Monitoring and change analysis of vegetation coverage in Ejin Oasis based on UAV-LiDAR

doi:10.7522/j.issn.1000-694X.2024.00080

Journal of Desert Research ›› 2024, Vol. 44 ›› Issue (5): 170-181.DOI: 10.7522/j.issn.1000-694X.2024.00080

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Monitoring and change analysis of vegetation coverage in Ejin Oasis based on UAV-LiDAR

Lang Zhang¹(), Guofeng Dang¹, Tengfei Yu²^,³(), Tuo Han²^,³, Yidan Yin², Yong Chen⁴

^1.School of Geography and Environmental Science，Northwest Normal University，Lanzhou 730070，China
^2.Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands /, Northwest Institute of Eco-Environment and Resources，Chinese Academy of Sciences，Lanzhou 730000，China
^3.Alxa Desert Eco-Hydrology Experimental Research Station, Northwest Institute of Eco-Environment and Resources，Chinese Academy of Sciences，Lanzhou 730000，China
^4.Administration of Populus euphratica Forest National Nature Reserve in Ejin Banner，Ejin Banner 735400，Inner Mongolia，China

Received:2024-04-01 Revised:2024-05-29 Online:2024-09-20 Published:2024-10-15
Contact: Tengfei Yu

Abstract

Abstract:

Vegetation coverage is the proportion of the vertical projected area of vegetation on the ground to the total area of a given statistical area， and it is an important parameter for characterizing vegetation growth and ecosystem changes. To scientifically evaluate the restoration effectiveness of the Heihe ecological water transfer project， this paper takes the Ejin Oasis Populus euphratica forest as the research object and uses a UAV-LiDAR system to simultaneously obtain ultra-high resolution （GSD<0.7 cm） visible light photos and ultra-high density LiDAR point clouds （3 000 points·m^-2） to calculate and investigate the vegetation coverage of the sample land. The relationship between vegetation coverage and the remote sensing vegetation index was established to infer the change of vegetation coverage in the Ejin Oasis from 1986 to 2023. The results showed the following：（1） The vegetation coverage calculated by LiDAR point cloud data was consistent with the visual interpretation results （R2=0.89， RMSE=0.07）. Compared with the pixel dichotomy method of remote sensing， R2 increased by 0.36 and RMSE decreased by 0.03， indicating that the LiDAR point cloud can accurately calculate the vegetation coverage of the plot. （2） The FVC of the study area varied from 0.11 to 0.60， with an average of 0.34. The optimal model of vegetation coverage （y）and improved soil-regulated Vegetation Index （x） based on LiDAR point cloud was： y=2.53x-0.07 （R2=0.68， RMSE=0.12）. （3） According to the model inversion， the vegetation coverage of Ejin Oasis fluctuated during 1986-2000 and increased during 2001-2012， with an annual growth rate of 0.31%. The increase slowed down slightly during 2013-2023， with an annual growth rate of 0.19%. Theil-Sen median and M-K test trend analysis showed that after the implementation of the ecological water transport project， vegetation cover reversed from a degradation trend to an improvement trend， indicating that the Heihe ecological water transport project has achieved remarkable results.

Key words: remote sensing, UAV-LiDAR, fractional vegetation cover, Ejin Oasis, ecological water transport

CLC Number:

Lang Zhang, Guofeng Dang, Tengfei Yu, Tuo Han, Yidan Yin, Yong Chen. Monitoring and change analysis of vegetation coverage in Ejin Oasis based on UAV-LiDAR[J]. Journal of Desert Research, 2024, 44(5): 170-181.

Figures/Tables 8

Fig.1 The location （A）， the type of desertification （B） of the study area and the distribution of land use and survey sites （C）

Fig.2 Study the technical process （A） and examples of vegetation coverage extraction by different methods （B）

Table 1 Basic information of Landsat satellite data sources

年份	数据集	卫星	使用波段
1986—1998	LANDSAT/LT05/C02/T1_L2	Landsat 5/TM	SR_B (1, 2, 3, 4, 5, 7)
1999—2012	LANDSAT/LE07/C02/T1_L2	Landsat 7/ETM+	SR_B (1, 2, 3, 4, 5, 7)
2012—2023	LANDSAT/LC08/C02/T1_L2	Landsat 8/OLI/TIRS	SR_B (2, 3, 4, 5, 6, 7)

Fig.3 FVC changes （A） and correlation （B） in different plots extracted by different methods

Fig.4 Relationship model between FVC and vegetation index based on LiDAR point cloud computing

Fig.5 Spatial distribution （A） and interannual variation （B） of FVC in Ejin Oasis from 1986 to 2023 based on LiDAR and MSAVI

Fig.6 Annual FVC spatial pattern of Ejin Oasis in 1986， 2000， and 2023

Fig.7 Vegetation changes in Ejin Oasis in 1986-2000， 2000-2023， 1986-2023

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Monitoring and change analysis of vegetation coverage in Ejin Oasis based on UAV-LiDAR

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