Comparative analysis of forest disturbance detection in the key state-owned forest region of the Greater Khingan Range of China based on different algorithms

The Greater Khingan Range of China has experienced varying levels of disturbance in history. To support sustainable management, this study used Landsat data (1986–2017) from GEE to establish a normalized burn ratio time series, compared the spatiotemporal accuracy of three change detection algorithms (BFAST, CCDC, LandTrendr), and analyzed their mapping differences. Results showed that: (1) all three algorithms can identify the major forest disturbances with a spatial accuracy higher than 80%, and LandTrendr performed the best (OA=86.2%). (2) All three algorithms can detect the occurrence time of major disturbances with a temporal accuracy higher than 70%, and LandTrendr achieved the highest accuracy (76.4%). (3) The highest fragmentation was observed using the CCDC (184,074 disturbance patches), while LandTrendr disturbance mapping was the most complete (102,143 disturbance patches). This study can provide technical references for regional-scale forest disturbance detection, and also provide optimal recommendations for the transfer application at different spatial scales.