Abstract
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.
Highlights
Forest ecosystems are an important component of terrestrial ecosystems
To construct a training sample set for automatic forest classification mapping, we introduced several sets of well–recognized land cover products in the field, including 2001–2015 year–on–year MCD12Q1 (MODIS land cover) products [40], 1998–2015 European Space Agency CCI (Climate Change Initiative) land cover products, 2000 and 2010 global surface coverage data developed by the China National Basic Geographic Information Center (GlobeLand30) [41], and 2015 global surface coverage data product FROM–GLC, developed by Tsinghua University, China [42]
The study was performed on the Google Earth Engine (GEE) cloud computing platform based on massive satellite remote sensing image data
Summary
Forest ecosystems are an important component of terrestrial ecosystems. According to the 2015 Global Forest Resources Assessment Report issued by the Food and Agriculture Organization (FAO) of the United Nations, the total area of global forest ecosystems is 3.99 billion hectares, accounting for 30.6% of the total land area. The timely and accurate monitoring of the status, changes (disturbance and recovery processes), and change–driven mechanisms of the forest ecosystems in Primorsky Krai, Russia, is of great significance for assessing the regional ecological environment and enhancing the sustainable development capacity [9]. In the existing research on forest change in the study area, Loboda et al [9] used Landsat data from 1972 to 2002 to obtain the forest disturbance dataset in the Far East for the past 30 years. The author extended the above method, and selected Primorsky Krai of the Russian Far East as the study area, integrated the 1998–2015 long–term Landsat SR dataset, and applied the RF classification algorithm to conduct land classification. WWhhaatt iiss tthhee rreellaattiioonnsshhiipp bbeettwweeeenn ffoorreesstt cchhaannggee ((eessppeecciiaallllyy ffoorreesstt ddiissttuurrbbaannccee)) aanndd rreeggiioonnaall pphhyyssiiccaall ggeeooggrraapphhyy aanndd eeccoonnoommiicc aanndd ssoocciieettaall ddeevveellooppmmeenntt??
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