Detecting Forest Disturbance and Recovery in Primorsky Krai, Russia, Using Annual Landsat Time Series and Multi–Source Land Cover Products

• Main Authors: Yang Hu, Yunfeng Hu
• Format: Article
• Language: English
• Published: MDPI AG 2020-01-01
• Series: Remote Sensing
• Subjects: temporal segmentation; change detection; landsat; time series; forest disturbance; forest recovery
• Online Access: https://www.mdpi.com/2072-4292/12/1/129

The spatial distribution and dynamic changes of the forests in Primorsky Krai, Russia, are of great significance for regional ecological security and sustainable economic and societal development. With the support of the Google Earth Engine cloud computing platform, we first synthesized yearly Landsat surface reflectance images of the best quality of the research area and then used the random forest method to calculate the forest classification probability of the study area year by year from 1998 to 2015. Furthermore, we used a time−series segmentation algorithm to perform temporal trajectory segmentation for forest classification probability estimation, and determined the spatial and temporal distribution characteristics and change laws of the forest. We extended the existing algorithms and parameters of forest classification probability trajectory analysis, achieving a high overall accuracy (86.2%) in forest change detection in the study area. The extended method can accurately capture the time node information of the changes. In the present research we observed: (1) that from 1998 to 2015, the forest area of the whole district showed a net loss state, with a loss area of 0.56 × 10⁶ ha, of which the cumulative forest disturbance area reached 1.12 × 10⁶ ha, and the cumulative forest recovery area reached 0.55 × 10⁶ ha; and (2) that more than 90% of the forest change occurred in areas with a slope of less than 18°, at a distance of less than 20 km from settlements, and at a distance of less than 10 km from roads. The forest disturbance monitoring results are consistent with the changes in official statistical results over time, but there was a 20% overestimation. The technical method we extended in this study can be used as a reference for large−scale and high−precision dynamic monitoring of the forests in Russia’s Far East and other regions of the world; it also provides a basis for estimating illegal timber harvesting and determining the appropriate amount of forest harvested.