Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2012, Vol. 24 Issue (2) : 98-104     DOI: 10.6046/gtzyyg.2012.02.18
Technology Application |
A Comprehensive Analysis of Remote Sensing Geological Characteristics and Ore Prospecting Perspective of Luchun Area,Yunnan Province
WANG Feng-de, ZHAO Zhi-fang, MAO Yu-jing, TAN Shu-cheng
College of Resource Environment and Earth Sciences, Research Center for Remote Sensing in Geology, Yunnan University, Kunming 650091, China
Download: PDF(7819 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  Luchun area in Yunnan Province is characterized by complex geological background,rugged terrain condition and thick vegetation coverage,and hence it is difficult to utilize traditional ground investigation methods for regional geological mapping. In this study,multi-source remote sensing data,such as TM,ETM+ and SPOT5, were adopted. Geological background was analyzed by extracting features from regional strata,rocks,and linear and circular structures,with emphasis placed on the 3D visualization technology. According to structural relationships and remote sensing image features,the study was focused on the analysis of regional stress as well. Finally, the prognosis of the mineral prospecting perspective was worked out by way of further research on typical ore deposits and extracting information related to mineralization from structure,lithologic character and altered anomaly of hydroxyl and iron ions. On such a basis,the geological information and prospecting clues are useful and valuable for deploying regional geological mapping.
Keywords hyper-spectral data      RBF neural network      LAI of grassland      retrieval     
:  TP 79  
Issue Date: 03 June 2012
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
BAO Gang
QIN Zhi-hao
ZHOU Yi
BAO Yu-hai
XIN Xiao-ping
HONG Yu
HAI Quan-sheng
Cite this article:   
BAO Gang,QIN Zhi-hao,ZHOU Yi, et al. A Comprehensive Analysis of Remote Sensing Geological Characteristics and Ore Prospecting Perspective of Luchun Area,Yunnan Province[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(2): 98-104.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2012.02.18     OR     https://www.gtzyyg.com/EN/Y2012/V24/I2/98
[1] 周成虎.遥感影像地学理解与分析[M].北京:科学出版社,2009.
[2] 陈小云,胡光道.GIS在矿产资源预测中的现状和研究思路[J].资源环境与工程,2006,20(1):56-59.
[3] 李文昌,赵志芳.云南省遥感地质应用[M].北京:地质出版社,2009.
[4] 刘纪选,曾朝铭,张秀茵.航天遥感图像数据索引及应用实例[M].北京:中国地质调查局,2004:37-58.
[5] 张玉君,杨建民,陈薇.ETM+(TM)蚀变遥感异常提取方法研究与应用——地质依据和波谱前提[J].国土资源遥感,2002(4):30-36.
[6] 张玉君,曾朝铭,陈薇.ETM+(TM)蚀变遥感异常提取方法研究与应用——方法选择和技术流程[J].国土资源遥感,2003(2):44-49.
[7] 韩林.蚀变信息的提取[J].遥感应用,2007(6):49-51.
[8] 吕凤军,邢立新,范继璋,等.基于蚀变信息场的遥感蚀变信息提取[J].地质与勘探,2006,42(2):65-68.
[9] 车永飞,李满根,刘林清,等.ETM+遥感影像铁化蚀变信息的提取方法[J].科技广场,2009(3):109-111.
[10] 张廷斌,唐菊兴,黄丁发.矿化蚀变信息提取的TM/ETM+遥感影像模式[J].遥感信息,2009(2):47-51.
[11] 徐明珠,杨洋,王海荣.GIS在矿产资源评价中的应用浅析[J].科技经济市场,2010(6):17-18.
[12] Zhang X F,Pazner M,Duke N.Lithologic and Mineral Information Extraction for Gold Exploration Using ASTER Data in the South Chocolate Mountains (California)[J].ISPRS Journal of Photogrammetry & Remote Sensing,2007,62(4):271-282.
[13] 潘彤,罗才让,尹有昌,等.青海省金属矿产成矿规律及成矿预测[M].北京:地震出版社,2006:19-27.
[1] REN Chaofeng, PU Yuchi, ZHANG Fuqiang. A method for extracting match pairs of UAV images considering geospatial information[J]. Remote Sensing for Natural Resources, 2022, 34(1): 85-92.
[2] FAN Jiazhi, LUO Yu, TAN Shiqi, MA Wen, ZHANG Honghao, LIU Fulai. Accuracy evaluation of the FY-3C/MWRI land surface temperature product in Hunan Province[J]. Remote Sensing for Land & Resources, 2021, 33(1): 249-255.
[3] YANG Lijuan. Estimating PM2.5 concentrations in eastern coastal area of China using a two-stage random forest model[J]. Remote Sensing for Land & Resources, 2020, 32(4): 137-144.
[4] Jiaqi ZUO, Zegen WANG, Jinhu BIAN, Ainong LI, Guangbin LEI, Zhengjian ZHANG. A review of research on remote sensing for ground impervious surface percentage retrieval[J]. Remote Sensing for Land & Resources, 2019, 31(3): 20-28.
[5] Chen GAO, Jian XU, Dan GAO, Lili WANG, Yeqiao WANG. Retrieval of concentration of total suspended matter from GF-1 satellite and field measured spectral data during flood period in Poyang Lake[J]. Remote Sensing for Land & Resources, 2019, 31(1): 101-109.
[6] Yun GE, Shunliang JIANG, Famao YE, Changlong JIANG, Ying CHEN, Yiling TANG. Aggregating CNN features for remote sensing image retrieval[J]. Remote Sensing for Land & Resources, 2019, 31(1): 49-57.
[7] Ying WU, Sulin JIANG, Zhenhui WANG. Effect of radio-frequency interference on the retrieval of land surface temperature from microwave radiation imager[J]. Remote Sensing for Land & Resources, 2018, 30(4): 90-96.
[8] Yiqun HU, Shaoguang ZHOU, Shun YUE, Xiaoqing LIU. Remote sensing image retrieval based on sparse local invariant features[J]. Remote Sensing for Land & Resources, 2018, 30(2): 38-44.
[9] YANG Ping, LI Yikun, HU Yuxi, YANG Shuwen. Remote sensing image retrieval based on tolerance granular computing theory[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 43-47.
[10] HU Yuxi, LI Yikun, YANG Ping. Retrieving of remote sensing images based on content-sensitive Bayesian networks and direction relations[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 70-76.
[11] LI Feng, LIANG Handong, ZHAO Xiaoping, BAI Jiangwei, CUI Yukun. Remote sensing monitoring and assessment of fire-fighting effects in Wuda coal field,Inner Mongolia[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 217-223.
[12] WEN Lujun, PENG Wenfu, YANG Huarong, WANG Huaiying, DONG Lijun, SHANG Xue. An analysis of land surface temperature (LST) and its influencing factors in summer in western Sichuan Plateau: A case study of Xichang City[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 207-214.
[13] LI Xiaomin, ZHANG Kun, LI Dongling, LI Delin, LI Zongren, ZHANG Xing. Remote sensing technology delineation method and its application to permafrost of Zhada area in the Tibetan Plateau[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 57-64.
[14] LI Li, WANG Di, PAN Caixia, NIU Huanna. Active microwave scattering models used in soil moisture retrieval[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 1-9.
[15] KONG Jinling, YANG Jing, SUN Xiaoming, YANG Shu, LIU Futian, DU Dong. Atmospheric correction and suspended sediment concentration retrieval based on multi-spectral remote sensing images: A case study of Caofeidian offshore area[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 130-137.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech