ORIGINAL RESEARCH
Monitoring Grassland Desertification in Zoige
County Using Landsat and UAV Image
More details
Hide details
1
Jiangxi Key Laboratory of Industrial Ecological Simulation and Environmental Health in the Yangtze River Basin,
Jiujiang University, Jiujiang 332005, China
2
Research Institute of Social Development, Southwestern University of Finance and Economics, Chengdu 610000, China
3
Sichuan Solid Waste and Chemicals Management Center, Chengdu 610000, China
Submission date: 2021-01-31
Final revision date: 2021-03-29
Acceptance date: 2021-04-27
Online publication date: 2021-10-13
Publication date: 2021-12-02
Corresponding author
Hong Lu
Research Institute of Social Development, Southwestern University of Finance and Economics, China
Pol. J. Environ. Stud. 2021;30(6):5789-5799
KEYWORDS
TOPICS
ABSTRACT
The increasing rate of sandy desertification lands in Zoige County has been regarded as an
imminent threat recently. There is an urgent need to monitor the status, trend of desertification. This
study used a land cover classification process of integrating Support Vector Machine classifier (SVM)
with threshold method to extract the sandy lands based on Landsat data collected from 1990 to 2017.
Furthermore, we quantitatively analyzed the evolution trend of the grassland desertification and verified
the effect of desertification control using the unmanned aerial vehicle (UAV) image acquired in 2019.
Three conclusions are drawn from the study. First, the sandy land is mainly distributed along the
directions of prevailing wind at the edges of mountains in the southwest. Second, the area of sandy
lands increased from 29.75 km2 in 1990 to 46.87 km2 in 2005 and then decreased to 22.58 km2 in
2013. While during 2014-2017, the area increased to 33.61 km2. Third, the increasing of sandy lands is
closely related to the terrain and water resources distribution. Ecological restoration policies, especially
the ecological recovery projects implemented are the main driving factor of desertification reversion.
The results of this study can provide effective data and decision support for combating desertification.