王金亮导师研究小组的论文在SCI期刊Remote Sensing上发表

2019年12月16日，以云南省高校资源与环境遥感重点实验室2016级硕士研究生杨艳林为第一作者、王金亮教授为通讯作者撰写的“Remote Sensing Monitoring of Grassland Degradation Based on the GDI in Shangri-La, China”论文在SCI期刊Remote Sensing上线发表了（Remote Sens. 2019, 11(24), 3030; https://doi.org/10.3390/rs11243030 (registering DOI) - 16 Dec 2019）。该论文以香格里拉退化草地为研究对象，实地调查退化草地，结合遥感技术，建立了基于GDI指数的草地退化遥感监测模型，高精度地评估了2008-2017年间香格里拉退化草地变化状况，可为高原地区草地监测和合理利用提高一定的依据。该论文得到了王金亮教授主持的国家自然科学基金(No.41271230和No.41961060)、云南省中青年学术技术带头人（No.2008PY056）、云南省高校科技创新团队支持计划和杨艳林申报的云南师范大学研究生科研创新基金项目（NO.2017058）的资助。

      论文相关信息

标题：Remote Sensing Monitoring of Grassland Degradation Based on the GDI in Shangri-La, China

作者：Yanlin Yang, Jinliang Wang*, Yun Chen, Feng Cheng, Guangjie Liu, and Zenghong He

通讯作者： Jinliang Wang, jlwang@ynnu.edu.cn

作者单位：1. College of Tourism and Geographic Sciences, Yunnan Normal University, Kunming 650500, China; 2. Key Laboratory of Resources and Environmental Remote Sensing, Universities in Yunnan, Kunming 650500, China, 3. Remote Sensing Research Laboratory, Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China

出版物：Remote Sensing

摘要：Grassland resources are important land resources. However, grassland degradation has become evident in recent years, which has reduced the function of soil and water conservation and restricted the development of animal husbandry. Timely and accurate monitoring of grassland changes and understanding the degree of degradation are the foundation for the scientific use of grasslands. The grassland degradation index of ground comprehensive evaluation (GDI_g) is a digital expression of grassland growth that can accurately indicate the degradation of grasslands. In this research, the accuracy of GDI_g in evaluating grassland degradation is discussed; the typical areas of grassland degradation in Shangri-La City, i.e., the towns of Jiantang and Xiaozhongdian, are selected as the research area. Through a field survey and spectroscopy combined with Huanjing-1 satellite image data (HJ-1), grassland degradation is monitored in the study area from 2008 to 2017. The results show that (1) GDI_g based on six indicators, namely, above-ground biomass, cover level, height, biomass of edible herbage, biomass of toxic weeds, and species richness, can effectively indicate grassland degradation, with the accuracy of the degradation grade assessment reaching 98.6%. (2) The correlation between the GDI_g and the grey values of 4 wavebands and 7 types of vegetation indexes derived from the HJ-1 is analysed, and the degraded grassland inversion model was built and revised based on HJ-1 data. The grassland degradation evaluation index of remote sensing (GDI_rs) model indicates that grassland degradation is proportional to the ratio vegetation index (RVI). (3) The grassland area was 405.40 km² in the initial monitoring period, accounting for 17.26% of the study area, while at the end of the monitoring period, the area was 338.87 km², with a loss of 66.53 km². From 2008 to 2017, the area of non-degraded and slightly degraded grassland in the study area presented a downward trend, with decreases of 59.87 km² and 49.93 km², respectively. In contrast, the area of moderately degraded grassland increased by 41.17 km² from 91.58 km² in 2008 to 132.74 km² in 2017, accounting for 39.17% of the grassland. The area of severely degraded grassland was 78.32 km², accounting for 23.11% of the grassland in 2017. (4) The degraded grasslands in the study area mainly transformed into the degradation-enhanced (deterioration) type. As the transformation rate gradually slows down, the current situation of grassland degradation is not hopeful.

关键字：Grassland Degradation; GDI; Remote Sensing Monitoring; Shangri-La