Author
Ying Sun, Lianhong Gu, Robert E Dickinson, et al
Journal
ENVIRONMENTAL RESEARCH LETTERS
Class
The damaged vegetation detection
Paper Keyword
Ice storm, large extreme events, forests, remote sensing, human intervention
Abstract
About 10% of China’s forests were impacted by a destructive ice storm and subsequently subjected to poorly planned salvage logging in 2008. We used the remote-sensing products of Enhanced Vegetation Indexes (EVI) corroborated with information gathered from ground visits to examine the spatial patterns and temporal trajectories of greenness of these nearly 20 million hectares of forests. We found (1) the EVI of about 50% of the impacted forests returned to normal status (i.e., within the 95% confidence interval of the long-term mean) within five months, and about 80% within one year after the storm, (2) the higher the pre-storm EVI (relative to the long-term mean), the slower the rebound of post-storm EVI, and (3) the rebound of greenness was slowest in forests that were moderately impacted by the ice storm only (i.e. before the occurrences of logging), resulting in a nonlinear relationship between greenness rebound time (GRT) and ice storm impact severity (IS). Ground visits suggested a hypothesis that the region-wide rebound in greenness was a consequence of resprouting of physically damaged trees and growth of understory plants including shrub, herbaceous and epiphytic species. These processes were facilitated by the rapid increase in temperature and ample moisture after the ice storm. Gap-phase dynamics could be responsible for the counterintuitive relationship between IS and GRT that was obtained. However, a more parsimonious explanation appears to be biased salvage logging, which may have selectively targeted lightly to moderately impacted forests for economic and accessibility reasons and thus adversely affected the GRT of these forests. Although a purely natural disturbance may result in forest greenness patterns different than those reported here, we suggest that remote-sensing-based dynamic analyses of greenness can play a major role in evaluating disturbance theories and in developing testable hypotheses to guide ground-based studies of the integrated effects of large extreme events and human intervention on forest ecosystems.
