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

• Published in: Remote sensing of environment Vol. 151; pp. 114 - 123
• Main Authors: Sulla-Menashe, Damien, Kennedy, Robert E., Yang, Zhiqiang, Braaten, Justin, Krankina, Olga N., Friedl, Mark A.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01-08-2014
• Subjects: Change detection; Disturbances; Forest disturbance; Forests; MODIS; Northwest; Northwest Forest Plan; Pixels; Segmentation; Temporal logic; Temporal segmentation; Time series; INE, USA, Pacific Northwest; USA, Pacific Northwest; Change detection; Temporal segmentation; MODIS; Forest disturbance; Time series; Northwest Forest Plan
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2013.07.042

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 a MODIS pixel. Our results also show that the size, severity, and timing of disturbance events, along with gridding artifacts inherent to MODIS data, interact in complex ways that influence the signature of forest disturbance events in MODIS 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.
•An algorithm for detecting forest disturbance was applied to MODIS data.•Disturbance size, severity, and timing influence disturbance detection.•Disturbances larger than 0.3MODISpixels were reliably detected using MODIS data.•Sub-pixel forest regrowth can mask the signature of disturbance in MODIS data.•Gridding artifacts inherent to MODIS data products also affected the results.