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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (2) : 255-263     DOI: 10.6046/zrzyyg.2022115
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High-efficiency supervision method for green geological exploration based on remote sensing
MA Shibin1,2,3(), PI Yingnan1,2,3(), WANG Jia1,2,3, ZHANG Kun1,2,3, LI Shenghui1,2,3, PENG Xi1,2,3
1. Institute of Geological Survey of Qinghai Province, Xining 810012, China
2. Qinghai-Tibet Plateau During the North Qilian Geology and Mineral Resources Laboratory of Qinghai Province, Xining 810012, China
3. Qinghai Remote Sensing Big Data Engineering Technology Research Center, Xining 810012, China
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Abstract  

Resources serve as the material guarantee of the existence and development of human society. However, as a basis for resource discovery, geological exploration tends to damage the ecological environment. With the official release of the Specification for Green Geological Survey and Mineral Exploration (DZ/T 0374—2021) in June 2021, green geological exploration has been officially promoted to the national level and was implemented nationwide in China. However, the supervision of green geological exploration faces many difficulties and challenges in practice. To meet the demands of responsible entities for the supervision, inspection, and management of green geological exploration projects, this study proposed a high-efficiency supervision method based on remote sensing. By applying this method to a polymetallic survey project in Qinghai Province, this study expounded the specific implementation process of the method, as well as its effectiveness in the supervision services for geological exploration projects. As indicated by the results, the method proposed in this study allows for ascertaining the basic external environment of the project area, following the project layout and implementation, and verifying the consistency with the project plan. In addition, through quantitative information investigation, this method allows for the full identification of the disturbance and damage to the ecological environment and its restoration during the project implementation. Therefore, this study can provide effective technical support and basic data for evaluating the performance of green geological exploration.

Keywords remote sensing      green geological exploration      supervision service      methodological system     
ZTFLH:  TP79  
Issue Date: 07 July 2023
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Shibin MA
Yingnan PI
Jia WANG
Kun ZHANG
Shenghui LI
Xi PENG
Cite this article:   
Shibin MA,Yingnan PI,Jia WANG, et al. High-efficiency supervision method for green geological exploration based on remote sensing[J]. Remote Sensing for Natural Resources, 2023, 35(2): 255-263.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022115     OR     https://www.gtzyyg.com/EN/Y2023/V35/I2/255
Fig.1  Target characteristic map under GF-2 satellite data
Fig.2  Remote sensing monitoring map of ecological environment disturbance
Fig.3  Field inspection photos
[1] 张勇. 十八届五中全会首次提出五大发展理念[N/OL]. 新华网,2016-06-20. http://www.qunzh.com/pub/jsqzw/xxzt/jd95zn/jdll/sgqm/201606/t20160620_21716.html.
url: http://www.qunzh.com/pub/jsqzw/xxzt/jd95zn/jdll/sgqm/201606/t20160620_21716.html
[1] Zhang Y. The Fifth Plenary Session of the 18th CPC Central Committee put forward five development concepts for the first time[N/OL]. Xinhuanet,2016-06-20. http://www.qunzh.com/pub/jsqzw/xxzt/jd95zn/jdll/sgqm/201606/t20160620_21716.html.
url: http://www.qunzh.com/pub/jsqzw/xxzt/jd95zn/jdll/sgqm/201606/t20160620_21716.html
[2] 王琼杰. 绿色勘查的美丽序曲[N/OL]. 中国矿业报,2016-09-20. https://www.cgs.gov.cn/gzdt/dzhy/201609/t20160920_402570.html.
url: https://www.cgs.gov.cn/gzdt/dzhy/201609/t20160920_402570.html
[2] Wang Q J. Beautiful prelude to green exploration[N/OL]. China Mining News,2016-09-20. https://www.cgs.gov.cn/gzdt/dzhy/201609/t20160920_402570.html.
url: https://www.cgs.gov.cn/gzdt/dzhy/201609/t20160920_402570.html
[3] 中国矿业联合会. T/CMAS 0001—2018绿色勘查指南[S]. 2018.
[3] China Mining Industry Association. T/CMAS 0001—2018 green exploration guide[S]. 2018.
[4] 自然资源部关于发布《绿色地质勘查工作规范》行业标准的公告[EB/OL]. 中国政府网, 2021-06-18[2021-06-22]. http://www.gov.cn/zhengce/zhengceku/2021-06/22/content_5620007.htm.
url: http://www.gov.cn/zhengce/zhengceku/2021-06/22/content_5620007.htm
[4] Announcement of the Ministry of natural resources on Issuing the industrial standard of the code for green geological exploration[EB/OL]. China Government Network, 2021-06-18[2021-06-22]. http://www.gov.cn/zhengce/zhengceku/2021-06/22/content_5620007.htm.
url: http://www.gov.cn/zhengce/zhengceku/2021-06/22/content_5620007.htm
[5] 戴阳利, 龚燃. 国外商业航天发展情况分析[J]. 卫星应用, 2020(10):22-26.
[5] Dai Y L, Gong R. Analysis of the development of foreign commercial aerospace[J]. Satellite Application, 2020(10):22-26.
[6] 毛凌野. 国内主要商业航天企业发展现状[J]. 卫星应用, 2017(10):28-32.
[6] Mao L Y. Development status of major domestic commercial aerospace companies[J]. Satellite Application, 2017(10):28-32.
[7] 技术研发知识服务融合发展[N/OL]. 中地数媒,2020-01-19. https://zhidao.baidu.com/question/559271323356147972.html.
url: https://zhidao.baidu.com/question/559271323356147972.html
[7] Integrated development of technology R & D knowledge service[N/OL]. Zhongdimedium,2020-01-19. https://zhidao.baidu.com/question/559271323356147972.html.
url: https://zhidao.baidu.com/question/559271323356147972.html
[8] 武海炜. 地质勘查经历的六大阶段[N/OL]. 中国矿业报,2018-02-01. https://www.sohu.com/a/220186793_99986028.
url: https://www.sohu.com/a/220186793_99986028
[8] Wu H H. Six stages of geological exploration[N/OL]. China Mining News,2018-02-01. https://www.sohu.com/a/220186793_99986028.
url: https://www.sohu.com/a/220186793_99986028
[9] 赵闯. 地质矿产勘查与生态环境保护协调发展研究[J]. 中国设备工程, 2021(11):246-247.
[9] Zhao C. Study on coordinated development of geological and mineral exploration and ecological environment protection[J]. China Equipment Engineering, 2021(11)246-247.
[10] 贺战朋, 赵祺彬, 李杏茹, 等. 基于绿色发展理念推进地质勘查标准化工作的思考与建议[J]. 中国矿业, 2021, 30(11):13-17.
[10] He Z P, Zhao Q B, Li X R, et al. Thoughts and suggestions on promoting the standardization of geological exploration based on the concept of green development[J]. China Mining, 2021, 30(11):13-17.
[11] 郑杰, 张福良, 靳松, 等. 绿色勘查项目示范进展与成效研究[J]. 中国矿业, 2021, 30(11):37-41.
[11] Zhen J, Zhang F L, Jin S, et al. Demonstration progress and effectiveness of green exploration project[J]. China Mining, 2021, 30(11):37-41.
[12] 庞博. 自然资源省级卫星中心建设实现全覆盖[N/OL]. 自然资源部网站,2020-09-11. http://www.gov.cn/xinwen/2020-09/11/content_5542544.htm.
url: http://www.gov.cn/xinwen/2020-09/11/content_5542544.htm
[12] Pang B. The construction of provincial satellite centers for natural resources has achieved full coverage[N/OL]. Website of the Ministry of Natural Resources,2020-09-11. http://www.gov.cn/xinwen/2020-09/11/content_5542544.htm.
url: http://www.gov.cn/xinwen/2020-09/11/content_5542544.htm
[13] 王懿哲, 刘国, 郭莉, 等. 基于高分一号WFV数据的正射校正与真彩色合成技术——以中巴经济走廊为例[J]. 国土资源遥感, 2020, 32(2):213-218.doi:10.6046/gtzyyg.2020.02.27.
doi: 10.6046/gtzyyg.2020.02.27
[13] Wang Y Z, Liu G, Guo L, et al. Research on ortho-rectification and true color synthesis technique of GF-1 WFV data in China-Pakistan Economic Corridor[J]. Remote Sensing for Land and Resources, 2020, 32(2):213-218.doi:10.6046/gtzyyg.2020.02.27.
doi: 10.6046/gtzyyg.2020.02.27
[14] 马世斌, 杨文芳, 张焜. SPOT6卫星图像处理关键技术研究[J]. 国土资源遥感, 2015, 27(3):30-35.doi:10.6046/gtzyyg.2015.03.06.
doi: 10.6046/gtzyyg.2015.03.06
[14] Ma S B, Yang W F, Zhang K. Study of key technology of SPOT6 satellite image processing[J]. Remote Sensing for Land and Resources, 2015, 27(3):30-35.doi:10.6046/gtzyyg.2015.03.06.
doi: 10.6046/gtzyyg.2015.03.06
[15] 石迎春, 叶浩, 郭娇, 等. 几何纠正模式对QuickBird全色影像定位精度的影响——以黄土高原为例[J]. 国土资源遥感, 2011, 23(3):135-139.doi:10.6046/gtzyyg.2011.03.24.
doi: 10.6046/gtzyyg.2011.03.24
[15] Shi Y C, Ye H, Guo J, et al. The effect of geometric rectification modes on positioning accuracy for QuickBird panchromatic image:A case study of loess plateau[J]. Remote Sensing for Land and Resources, 2011, 23(3):135-139.doi:10.6046/gtzyyg.2011.03.24.
doi: 10.6046/gtzyyg.2011.03.24
[16] 孙攀, 董玉森, 陈伟涛, 等. 高分二号卫星影像融合及质量评价[J]. 国土资源遥感, 2016, 28(4):108-113.doi:10.6046/gtzyyg.2016.04.17.
doi: 10.6046/gtzyyg.2016.04.17
[16] Sun P, Dong Y S, Chen W T, et al. Research on fusion of GF-2 imagery and quality evaluation[J]. Remote Sensing for Land and Resources, 2016, 28(4):108-113.doi:10.6046/gtzyyg.2016.04.17.
doi: 10.6046/gtzyyg.2016.04.17
[17] 胥兵, 方臣. ZY-1 02C星图像与ETM+图像融合方法及效果评价[J]. 国土资源遥感, 2014, 26(3):80-85.doi:10.6046/gtzyyg.2014.03.13.
doi: 10.6046/gtzyyg.2014.03.13
[17] Xu B, Fang C. Data fusion methods of ZY-1 02C and ETM+ images and effect evaluation[J]. Remote Sensing for Land and Resources, 2014, 26(3):80-85.doi:10.6046/gtzyyg.2014.03.13.
doi: 10.6046/gtzyyg.2014.03.13
[18] 王华斌, 李国元, 张本奎, 等. 资源三号卫星影像融合算法对比分析[J]. 测绘科学, 2015, 40(1):47-51.
[18] Wang H B, Li G Y, Zhang B K, et al. Contrast and analysis of different fusion algorithms for ZY-3 satellite images[J]. Science of Surveying and Mapping, 2015, 40(1):47-51.
[19] 郭蕾, 杨冀红, 史良树, 等. SPOT6遥感图像融合方法比较研究[J]. 国土资源遥感, 2014, 26(4):71-77.doi:10.6046/gtzyyg.2014.04.12.
doi: 10.6046/gtzyyg.2014.04.12
[19] Guo L, Yang J H, Shi L S, et al. Comparative study of image fusion algorithms for SPOT6[J]. Remote Sensing for Land and Resources, 2014, 26(4):71-77.doi:10.6046/gtzyyg.2014.04.12.
doi: 10.6046/gtzyyg.2014.04.12
[20] 初禹, 单久库, 侯建国. GeoEye-1遥感影像融合效果的比较分析[J]. 测绘与空间地理信息, 2011, 34(3):21-26.
[20] Chu Y, Shan J K, Hou J G. Comparison and analysis of the fusion results of GeoEye-1 remote sensing imagery[J]. Geomatics and Spatial Information Technology, 2011, 34(3):21-26.
[21] 刘大伟, 韩玲, 韩晓勇. 基于深度学习的高分辨率遥感影像分类研究[J]. 光学学报, 2016(4):306-314.
[21] Liu D W, Han L, Han X Y. Research on high resolution remote sensing image classification based on deep learning[J]. Journal of Optics, 2016(4):306-314.
[22] 冯丽英. 基于深度学习技术的高分辨率遥感影像建设用地信息提取研究[D]. 杭州: 浙江大学, 2017.
[22] Feng L Y. Research on construction land information extraction from high-resolution remote sensing images based on deep learning technology[D]. Hangzhou: Zhejiang University, 2017.
[23] 王小娜, 田金炎, 李小娟, 等. Google Earth Engine云平台对遥感发展的改变[J]. 遥感学报, 2022, 26(2):299-309.
[23] Wang X N, Tian J Y, Li X J, et al. Benefits of Google Earth Engine in remote sensing[J]. National Remote Sensing Bulletin, 2022, 26(2):299-309.
doi: 10.11834/jrs.20211317 url: http://www.ygxb.ac.cn/zh/article/doi/10.11834/jrs.20211317/
[24] 付东杰, 肖寒, 苏奋振, 等. 遥感云计算平台发展及地球科学应用[J]. 遥感学报, 2021, 25(1):220-230.
[24] Fu D J, Xiao H, Su F Z, et al. Remote sensing cloud computing platform development and earth science application[J]. National Remote Sensing Bulletin, 2021, 25(1):220-230.
doi: 10.11834/jrs.20210447 url: http://www.ygxb.ac.cn/zh/article/doi/10.11834/jrs.20210447/
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