High Spatial Resolution Remotely Sensed Data for Ecosystem Characterization

Abstract

R emotely sensed data have been employed for the characterization of ecologically important variables from local through global contexts. These data may be used to generate a wide range of estimates that are valuable to ecologists, including information on land cover, vegetation cover, habitat, forest structure, and forest function Recent technological developments in remote sensing have resulted in new capabilities for data capture and data processing, making it possible to generate and analyze digital images at high spatial resolution (fine grain, defined here as a pixel size of 16 square meters [m 2 ] or less). A wide variety of options exists for using data processing and data analysis to estimate a range of ecologically important attributes. For instance, in the characterization of vegetation, applications have been developed for the estimation of stand structural attributes and leaf area. These applications have been developed using airborne sensors, and the lessons learned are currently being applied to spaceborne data at high spatial resolution. For example, it is now possible to identify and map individual trees and groups of trees over large areas, or as part of a strategy for forest sampling.