Integrating digital terrain and satellite image data with soils data for small-scale mapping of soils
Abstract
As a significant and dynamic component of the earth system, soil has come to the forefront of the environmental agenda during the past decade as it has never done previously. Spectral patterns and geomorphometry are shown to capture those attributes of the soil surface necessary for classification of landscape units. Digital data and methods may offer significant improvements in terms of survey reliability, accuracy, and repeatability. The major objective of this study was to assess the utility of satellite multispectral images and digital topographic data as supplemental tools for the generation of small scale soil maps and recommendations for the possible improvement of existing small scale maps. Three generalized representations or maps (Soil Associations (STATSGO), Major Land Resource Areas, Soil Region Maps) of the soils of Illinois and Indiana were used in this study as the standard or "ground truth" with which to compare quantitatively maps or images derived from the integration and classification of satellite images and topographic data. Advanced Very High Resolution Radiometer (AVHRR) data and topographic information (elevation, slope, aspect, relief) produced from a digital elevation model (DEM) were combined to determine geographic locations of the soil regions that occur in Indiana and Illinois. Satellite data from several dates were examined. Both single date data and combinations of multiple date data were studied. In general, the classification results with the 10-day composite, multi-date AVHRR data integrated with topographic component data gave better agreement with all soil maps than did single date AVHRR data. Maximum likelihood classification algorithms was used with and without weightings. In all cases the classification results which included weightings gave better accuracies than did the classification of unweighted data. Several different components of digital topographic data were integrated with the different combinations of satellite data to create a broad array of data sets for supervised classification.
Degree
Ph.D.
Advisors
Baumgardner, Purdue University.
Subject Area
Agronomy|Environmental science|Geography|Remote sensing
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