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REMOTE SENSING FOR LAND & RESOURCES    2015, Vol. 27 Issue (4) : 144-149     DOI: 10.6046/gtzyyg.2015.04.22
Technology Application |
Land use dynamic remote sensing monitoring at the initial stage of exploitation of the Xinjie Taigemiao mining area
ZHAO Peng
Shenhua Geological Exploration Co.Ltd., Beijing 100085, China
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Abstract  

In order to identify the land use change at the initial stage of exploitation of the Xinjie Taigemiao mining area,the author selected SPOT5 images of 2007,ALOS images of 2011,WorldView images of 2012 and QuickBird images of 2013 to extract change information.Then the land use changes and conversions were made clear through analyzing,and their change rules and driving force were obtained.According to the results obtained, the cultivated land showed rapid increase from 2007 to 2013. In this period, the driving force came from the residents' expectation of the land compensation; in addition, such factors as the settlement area,the water area,the greenhouse and aquatic operations were also directly responsible for the driving force.The forest land and the land for transportation area increased mainly concentratedly in the period of 2007-2011,with the former land attributed to afforestation project,and the latter to the Xinen Railway and the Langa Highway. The land for mining and industry continued to increase because of industrial construction and natural gas exploitation, and it is still maintaining a growing trend now. As the only net decrease of land use types,the grazing land that occupied a large area of the grazing land was being driven by the interests of the inevitable choice.In addition,the wasteland change represented the intermediate process of the conversion of the grazing land into other land use types.

Keywords remote sensing image      multi-target retrieval      accuracy evaluation model     
:  TP79  
Issue Date: 23 July 2015
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ZENG Zhi
ZHOU Yongfu
DU Zhenhong
LIU Renyi
Cite this article:   
ZENG Zhi,ZHOU Yongfu,DU Zhenhong, et al. 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.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2015.04.22     OR     https://www.gtzyyg.com/EN/Y2015/V27/I4/144

[1] 聂洪峰, 杨金中, 王晓红, 等.矿产资源开发遥感监测技术问题与对策研究[J].国土资源遥感, 2007, 19(4):11-13.doi:10.6046/gtzyyg.2007.04.03. Nie H F, Yang J Z, Wang X H, et al.The problems in the remote sensing monitoring technology for the exploration of mineral resources and the countermeasures[J].Remote Sensing for Land and Resources, 2007, 19(4):11-13.doi:10.6046/gtzyyg.2007.04.03.

[2] 漆小英, 晏明星.多时相遥感数据在矿山扩展动态监测中的应用[J].国土资源遥感, 2007, 19(3):85-88.doi:10.6046/gtzyyg.2007.03.20. Qi X Y, Yan M X.Dynamic monitoring of mining area expansion based on multitemporal remote sensing images[J].Remote Sensing for Land and Resources, 2007, 19(3):85-88.doi:10.6046/gtzyyg.2007.03.20.

[3] 张磊, 吴炳方.关于土地覆被遥感监测的几点思考[J].国土资源遥感, 2011, 23(1):15-20.doi:10.6046/gtzyyg.2011.01.03. Zhang L, Wu B F.A discussion on land cover mapping[J].Remote Sensing for Land and Resources, 2011, 23(1):15-20.doi:10.6046/gtzyyg.2011.01.03.

[4] 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. 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.

[5] 曹子剑, 吴学瑜, 高振宇, 等.基于发展压力状态的土地利用动态遥感监测区域划分方法——以天津市津南区为例[J].国土资源遥感, 2013, 25(3):124-129.doi:10.6046/gtzyyg.2013.03.21. Cao Z J, Wu X Y, Gao Z Y, et al.Land use dynamic remote sensing monitoring region partitioning method based on the development pressure state:A case study of Jinnan District, Tianjin City[J].Remote Sensing for Land and Resources, 2013, 25(3):124-129.doi:10.6046/gtzyyg.2013.03.21.

[6] 尚慧, 倪万魁.石嘴山矿区地表环境动态变化遥感监测[J].国土资源遥感, 2013, 25(2):113-120.doi:10.6046/gtzyyg.2013.02.20. Shang H, Ni W K.Remote sensing monitoring of dynamic changes of surface environment in Shizuishan mining area[J].Remote Sensing for Land and Resources, 2013, 25(2):113-120.doi:10.6046/gtzyyg.2013.02.20.

[7] 张和生, 吕强, 霍官印.矿区开发不同阶段土地复垦与生态系统重建[J].煤矿环境保护, 2002, 16(1):15-17. Zhang H S, Lv Q, Huo G Y.Land reclamation and ecological rehabilitation of development different stage in mining areas[J].Coal Mine Environmental Protection, 2002, 16(1):15-17.

[8] 车仁浦.神华新街矿区三维数字化管理平台的建设研究[J].中国煤炭, 2012, 38(7):54-58. Che R P.Research on construction of 3D digital management platform for Xinjie mining area of Shenhua Group[J].China Coal, 2012, 38(7):54-58.

[9] 车仁浦.科学规划持续创新高起点建设神华新街矿区[J].煤炭经济研究, 2012, 32(7):25-27. Che R P.Scientific planning and sustainable innovation to construct Shenhua Xinjie mining area at high level[J].Coal Economic Research, 2012, 32(7):25-27.

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