Analysis on characteristics of mine geological environment and ecological restoration changes in Jilin Province based on dynamic remote sensing monitoring

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Abstract

This study aims to understand the variation trend of the geological environment and the ecological restoration prospect of mines in Jilin Province. Using the 2015—2019 high-resolution remote sensing data from a domestic satellite and other multi-source information, this study carried out the remote sensing-based dynamic monitoring of the geological environment and ecological restoration of mines in Jilin Province by means of automatic information extraction, human-machine interactive interpretation, in-door comprehensive research, and field surveys and verification. The analysis of the changes in the geological environment and ecological restoration of the mines based on the monitoring results allowed for basically ascertaining the current situations and variation trend of the occupation of land resources by mines, damage to land resources by mines, and the geological disasters, environmental pollution, and ecological restoration of mines in Jilin Province. The analysis results are objective and accurate, indicating good application results of remote sensing-based monitoring. The results of this study can provide references and bases for further promoting the ecological protection and restoration engineering of mountains, rivers, forests, lakes, grass, and sand in Jilin Province.

Keywords remote sensing mine development mine geological environment ecological restoration dynamic monitoring

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TP79

Issue Date: 21 September 2022

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	Junhua GAO
	Lianxue ZOU
	Huan LONG
	Shuitao CHU

Cite this article:

Junhua GAO,Lianxue ZOU,Huan LONG, et al. Analysis on characteristics of mine geological environment and ecological restoration changes in Jilin Province based on dynamic remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(3): 240-248.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021278 OR https://www.gtzyyg.com/EN/Y2022/V34/I3/240

Fig.1 Work flow chart

Fig.2 Statistical histogram of domestic satellite remote sensing data sources in Jilin Province during 2015 to 2019

类别	地物和影像特征	解译标志
矿山开发损毁土地资源	露天采场	影像上纹理、色调与周边地物差异显著,区别于一般的裸露土地,一般低于周围地形,采场边坡呈阶梯弧形、环形纹理(见图3)
	井口(硐口)	在高分辨率影像上可被明显识别,井口上方有井架及井架阴影,周边矿石堆常以发射状分布,呈扇形或圆形; 硐口出口处常有矿石输送巷道或铁轨,周边矿石堆常于硐口正前方呈锥型分布
	塌陷坑(地裂缝)	影像上塌陷坑呈独立的环形或椭圆形斑点、斑块状,呈独立个体成群分布,色调明暗不同,有一定深度的负地形,坑内植被呈微红色。地裂缝呈线状(直线、断续或交叉)、条带状影像特征,裂缝内有植被呈暗红色
类别	地物和影像特征	解译标志
矿山地质灾害	崩塌	影像上陡崖色调较浅,陡坡有粗糙感或花瓣状锥状地形
矿山地质灾害	滑坡	多在峡谷中缓坡、分水岭阴坡、主沟与支沟交会处,呈簸箕型、舌型、弧形和不规则型,局部平缓斜坡明显界限与周围分割
矿山环境污染	粉尘污染	污染源周围植被稀疏,影像不清晰,昏暗、轮廓暗淡,色调上呈现不均匀浑黄褐色、褐红色
矿山环境污染	水体污染	影像上从污染源向外扩散呈弧扇状、条状或块状分布,蓝色中带有不均匀的黑色调
矿山开发压占土地资源	矿山建筑	基础设施建设的场地和生活办公用地,在影像上特征明显,且比较整齐规则
	固体废弃物	排土场高于周边地形,边缘多呈裙状,颗粒状纹理,色调较亮; 尾矿库位于沟谷内,前有坝体,后部沿沟展布,多呈树枝状,纹理细致,色调较亮; 煤矸石堆一般高于周边地形,多呈锥型,色调较暗,多为灰黑色(见图4)
	中转场地	用来堆放矿石等相关产品的场所,一般与采场(井口、硐口)距离较近,选矿厂多数和尾矿库相连接
矿山生态修复	耕地	灌溉、湿度大、生长着的色调较暗,反之较浅。具有规则几何形状,纹理平滑细腻,良地多为均匀平滑的绿色,其边界多有路、渠
	园地	位于地势较高排水通畅处,规则颗粒状,光谱特征与林地相似,株距较大。果树颗粒状,成行成列,规则分布、一般规模化的果园色彩、纹理都比较均一
	林地	分布在山区,不规则颗粒状,色彩较浓。自然林地不规则,人工林地区别于果园,株距更小、植株更密集,颗粒状不如园地明显
	草地	草地植被生物量小,颜色较浅,但内部均一度较高,纹理细腻。人工草地颜色较天然草地深(见图4)

Tab.1 Remote sensing image features and interpretation marks of mine features

Fig.3 Change of remote sensing image characteristics of open pit

Fig.4 Change of remote sensing image characteristics of solid waste and mine ecological restoration

Fig.5 Statistical histogram of changes in the ratio of mine ecological restoration area in Jilin Province during 2015 to 2019

[1]	王国才. 吉林省矿山生态环境问题防治对策研究[D]. 长春: 东北师范大学, 2004:5-15.
[1]	Wang G C. Countermeasures for prevention and control of mining ecological environment problems in Jilin Province[D]. Changchun: Northeast Normal University, 2004:5-15.
[2]	胡明忠, 王小雨. 吉林省矿山生态环境防治对策研究[J]. 环境科学与管理, 2006, 31(3):132-134.
[2]	Hu R Z, Wang X Y. Study on prevention and control countermeasures of mine ecological environment in Jilin Province[J]. Environmental Science and Management, 2006, 31 (3):132-134.
[3]	刘传深. 吉林省矿山地质环境综合研究[D]. 长春: 吉林大学, 2006:18-38.
[3]	Liu C S. Comprehensive study on the geological environment of mines in Jilin Province[D]. Changchun: Jilin University, 2006:18-38.
[4]	陈力, 赵彦宁, 姚维斌. 吉林省不同类型矿山地质环境问题及恢复治理对策初探[J]. 地质与资源, 2010, 19(2):115-118.
[4]	Chen L, Zhao Y N, Yao W B. Geological environment problems of different types of mines in Jilin Province and countermeasures for restoration and treatment[J]. Geology and Resources, 2010, 19 (2):115-118.
[5]	张志, 杨金中, 张高华, 等. 生态文明视角下的湖北省东部矿山地质环境遥感调查与分析[J]. 中国地质调查, 2016, 3(5):21-27.
[5]	Zhang Z, Yang J Z, Zhang G H, et al. Remote sensing survey and analysis of mine geological environment in eastern Hubei Province from the perspective of ecological civilization[J]. China Geological Survey, 2016, 3(5):21-27.
[6]	杨金中, 聂洪峰, 荆青青. 初论全国矿山地质环境现状与存在问题[J]. 国土资源遥感. 2017, 29(2):1-7.doi: 10.6046/gtzyyg.2017.02.01. doi: 10.6046/gtzyyg.2017.02.01
[6]	Yang J Z, Nie H F, Jing Q Q. Preliminary discussion on the current situation and existing problems of mining geological environment in China[J]. Remote Sensing for Land and Resources. 2017, 29(2):1-7.doi: 10.6046/gtzyyg.2017.02.01. doi: 10.6046/gtzyyg.2017.02.01
[7]	王海庆, 李丽, 许文佳, 等. 西藏生产矿山在用土地面积遥感调查与分析[J]. 矿产保护与利用, 2019(5):146-150.
[7]	Wang H Q, Li L, Xu W J, et al. Remote sensing survey and analysis of land area in use of production mines in Tibet[J]. Mineral Protection and Utilization, 2019(5):146-150.
[8]	高俊华, 刘立, 杨金中, 等. 在建和生产矿山环境遥感监测图制作方法初探[J]. 地理空间信息, 2020, 18(6):70-72,7.
[8]	Gao J H, Liu L, Yang J Z, et al. Preliminary study on the method of making remote sensing monitoring map of mine environment under construction and production[J]. Geospatial Information, 2020, 18(6):70-72,7.
[9]	汪洁, 殷亚秋, 于航, 等. 基于RS和GIS的浙江省矿山地质环境遥感监测[J]. 国土资源遥感, 2020, 32(1):232-236.doi: 10.6046/gtzyyg.2020.01.31. doi: 10.6046/gtzyyg.2020.01.31
[9]	Wang J, Yin Y Q, Yu H, et al. Remote sensing monitoring of mine geological environment in Zhejiang Province based on RS and GIS[J]. Remote Sensing for Land and Resources, 2020, 32(1):232-236.doi: 10.6046/gtzyyg.2020.01.31. doi: 10.6046/gtzyyg.2020.01.31
[10]	姚维岭, 荆青青, 周英杰, 等. 基于遥感动态监测的山东省矿山地质环境恢复治理典型模式分析[J]. 矿产勘查, 2015, 6(5):627-634.
[10]	Yao W L, Jing Q Q, Zhou Y J, et al. Analysis on typical model of mine geological environment restoration and management in Shandong Province based on remote sensing dynamic monitoring[J]. Mineral Exploration, 2015, 6(5):627-634.
[11]	王耿明, 朱俊凤, 朱鑫, 等. 基于国产卫星的广东省矿山地质环境恢复治理遥感监测[J]. 地矿测绘, 2017, 33(2):4-7.
[11]	Wang G M, Zhu J F, Zhu X, et al. Remote sensing monitoring of mine geological environment restoration in Guangdong Province based on domestic satellite[J]. Geological and Mineral Mapping, 2017, 33(2):4-7.
[12]	耿娇娇, 赵志芳, 牛磊, 等. 矿山生态恢复治理与矿山开发关系分析——以X省域为例[J]. 中国高新科技, 2020(15):156-158.
[12]	Geng J J, Zhao Z F, Niu L, et al. Analysis of the relationship between mine ecological restoration and mining development:Taking X Province as an example[J]. China High Technology, 2020(15):156-158.
[13]	马世斌, 李生辉, 安萍, 等. 青海省聚乎更煤矿区矿山地质环境遥感监测及质量评价[J]. 国土资源遥感, 2015, 27(2):139-145.doi: 10.6046/gtzyyg.2015.02.22. doi: 10.6046/gtzyyg.2015.02.22
[13]	Ma S B, Li S H, An P, et al. Remote sensing monitoring and quality evaluation of mine geological environment in Juhugeng coal mine area,Qinghai Province[J]. Remote Sensing for Land and Resources, 2015, 27(2):139-145.doi: 10.6046/gtzyyg.2015.02.22. doi: 10.6046/gtzyyg.2015.02.22
[14]	高永志, 郑卫政, 初禹. 基于RS与GIS的黑龙江省矿山地质环境评价研究[J]. 地质与资源, 2016, 25(2):171-175.
[14]	Gao Y Z, Zheng W Z, Chu Y. Evaluation of mine geological environment in Heilongjiang Province based on RS and GIS[J]. Geology and Resources, 2016, 25(2):171-175.
[15]	赵玉灵. 基于层次分析法的矿山环境评价方法研究——以海南岛为例[J]. 国土资源遥感, 2020, 32(1):148-153.doi: 10.6046/gtzyyg.2020.01.20. doi: 10.6046/gtzyyg.2020.01.20
[15]	Zhao Y L. Research on mine environmental assessment method based on analytic hierarchy process:A case study of Hainan Island[J]. Remote Sensing for Land and Resources, 2020, 32(1):148-153.doi: 10.6046/gtzyyg.2020.01.20. doi: 10.6046/gtzyyg.2020.01.20
[16]	高俊华, 刘莎莎, 杨金中, 等. 基于遥感的露天煤矿集中区地质环境灰关联评价——以准格尔煤田为例[J]. 国土资源遥感, 2021, 33(1):183-190.doi: 10.6046/gtzyyg.2020006. doi: 10.6046/gtzyyg.2020006
[16]	Gao J H, Liu S S, Yang J Z, et al. Remote sensing based grey correlation evaluation of geological environment in open pit coal mine concentration area:A case study of Zhungeer coalfield[J]. Remote Sensing for Land and Resources, 2021, 33(1):183-190.doi: 10.6046/gtzyyg.2020006. doi: 10.6046/gtzyyg.2020006
[17]	贾汝颖, 薄桂秋. 吉林省矿产资源概况[J]. 中国地质, 1987(9):25-26,28.
[17]	Jia R Y, Bo G Q. General situation of mineral resources in Jilin Province[J]. Geology of China, 1987(9):25-26,28.
[18]	杨继红, 王冬艳. 浅谈吉林省矿产资源利用与可持续发展[J]. 吉林地质, 2010, 29(2):161-164.
[18]	Yang J H, Wang D Y. Discussion on the utilization and sustainable development of mineral resources in Jilin Province[J]. Jilin Geology, 2010, 29(2):161-164.

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