High Spatial Resolution Remotely Sensed Data for Ecosystem Characterization

  • Wulder M
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Articles 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 (Kerr and Ostrovsky 2003), and to track changes in these variables. Recent technological developments in remote sensing have resulted in new capabilities for data capture and data pro-cessing, 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 devel-oped using airborne sensors, and the lessons learned are cur-rently 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. The information that may be generated from remotely sensed data is directly linked to the sensor and to the related characteristics of the images it produces: spatial resolution (pixel size; table 1), spectral resolution (wavelength ranges uti-lized), temporal resolution (when and how often images are collected), and spatial extent (ground area represented) (Turner W et al. 2003). Thus, the most appropriate remotely sensed data may be selected for a given application (Lefsky and Cohen 2003). The sensor and image characteristics of the MODIS (Moderate Resolution Imaging Spectroradiometer) satellite, for instance, allow for regular monitoring of global vegetation productivity (Running et al. 2004) but not for spatially detailed characterization of vegetation. Rather, in-formation at the national scale is commonly generated using remotely sensed data at coarse spatial resolution for attributes such as fractional cover, land cover and change, fire, leaf area, and productivity (see Cihlar and colleagues for overviews of methods [2003a] and applications [2003b]). In many cases, information generated to represent ecological phenomena at the global scale is too general to meet regional or local ob-jectives. Sensors such as the Landsat series (see Cohen and

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  • Michael A Wulder

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