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The Spatial Manifestation of Forest Succession in Optical Imagery – The Potential of Multiresolution Imagery


Song, Conghe H. & Woodcock, Curtis E. (2002). The Spatial Manifestation of Forest Succession in Optical Imagery - The Potential of Multiresolution Imagery. Remote Sensing of Environment, 82(2-3), 271-284.


Forest succession is a fundamental ecological phenomenon, which has significant implications for sustainable ecosystem management as well as biological, biophysical, and biogeochemical processes. Remote sensing is perhaps the only viable option for monitoring changes in forest ecosystems over large areas in a timely and cost efficient manner. This study investigates the spatial manifestation of forest succession in optical imagery through three types of models: a two-component spatial model, a canopy reflectance model (Geometric-Optical and Radiative Transfer, GORT) and a forest ecosystem dynamics model (ZELIG). The latter two models provide inputs to the former one to predict the spatial properties of images as a function of the combined effects of tree size and density, the spectral signatures of scene components and pixel size. An important source of information that is diagnostic of canopy structure has been identified: the spatial properties of multiresolution imagery. The sill of variograms of images of forest stands decrease with regularization, and in particular the rate of decrease is related to the size of trees. For stands with larger trees the sills of variograms decrease more slowly with increasing regularization than for stands with smaller trees. However, the spatial patterns for a scene with multiresolution imagery are also dependent on tree cover. This implies that the use of spatial patterns to estimate tree size will require independent estimates of tree cover as a preliminary step. Concept verification with an Ikonos 1-m panchromatic image for stands at the H.J. Andrews Experimental Forest in the Cascade Range of Oregon indicates the simulated spatial patterns exist in multiresolution imagery. This study demonstrates the potential to map tree size automatically from multiresolution imagery. (C) 2002 Elsevier Science Inc. All rights reserved.


Reference Type

Journal Article

Year Published


Journal Title

Remote Sensing of Environment


Song, Conghe H.
Woodcock, Curtis E.