Stephen L. Egbert

A spatial model for studying the effects of climatic change on the structure of landscapes subject to large disturbances

Global warming may have many consequences for natural ecosystems, including a change in disturbance regimes. No current model of landscapes subject to disturbance incorporates the effect of climatic change on disturbances on decade to century time scales, or addresses quantitative changes in landscape structure as disturbances occur. A new computer simulation model, DISPATCH, which makes use of a geographical information system for managing spatial data, has been developed for these purposes. The concept and structure of the DISPATCH model are described here, and a hypothetical example of its use is illustrated, but the model requires refinement before it can be used to predict the effects of global warming on specific landscapes. The model includes provisions for (1) temporally varying weather conditions and their effect on disturbance sizes, and (2) the effect of spatial variation in vegetation condition and physical setting on the probability of disturbance initiation and spread. The potential use of the model is illustrated with a hypothetical example in which the age structure of disturbance patches is monitored for a 250-year period as weather fluctuates. The model run suggests that landscape structure fluctuates even if a disturbance regime remains constant.

Using USDA Crop Progress Data for the Evaluation of Greenup Onset Date Calculated from MODIS 250-Meter Data

Identification of the onset of vegetation greenup is a key factor in characterizing and monitoring vegetation dynamics over large areas. However, the relationship between greenup onset dates estimated from satellite imagery and the actual growth stage of vegetation is often unclear. Herein, we present an approach for comparing pixel-level onset dates to regional planting and emergence information for agricultural crops, with the goal of drawing reliable conclusions regarding the physical growth stage of the vegetation of interest at the time of greenup onset. To accomplish this, we calculated onset of greenup using MODIS 250 m, 16-day composite NDVI time series data for Kansas for 2001 and a recently proposed methodology for greenup detection. We then evaluated the estimated greenup dates using the locations of 1,417 large field sites that were planted to corn, soybeans, or sorghum in 2001, in conjunction with United States Department of Agriculture (USDA) weekly crop progress reports containing crop planting and emergence percentage estimates. Average greenup onset dates calculated for the three summer crops showed that the dates were consistent with the relative planting order of corn, sorghum, and soybeans across the state. However, the influence of pre-crop vegetation (weeds and “volunteer” crops) introduced an early bias for the greenup onset dates calculated for many field sites. This pre-crop vegetation signal was most pronounced for the later planted summer crops (soybeans and sorghum) and in areas of Kansas that receive higher annual precipitation. The most reliable results were obtained for corn in semi-arid western Kansas, where pre-crop vegetation had considerably less influence on the greenup onset date calculations. The greenup onset date calculated for corn in western Kansas was found to occur 23 days after 50 percent of the crop had emerged. Corn’s greenup onset was detected, on average, at the agronomic stage where plants are 15 to 45 cm (6 to 18 inches) tall and the crop begins its rapid growth.

Using conservation reserve program maps derived from satellite imagery to characterize landscape structure

Abstract The Conservation Reserve Program (CRP) instituted one of the largest and most rapid land use/land cover conversions in US history. Approximately 14.8 million ha (36.5 million acres) of cropland were converted to grassland, woodland, and other conservation uses between 1986 and 1995. As policy makers continue to evaluate the future of the program and as scientists examine its effects, it is critical that the impact of CRP on landscape structure be considered because of its potential influence on wildlife populations. Utilizing multi-seasonal Landsat thematic mapper imagery in an unsupervised classification technique, we produced highly accurate maps of cropland and grassland for 1987 and 1992 for Finney County, Kansas. Post-classification differencing identified regions of cropland that had been converted to CRP. We then used the Finney County CRP map to examine changes in landscape structure caused by the introduction of CRP. Using the fragstats spatial pattern analysis program, we calculated the number of patches, mean patch size, patch density, edge density, mean shape index, nearest neighbor distance, and an interspersion/juxtaposition index. In addition, we calculated total grassland area and percent of area in grassland for the pre- and post-CRP enrollment years. We found that the total grassland area and the percent area in grassland in Finney County increased due to CRP and that mean grassland patch size also increased. The total number of grassland patches decreased, however, due to coalescence of smaller grassland patches. Patch density, edge density, mean shape index, nearest neighbor distance, and the interspersion/juxtaposition index all showed relatively small changes. These small changes appear to reflect geographic differences in CRP effects within the county—large aggregating patches in the northeast were offset by a number of isolated patches of CRP in other areas. The implication of these findings for wildlife managers is that, for species that require large areas of grassland habitat, especially habitat that is contiguous, CRP in Finney County represents a substantial increase in potential habitat. This holds for species at all levels of management interest, ranging from economically valuable species to species that are rare, threatened, and endangered. These findings emphasize the importance of CRP for wildlife conservation and should further inform ongoing debate concerning the importance of the CRP.

A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas

Multi-temporal vegetation index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are becoming widely used for large-area crop classification. Most crop-mapping studies have applied enhanced vegetation index (EVI) data from MODIS instead of the more traditional normalized difference vegetation index (NDVI) data because of atmospheric and background corrections incorporated into EVIs calculation and the indexs sensitivity over high biomass areas. However, the actual differences in the classification results using EVI versus NDVI have not been thoroughly explored. This study evaluated time-series MODIS 250-m EVI and NDVI for crop-related land use/land cover (LULC) classification in the US Central Great Plains. EVI- and NDVI-derived maps classifying general crop types, summer crop types and irrigated/non-irrigated crops were produced for southwest Kansas. Qualitative and quantitative assessments were conducted to determine the thematic accuracy of the maps and summarize their classification differences. For the three crop maps, MODIS EVI and NDVI data produced equivalent classification results. High thematic accuracies were achieved with both indices (generally ranging from 85% to 90%) and classified cropping patterns were consistent with those reported for the study area (> 0.95 correlation between the classified and USDA-reported crop areas). Differences in thematic accuracy (< 3% difference), spatially depicted patterns (> 90% pixel-level thematic agreement) and classified crop areas between the series of EVI- and NDVI-derived maps were negligible. Most thematic disagreements were restricted to single pixels or small clumps of pixels in transitional areas between cover types. Analysis of MODIS composite period usage in the classification models also revealed that both VIs performed equally well when periods from a specific growing season phase (green, peak or senescence) were heavily utilized to generate a specific crop map.

Mapping Land Cover in a High Plains Agro-ecosystem Using a Multidate Landsat Thematic Mapper Modeling Approach

The objective of this study was to develop a repeatable procedure for modeling land use and land cover (LULC) within one of the most agriculturally developed and economically significant areas of the High Plains region: Finney County in southwestern Kansas. The technique involved the use of Landsat Thematic Mapper (TM) images for three seasons for each of three years (1987, 1989, and 1992). Through a series of image preprocessing and automated classification procedures we were able to discriminate between grassland and croplands more than 95% of the time (previous to this study, less than 70% classification accuracy was usual). As we refined the approach further, we were able to identify crop types: wheat, grain sorghum (milo), corn, and alfalfa, and fallowed lands with greater than 80% accuracy for all five classes, with most crop types mapped at more than 90% accuracy. We also developed a technique that correctly mapped U.S. Department of

MODIS land surface temperature composite data and their relationships with climatic water budget factors in the central Great Plains

Daily land surface temperatures (Ts ) derived from moderate resolution imaging spectroradiometer (MODIS) data were correlated with concurrent climatic water budget variables. Using a climatic water budget program, four daily water budget factors—percentage soil moisture (SM), actual/potential evapotranspiration ratio (AE/PE), moisture deficit (MD), and moisture deficit/potential evapotranspiration ratio (MD/PE)—were calculated at six weather stations across western and central Kansas. Correlation analysis showed that Ts deviations from air temperature had a significant relationship with the water budget factors. To do the analysis on a weekly basis, daily MODIS data were integrated into three different types of weekly composites, including maximum Ts , driest‐day, and maximum Ts deviation (from maximum air temperature, or maxTa ). Results showed that the maximum Ts deviation (Ts –maxTa ) temperature composite had the highest correlation with the climatic water budget parameters. Time‐integrated, or cumulative values and the moving average of the Ts deviation were meaningful measures of the relationship, but effective moving average periods varied spatially. Correction for different data acquisition times of MODIS thermal imagery improved the representativeness of signals for surface moisture conditions. The driest‐day composite was most sensitive to time correction. After time correction, its relationship with soil moisture content improved by 11.1% on average, but the degree of correlation improvement varied spatially. Despite this improvement, the driest‐day composite dataset did not have as strong a correlation with water budget factors as that of the maximum Ts deviation composite method.

Mapping conservation reserve program (CRP) grasslands using multi‐seasonal thematic mapper imagery

Abstract This is a critical time for evaluating the status and success of the U.S. Conservation Reserve Program (CRP), a program that has resulted in the conversion of millions of hectares of cropland to grassland, woodland, and other conservation uses. In order to evaluate the effects of CRP on soil erosion, wildlife habitat, water pollution, and groundwater recharge, however, it is essential to have detailed digital maps that accurately identify CRP lands. Remote sensing techniques offer a means for developing such a database in an economical and accurate way. Utilizing multi‐seasonal imagery in an unsupervised classification technique, highly accurate maps of cropland and grassland were produced for 1987 and 1992 for Finney County, Kansas. Post‐classification differencing identified regions of cropland that had changed to grassland between the two years, indicating land that had been converted to CRP. Comparison of the CRP map with ground truth sources produced an accuracy of approximately 88%. Digital...

A State-Level Comparative Analysis of the GAP and NLCD Land-Cover Data Sets

Two nationwide land-cover mapping efforts, the GAP Analysis Program (GAP) and the USGS National Land Cover Data (NLCD) program, address the need for intermediate-scale land-cover information to support a diverse user community. The data sets are comparable, but have different objectives, classification systems, and methodologies. A comparative analysis of the GAP and NLCD data sets is needed throughout the United States to determine their relative strengths and limitations and better inform the user community of their applicability for various applications. This study conducted comparative analyses of the GAP and NLCD data sets for Kansas. The data sets were generalized to a common set of land-cover classes, and pixellevel comparisons were made at the state and ecoregion levels. The GAP and NLCD had an overall classification agreement of 67 percent at the state level, with most of the classification disagreement occurring between cropland and grassland. The cropland area classified by GAP was comparable to cropland area estimates reported by the USDA, while NLCD appeared to underestimate cropland and overestimate grassland. The single-date/multiple-data source classification approach and the sub-optimal early-spring dates of Landsat TM data used to produce NLCD resulted in substantial confusion in croplandgrassland discrimination. The multiple-date classification approach used by Kansas GAP provided better discrimination of most land-cover classes. Some classification disagreement, however, was attributable to methodological differences between GAP and NLCD. Accuracy assessment found an overall accuracy of 87 percent for GAP and 81 percent for NLCD, and GAP had higher accuracies for most individual land-cover classes. The Kansas GAP and NLCD land-cover products were found to be comparable in terms of characterizing broad scale land-cover patterns, but the Kansas GAP land-cover map appears to be more appropriate for localized applications that require detailed and accurate land-cover information.

VEGETATION-INDEX MODELS PREDICT AREAS VULNERABLE TO PURPLE LOOSESTRIFE (LYTHRUM SALICARIA) INVASION IN KANSAS

Abstract Purple loosestrife (Lythrum salicaria) constitutes an invasive species detrimental to wetland habitats in North America. To estimate areas vulnerable to it in Kansas, we modeled the potential geographic distribution of the species by using current records in the state, remotely sensed vegetation-index data from the Moderate Resolution Imaging Spectrometer (MODIS), and the Genetic Algorithm for Rule-Set Prediction (GARP). Models built using only localities from northeastern Kansas (the origin of invasion within the state) consistently predicted test localities in other parts of the state with negligible omission. An additional analysis using records from all counties where the species is known showed a similar prediction. All models indicated suitable conditions for purple loosestrife in much of eastern and central Kansas, as well as in riverine and irrigated areas in the western part of the state. The approach presented here might prove useful for assessing the regional colonization potential of other newly detected invasive species before other studies can be undertaken.

Knowledge Acquisition from Choropleth Maps

Todays computer graphics technology enables map users to acquire spatial knowledge in ways not possible with traditional static displays; for example, classes of data on a choropleth map can be sequenced from low to high values. Although sequencing and related approaches are often judged to be novel and exciting, it is unknown whether such approaches enhance or diminish knowledge acquisition. In a broader vein, we might ask what is the optimal technique for acquiring knowledge from a choropleth map? The optimal technique might involve a novel display approach, or modifying a method for learning a traditional static display. In this vein, two experiments were conducted. In the first, learning procedures common to experienced choropleth map users were ascertained. Using these procedures and others developed in a prior study, and their knowledge as cartographers, the authors developed a set of presumably effective procedures. In the second experiment, three choropleth display approaches were compared for th...