Assessing the Spatiotemporal Evolution and Drivers of Ecological Environment Quality Using an Enhanced Remote Sensing Ecological Index in Lanzhou City, China

被引:27
作者
Duo, Linghua [1 ,2 ]
Wang, Junqi [1 ,2 ]
Zhang, Fuqing [1 ,2 ]
Xia, Yuanping [1 ,2 ]
Xiao, Sheng [1 ,2 ]
He, Bao-Jie [3 ,4 ,5 ]
机构
[1] East China Universtiy Technol, Minist Nat Rsources, Key Lab Mine Environm Monitoring & Improving Poyan, Nanchang 330013, Peoples R China
[2] East China Univ Technol, Sch Surveying & Geoinformat Engn, Nanchang 330013, Peoples R China
[3] Chongqing Univ, Ctr Climate Resilient & Low Carbon Cities, Sch Architecture & Urban Planning, Chongqing 400030, Peoples R China
[4] Hiroshima Univ, Network Educ & Res Peace & Sustainabil NERPS, Hiroshima 7398530, Japan
[5] Chongqing Univ, Chongqing Univ Liyang, Smart City Res Inst, Chongqing 213300, Peoples R China
关键词
Lanzhou City; desertification index; remote sensing ecological assessment; Google Earth Engine (GEE); RIVER-BASIN; CONSERVATION; EROSION; SOIL;
D O I
10.3390/rs15194704
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Lanzhou City is located in the semi-arid region of northwest China, which experiences serious desertification. Moreover, the high intensity of land development, with the accelerated industrialization and urbanization, causes increasingly aggravated conflict between humans and the environment. Exploring the response of the ecological environment quality to the natural environment and anthropogenic activities is important to protect the sustainable development of urban economic construction and the environment. Based on the Google Earth Engine (GEE) platform, this paper constructed a modified Remote Sensing Ecological Index (MRSEI) model which could reflect the ecological environment quality by integrating the desertification index (DI) into the Remote Sensing Ecological index (RSEI) model. This paper explores the spatiotemporal variation in the environmental quality from 2000 to 2020 in Lanzhou, China, and analyzes the natural and anthropogenic factors affecting the environment quality in terms of temperature, precipitation, gross domestic product (GDP), land use, night lighting, and population. The results showed that the mean value of MRSEI ranged from 0.254 to 0.400. The area undergoing fast growth in ecological quality was in the northwestern part of Lanzhou, and the area of decrease was in the central part. Various factors have different degrees of influence on the ecosystem, with temperature, precipitation, and land use having a greater impact, and GDP and population having a limited impact. Precipitation and temperature showed a strong impact when interacting with other factors, demonstrating that precipitation and temperature were also key factors affecting MRSEI. Overall, climate change and the implementation of ecological restoration projects have led to an improvement in the quality of the ecological environment in Lanzhou. This study provides a reference for understanding the spatiotemporal changes in the ecological environment in semi-arid Lanzhou and is conducive to formulating proper protection strategies.
引用
收藏
页数:24
相关论文
共 57 条
[1]   Spatiotemporal change of ecologic environment quality and human interaction factors in three gorges ecologic economic corridor, based on RSEI [J].
An, Min ;
Xie, Ping ;
He, Weijun ;
Wang, Bei ;
Huang, Jin ;
Khanal, Ribesh .
ECOLOGICAL INDICATORS, 2022, 141
[2]   Drivers of eco-environmental quality in China from 2000 to 2017 [J].
Bai, Tingting ;
Cheng, Jie ;
Zheng, Zihao ;
Zhang, Qifei ;
Li, Zihao ;
Xu, Dong .
JOURNAL OF CLEANER PRODUCTION, 2023, 396
[3]   Spatiotemporal change and driving factors of ecological status in Inner Mongolia based on the modified remote sensing ecological index [J].
Bai, Zongfan ;
Han, Ling ;
Liu, Huiqun ;
Jiang, Xuhai ;
Li, Liangzhi .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (18) :52593-52608
[4]  
Cheng J., 2022, ARID LAND GEOGRAPHY, V45, P1637
[5]  
Ding Q., 2013, Soil Water Conserv. China, V08, P29
[6]   Land Surface Ecological Status Composition Index (LSESCI): A novel remote sensing-based technique for modeling land surface ecological status [J].
Firozjaei, Mohammad Karimi ;
Fathololoumi, Solmaz ;
Kiavarz, Majid ;
Biswas, Asim ;
Homaee, Mehdi ;
Alavipanah, Seyed Kazem .
ECOLOGICAL INDICATORS, 2021, 123
[7]  
Gansu Provincial Forestry and Grassland Bureau, 2023, Gansu Dly, V6, P10
[8]   Google Earth Engine: Planetary-scale geospatial analysis for everyone [J].
Gorelick, Noel ;
Hancher, Matt ;
Dixon, Mike ;
Ilyushchenko, Simon ;
Thau, David ;
Moore, Rebecca .
REMOTE SENSING OF ENVIRONMENT, 2017, 202 :18-27
[9]   Eco-Environmental Quality Monitoring in Beijing, China, Using an RSEI-Based Approach Combined With Random Forest Algorithms [J].
Gou, Ruikun ;
Zhao, Jun .
IEEE ACCESS, 2020, 8 :196657-196666
[10]  
Hang Xin, 2020, Yingyong Shengtai Xuebao, V31, P219, DOI 10.13287/j.1001-9332.202001.030