Dynamic changes of vegetation cover in natural forest area of western Sichuan in recent 29 years based on RS
Jiaming LAI1,2, Wunian YANG1()
1. College of Earth Science, Chengdu University of Technology, Chengdu 610059, China 2. College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
Monitoring the effectiveness of natural forest protection project (NFPP) is of great significance. In order to know about the changes of natural forest in western Sichuan before and after the implementation of the NFPP, based on the comparative analysis of land cover classification results of Landsat series remote sensing images in 1989, 2000 and 2017, the characteristics of vegetation cover change in western Sichuan natural forest area in recent 29 years were compared and analyzed. The results showed that the vegetation coverage of the natural forest area in western Sichuan was declining as a whole before the NFPP from 1989 to 2000, and it showed a slow rising trend after the projection from 2000 to 2017. The vegetation coverage area which were middle grade and above decreased 6 291.56 hm 2 before the projection and 4 384.01 hm 2 increased after that. The land cover types in the study area in the last 29 years were dominated by arbor forest and shrub forest, and the area transfer occurred mainly between them. In the past 29 years, the change of farmland area was obvious, which increased before the NFPP and decreased after that. In the last 29 years, the forest coverage rate in the study area was rapidly decreasing at first and then slowly increasing. The rate dropped to 61.77% from 1989 to 2000, a decrease of 2.57%, then slowly rose to 65.61% from 2000 to 2017, and the increase was 3.84%. The results of the study show that the implementation of the NFPP has been effectively protecting the natural forest resources in western Sichuan.
赖家明, 杨武年. 基于RS的川西天然林区近29年植被覆盖动态变化[J]. 国土资源遥感, 2018, 30(4): 132-138.
Jiaming LAI, Wunian YANG. Dynamic changes of vegetation cover in natural forest area of western Sichuan in recent 29 years based on RS. Remote Sensing for Land & Resources, 2018, 30(4): 132-138.
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