Detecting Forest Disturbance and Recovery in Primorsky Krai, Russia, Using Annual Landsat Time Series and Multi-Source Land Cover Products | |
Hu, Yang1,2; Hu, Yunfeng1,2 | |
刊名 | REMOTE SENSING |
2020 | |
卷号 | 12期号:1页码:21 |
关键词 | temporal segmentation change detection landsat time series forest disturbance forest recovery |
DOI | 10.3390/rs12010129 |
通讯作者 | Hu, Yunfeng(huyf@lreis.ac.cn) |
英文摘要 | 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 x 10(6) ha, of which the cumulative forest disturbance area reached 1.12 x 10(6) ha, and the cumulative forest recovery area reached 0.55 x 10(6) ha; and (2) that more than 90% of the forest change occurred in areas with a slope of less than 18 degrees, 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. |
资助项目 | National Key Research and Development Plan Program in China[2016YFB0501502] ; National Key Research and Development Plan Program in China[2016YFC0503701] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA20010202] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA19040301] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA23100201] ; Key Project of High Resolution Earth Observation System in China[00-Y30B14-9001-14/16] |
WOS关键词 | TROPICAL DEFORESTATION ; TEMPORAL SEGMENTATION ; MODIS ; AREA ; CLASSIFICATION ; ABANDONMENT ; LANDTRENDR ; MANAGEMENT ; CLOUD ; BURNT |
WOS研究方向 | Remote Sensing |
语种 | 英语 |
出版者 | MDPI |
WOS记录号 | WOS:000515391700129 |
资助机构 | National Key Research and Development Plan Program in China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Key Project of High Resolution Earth Observation System in China |
内容类型 | 期刊论文 |
源URL | [http://ir.igsnrr.ac.cn/handle/311030/132837] |
专题 | 中国科学院地理科学与资源研究所 |
通讯作者 | Hu, Yunfeng |
作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
推荐引用方式 GB/T 7714 | Hu, Yang,Hu, Yunfeng. Detecting Forest Disturbance and Recovery in Primorsky Krai, Russia, Using Annual Landsat Time Series and Multi-Source Land Cover Products[J]. REMOTE SENSING,2020,12(1):21. |
APA | Hu, Yang,&Hu, Yunfeng.(2020).Detecting Forest Disturbance and Recovery in Primorsky Krai, Russia, Using Annual Landsat Time Series and Multi-Source Land Cover Products.REMOTE SENSING,12(1),21. |
MLA | Hu, Yang,et al."Detecting Forest Disturbance and Recovery in Primorsky Krai, Russia, Using Annual Landsat Time Series and Multi-Source Land Cover Products".REMOTE SENSING 12.1(2020):21. |
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