TY - JOUR
T1 - Predictive mapping of soil-landscape relationships in the arid Southwest United States
AU - Regmi, Netra R.
AU - Rasmussen, Craig
N1 - Funding Information:
This research was funded by a Cooperative Ecosystem Studies Unit agreement ( W9126G-14-2-0032 ) between the MCAS Yuma and the University of Arizona and supported by the Arizona Agricultural Experiment Station. We appreciate the support provided by Abigail Rosenberg, Natural Resource Specialist with MCAS Yuma's Conservation Program, for project administration and coordination of field surveys. The views and conclusions contained in this paper are those of the authors and should not be interpreted as representing official policies, either expressed or implied, of the U.S. government.
Publisher Copyright:
© 2018
PY - 2018/6
Y1 - 2018/6
N2 - 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.
AB - 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.
KW - Digital soil mapping
KW - Quaternary alluvial and eolian deposit
KW - Soil-landscape evolution
KW - Soil-landscape relationship
UR - http://www.scopus.com/inward/record.url?scp=85042870774&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85042870774&partnerID=8YFLogxK
U2 - 10.1016/j.catena.2018.02.031
DO - 10.1016/j.catena.2018.02.031
M3 - Article
AN - SCOPUS:85042870774
SN - 0341-8162
VL - 165
SP - 473
EP - 486
JO - Catena
JF - Catena
ER -