Dynamic Change Analysis of Forest Disturbance and Forest Recovery in Guangdong Province From 1990 to 2020 Using the LandTrendr Algorithm

In order to accurately understand the dynamic characteristics of forest disturbance and forest recovery in Guangdong Province, a Landsat long time-series annual cloud-free surface reflectance dataset is established based on the GEE cloud platform. Then, the dataset is combined with the LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) algorithm to explore the characteristics of the spatial and temporal distribution of forest disturbances and forest restoration in Guangdong Province from 1990 to 2020. Finally, the driving factors of forest evolution and the contrast of the disturbance and recovery characteristics of different forest types are analyzed. The results indicated that: (1)From 1990 to 2020, the total area of forest disturbance in Guangdong Province is 1.35×10⁴ km², concentrated in the western, eastern, and small areas of central regions. The three cities with the largest forest disturbance area are Shaoguan, Meizhou, and Qingyuan. The total area of forest recovery is 1.91×10⁴ km², concentrated in the northern and western regions. The three cities with the largest forest recovery area are Shaoguan, Qingyuan, and Zhaoqing. (2)Forest disturbance and forest recovery in Guangdong Province are concentrated in areas less than or equal to 600 m, and the area of forest is relatively stable in high-altitude areas. Forest disturbance in Guangdong Province is concentrated in slopes less than or equal to 25°, while forest recovery is concentrated in slopes less than or equal to 35°. (3)Forest disturbance in Guangdong Province occurred more frequently after 1996, with the largest disturbance area occurring in 2011. Forest recovery in Guangdong Province occurred more frequently from 2001 to 2016, with the largest forest recovery area in 2012. (4)Forest disturbance and forest recovery in Guangdong Province are mainly affected by natural factors (rain, snow, and freezing disasters, typhoons, and insect pests) and human factors (forest fire, urbanization, cutting, and forestry policies). The impact of rain, snow, and freezing disasters on evergreen needle-leaved forests is significant, and this type of forest recovers quickly.