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G.L. Anderson

Publications and source records attributed to G.L. Anderson.

3 recordsLinked to original sources

The use of landsat 7 enhanced thematic mapper plus for mapping leafy spurge

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 $87 million annually in 1991, to the state's 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.

Rangeland Ecology and Management

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 ( r 2 =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.

Remote Sensing of Environment

Combining remote sensing techniques with simulation modelling for assessing rangeland resources

Simulation modelling and remote sensing techniques can be used to describe ecological processes. When used together, these tools can increase our understanding of the spatial variability of ecosystems. Remotely sensed data can be combined with simulation models for predicting the accumulation of plant biomass, as surrogate variables within the models, or as correlative information for monitoring entire eco‐regions.

Geocarto International