Coops, NC, Wulder, MA, Iwanicka, D (2009). Large area monitoring with a MODIS-based Disturbance Index (DI) sensitive to annual and seasonal variations. REMOTE SENSING OF ENVIRONMENT, 113(6), 1250-1261.
Disturbance of the vegetated land surface, due to factors such as fire, insect infestation, windthrow and harvesting, is a fundamental driver of the composition forested landscapes with information on disturbance providing critical insights into species composition, vegetation condition and structure. Long-term climate variability is expected to lead to increases in both the magnitude and distribution of disturbances. As a consequence it is important to develop monitoring systems to better understand these changes in the terrestrial biosphere as well to inform managers about disturbance agents more typically captured through specific monitoring programs (such as focused on insect. fire, or agricultural conditions). Changes in the condition, composition and distribution pattern of vegetation can lead to changes in the spectral and thermal signature of the land surface. Using a 6-year time series of MODerate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) data we apply a previously proposed Disturbance Index (DI) which has been shown to be sensitive to both continuous and discontinuous change. Using Canada as an example area, we demonstrate the capacity of this Disturbance Index to monitor land dynamics over time. As expected, our results confirm a significant relationship between area flagged as disturbed by the index and area burnt as estimated from other satellite sources (R-2=0.78, p<0.0001). The DI also demonstrates a sensitivity to capture and depict changes related to insect infestations. Further, on a regional basis the DI produces change information matching measured wide-area moisture conditions (i.e., drought) and corresponding agricultural outputs. These findings indicate that for monitoring a large area, such as Canada, the time series based DI is a useful tool to aid in change detection and national monitoring activities. (C) 2009 Elsevier Inc. All rights reserved.