Landform-Vegetation Relationships in the Northern Chihuahuan Desert,

Abstract

The description and monitoring of environmental resources in arid regions can be a formidable undertaking requiring substantial resources. These efforts can be expedited by using remote sensing techniques. There remains, however, a need for correlating features that are readily extracted from the imagery with anticipated soil and vegetation conditions on the ground. Landform features provide a basis for the assessment of soil and vegetation conditions and these features can be readily identified from aerial photography and, to a certain extent, from Landsat imagery. The purpose of this study was to evaluate landform features as a basis for the assessment of soil and vegetation conditions. The study was conducted on 650,000 hectares in the northern Chihuahuan Desert (south-central New Mexico and western Texas). Landform conditions and plant communities were identified from an analysis of stereo panchromatic aerial photography, and were evaluated in detail by intensive field investigations. Soil conditions of the various landforms identified from the imagery were established by the laboratory analysis of field samples. The distribution of the plant communities was closely correlated to landform conditions and the edaphic factors affecting plant-available soil-water, soil texture, soil depth, infiltration, and slope.

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Document Details

Document Type
Technical Report
Publication Date
Mar 29, 1981
Accession Number
ADA102896

Entities

People

  • Judy Ehlen
  • Melvin B. Satterwhite

Organizations

  • Geospatial Research Laboratory

Tags

Communities of Interest

  • C4I
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Aerial Photography
  • Data Science
  • Depression
  • Forests
  • Frequency
  • Indicators
  • Information Science
  • Landforms
  • Moisture
  • Monitoring
  • Mountains
  • New Mexico
  • Photography
  • Plants
  • Remote Sensing
  • Statistics
  • Vegetation

Readers

  • Agricultural Chemistry/Soil Science
  • Archaeological Resource Survey
  • Computer Vision.