Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation

摘要

Fire, insects, and human activities are the dominant drivers of forest disturbance at the global scale. Because forests are geographically extensive and are often remote, the Moderate Resolution Imaging Spectroradiometer (MODIS) is uniquely suited to monitor the state and health of forested ecosystems. However, the extent to which coarse-resolution remote sensing data can accurately capture spatial and temporal patterns of disturbance is unclear. To investigate this, we developed an 11-year time series of MODIS Normalized Burn Ratio images corresponding to peak-growing season conditions for a study area located in the Pacific Northwest of the conterminous United States. Using a temporal segmentation algorithm that was originally developed using Landsat TM and ETM data, we created annual maps of forest disturbance from these time series. We then compared these maps to a database of annual forest disturbance that was compiled using Landsat TM/ETM data for the same region. Results from this comparison revealed that about half of all pixels affected by disturbances that occupied more than 5% of a MODIS pixel were correctly identified as disturbed, including 79% of those that were affected by disturbances larger than one-third of aMODIS pixel. Our results also show that the size, severity, and timing of disturbance events, alongwith gridding artifacts inherent toMODIS data, interact in complexways that influence the signature of forest disturbance events inMODIS data. These results demonstrate both the utility as well as the limitations of MODIS and other coarse spatial resolution sensors for monitoring forest disturbance at regional to global scales.