The spectral/temporal manifestation of forest succession in optical imagery: The potential of multitemporal imagery

Abstract

Forest succession is a fundamental ecological process, which has significant implications for sustainable natural resource management as well as ecosystem biological, biophysical, and biogeochemical processes. Remote sensing is perhaps the only viable option to monitor succession in forest ecosystems over large areas in a timely and cost efficient manner. This study integrates the geometric-optical and radiative transfer (GORT) canopy reflectance model with the ZELIG forest ecosystem dynamics model to study the manifestation of forest succession in optical imagery. The spectral/temporal trajectories associated with forest succession are highly nonlinear. The rate of change in spectral space through time is faster during the early years of succession. The succession trajectories do not proceed linearly in the spectral space, but rather curvilinearly, and are strongly influenced by initial background/understory conditions and topography. The nonlinear nature of the trajectories implies that adequate characterization of forest development with remote sensing requires multiple images through time. Validation using multiple Landsat TM images for the H.J. Andrews Experimental Forest (HJA) in the Cascade Range of Oregon found the expected successional trajectories in successfully regenerated young stands. The simulated effects of topography on successional trajectories exist in the Landsat data. The primary challenge in using multitemporal imagery to monitor forest succession is to minimize the effects of various kinds of noise, including varying atmospheric effects, phenology, changes in sun and viewing angles, and topography. The current linkage of GORT–ZELIG only captures the spectral/temporal changes for young stands. Successional changes associated with mature and old-growth stands due to changes in the materials comprising the canopy are not captured. Further research is necessary to include changes associated with mature and old-growth forests, and to develop operational algorithms to utilize information from multitemporal imagery to monitor forest succession.