Object-based change detection for vegetation disturbance and recovery using Landsat time series

Abstract

Accurate characterization of historical trends in vegetation change at the landscape scale is necessary for resource management and ecological assessment. Vegetation disturbance and recovery are coherent spatial and temporal processes. Pixel-based change detection methods often struggle to provide reliable estimates of change events because they neglect spatial contextual information and are affected by salt-and-pepper noise. To address such problems, we propose a new approach, “object-based change detection of trends in disturbance and recovery” (Object-LT), which introduces object-based image analysis (OBIA) into the current framework of LandTrendr algorithm. We then applied this approach to detect vegetation changes during 2000–2020 in the ecologically fragile region of Guyuan, Ning Xia, China. Accuracy assessment indicated that Object-LT could accurately identify disturbance and recovery trends in vegetation with overall accuracies of 90.05% and 87.50%, respectively. Compare with pixel-based LandTrendr algorithm, Object-LT significantly improved user’s accuracy and removed salt-and-pepper noise. Spatial–temporal maps of vegetation change showed that the recovery area was 571.27 km2 while the disturbed area was 297.65 km2, accounting for 5.44% and 2.83% of the study area, respectively. This indicates a general vegetation recovery trend in the study area. Object-LT allowed for an accurate and comprehensive characterization of vegetation change over large areas, which contributes to a better understanding of change processes of vegetation landscape over time.

Keywords

• object-based change detection
• vegetation disturbance and recovery
• landtrendr
• time series