Rangeland monitoring using remote sensing: comparison of cover estimates from field measurements and image analysis

Abstract

Rangeland monitoring is important for evaluating and assessing semi-arid plant communities. Remote sensing provides an effective tool for rapidly and accurately assessing rangeland vegetation and other surface attributes such as bare soil and rock. The purpose of this study was to evaluate the efficacy of remote sensing as a surrogate for field-based sampling techniques in detecting ground cover features (i.e., trees, shrubs, herbaceous cover, litter, surface), and comparing results with field-based measurements collected by the Utah Division of Wildlife Resources Range Trent Program. In the field, five 152 m long transects were used to sample plant, litter, rock, and bare-ground cover using the Daubenmire ocular estimate method. At the same location of each field plot, a 4-band (R,G,B,NIR), 25 cm pixel resolution, remotely sensed image was taken from a fixed-wing aircraft. Each image was spectrally classified producing 4 cover classes (tree, shrub, herbaceous, surface). No significant differences were detected between canopy cover collected remotely and in the field for tree (P = 0.652), shrub (P = 0.800), and herbaceous vegetation (P = 0.258). Surface cover was higher in field plots (P < 0.001), likely in response to the methods used to sample surface features by field crews. Accurately classifying vegetation and other features from remote sensed information can improve the efficiency of collecting vegetation and surface data. This information can also be used to improve data collection frequency for rangeland monitoring and to efficiently quantify ecological succession patterns.

Keywords

• Image classification
• canopy cover
• rangeland monitoring
• remote sensing