Digital Soil Mapping in the Absence of Field Training Data: A Case Study Using Terrain Attributes and Semiautomated Soil Signature Derivation to Distinguish Ecological Potential
Figure 3
Supervised classification representing soil classes for the 150 ha study area on the Chihuahuan Desert Rangeland Research Center in southern New Mexico. Spectral classes were derived in an automated fashion using the Transformed Divergence feature in ERDAS Imagine. A field assessment of 24 points (blue triangles) was conducted by generating a stratified random sample to characterize soils within mapped classes (a); labels correspond to mapped soil classes. Mapped soil classes are depicted in the context of mapped landforms of the Jornada Basin (b); labels coincide with landforms described by Monger et al. [22].
