Investigation of digital landsat data for mapping soils under range vegetation

Abstract

Computer-pattern-recognition techniques were used to investigate relationships of Landsat multispectral data to soil patterns under range vegetation. A semiarid region in Park County, Colorado, was selected for study. Landsat imagery was analyzed and class statistics developed by three methods: a conventional supervised method, a “cleaned” supervised method, and an unsupervised (clustering) method. Landsat classification maps displaying eleven different soil units were produced by each of the three analysis methods. The Landsat classification maps produced by the unsupervised method had very low agreement with ground information. The “cleaned” supervised method produced maps that agreed with soil survey information developed by conventional means 47% of the time, while those maps produced using the conventional supervised method agreed with ground information only 33% of the time. Boundaries on Landsat computer classification maps produced by the “cleaned” supervised method compared favorably with soil boundaries on soil maps produced by conventional soil survey techniques.