1990—2020年中国能源开采和加工场地多源数据综合制图与时空变化分析  

Mapping and Spatio-temporal Changes Analysis of Energy Mining and Producing Sites in China Using Multi-source Data from 1990 to 2020

作　　者：郭长庆 迟文峰 匡文慧[1,3] 窦银银 傅舒婧 雷梅 GUO Changqing;CHI Wenfeng;KUANG Wenhui;DOU Yinyin;FU Shujing;LEL Mei(Key Laboratory of Land Surface Pattern and Simulation,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;College of Resources and Environment Economy,Inner Mongolia University of Finance and Economics,Hohhot 010070,China;University of Chinese Academy of Sciences,Beijing 100049,China;Key Laboratory of State Forestry Administration of Soil and Water Conservation,School of Soil and Water Conservation,Beijing Forestry University,Beijing 100083,China;Key Laboratory of Resources Use and Environmental Remediation,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China)

机构地区：[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院重点实验室,北京100101 [2]内蒙古财经大学资源与环境经济学院,呼和浩特010070 [3]中国科学院大学,北京100049 [4]北京林业大学水土保持学院,水土保持国家林业局重点实验室,北京100083 [5]中国科学院地理科学与资源研究所,资源利用与环境修复重点实验室,北京100101

出　　处：《地球信息科学学报》2022年第1期127-140,共14页Journal of Geo-information Science

基　　金：国家重点研发计划项目(2018YFC1800103、2018YFC1800106、2018YFC1800102);中国科学院A类战略性先导科技专项(XDA23100201)。

摘　　要：由于当前缺乏有效的能源开采和加工场地精细化遥感探测方法和高精度的数据产品,全国尺度的能源开采和加工场地时空分布规律的认识仍显不足。本研究基于高分辨率遥感影像、土地利用/覆盖数据、网络爬虫数据、OSM地图数据和环境专题数据等信息,发展了基于多源数据融合和专家知识参与获取的能源开采和加工场地遥感识别和精细化制图的技术方法,研发了1990、2000、2010和2020年共4期的中国能源开采和加工场地分布数据产品及2010—2020年场地植被恢复信息数据产品,作为中国土地利用/覆盖变化数据的组成部分(CLUD-Mining)。CLUD-Mining具有较高的质量和可靠性,数据产品平均精度为91.75%;中国能源开采和加工场地开发建设的面积呈现先增长后减少的发展趋势,1990—2010年,面积增长速度从55.22 km^(2)/a上升到95.51 km^(2)/a,而2010—2020年呈现负增长,平均每年减少27.28 km^(2);此外,2010—2020年场地植被恢复面积达746.76 km^(2),主要集中在华北区和西南区;中国能源开采和加工场地分布格局逐渐由东部地区向西部地区转移。本研究对提升中国能源开采和加工场地时空分布特征的认识具有重要意义,可为场地污染治理和生态修复提供重要的数据基础。The understanding of spatio-temporal distribution of energy mining and producing sites at the national scale is still insufficient, due to the lack of effective remote sensing detection methods and high-precision data products. This study developed a new method for identifying and mapping energy mining and producing sites based on multi-source data integration and expert knowledge participation, including information from highresolution remote sensing images, land use/cover data, web crawler data, OSM map data, and environmental thematic data. Energy mining and producing sites data as well as vegetation restoration data were produced in China for 1990, 2000, 2010, and 2020. These data products were as a part of China’s land use/cover change datasets(CLUD-mining). The data products of China’s energy mining and producing sites can fulfill the demands of 1:25000 mapping, with an average accuracy of 91.75%. In general, the data products have excellent quality and reliability. Results show that the construction area of China’s energy mining and processing sites showed a trend of growth followed by decline. From 1990 to 2020, the area of China’s energy mining and processing grew by 116.90%, from 1055.94 km^(2)to 2290.36 km^(2). From 1990 to 2010, the growth rate of the area of energy mining and processing site increased from 55.22 km^(2)/a in 1990—2000 to 95.51 km^(2)/a in 2000—2010,while the growth rate from 2010 to 2020 was negative, with an average annual decrease of 27.28 km^(2). In particular, the area of energy fields in Northwest China and Qinghai Tibet Plateau continued to grow, with a total increase of 117.42 km^(2). In addition, the vegetation restoration area of energy sites reached 746.76 km^(2)from 2010 to 2020 and mainly concentrated in North China and Southwest China, accounting for 51.41% of the total vegetation restoration area. Overall, the spatial patterns of energy mining and producing sites in China has gradually shifted from the eastern zone to the western zone. This study is of great

关 键 词：多源数据 数据融合 能源开采 能源加工 植被恢复 综合制图 空间格局 时空变化 

分 类 号：P285[天文地球—地图制图学与地理信息工程] P208[天文地球—测绘科学与技术]