Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI)

被引:290
|
作者
Xu, Hanqiu [1 ,2 ]
Wang, Meiya [1 ,2 ]
Shi, Tingting [1 ,2 ]
Guan, Huade [3 ]
Fang, Canying [1 ,2 ]
Lin, Zhongli [2 ]
机构
[1] Fuzhou Univ, Minist Educ, Key Lab Spatial Data Min & Informat Sharing, Coll Environm & Resources, Fuzhou 350116, Fujian, Peoples R China
[2] Fuzhou Univ, Fujian Prov Key Lab Remote Sensing Soil Eros, Inst Remote Sensing Informat Engn, Fuzhou 350116, Fujian, Peoples R China
[3] Flinders Univ S Australia, Coll Sci & Engn, Adelaide, SA 5001, Australia
基金
中国国家自然科学基金;
关键词
Model prediction; Remote sensing-based ecological index (RSEI); Impervious surface; Population growth; Xiong'an New Area; TEMPERATURE RETRIEVAL; ECOSYSTEM SERVICES; VEGETATION INDEX; MANAGEMENT; INDICATOR; IMAGERY; COVER; TRANSFORMATION; ENVIRONMENT; IMPACT;
D O I
10.1016/j.ecolind.2018.05.055
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
The ecological impact of urban development and population increase is an area of increasing relevance as human modification of the landscape continues unabated. The prediction of this impact will help inform urban planning and decision making around population growth, impervious surface (IS) increase and associated ecological effects. The Xiong'an New Area is a state-level new area to be established in North China. The population growth goal for the area is going to reach 2.5 million and the area is planned to expand to 2000 km(2). The potential population growth and area expansion will result in a massive increase in IS area and thus may impact the regional ecological quality. A clear understanding of the impact would help to minimize the influence of the new area's development on regional ecological quality. Therefore, this study investigated current land cover types and ecological status in the Xiong'an New Area using feature inversion techniques and the improved remote sensing-based ecological index (RSEI). Statistical models were developed to predict ecological effects responding to the forthcoming population and associated IS increase in the new area. This was achieved by relating population growth to IS area increase and exploring the relationships between IS area and RSEI. The results show that the area's land surface has not been intensively developed and the current ecological status is good. The RSEI-based prediction shows that IS area has a noteworthy effect on regional ecological conditions. The variation of IS proportions in the new area can result in a significant shift of RSEI. A balance amount of total IS area in the 2000 km(2) new area is 433 km(2). Exceeding/reducing the amount would result in a decline/rise of the area's ecological quality. Introducing a quantity of IS area-related population density (IPD) reveals that the area's ecological quality is actually related to IPD rather than to traditional population density when the total area and future population of the new area are given. Therefore, the forthcoming regional master planning for the new area should include specific efforts to control IS area increase.
引用
收藏
页码:730 / 740
页数:11
相关论文
共 50 条
  • [1] Comparison between modified remote sensing ecological index and RSEI
    Liu Y.
    Dang C.
    Yue H.
    Lyu C.
    Qian J.
    Zhu R.
    National Remote Sensing Bulletin, 2022, 26 (04): : 683 - 697
  • [2] Ecological vulnerability assessment based on remote sensing ecological index (RSEI): A case of Zhongxian County, Chongqing
    Jiang, Xiaolan
    Guo, Xianhua
    Wu, Yan
    Xu, Denghui
    Liu, Yixuan
    Yang, Yuzheng
    Lan, Guoxin
    FRONTIERS IN ENVIRONMENTAL SCIENCE, 2023, 10
  • [3] Ecological quality assessment and monitoring using a time-series remote sensing-based ecological index (ts-RSEI)
    Sun, Chao
    Li, Jialin
    Liu, Yongchao
    Cao, Luodan
    Zheng, Jiahao
    Yang, Zhenjie
    Ye, Junwei
    Li, Yue
    GISCIENCE & REMOTE SENSING, 2022, 59 (01) : 1793 - 1816
  • [4] Application of a novel remote sensing ecological index (RSEI) based on geographically weighted principal component analysis for assessing the land surface ecological quality
    Mondal J.
    Basu T.
    Das A.
    Environmental Science and Pollution Research, 2024, 31 (22) : 32350 - 32370
  • [5] Detecting Ecological Changes with a Remote Sensing Based Ecological Index (RSEI) Produced Time Series and Change Vector Analysis
    Xu, Hanqiu
    Wang, Yifan
    Guan, Huade
    Shi, Tingting
    Hu, Xisheng
    REMOTE SENSING, 2019, 11 (20)
  • [6] Instability of remote sensing based ecological index (RSEI) and its improvement for time series analysis
    Zheng, Zihao
    Wu, Zhifeng
    Chen, Yingbiao
    Guo, Cheng
    Marinello, Francesco
    SCIENCE OF THE TOTAL ENVIRONMENT, 2022, 814
  • [7] Beyond the Remote Sensing Ecological Index: A Comprehensive Ecological Quality Evaluation Using a Deep-Learning-Based Remote Sensing Ecological Index
    Gong, Xi
    Li, Tianqi
    Wang, Run
    Hu, Sheng
    Yuan, Shuai
    REMOTE SENSING, 2025, 17 (03)
  • [8] Ecological environment assessment of Shanxi Province and planned mining area based on coupling Remote Sensing Ecological Index (RSEI) model
    Ji X.
    Yan Y.
    Guo W.
    Teng Y.
    Zhao C.
    Meitiandizhi Yu Kantan/Coal Geology and Exploration, 2023, 51 (03): : 103 - 112
  • [9] Impact of Land Cover Change on a Typical Mining Region and Its Ecological Environment Quality Evaluation Using Remote Sensing Based Ecological Index (RSEI)
    Tang, Huan
    Fang, Jiawei
    Xie, Ruijie
    Ji, Xiuli
    Li, Dayong
    Yuan, Jing
    SUSTAINABILITY, 2022, 14 (19)
  • [10] Ecological vulnerability assessment based on remote sensing ecological index (RSEI): a case of Zhongxian County, Chongqing (vol 10, 1074376, 2023)
    Lan, Guoxin
    Jiang, Xiaolan
    Xu, Denghui
    Guo, Xianhua
    Wu, Yan
    Liu, Yixuan
    Yang, Yuzheng
    FRONTIERS IN ENVIRONMENTAL SCIENCE, 2023, 11