Please wait a minute...
 
自然资源遥感  2021, Vol. 33 Issue (3): 148-155    DOI: 10.6046/zrzyyg.2020358
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
煤炭开采背景下的伊金霍洛旗土地利用变化强度分析
桑潇1(), 张成业1,2, 李军1,2(), 朱守杰1, 邢江河1, 王金阳1, 王兴娟1, 李佳瑶1, 杨颖1
1.中国矿业大学(北京)地球科学与测绘工程学院,北京 100083
2.中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京 100083
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
全文: PDF(3123 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

为探索矿区煤炭开采活动在不同阶段对各类型土地利用类型的影响差异和特征,以我国重要产煤区伊金霍洛旗为研究区,以1990—2019年近30 a间的多期Landsat遥感影像为主要数据源,在Google Earth Engine平台上采用随机森林分类法提取土地利用分布信息,结合煤炭开采统计数据,利用强度分析理论对煤炭开采3个阶段的矿区土地利用变化特征进行分析。结果表明: ①强度变化理论可对土地利用变化从间隔层次、类别层次、转化层次进行全面分析,同时更加系统地展示出研究区的土地利用变化特征及人类活动产生的影响,对深入理解土地利用变化过程具有重要意义; ②煤炭开采对不同地类的影响具有差异,其主要影响地类为植被、水域、裸地; ③煤炭开采在不同阶段对各类用地的影响作用具有差异,在煤炭开采起步阶段,对各种类型用地影响较小; 在煤炭开采高速发展阶段,煤炭开采对各类型用地的影响加大,主要影响矿区及周边植被、裸地和水域; 在煤炭开采平稳发展阶段,对各地类的影响强度减小。研究结果可服务于制定在不同阶段对不同地类的精准防护实施方案,为矿区生态环境的保护提供科学依据。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
桑潇
张成业
李军
朱守杰
邢江河
王金阳
王兴娟
李佳瑶
杨颖
关键词 煤炭开采Landsat遥感影像土地利用变化强度分析理论    
Abstract

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.

Key wordscoal mining    Landsat remote sensing images    land use change    intensity analysis theory
收稿日期: 2020-11-17      出版日期: 2021-09-24
ZTFLH:  TP79  
基金资助:中国矿业大学(北京)越崎青年学者资助计划;中央高校基本科研业务费项目“露天矿区生态环境协同演变遥感大数据监测与分析”(2021YQDC02);大学生创新训练项目“基于多源遥感数据的矿产资源开发监测与影响评价”(C202002179)
通讯作者: 李军
作者简介: 桑 潇(1993-),女,博士研究生,主要从事自然资源监测与评价研究。Email: sangxiao@student.cumtb.edu.cn
引用本文:   
桑潇, 张成业, 李军, 朱守杰, 邢江河, 王金阳, 王兴娟, 李佳瑶, 杨颖. 煤炭开采背景下的伊金霍洛旗土地利用变化强度分析[J]. 自然资源遥感, 2021, 33(3): 148-155.
SANG Xiao, ZHANG Chengye, LI Jun, ZHU Shoujie, XING Jianghe, WANG Jinyang, WANG Xingjuan, LI Jiayao, YANG Ying. Application of intensity analysis theory in the land use change in Yijin Holo Banner under the background of coal mining. Remote Sensing for Natural Resources, 2021, 33(3): 148-155.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2020358      或      https://www.gtzyyg.com/CN/Y2021/V33/I3/148
Fig.1  研究区范围
Fig.2  伊金霍洛旗历年原煤产量
Fig.3  研究区土地利用专题图
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  分类结果精度
土地利
用类型
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  研究区各土地利用类型面积及占比
Fig.4  土地利用变化强度-间隔层次
类别 1990—2000年
(S=3.38%)
2000—2005年
(S=6.08%)
2005—2010年
(S=5.74%)
2010—2015年
(S=5.57%)
2015—2019年
(S=6.05%)
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  土地利用变化强度-类别层次
Fig.5  土地利用变化强度-转化层次
[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
[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
[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
[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.
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
[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
[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
[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
[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
[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
[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
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
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.
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
[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
[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
[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
[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.
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.
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
[1] 杨潋威, 赵娟, 朱家田, 刘雷, 张平. 基于PLUS和InVEST模型的西安市生态系统碳储量时空变化与预测[J]. 自然资源遥感, 2022, 34(4): 175-182.
[2] 宋奇, 冯春晖, 马自强, 王楠, 纪文君, 彭杰. 基于1990—2019年Landsat影像的干旱区绿洲土地利用变化与模拟[J]. 自然资源遥感, 2022, 34(1): 198-209.
[3] 汪清川, 奚砚涛, 刘欣然, 周文, 徐欣冉. 生态服务价值对土地利用变化的时空响应研究——以徐州市为例[J]. 自然资源遥感, 2021, 33(3): 219-228.
[4] 郭瑞琦, 陆波, 陈恺霖. 基于CLUMondo模型的多情景土地利用变化动态模拟——以广西沿海城市为例[J]. 国土资源遥感, 2020, 32(1): 176-183.
[5] 张瑜, 赵晓丽, 左丽君, 张增祥, 徐进勇. 黄土高原土地利用变化对生态系统服务价值的影响[J]. 国土资源遥感, 2019, 31(3): 132-139.
[6] 詹雅婷, 朱叶飞, 苏一鸣, 崔艳梅. 基于国土资源卫星的盐城海岸带生态环境变化调查[J]. 国土资源遥感, 2017, 29(s1): 160-165.
[7] 张登荣, 许思莹, 谢斌, 吴文渊, 路海峰. 近40年椒江-台州湾滩涂围垦土地利用变化的遥感调查[J]. 国土资源遥感, 2016, 28(1): 101-106.
[8] 杨娅楠, 王金亮, 陈光杰, 习晓环, 王成. 抚仙湖流域土地利用格局与水质变化关系[J]. 国土资源遥感, 2016, 28(1): 159-165.
[9] 赵鹏. 新街台格庙矿区开发初期土地利用动态遥感监测[J]. 国土资源遥感, 2015, 27(4): 144-149.
[10] 方秀琴, 任立良, 李琼芳. 近40年老哈河流域土地利用变化监测与分析 [J]. 国土资源遥感, 2012, 24(2): 125-131.
[11] 魏显虎, 张增祥, 胡顺光, 刘芳. 北京山区土地利用转移的系统性和随机性[J]. 国土资源遥感, 2010, 22(4): 77-84.
[12] 冯永玖, 韩震. 基于遥感的黄浦江沿岸土地利用时空演化特征分析[J]. 国土资源遥感, 2010, 22(2): 91-96.
[13] 高迎春, 王利香, 佟连军, 尹君. 石家庄城市边缘区土地利用变化分析[J]. 国土资源遥感, 2010, 22(1): 107-111.
[14] 燕云鹏, 和正民, 李建存, 曾福年. 环北京地区土地利用变化监测与分析[J]. 国土资源遥感, 2008, 20(1): 64-67.
[15] 路云阁, 蔡运龙. 基于空间连续数据的小流域景观格局破碎化研究[J]. 国土资源遥感, 2007, 19(2): 60-64.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
版权所有 © 2015 《自然资源遥感》编辑部
地址:北京学院路31号中国国土资源航空物探遥感中心 邮编:100083
电话:010-62060291/62060292 E-mail:zrzyyg@163.com
本系统由北京玛格泰克科技发展有限公司设计开发