[1]白宇,郑志忠,修连存,等.无人机高光谱遥感技术在自然资源调查中的应用进展[J].华东地质,2022,43(04):527-538.[doi:10.16788/j.hddz.32-1865/P.2022.04.011]
 BAI Yu,ZHENG Zhizhong,XIU Liancun,et al.UAV hyperspectral remote sensing technology and its application progress in natural resources survey[J].East China Geology,2022,43(04):527-538.[doi:10.16788/j.hddz.32-1865/P.2022.04.011]
点击复制

无人机高光谱遥感技术在自然资源调查中的应用进展()
分享到:

《华东地质》[ISSN:2096-1871/CN:32-1865/P]

卷:
43
期数:
2022年04期
页码:
527-538
栏目:
其他研究
出版日期:
2022-12-23

文章信息/Info

Title:
UAV hyperspectral remote sensing technology and its application progress in natural resources survey
作者:
白宇1 郑志忠1 修连存1 周航建12 肖盈蓄13
1. 中国地质调查局南京地质调查中心, 江苏 南京 210016;
2. 中国地质大学(武汉)自动化学院, 湖北 武汉 430074;
3. 中国地质大学(武汉)地球物理与空间信息学院, 湖北 武汉 430074
Author(s):
BAI Yu1 ZHENG Zhizhong1 XIU Liancun1 ZHOU Hangjian12 XIAO Yingxu13
1. Nanjing Center, China Geological Survey, Nanjing 210016, Jiangsu, China;
2. School of Automation, China University of Geosciences (Wuhan), Wuhan 430074, Hubei, China;
3. Institute of Geophysics & Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, Hubei, China
关键词:
无人机高光谱遥感技术地质矿产填图水体质量监测森林资源调查土壤质量评估
Keywords:
UAVhyperspectral remote sensinggeological and mineral mappingwater quality monitoringforest resources surveysoil quality evaluation
分类号:
TP79
DOI:
10.16788/j.hddz.32-1865/P.2022.04.011
摘要:
无人机高光谱遥感技术是遥感领域的重要研究方向,可快速、高效地获取地物空间信息和光谱信息,具有机动灵活、成本低廉等优势,近些年来受到广泛关注。文章总结了无人机高光谱成像仪的特点及发展现状,阐述了基于高光谱成像仪的无人机遥感系统组成和研究现状,重点介绍了无人机高光谱遥感技术在地质矿产填图、水体质量监测、森林资源调查、土壤质量评估等自然资源调查领域的最新应用进展。文章针对当前无人机高光谱遥感技术存在的问题,提出了系统微小型化、多波段集成和多源数据融合的未来发展预测,指出其在一体化自然资源调查监测技术体系中将具有更广泛的应用前景。
Abstract:
UAV hyperspectral technology is an important research direction in the field of remote sensing, which can obtain spatial and spectral information of geological features quickly and efficiently. With the advantages of flexibility and low cost, it has received wide attention from experts in recent years. This paper summarizes the characteristics and current status of UAV hyperspectral imager, describes the composition and research status of UAV hyperspectral remote sensing system, and focuses on the latest application progress in the investigation on natural resources, such as mineral mapping, water quality monitoring, forest resources survey and soil quality evaluation. In terms of the current problems of UAV hyperspectral remote sensing technology, three predictions are proposed, i.e. system miniaturization, multi-band integration and multi-source data fusion. It also points out that UAV hyperspectral remote sensing technology will have a wider application prospect in the technology system of natural resources integrated survey and monitoring.

参考文献/References:

[1] 刘银年.高光谱成像遥感载荷技术的现状与发展[J].遥感学报, 2021, 25(1):439-459.LIU Y N. Development of hyperspectral imaging remote sensing technology[J].National Remote Sensing Bulletin, 2021,25(1):439-459.
[2] MOUROULIS P, GREEN R O. Review of high fidelity imaging spectrometer design for remote sensing[J].Optical Engineering, 2018, 57(4):040901.
[3] 李月,杨灿坤,周春平,等.无人机载高光谱成像设备研究及应用进展[J].测绘通报, 2019(9):1-6.LI Y, YANG C K, ZHOU C P, et al. Advance and application of UAV hyperspectral imaging equipment[J]. Bulletin of Surveying and Mapping, 2019(9):1-6.
[4] 金伟,葛宏立,杜华强,等.无人机遥感发展与应用概况[J].遥感信息, 2009(1):88-92.JIN W,GE H L,DU H Q, et al. Development and application of UAV remote sensing[J]. Remote Sensing Information,2009(1):88-92.
[5] BIOUCAS-DIAS J M, PLAZA A, CAMPS-VALLS G, et al. Hyperspectral remote sensing data analysis and future challenges[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(2):6-36.
[6] 刘洪麟.机载高光谱成像仪光谱定标关键技术研究[D].上海:中国科学院大学(中国科学院上海技术物理研究所), 2020.LIU H L. Study on The Key Technologies of Spectral Calibration for Airborne Hyperspectral Imager[D].Shanghai:University of Chinese Academy of Sciences(Shanghai Institute of Technical Physics,Chinese Academy of Sciences), 2020.
[7] HAGEN N A, KUDENOV M W. Review of snapshot spectral imaging technologies[J]. Optical Engineering, 2013, 52(9):090901.
[8] M?YNEN J, HOLMLUND C, SAARI H, et al. Unmanned aerial vehicle (UAV) operated megapixel spectral camera[C]//Electro-Optical Remote Sensing, Photonic Technologies, and Applications V. International Society for Optics and Photonics, 2011, 8186:295-303.
[9] GRUSCHE S. Basic slit spectroscope reveals three-dimensional scenes through diagonal slices of hyperspectral cubes[J]. Applied Optics, 2014, 53(20):4594-4603.
[10] HONKAVAARA E, ESKELINEN M A, P L NEN I, et al. Remote sensing of 3-D geometry and surface moisture of a peat production area using hyperspectral frame cameras in visible to short-wave infrared spectral ranges onboard a small unmanned airborne vehicle (UAV)[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(9):5440-5454.
[11] ZHANG H, ZHANG B, WEI Z, et al. Lightweight integrated solution for a UAV-borne hyperspectral imaging system[J]. Remote Sensing, 2020, 12(4):657.
[12] SAARI H, PELLIKKA I, PESONEN L, et al. Unmanned Aerial Vehicle (UAV) operated spectral camera system for forest and agriculture applications[C]//Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII. International Society for Optics and Photonics, 2011, 8174:170-184.
[13] HRUSKA R, MITCHELL J, ANDERSON M, et al. Radiometric and geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle[J]. Remote Sensing, 2012, 4(9):2736-2752.
[14] ZARCO-TEJADA P J, GONZáLEZ-DUGO V, BERNI J A. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera[J]. Remote Sensing of Environment, 2012, 117:322-337.
[15] LUCIEER A, MALENOVSKY’ Z, VENESS T, et al. HyperUAS-Imaging spectroscopy from a multirotor unmanned aircraft system[J]. Journal of Field Robotics, 2014, 31(4):571-590.
[16] FOSSI A P, FERREC Y, COUDRAIN C, et al. Compact hyperspectral camera in the mid-infrared for small UAVs[C]//Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII. International Society for Optics and Photonics, 2016, 9840:482-490.
[17] ARROYO-MORA J P, KALACSKA M, INAMDAR D, et al. Implementation of a UAV-hyperspectral pushbroom imager for ecological monitoring[J]. Drones, 2019, 3(1):12.
[18] 李迁.低空无人机遥感在矿山监测中的应用研究[D].北京:中国地质大学(北京),2013.LI Q. Application of low-altitude UAV remote sensing in mine monitoring[D]. Beijing:China University of Geosciences(Beijing),2013.
[19] 李传荣.无人机遥感载荷综合验证系统技术[M].北京:科学出版社,2014.LI C R. UAV Remote Sensing Load Comprehensive Verification System Technology[M]. Beijing:China Science Publishing, 2014.
[20] 康孝岩,张爱武,庞海洋.基于光谱重建优化的无人机高光谱影像估算牧草生物量[J].光谱学与光谱分析, 2021,41(1):250.KANG X Y, ZHANG A W, PANG H Y. Estimation of Grassland Aboveground Biomass From UAV-Mounted Hyperspectral Image by Optimized Spectral Reconstruction[J]. Spectroscopy and Spectral Analysis, 2021, 41(1):250
[21] ZHANG H, ZHANG B, WEI Z, et al. Lightweight integrated solution for a UAV-borne hyperspectral imaging system[J]. Remote Sensing, 2020, 12(4):657.
[22] KIRSCH M, LORENZ S, ZIMMERMANN R, et al. Integration of terrestrial and drone-borne hyperspectral and photogrammetric sensing methods for exploration mapping and mining monitoring[J]. Remote Sensing, 2018, 10(9):1366.
[23] HUYNH H H, YU J, WANG L, et al. Integrative 3D Geological Modeling Derived from SWIR Hyperspectral Imaging Techniques and UAV-Based 3D Model for Carbonate Rocks[J]. Remote Sensing, 2021, 13(15):3037.
[24] BOOYSEN R, JACKISCH R, LORENZ S, et al. Detection of REEs with lightweight UAV-based hyperspectral imaging[J]. Scientific Reports, 2020, 10(1):1-12.
[25] WEI L, WANG Z, HUANG C, et al. Transparency estimation of narrow rivers by UAV-borne hyperspectral remote sensing imagery[J]. IEEE Access, 2020, 8:168137-168153.
[26] WEI L, HUANG C, WANG Z, et al. Monitoring of urban black-odor water based on nemerow index and gradient boosting decision tree regression using uav-borne hyperspectral imagery[J]. Remote Sensing, 2019, 11(20):2402.
[27] CUI M, SUN Y, HUANG C, et al. Water Turbidity Retrieval Based on UAV Hyperspectral Remote Sensing[J]. Water, 2022, 14(1):128.
[28] ROSSITER T, FUREY T, MCCARTHY T, et al. UAV-mounted hyperspectral mapping of intertidal macroalgae[J]. Estuarine, Coastal and Shelf Science, 2020, 242:106789.
[29] BALSI M, MORONI M, CHIARABINI V, et al. High-resolution aerial detection of marine plastic litter by hyperspectral sensing[J]. Remote Sensing, 2021, 13(8):1557.
[30] 郑迪,沈国春,王舶鉴,等.基于无人机高光谱影像和深度学习算法的长白山针阔混交林优势树种分类[J].生态学杂志, 2022, 41(5):1024-1032.ZHENG D, SHEN G C, WANG B J, et al. Classification of dominant species in coniferous and broad-leaved mixed forest on Changbai Mountain based on UAV-based hyperspectral image and deep learning algorithm[J]. Chinese Journal of Ecology, 2022, 41(5):1024-1032.
[31] ZHANG N, WANG Y, ZHANG X. Extraction of tree crowns damaged by Dendrolimus tabulaeformis Tsai et Liu via spectral-spatial classification using UAV-based hyperspectral images[J]. Plant Methods, 2020, 16(1):1-19.
[32] NEVALAINEN O, HONKAVAARA E, TUOMINEN S, et al. Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging[J]. Remote Sensing, 2017, 9(3):185.
[33] 徐明钻,梁森,石剑龙,等.航空高光谱反演耕地土壤重金属分布特征——以苏北灌河地区为例[J].华东地质,2021, 42(1):100-107.XU M Z,LIANG S,SHI J L, et al. Airborne hyperspectral inversion of heavy metal distribution in cultivated soil:A case study of the Guanhe area,north of Jiangsu Province[J].East China Geology,2021,42(1):100-107.
[34] NATESAN S, ARMENAKIS C, BENARI G, et al.Use of UAV-borne spectrometer for land cover classification[J]. Drones, 2018, 2(2):16.
[35] 王丹阳,陈红艳,王桂峰,等.无人机多光谱反演黄河口重度盐渍土盐分的研究[J].中国农业科学,2019, 52(10):1698-1709.WANG D Y,CHEN H Y,WANG G F, et al. Salinity Inversion of Severe Saline Soil in the Yellow River Estuary Based on UAV Multi-Spectra[J]. Scientia Agricultura Sinica,2019,52(10):1698-1709.
[36] HU J, PENG J, ZHOU Y, et al. Quantitative estimation of soil salinity using UAV-borne hyperspectral and satellite multispectral images[J]. Remote Sensing, 2019, 11(7):736.
[37] GE X, WANG J, DING J, et al. Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring[J]. PeerJ, 2019, 7:e6926.
[38] 中华人民共和国自然资源部.自然资源调查监测技术体系总体设计方案(试行)[EB/OL].[2022-03-02]. http://zrzy.hebei.gov.cn/heb/gongk/gkml/gggs/tz/zyzy/10696335938025320448.html. Ministry of Natural Resources of the People’s Republic of China. Overall design scheme of natural res-ources investigation and monitoring technology system (Trial)[EB/OL]. http://zrzy.hebei.gov.cn/heb/gongk/gkml/gggs/tz/zyzy/10696335938025320448.html.

备注/Memo

备注/Memo:
收稿日期:2022-05-10;改回日期:2022-07-14。
基金项目:江苏省自然资源发展专项(海洋科技创新)"江苏海岸带灾害承载体脆弱新调查与评价(编号:JSZRHYKJ202007)"项目资助。
作者简介:白宇,1995年生,男,硕士研究生,主要从事光谱探测信号处理工作。Email:baiyu95@163.com。
通讯作者:郑志忠,1980年生,男,教授级高级工程师,博士,主要从事高光谱遥感技术研究工作。Email:zhengzz_js@126.com。
更新日期/Last Update: 1900-01-01