Mapping Sagebrush Distribution Using Fusion of Hyperspectral and Lidar Classifications

Abstract

The applicability of high spatial resolution hyperspectral data and small-footprint Light Detection and Ranging (lidar) data to map and describe sagebrush in a semi-arid shrub steppe rangeland is demonstrated. Hyperspectral processing utilized a spectral subset (605 nm to 984 nm) of the reflectance data to classify sagebrush presence to an overall accuracy of 74 percent. With the inclusion of co-registered lidar data, this accuracy increased to 89 percent. Furthermore, lidar data were utilized to generate stand specific descriptive information in areas of sagebrush presence and sagebrush absence. The methods and results of this study lay the framework for utilizing co-registered hyperspectral and lidar data to describe semi-arid shrubs in greater detail than would be feasible using either dataset independently or by most ground based surveys.