Time Series Remote Sensing of Landscape-Vegetation Interactions in the Southern Great Plains

Transcription

1 Time Series Remote Sensing of Landscape-Vegetation Interactions in the Southern Great Plains Mark E. Jakubauskas, Dana L. Peterson, Jude H. Kastens, and David R. Legates Abstract The southern Great Plains may be one of the first areas in the United States to show significant and detectable changes in vegetation cover as a result of global climate change. The objective of this project was to examine interactions between landscape environmental factors and interannual variability of land-cover types in this region. Harmonic analysis of a nine- year time series ( ) of NOAA Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) biweekly composite data was used to quantify interannual changes in natural and managed vegetation. An index of interannual landscape variability was developed based on the weighted circular variance in phase values produced by the harmonic analysis. Results indicate that landscape variability, as quantified by the weighted circular variance, is significantly different among three soil texture classes and five land-use/land-cover types. Harmonic analysis of time-series data offers considerable promise as a tool for monitoring landscape change. Introduction The southern Great Plains is a critical area for monitoring land- use and land-cover change. Because of its sensitivity to varia- tions in climate, this region may be one of the first areas in the United States to exhibit both ephemeral and persistent effects resulting from natural environmental variation and anthropogenic changes in the landscape. The history of land manage- ment and climate interactions in this region provides an example of this sensitivity in the Oklahoma/Kansas/Texas Dust Bowl of the 1930s. Human modification of the natural land cover by conversion to agriculture altered the preexisting relationship between natural vegetation and climatological conditions, triggering significant changes in land condition (Riney-Kehrberg, 1994; Worster, 1979). Changes in precipita- tion and temperature patterns thus may be manifested as changes in land use and land cover (LULC), particularly in land- cover types that already exhibit a high degree of sensitivity to environmental conditions. The objective of the research described in this paper was to examine relationships between landscape characteristics and interannual variability in LULC in the southern Great Plains. This objective falls under a broader research goal of developing protocols for identifying environmentally sensitive regions that may be the first to exhibit signs of environmental degradation. An index of interannual variability for natural and managed LULC types was developed using phase components derived from harmonic analysis of a nine-year ( ) Advanced Very High Resolution Radiometer (AVHRR) normal- ized difference vegetation index (NDVI) time-series dataset. Landscape characteristics used include land-cover type, soil texture, and available water capacity. Time-Series Remote Sensing for Landscape Change Characterization Recent advances in remote sensing technology and theory have expanded opportunities to characterize the seasonal and interannual dynamics of vegetation communities and monitor change in sensitive environments. Studies have shown that the temporal domain of multispectral data frequently provides more information about vegetation condition than do the spa- tial, spectral, or radiometric domains (Briggs and Nellis, 1991; Kremer and Running, 1993; Eastman and Fulk, 1993; Samson, 1993; Wynne and Lillesand, 1993; Reed et al., 1994; Jaku- bauskas et al., 2001; Moody and Johnson, 2001). Vegetation phenology, or the seasonal changes in vegetation associated with spring green-up and fall senescence, can be monitored using a time series of satellite images. Many land-cover types exhibit a strongly periodic pattern of change in the normalized difference vegetation index (NDVI) over the course of a year. Seasonal and interannual perturbations to this periodicity can be introduced by variability in climatological conditions (temperature and precipitation), or by effects of land management, such as overgrazing, land abandonment, or crop rotation. Phenological studies using time-series remotely sensed imagery have varied considerably in their approaches, from standardized principal component analysis (Eastman and Fulk, 1993), to textural analysis (Briggs and Nellis, 1991), to the development of phenological metrics that describe seasonal changes in the normalized difference vegetation index (Lloyd, 1990; Samson, 1993; Reed et al., 1994). More recently, harmonic (Fourier) analysis has been used for analyzing sets of successive regular multidate samples of satellite remotely sensed imagery (Olsson and Eklundh, 1994; Andres et al., 1994; Verhoef et al., 1996; Rogers et al., 1996; Azzali and Men- enti, 2000; Jakubauskas et al., 2001; Moody and Johnson, 2001). Harmonic analysis is particularly amenable for detecting periodic patterns in a time series of satellite imagery and for quantifying changes in periodic seasonal NDVI over time. M.E. Jakubauskas, D.L. Peterson, and J.H. Kastens are with the Kansas Applied Remote Sensing (KARS) Program, 2335 Irving Photogrammetric Engineering & Remote Sensing Hill Road, University of Kansas, Lawrence, KS Vol. 68, No. 10, October 2002, pp (mjakub@ku.edu) /02/ $3.00/0 D.R. Legates is with the Department of Geography, 227A Pearson 2002 American Society for Photogrammetry Hall, University of Delaware, Newark, DE and Remote Sensing PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING October

2 Methods Study Area The study area lies in the southern Great Plains region approximately within 39 N 104 W to36 N 100 W (Figure 1). The region has a relatively flat landscape with the exception of the sandhills along the steep slopes of river systems extending from eastern Colorado to southwest Kansas and down to the Oklahoma and Texas panhandles. The average annual temperature in the southern Great Plains ranges from 10 Cinsouthern Nebraska to 13.3 Cinnorthern Texas. Seasonal temperatures are highly variable with low mean monthly temperatures of 4 C and 7 CinJanuary and high mean monthly temperatures of 31 C and 34 C injuly for southern Nebraska and northern Texas, respectively. Average annual precipitation increases from east to west with approximately 440 mm falling in eastern Colorado and 700 mm in central Kansas. Land-cover/land-use types in the southern Great Plains can be roughly categorized into three groups: natural vegetation (shrublands and tallgrass/shortgrass prairies), non-irri- gated small grains cropland (predominantly winter wheat), and irrigated cropland (principally the row crops of corn, milo, and soybeans), concentrated in areas along the Arkansas River and in the lower portion of the Texas panhandle. In the 20th century, agricultural development in this region focused on commodity crops (Worster, 1979). Small grains (principally hard red winter wheat) can be successfully grown without irrigation in this region, despite the low annual rainfall, given careful field management practices and a system of two- or threeyear crop rotation, in which fields are allowed to remain fallow and regain soil moisture. Irrigation was relatively limited until after World War II, when powerful pumps and center-pivot sys- tems allowed farmers to expand cropped acreage into cheap, poorer lands that were previously unfarmable, such as the rolling sand-sage prairies south of the Arkansas River (Riney-Kehrberg, 1994; Worster, 1979). Despite the low available water capacity of these sandy soils, irrigation, by providing a con- stant source of moisture, allowed continuous farming of row crops with high water requirements (e.g., corn, alfalfa). Database Development Seasonal and interannual vegetation condition is closely linked to important physical processes occurring at the land-surface/ atmosphere interface, including evapotranspiration, precipita- tion, and soil moisture. Vegetation, whether natural or agricul- tural, depends on soil to supply water to meet plant needs between rainfall or irrigation events. Naturally occurring vegetation communities occupy particular sites based on specific adaptations to gradients of temperature and moisture, while croplands are managed to produce specific conditions for a given crop. Three landscape attributes relating to the ability of the soil to provide water to plants were chosen as the environmental variables (soil texture, available water capacity in the top 100 cm of the soil profile, and total available water capacity). The NDVI was used as a landscape-scale surrogate of biweekly vegetation conditions, measured over the course of a year and interannually. Soil Texture The state soil geographic (STATSGO) database developed by the U.S. Department of Agriculture s (USDA) Natural Resources Conservation Service (NRCS, 1994) was downloaded and imported into Access database software. The STATSGO data- base is organized by map units and contains a variety of soil attributes, including soil texture. Soil texture data for the study area were extracted from the database, and vector data files of the percent of each soil texture class within each map unit were produced. After converting the vector files for each soil texture class to 1-km resolution raster files, the soil texture class representing the highest percentage within each map unit was identified and assigned to the pixels within each map unit. Soil texture classes were aggregated based on texture composition to produce four soil texture classes: sandy loam, loamy sand, and silt loam, and clay (Figure 2, Table 1). Available Water Capacity Soil texture alone does not directly and completely express the amount of water available to plants. A more direct measure is the total available water capacity (AWC). The NRCS defines available water capacity as the volume of water that should be available to plants if the soil were at field capacity (NRCS, 1993). Using the STATSGO database, the mean total available water capacity was calculated for each map unit using methods described in the STATSGO data manual (NRCS, 1993) (Figure 3). Total available water capacity is sometimes restricted to a depth maximum of 100 cm, because most crop types have relatively shallow root systems and cannot take advantage of deep subsurface moisture. Both total AWC and AWC at 100 cm were included in the analysis in order to examine the effects of differing moisture availability on different LULC types. Available water capacity at a depth of 100 cm was obtained from the Earth System Science Center s (ESSC) Conterminous United States soil data set (CONUS-SOIL) (ESSC, 1999) (Figure 4). Figure 1. Areas subject to severe wind erosion in the 1930s Dust Bowl (adapted from Worster (1979)). The study area, indicated by a dashed line, is located in the southern Great Plains region. AVHRR Times-Series NDVI NOAA-AVHRR NDVI biweekly composites for 1989 through 1997 were acquired from the U.S. Geological Survey EROS Data Center in Sioux Falls, South Dakota. Each biweekly NDVI composite image consists of the maximum NDVI value within a defined two-week period for each pixel (Eidenshink, 1992). Vegetation index data are rescaled by EROS during processing from a range 1022 October 2002 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

3 Figure 2. Soil texture classes by map unit. Map units were assigned a soil texture class based on a majority function. Figure 3. Average total available water capacity (cm) levels by map unit. of 1.0 to 1.0, to 0 to 200. Values less than 100 typically represent snow, ice, water, and other non-vegetated earth surfaces while values between 100 to 200 represent a vegetated feature. Images for missing biweekly composite periods were created by averaging image data for periods bracketing the missing period (e.g., the previous and succeeding periods). Each year of the nine-year data set contained 26 biweekly images. Land Use/Land Cover An unsupervised classification approach was used on 1997 AVHRR NDVI time-series data to generate a general LULC map of the study area (Plate 1). Twenty-five initial spectral classes were generated using the ISODATA clustering algorithm and maximum-likelihood classifier. Using other land-cover maps such as the Kansas GAP Vegetation Map (KARS, 2000) and the Texas Vegetation Map (TNRIS, 2000), pure areas ( 1 km) of each land-cover type were identified, and temporal spectral TABLE 1. Original Soil Texture Classes Very fine sandy loam Fine sandy loam Sandy loam Coarse sandy loam Loamy very fine sand Loamy fine sand Loamy sand Loamy coarse sand Sandy clay loam Silt Loam Silty clay loam Clay Clay loam THE AGGREGATION OF SOIL TEXTURE CLASSES USED FOR STATISTICAL ANALYSIS Aggregated Soil Texture Classes Sandy Loam Loamy Sand Silt Loam Clay/Clay Loam Figure 4. Available water capacity (cm) levels at a depth of 100 cm by map unit. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING October

4 defined: natural vegetation, subdivided into shrubland/rangeland, tallgrass/mixed prairie, and shortgrass/mixed prairie; irrigated row crops, divided into generic row crops (principally corn and milo) and alfalfa; and nonirrigated small grains cropland (predominantly winter wheat). Alfalfa, although an irrigated row crop, was not used in further analysis because it occupied only a relatively small percentage of the total area and was restricted to a single soil type along the Arkansas River. Two additional classes defined and mapped but not used in further analysis were water bodies and mixed cropland/grassland, where pixels could not be reliably assigned to either category. Level III ecoregions (EPA, 2000) were used to stratify the grassland class into shortgrass/mixed grass and tallgrass/ mixed prairie. Harmonic Analysis Briefly defined, harmonic analysis permits a complex curve to be expressed as the sum of a series of cosine waves (terms) and an additive term (Davis, 1986; Rayner, 1971). Each wave is defined by unique amplitude and phase angle values, where the amplitude value is half the height of a wave, and the phase angle (or simply, phase) defines the offset between the origin and the peak of the wave over the range 0 to 2 (Figure 5a). Each term designates the number of complete cycles completed by a wave over the defined interval (e.g., the second term completes two cycles) (Figure 5b). Successive harmonic terms are added to produce a complex curve (Figure 5c), and each component curve, or term, accounts for a percentage of the total variance in the original time-series data set. Interpreting harmonic analysis as applied to biweekly NDVI data, the additive or zero term is the arithmetic mean over the time series (26 periods) and represents the overall greenness of a land cover type (Jakubauskas et al., 2001; Moody and Johnson, 2001). High amplitude values for a given term indicate a high level of variation in temporal NDVI, and the term in which that variation occurs indicates the periodicity of the event. For example, high first-term amplitude values indicate a unimodal temporal NDVI pattern, where an LULC has a wide seasonal NDVI range. Phase indicates the time of year at which the peak value for a term occurs (Jakubauskas et al., 2001; Moody and Johnson, 2001). For LULC types with unimodal NDVI phenological profiles that peak in midsummer (such as row crops or warm-season grasslands), phase values typically are near, over a possible range of 0 to 2 (Jakubauskas et al., 2001). An LULC with a stable, consistent vegetation cover that exhibits a strongly periodic pattern of seasonal NDVI (Figure 6) should in Plate 1. A land-use/land-cover map derived from 1997 AVHRR NDVI biweekly composites depicting regional trends in vegetation types. profiles were extracted from the 1997 NDVI composite image. The spectral profiles for each spectral class were compared to the pure spectral profiles and assigned to one of the LULC classes. Spectral classes containing more than one land-cover type underwent a second unsupervised classification (Jensen, 2000). Within the three broad categories of LULC defined in the study area description, six vegetation LULC classes were (c) (a) (b) Figure 5. Overview of harmonic (Fourier) analysis. (a) A simple cosine curve representative of the first harmonic. (b) Curves for harmonic terms 1, 2, and 3. (c) Curve produced from addition of curves in (b) Oc tobe r PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

5 Figure 6. An NDVI spectral profile illustrating a strong periodic pattern. basis. To compute the percent variance requires calculating the total variance of the amplitude values for each term j where j 1ton (Davis, 1986): i.e., (amplitude j ) Total variance 1 2. to n 2 The percent variance for a given term is computed by dividing the individual variance for that term by the total vari- ance. The average percent variance across the nine-year period for each term was computed to permit trends in variance among the various LULC types to be identified. theory exhibit little year-to-year (interannual) change in annual phase values for a given harmonic term. Conversely, an LULC that is highly variable in seasonal NDVI from year to year (Figure 7) should exhibit a wide range in annual phase values for a given harmonic term. Images of the additive term, and amplitude and phase angle for each term to the eighth harmonic, were produced on a per-pixel basis for each year of data (26 periods/year) in the nine-year NDVI data set, using the procedures described by Jakubauskas et al. (2001), modified from Davis (1986). Using the amplitude values in each term, the percent variance explained by each term was then calculated on a per-pixel Figure 7. An NDVI spectral profile illustrating a less periodic or more chaotic pattern. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING October

6 Weighted Circular Variance TABLE 2. SUMMARY STATISTICS OF WEIGHTED CIRCULAR VARIANCE IN FIRST- Because phase angles are directional, circular variance was calculated TERM PHASE ANGLE STRATIFIED BY LAND USE/LAND COVER AND SOIL TEXTURE for the first-term phase as a measure of interannual variation (Davis, 1986). Circular variance in each image is cal- 1 st term culated by first calculating the length of the vector resultant Factors Class n Mean Std where X r and Y r are equal to the sum of the cosines and sines, Land Use/Land Cover Small Grains respectively, of the individual vectors: i.e., Row Crops Tallgrass/Mixed R X 2 r Y 2 r. Mixed/Shortgrass Shrubland/Rangeland To calculate dispersion in the data, the resultant length R is subtracted from the number of observations n and then divided by the number of observations (Davis, 1986): i.e., Soil Texture Silt Loam Sandy Loam Loamy Sand s 2 o (n R)/n. Low circular variance indicates that phase values are closely clustered, e.g., they occur within 1 to 2 periods), while high circular variance indicates that phase values occur in multiple dispersed periods. To account for the variance explained by each harmonic component, weighted circular variance was calculated for first-term phase by multiplying the circular vari- ance by the mean percent variance for that term. term, and small grains (winter wheat) had the highest amplitude in the second harmonic term. Row crops and all grasslands had first-term phase angles close to, indicating that the peak greenness period is close to midsummer. A small grains land- cover type such as winter wheat has a bimodal greenness pat- tern and a lower first-term phase angle that indicates that a firstterm amplitude peak occurs early in the growing season. Shrublands had a first-term phase angle close to four, indicating a late summer first-term amplitude peak. Statistical Analysis To determine if first-term weighted circular variance differed across LULC types and soil texture classes within the study Summary Statistics area, 175 pixels for each subclass (LULC type and soil texture class combined) were randomly selected. Due to the limited Weighted Circular Variance area of tallgrass prairie on silt loam texture within the study A visual comparison of the weighted circular variance and the area, a random sample of 125 pixels was selected for tallgrass LULC map indicates that irrigated row crops along the Arkansas prairie in the three soil texture classes. The clay soil texture River and naturally vegetated areas (grasslands and class was not included in the statistical analysis due to small shrublands) throughout the study area were less variable in sample size. first-term phase angle than areas dominated by small grain The values in the randomly selected pixels were extracted crops (Plates 1 and 2). Summary statistics based on the random from the data sets and imported into SPSS for statistical analyment sample of pixels for each LULC type confirmed the visual assess- sis. The data were then stratified and summary statistics of with small grains having the highest weighted circular weighted circular variance for first-term phase angle were calcover variance (Table 2). Row crops, native grasslands, and shrubland culated to illustrate how weighted circular variance varied by types have low mean weighted circular variance with LULC type, soil texture class, and subclass. Next, LULC data and shrubland land-cover types having the lowest weighted circusoil texture information were entered as factors into an analysis lar variance of all land-cover types. of variance (ANOVA). Factorial ANOVA tests the main effects of Summary statistics stratified by soil texture indicate that the factor variables (LULC type and soil texture class) and the vegetation on soil with silt loam texture has higher first-term interaction between the factor variables. A second set of ANOVA weighted circular variance than vegetation on soils with sandy tests identified which subclasses were significantly different loam or loamy sand soil texture classes (Table 2). Summary sta- from one another in the weighted circular variance of the tistics for LULC type combined with soil texture revealed that phase. small grains consistently had the highest mean weighted circu- To identify associations between available water capacity lar variance across the three soil texture classes (Table 3). (at a depth of 100 cm and total available water capacity) and weighted circular variance stratified by soil texture, summary statistics and Kendall s tau c (Kendall, 1955) were calculated. TABLE 3. SUMMARY STATISTICS FOR CIRCULAR VARIANCE IN FIRST-TERM PHASE Because the data were not normally distributed, a nonparamet- ANGLE STRATIFIED BY BOTH LAND USE/LAND COVER AND SOIL TEXTURE ric test was used as a measure of association. The data were first stratified by subclass (LULC type and soil texture class com- Soil Texture Land Use/Land Cover n Mean Std bined) and were ranked in ascending order by available water Silt Loam Small Grains capacity and then by weighted circular variance of the first- Row Crops term phase angle. The ranked variables were used to calculate Tallgrass/Mixed Kendall s tau c. Mixed/Shortgrass Shrubland/Rangeland Sandy Loam Small Grains Results Harmonic Characteristics Row Crops Tallgrass/Mixed Land-use and land-cover types in the study area exhibited char- Mixed/Shortgrass acteristic additive term, amplitude, and phase components Shrubland/Rangeland derived from the harmonic analysis of NDVI biweekly compos- Loamy Sand Small Grains ites. Native grasslands and shrublands had lower mean Row Crops rescaled Tallgrass/Mixed NDVI values ( 120) than did small grains and irrigated Mixed/Shortgrass row crops ( 140). Row crops, native grasslands, and Shrubland/Rangeland shrublands had the highest amplitude in the first harmonic 1026 October 2002 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

7 TABLE 4. SUMMARY STATISTICS OF AVAILABLE WATER CAPACITY STRATIFIED BY SUBCLASS (LAND USE/LAND COVER AND SOIL TEXTURE) TABLE 6. POST HOC ANALYSIS OF THE FACTORIAL ANOVA. THE RESULTS SHOW THAT WEIGHTED CIRCULAR VARIANCE IN THE FIRST-TERM PHASE ANGLE IS SIGNIFICANTLY DIFFERENT AMONG THE THREE SOIL TEXTURE CLASSES AND AMONG AWC (cm) SMALL GRAINS AND SHRUBLAND/RANGELAND LAND-USE/LAND-COVER TYPES at 100cm Total AWC depth (cm) Post Hoc Land Use/Land Comparison Soil Texture Cover n Mean Std Mean Std Class 1 Class 2 Sig Soil Texture Sandy Loam Loamy Sand Silt Loam Row Crops Sandy Loam Silt Loam Small Grains Loamy Sand Silt Loam Tallgrass/Mixed Land Use/ Small Grains Row Crops Mixed/Shortgrass Land Cover Tallgrass/Mixed Shrubland/Rangeland Mixed/Short Sandy Loam Row Crops Shrub/Rangeland Small Grains Row Crops Tallgrass/Mixed Tallgrass/Mixed Mixed/Short Mixed/Shortgrass Shrub/Rangeland Shrubland/Rangeland Tallgrass/Mixed Mixed/Short Loamy Sand Row Crops Shrub/Rangeland Small Grains Mixed/Short Shrub/Rangeland Tallgrass/Mixed Mixed/Shortgrass Shrubland/Rangeland Available Water Capacity Summary statistics indicate that LULC types on soil with loamy sand texture have lower available water capacity (both at 100- cm depth and total) than LULC types on soil with silt loam and sandy loam textures (Table 4). Furthermore, of the three soil texture classes, silt loam was the least variable in available water capacity. Shrubland/rangeland and shortgrass/mixed grass prairie occupy areas having lower and more variable available water capacity levels. Factorial ANOVA The results show that the main effects of LULC and soil texture are significant as well as their interaction (ANOVA,P 0.000, Table 5). Post hoc analysis showed that weighted circular variance for first-term phase angle for small grains and shrubland/ rangeland were significantly different from the other three LULC types (Table 6). There were no significant differences among row crops, shortgrass/mixed grass prairie, and tallgrass/mixed prairie land-cover types. soil-stratified land-cover types (Table 7). Correlation coefficients were highest for LULC types on the silt loam soil texture class and the least number of significant relationships and lowest coefficients were observed for LULC types occurring on loamy sand soils. One factor that may be contributing to the low Kendall s tau c values in all classes is that there is insuffi- cient variation in available water capacity within each sub- class (e.g., row crops on silt loam soils) to show a strong association between weighted circular variance and available water capacity. Generalizing the data to examine all land-cover classes combined within a soil texture class might increase the strength of the relationships, but with the disadvantage of discerning key within-class differences that might aid in discriminating what specific LULC types are most variable on a given soil type. Discussion The observed low weighted circular variance for row crops and native grasslands/shrublands may be due in part to the vegeta- tion phenology of these LULC types. Row crops and grasslands ANOVA TABLE 7. KENDALL S TAU c RANKED CORRELATION COEFFICIENTS BETWEEN Weighted circular variance significantly differed among sub- WEIGHTED CIRCULAR VARIANCE IN THE FIRST-TERM PHASE ANGLE, AVAILABLE WATER classes (ANOVA, P 0.000, F ). Post hoc results CAPACITY AT 100 CM DEPTH, AND TOTAL AVAILABLE WATER CAPACITY FOR EACH SUBCLASS indicate that weighted circular variance for small grains significantly differed from most other subclasses except when grow- AWC ing on soils with silt loam and sandy loam texture (ANOVA,P (cm 3 )at Total 0.915) or on soils with silt loam texture (ANOVA,P 0.740, 100 cm AWC Table 6). Additionally, weighted circular variance did not differ Soil Texture Land Use/Land Cover n depth (cm 3 ) across soil texture classes for row crops, shrubland/rangeland, Silt Loam Row Crops ** 0.23** shortgrass/mixed grass prairie, and tallgrass/mixed prairie. Small Grains ** Correlation Analysis Tallgrass/Mixed * 0.24** Mixed/Shortgrass ** 0.31** Results of the correlation analysis indicate that statistically sig- Shrubland/Rangeland ** 0.50** nificant but weak correlations exist between available water Sandy Loam Row Crops ** 0.27** capacity and weighted circular variance for nearly all of the Small Grains Tallgrass/Mixed ** 0.03 Mixed/Shortgrass ** 0.39** Shrubland/Rangeland ** 0.14** TABLE 5. FACTORIAL ANOVA RESULTS SHOWING A SIGNIFICANT INTERACTION Loamy Sand Row Crops ** 0.10 Small Grains ** 0.03 BETWEEN LAND USE/LAND COVER AND SOIL TEXTURE Tallgrass/Mixed ** 0.18** Multivariate F Sig Partial Eta 2 Mixed/Shortgrass Soil Texture Shrubland/Rangeland Land Use/Land Cover *Significant at alpha 0.05 Soil Texture* LULC **Significant at alpha 0.01 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING October

8 Plate 2. Weighted circular variance for first-term phase. typically have strongly unimodal seasonal NDVI curves and inter-annual variation in the timing, peak, and duration of greenness for row crops is minimal, producing relatively low weighted circular variance. In western Kansas, the natural vegetation consists of a mixture of sandsage shrubland, shortgrass prairie, mixed prairie, and sand prairie. Furthermore, various plant species have differing adaptations to moisture stress. Weighted circular variance may be detecting the differing responsiveness of grassland plant species to changes in timing and levels of precipitation events. Certain native grassland species (e.g., blue grama, Bouteloua gracilis) respond to water stress by becoming dormant and shutting down, but rapidly green-up again after moisture is received. Other species, especially native shrubland species (e.g., mesquite, Prosopis spp.), possess extensive root systems or deep tap roots that reach subsurface moisture in times of drought, allowing them to maintain photosynthetic activity. Values of the mean and standard deviation in phase (Table 2) for shrublands are the lowest of all LULC types in the study area, indicating that this community exhibits little interannual variation in timing of peak greenness during the nine-year study period. Heterogeneity of the LULC types and effects of crop rotation in part may be a driver of high first-term weighted circular variance. A 1-km grid cell size, corresponding to the resolution of the AVHRR NDVI biweekly composite data, was used for the analyses. The coarse resolution of the AVHRR sensor (1100 m) dictates that several different land-cover types typically will contribute to the spectral reflectance value for a given pixel. In addition to LULC heterogeneity, the relatively high circular variance for some areas of winter wheat in the first-term phase may be a function of land management practices. Moderate circular variance in grasslands potentially may be a function of local- to landscape-scale variations in grazing intensity that produces small-scale heterogeneity in landscape pattern and con- sequent higher variability in CV. Crop rotation, such as the wheat-fallow two-year rotation and the wheat-corn-fallow three-year rotation patterns common in areas of western Kansas, would increase the biannual variability in vegetation greenness patterns and thus increase the weighted circular variance of the first-term phase angle in this region. The interaction between inter-annual variability in vegetation condition (as quantified by the weighted circular variance in harmonic phase) and the environmental factors of soil type, available water capacity, and land cover is complex and initially appears contradictory. We expected that areas with low available water capacity and sand soils (loamy sand and sandy loam) would exhibit the highest weighted circular variance, but the analysis indicates exactly the opposite effect is occurring. One possible interpretation of the data with regard to the cropping and land management practices in the southern Great Plains, however, suggests a possible model or framework for landscape variability and stability in this region. Viewed in the context of this framework, shrub/prairie and irrigated cropland can represent two endpoints in a continuum of landscape stability and human interactions (Figure 8). Between these two endpoints of stability (quantified in this paper as low circular variance of the weighted phase term of an annual NDVI profile), non-irrigated cropland exhibits high variability in phenological patterns. Natural vegetation is highly adapted to seasonal and interannual water stress, representing the low input endpoint of stability. Irrigated cropland, in contrast, requires a significant investment of time, energy, and resources by humans to maintain essentially an artificial LULC, and thus represents the high input endpoint of stability (Forman and Godron, 1986). In this sense, the varianceweighted phase term could be used as a regional-scale surrogate for landscape stability (sensu Forman and Godron, 1986). Changes in landscape variability, as expressed by changes in the phase term, may signal transitions between landscape states, with lower circular variance at metastable states and high circular variance of the phase term indicating transition points (Forman and Godron, 1986; Westoby et al., 1989; Frie- del, 1991; Laycock, 1991). Transition events can be triggered by either natural disturbance or by management actions (Westoby et al., 1989). In the context of the LULC of the southern Great Plains agroecosystem, this provides a mechanism for detecting regional-scale landscape changes. For example, if intensively managed irrigated croplands were converted to dry cropland Figure 8. Hypothesized continuum of landscape stability and human inputs in the southern Great Plains October 2002 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

9 (decreased resource inputs), under the proposed framework EPA (Environmental Protection Agency), Level III Ecoregions of (Figure 8), observed circular variance should be expected to the United States, National Health and Environmental Effects subsequently increase over a period of time. If those irrigated Research Laboratory, Western Ecology Division, Corvallis, Oregon, URL: iii.htm croplands were simply abandoned, perhaps as a result of aqui- (last date accessed 05 June 2002). fer depletion (a critical issue in this region), observed circular variance might initially increase, and then decrease as nonronmental Modeling and Ecosystem Management, College of ESSC (Earth System Science Center), Soil Information for Envi- agricultural vegetation colonizes the site and becomes estab- Earth and Mineral Sciences, Pennsylvania State University, State lished. This framework for landscape change in the southern College, Pennsylvania, URL: Great Plains agroecosystem will be tested in future studies and soil info/index.cgi?soil data&conus (last date accessed 05 will incorporate climatological data and soil moisture model- June 2002). ing. Decreases in irrigated acreage in southwestern Kansas over Forman, R.T.T., and M. Godron, Landscape Ecology, John Wiley the past decade as a result of declining aquifer levels, and con- and Sons, New York, N.Y., 619 p. version of cropland to grassland under the Conservation Friedel, M.H., Range condition assessment and the concept of Reserve Program, present two opportunities to test the scenarios thresholds: A viewpoint, Journal of Range Management, presented above. By quantifying changes in weighted circular 44(5): variance, areas of LULC conversion to and from hypothesized Jakubauskas, M.E., D.R. Legates, and J. Kastens, Harmonic analy- stable and unstable states should be identifiable. sis of time-series AVHRR NDVI data, Photogrammetric Engineering & Remote Sensing, 67(4): Conclusions Jensen, J.R., Remote Sensing of the Environment: An Earth Harmonic analysis of time-series data provides an unbiased, Resource Perspective, Prentice-Hall, Inc., Upper Saddle River, replicable method for assessing interannual change in sensitive New Jersey, 544 p. landscapes, and offers considerable promise as a tool for KARS (Kansas Applied Remote Sensing), The Kansas GAP Anal- monitoring directional landscape change. The use of newer ysis Land Cover Database, Data Access and Support Center, Kan- data sets with higher spatial resolution in future studies may sas Geological Survey, Lawrence, Kansas, URL: reduce within-pixel mixing of disparate LULC classes and offer gisdasc.kgs.ku.edu (last date accessed 05 June 2002). higher temporal resolution than the AVHRR biweekly composite Kendall, M.G., Rank Correlation Methods, Hafner Publishing data set. Data from the Moderate-Resolution Imaging Spectroradiometer Company, New York, N.Y., 196 p. (MODIS) aboard the NASA Terra satellite offer an Kremer, R.G., and S.W. Running, Community type differentiation opportunity to extend the concepts described above to new using NOAA/AVHRR data within a sagebrush-steppe ecosystem, image data sets and overcome some of the spatial and temporal Remote Sensing of Environment, 46: resolution limitations of the AVHRR data. The MODIS sensor Laycock, W.A., Stable states and thresholds of range condition acquires multispectral imagery with spatial resolutions of 250 on North American rangelands: A viewpoint, Journal of Range m, 500 m, and 1000 m over 36 visible-to-infrared spectral Management, 44(5): bands. A swath width of 2330 km allows a repeat cycle of 1 to Lloyd, D., A phenological classification of terrestrial vegetation 2 days. cover using shortwave vegetation index imagery, International Journal of Remote Sensing, 11(12): Moody, A., and D.M. Johnson, Land-surface phenologies from Acknowledgments AVHRR using the discrete Fourier transform, Remote Sensing of This project was conducted at the Kansas Applied Remote Environment, 75(3): Sensing (KARS) Program (Edward A. Martinko, Director). The NRCS (Natural Resources Conservation Service), Soil Survey research described in this paper was funded in part by the Manual, U.S. Department of Agriculture Handbook 18, Soil Sur- National Institute for Global Environmental Change (NIGEC) vey Division Staff, Natural Resources Conservation Service, U.S. (SouthCentral Regional Center, Tulane University, David Department of Agriculture, Washington, D.C., 437 p. Sailor, Director), through the U.S. Department of Energy, National STATSGO Database, USDA-NRCS Soil Survey (Cooperative Agreement No. DE-FC03-90ER61010). Any opinions, Division, Fort Worth, Texas, URL: findings, and conclusions or recommendations expressed in stat data.html (last date accessed 05 June 2002). this publication are those of the authors and do not necessarily Olsson, L., and L. Eklundh, Fourier series for analysis of temporal reflect the views of the DOE. sequences of satellite sensor imagery, International Journal of Remote Sensing, 5: References Rayner, J.N., An Introduction to Spectral Analysis, Pion Ltd., London, United Kingdom, 174 p. Andres, L., W.A. Salas, and D. Skole, Fourier analysis of multi- Reed, B., J. Brown, D. VanderZee, T. Loveland, J. Merchant, and D.O. temporal AVHRR data applied to a land cover classification, Interimagery, Journal of Vegetation Science, 5: Ohlen, Measuring phenological variability from satellite national Journal of Remote Sensing, 15(5): Azzali, S., and M. Menenti, Mapping vegetation-soil-climate Riney-Kehrberg, P., Rooted in Dust: Surviving Drought and complexes in southern Africa using temporal Fourier analysis Depression in Southwestern Kansas, University Press of Kansas using NOAA-AVHRR-NDVI data, International Journal of Remote Press, Lawrence, Kansas, 249 p. Sensing, 21(5): Rogers, D.J., S.I. Hay, and M.J. Packer, Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed Briggs, J., and M.D. Nellis, Seasonal variation of heterogeneity meteorological satellite data, Annals of Tropical Medicine and in the tallgrass prairie: A quantitative measure using remote sens- Parasitology, 90: ing, Photogrammetric Engineering & Remote Sensing, 57: Samson, S.A., Two indices to characterize temporal patterns in Davis, J.C., Statistics and Data Analysis in Geology, Second the spectral response of vegetation, Photogrammetric Engi- Edition, J. Wiley and Sons, New York, N.Y., 646 p. neering & Remote Sensing, 59(4): Eastman, J.R., and M. Fulk, Long sequence time series evaluation TNRIS (Texas Natural Resources Information System), The Vegeusing standardized principal components, Photogrammetric tation Types of Texas, Texas Natural Resources Information Sys- Engineering & Remote Sensing, 59: tem, Texas Water Development Board, Austin, Texas, URL: Eidenshink, J.C., The 1990 conterminous U.S. AVHRR data set, Photogrammetric Engineering & Remote Sensing, 58(6): (last date accessed 05 June 2002). PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING October

10 Verhoef, W., M. Menenti, and S. Azzali, A colour composite of NOAA-AVHRR-NDVI based on time series analysis ( ), International Journal of Remote Sensing, 17(2): Westoby, M., B. Walker, and I. Noy-Meir, Opportunistic management for rangelands not at equilibrium, Journal of Range Management, 42(4): Worster, D., Dust Bowl: The Southern Great Plains in the 1930 s, Oxford University Press, New York, N.Y., 277 p. Wynne, R.H., and T.M. Lillesand, Satellite observation of lake ice as a climate indicator: Initial results from statewide monitoring in Wisconsin, Photogrammetric Engineering & Remote Sensing, 59(6): October 2002 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING