Surface Collapse Identification and Its Boundary Extraction Using High Resolution Remote Sensing

doi:10.6046/gtzyyg.2011.03.20

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Abstract

In order to extract surface collapse’s boundary and calculate its area precisely,this paper summed up the identification keys to hazards and introduced a method for extracting the boundary of surface collapse. In this method,high resolution remote sensing image is used as the data source. Based on image fusion,the authors applied the Robert and Directional operators to the fused image respectively. Next,the first band in the original and two enhanced images were used to form false color image(Original,Robert and Direction enhanced image,ORD). Finally,the surface collapse area was calculated using ArcGIS software. The results show that the method can highlight the boundary of the surface collapse and reduce the error of the area calculation effectively.

Keywords ASTER SWIR Alteration anomalies Chambishi Relative absorption-Band Depth (RBD)

TP 751.1

Issue Date: 07 September 2011

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	YU Jian
	DONG Yu-sen
	ZHANG Zhi
	YANG Ri-hong

Cite this article:

YU Jian,DONG Yu-sen,ZHANG Zhi, et al. Surface Collapse Identification and Its Boundary Extraction Using High Resolution Remote Sensing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(3): 113-116.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2011.03.20 OR https://www.gtzyyg.com/EN/Y2011/V23/I3/113

[1] 黎来福,王秀丽.SPOT-5卫星遥感数据在煤矿塌陷区监测中的应用
[J].矿山测量,2008(2):45-47.

[2] 李成尊,聂洪峰,汪劲,等.矿山地质灾害特征遥感研究
[J].国土资源遥感,2005(1):45-49.

[3] 许长辉,高井祥,王坚,等.多源多时相遥感数据融合在煤矿塌陷地中应用研究
[J].水土保持研究,2008,15(1):92-95.

[4] 彭苏萍,王磊,孟召平,等.遥感技术在煤矿区积水塌陷动态监测中的应用——以淮南矿区为例
[J].煤炭学报,2002,27(4):374-378.

[5] 丁爱华,鲁明星,吕冰.基于遥感技术的采煤塌陷区时空演变研究
[J].安徽农业科学,2010,38(7):3723-3725.

[6] 张敦虎,卢中正,强建华,等.地面塌陷特征及其遥感识别方法研究
[J].地质灾害与环境保护,2009,20(3):8-12.

[7] Wang J A,Shang X C,Ma H T.Investigation of Catastrophic Ground Collapse in Xingtai Gypsum Mines in China
[J].International Journal of Rock Mechanics & Mining Sciences,2008,45(8):1480-1499.

[8] Hervas J,Barredo J I,Rosin P L,et al.Monitoring Landslides from Optical Remotely Sensed Imagery:the Case History of Tessina landslide,Italy
[J].Geomorphology,2003,54(1-2):63-75.

[9] Eeckhaut M V D,Vanwalleghem T,Poesen J,et al.Prediction of Landslide Susceptibility Using Rare Events Logistic Regression:a Case Study in the Flemish Ardennes,Belgium
[J].Geomorphology,2006,76:392-410.

[10] 中华人民共和国区域地质调查报告(清水幅,J-50-4-A,1∶50 000)
[R].北京:北京市地质局,1983.

[11] 谢立民,王长青,王文武,等.地面塌陷及地裂缝的成因及防治——葫芦岛南票区及连山区矿产开采引起的地质灾害
[J].化工矿产地质,2007,29(2):101-106.

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