Highway Ecological Environmental Assessment Based on Modified Remote Sensing Index—Taking the Lhasa–Nyingchi Motorway as an Example

Author:

Wang Xinghan12,Liu Qi2,Jia Pengfei3,Huang Xifeng4,Yang Jianhua5,Mao Zhengjun6ORCID,Shen Shengyu78ORCID

Affiliation:

1. College of Civil Engineering, Tianjin University, Tianjin 300350, China

2. Research Center on Flood & Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

3. Citic Construction Co., Ltd., Beijing 100027, China

4. Shaanxi Provincial Water Resources Department, Xi’an 710004, China

5. Academy of Eco-Civilization Development for Jing-Jin-Ji Megalopolis, Tianjin Normal University, Tianjin 300387, China

6. College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China

7. Department of Soil and Water Conservation, Changjiang River Scientific Research Institute (CRSRI), Wuhan 430010, China

8. Research Center on Mountain Torrent & Geologic Disaster Prevention of the Ministry of Water Resources, Wuhan 430010, China

Abstract

The Lhasa to Nyingchi Expressway in Xizang made efforts to protect the ecological environment during its construction, but it still caused varying degrees of damage to the fragile ecosystems along the route. Accurately assessing the process of change in the ecological environment quality in this region holds significant research value. This study selected the Linzhi-to-Gongbo’gyamda section of the Lhasa-to-Nyingchi Expressway as the research area. Firstly, based on the remote sensing ecological index (RSEI), this study constructed an ecological environmental quality evaluation system for the Xizang region. Subsequently, using the Google Earth Engine (GEE) platform, sub-indicators were extracted, and the combination weighting method of game theory was employed to determine indicator weights. This process resulted in the calculation of the MRSEI for the study area from 2012 to 2020. Finally, by utilizing the spatial distribution of the MRSEI, monitoring the level of MRSEI changes, and employing the transition matrix, this study analyzed the changing trend of the ecological environmental quality from 2012 to 2020. The results indicate that the MRSEI are 0.5885, 0.5951, 0.5296, 0.6202, 0.59, 0.5777, 0.5898, 0.5703, and 0.5987, showing a gradual increasing trend with an initial decrease followed by an ascent. This trend is mainly attributed to concentrated road construction and subsequent ecological restoration, leading to an improvement in the restoration effect. Simultaneously, the ecological environmental quality remains relatively stable, with 69.5% of the region showing no change, and the remaining 30.5% experiencing improvement exceeding degradation. Specifically, there were significant improvements in the land with ecological quality levels categorized as poor, fair, moderate, and good. The types of degradation primarily involved lands originally classified as excellent and good degrading to good and moderate levels, respectively. The above results serve as a theoretical reference for the ecological restoration project of the Lhasa-to-Nyingchi Expressway.

Funder

National Natural Science Foundation of China

a study on flash flood risk assessment method based on ensemble learning

a flash flood warning method coupled with disaster-causing mechanism

research on key technologies for flood disaster defense based on machine learning multi-models

Publisher

MDPI AG

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