Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (3) : 156-162     DOI: 10.6046/gtzyyg.2017.03.23
|
Remote sensing investigation of restoration and management situation in coal mine subsidence areas
WANG Haiqing, YANG Jinzhong, CHEN Ling, WANG Jie, ZHOU Yingjie, YAO Weiling
China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China
Download: PDF(7646 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  In this paper, the authors studied restoration and management situation in coal mine subsidence areas using remote sensing images. According to the research objective, the coal mine subsidence area in Shandong Province was chosen as the study area. Multi-stage optical remote sensing images and the technical method which included indoor research and field survey were used. Some conclusions have been reached: ① Coal mine subsidence in this area was very serious, by 2014, water area had reached 13.62 km2; ② Coal mine subsidence was developed rapidly in this area, from 2006 to 2014, coal mine subsidence water area increased by 7.78 km2 totally, and the growth rate was 133%; ③ Restoration and management situation was good in study area, its area reached 11.70 km2, and its rate was 85.90%; ④ Original restoration and management project could be destroyed by flow up coal mine subsidence, and the area needs restoration and management again; ⑤ In remote sensing images, restoration and management area is easily confused with other features, and hence it is necessary to use multi-temporal remote sensing data for careful comparison.
Keywords spatial relationship      object histogram      G statistics      land cover adjacency probability      image classification     
Issue Date: 15 August 2017
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
LI Liang
ZHANG Yun
LI Sheng
YING Guowei
Cite this article:   
LI Liang,ZHANG Yun,LI Sheng, et al. Remote sensing investigation of restoration and management situation in coal mine subsidence areas[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 156-162.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.03.23     OR     https://www.gtzyyg.com/EN/Y2017/V29/I3/156
[1] 高小六.基于D-InSAR技术的煤矿沉陷监测[J].煤炭技术,2013,32(6):78-80.
Gao X L.Coal mine subsidence monitoring based on D-InSAR technology[J].Coal Technology,2013,32(6):78-80.
[2] 李 勇,王志勇,马全明.利用3S集成技术进行矿区沉陷精确调查与分析[J].测绘通报,2014(7):63-65.
Li Y,Wang Z Y,Ma Q M.Precisely investigating and analyzing the mining subsidence based on 3S integrated technology[J].Bulletin of Surveying and Mapping,2014(7):63-65.
[3] Wang H Q,Zhou Y J,Chen L,et al.The dynamic monitoring on mining collapsing around Xinglongzhuang coal mine based on remote sensing images[J].Advanced Materials Research,2013,726-731:4625-4630.
[4] Wang H Q.Mining subsidence monitoring around Longgu coal mine based on remote sensing[J].Advanced Materials Research,2014,1010-1012:489-495.
[5] 王海庆,陈 玲.山东省济宁市煤矿矿集区地面沉陷现状遥感调查[J].中国地质灾害与防治学报,2011,22(1):87-93.
Wang H Q,Chen L.Investigation of current surface subsidence situation using remote semsing images,in Jining coal mine concentration area,Shandong Province[J].The Chinese Journal of Geological Hazard and Control,2011,22(1):87-93.
[6] 王海庆,聂洪峰,陈 玲,等.采矿沉陷遥感调查与危害性研究[J].国土资源遥感,2016,28(1):114-121.doi:10.6046/gtzyyg.2016.01.17"> doi:10.6046/gtzyyg.2016.01.17.
Wang H Q,Nie H F,Chen L,et al.Remote sensing investigation of mining subsidence and harmfulness research[J].Remote Sensing for Land and Resources,2016,28(1):114-121.doi:10.6046/gtzyyg.2016.01.17"> doi:10.6046/gtzyyg.2016.01.17.
[7] 王钦军,陈 玉,蔺启忠.矿山地面塌陷的高分辨率遥感识别与边界提取[J].国土资源遥感,2011,23(3):113-116.doi:10.6046/gtzyyg.2011.03.20"> doi:10.6046/gtzyyg.2011.03.20.
Wang Q J,Chen Y,Lin Q Z.Surface collapse identification and its boundary extraction using high resolution remote sensing[J].Remote Sensing for Land and Resources,2011,23(3):113-116.doi:10.6046/gtzyyg.2011.03.20"> doi:10.6046/gtzyyg.2011.03.20.
[8] 许 冬,吴 侃.济宁煤矿区地表塌陷积水时空演变[J].辽宁工程技术大学学报(自然科学版),2014,33(10):1307-1311.
Xu D,Wu K.Spatial-temporal evolution of collapse waterlogged region in Jining coal mining district[J].Journal of Liaoning Technical University:Natural Science,2014,33(10):1307-1311.
[9] 姚丹丹,吴 侃,何 强.基于D-InSAR技术的煤矿区沉陷监测[J].金属矿山,2014,32(11):151-155.
Yao D D,Wu K,He Q.Coal mine subsidence monitoring based on D-InSAR technology[J].Metal Mine,2014,32(11):151-155.
[10] 王 强.采煤塌陷区综合治理初探[J].能源与节能,2016(1):116-117.
Wang Q.On the comprehensive management used in coal mining subsidence[J].Energy and Energy Conservation,2016(1):116-117.
[11] 朱思东.煤矿塌陷区的复垦与展望[J].山东煤炭科技,2015(11):209-210.
Zhu S D.Reclamation and prospect of coal mine subsidence area[J].Shandong Coal Science and Technology,2015(11):209-210.
[12] 黄晓娜,李新举,刘 宁,等.煤矿塌陷区不同复垦年限土壤颗粒组成分形特征[J].煤炭学报,2014,39(6):1140-1146.
Huang X N,Li X J,Liu N,et al.Characteristics of soil particles fractal dimension under different reclamation years in coal mining subsidence[J].Journal of China Coal Society,2014,39(6):1140-1146.
[13] 王洪丹,王金满,曹银贵,等.黄土区露天煤矿排土场土壤与地形因子对植被恢复的影响[J].生态学报,2016,36(16):5098-5108.
Wang H D,Wang J M,Cao Y G,et al.Effect of soil and topography on vegetation restoration in an opencast coal mine dump in a loess area[J].Acta Ecologica Sinica,2016,36(16):5098-5108.
[14] 王 平,王金满,刘伟红.黄土丘陵区露天煤矿复垦排土场地形因子对土壤有机碳的影响[J].中国煤炭,2015(9):128-131.
Wang P,Wang J M,Liu W H.Impact of topographical factor on soil organic carbon in reclamation spoibank of an opencast coal mine in loess hilly area[J].China Coal,2015(9):128-131.
[15] 陈孝杨,王 芳,严家平,等.覆土厚度对矿区复垦土壤呼吸昼夜变化的影响[J].中国矿业大学学报,2016,45(1):163-169.
Chen X Y,Wang F,Yan J P,et al.Effect of coversoil thickness on diurnal variation characteristics of reclaimed soil respiration in coal mining areas[J].Journal of China University of Mining and Technology,2016,45(1):163-169.
[16] 杨勤学,赵冰清,郭东罡.中国北方露天煤矿区植被恢复研究进展[J].生态学杂志,2015,34(4):1152-1157.
Yang Q X,Zhao B Q,Guo D G.A review on vegetation restoration of opencast coal mine areas in northern China[J].Chinese Journal of Ecology,2015,34(4):1152-1157.
[17] 张紫昭,郭瑞清,周天生,等.新疆煤矿土地复垦为草地的适宜性评价方法与应用[J].农业工程学报,2015,31(11):278-286.
Zhang Z Z,Guo R Q,Zhou T S,et al.Suitability evaluation method and application for land reclamation to grassland in Xinjiang coal mines[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(11):278-286.
[18] 付梅臣,郭卫斌,李建民,等.我国煤矿区低碳型土地复垦现状与展望[J].中国矿业,2015,24(5):49-52.
Fu M C,Guo W B,Li J M,et al.Low-carbon land reclamation in China’s coal mining areas:Current situation and prospects[J].China Mining Magazine,2015,24(5):49-52.
[19] 杨 逾,张培兰.牛心台煤矿采煤沉陷区土地复垦工程分析[J].辽宁工程技术大学学报(自然科学版),2015,34(8):926-929.
Yang Y,Zhang P L.Analysis on land reclamation in Niuxintai coal mining subsidence area[J].Journal of Liaoning Technical University(Natural Science),2015,34(8):926-929.
[20] 王 琛,孙庆业,韦绪好,等.堆肥施用和牧草种植对煤矿复垦地土壤肥力的影响[J].土壤通报,2015,46(5):1174-1180.
Wang C,Sun Q Y,Wei X H,et al.Effect of compost application and forage sowing on soil physical and chemical properties in reclamation land of coal-mine[J].Chinese Journal of Soil Science,2015,46(5):1174-1180.
[21] 吴国伟,赵艳玲,付艳华,等.复垦矿区土地利用类型变化对植被碳储量的影响[J].中国生态农业学报,2015,23(11):1437-1444.
Wu G W,Zhao Y L,Fu Y H,et al.Impact of reclamation-driven land use change on vegetation carbon store in mining areas[J].Chinese Journal of Eco-Agriculture,2015,23(11):1437-1444.
[22] 黄元仿,张世文,张立平,等.露天煤矿土地复垦生物多样性保护与恢复研究进展[J].农业机械学报,2015,46(8):72-82.
Huang Y F,Zhang S W,Zhang L P,et al.Research progress on conservation and restoration of biodiversity in land reclamation of opencast coal mine[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(8):72-82.
[23] 吕玉凤.遥感技术在矿山环境恢复治理中的应用研究[J].科技广场,2015(8):42-46.
Lyu Y F.Application research of remote sensing technology in mine environment restoration[J].Science Mosaic,2015(8):72-82.
[24] 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.
Yao W L,Jing Q Q,Zhou Y J,et al.Analysis on typical pattern of mine geological environment restoration and governance based on remote sensing dynamic monitoring in Shandong[J].Mineral Exploration,2015,6(5):627-634.
[25] Jiang H B,Su Y Y,Jiao Q S,et al.Typical geologic disaster surveying in Wenchuan 8.0 earthquake zone using high resolution ground LiDAR and UAV remote sensing[C]//Proceedings SPIE 9262,Lidar Remote Sensing for Environmental Monitoring XIV.Beijing,China:SPIE,2014:926219.
[1] BAI Junlong, WANG Zhangqiong, YAN Haitao. A K-means clustering-guided threshold-based approach to classifying UAV remote sensed images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 114-120.
[2] HAN Yanling, CUI Pengxia, YANG Shuhu, LIU Yekun, WANG Jing, ZHANG Yun. Classification of hyperspectral image based on feature fusion of residual network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 11-19.
[3] SUN Ke. Remote sensing image classification based on super pixel and peak density[J]. Remote Sensing for Land & Resources, 2020, 32(4): 41-45.
[4] CAI Zhiling, WENG Qian, YE Shaozhen, JIAN Cairen. Remote sensing image scene classification based on Inception-V3[J]. Remote Sensing for Land & Resources, 2020, 32(3): 80-89.
[5] Pengyan HUANG, Lijing BU, Yongliang FAN. Integrating visual features in polarimetric SAR image classification[J]. Remote Sensing for Land & Resources, 2020, 32(2): 88-93.
[6] Benzuo YAO, Fang HE. Spatial and spectral feature hierarchical fusion for hyperspectral image feature extraction[J]. Remote Sensing for Land & Resources, 2019, 31(3): 59-64.
[7] Wanjun LIU, Tianhui LI, Haicheng QU. Hyperspectral similar sample classification algorithm based on Fisher criterion and TrAdaboost[J]. Remote Sensing for Land & Resources, 2018, 30(4): 41-48.
[8] Jia XU, Chunqi YUAN, Yuane CHENG, chenyu ZENG, Kang XU. Active deep learning based polarimetric SAR image classification[J]. Remote Sensing for Land & Resources, 2018, 30(1): 72-77.
[9] BU Lijing, HUANG Pengyan, SHEN Lu. Integrating color features in polarimetric SAR image classification[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 59-64.
[10] LI Liang, ZHANG Yun, LI Sheng, YING Guowei. Classification of remote sensing images based on the fusion of spatial relationship[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 10-16.
[11] LI Liang, ZHOU Yaguang, LIANG Bin, XU Qing. Remote sensing image classification based on fusion of temporal features[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 36-42.
[12] LI Liang, LIANG Bin, XUE Peng, YING Guowei. Remote sensing image classification based on G statistics of object histogram[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 71-76.
[13] LI Liang, YING Guowei, WEN Xuehu, HE Xin. Classification of high spatial resolution remotely sensed images by temporal feature fusion[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 91-98.
[14] ZHANG Huanxue, LI Qiangzi, WEN Ning, Du Xin, TAO Qingshan, TIAN Yichen. Important factors affecting crop acreage estimation based on remote sensing image classification technique[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(4): 54-61.
[15] HOU Qunqun, WANG Fei, YAN Li. Extraction of color image texture feature based on gray-level co-occurrence matrix[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(4): 26-32.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech