干旱区生态环境知识资源中心

Multi-scale geospatial and absolute variation of surface and near-surface soil physical and chemical properties can be mapped and quantified by coupling digital soil mapping techniques with high resolution remote sensing products. The goal of this research was to advance data-driven digital soil mapping techniques by developing an approach that can integrate multi-scale digital surface topography and reflectance-derived remote sensing products, and characterize multi-scale soil-landscape relations of Quaternary alluvial and eolian deposits. The study area spanned the arid landscape encompassed by the Barry M. Goldwater Range West (BMGRW), which is administered by the Marine Corps Air Station Yuma, in southwestern Arizona, USA. An iterative principal component analysis (iPCA) was implemented for LiDAR elevation- and Landsat ETM + -derived soil predictors, termed environmental covariates. Principal components that characterize > 95% of covariate space variability were then integrated and classified using an ISODATA (Iterative Self-Organizing Data) unsupervised technique. The classified map was further segmented into polygons based on a region growing algorithm, yielding multi scale maps of soil-landscape relations that were compared with maps of soil landforms identified from aerial photographs, satellite images and field observation. The approach identified and mapped the spatial variability of soil-landscape relationships in alluvial and eolian deposits and illustrated the applicability of coupling covariate selection and integration by iPCA, ISODATA classification of integrated data layers, and image segmentation for effective spatial prediction of soil landscape characteristics. The approach developed here is data driven, applicable for multi-scale mapping, allows incorporation of a wide variety of covariates, and maps spatially homogenous soil-landscape units that are necessary for hydrologic models, land and ecosystem management decisions, and hazard assessment.