Please wait a minute...
Remote Sensing for Natural Resources    2021, Vol. 33 Issue (3) : 148-155     DOI: 10.6046/zrzyyg.2020358
Application of intensity analysis theory in the land use change in Yijin Holo Banner under the background of coal mining
SANG Xiao1(), ZHANG Chengye1,2, LI Jun1,2(), ZHU Shoujie1, XING Jianghe1, WANG Jinyang1, WANG Xingjuan1, LI Jiayao1, YANG Ying1
1. College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083,China
2. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology-Beijing, Beijing 100083,China
Download: PDF(3123 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    

This study aims to explore the differences and characteristics of the impacts of coal mining activities at different stages on various land use types in mining areas. Taking Yijin Huoluo Banner-a major coal-producing area in China-as the study area and multi-stage Landsat remote sensing images of nearly 30 years during 1990-2019 as the main data source, this study extracted land use distribution information using the random forest classification method on the Google Earth Engine platform. Based on this as well as coal mining statistical data, this paper analyzed the characteristics of land use changes at three stages of coal mining using the intensity analysis theory. The results are as follows. ① The intensity change theory can be used to comprehensively analyze the land use change from the aspects of intervals, categories, and transformation and to more systematically exhibit the characteristics of land use changes and the impacts of human activities in the study area. These are greatly significant for the in-depth understanding of the land use change process. ② Coal mining produces different impacts on different types of land, and it primarily affects the vegetation, water areas, and bare land. ③ Coal mining imposes different impacts on various types of land at different stages. It produces slight impacts on various types of land at the initial stage. It produces increasing impacts on various types of land at the high-speed development stage, during which it mainly affects vegetation, bare land, and water areas in and around the mining area. Then the impacts decrease at the steady development stage of coal mining. The results of this study can serve the implementation of precise protection plans for different types of land at different coal mining stages and provide a scientific basis for the protection of the ecological environment in the mining area.

Keywords coal mining      Landsat remote sensing images      land use change      intensity analysis theory     
ZTFLH:  TP79  
Corresponding Authors: LI Jun     E-mail:;
Issue Date: 24 September 2021
E-mail this article
E-mail Alert
Articles by authors
Chengye ZHANG
Jun LI
Shoujie ZHU
Jianghe XING
Jinyang WANG
Xingjuan WANG
Jiayao LI
Cite this article:   
Xiao SANG,Chengye ZHANG,Jun LI, et al. Application of intensity analysis theory in the land use change in Yijin Holo Banner under the background of coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(3): 148-155.
URL:     OR
Fig.1  Study area
Fig.2  Raw coal production in Yijin Holo Banner
Fig.3  Land use thematic map of study area
年份 总体精度 Kappa系数
1990年 0.91 0.81
2000年 0.91 0.81
2005年 0.94 0.85
2010年 0.91 0.78
2015年 0.95 0.86
2019年 0.90 0.73
Tab.1  Classification results accuracy
1990年 2000年 2005年 2010年 2015年 2019年
面积/km2 占比/% 面积/km2 占比/% 面积/km2 占比/% 面积/km2 占比/% 面积/km2 占比/% 面积/km2 占比/%
水域 150.89 2.75 123.14 2.25 91.99 1.68 57.18 1.04 74.25 1.35 87.79 1.60
植被 3 898.04 71.07 3 931.19 71.66 4 115.91 75.03 4 332.48 78.99 4 388.27 80.00 4 547.15 82.91
耕地 43.02 0.78 128.74 2.35 214.25 3.91 320.12 5.84 389.98 7.11 459.45 8.38
人工用地 8.36 0.15 31.10 0.57 42.77 0.78 65.52 1.19 112.34 2.05 146.69 2.67
采矿用地 0 0 3.19 0.06 21.32 0.39 42.27 0.77 68.51 1.25 75.90 1.38
裸地 1 384.74 25.25 1 267.69 23.11 998.81 18.21 667.48 12.17 451.70 8.24 168.07 3.06
Tab.2  Area and ratio of land use types in study area
Fig.4  Land use intensity change of study area at interval level
类别 1990—2000年
增加 减少 增加 减少 增加 减少 增加 减少 增加 减少
水域 3.94 5.78 6.71 11.77 4.44 12.01 14.65 8.68 14.67 10.11
植被 2.33 2.24 4.51 3.67 4.15 3.10 3.44 3.18 4.01 3.10
耕地 29.20 9.27 28.46 15.17 25.10 15.22 25.32 16.40 22.87 18.42
人工用地 35.07 7.87 25.36 17.85 26.85 16.21 27.17 12.88 21.45 13.81
采矿用地 127.90 14.23 35.17 15.52 25.40 12.99 17.07 14.37
裸地 5.26 6.11 7.86 12.10 6.74 13.37 11.04 14.44 4.42 20.12
Tab.3  Land use intensity change of study area at category level
Fig.5  Land use intensity change of study area at transition level
[1] Liu J, Yang T, Wang L, et al. Research progress in coal and gas co-mining modes in China[J]. Energy Science and Engineering, 2020, 8:3365-3376.
doi: 10.1002/ese3.v8.9 url:
[2] Wang G F, Xu Y X, Ren H W. Intelligent and ecological coal mining as well as clean utilization technology in China:Review and prospects[J]. International Journal of Mining Science and Technology, 2019, 29:161-169.
doi: 10.1016/j.ijmst.2018.06.005 url:
[3] Zhang M, Wang J M, Li S J, et al. Dynamic changes in landscape pattern in a large-scale opencast coal mine area from 1986 to 2015:A complex network approach[J]. Catena, 2020(194):104738.
[4] Gorokhovich Y, Reid M, Mignone E, et al. Prioritizing abandoned coal mine reclamation projects within the contiguous United States using geographic information system extrapolation[J]. Environmental Management, 2003, 32(4):527-534.
pmid: 14986901
[5] Qian D, Yan C, Xing Z, et al. Monitoring coal mine changes and their impact on landscape patterns in an alpine region:A case study of the Muli coal mine in the Qinghai-Tibet Plateau[J]. Environmental Monitoring and Assessment, 2017, 189(11),559-570.
doi: 10.1007/s10661-017-6284-9 url:
[6] 战甜. 霍林河南露天矿土地利用与景观格局变化研究[D]. 呼和浩特:内蒙古农业大学, 2017.
[6] Zhan T. Study on land use and landscape pattern change in Huolinhenan open-pit coal mine[D]. Hohhot:Inner Mongolia Agricultural University, 2017.
[7] Feng Y, Wang J, Bai Z, et al. Effects of surface coal mining and land reclamation on soil properties:A review[J]. Earth-Science Reviews, 2019(191):12-25.
[8] Li J, Pei Y Q, Zhao S H, et al. A review of remote sensing for environmental monitoring in China[J]. Remote Sensing, 2020, 12(7):1130.
doi: 10.3390/rs12071130 url:
[9] Zhang M, Wang J M, Feng Y. Temporal and spatial change of land use in a large-scale opencast coal mine area:A complex network approach[J]. Land Use Policy, 2019, 86:375-386.
doi: 10.1016/j.landusepol.2019.05.020
[10] Rendenieks Z, Nita M D, Nikodemus O, et al. Half a century of forest cover change along the Latvian-Russian border captured by object-based image analysis of Corona and Landsat TM/OLI data[J]. Remote Sensing of Environment, 2020, 249:112010.
doi: 10.1016/j.rse.2020.112010 url:
[11] Negri R G, da Silva E A, Casaca W. Inducing contextual classifications with kernel functions into support vector machines[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(6):962-966.
doi: 10.1109/LGRS.2018.2816460 url:
[12] Berhane T M, Lane C R, Wu Q S, et al. Decision-tree,rule-based,and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory[J]. Remote Sensing, 2018, 10(4):580.
doi: 10.3390/rs10040580 pmid: 30147945
[13] Garosi Y, Sheklabadi M, Pourghasemi H R, et al. Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping[J]. Geoderma, 2018, 330:65-78.
doi: 10.1016/j.geoderma.2018.05.027 url:
[14] Alimjan G, Sun T L, Liang Y, et al. A new technique for remote sensing image classification based on combinatorial algorithm of SVM and KNN[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2018, 32(7):1859012.
doi: 10.1142/S0218001418590127 url:
[15] Bazi Y, Melgani F. Convolutional SVM networks for object detection in UAV imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(6):3107-3118.
doi: 10.1109/TGRS.2018.2790926 url:
[16] 桑潇, 国巧真, 潘应阳, 等. 基于TM和OLI数据山西省潞城市土地利用动态变化分析与预测[J]. 国土资源遥感, 2018, 30(2):125-131.doi: 10.6046/gtzyyg.2018.02.17.
doi: 10.6046/gtzyyg.2018.02.17
[16] Sang X, Guo Q Z, Pan Y Y, et al. Research on land use dynamic change and prediction in Lucheng City of Shanxi Province based on TM and OLI[J]. Remote Sensing for Land and Resources, 2018, 30(2):125-131.doi: 10.6046/gtzyyg.2018.02.17.
doi: 10.6046/gtzyyg.2018.02.17
[17] Aldwaik S Z Pontius R G J. Intensity analysis to unify measurements of size and stationarity of land changes by interval,category,and transition[J]. Landscape Urban Planning, 2012(106):103-114.
[18] 孙云华, 郭涛, 崔希民. 昆明市土地利用变化的强度分析与稳定性研究[J]. 地理科学进展, 2016, 35(2):245-254.
doi: 10.18306/dlkxjz.2016.02.011
[18] Sun Y H, Guo T, Cui X M. Intensity analysis and stationarity of land use change in Kunming City[J]. Progress in Geography, 2016, 35(2):245-254.
[19] Sang X, Guo Q Z, Wu X X, et al. Intensity and stationarity analysis of land use change based on CART algorithm[J]. Scientific Reports, 2019(9):12279.
[20] 邵亚奎, 王蕾, 朱长明, 等. GEE云平台支持下的西天山森林遥感监测与时空变化分析[J]. 测绘通报, 2020(8):13-17.
[20] Shao Y K, Wang L, Zhu C M, et al. Forest survey and spatio-temporal analysis in West Tianshan mountains supported by Google Earth Engine[J]. Bulletin of Surveying and Mapping, 2020(8):13-17.
[21] Gong P, Li X C, Wang J, et al. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018[J]. Remote Sensing of Environment, 2020, 236:111510.
doi: 10.1016/j.rse.2019.111510 url:
[22] Parente L, Mesquita V, Miziara F, et al. Assessing the pasturelands and livestock dynamics in Brazil,from 1985 to 2017:A novel approach based on high spatial resolution imagery and Google Earth Engine cloud computing[J]. Remote Sensing of Environment, 2019(232):111301.
[23] Phan T N, Kuch V, Lehnert L W. Land cover classification using Google Earth Engine and random forest classifier:The role of image composition[J]. Remote Sensing, 2020, 12(15):2411.
doi: 10.3390/rs12152411 url:
[24] Tassi A, Vizzari M. Object-oriented LULC classification in Google Earth Engine combining SNIC,GLCM,and machine learning algorithms[J]. Remote Sensing, 2020, 12(22):3776.
doi: 10.3390/rs12223776 url:
[25] Lin L L, Hao Z B, Post C J, et al. Monitoring land cover change on a rapidly urbanizing island using Google Earth Engine[J]. Applied Sciences-Basel, 2020, 10(25):7336.
[26] Fashae O A, Adagbasa E G, Olusola A O, et al. Land use/land cover change and land surface temperature of Ibadan and environs,Nigeria[J]. Environmental Monitoring and Assessment, 2020, 192(2):109.
doi: 10.1007/s10661-019-8054-3 url:
[27] 董欣, 刘鹏程. 基于GEE的土地利用变化对生态系统服务价值的影响研究——以京津冀地区为例[J]. 华中师范大学学报(自然科学版), 2020, 54(4):670-678.
[27] Dong X, Liu P C. Impacts study of GEE-based land use changes on ecosystem service value(ESV):Take the Beijing-Tianjin-Hebei Region as an example[J]. Journal of Central China Normal University (Natural Sciences), 2020, 54(4):670-678.
[28] 杨可明. 遥感原理与应用[M]. 北京: 中国矿业大学出版社, 2016:231-234.
[28] Yang K M. Remote sensing principle and applications[M]. Beijing: China University of Mining and Technology Press, 2016:231-234.
[29] Cerrillo R M N, Rodriguez G P, Rumbao I C, et al. Modeling major rural land-use changes using the GIS-based cellular automata metronamica model:The case of andalusia (Southern Spain)[J]. ISPRS International Journal of Geo-Information, 2020, 9(7):458.
doi: 10.3390/ijgi9070458 url:
[1] YANG Lianwei, ZHAO Juan, ZHU Jiatian, LIU Lei, ZHANG Ping. Spatial-temporal change and prediction of carbon stock in the ecosystem of Xi’an based on PLUS and InVEST models[J]. Remote Sensing for Natural Resources, 2022, 34(4): 175-182.
[2] XU Zixing, JI Min, ZHANG Guo, CHEN Zhenwei. Method for dynamic prediction of mining subsidence based on the SBAS-InSAR technology and the logistic model[J]. Remote Sensing for Natural Resources, 2022, 34(2): 20-29.
[3] SONG Qi, FENG Chunhui, MA Ziqiang, WANG Nan, JI Wenjun, PENG Jie. Simulation of land use change in oasis of arid areas based on Landsat images from 1990 to 2019[J]. Remote Sensing for Natural Resources, 2022, 34(1): 198-209.
[4] WANG Qingchuan, XI Yantao, LIU Xinran, ZHOU Wen, XU Xinran. Spatial-temporal response of ecological service value to land use change: A case study of Xuzhou City[J]. Remote Sensing for Natural Resources, 2021, 33(3): 219-228.
[5] Ruiqi GUO, Bo LU, Kailin CHEN. Dynamic simulation of multi-scenario land use change based on CLUMondo model: A case study of coastal cities in Guangxi[J]. Remote Sensing for Land & Resources, 2020, 32(1): 176-183.
[6] Yu ZHANG, Xiaoli ZHAO, Lijun ZUO, Zengxiang ZHANG, Jinyong XU. The impact of land use change on ecosystem services value in Loess Plateau[J]. Remote Sensing for Land & Resources, 2019, 31(3): 132-139.
[7] ZHAN Yating, ZHU Yefei, SU Yiming, CUI Yanmei. Eco-environmental changes in Yancheng coastal zone based on the domestic resource satellite data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(s1): 160-165.
[8] ZHANG Dengrong, XU Siying, XIE Bin, WU Wenyuan, LU Haifeng. Land use change of reclaimed mud flats in Jiaojiang-Taizhou Estuary in the past 40 years based on remote sensing technology[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 101-106.
[9] YANG Yanan, WANG Jinliang, CHEN Guangjie, XI Xiaohuan, WANG Cheng. Relationship between land use pattern and water quality change in Fuxian Lake basin[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 159-165.
[10] ZHAO Peng. Land use dynamic remote sensing monitoring at the initial stage of exploitation of the Xinjie Taigemiao mining area[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(4): 144-149.
[11] FANG Xiu-qin, REN Li-liang, LI Qiong-fang. The Detection and Analysis of Land Use Change in the Laoha River Basin During the Past Four Decades[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(2): 125-131.
[12] WEI Xian-Hu, ZHANG Zeng-Xiang, HU Shun-Guang, LIU Fang. Random and Systematic Land-use Transitions in Mountainous Area of Beijing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(4): 77-84.
[13] FENG Yong-Jiu, HAN Zhen. Remote Sensing Based Spatio-temporal Evolution of Land Use Pattern in Huangpu River Coast[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(2): 91-96.
[14] DU Jun, YANG Qing-Hua. An Analysis of Regional Ecological Risk Based on Land Use Change  and
Spatial Statistics: A Case Study in Wuhan, Hubei Province
[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(2): 102-106.
[15] GAO Ying-Chun, WANG Li-Xiang, TONG Lian-Jun, YIN Jun. An Analysis of Land Use Change in Urban Boroler Areas of Shijiazhuang City[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(1): 107-111.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech