High Spatial Resolution Remote Sensing Data for Forest Ecosystem Classification: An Examination of Spatial Scale

Abstract

Detailed forest ecosystem classifications have been developed for large regions of northern Ontario, Canada. These ecosystem classifications provide tools for ecosystem management that constitute part of a larger goal of integrated management of forest ecosystems for long-term sustainability. These classification systems provide detailed stand-level characterization of forest ecosystems at a local level. However, for ecological approaches to forest management to become widely accepted by forest managers, and for these tools to be widely used, methods must be developed to characterize and map or model ecosystem classes at landscape scales for large regions. In this study, the site-specific Northwestern Ontario Forest Ecosystem Classification (NWO FEC) was adapted to provide a landscape-scale (1:20 000) forest ecosystem classification for the Rinker Lake Study Area located in the boreal forest north of Thunder Bay, Ontario. High spatial resolution remote sensing data were collected using the Compact Airborne Spectrographic Imager (CASI) and analyzed using geostatistical techniques to obtain an understanding of the nature of the spatial dependence of spectral reflectance for selected forest ecosystems at high spatial resolutions. Based on these analyses it was determined that an optimal size of support for characterizing forest ecosystems (i.e., optimal spatial resolution), as estimated by the mean ranges of a series of experimental variograms, differs based on (i) wavelength, (ii) forest ecosystem class, and (iii) mean maximum canopy diameter (MMCD). In addition, maximum semivariance as estimated from the sills of the experimental variograms increased with density of understory.