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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (1) : 1-7     DOI: 10.6046/gtzyyg.2019.01.01
An overview of applying high resolution remote sensing to natural resources survey
Ling CHEN1, Jia JIA2, Haiqing WANG1
1.China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
2.Henan College of Surveying and Mapping, Zhengzhou 451464, China
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With the launch of high-resolution satellites, their applications in geological survey become more prominent. Based on the introduction of various kinds of high resolution data which include high spatial resolution satellite remote sensing, high temporal resolution satellite remote sensing, high spectral resolution satellite remote sensing and high radiation resolution satellite remote sensing, this paper gives a review of the application of optical high resolution images to geological survey, mainly in the aspects of dynamic monitoring of land use, basic geological and resources survey, mineral resources development and ecological monitoring, ecological environment investigation, geological disaster and emergency investigation. It is found that high resolution remote sensing images have great potential in natural resources survey,and collaborative application of multi-source multi-scale high resolution remote sensing data will play an increasingly important role in natural resources survey in the future.

Keywords high resolution remote sensing data      natural resources survey      application      development tendency     
:  TP79  
Issue Date: 15 March 2019
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Haiqing WANG
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Ling CHEN,Jia JIA,Haiqing WANG. An overview of applying high resolution remote sensing to natural resources survey[J]. Remote Sensing for Land & Resources, 2019, 31(1): 1-7.
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传感器类型 波段号 光谱范围/nm 空间分辨率/m 重访周期/d 位深/bit 快速响应能力
GF-2 全色 450900 1 5 10 一般
1 450520 4
2 520590 4
3 630690 4
4 770890 4
GF-4 1 可见光 50 分钟级别 10 可接受紧急任务,应急响应高效
2 中波红外 400
WorldView-2 全色 450800 0.5 1.1 11 具备快速响应机制
1 400450 1.8
2 450510 1.8
3 510580 1.8
4 585625 1.8
5 630690 1.8
6 705745 1.8
7 770895 1.8
8 8601 040 1.8
WorldView-3 全色 450800 0.31 13 11 具备快速响应机制
1 400450 1.24
2 450510 1.24
3 510580 1.24
4 585625 1.24
5 630690 1.24
6 705745 1.24
7 770895 1.24
8 8601 040 1.24
9 1 1951 225 3.7
10 1 5501 590 3.7
11 1 6401 680 3.7
12 1 7101 750 3.7
13 2 1452 185 3.7
14 2 1852 225 3.7
15 2 2352 285 3.7
16 2 2952 365 3.7
传感器类型 波段号 光谱范围/nm 空间分辨率/m 重访周期/d 位深/bit 快速响应能力
GeoEye-1 全色 450800 0.5 23 11 具备快速响应机制
1 450510 2
2 510580 2
3 655690 2
4 780920 2
SPOT6/7 全色 450745 1.5 45 11 接收计划每4 h上传一次,可接受紧急任务
1 455520 6
2 530590 6
3 625695 6
4 760890 6
QuickBird 全色 445900 0.61 16 11 一般
1 450520 2.44
2 520600 2.44
3 630690 2.44
4 760900 2.44
Tab.1  Technical index of high spatial, high time and high radiometric resolution data in natural resources survey
国家/地区 光谱范围/
波段数 空间分
机载 HyMap 澳大利亚 4002 500 128 根据飞行高度不同而不同
AVIRIS 美国 4002 500 224
CASI 加拿大 3801 050 288
SASI 加拿大 9502 450 100
TASI 加拿大 8 00011 500 32
PHI 中国 400850 244
星载 Hyperion 美国 3572 576 220 30
CHRIS 欧盟 4151 050 62/34/18 34/17
HJ-1A 中国 450950 115 100
天宫一号 中国 4002 500 128 20/30
高分五号 中国 4002 500 330 30
Tab.2  Technical index for hyperspectral remote sensing data in natural resources survey
[1] Filion R, Bernier M, Paniconi C , et al.Remote sensing for mapping soil moisture and drainage potential in semi-arid regions:Applications to the Campidano plain of Sardinia, Italy[J].Science of the Total Environment, 2016, 543, Part B: 862-876.
doi: 10.1016/j.scitotenv.2015.07.068 pmid: 26254021 url:
[2] 王润生 . 遥感地质技术发展的战略思考[J]. 国土资源遥感. 2008,20(1):1-12.doi: 10.6046/gtzyyg.2008.01.01.
doi: 10.6046/gtzyyg.2008.01.01 url:
[2] Wang R S . On the development strategy of remote sensing technology in geology[J]. Remote Sensing for Land and Resources, 2008,20(1):1-12.doi: 10.6046/gtzyyg.2008.01.01.
[3] 张达, 郑玉权 . 高光谱遥感的发展与应用[J]. 光学与光电技术, 2013,11(3):67-73.
[3] Zhang D, Zheng Y Q . Hyperspectral remote sensing and its development and application review[J]. Optics and Precision Engineering, 2013,11(3):67-73.
[4] Li M M, Stein A, Bijker W , et al. Urban land use extraction from very high resolution remote sensing imagery using a Bayesian network[J]. International Society for Photogrammetry and Remote Sensing Journal of Photogrammetry and Remote Sensing, 2016,122:192-205.
doi: 10.1016/j.isprsjprs.2016.10.007 url:
[5] Wu M Q, Huang W J, Niu Z , et al. Fine crop mapping by combining high spectral and high spatial resolution remote sensing data in complex heterogeneous areas[J]. Computers and Electronics in Agriculture, 2017,139(Supplement C):1-9.
doi: 10.1016/j.compag.2017.05.003 url:
[6] Zhou Q B, Yu Q Y, Liu J , et al. Perspective of Chinese GF-1 high-resolution satellite data in agricultural remote sensing monitoring[J]. Journal of Integrative Agriculture, 2017,16(2):242-251.
doi: 10.1016/S2095-3119(16)61479-X url:
[7] Zhang X L, Xiao P F, Feng X Z , et al. Separate segmentation of multi-temporal high-resolution remote sensing images for object-based change detection in urban area[J]. Remote Sensing of Environment, 2017,201(Supplement C):243-255.
doi: 10.1016/j.rse.2017.09.022 url:
[8] 孙在宏, 吴长彬 . 基于“一张图”的土地动态监测系统研究[J].测绘通报, 2012(6):22-24,100.
[8] Sun Z H, Wu C B . Land dynamic monitoring system based on “One Land Map”[J].Bulletin of Survey and Mapping, 2012(6):22-24,100.
[9] Asner G P, Heidebrecht K B . Imaging spectroscopy for desertification studies:Comparing AVIRIS and EO-1 Hyperion in Argentina drylands[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003,41(6):1283-1296.
doi: 10.1109/TGRS.2003.812903 url:
[10] Okin G S, Murray B, Schlesinger W H . Degradation of sandy arid shrubland environments:Observations, process modelling,and management implications[J]. Journal of Arid Environments, 2001,47(2):123-144.
doi: 10.1006/jare.2000.0711 url:
[11] 王静, 何挺, 李玉环 . 基于高光谱遥感技术的土地质量信息挖掘研究[J]. 遥感学报, 2005,9(4):438-445.
doi: 10.3321/j.issn:1007-4619.2005.04.015 url:
[11] Wang J, He T, Li Y H . Studying on extraction for land quality information based on hyperspectral data[J]. Journal of Remote Sensing, 2005,9(4):438-445.
[12] 杨金中, 聂洪峰, 荆青青 . 初论全国矿山地质环境现状与存在问题[J]. 国土资源遥感, 2017,29(2):1-7.doi: 10.6046/gtzyyg.2007.02.01.
doi: 10.6046/gtzyyg.2017.02.01 url:
[12] Yang J Z, Nie H F, Jing Q Q . Preliminary analysis of mine geo-environment status and existing problems in China[J]. Remote Sensing for Land and Resources, 2017,29(2):1-7.doi: 10.6046/gtzyyg.2007.02.01.
[13] 吴亚楠, 代晶晶, 周萍 . 基于高空间分辨率遥感数据的稀土矿山监测研究[J]. 中国稀土学报, 2017,35(2):262-271.
doi: 10.11785/S1000-4343.20170214 url:
[13] Wu Y N, Dai J J, Zhou P . Research of rare earth minerals monitoring based on high resolution remote sensing[J]. Journal of the Chinese Society of Rare Earths, 2017,35(2):262-271.
[14] 王燕波, 罗伟, 李名勇 , 等. 基于高分辨率遥感影像的矿山开发监测研究[J]. 热带地理, 2011,31(4):377-382.
doi: 10.3969/j.issn.1001-5221.2011.04.008 url:
[14] Wang Y B, Luo W, Li M Y , et al. Mine development monitoring based on high-resolution remote sensing images[J]. Tropical Geography, 2011,31(4):377-382.
[15] 陈圣波, 谢明辉, 路鹏 , 等. 基于Hyperion数据的矿区废弃土土壤恢复潜力[J]. 地球科学, 2015,40(8):1353-1358.
doi: 10.3799/dqkx.2015.117 url:
[15] Chen S B, Xie M H, Lu P , et al. Restoration potential of abandoned soil at Dexing mine of Jiangxi Province based on Hyperion data[J]. Earth Science, 2015,40(8):1353-1358.
[16] 李万伦, 甘甫平 . 矿山环境高光谱遥感监测研究进展[J]. 国土资源遥感, 2016,28(2):1-7.doi: 10.6046/gtzyyg.2016.02.01.
doi: 10.6046/gtzyyg.2016.02.01 url:
[16] Li W L, Gan F P . Progress in hyperspectral research and monitoring in mine environment[J]. Remote Sensing for Land and Resources, 2016,28(2):1-7.doi: 10.6046/gtzyyg.2016.02.01.
[17] Chen N H, Dong J J, Chen J Y , et al. Geometry and emplacement of the Late Cretaceous mafic dyke swarms on the islands in Zhejiang Province,Southeast China:Insights from high-resolution satellite images[J]. Journal of Asian Earth Sciences, 2014,79:302-311.
doi: 10.1016/j.jseaes.2013.10.001 url:
[18] 王晓鹏, 杨志强, 康高峰 , 等. WorldView-2高分辨率卫星数据在西昆仑塔什库尔干地区遥感地质调查中的应用[J]. 地质找矿论丛, 2014,29(3):428-432.
doi: 10.6053/j.issn.1001-1412.2014.03.018 url:
[18] Wang X P, Yang Z Q, Kang G F , et al. Application of WorldView-2 data to remote sensing geological survey in Tashkurgan Area at west Kunlun[J]. Contributions to Geology and Mineral Resources Research, 2014,29(3):428-432.
[19] 任广利, 杨军录, 杨敏 , 等. 高光谱遥感异常提取在甘肃北山金滩子—明金沟地区成矿预测中的应用[J]. 大地构造与成矿学, 2013,37(4):765-776.
[19] Ren G L, Yang J L, Yang M , et al. Application of hyperspectral remote sensing anomaly information on metallogenic prediction in the Jintanzi—Mingjingou area of Beishan,Gansu[J]. Geotectonica et Metaalogenia, 2013,37(4):765-776.
[20] 王润生, 熊盛青, 聂洪峰 , 等. 遥感地质勘查技术与应用研究[J]. 地质学报, 2011,85(11):1699-1743.
[20] Wang R S, Xiong S Q, Nie H F , et al. Remote sensing technology and its application in geological exploration[J]. Acta Geological Sinica, 2011,85(11):1699-1743.
[21] 周子勇 . 高光谱遥感油气勘探进展[J]. 遥感技术与应用, 2014,29(2):352-361.
doi: 10.11873/j.issn.1004-0323.2014.2.0352 url:
[21] Zhou Z Y . Progress in hyperspectral remote sensing for petroleum prospecting[J]. Remote Sensing Technology and Application, 2014,29(2):352-361.
[22] 胡畔, 田庆久, 闫柏琨 . 柴达木盆地烃蚀变矿物高光谱遥感识别研究[J]. 国土资源遥感, 2009,21(2):54-61.doi: 10.6046/gtzyyg.2009.02.12.
doi: 10.6046/gtzyyg.2009.02.12 url:
[22] Hu P, Tian Q J, Yan B K . The application of hyperspectral remote sensing to the identification of hydrocarbon alteration minerals in Qaidam basin[J]. Remote Sensing for Land and Resources, 2009,21(2):54-61.doi: 10.6046/gtzyyg.2009.02.12.
[23] Xu D Q, Ni G Q, Jiang L L , et al. Exploring for natural gas using reflectance spectra of surface soils[J]. Advances in Space Research, 2008,41(11):1800-1817.
doi: 10.1016/j.asr.2007.05.073 url:
[24] Willis K S . Remote sensing change detection for ecological monitoring in United States protected areas[J]. Biological Conservation, 2015,182:233-242.
doi: 10.1016/j.biocon.2014.12.006 url:
[25] 腾明君, 曾立雄, 肖文发 , 等. 长江三峡库区生态环境变化遥感研究进展[J]. 应用生态学报, 2014,25(12):3683-3693.
[25] Teng M J, Zeng L X, Xiao W F , et al. Research progress on remote sensing of ecological and environmental changes in the Three Gorges[J]. Chinese Journal of Applied Ecology, 2014,25(12):3683-3693.
[26] 韩阳, 秦伟超, 王野乔 . 吉林省西部典型盐渍化土壤偏振反射高光谱特征与模型研究[J]. 光谱学与光谱分析, 2014,34(6):1640-1644.
[26] Han Y, Qin W C, Wang Y Q . Study on the polarized reflectance hyperspectral characteristics and models of typical saline soil in the west of Jilin Province,China[J]. Spectroscopy and Spectral Analysis, 2014,34(6):1640-1644.
[27] Chu T A, Guo X L, Takeda K . Remote sensing approach to detect post-fire vegetation regrowth in Siberian boreal larch forest[J]. Ecological Indicators, 2016,62:32-46.
doi: 10.1016/j.ecolind.2015.11.026 url:
[28] Garni R, Tran A, Guis H , et al. Remote sensing,land cover changes, and vector-borne diseases:Use of high spatial resolution satellite imagery to map the risk of occurrence of cutaneous leishmaniasis in Ghardaia,Algeria[J]. Infection,Genetics and Evolution, 2014,28:725-734.
doi: 10.1016/j.meegid.2014.09.036 pmid: 25305006 url:
[29] Jiang S, Wen B P, Zhao C , et al. Kinematics of a giant slow-moving landslide in Northwest China:Constraints from high resolution remote sensing imagery and GPS monitoring[J]. Journal of Asian Earth Sciences, 2016,123:34-46.
doi: 10.1016/j.jseaes.2016.03.019 url:
[30] 苏凤环, 刘洪江, 韩用顺 . 汶川地震山地灾害遥感快速提取及其分布特点分析[J]. 遥感学报, 2008,12(6):956-963.
doi: 10.3321/j.issn:1007-4619.2008.06.019 url:
[30] Su F H, Liu H J, Han Y S . The extraction of mountain hazard induced by Wenchuan earthquake and analysis of its distributing characteristic[J]. Journal of Remote Sensing, 2008,12(6):956-963.
[31] 朱静, 唐川 . 遥感技术在我国滑坡研究中的应用综述[J]. 遥感技术与应用, 2012,27(3):458-464.
[31] Zhu J, Tang C . An overview of remote sensing applications for landslides research in China[J]. Remote Sensing Technology and Application, 2012,27(3):458-464.
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