Gerald L. Anderson

Evaluating landsat thematic mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands

Ground data from the Central Plains Experimental Range in northeast Colorado and Landsat satellite images of that area acquired in August 1989, June 1990, and September 1990 were used to evaluate the level of association that can be expected from a univariate model relating spectrally derived vegetation indices (difference, ratio, and normalized difference vegetation indices) and dried green vegetation biomass. The vegetation indices were related to the ground sample estimates using a sample point, spectral class, and greenness strata approach. No strong relationships were found between the vegetation indices and sample estimates of dried green biomass using the sample point approach. The spectral class approach produced significant results only for the June 1990 sample period (r=0.96). Significant relationships were found for the August 1989, June 1990, and September 1990 samples periods (r2=0.95, 0.71, and 0.95, respectively) when the data were aggregated by greenness strata. The high degree of association between green biomass and the NDVI, obtained when the data were combined into greenness strata, indicated that it is possible to predict green biomass levels on semiarid rangelands using univariate regression models.

Applications and Research Using Remote Sensing for Rangeland Management

Rangelands are grasslands, shrublands, and savannas used by wildlife for habitat and livestock in order to produce food and fiber. Assessment and monitoring of rangelands are currently based on comparing the plant species present in relation to an expected successional end-state defined by the ecological site. In the future, assessment and monitoring may be based on indicators of ecosystem health, including sustainability of soil, sustainability of plant production, and presence of invasive weed species. USDA Agricultural Research Service (ARS) scientists are actively engaged in developing quantitative, repeatable, and low-cost methods to measure indicators of ecosystem health using remote sensing. Noxious weed infestations can be determined by careful selection of the spatial resolution, spectral bands, and timing of image acquisition. Rangeland productivity can be estimated with either Landsat or Advanced Very High Resolution Radiometer data using models of gross primary production based on radiation use efficiency. Lidar measurements are useful for canopy structure and soil roughness, indicating susceptibility to erosion. The value of remote sensing for rangeland management depends in part on combining the imagery with other spatial data within geographic information systems. Finally, ARS scientists are developing the knowledge on which future range-land assessment and monitoring tools will be developed.

Using classification and NDVI differencing methods for monitoring sparse vegetation coverage: a case study of saltcedar in Nevada, USA

A change detection experiment for an invasive species, saltcedar, near Lovelock, Nevada, was conducted with multidate Compact Airborne Spectrographic Imager (CASI) hyperspectral datasets. Classification and NDVI differencing change detection methods were tested. In the classification strategy, a principal component analysis (PCA) was performed on single‐date CASI imagery separately in the visible bands and NIR bands. Then the first five PCs from the visible bands and the first five PCs from the NIR bands were used to classify six to eight cover types with a maximum likelihood classifier. A complete matrix of change information and change/no‐change maps were produced by overlaying two single‐date classification maps. In the NDVI differencing strategy, a linear regression model was developed between two Normalized Difference Vegetation Index (NDVI) images to normalize the index differences caused by factors not related to land cover change. Then the actual time 2 NDVI image was subtracted by the predicted time 2 NDVI image to obtain the differencing image. The NDVI differencing image was further processed with a new threshold method into change/no‐change of saltcedar. By testing the single‐date classification results and validating the change/no‐change results, both change detection results indicated that CASI hyperspectral data have the potential to map and monitor the change of saltcedar. However, the accuracy assessment and change/no‐change validation results (overall accuracy 91.56% and kappa value 0.618 for the classification method against corresponding values of 93.04% and 0.684 for the NDVI differencing method) indicate that the NDVI differencing method outperformed the classification method in this particular study. In addition, use of the new method of determining thresholds for differentiating change pixels from no‐change pixels from the NDVI differencing image improved the change detection accuracy compared to a traditional method (kappa value increased from 0.813 to 0.888 from a test sample). Therefore, according to the criteria of higher accuracy of change/no‐change maps and fewer spectral bands, the NDVI differencing method is recommended for use if a suitable spectral normalization between multi‐temporal images can be carried out before performing image differencing.

Mapping leafy spurge (Euphorbia esula) infestations using aerial photography and geographic information systems

Abstract Leafy spurge is a troublesome weed on the northern Great Plains of the United States that chemicals and grazing management have not controlled. Remote sensing and geographic information system (GIS) technology have been used to detect and monitor numerous grassland related problems. The objectives of this study were to use both technologies to map and quantify the extent of leafy spurge within Theodore Roosevelt National Park and to provide information for managing the infestation. Analysis of the data indicated that 702 ha of the 18,680 ha park were infested by leafy spurge; however, leafy spurge populations occurring under dense woody canopies, in deep stream channels, and on steep slopes were not always detected. Infestations were especially dense in the western and southeast portions of the park. Most infestations were restricted to riparian zones and smaller drainage channels. Leafy spurge infestations decreased exponentially as distance from stream channels increased (r2=0.98). The signific...

Airborne Videography to Identify Spatial Plant Growth Variability for Grain Sorghum

Much research has focused on the use of intensive grid soil sampling and yield monitors to identify within-field spatial variability in precision farming. This paper reports on the use of airborne videography to identify spatial plant growth patterns for grain sorghum. Color-infrared (CIR) digital video images were acquired from two grain sorghum fields in south Texas several times during the 1995 and 1996 growing seasons. The video images were registered, and classified into several zones of homogeneous spectral response using an unsupervised classification procedure. Ground truthing was performed upon a limited number of sites within each zone to determine plant density, plant height, leaf area index, biomass, and grain yield. Results from both years showed that the digital video imagery identified within-field plant growth variability and that classification maps effectively differentiated grain production levels and growth conditions within the two fields. A temporal comparison of the images and classification maps indicated that plant growth patterns differed somewhat between the two successive growing seasons, though areas exhibiting consistently high or low yield were identified within each field.

Using CASI Hyperspectral Imagery to Detect Mortality and Vegetation Stress Associated with a New Hardwood Forest Disease

A Compact Airborne Spectrographic Imager-2 (CASI) dataset was used for detecting mortality and vegetation stress associated with a new forest disease. We first developed a multilevel classification scheme to improve classification accuracy. Then, the CASI raw data were transformed to reflectance and corrected for topography, and a principal component (PC) transformation of all 48 bands and the visible bands and NIR bands were separately conducted to extract features from the CASI data. Finally, we classified the calibrated and corrected CASI imagery using a maximum likelihood classifier and tested the relative accuracies of classification across the scheme. The multilevel scheme consists of four levels (Levels 0 to 3). Level 0 covered the entire study area, classifying eight classes (oak trees, California bay trees, shrub areas, grasses, dead trees, dry areas, wet areas, and water). At Level 1, the vegetated and non-vegetated areas were separated. The vegetated and nonvegetated areas were further subdivided into four vegetated (oak trees, California bay trees, shrub areas, grasses) and four non-vegetated (dead trees, dry areas, wet areas, and water) classes at Level 2. Level 3 identified stressed and non-stressed oak trees (two classes). The ten classes classified at different levels are defined as final classes in this study. The experimental results indicated that classification accuracy generally increased as the detailed classification level increased. When the CASI topographically corrected reflectance data were processed into ten PCs (five PCs from the visible region and five PCs from NIR bands), the classification accuracy for Level 2 vegetated classes (non-vegetated classes) increased to 80.15 percent (94.10 percent) from 78.07 percent (92.66 percent) at Level 0. The accuracy of separating stressed from non-stressed oak trees at Level 3 was 75.55 percent. When classified as a part of Level 0, the stressed and non-stressed were almost inseparable. Furthermore, we found that PCs derived from visible and NIR bands separately yielded more accurate results than the PCs from all 48 CASI bands.

Mapping Grain Sorghum Yield Variability Using Airborne Digital Videography

Mapping crop yield variability is one important aspect of precision agriculture. Combine-mounted yield monitors are becoming widely available for measuring and mapping yields for different crops. This study was designed to assess airborne digital videography as a tool for mapping grain sorghum yields for precision farming. Color-infrared (CIR) imagery was acquired with a three-camera digital video imaging system from two grain sorghum fields in south Texas over the 1995 and 1996 growing seasons. The multispectral video data obtained during the bloom to soft dough stages of plant development were related to hand-harvested grain yields at sampling sites determined from unsupervised image classification maps of the two fields. Significant correlations were found between grain yields and the red band, the green band, and the normalized difference vegetation index (NDVI). Regression equations were developed to describe the relations between grain yields and each of the three significant spectral variables using an exponential model and two segmented models. Multiple linear regression equations were also determined to relate grain yields to the three bands and NDVI. These equations were then used to estimate grain yields at each video image pixel within each field and to generate grain yield maps. Comparisons of the estimated average yields from the regression equations with the actual yields indicated that yield estimation errors from the equations ranged from 0.0 to 10.0% in 1995 and from 0.2 to 7.3% in 1996 for field 1, and from 4.0 to 11.2% in 1995 and 6.3 to 12.5% in 1996 for field 2. Although the equations developed for one field in a given year may not apply to the same field in any other year, the practical value of these relationships is for mapping within-field grain yield variations. The results from this study showed that airborne digital videography, combined with ground sampling, regression analysis, and image processing, could be a useful approach for mapping spatial crop yield variability within fields.

Evaluating hand‐held radiometer derived vegetation indices for estimating above ground biomass

Abstract The potential for using vegetation indices developed from remote sensing devices, such as the GSFC Mark‐II hand‐held radiometer, to evaluate biomass has been examined in a number of studies. Relationships range from very good (r2=0.96) to very poor (r2=0.029). One problem with most of the studies is that the data were only collected during a single year, therefore, the usefulness of the relationships within and between years is difficult to evaluate. This study examined the usefulness of vegetation indices, calculated from data gathered by the GSFC Mark‐II radiometer, in assessing the amount of biomass on native grasslands. Vegetation indices derived from data gathered over two years (1989 and 1990) and during four periods throughout each growing season indicate that the ratio between near‐infrared and red spectral responses (ratio vegetation index) performed better then either the difference between the two bands or the normalized difference vegetation indices. Poor relationships were observed (...

The Use of Landsat 7 Enhanced Thematic Mapper Plus for Mapping Leafy Spurge

Abstract Euphorbia esula L. (leafy spurge) is an invasive weed that is a major problem in much of the Upper Great Plains region, including parts of Montana, South Dakota, North Dakota, Nebraska, and Wyoming. Infestations in North Dakota alone have had a serious economic impact, estimated at

Leafy Spurge Suppression by Flea Beetles in The Little Missouri Drainage Basin, USA

87 million annually in 1991, to the states wildlife, tourism, and agricultural economy. Leafy spurge degrades prairie and badland ecosystems by displacing native grasses and forbs. It is a major threat to protected ecosystems in many national parks, national wild lands, and state recreational areas in the region. This study explores the use of Landsat 7 Enhanced Thematic Mapper Plus (Landsat) imagery and derived products as a management tool for mapping leafy spurge in Theodore Roosevelt National Park, in southwestern North Dakota. An unsupervised clustering approach was used to map leafy spurge classes and resulted in overall classification accuracies of approximately 63%. The uses of Landsat imagery did not provide the accuracy required for detailed mapping of small patches of the weed. However, it demonstrated the potential for mapping broad-scale (regional) leafy spurge occurrence. This paper offers recommendations on the suitability of Landsat imagery as a tool for use by resource managers to map and monitor leafy spurge populations over large areas.