The radiometric measurements were then converted to biophysical parameters and spatially interpolated using geostatistical kriging. Grid-based biophysical parameters were monitored through the growing season and regressed against Landsat Thematic Mapper (TM) NDVI (r2 = .92 – .94). These regression equations were used to estimate biophysical parameters for grassland TM pixels over the Tallgrass Prairie Preserve in 1996. This method maintained consistent regression development and prediction scales and attempted to minimize scaling problems associated with mixed land cover pixels. A method for scaling Landsat biophysical parameters to coarser resolution satellite data sets (1 km2) was also investigated. D 2002 Elsevier Science Inc. All rights reserved. Keywords: Grassland; Biophysical; Scaling; Satellite; NDVI; Biomass
1. Introduction and background A fundamental problem with the use of biophysical quantities for modeling and monitoring applications across large areas is how to describe the complexities of terrestrial ecosystems with a small number of observations. As discussed by Graetz (1989), the problem is a balancing act between highly site-specific observations made by traditional ecologists and the need for remote sensing to assist in ecological studies at biome to global scales. He notes that the most straightforward means for characterizing an ecosystem using remote sensing is through its primary trophic level,
* Corresponding author. Tel.: +1-605-594-6114; fax: +1-605-594-6529. E-mail address: [email protected] (B.K. Wylie).
the vegetation, characterized by three properties: structure, dynamics, and taxonomy. Structural descriptors at the canopy scale are density and height, which are related to quantities such as biomass and leaf area index (LAI). Dynamics can be inferred by repeated observation within a growing season (Mannel, 1999) as well as interannually (Reed et al., 1994), as remote sensing observations record phenological, climatic, and land cover changes. Taxonomy, the distribution of species within the system based on phylogenetic association or evolutionary affinities, remains an elusive goal for remote sensing but becomes less important if ecosystems are considered within a functional framework. Characteristics such as evergreen vs. deciduous and C3 vs. C4 metabolism (Tieszen, Reed, Bliss, Wylie, & DeJong, 1997) are more important descriptors of ecosystem function than phylogenetic affinities (Graetz, 1989). Within
0034-4257/01/$ – see front matter D 2002 Elsevier Science Inc. All rights reserved. PII: S 0 0 3 4 - 4 2 5 7 ( 0 1 ) 0 0 2 7 8 - 4
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a functional framework, the fraction of absorbed photosynthetically active radiation (f PAR), the uptake of solar energy by plants to drive photosynthesis, plays an increasingly important role. Most terrestrial process models require vegetation descriptors that adequately represent the dynamic nature of ecosystem processes (Hall, Townshend, & Engman, 1995). Primary examples are models computing productivity estimates and the storage and cycling of carbon and nitrogen within ecosystems (Ojima, Schimel, & Parton, 1994; Prince &
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Goward, 1995; Running & Hunt, 1993; Ruimy, Saugier, & Dedieu, 1994). Modeling efforts often use satellite-derived indices, such as the normalized difference vegetation index (NDVI), which are sensitive to changes in surface biophysics (Gamon et al., 1995; Sellers, 1987). Biophysical parameters are needed to specify lower boundary conditions in coupled surface/atmosphere models (Dickinson, Henderson-Sellers, Kennedy, & Wilson, 1986; Sellers, Mintz, Sud, & Dalcher, 1986), often by using land cover class indices (Loveland, Merchant, Ohlen, & Brown, 1991) as pointers to compiled
Fig. 1. The 1995 study areas within the North American Great Plains. Grasslands National Park in Saskatchewan represents a dominant cool season system, Niobrara Valley Preserve a mixed system, and Tallgrass Prairie Preserve a dominant warm season system.
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lists of parameters (Fennessey & Xue, 1997; Pielke et al., 1996). Increasingly, parameters derived from remotely sensed observations are being used directly within models (Carlson & Ripley, 1997; Running, Loveland, Pierce, Nemani, & Hunt, 1995; Yang, 2000) to provide the model with the true ecosystem dynamic profiles on a pixel-by-pixel basis rather than simplified profiles that are spatially uniform within a land cover class (Dorman & Sellers, 1989). The focus of this paper is to describe the development of a method for pixel-by-pixel mapping of biomass, f PAR, and LAI using a scheme designed to monitor these parameters over extensive areas of a specific biome, the North American grasslands, based on relationships developed at the ground and scaled to satellite imagery. Our study focuses on the North American Great Plains grassland biome. The geographic extent of the biome is illustrated in Fig. 1, where the boundaries define the putative extent of presettlement continuous grassland. The boundary on the north is defined on the basis of soil characteristics that distinguish forest from grassland derivations; the boundary on the west coincides with the Rocky Mountain uplift and the development of forest; and the boundary on the east, which is less definitive, approaches more or less continuous extents of the deciduous forests. This large grassland biome has been substantially modified by human activities as is illustrated by the array of current land cover types on former grasslands. Clearly, much of the native grassland has been converted to other uses, and existing grasslands are largely restricted to the drier shortgrass components or exist as relicts of various sizes that could not be converted to intensive agriculture.
2. Methods The ultimate goal of this study was to develop methods for monitoring grassland ecosystem function at the biome scale using satellite observations. The strategy employed here focused on regression analysis between field biophysical observations (biomass, f PAR, and LAI) and NDVI derived from radiometric measurements made at the same field sites (quadrats). The results of these regressions are used along with a more spatially extensive set of field radiometric measurements to distribute biophysical estimates within ‘‘pixel-sized’’ grids, whose spatial dimensions correspond to the resolution of the imaging system used to monitor the system at the landscape scale. Biophysical quantities were estimated at the grid scale using geostatistical techniques, and ensembles of these estimates were subsequently regressed against corresponding satellite NDVI values to scale these estimates over a landscape. The change of scale between landscape and biome was achieved through correlating landscape-scale indices computed to those derived from coarser-scale satellite observations. In essence, the only measurements used to change scale were those that were directly transformed from the satellite-observable
quantity, radiance. Table 1 lists the steps involved at each scale to construct a satellite-based map of biophysical quantities required for ecosystem monitoring. Measurements of f PAR, LAI, and biomass were made at quadrats having areas of 0.5 m2, concurrently with radiometric measurements from which reflectance and NDVI were computed. Numerous biophysical and radiometric measurements were collected at three different grasslands sites (and on several dates at one site) in 1995 to capture as wide a range of biomass, f PAR, and LAI variability as possible. Field regression analysis specified relationships between biophysical quantities and NDVI at the quadrat scale. These relationships were then tested against measurements collected in 1996 at one of the sites for consistency. Once validated, the field relationships were transferred to the landscape scale using radiometric measurements made within 30 30 m grids that correspond to the spatial resolution and location of image pixels observed by the Landsat Thematic Mapper (TM). The transfer was accomplished by the geostatistical/regression analysis outlined above, yielding biophysical estimates at each grid and a regression equation between grid biophysics and satellite NDVI. At the landscape scale, these relationships were applied to pixels identified as grassland based on a land cover map, also derived from the TM data. The exclusion of cover types for which relationships were not developed (e.g., agriculture, forests, water, etc.) minimizes scaling problems associated with mixed pixels (Chen, 1999). Finally, the biophysical quantities developed at the landscape scale were transferred to the biome scale by developing relationships between the TM-derived results and lower resolution sensors such as the Advanced Very High Resolution Radiometer (AVHRR). A key feature of this strategy is that no attempt was made to compare measurements made at one spatial scale (quadrat biophysics) directly to a different quantity observed at a different scale (reflectance-derived NDVI over a TM pixel). Table 1 Diagram of regression/scaling methodology Scale
Procedures
Quadrat relationships
From 1995 dataset: biophysicsquadrat = f(NDVIquadrat) Test: Does the 1996 biophysical measurements fall within 95% confidence interval specified by the 1995 relationships? Distribute quadrat biophysics estimates over pixel-sized ‘‘grids’’ using NDVI measurements and quadrat regressions Determine pixel-scale biophysical estimates for each grid using kriging Distribute grids across various ‘‘treatments’’ within a landscape Landscape-scale biophysics from TM-based NDVI Aggregate NDVITM to AVHRR scale Biome-scale biophysics from AVHRR-based NDVI
Quadrat to grid
Grid to landscape
Landscape to biome
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Rather, correlations were made at the quadrat scale between biophysical quantities and NDVI. Then, extensive NDVI measurements were made to distribute the relationships over areas corresponding to TM pixels (the grids). These grids were in turn distributed across the landscape under observation. This method has several advantages over comparing quadrat biophysics directly to TM pixels. Because field biophysical measurements can be laborious, one is always faced with a tradeoff between adequate spatial sampling density and complex field logistics; the method described here developed the relationships at quadrats over a small area then distributed these relationships over larger areas using relatively fast spectroradiometric field observations. This allowed much more rapid sampling of landscape-scale processes than would be possible with a similar density of biophysical measurements, given limits on time, resources, and observing opportunities. Also, this strategy minimized destructive sampling on the grids, which often must be sampled repeatedly over time.
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spectral reflectance (Asner, 1998; Gamon et al., 1995; Huete & Jackson 1987). Three treatments were used to represent varying levels of senescent vegetation in the grass canopy. The (1) unburned/ungrazed treatment represented high levels of senescent vegetation and litter. The (2) burned treatment included both burned/grazed (bison) and burned/ungrazed areas. The burns in the bison pastures were small and dispersed but had concentrated grazing pressure. The burned/ungrazed component was included to insure that high levels of biomass were represented in the burned treatment. A third treatment, labeled (3) ‘‘traditional,’’ was introduced at the Tallgrass site to represent a typical management scenario widely used in the Flint Hills region: burned in the spring and subsequently grazed somewhat heavily by cattle (not bison). Note that similar treatments were also used in the 1995 studies, as discussed in Results and Discussion. 2.2. Model development and testing: NDVI to biophysics at the quadrat scale
2.1. Study area The scaling strategy used three test sites located within the Great Plains that together captured a wide range of variability in ecosystem function, from dominant cool season grasslands in the north to dominant warm season grasslands in the south. The northernmost site was the Grasslands National Park in Saskatchewan, where greater than 60% of grassland cover is represented by C3 grass species. The central site was the Niobrara Valley Preserve and included dominant C4 species in the Sand Hills system in the southern part of the preserve and largely introduced dominant C3 species to the north. The southernmost site was the Tallgrass Prairie Preserve, a dominant C4 grassland located in the southern Flint Hills in northeastern Oklahoma. At each of these preserved relic grasslands sites, 100 100 km study sites were defined within which we obtained detailed quadrat, TM, and AVHRR data. These sites were selected not only because they were representative of different grassland types but also because they had protected status and are being used to compare the land cover performance, e.g., carbon flux or annual NDVI, of managed components with adjacent reference baseline native grassland. The Niobrara and Tallgrass sites are part of The Nature Conservancy’s preserve system. The Tallgrass Prairie Preserve, remeasured in 1996 to validate the 1995 relationships, represents the southern extent of the Flint Hills tallgrass prairie and on the average receives 584 mm of precipitation between April and September (N.C.S.S. 1979). Because of shallow rocky soils, the Flint Hills was one of the few areas of the original C4-dominated tallgrass prairie that was not largely converted to farming. Livestock operations use spring burning of grasslands to stimulate forage production and control woody species invasion. Unburned grasslands can contain large volumes of senescent vegetation affecting
To develop general relationships between biophysics and NDVI over a large area, we collected quadrat (0.5 m2) data at the three sites within 1 year (1995). At Niobrara in 1995, two dates were used in this study (mid-July and late August). The Grasslands National Park data were collected in early June, near peak greenness for the cool season system at that location. The 1995 Tallgrass data were collected in mid-August to coincide with the warm season peak greenness there. From these campaigns, regression models were developed to be representative of all the sites, although some models were necessarily treatment specific, as discussed in Results and Discussion. The sampling strategy placed several quadrats in each treatment along with grids to be used later for landscapescale mapping. In 1995, quadrats were colocated with grids, as depicted in Fig. 2. Multiple quadrats were often sampled within a grid to insure an accurate representation of the full range of biomass variability within the grid. Subsequently in 1996 at Tallgrass, quadrat sampling occurred along transects defined within the various treatments to sample across biomass gradients within the treatment. Seven quadrats were defined per transect, two quadrats of which were reserved for destructive sampling. The quadrat transects were located within the same treatment as the grids used for scaling but were not necessarily colocated with them: the transects sampled gradients, while the grids required larger, uniform areas viewable from the TM system. This method allowed more frequent sampling of the quadrats than would be logistically feasible for the more labor-intensive grids while preventing the disturbance caused by quadrat sampling from affecting overall grid reflectance. The LAI measurements were taken with the LI-COR LAI 2000 Plant Canopy Analyzer and the f PAR measurements were taken with a Decagon AccuPAR ceptometer. Herbaceous standing biomass was clipped at ground level and
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sorted into senesced and live components. The oven-dried weights were used as biomass estimates. The percent of live biomass of the total quadrat biomass was used to convert both LAI and f PAR to live LAI and live f PAR estimates. This assumed that standing senesced grasses absorbed almost as much PAR energy as live grasses, as described by Asner et al. (1998). Spectral reflectance of the quadrat was obtained using a handheld ground radiometer (Cropscan MSR-16), which simultaneously measured incident and reflected light for nine spectral bands. Eight bands were sampled from approximately 460 to 810 nm, with band centers spaced 50 nm apart and having full width, half-maximum bandwidths of 30 nm, and a ninth band sampled the 1550 – 1750 nm wavelength interval (Table 2). The MSR-16 instrument collects the upwelling and downwelling fields simultaneously at each waveband, using a cosine diffuser for the
Table 2 Cropscan MSR-16 spectral channel characteristics (values in nanometers) Central wavelength 460 510 560 610 660a 710 760 810a 1650 Effective bandwidth 30 30 30 30 30 30 30 30 200 a
Denotes channels used in NDVI computation.
downwelling measurement. Thus, the MSR-16 is able to compute reflectance directly rather than through the use of a spectrally flat reference panel and therefore can collect reflectance observations very rapidly. NDVI was calculated using the 810 and 660 nm Cropscan bands as: NDVI ¼
ðr813 r662 Þ ðr813 þ r662 Þ
Where r is spectral reflectance. At each of the clipped quadrats, spectral reflectance and f PAR measurements were taken before and after clipping. In the case of f PAR, the postclipped measurement was used to estimate the soil contribution to the overall f PAR observation. The clipped quadrats were used in place of nearby bare areas, because the latter would typically be significantly depleted of soil organic matter and would yield an overestimate of the lower boundary contribution to the upwelling radiation field. Based on this, f PAR is computed as: f PAR ¼ 1 t rc þ trs
Fig. 2. Field sampling scheme for the study. Quadrats located within (or sometimes near) grids are used to estimate field relationships between NDVI and biophysical quantities (biomass, f PAR, and LAI). These relationships are distributed across the 30 30 m grids using geostatistical techniques; then, the grids are distributed across the landscape within various ‘‘treatments’’ (burning, grazing, etc.).
where t is the fraction of the downwelling radiation transmitted through the canopy (one way), rc is the reflectance of the canopy as measured from above, and rs is the reflectance of the soil measured after clipping. The average rs for the two clipped transect quadrats within each treatment was used for all f PAR calculations for the given treatment on a given date of observation. Regression analysis was used to develop predictive relationships between NDVI and biophysical parameters. Previous work in the Nebraska Sand Hills with other indices such as the modified soil adjusted vegetation index (MSAVI) and the simple red/infrared ratio (VI) indicated little or no improved predictability by other indices (Wylie, DeJong, & Tieszen, 1996). Nonlinear trends in predictive relationships were approximated with natural logarithmic transformations of the dependent variable. Nonlinear relationships pose only minor scaling problems above linear relationships in pixels of uniform land cover (Chen, 1999). Nonhomogeneity associated with the regression variance was assessed by visual inspection of residual plots. Interaction and indicator treatment variables were tested for possible differences in slope and intercept terms for treatment effects using stepwise regression techniques ( P < .01). Estimations made from the 1995 data sets were tested against independent data collected at Tallgrass Prairie in July of 1996. These data were collected in July using seven quadrats from each of three treatments using the same quadrat
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based scheme used in 1995. The 95% confidence intervals for observations from the respective biophysical parameter estimation regressions were plotted and compared to observed 1996 data to see if the 95% confidence intervals contained the values for the 1996 data. 2.3. Landscape-scale estimation The means by which field observations were scaled to the satellite included estimation of f PAR, LAI, and biomass for the live components over the grids and as regressions of these estimations against NDVI computed from TM data. Within each treatment, seven spatially uniform grids considered to be representative of the treatment were selected and located to within 5 m using a GPS receiver. Seven of these 21 grids were identified as priority ‘‘pixels’’ where handheld reflectance would be obtained as frequently as possible to insure a record of seasonal evolution of grid biophysical parameters within the narrow windows of opportunity dictated by weather. The remaining grids were sampled monthly. Systematic handheld radiometer readings were taken every 6 m across each 30 30 m grid (Fig. 2). The predictive equations derived from the quadrat data sets allowed estimation of the biophysical parameters at each systematic sample point within a grid. This insured that ground radiometer estimates of biophysical parameters were applied at the same scale at which they were developed, as recommended by Chen (1999). The biophysical parameters were then interpolated across the grid by anisotropic punctual kriging (GS+). The software automatically selected best-fit models that varied between linear, spherical, and exponential. The average and error terms were derived for the interpolated biophysical parameters from each grid. Error terms incorporated error components from kriging, handheld radiometer regressions, and the spatial covariance. Grid biophysical parameters were temporally interpolated linearly to provide biophysical parameter estimates at the date of satellite overpass. Landsat TM data sets for the year 1996 were obtained for the study area on 3/25, 4/10, 4/26, 5/28, 6/29, and 7/15. The 5/28 image had scattered, fair weather cumulus clouds in the study area. The scenes were georegistered to an UTM projection using cubic convolution image-to-image techniques. The TM spectral bands 3 and 4 were converted to atsensor reflectance using methods described by Markham and Barker (1986). Grid locations were superimposed over the imagery and grid number/date combinations that occurred in cloud, or cloud shadows were eliminated from the analysis. To compensate for ground and satellite locational errors, NDVI for each grid location consisted of the average of the pixels, with a majority of their area falling with in a 30 m radius of the GPS coordinates. We performed regressions between surface biophysics estimated at the grid and NDVI computed from TM as described above. Grids were sampled repeatedly through the spring and summer growing season. Thus, the data points were not truly independent. The Durbin – Watson test
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revealed that temporal autocorrelation was a factor. This implied that the regression coefficients were unbiased but the error terms may have been underestimated. The ability to temporally interpolate biophysical parameters to the date of satellite overpass was considered more important in this study than the effects of temporal autocorrelation. Spatial autocorrelation effects on satellite biophysical regressions in this study were not quantified. Distances between grid locations varied from a minimum of 150 m to a maximum of around 6 km. If spatial autocorrelation effects existed, they would have caused artificially low errors in the predicted biophysical quantities. Simulation studies using Landsat TM data and simulated TM data on a grassland site showed that regression slope and intercept terms were largely unaffected by correlation structures, and only variance and correlation were biased (Harris & Johnson, 1996). Similar regression fitting approaches and treatment testing strategies that were used on the ground radiometer relationships were also applied to the satellite NDVI to grid-based biophysical parameter regressions. These predictive relationships were then used to estimate biophysical parameters for grasslands across the study area at the Landsat TM scale. 2.4. Biome-scale estimation The climate change and carbon flux modeling communities are often interested in regional, continental, or global scale estimates of biophysical parameters for model inputs. In order to obtain such large area estimates, coarser-scale satellite data such as AVHRR are often utilized. These data sets have a nominal ground resolution of around 1 km2. The percent of grasslands and the coefficient of variation for Landsat TM was calculated for 3 3 km windows for an area corresponding to the Pawhuska 1:100,000 USGS topographic map. In addition to measurements of canopy biophysical features, we obtained a continuous record of phenological parameters at each site. This information was obtained by cooperating station managers who assessed the status of the vegetation at 2-week intervals throughout the season and estimated many features, including canopy height, onset of growth by select species and C3 and C4 types, floral emergence, bud break, senescence, and others as well as seasonal weather indicators including first frost, snow cover, ice cover, etc. These independent observations were intended to provide interpretations of phenological events that were independent of the satellite observations.
3. Results and discussion The spring and summer precipitation at the Tallgrass Preserve was below normal in 1996. Precipitation for January to July at Pawhuska, OK was 1051 mm in 1995. This was well above the normal rainfall for that time period of 390 mm (N.C.S.S., 1979). However, only 262 mm was received during that time period in 1996. This facilitated
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Fig. 3. Field phenological observations at the Tallgrass Preserve during the 1995 and 1996 growing seasons. The right-hand vertical axis represents AVHRRderived NDVI values. The black line in the plot is NDVI derived from 10-day maximum value composites, the red line is smoothed NDVI using a technique described in Reed et al. (1994), and the numbers above the NDVI curves are annual time integrated NDVI.
cloud-free Landsat TM acquisitions but put severe moisture constraints on vegetation and limited plant growth as seen in
Fig. 3. The field data show markedly different patterns in the 2 years, a clear effect of a substantial drought in 1996 that
Fig. 8. Temporal and spatial dynamics of Tallgrass Prairie Preserve herbaceous biomass in 1996.
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Table 3 Regressions using ground radiometer NDVI to estimate biophysical parameters Dependent variable 2
LN [biomass (g/m )] LN [biomass (g/m2)] f PAR LN (LAI)
Treatment
Intercept
Slope
r2
Standard error
n
P
Unburned and ungrazed Remaining treatments All treatments All treatments
1.32 2.01 0.28 3.22
5.22 4.41 1.01 4.88
.85 .72 .66 .7
0.42 0.41 0.13 0.54
54 144 154 198
< .0001 < .0001 < .0001 < .0001
reduced canopy height as well as AVHRR-derived NDVI values. The satellite estimates of start of season and end of season (Reed et al., 1994) agreed closely with the interpretations made by the ground observer. The early end of season in 1996 was associated with simultaneous ground observations of freezing temperatures that resulted in tissue damage. The increase in NDVI at this location during September was a response to precipitation that broke the extended drought but only resulted in minor increases in canopy height. 3.1. Model testing: 1996 Tallgrass OK data The quadrat regressions between ground radiometer NDVI and biophysical parameters had r2 values ranging from .66 to .85 (Table 3) for the 1995 data sets. Biomass estimation was significantly different for the unburned and ungrazed treatment where litter and standing dead levels were highest. This was consistent with other studies that indicated treatment practices could affect regression results. For example, Price, Varner, Rundquist, and Peake (1993) found that land management practices can affect the use of
remotely sensed imagery to measure plant biophysical properties in northern Kansas prairies. In a study over eastern Colorado grasslands, Todd, Hoffer, and Milchunas (1998) found that accounting for grazing treatments improves the relationship between remotely sensed quantities and biomass. Other variables, some not accounted for here, may also play a role. For example, Friedl, Michaelson, Davis, Walker, and Schimel (1994) found that burning and terrain characteristics could affect biophysical relationships to reflectancederived quantities. Fig. 4 illustrates the complementary nature of the various sites in the biomass regression for the combination of both the burned and the traditional treatments. The lower predictability associated with LAI values greater than 2 (and subsequently higher biomass) was consistent with Gamon et al. (1995). The majority of the 1996 test field data fell within the 95% confidence limits for individual observations from 1995 and indicated no clear biases (Fig. 5). Because of limited biomass sampling in 1996, this test may not be stringent. Small percentages of f PAR (10%) and LAI (19%) validation points were above the upper confidence intervals,
Fig. 4. Quadrate biomass NDVI regression for all 1995 sites.
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Fig. 5. The 1995 ground radiometer to biophysical parameter [biomass (g/m2), f PAR, and LAI] regression 95% confidence intervals for observations compared with 1996 data (UU = unburned ungrazed treatment).
but the bulk of the observations were centrally aligned within the confidence intervals. The predictive ground radiometer regressions based on 1995 observations provided reasonably robust estimates of biophysical properties for Tallgrass Prairie in 1996. This was true despite 1995 being a wet year and 1996 being a drought year. After validation, these predictive regressions were used to estimate biophysical parameters at the seven grids within each treatment (Fig. 6). This analysis was completed for all three treatments (burned, unburned/ungrazed, and traditional) for f PAR and LAI, as well as biomass. Three grids within each treatment (1, 3, and 6) were designated as
‘‘priority’’ grids and were sampled more frequently (Fig. 6). The temporal dynamics of grid biomass show higher biomass from the burned grids 5 –7, which were ungrazed, than the grazed grids. The vegetation with these grids senesced more quickly than the grazed burned grids, probably because the larger biomass levels depleted available soil water more quickly than the grazed grids. 3.2. Landscape scale The satellite-to-biophysical parameter regressions (Table 4) had higher r 2 values than the ground radiometer
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the lower biomass values early in the year imposed an important physical constraint on the relationship. Treatment effects were not significant ( P < .01) in the f PAR and biomass regressions. However, the LAI regression had a significantly lower intercept associated with the burned treatment. The incorporation of this treatment effect increased the r 2 by 0.007 and decreased the standard error by 0.014. Because of this and difficulties in knowing the spatial extents of treatments, nontreatment specific regression equations were utilized. Landsat TM NDVI values used in these regression analyses came from the six TM scenes acquired throughout the growing season. These regressions include errors associated with atmospheric effects on the different dates. Atmospheric corrections would probably improve these relationships further (Goetz, 1997), but the necessary atmospheric data were not available on all dates. Since 1996 was a drought year, water vapor atmospheric effects may have been relatively insignificant. The above relationships between Landsat TM NDVI and biophysical parameters were applied to all Landsat pixels that had been classified as grasslands in a TM land cover product. These were used to make grassland biophysical parameter estimates for the entire Tallgrass Preserve (Fig. 8). Black areas within the preserve represent areas that are not grassland and no estimates for biophysical parameters were made. The spatial average biomass for the preserve on each date indicates a germination/emergence date of grasslands in mid to late April. This is similar to the observed date of C4 grasses that dominate these grasslands and observations of grass height (Fig. 3). Using rangeland production estimates for a Fig. 6. Temporal dynamics in 1996 Tallgrass Prairie Preserve grid biomass estimated from the kriged hand held radiometer observations.
regressions (Table 3). This is probably because these analyses were between grid NDVI scaled to biomass and Landsat TM NDVI. However, the r 2 statistics is sensitive to the distribution of the data points. Fig. 7 illustrates the distribution problem caused by a lack of measurements from April 2 to May 21. This problem also affected the LAI and f PAR relationships to Landsat TM NDVI and probably produced artificially high r 2 values. Rather than limit the regression to the late season values, the authors thought it was important to insure that the predictive capabilities of the regression equation were applicable to all observed NDVI values, i.e., that
Table 4 Regressions using Landsat TM NDVI to estimate biophysical parameters Dependent variable
Intercept
Slope
r2
Standard error
n
P
LN [biomass (g/m2)] f PAR LN (LAI)
1.17 0.55 4.16
6.26 1.68 7.45
.092 .92 .94
0.24 0.06 0.24
64 66 65
< .0001 < .0001 < .0001
Fig. 7. Herbaceous grid-based aboveground biomass regressed on Landsat TM NDVI.
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Fig. 9. Potential uniform grassland areas for scaling from Landsat TM to AVHRR for the area around the Tallgrass Preserve (CV is the coefficient of variation).
normal year derived from the soil survey (NCSS 1979) masked to exclude nongrassland areas, a production estimate for a normal year for the preserve was estimated at 386 g/m2. Peak grassland biomass occurred on 6/29/96 (Fig. 8) and was only 32% of the estimated production of a normal year. 3.3. Biome scale A major problem with scaling biophysical quantities to AVHRR images is the identification of grids having sufficient uniformity at the 1 km scale. This is further complicated by image registration errors and pixel size variability, requiring uniformity over a 3 3 km area to compensate for these factors (Meyer, 1996). Fig. 9 depicts areas in the vicinity of the Tallgrass Preserve that, within 3 3 km neighborhoods, were dominated by grassland (based on a land cover classification derived from the multidate TM data set) and had low Landsat TM NDVI spatial variability (based on 6/29 TM image). The Preserve boundaries are superimposed on the image. Dark gray regions within this figure indicate areas that would contain likely candidates for surface grids for the biome-scale study. This will be used subsequently to select AVHRR pixels, which are relatively uniform, to minimize scale problems associated with heterogeneous and mixed land cover areas (Chen, 1999) and to establish relationships between AVHRR NDVI and Landsat TM biophysical param-
eters. This approach conducted across several dates and several sites throughout the Great Plains will be used to produce biophysical parameters at the AVHRR scale for regional analyses. Studies to apply these relationships to data acquired from the System pour l’Observation de la Terre (SPOT) Vegetation Monitoring Instrument and the Moderating Resolution Imaging Spectrometer (MODIS) are also underway. This approach would not only produce coarse biome-scale estimates of grassland biophysical parameters for the modeling community but also a subsample of more detailed study areas at the Landscape scale.
4. Conclusions The sampling design described herein suggests three conclusions. First, the 1995 quadrat relationships between ground radiometer data and field-sampled biophysics was robust when applied the 1996 Tallgrass data set. This implies that the methodology can be applied consistently between years and between various sites within the grasslands. The inclusion of more sites and additional collection seasons would strengthen this analysis, and further studies are underway at other sites toward that end. Second, this methodology permits the estimation of biophysical parameters with a minimum of destructive samples. This allowed nonintrusive,
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repetitive sampling to occur within permanent field plots in a manner having minimal impact on ecosystem dynamics. Third, our approach separates the processes of scaling and sample distribution by developing regression relationships at a consistent scale and avoiding correlations between variables measured at dissimilar scales. This strategy also provides the ability to estimate multiple parameters (f PAR, LAI, and biomass) via one set of distributed ground measurements (NDVI) at a given scale. The use of relatively uniform grids avoids scaling problems associated with heterogeneous areas. The approach outlined in this study will allow robust spatial extrapolations of biophysical data with minimal destructive sampling. The application across broad expanses of a land cover type, however, still requires accurate and automated atmospheric corrections of reflectance signals. This should be achievable with MODIS data.
Acknowledgments This work was carried out with support from CNES SPOT VEGETATION (95/CNES/0395), The Nature Conservancy Ecosystem Research Program (TNC-Mellon), U.S. Geological Survey, Cooperative Agreement No. 1434-93-A00761, and the Department of Energy through the Great Plains National Institute for Global Change Program (LWT 52-123-06505). Drs. Wylie and Meyer’s work was performed under U.S. Geological Survey contract 1434-CR-97CN-40274.
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NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications, 5, 28 – 41. Goetz, S. J. (1997). Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site. International Journal of Remote Sensing, 18, 71 – 94. Graetz, R. D. (1989). Remote sensing of terrestrial ecosystem structure: an ecologist’s pragmatic view. In: R. J. Hobbs, & H. A. Mooney (Eds.), Remote sensing of biosphere functioning ( pp. 5 – 30). New York: Springer-Verlag. Hall, F. G., Townshend, J. R., & Engman, E. T. (1995). Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sensing of Environment, 51, 138 – 156. Harris, R. T., & Johnson, D. E. (1996). A regression model with spatially correlated errors for comparing remote sensing and in-situ measurements of a grassland site. Journal of Agricultural, Biological, and Environmental Statistics, 1, 190 – 204. Huete, A. R., & Jackson, R. D. (1987). Suitability of spectral indices for evaluating vegetation characteristics on arid rangelands. Remote Sensing of Environment, 23, 213 – 232. Loveland, T. R., Merchant, J. W., Ohlen, D. O., & Brown, J. F. (1991). Development of a land-cover characteristics database for the conterminous U.S. Photogrammetric Engineering and Remote Sensing, 57 (11), 1453 – 1463. Mannel, S. (1999). Biophysical characterization of the Tallgrass prairie in Oklahoma: grassland canopy parameters and their relationships to satellite derived NDVI. Master Thesis, Landscape Ecology, University of Potsdam, p. 91. Markham, B. L., & Barker, J. L. (1986). Landsat MSS and TM post-calibration dynamic ranges, exoatmosphere reflectances and at-satellite temperatures. EOSAT Landsat Technical Notes, 3 – 8. Meyer, D. J. (1996). Estimating the effective spatial resolution of an AVHRR time series. International Journal of Remote Sensing, 17, 2971 – 2980. N.C.S.S. (1979). Soil survey of Osage County, Oklahoma ( p. 164). USDA, Washington, DC: U.S.D.A. S.C.S. and U.S.D.I. B.I.A. Ojima, D. S., Schimel, D. S., & Parton, W. J. (1994). Long- and short-term effects of fire on nitrogen cycling in tallgrass prairie. Journal of Information Biogeochemistry, 24, 67 – 75. Pielke, R., Lee, T. J., Copeland, J. H., Eastman, J. L., Ziegler, C. L., & Finley, C. A. (1996). The use of USGS land surface data sets to improve weather and climate simulations. Ecological Applications, 7 (1), 3 – 21. Price, K. P., Varner, V. C., Rundquist, D. C., & Peake, J. S. (1993). Influences of land management and weather on plant biophysical and hyper-spectral response patterns of tallgrass prairies in northeastern Kansas (pp. 441 – 450). PECORA12, Sioux Falls, South Dakota, Aug. 24 – 26. Bethesda, MD: American Society for Photogrammetry and Remote Sensing. Prince, S. D., & Goward, S. N. (1995). Global primary production: a remote sensing approach. Journal of Biogeography, 22, 815 – 835. Reed, B. C., Brown, J. F., VanderZee, D., Loveland, T. R., Merchant, J. W., & Ohlen, D. O. (1994). Measuring phenological variability from satellite imagery. Journal of Vegetation Science, 5, 703 – 714. Ruimy, A., Saugier, B., & Dedieu, G. (1994). Methodology for the estimation of net primary production from remotely sensed data. Journal of Geophysical Research, 99, 5263 – 5283. Running, S. W., & Hunt, E. R. (1993). Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. In: J. R. Ehleringer, & C. B. Field (Eds.), Scaling ecological processes: leaf to globe ( pp. 141 – 158). San Diego: Academic Press. Running, S. W., Loveland, T. R., Pierce, L. L., Nemani, R. R., & Hunt, E. R. (1995). A remote sensing based vegetation classification logical for global land cover analysis. Remote Sensing of Environment, 51, 39 – 48. Sellers, P. J. (1987). Canopy reflectance, photosynthesis and transpiration: II. Theory of biophysics in the linearity of their interdependence. Remote Sensing of Environment, 21, 143 – 183.
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on grazed and ungrazed rangelands using spectral indices. International Journal of Remote Sensing, 19, 427 – 438. Wylie, B. K., DeJong, D. D., & Tieszen, L. L. (1996). Grassland canopy parameters and their relationship to remotely sensed vegetation indices in the Nebraska Sand Hills. GeoCarto International, 11, 39 – 52. Yang, L. (2000). Integration of a numerical model and remotely sensed data to study urban/rural land surface climate processes. Computers and Geoscience, 26, 451 – 468.
Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands A case study B.K. Wyliea,*, D.J. Meyera, L.L. Tieszenb, S. Mannelc a
Raytheon Systems Company, EROS Data Center, Sioux Falls, SD 57198, USA b U.S. Geological Survey, EROS Data Center, Sioux Falls, SD 57198, USA c Department of Geo-Ecology, University of Potsdam, Potsdam Germany
Received 25 August 1999; received in revised form 21 June 2000; accepted 21 July 2000
Abstract Quantification of biophysical parameters is needed by terrestrial process modeling and other applications. A study testing the role of multispectral data for monitoring biophysical parameters was conducted over a network of grassland field sites in the Great Plains of North America. Grassland biophysical parameters [leaf area index (LAI), fraction of absorbed photosynthetically active radiation (f PAR), and biomass] and their relationships with ground radiometer normalized difference vegetation index (NDVI) were established in this study (r2 = .66 – .85) from data collected across the central and northern Great Plains in 1995. These spectral/biophysical relationships were compared to 1996 field data from the Tallgrass Prairie Preserve in northeastern Oklahoma and showed no consistent biases, with most regression estimates falling within the respective 95% confidence intervals. Biophysical parameters were estimated for 21 ‘‘ground pixels’’ (grids) at the Tallgrass Prairie Preserve in 1996, representing three grazing/burning treatments. Each grid was 30 30 m in size and was systematically sampled with ground radiometer readings. The radiometric measurements were then converted to biophysical parameters and spatially interpolated using geostatistical kriging. Grid-based biophysical parameters were monitored through the growing season and regressed against Landsat Thematic Mapper (TM) NDVI (r2 = .92 – .94). These regression equations were used to estimate biophysical parameters for grassland TM pixels over the Tallgrass Prairie Preserve in 1996. This method maintained consistent regression development and prediction scales and attempted to minimize scaling problems associated with mixed land cover pixels. A method for scaling Landsat biophysical parameters to coarser resolution satellite data sets (1 km2) was also investigated. D 2002 Elsevier Science Inc. All rights reserved. Keywords: Grassland; Biophysical; Scaling; Satellite; NDVI; Biomass
1. Introduction and background A fundamental problem with the use of biophysical quantities for modeling and monitoring applications across large areas is how to describe the complexities of terrestrial ecosystems with a small number of observations. As discussed by Graetz (1989), the problem is a balancing act between highly site-specific observations made by traditional ecologists and the need for remote sensing to assist in ecological studies at biome to global scales. He notes that the most straightforward means for characterizing an ecosystem using remote sensing is through its primary trophic level,
* Corresponding author. Tel.: +1-605-594-6114; fax: +1-605-594-6529. E-mail address: [email protected] (B.K. Wylie).
the vegetation, characterized by three properties: structure, dynamics, and taxonomy. Structural descriptors at the canopy scale are density and height, which are related to quantities such as biomass and leaf area index (LAI). Dynamics can be inferred by repeated observation within a growing season (Mannel, 1999) as well as interannually (Reed et al., 1994), as remote sensing observations record phenological, climatic, and land cover changes. Taxonomy, the distribution of species within the system based on phylogenetic association or evolutionary affinities, remains an elusive goal for remote sensing but becomes less important if ecosystems are considered within a functional framework. Characteristics such as evergreen vs. deciduous and C3 vs. C4 metabolism (Tieszen, Reed, Bliss, Wylie, & DeJong, 1997) are more important descriptors of ecosystem function than phylogenetic affinities (Graetz, 1989). Within
0034-4257/01/$ – see front matter D 2002 Elsevier Science Inc. All rights reserved. PII: S 0 0 3 4 - 4 2 5 7 ( 0 1 ) 0 0 2 7 8 - 4
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a functional framework, the fraction of absorbed photosynthetically active radiation (f PAR), the uptake of solar energy by plants to drive photosynthesis, plays an increasingly important role. Most terrestrial process models require vegetation descriptors that adequately represent the dynamic nature of ecosystem processes (Hall, Townshend, & Engman, 1995). Primary examples are models computing productivity estimates and the storage and cycling of carbon and nitrogen within ecosystems (Ojima, Schimel, & Parton, 1994; Prince &
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Goward, 1995; Running & Hunt, 1993; Ruimy, Saugier, & Dedieu, 1994). Modeling efforts often use satellite-derived indices, such as the normalized difference vegetation index (NDVI), which are sensitive to changes in surface biophysics (Gamon et al., 1995; Sellers, 1987). Biophysical parameters are needed to specify lower boundary conditions in coupled surface/atmosphere models (Dickinson, Henderson-Sellers, Kennedy, & Wilson, 1986; Sellers, Mintz, Sud, & Dalcher, 1986), often by using land cover class indices (Loveland, Merchant, Ohlen, & Brown, 1991) as pointers to compiled
Fig. 1. The 1995 study areas within the North American Great Plains. Grasslands National Park in Saskatchewan represents a dominant cool season system, Niobrara Valley Preserve a mixed system, and Tallgrass Prairie Preserve a dominant warm season system.
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lists of parameters (Fennessey & Xue, 1997; Pielke et al., 1996). Increasingly, parameters derived from remotely sensed observations are being used directly within models (Carlson & Ripley, 1997; Running, Loveland, Pierce, Nemani, & Hunt, 1995; Yang, 2000) to provide the model with the true ecosystem dynamic profiles on a pixel-by-pixel basis rather than simplified profiles that are spatially uniform within a land cover class (Dorman & Sellers, 1989). The focus of this paper is to describe the development of a method for pixel-by-pixel mapping of biomass, f PAR, and LAI using a scheme designed to monitor these parameters over extensive areas of a specific biome, the North American grasslands, based on relationships developed at the ground and scaled to satellite imagery. Our study focuses on the North American Great Plains grassland biome. The geographic extent of the biome is illustrated in Fig. 1, where the boundaries define the putative extent of presettlement continuous grassland. The boundary on the north is defined on the basis of soil characteristics that distinguish forest from grassland derivations; the boundary on the west coincides with the Rocky Mountain uplift and the development of forest; and the boundary on the east, which is less definitive, approaches more or less continuous extents of the deciduous forests. This large grassland biome has been substantially modified by human activities as is illustrated by the array of current land cover types on former grasslands. Clearly, much of the native grassland has been converted to other uses, and existing grasslands are largely restricted to the drier shortgrass components or exist as relicts of various sizes that could not be converted to intensive agriculture.
2. Methods The ultimate goal of this study was to develop methods for monitoring grassland ecosystem function at the biome scale using satellite observations. The strategy employed here focused on regression analysis between field biophysical observations (biomass, f PAR, and LAI) and NDVI derived from radiometric measurements made at the same field sites (quadrats). The results of these regressions are used along with a more spatially extensive set of field radiometric measurements to distribute biophysical estimates within ‘‘pixel-sized’’ grids, whose spatial dimensions correspond to the resolution of the imaging system used to monitor the system at the landscape scale. Biophysical quantities were estimated at the grid scale using geostatistical techniques, and ensembles of these estimates were subsequently regressed against corresponding satellite NDVI values to scale these estimates over a landscape. The change of scale between landscape and biome was achieved through correlating landscape-scale indices computed to those derived from coarser-scale satellite observations. In essence, the only measurements used to change scale were those that were directly transformed from the satellite-observable
quantity, radiance. Table 1 lists the steps involved at each scale to construct a satellite-based map of biophysical quantities required for ecosystem monitoring. Measurements of f PAR, LAI, and biomass were made at quadrats having areas of 0.5 m2, concurrently with radiometric measurements from which reflectance and NDVI were computed. Numerous biophysical and radiometric measurements were collected at three different grasslands sites (and on several dates at one site) in 1995 to capture as wide a range of biomass, f PAR, and LAI variability as possible. Field regression analysis specified relationships between biophysical quantities and NDVI at the quadrat scale. These relationships were then tested against measurements collected in 1996 at one of the sites for consistency. Once validated, the field relationships were transferred to the landscape scale using radiometric measurements made within 30 30 m grids that correspond to the spatial resolution and location of image pixels observed by the Landsat Thematic Mapper (TM). The transfer was accomplished by the geostatistical/regression analysis outlined above, yielding biophysical estimates at each grid and a regression equation between grid biophysics and satellite NDVI. At the landscape scale, these relationships were applied to pixels identified as grassland based on a land cover map, also derived from the TM data. The exclusion of cover types for which relationships were not developed (e.g., agriculture, forests, water, etc.) minimizes scaling problems associated with mixed pixels (Chen, 1999). Finally, the biophysical quantities developed at the landscape scale were transferred to the biome scale by developing relationships between the TM-derived results and lower resolution sensors such as the Advanced Very High Resolution Radiometer (AVHRR). A key feature of this strategy is that no attempt was made to compare measurements made at one spatial scale (quadrat biophysics) directly to a different quantity observed at a different scale (reflectance-derived NDVI over a TM pixel). Table 1 Diagram of regression/scaling methodology Scale
Procedures
Quadrat relationships
From 1995 dataset: biophysicsquadrat = f(NDVIquadrat) Test: Does the 1996 biophysical measurements fall within 95% confidence interval specified by the 1995 relationships? Distribute quadrat biophysics estimates over pixel-sized ‘‘grids’’ using NDVI measurements and quadrat regressions Determine pixel-scale biophysical estimates for each grid using kriging Distribute grids across various ‘‘treatments’’ within a landscape Landscape-scale biophysics from TM-based NDVI Aggregate NDVITM to AVHRR scale Biome-scale biophysics from AVHRR-based NDVI
Quadrat to grid
Grid to landscape
Landscape to biome
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Rather, correlations were made at the quadrat scale between biophysical quantities and NDVI. Then, extensive NDVI measurements were made to distribute the relationships over areas corresponding to TM pixels (the grids). These grids were in turn distributed across the landscape under observation. This method has several advantages over comparing quadrat biophysics directly to TM pixels. Because field biophysical measurements can be laborious, one is always faced with a tradeoff between adequate spatial sampling density and complex field logistics; the method described here developed the relationships at quadrats over a small area then distributed these relationships over larger areas using relatively fast spectroradiometric field observations. This allowed much more rapid sampling of landscape-scale processes than would be possible with a similar density of biophysical measurements, given limits on time, resources, and observing opportunities. Also, this strategy minimized destructive sampling on the grids, which often must be sampled repeatedly over time.
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spectral reflectance (Asner, 1998; Gamon et al., 1995; Huete & Jackson 1987). Three treatments were used to represent varying levels of senescent vegetation in the grass canopy. The (1) unburned/ungrazed treatment represented high levels of senescent vegetation and litter. The (2) burned treatment included both burned/grazed (bison) and burned/ungrazed areas. The burns in the bison pastures were small and dispersed but had concentrated grazing pressure. The burned/ungrazed component was included to insure that high levels of biomass were represented in the burned treatment. A third treatment, labeled (3) ‘‘traditional,’’ was introduced at the Tallgrass site to represent a typical management scenario widely used in the Flint Hills region: burned in the spring and subsequently grazed somewhat heavily by cattle (not bison). Note that similar treatments were also used in the 1995 studies, as discussed in Results and Discussion. 2.2. Model development and testing: NDVI to biophysics at the quadrat scale
2.1. Study area The scaling strategy used three test sites located within the Great Plains that together captured a wide range of variability in ecosystem function, from dominant cool season grasslands in the north to dominant warm season grasslands in the south. The northernmost site was the Grasslands National Park in Saskatchewan, where greater than 60% of grassland cover is represented by C3 grass species. The central site was the Niobrara Valley Preserve and included dominant C4 species in the Sand Hills system in the southern part of the preserve and largely introduced dominant C3 species to the north. The southernmost site was the Tallgrass Prairie Preserve, a dominant C4 grassland located in the southern Flint Hills in northeastern Oklahoma. At each of these preserved relic grasslands sites, 100 100 km study sites were defined within which we obtained detailed quadrat, TM, and AVHRR data. These sites were selected not only because they were representative of different grassland types but also because they had protected status and are being used to compare the land cover performance, e.g., carbon flux or annual NDVI, of managed components with adjacent reference baseline native grassland. The Niobrara and Tallgrass sites are part of The Nature Conservancy’s preserve system. The Tallgrass Prairie Preserve, remeasured in 1996 to validate the 1995 relationships, represents the southern extent of the Flint Hills tallgrass prairie and on the average receives 584 mm of precipitation between April and September (N.C.S.S. 1979). Because of shallow rocky soils, the Flint Hills was one of the few areas of the original C4-dominated tallgrass prairie that was not largely converted to farming. Livestock operations use spring burning of grasslands to stimulate forage production and control woody species invasion. Unburned grasslands can contain large volumes of senescent vegetation affecting
To develop general relationships between biophysics and NDVI over a large area, we collected quadrat (0.5 m2) data at the three sites within 1 year (1995). At Niobrara in 1995, two dates were used in this study (mid-July and late August). The Grasslands National Park data were collected in early June, near peak greenness for the cool season system at that location. The 1995 Tallgrass data were collected in mid-August to coincide with the warm season peak greenness there. From these campaigns, regression models were developed to be representative of all the sites, although some models were necessarily treatment specific, as discussed in Results and Discussion. The sampling strategy placed several quadrats in each treatment along with grids to be used later for landscapescale mapping. In 1995, quadrats were colocated with grids, as depicted in Fig. 2. Multiple quadrats were often sampled within a grid to insure an accurate representation of the full range of biomass variability within the grid. Subsequently in 1996 at Tallgrass, quadrat sampling occurred along transects defined within the various treatments to sample across biomass gradients within the treatment. Seven quadrats were defined per transect, two quadrats of which were reserved for destructive sampling. The quadrat transects were located within the same treatment as the grids used for scaling but were not necessarily colocated with them: the transects sampled gradients, while the grids required larger, uniform areas viewable from the TM system. This method allowed more frequent sampling of the quadrats than would be logistically feasible for the more labor-intensive grids while preventing the disturbance caused by quadrat sampling from affecting overall grid reflectance. The LAI measurements were taken with the LI-COR LAI 2000 Plant Canopy Analyzer and the f PAR measurements were taken with a Decagon AccuPAR ceptometer. Herbaceous standing biomass was clipped at ground level and
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sorted into senesced and live components. The oven-dried weights were used as biomass estimates. The percent of live biomass of the total quadrat biomass was used to convert both LAI and f PAR to live LAI and live f PAR estimates. This assumed that standing senesced grasses absorbed almost as much PAR energy as live grasses, as described by Asner et al. (1998). Spectral reflectance of the quadrat was obtained using a handheld ground radiometer (Cropscan MSR-16), which simultaneously measured incident and reflected light for nine spectral bands. Eight bands were sampled from approximately 460 to 810 nm, with band centers spaced 50 nm apart and having full width, half-maximum bandwidths of 30 nm, and a ninth band sampled the 1550 – 1750 nm wavelength interval (Table 2). The MSR-16 instrument collects the upwelling and downwelling fields simultaneously at each waveband, using a cosine diffuser for the
Table 2 Cropscan MSR-16 spectral channel characteristics (values in nanometers) Central wavelength 460 510 560 610 660a 710 760 810a 1650 Effective bandwidth 30 30 30 30 30 30 30 30 200 a
Denotes channels used in NDVI computation.
downwelling measurement. Thus, the MSR-16 is able to compute reflectance directly rather than through the use of a spectrally flat reference panel and therefore can collect reflectance observations very rapidly. NDVI was calculated using the 810 and 660 nm Cropscan bands as: NDVI ¼
ðr813 r662 Þ ðr813 þ r662 Þ
Where r is spectral reflectance. At each of the clipped quadrats, spectral reflectance and f PAR measurements were taken before and after clipping. In the case of f PAR, the postclipped measurement was used to estimate the soil contribution to the overall f PAR observation. The clipped quadrats were used in place of nearby bare areas, because the latter would typically be significantly depleted of soil organic matter and would yield an overestimate of the lower boundary contribution to the upwelling radiation field. Based on this, f PAR is computed as: f PAR ¼ 1 t rc þ trs
Fig. 2. Field sampling scheme for the study. Quadrats located within (or sometimes near) grids are used to estimate field relationships between NDVI and biophysical quantities (biomass, f PAR, and LAI). These relationships are distributed across the 30 30 m grids using geostatistical techniques; then, the grids are distributed across the landscape within various ‘‘treatments’’ (burning, grazing, etc.).
where t is the fraction of the downwelling radiation transmitted through the canopy (one way), rc is the reflectance of the canopy as measured from above, and rs is the reflectance of the soil measured after clipping. The average rs for the two clipped transect quadrats within each treatment was used for all f PAR calculations for the given treatment on a given date of observation. Regression analysis was used to develop predictive relationships between NDVI and biophysical parameters. Previous work in the Nebraska Sand Hills with other indices such as the modified soil adjusted vegetation index (MSAVI) and the simple red/infrared ratio (VI) indicated little or no improved predictability by other indices (Wylie, DeJong, & Tieszen, 1996). Nonlinear trends in predictive relationships were approximated with natural logarithmic transformations of the dependent variable. Nonlinear relationships pose only minor scaling problems above linear relationships in pixels of uniform land cover (Chen, 1999). Nonhomogeneity associated with the regression variance was assessed by visual inspection of residual plots. Interaction and indicator treatment variables were tested for possible differences in slope and intercept terms for treatment effects using stepwise regression techniques ( P < .01). Estimations made from the 1995 data sets were tested against independent data collected at Tallgrass Prairie in July of 1996. These data were collected in July using seven quadrats from each of three treatments using the same quadrat
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based scheme used in 1995. The 95% confidence intervals for observations from the respective biophysical parameter estimation regressions were plotted and compared to observed 1996 data to see if the 95% confidence intervals contained the values for the 1996 data. 2.3. Landscape-scale estimation The means by which field observations were scaled to the satellite included estimation of f PAR, LAI, and biomass for the live components over the grids and as regressions of these estimations against NDVI computed from TM data. Within each treatment, seven spatially uniform grids considered to be representative of the treatment were selected and located to within 5 m using a GPS receiver. Seven of these 21 grids were identified as priority ‘‘pixels’’ where handheld reflectance would be obtained as frequently as possible to insure a record of seasonal evolution of grid biophysical parameters within the narrow windows of opportunity dictated by weather. The remaining grids were sampled monthly. Systematic handheld radiometer readings were taken every 6 m across each 30 30 m grid (Fig. 2). The predictive equations derived from the quadrat data sets allowed estimation of the biophysical parameters at each systematic sample point within a grid. This insured that ground radiometer estimates of biophysical parameters were applied at the same scale at which they were developed, as recommended by Chen (1999). The biophysical parameters were then interpolated across the grid by anisotropic punctual kriging (GS+). The software automatically selected best-fit models that varied between linear, spherical, and exponential. The average and error terms were derived for the interpolated biophysical parameters from each grid. Error terms incorporated error components from kriging, handheld radiometer regressions, and the spatial covariance. Grid biophysical parameters were temporally interpolated linearly to provide biophysical parameter estimates at the date of satellite overpass. Landsat TM data sets for the year 1996 were obtained for the study area on 3/25, 4/10, 4/26, 5/28, 6/29, and 7/15. The 5/28 image had scattered, fair weather cumulus clouds in the study area. The scenes were georegistered to an UTM projection using cubic convolution image-to-image techniques. The TM spectral bands 3 and 4 were converted to atsensor reflectance using methods described by Markham and Barker (1986). Grid locations were superimposed over the imagery and grid number/date combinations that occurred in cloud, or cloud shadows were eliminated from the analysis. To compensate for ground and satellite locational errors, NDVI for each grid location consisted of the average of the pixels, with a majority of their area falling with in a 30 m radius of the GPS coordinates. We performed regressions between surface biophysics estimated at the grid and NDVI computed from TM as described above. Grids were sampled repeatedly through the spring and summer growing season. Thus, the data points were not truly independent. The Durbin – Watson test
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revealed that temporal autocorrelation was a factor. This implied that the regression coefficients were unbiased but the error terms may have been underestimated. The ability to temporally interpolate biophysical parameters to the date of satellite overpass was considered more important in this study than the effects of temporal autocorrelation. Spatial autocorrelation effects on satellite biophysical regressions in this study were not quantified. Distances between grid locations varied from a minimum of 150 m to a maximum of around 6 km. If spatial autocorrelation effects existed, they would have caused artificially low errors in the predicted biophysical quantities. Simulation studies using Landsat TM data and simulated TM data on a grassland site showed that regression slope and intercept terms were largely unaffected by correlation structures, and only variance and correlation were biased (Harris & Johnson, 1996). Similar regression fitting approaches and treatment testing strategies that were used on the ground radiometer relationships were also applied to the satellite NDVI to grid-based biophysical parameter regressions. These predictive relationships were then used to estimate biophysical parameters for grasslands across the study area at the Landsat TM scale. 2.4. Biome-scale estimation The climate change and carbon flux modeling communities are often interested in regional, continental, or global scale estimates of biophysical parameters for model inputs. In order to obtain such large area estimates, coarser-scale satellite data such as AVHRR are often utilized. These data sets have a nominal ground resolution of around 1 km2. The percent of grasslands and the coefficient of variation for Landsat TM was calculated for 3 3 km windows for an area corresponding to the Pawhuska 1:100,000 USGS topographic map. In addition to measurements of canopy biophysical features, we obtained a continuous record of phenological parameters at each site. This information was obtained by cooperating station managers who assessed the status of the vegetation at 2-week intervals throughout the season and estimated many features, including canopy height, onset of growth by select species and C3 and C4 types, floral emergence, bud break, senescence, and others as well as seasonal weather indicators including first frost, snow cover, ice cover, etc. These independent observations were intended to provide interpretations of phenological events that were independent of the satellite observations.
3. Results and discussion The spring and summer precipitation at the Tallgrass Preserve was below normal in 1996. Precipitation for January to July at Pawhuska, OK was 1051 mm in 1995. This was well above the normal rainfall for that time period of 390 mm (N.C.S.S., 1979). However, only 262 mm was received during that time period in 1996. This facilitated
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Fig. 3. Field phenological observations at the Tallgrass Preserve during the 1995 and 1996 growing seasons. The right-hand vertical axis represents AVHRRderived NDVI values. The black line in the plot is NDVI derived from 10-day maximum value composites, the red line is smoothed NDVI using a technique described in Reed et al. (1994), and the numbers above the NDVI curves are annual time integrated NDVI.
cloud-free Landsat TM acquisitions but put severe moisture constraints on vegetation and limited plant growth as seen in
Fig. 3. The field data show markedly different patterns in the 2 years, a clear effect of a substantial drought in 1996 that
Fig. 8. Temporal and spatial dynamics of Tallgrass Prairie Preserve herbaceous biomass in 1996.
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Table 3 Regressions using ground radiometer NDVI to estimate biophysical parameters Dependent variable 2
LN [biomass (g/m )] LN [biomass (g/m2)] f PAR LN (LAI)
Treatment
Intercept
Slope
r2
Standard error
n
P
Unburned and ungrazed Remaining treatments All treatments All treatments
1.32 2.01 0.28 3.22
5.22 4.41 1.01 4.88
.85 .72 .66 .7
0.42 0.41 0.13 0.54
54 144 154 198
< .0001 < .0001 < .0001 < .0001
reduced canopy height as well as AVHRR-derived NDVI values. The satellite estimates of start of season and end of season (Reed et al., 1994) agreed closely with the interpretations made by the ground observer. The early end of season in 1996 was associated with simultaneous ground observations of freezing temperatures that resulted in tissue damage. The increase in NDVI at this location during September was a response to precipitation that broke the extended drought but only resulted in minor increases in canopy height. 3.1. Model testing: 1996 Tallgrass OK data The quadrat regressions between ground radiometer NDVI and biophysical parameters had r2 values ranging from .66 to .85 (Table 3) for the 1995 data sets. Biomass estimation was significantly different for the unburned and ungrazed treatment where litter and standing dead levels were highest. This was consistent with other studies that indicated treatment practices could affect regression results. For example, Price, Varner, Rundquist, and Peake (1993) found that land management practices can affect the use of
remotely sensed imagery to measure plant biophysical properties in northern Kansas prairies. In a study over eastern Colorado grasslands, Todd, Hoffer, and Milchunas (1998) found that accounting for grazing treatments improves the relationship between remotely sensed quantities and biomass. Other variables, some not accounted for here, may also play a role. For example, Friedl, Michaelson, Davis, Walker, and Schimel (1994) found that burning and terrain characteristics could affect biophysical relationships to reflectancederived quantities. Fig. 4 illustrates the complementary nature of the various sites in the biomass regression for the combination of both the burned and the traditional treatments. The lower predictability associated with LAI values greater than 2 (and subsequently higher biomass) was consistent with Gamon et al. (1995). The majority of the 1996 test field data fell within the 95% confidence limits for individual observations from 1995 and indicated no clear biases (Fig. 5). Because of limited biomass sampling in 1996, this test may not be stringent. Small percentages of f PAR (10%) and LAI (19%) validation points were above the upper confidence intervals,
Fig. 4. Quadrate biomass NDVI regression for all 1995 sites.
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Fig. 5. The 1995 ground radiometer to biophysical parameter [biomass (g/m2), f PAR, and LAI] regression 95% confidence intervals for observations compared with 1996 data (UU = unburned ungrazed treatment).
but the bulk of the observations were centrally aligned within the confidence intervals. The predictive ground radiometer regressions based on 1995 observations provided reasonably robust estimates of biophysical properties for Tallgrass Prairie in 1996. This was true despite 1995 being a wet year and 1996 being a drought year. After validation, these predictive regressions were used to estimate biophysical parameters at the seven grids within each treatment (Fig. 6). This analysis was completed for all three treatments (burned, unburned/ungrazed, and traditional) for f PAR and LAI, as well as biomass. Three grids within each treatment (1, 3, and 6) were designated as
‘‘priority’’ grids and were sampled more frequently (Fig. 6). The temporal dynamics of grid biomass show higher biomass from the burned grids 5 –7, which were ungrazed, than the grazed grids. The vegetation with these grids senesced more quickly than the grazed burned grids, probably because the larger biomass levels depleted available soil water more quickly than the grazed grids. 3.2. Landscape scale The satellite-to-biophysical parameter regressions (Table 4) had higher r 2 values than the ground radiometer
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the lower biomass values early in the year imposed an important physical constraint on the relationship. Treatment effects were not significant ( P < .01) in the f PAR and biomass regressions. However, the LAI regression had a significantly lower intercept associated with the burned treatment. The incorporation of this treatment effect increased the r 2 by 0.007 and decreased the standard error by 0.014. Because of this and difficulties in knowing the spatial extents of treatments, nontreatment specific regression equations were utilized. Landsat TM NDVI values used in these regression analyses came from the six TM scenes acquired throughout the growing season. These regressions include errors associated with atmospheric effects on the different dates. Atmospheric corrections would probably improve these relationships further (Goetz, 1997), but the necessary atmospheric data were not available on all dates. Since 1996 was a drought year, water vapor atmospheric effects may have been relatively insignificant. The above relationships between Landsat TM NDVI and biophysical parameters were applied to all Landsat pixels that had been classified as grasslands in a TM land cover product. These were used to make grassland biophysical parameter estimates for the entire Tallgrass Preserve (Fig. 8). Black areas within the preserve represent areas that are not grassland and no estimates for biophysical parameters were made. The spatial average biomass for the preserve on each date indicates a germination/emergence date of grasslands in mid to late April. This is similar to the observed date of C4 grasses that dominate these grasslands and observations of grass height (Fig. 3). Using rangeland production estimates for a Fig. 6. Temporal dynamics in 1996 Tallgrass Prairie Preserve grid biomass estimated from the kriged hand held radiometer observations.
regressions (Table 3). This is probably because these analyses were between grid NDVI scaled to biomass and Landsat TM NDVI. However, the r 2 statistics is sensitive to the distribution of the data points. Fig. 7 illustrates the distribution problem caused by a lack of measurements from April 2 to May 21. This problem also affected the LAI and f PAR relationships to Landsat TM NDVI and probably produced artificially high r 2 values. Rather than limit the regression to the late season values, the authors thought it was important to insure that the predictive capabilities of the regression equation were applicable to all observed NDVI values, i.e., that
Table 4 Regressions using Landsat TM NDVI to estimate biophysical parameters Dependent variable
Intercept
Slope
r2
Standard error
n
P
LN [biomass (g/m2)] f PAR LN (LAI)
1.17 0.55 4.16
6.26 1.68 7.45
.092 .92 .94
0.24 0.06 0.24
64 66 65
< .0001 < .0001 < .0001
Fig. 7. Herbaceous grid-based aboveground biomass regressed on Landsat TM NDVI.
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Fig. 9. Potential uniform grassland areas for scaling from Landsat TM to AVHRR for the area around the Tallgrass Preserve (CV is the coefficient of variation).
normal year derived from the soil survey (NCSS 1979) masked to exclude nongrassland areas, a production estimate for a normal year for the preserve was estimated at 386 g/m2. Peak grassland biomass occurred on 6/29/96 (Fig. 8) and was only 32% of the estimated production of a normal year. 3.3. Biome scale A major problem with scaling biophysical quantities to AVHRR images is the identification of grids having sufficient uniformity at the 1 km scale. This is further complicated by image registration errors and pixel size variability, requiring uniformity over a 3 3 km area to compensate for these factors (Meyer, 1996). Fig. 9 depicts areas in the vicinity of the Tallgrass Preserve that, within 3 3 km neighborhoods, were dominated by grassland (based on a land cover classification derived from the multidate TM data set) and had low Landsat TM NDVI spatial variability (based on 6/29 TM image). The Preserve boundaries are superimposed on the image. Dark gray regions within this figure indicate areas that would contain likely candidates for surface grids for the biome-scale study. This will be used subsequently to select AVHRR pixels, which are relatively uniform, to minimize scale problems associated with heterogeneous and mixed land cover areas (Chen, 1999) and to establish relationships between AVHRR NDVI and Landsat TM biophysical param-
eters. This approach conducted across several dates and several sites throughout the Great Plains will be used to produce biophysical parameters at the AVHRR scale for regional analyses. Studies to apply these relationships to data acquired from the System pour l’Observation de la Terre (SPOT) Vegetation Monitoring Instrument and the Moderating Resolution Imaging Spectrometer (MODIS) are also underway. This approach would not only produce coarse biome-scale estimates of grassland biophysical parameters for the modeling community but also a subsample of more detailed study areas at the Landscape scale.
4. Conclusions The sampling design described herein suggests three conclusions. First, the 1995 quadrat relationships between ground radiometer data and field-sampled biophysics was robust when applied the 1996 Tallgrass data set. This implies that the methodology can be applied consistently between years and between various sites within the grasslands. The inclusion of more sites and additional collection seasons would strengthen this analysis, and further studies are underway at other sites toward that end. Second, this methodology permits the estimation of biophysical parameters with a minimum of destructive samples. This allowed nonintrusive,
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repetitive sampling to occur within permanent field plots in a manner having minimal impact on ecosystem dynamics. Third, our approach separates the processes of scaling and sample distribution by developing regression relationships at a consistent scale and avoiding correlations between variables measured at dissimilar scales. This strategy also provides the ability to estimate multiple parameters (f PAR, LAI, and biomass) via one set of distributed ground measurements (NDVI) at a given scale. The use of relatively uniform grids avoids scaling problems associated with heterogeneous areas. The approach outlined in this study will allow robust spatial extrapolations of biophysical data with minimal destructive sampling. The application across broad expanses of a land cover type, however, still requires accurate and automated atmospheric corrections of reflectance signals. This should be achievable with MODIS data.
Acknowledgments This work was carried out with support from CNES SPOT VEGETATION (95/CNES/0395), The Nature Conservancy Ecosystem Research Program (TNC-Mellon), U.S. Geological Survey, Cooperative Agreement No. 1434-93-A00761, and the Department of Energy through the Great Plains National Institute for Global Change Program (LWT 52-123-06505). Drs. Wylie and Meyer’s work was performed under U.S. Geological Survey contract 1434-CR-97CN-40274.
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