Three-dimensional Discriminate of Mine Hidden Geological Disaster Based on 3S Technology of Anning Phosphate Area

doi:10.6046/gtzyyg.2012.02.25

Download: PDF(1031 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract With the Anning phosphate mine as the study area and on the basis of the existing interpretation of signs, the high resolution remote sensing images and ArcGIS software were used to delineate and calculate the areas of geological disaster and affected objects and verify the correctness or incorrectness of the information in field. This method is highly feasible and has effective visualization, and the results achieved can provide basic information and decision support for mining business owners and relevant authorities.

Keywords spectrometer remote sensing image stripe noise normalized grey level

TP 79

Issue Date: 03 June 2012

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	LAN Qiong-qiong
	ZHANG Li-fu
	WU Tai-xia

Cite this article:

LAN Qiong-qiong,ZHANG Li-fu,WU Tai-xia. Three-dimensional Discriminate of Mine Hidden Geological Disaster Based on 3S Technology of Anning Phosphate Area[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(2): 138-142.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2012.02.25 OR https://www.gtzyyg.com/EN/Y2012/V24/I2/138

[1] 罗真富,齐信,易静.遥感三维可视化在南沟泥石流调查中的运用[J].环境科学与管理,2010,35(1):144-146.
[2] 高山,冯光胜.三维遥感铁路工程地质勘察技术应用研究[J].铁道勘察,2009(1):36-39.
[3] 戴晓爱.遥感图像三维可视化及在腾冲机场建设中的应用研究[D].四川:成都理工大学,2005.
[4] 唐小明,冯杭建,赵建康.基于虚拟GIS和空间分析的小流域泥石流地质灾害遥感解译——以嵊州市为例[J].地质科技情报,2008,27(2):12-13.
[5] 袁忠玉.云南省矿山地质灾害及防治[J].中国地质,2000(8):36-37.
[6] 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.
[7] 张红兵.云南省东川区泥石流灾害SPOT5遥感影像特征[J].灾害学,2010,25(4):60-67.
[8] 吕战锋,毛延兵.浅谈小型露天开采矿山地质灾害分析与防治[J].工程技术,2010(12):169.
[9] 王治华.遥感技术在地质灾害调查、监测和防治中的应用[J].环境遥感,1992,7(3):190-195.

[1]	ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[2]	XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[3]	SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[4]	LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
[5]	LIU Zhizhong, SONG Yingxu, YE Runqing. An analysis of rainstorm-induced landslides in northeast Chongqing on August 31, 2014 based on interpretation of remote sensing images[J]. Remote Sensing for Natural Resources, 2021, 33(4): 192-199.
[6]	ZHANG Chengye, XING Jianghe, LI Jun, SANG Xiao. Recognition of the spatial scopes of tailing ponds based on U-Net and GF-6 images[J]. Remote Sensing for Natural Resources, 2021, 33(4): 252-257.
[7]	WANG Yiuzhu, HUANG Liang, CHEN Pengdi, LI Wenguo, YU Xiaona. Change detection of remote sensing images based on the fusion of co-saliency difference images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 89-96.
[8]	LIU Wanjun, GAO Jiankang, QU Haicheng, JIANG Wentao. Ship detection based on multi-scale feature enhancement of remote sensing images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 97-106.
[9]	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[J]. Remote Sensing for Natural Resources, 2021, 33(3): 148-155.
[10]	LU Qi, QIN Jun, YAO Xuedong, WU Yanlan, ZHU Haochen. Buildings extraction of GF-2 remote sensing image based on multi-layer perception network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 75-84.
[11]	HU Suliyang, LI Hui, GU Yansheng, HUANG Xianyu, ZHANG Zhiqi, WANG Yingchun. An analysis of land use changes and driving forces of Dajiuhu wetland in Shennongjia based on high resolution remote sensing images: Constraints from the multi-source and long-term remote sensing information[J]. Remote Sensing for Land & Resources, 2021, 33(1): 221-230.
[12]	LIU Zhao, ZHAO Tong, LIAO Feifan, LI Shuai, LI Haiyang. Research and comparative analysis on urban built-up area extraction methods from high-resolution remote sensing image based on semantic segmentation network[J]. Remote Sensing for Land & Resources, 2021, 33(1): 45-53.
[13]	WANG Xiaobing. Denoising algorithm based on the fusion of lifting wavelet thresholding and multidirectional edge detection of remote sensing image of mining area[J]. Remote Sensing for Land & Resources, 2020, 32(4): 46-52.
[14]	WEI Hongyu, ZHAO Yindi, DONG Jihong. Cooling tower detection based on the improved RetinaNet[J]. Remote Sensing for Land & Resources, 2020, 32(4): 68-73.
[15]	LIU Shangwang, CUI Zhiyong, LI Daoyi. Multi-task learning for building object semantic segmentation of remote sensing image based on Unet network[J]. Remote Sensing for Land & Resources, 2020, 32(4): 74-83.