Analysis of forest fires in Northeast China from 2003 to 2011

Abstract

Wildfires, a common disturbance in ecosystems, can be an immediate and dominant source of interannual carbon variability. In this study, we used an instantaneous Moderate Resolution Imaging Spectroradiometer (MODIS) global disturbance index algorithm to explore continuous spatiotemporal patterns of forest fires in Northeast China. The forest fires that were sensed remotely were then validated by field records. The findings suggest that the disturbance index is effective in locating forest fires in Northeast China, as evidenced by a close match with field fire records. We found that the incidence of forest fires was closely linked to extreme conditions of climate warming and drought, and more fires occurred in dry years than in wet years. Among different forest types, shrublands, mixed forest, and deciduous needleleaf forests were more prone to wildfires because of their fire regime characteristics. The study demonstrates that the algorithm was effective in detecting forest fires from 2003 to 2011 in Northeast China, providing fundamental data for forest inventory and large-scale ecological applications.