Journal
GEODERMA REGIONAL
Volume 21, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.geodrs.2020.e00268
Keywords
Digital elevation model; Morphometry; Multivariate analysis; Machine learning
Categories
Funding
- Geoprocessing Laboratory in conjunction with the Cluster Environment of the Federal University of Vicosa, Brazil
- National Council for Scientific and Technological Development - CNPq
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Terrain attributes are used as auxiliary variables in spatial prediction of soil classes and properties, due to their important role in the pedogenetic process and the increasing availability of digital elevation models (DEMs) in different resolutions. This work analyzed the effect of the different spatial resolutions of the DEMs and attributes derived from terrain and their implications for application in DSM predictive models. We used three spatial resolutions from different DEMs: (1) LIDAR - 2 m; (2) ALOS PALSAR - 12.5 and 30 m; (3) SRTM - 30 m; and (4) ASTER GDEM - 30 m. Multivariate analyses were performed determined by the Pearson linear correlation coefficient (r), the K-means cluster analysis, and the principal component analysis (PCA). The prediction of soil classes was performed using the terrain attributes grouped as to sensitivity to resolution, for different spatial resolutions, applying the machine learning algorithms Random Forest and Support Vector Machine. The cluster analysis indicated that most attributes remained within the same group of resolution sensitivity with changes in the cell size of reference DEM. The attributes of the terrain grouped low sensitive to resolution derived from the SRTMDEM showed better precision and the main advantage was the low cost and facilitating computational processing for application in the DSM. (C) 2020 Elsevier B.V. All rights reserved.
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