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

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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.