自然资源学报 ›› 2021, Vol. 36 ›› Issue (5): 1176-1185.doi: 10.31497/zrzyxb.20210507

• "中国'三生空间'统筹优化的方法与实践"专栏 • 上一篇    下一篇

2000年来中国生态状况时空变化格局

何盈利1,2(), 尤南山1,2, 崔耀平3, 肖桐4, 郝媛媛5, 董金玮1()   

  1. 1.中国科学院地理科学与资源研究所陆地表层格局与模拟重点实验室,北京 100101
    2.中国科学院大学资源与环境学院,北京 100049
    3.河南大学环境与规划学院,开封 475004
    4.生态环境部卫星环境应用中心,北京 100094
    5.蒙草生态大数据研究院,呼和浩特 010000
  • 收稿日期:2020-05-11 修回日期:2021-01-14 出版日期:2021-05-28 发布日期:2021-07-28
  • 通讯作者: 董金玮(1982- ),男,山东潍坊人,博士,研究员,研究方向为土地利用与全球变化遥感。E-mail: dongjw@igsnrr.ac.cn
  • 作者简介:何盈利(1997- ),男,山东日照人,博士研究生,研究方向为全球变化遥感。E-mail: heyl.19b@igsnrr.ac.cn
  • 基金资助:
    中国科学院战略性先导科技专项(XDA19040301)

Spatio-temporal changes in remote sensing-based ecological index in China since 2000

HE Ying-li1,2(), YOU Nan-shan1,2, CUI Yao-ping3, XIAO Tong4, HAO Yuan-yuan5, DONG Jin-wei1()   

  1. 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    3. School of Environment and Planning, Henan University, Kaifeng 475004, Henan, China
    4. Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment, Beijing 100094, China
    5. Big Data Research Institute of Mongolian Grassland Ecology, Hohhot 010000, China
  • Received:2020-05-11 Revised:2021-01-14 Online:2021-05-28 Published:2021-07-28

摘要:

21世纪以来,我国在经济快速发展的同时高度重视生态环境保护,一系列生态修复工程和空间管控措施使生态状况发生了巨大变化;然而目前对于全国生态状况宏观格局的认识仍十分有限。借助 Google Earth Engine(GEE)遥感云计算平台,采用主成分分析方法和MODIS数据构建的绿度NDVI、热度LST、湿度WET和干度NDSI四个指标,生成长时间序列的遥感生态指数RSEI数据集,完整刻画了中国2000年来生态状况的时空连续变化格局。研究发现:在空间格局上,东南沿海地区生态状况优于西北地区;变化趋势上,全国生态状况除上海、西藏和澳门之外均显著改善,RSEI增长最多的三个省份为山西、陕西和河北。进一步采用遥感云计算定量评价了2000年来生态状况变化的宏观格局,以期为国土空间管控和生态保护提供科学支持。

关键词: 生态状况, 遥感生态指数RSEI, 遥感云计算, MODIS, GEE, 时空分析

Abstract:

Since the beginning of the 21st century, China has responded to a national land-system sustainability emergency via an integrated portfolio of large-scale programmes. A series of ecological restoration projects and land regulating and planning policies have been implemented for sustainable development, which substantially improved the security status of the country's ecology. However, comprehensive assessments of the ecological status based on objective data and framework are still limited. Remote sensing-based ecological index (RSEI) has been proposed as an objective and effective approach for assessing ecological security on a regional scale. However, a national scale application has not been conducted yet. Here we generated the annual RSEI products from 2000 to 2019 by using four indicators (Normalized Difference Vegetation Index (NDVI), Normalized Difference Soil Index (NDSI), Wetness Index (Wet), and Land Surface Temperature (LST) based on Moderate Resolution Imaging Spectroradiometer (MODIS) data as well as the Google Earth Engine (GEE)-a cloud computing platform. The results showed that the multi-year average RSEI showed higher values in the southeast coastal regions compared with the northwestern regions, the regions with superior hydrothermal conditions have high RSEI values, while the arid and semi-arid inland areas with higher elevations and cold-dry climates have low RSEI values and fragile ecological conditions. In general, the whole country experienced a significant improvement of RSEI, and all the provincial-level regions in China, except Shanghai, Tibet, and Macao, have shown an increasing RSEI. The three provinces with the fastest growing rates were Shanxi, Shaanxi, and Hebei, with increases of 0.29, 0.25, and 0.19, respectively. The RSEI increased significantly in the Northeast China Plain, Loess Plateau, south and north of the North China Plain, the north of the middle and lower reaches of the Yangtze River Plain, and the south of the Junggar Basin in the northwest desert region, while the RSEI decreased in the Tianshan Mountain range, the southwest of the Qinghai-Tibet Plateau, the central part of the North China Plain and the Yangtze River Delta. This study quantitatively evaluated the macro patterns of RSEI changes based on GEE since 2000, and expecte to support decision making on land use management and ecological protection.

Key words: ecological status, remote sensing-based ecological index (RSEI), remote sensing cloud computing, MODIS, Google Earth Engine (GEE), spatio-temporal analysis