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

doi:10.12082/dqxxkx.2022.210577

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (1): 127-140.doi: 10.12082/dqxxkx.2022.210577

• 地理空间分析综合应用 • 上一篇下一篇

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

郭长庆¹(), 迟文峰², 匡文慧¹^,³, 窦银银¹^,^*(), 傅舒婧⁴, 雷梅³^,⁵

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

收稿日期:2021-09-25 修回日期:2021-11-12 出版日期:2022-01-25 发布日期:2022-03-25
通讯作者: * 窦银银（1986—）,女,山东高唐人,博士,助理研究员,主要从事城市生态遥感研究。E-mail: douyinyin@igsnrr.ac.cn
作者简介:郭长庆（1994—）,男,山东济宁人,硕士生,主要从事生态环境遥感研究。E-mail: guocq@igsnrr.ac.cn
基金资助:
国家重点研发计划项目(2018YFC1800103);国家重点研发计划项目(2018YFC1800106);国家重点研发计划项目(2018YFC1800102);中国科学院A类战略性先导科技专项(XDA23100201)

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

GUO Changqing¹(), CHI Wenfeng², KUANG Wenhui¹^,³, DOU Yinyin¹^,^*(), FU Shujing⁴, LEL Mei³^,⁵

1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. College of Resources and Environment Economy, Inner Mongolia University of Finance and Economics, Hohhot 010070, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
4. Key Laboratory of State Forestry Administration of Soil and Water Conservation, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
5. Key Laboratory of Resources Use and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Received:2021-09-25 Revised:2021-11-12 Online:2022-01-25 Published:2022-03-25
Contact: DOU Yinyin
Supported by:
National Key Research and Development Program of China(2018YFC1800103);National Key Research and Development Program of China(2018YFC1800106);National Key Research and Development Program of China(2018YFC1800102);Strategic Priority Research Program(A) of Chinese Academy of Sciences(XDA23100201)

摘要/Abstract

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 km²/a上升到95.51 km²/a,而2010—2020年呈现负增长,平均每年减少27.28 km²;此外,2010—2020年场地植被恢复面积达746.76 km²,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。

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

Abstract:

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 high-resolution 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 km² to 2290.36 km². From 1990 to 2010, the growth rate of the area of energy mining and processing site increased from 55.22 km²/a in 1990—2000 to 95.51 km²/a in 2000—2010, while the growth rate from 2010 to 2020 was negative, with an average annual decrease of 27.28 km². In particular, the area of energy fields in Northwest China and Qinghai Tibet Plateau continued to grow, with a total increase of 117.42 km². In addition, the vegetation restoration area of energy sites reached 746.76 km² 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 significance in improving the understanding of the spatial-temporal distribution characteristics of energy mining and producing sites in China and can provide an important data basis for site pollution management and ecological restoration.

Key words: multi-source data, data integration, energy mining, energy producing, vegetation restoration, composite mapping, spatial pattern, spatio-temporal changes

郭长庆, 迟文峰, 匡文慧, 窦银银, 傅舒婧, 雷梅. 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[J]. 地球信息科学学报, 2022, 24(1): 127-140.DOI:10.12082/dqxxkx.2022.210577

GUO Changqing, CHI Wenfeng, KUANG Wenhui, DOU Yinyin, FU Shujing, LEL Mei. Mapping and Spatio-temporal Changes Analysis of Energy Mining and Producing Sites in China Using Multi-source Data from 1990 to 2020[J]. Journal of Geo-information Science, 2022, 24(1): 127-140.DOI:10.12082/dqxxkx.2022.210577

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数据类型	数据源名称	时间/年份	分辨率	数据格式	数据来源
高分辨率遥感影像	GF-2影像数据	2016	1 m	栅格	中国科学院遥感与数字地球研究所（http://www.ceode.cas.cn/）
高分辨率遥感影像	Google Earth影像数据	1990—2020	0.5 m/1 m/ 2 m	栅格	http://google-earth.en.softonic.com/
土地利用/覆盖数据	中国土地利用/覆盖数据集（CLUD）	1990—2020	-	矢量	中国科学院地理科学与资源研究所（http://www.igsnrr.ac.cn/）
网络爬虫数据	网络爬虫数据	1990—2020	-	表格	网络爬虫技术/全国排污许可证管理信息平台（公开端）（http://permit.mee.gov.cn/）/绿网环境数据中心（http://www.lvwang.org.cn/）/天眼查（https://www.tianyancha.com/）/企查查（https://www.qcc.com/）/百度电子地图（https://map.baidu.com/）
OSM地图数据	OSM地图数据	2020	-	矢量	Open Street Map（https://www.openstreetmap.org/#map=4/36.96/104.17）
环境专题数据	调研场地信息数据	2018—2020	-	表格	实地调研获取
环境专题数据	Landsat NDVI数据	2010—2020	30 m	栅格	https://earthengine.google.com/
基础地理数据	行政区划数据	2018	1:100万	矢量	国家基础地理信息中心（http://www.ngcc.cn/）

地理分区	指标	1990年	2000年	2010年	2020年	2010—2020年植被恢复信息	平均精度^∗/%
华北区	总采样数/个	286	398	377	390	506	91.82
	正确数/个	255	360	344	376	464
	错误数/个	31	38	33	14	42
	产品精度/%	89.16	90.45	91.25	96.41	91.70
东北区	总采样数/个	123	188	157	139	201	91.82
	正确数/个	111	170	145	131	186
	错误数/个	12	18	12	8	15
	产品精度/%	90.24	90.43	92.36	94.24	92.54
西北区	总采样数/个	138	182	213	239	124	91.30
	正确数/个	127	165	190	223	117
	错误数/个	11	17	23	16	7
	产品精度/%	92.03	90.66	89.20	93.31	94.35
东南区	总采样数/个	161	180	185	176	259	90.78
	正确数/个	140	163	171	164	228
	错误数/个	21	17	14	12	31
	产品精度/%	86.96	90.56	92.43	93.18	88.03
西南区	总采样数/个	140	183	208	168	264	93.11
	正确数/个	134	161	197	158	230
	错误数/个	6	22	11	10	34
	产品精度/%	95.71	87.98	94.71	94.05	87.12
青藏高原区	总采样数/个	21	26	33	30	25	91.76
	正确数/个	19	24	30	28	22
	错误数/个	2	2	3	2	3
	产品精度/%	90.48	92.31	90.91	93.33	88.00
中国大陆	总采样数/个	869	1157	1173	1142	1379	91.75
	正确数/个	786	1043	1077	1080	1247
	错误数/个	83	114	96	62	132
	产品精度/%	90.45	90.15	91.82	94.57	90.43

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

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