扫 描 看 全 文
引用
李玉美，郭庆华，万波，秦宏楠，王德智，徐可心，宋师琳，孙千惠，赵晓霞，杨默含，吴晓永，魏邓杰，胡天宇，苏艳军.2021.基于激光雷达的自然资源三维动态监测现状与展望.遥感学报，25（1）： 381-402
Li Y M，Guo Q H，Wan B，Qin H N，Wang D Z，Xu K X，Song S L，Sun Q H，Zhao X X，Yang M H，Wu X Y，Wei D J，Hu T Y and Su Y J. 2021. Current status and prospect of three-dimensional dynamic monitoring of natural resources based on LiDAR. National Remote Sensing Bulletin， 25（1）：381-402
激光雷达作为一种主动的三维遥感观测技术，在不同尺度的土地、矿产、森林、草原、湿地、水、海洋等自然资源的三维动态监测中发挥着越来越重要的作用。本文将在简要介绍激光雷达技术发展现状的基础上，重点阐述激光雷达技术在各类自然资源三维动态监测中的应用现状，同时对激光雷达在自然资源调查中的应用潜力和局限性进行综合分析，最后探讨以激光雷达技术为基础的自然资源三维动态监测的未来发展趋势和方向。随着激光雷达技术和平台的不断发展以及激光雷达信息的深入挖掘，将不断促进激光雷达技术在自然资源三维动态监测应用中的纵深发展。然而单一激光雷达数据由于其本身存在的局限性，难以满足自然资源全要素、全流程、全覆盖、高精度、高效率的现代化动态监测的要求，如何将多源、多尺度、多平台遥感数据与人工智能相结合，构建“天—空—地”一体化的自然资源调查监测技术体系，是未来自然资源三维动态监测的发展方向。
Natural resources, as the necessary conditions for human survival and development, play an important role of driving force and main support for achieving high-quality and sustainable economic development, and are also the fundamental carrier for building a beautiful China and deepening the system reform of ecological civilization. Thus it is of great significance for human survival and development to achieve the high-precision and high-efficiency investigation, evaluation and monitoring of various natural resources. As an active three-dimensional remote sensing observation technology, Light Detection and Ranging (LiDAR) is playing an increasingly important role in the three-dimensional dynamic monitoring of multi-scale natural resources, such as land, mineral, forest, grassland, wetland, water, and ocean resources. To better understand the development and application situation of LiDAR in the three-dimensional dynamic monitoring of multi-scale natural resources, in this paper we first briefly introduced the current development status of LiDAR technology, including the review of technological development history of LiDAR and the brief elaboration of different LiDAR platforms (e.g. spaceborne LiDAR systems, airborne LiDAR systems, terrestrial LiDAR systems, etc) and their components. Then we reviewed respectively the application of LiDAR technology in three-dimensional dynamic monitoring of land, mineral, forest, grassland, wetland, water as well as ocean resources, and preliminarily analyzed the potentials and limitations of different LiDAR platforms in the three-dimensional dynamic monitoring of multi-scale natural resources. Based on the above review, we then comprehensively analyzed the potentials and limitations of applying LiDAR in natural resource surveys. The analysis showed that it is no doubt that the LiDAR technology will show the enormous advantages and potential in the three-dimensional dynamic monitoring of multi-scale natural resources in the future, as the fast development of the single photon LiDAR, multispectral LiDAR, hyperspectral LiDAR as well as Unmanned Aerial Vehicle (UAV) LiDAR platform. Certainly, LiDAR technology also demonstrated some limitations in natural resource surveys, which were mainly embodied in the following four aspects: (1) it was difficult for LiDAR technology to provide rich spectral information of natural resource; (2) A wide range of the all-weather and full-coverage LiDAR data normally was inaccessible; (3) The full three-dimensional information of natural resource was hard to be generated from a single LiDAR platform; (4) The data processing and information extraction algorithms of LiDAR were not yet systematic and prefect. Finally, we discussed the future development trend and direction of the three-dimensional dynamic monitoring of natural resources based on LiDAR technology. It is believed that the continuous development in LiDAR hardware and software platforms will continue to promote the in-depth mining of LiDAR data in the applications of three-dimensional dynamic monitoring of natural resources. However, current LiDAR technology still cannot meet the requirements of full-element, full-processes, full-coverage, high-precision and high-efficiency monitoring of natural resources, owing to its shortcoming of lack of spectral information, full three-dimensional information as well as all-weather and full-coverage data. Therefore, how to fuse multi-source, multi-scale, and multi-platform remote sensing data by taking advantages of artificial intelligence to build an integrated natural resource monitoring system is the future direction of three-dimensional dynamic monitoring of natural resources.
遥感激光雷达自然资源三维信息动态监测数据融合
remote sensingLiDARnatural resourcesthree-dimensional informationdynamic monitoringdata fusion
Alexander C, Tansey K, Kaduk J, Holland D and Tate N J. 2010. Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 65(5): 423-432 [DOI: 10.1016/j.isprsjprs.2010.05.002http://dx.doi.org/10.1016/j.isprsjprs.2010.05.002]
Anderson K E, Glenn N F, Spaete L P, Shinneman D J, Pilliod D S, Arkle R S, McIlroy S K and Derryberry D R. 2018. Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning. Ecological Indicators, 84: 793-802 [DOI: 10.1016/j.ecolind.2017.09.034http://dx.doi.org/10.1016/j.ecolind.2017.09.034]
Armstrong C. 2013. Natural resources: the demands of equality. Journal of Social Philosophy, 44(4): 331-347 [DOI: 10.1111/josp.12040http://dx.doi.org/10.1111/josp.12040]
Arndt N T, Fontboté L, Hedenquist J W, Kesler S E, Thompson J F H and Wood D G. 2017. Future global mineral resources. Geochemical Perspectives, 6(1): 1-171 [DOI: 10.7185/geochempersp.6.1http://dx.doi.org/10.7185/geochempersp.6.1]
Bandini F, Sunding T P, Linde J, Smith O, Jensen I K, Köppl C J, Butts M and Bauer-Gottwein P. 2020. Unmanned Aerial System (UAS) observations of water surface elevation in a small stream: comparison of radar altimetry, LIDAR and photogrammetry techniques. Remote Sensing of Environment, 237: 111487 [DOI: 10.1016/j.rse.2019.111487http://dx.doi.org/10.1016/j.rse.2019.111487]
Barbini R, Colao F, Fantoni R, Palucci A and Ribezzo S. 2001. Differential lidar fluorosensor system used for phytoplankton bloom and seawater quality monitoring in Antarctica. International Journal of Remote Sensing, 22(2/3): 369-384 [DOI: 10.1080/014311 601449989http://dx.doi.org/10.1080/014311601449989]
Behrangi A, Bormann K J and Painter T H. 2018. Using the airborne snow observatory to assess remotely sensed snowfall products in the California Sierra Nevada. Water Resources Research, 54(10): 7331-7346 [DOI: 10.1029/2018WR023108http://dx.doi.org/10.1029/2018WR023108]
Benediktsson J, Chanussot J and Fauvel M. 2007. Multiple classifier systems in remote sensing: from basics to recent developments // Multiple Classifier Systems. MCS 2007. Lecture Notes in Computer Science, vol 4472. Berlin, Heidelberg: Springer: 501-512 [DOI: 10.1007/978-3-540-72523-7_50http://dx.doi.org/10.1007/978-3-540-72523-7_50]
Bernard É, Friedt J M, Tolle F, Griselin M, Marlin C and Prokop A. 2017. Investigating snowpack volumes and icing dynamics in the moraine of an arctic catchment using UAV photogrammetry. The Photogrammetric Record, 32(160): 497-512 [DOI: 10.1111/phor.12217http://dx.doi.org/10.1111/phor.12217]
Bianchi C N and Morri C. 2000. Marine biodiversity of the Mediterranean Sea: situation, problems and prospects for future research. Marine Pollution Bulletin, 40(5): 367-376 [DOI: 10.1016/S0025-326X(00)00027-8http://dx.doi.org/10.1016/S0025-326X(00)00027-8]
Brock J C and Purkis S J. 2009. The emerging role of lidar remote sensing in coastal research and resource management. Journal of Coastal Research, 10053: 1-5 [DOI: 10.2112/SI53-001.1http://dx.doi.org/10.2112/SI53-001.1]
Brock J C, Wright C W, Kuffner I B, Hernandez R and Thompson P. 2006. Airborne lidar sensing of massive stony coral colonies on patch reefs in the northern Florida reef tract. Remote Sensing of Environment, 104(1): 31-42 [DOI: 10.1016/j.rse.2006.04.017http://dx.doi.org/10.1016/j.rse.2006.04.017]
Casella E, Collin A, Harris D, Ferse S, Bejarano S, Parravicini V, Hench J L and Rovere A. 2017. Mapping coral reefs using consumer-grade drones and structure from motion photogrammetry techniques. Coral Reefs, 36(1): 269-275 [DOI: 10.1007/s00338-016-1522-0http://dx.doi.org/10.1007/s00338-016-1522-0]
Caudal P, Grenon M, Turmel D and Locat J. 2017. Analysis of a large rock slope failure on the east wall of the LAB Chrysotile mine in Canada: LiDAR monitoring and displacement analyses. Rock Mechanics and Rock Engineering, 50(4): 807-824 [DOI: 10.1007/s00603-016-1145-3http://dx.doi.org/10.1007/s00603-016-1145-3]
Chadwick J. 2011. Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties. International Journal of Remote Sensing, 32(21): 6765-6781 [DOI: 10.1080/01431161.2010.512944http://dx.doi.org/10.1080/01431161.2010.512944]
Chave J, Andalo C, Brown S, Cairns M A, Chambers J Q, Eamus D, Fölster H, Fromard F, Higuchi N, Kira T, Lescure J P, Nelson B W, Ogawa H, Puig H, Riéra B and Yamakura T. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145(1): 87-99 [DOI: 10.1007/s00442-005-0100-xhttp://dx.doi.org/10.1007/s00442-005-0100-x]
Chehata N, Guo L and Mallet C. 2009. Airborne LIDAR feature selection for urban classification using random forests //Laser scanning 2009, IAPRS, Vol. XXXVIII, Part 3/W8. (Paris, France: [s.
n.]: 207-212
Chen G, Yang J, Zhang B T and Ma C Y. 2019. Thoughts and prospects on the new generation of marine science satellites. Periodical of Ocean University of China, 49(10): 110-117
陈戈, 杨杰, 张本涛, 马纯永 2019. 新一代海洋科学卫星的思考与展望. 中国海洋大学学报, 49(10): 110-117 [DOI: 10.16441/j.cnki.hdxb.20190226http://dx.doi.org/10.16441/j.cnki.hdxb.20190226]
Chen J K, Du P J, Wu C S, Xia J S and Chanussot J. 2018. Mapping urban land cover of a large area using multiple sensors multiple features. Remote Sensing, 10: 872 [DOI: 10.3390/rs10060872http://dx.doi.org/10.3390/rs10060872]
Chen P, Mao Z H, Zhang Z H, Liu H and Pan D L. 2020. Detecting subsurface phytoplankton layer in Qiandao Lake using shipborne lidar. Optics Express, 28(1): 558-569 [DOI: 10.1364/OE.381617http://dx.doi.org/10.1364/OE.381617]
Chen Y N, Li Z, Fan Y T, Wang H J and Fang G H. 2014. Research progress on the impact of climate change on water resources in the arid region of Northwest China. Acta Geographica Sinica, 69(9): 1295-1304
陈亚宁, 李稚, 范煜婷, 王怀军, 方功焕. 2014. 西北干旱区气候变化对水文水资源影响研究进展. 地理学报, 69(9): 1295-1304 [DOI: 10.11821/dlxb201409005http://dx.doi.org/10.11821/dlxb201409005]
Chen Y Y and Lv X G. 2003. The wetland function and research Tendency of wetland science. Wetland Science, 1(1): 7-11
陈宜瑜, 吕宪国. 2003. 湿地功能与湿地科学的研究方向. 湿地科学, 1(1): 7-11 [DOI: 10.3969/j.issn.1672-5948.2003.01.003http://dx.doi.org/10.3969/j.issn.1672-5948.2003.01.003]
Clark M L, Clark D B and Roberts D A. 2004. Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape. Remote Sensing of Environment, 91(1): 68-89 [DOI: 10.1016/j.rse.2004.02.008http://dx.doi.org/10.1016/j.rse.2004.02.008]
Cooper S D, Roy D P, Schaaf C B and Paynter I. 2017. Examination of the potential of terrestrial laser scanning and structure-from-motion photogrammetry for rapid nondestructive field measurement of grass biomass. Remote Sensing, 9(6): 531 [DOI: 10.3390/rs9060531http://dx.doi.org/10.3390/rs9060531]
Dare P M. 2005. Shadow analysis in high-resolution satellite imagery of urban areas. Photogrammetric Engineering and Remote Sensing, 71(2): 169-177 [DOI: 10.14358/PERS.71.2.169http://dx.doi.org/10.14358/PERS.71.2.169]
Deems J S, Painter T H and Finnegan D C. 2013. Lidar measurement of snow depth: a review. Journal of Glaciology, 59(215): 467-479 [DOI: 10.3189/2013JoG12J154http://dx.doi.org/10.3189/2013JoG12J154]
Dickey T, Lewis M and Chang G. 2006. Optical oceanography: recent advances and future directions using global remote sensing and in situ observations. Reviews of Geophysics, 44: RG1001 [DOI: 10.1029/2003RG000148http://dx.doi.org/10.1029/2003RG000148]
Eitel J U H, Höfle B, Vierling L A, Abellán A, Asner G P, Deems J S, Glennie C L, Joerg P C, LeWinter A L, Magney T S, Mandlburger G, Morton D C, Müller J and Vierling K T. 2016. Beyond 3-D: the new spectrum of lidar applications for earth and ecological sciences. Remote Sensing of Environment, 186: 372-392 [DOI: 10.1016/j.rse.2016.08.018http://dx.doi.org/10.1016/j.rse.2016.08.018]
Errington A F C, Daku B L F and Prugger A F. 2016. Clay mapping in underground potash mines: an initial investigation into the use of corrected intensity terrestrial LiDAR data//2016 IEEE International Conference on Imaging Systems and Techniques. Chania, Greece: IEEE: 94-99 [DOI: 10.1109/IST.2016.7738204http://dx.doi.org/10.1109/IST.2016.7738204]
Fan L, Powrie W, Smethurst J, Atkinson P M and Einstein H. 2014. The effect of short ground vegetation on terrestrial laser scans at a local scale. ISPRS Journal of Photogrammetry and Remote Sensing, 95: 42-52 [DOI: 10.1016/j.isprsjprs.2014.06.003http://dx.doi.org/10.1016/j.isprsjprs.2014.06.003]
Finkl C W, Benedet L and Andrews J L. 2005. Interpretation of seabed geomorphology based on spatial analysis of high-density airborne laser bathymetry. Journal of Coastal Research, 213: 501-514 [DOI: 10.2112/05-756A.1http://dx.doi.org/10.2112/05-756A.1]
Fiorani L, Angelini F, Artuso F, Cataldi D and Colao F. 2019. Lidar monitoring of chlorophyll a during the XXIX and XXXI Italian Antarctic expeditions. International Journal of Environmental Research, 13(2): 253-263 [DOI: 10.1007/s41742-019-00169-whttp://dx.doi.org/10.1007/s41742-019-00169-w]
Fisher R J, Sawa B and Prieto B. 2018. A novel technique using LiDAR to identify native-dominated and tame-dominated grasslands in Canada. Remote Sensing of Environment, 218: 201-206 [DOI: 10.1016/j.rse.2018.10.003http://dx.doi.org/10.1016/j.rse.2018.10.003]
Fragoso-Campón L, Quirós E, Mora J, Gallego J A G and Durán-Barroso P. 2020. Overstory-understory land cover mapping at the watershed scale: accuracy enhancement by multitemporal remote sensing analysis and LiDAR. Environmental Science and Pollution Research, 27(1): 75-88 [DOI: 10.1007/s11356-019-04520-8http://dx.doi.org/10.1007/s11356-019-04520-8]
Gao Z Y. 2019. Spatial and Temporal Variation in the Elevation and Mass of the Antarctic Ice Sheet Were Studied by Altimetry and Gravity Satellites. Xi’an: Northwest University
高志远. 2019. 利用测高和重力卫星研究南极冰盖高程及质量时空格局变化. 西安: 西北大学
Ge Q S, Fang C L and Jiang D. 2020. Geographical missions and coupling ways between human and nature for the Beautiful China Initiative. Acta Geographica Sinica, 75(6): 1109-1119
葛全胜, 方创琳, 江东. 2020. 美丽中国建设的地理学使命与人地系统耦合路径. 地理学报, 75(6): 1109-1119 [DOI: 10.11821/dlxb202006001http://dx.doi.org/10.11821/dlxb202006001]
Ghosh S, Nandy S, Patra S, Kushwaha S P S, Kumar A S and Dadhwal V K. 2017. Land cover classification using ICESat/GLAS full waveform data. Journal of the Indian Society of Remote Sensing, 45: 327-335 [DOI: 10.1007/s12524-016-0602-5http://dx.doi.org/10.1007/s12524-016-0602-5]
Goodenough K M, Wall F and Merriman D. 2018. The rare earth elements: demand, global resources, and challenges for resourcing future generations. Natural Resources Research, 27: 201-216 [DOI: 10.1007/s11053-017-9336-5http://dx.doi.org/10.1007/s11053-017-9336-5]
Gottfried M, Hollaus M, Glira P, Wieser M, Milenković M, Riegl U and Pfennigbauer M. 2015. First examples from the RIEGL VUX-SYS for forestry applications//SilviLaser 2015. La Grande-Motte, France: [s.
n.]: 105-107
Greaves H E, Vierling L A, Eitel J U H, Boelman N T, Magney T S, Prager C M and Griffin K L. 2015. Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR. Remote Sensing of Environment, 164: 26-35 [DOI: 10.1016/j.rse.2015.02.023http://dx.doi.org/10.1016/j.rse.2015.02.023]
Greaves H E, Vierling L A, Eitel J U H, Boelman N T, Magney T S, Prager C M and Griffin K L. 2016. High-resolution mapping of aboveground shrub biomass in Arctic tundra using airborne lidar and imagery. Remote Sensing of Environment, 184: 361-373 [DOI: 10.1016/j.rse.2016.07.026http://dx.doi.org/10.1016/j.rse.2016.07.026]
Guan H C, Su Y J, Hu T Y, Wang R, Ma Q, Yang Q L, Sun X L, Li Y M, Jin S C, Zhang J, Ma Q, Liu M, Wu F Y and Guo Q H. 2020. A novel framework to automatically fuse multiplatform LiDAR data in forest environments based on tree locations. IEEE Transactions on Geoscience and Remote Sensing, 58(3): 2165-2177 [DOI: 10.1109/TGRS.2019.2953654http://dx.doi.org/10.1109/TGRS.2019.2953654]
Guo C. 2014. Data Processing and Application of 3D Laser Scanning to Dam Subsidence Monitoring in Mining Area. Jilin: Jilin University
郭超. 2014. 三维激光扫描数据处理及在矿区大坝沉陷监测中的应用研究. 吉林: 吉林大学
Guo Q H, Liu J, Tao S L, Xue B L, Li L, Xu G C, Li W K, Wu F F, Li Y M, Chen L H and Pang S X. 2014. Perspectives and prospects of LiDAR in forest ecosystem monitoring and modeling. Chinese Science Bulletin, 59(6): 459-478
郭庆华, 刘瑾, 陶胜利, 薛宝林, 李乐, 徐光彩, 李文楷, 吴芳芳, 李玉美, 陈琳海, 庞树鑫. 2014. 激光雷达在森林生态系统监测模拟中的应用现状与展望. 科学通报, 59(6): 459-478 [DOI: 10.1360/972013-592http://dx.doi.org/10.1360/972013-592]
Guo Q H, Su Y J, Hu T Y and Liu J. 2018. LiDAR Principles, Processing and Applications in Forest Ecology. Beijing: Higher Education Press
郭庆华, 苏艳军, 胡天宇, 刘瑾. 2018. 激光雷达森林生态应用——理论、方法及实例. 北京: 高等教育出版社
Guo Q H, Su Y J, Hu T Y, Zhao X Q, Wu F F, Li Y M, Liu J, Chen L H, Xu G C, Lin G H, Zheng Y, Lin Y Q, Mi X C, Fei L and Wang X G. 2017. An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China. International Journal of Remote Sensing, 38(8/10): 2954-2972 [DOI: 10.1080/01431161.2017.1285083http://dx.doi.org/10.1080/01431161.2017.1285083]
Guo S Y, Hu X, Yan Z A, Cheng Y Q and Guo W J. 2016. Research development of space-borne lidar in foreign countries. Laser Technology, 40(5): 772-778
郭商勇, 胡雄, 闫召爱, 程永强, 郭文杰. 2016. 国外星载激光雷达研究进展. 激光技术, 40(5): 772-778 [DOI: 10.7510/jgjs.issn.1001-3806.2016.05.032http://dx.doi.org/10.7510/jgjs.issn.1001-3806.2016.05.032]
Harder P, Pomeroy J W and Helgason W D. 2020. Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques. Cryosphere, 14(6): 1919-1935 [DOI: 10.5194/tc-14-1919-2020http://dx.doi.org/10.5194/tc-14-1919-2020]
Harpold A A, Guo Q H, Molotch N, Brooks P D, Bales R, Fernandez-Diaz J C, Musselman K N, Swetnam T L, Kirchner P, Meadows M W, Flanagan J and Lucas R. 2014. Lidar-derived snowpack data sets from mixed conifer forests across the western United States. Water Resources Research, 50(3): 2749-2755 [DOI: 10.1002/2013WR013935http://dx.doi.org/10.1002/2013WR013935]
Hartfield K A, Landau K I and Van Leeuwen W J D. 2011. Fusion of high resolution aerial multispectral and LiDAR data: land cover in the context of urban mosquito habitat. Remote Sensing, 3(11): 2364-2383 [DOI: 10.3390/rs3112364http://dx.doi.org/10.3390/rs3112364]
Hickman G D and Hogg J E. 1969. Application of an airborne pulsed laser for near shore bathymetric measurements. Remote Sensing of Environment, 1(1): 47-58 [10.1016/S0034-4257(69)90088-1]
Hoge F E, Swift R N and Frederick E B. 1980. Water depth measurement using an airborne pulsed neon laser system. Applied Optics, 19(6): 871-883 [DOI: 10.1364/AO.19.000871http://dx.doi.org/10.1364/AO.19.000871]
Hostetler C A, Behrenfeld M J, Hu Y, Hair J W and Schulien J A. 2018. Spaceborne lidar in the study of marine systems. Annual Review of Marine Science, 10: 121-147 [DOI: 10.1146/annurev-marine-121916-063335http://dx.doi.org/10.1146/annurev-marine-121916-063335]
Hu H Q, Luo B Z, Luo S S, Wei S J, Wang Z S, Li X C and Liu F. 2020. Research progress on effects of forest fire disturbance on carbon pool of forest ecosystem. Scientia Silvae Sinicae, 56(4): 160-169
胡海清, 罗碧珍, 罗斯生, 魏书精, 王振师, 李小川, 刘菲. 2020. 林火干扰对森林生态系统碳库的影响研究进展. 林业科学, 56(4): 160-169 [DOI: 10.11707/j.1001-7488.20200418http://dx.doi.org/10.11707/j.1001-7488.20200418]
Hu S X, Li J R and Liang Y Q. 2020. Real-time monitoring method of land use change based on lidar surveying and mapping. Laser Journal, 41(5): 86-90
胡少雄, 李洁茹, 梁彦庆. 2020. 基于激光雷达测绘的土地利用变动实时监测方法. 激光杂志, 41(5): 86-90 [DOI: 10.14016/j.cnki.jgzz.2020.05.086http://dx.doi.org/10.14016/j.cnki.jgzz.2020.05.086]
Hu T Y, Ma Q, Su Y J, Battles J J, Collins B M, Stephens S L, Kelly M and Guo Q H. 2019. A simple and integrated approach for fire severity assessment using bi-temporal airborne LiDAR data. International Journal of Applied Earth Observation and Geoinformation, 78: 25-38 [DOI: 10.1016/j.jag.2019.01.007http://dx.doi.org/10.1016/j.jag.2019.01.007]
Hu T Y, Su Y J, Xue B L, Liu J, Zhao X Q, Fang J Y and Guo Q H. 2016. Mapping global forest aboveground biomass with spaceborne LiDAR, optical imagery, and forest inventory data. Remote Sensing, 8(7): 565 [DOI: 10.3390/rs8070565http://dx.doi.org/10.3390/rs8070565]
Hu T Y, Zhang Y Y, Su Y J, Zheng Y, Lin G H and Guo Q H. 2020. Mapping the global mangrove forest aboveground biomass using multisource remote sensing data. Remote Sensing, 12(10): 1690 [DOI: 10.3390/rs12101690http://dx.doi.org/10.3390/rs12101690]
Huang H B, Li Z, Gong P, Cheng X, Clinton N, Cao C X, Ni W J and Wang L. 2011. Automated methods for measuring DBH and tree heights with a commercial scanning lidar. Photogrammetric Engineering and Remote Sensing, 77(3): 219-227 [DOI: 10.14358/PERS.77.3.219http://dx.doi.org/10.14358/PERS.77.3.219]
Huang L and Zhang X L. 2006. Applications of Lidar and 3D remote sensing in forestry. World Forestry Research, 19(4): 11-17
黄麟, 张晓丽. 2006. 三维成像激光雷达遥感技术在林业中的应用. 世界林业研究, 19(4): 11-17 [DOI: 10.3969/j.issn.1001-4241.2006.04.003http://dx.doi.org/10.3969/j.issn.1001-4241.2006.04.003]
Im J, Jensen J R and Hodgson M E. 2008. Object-based land cover classification using high-posting-density LiDAR data. GIScience and Remote Sensing, 45(2): 209-228 [DOI: 10.2747/1548-1603.45.2.209http://dx.doi.org/10.2747/1548-1603.45.2.209]
Jansen V S, Kolden C A, Greaves H E and Eitel J U H. 2019. Lidar provides novel insights into the effect of pixel size and grazing intensity on measures of spatial heterogeneity in a native bunchgrass ecosystem. Remote Sensing of Environment, 235: 111432 [DOI: 10.1016/j.rse.2019.111432http://dx.doi.org/10.1016/j.rse.2019.111432]
Ji X F. 2020. Application of rotor UAV LiDAR in real estate property investigation in rural area. Bulletin of Surveying and Mapping, (7): 152-155, 158
吉绪发. 2020. 旋翼无人机载激光雷达在农村不动产权籍调查中的应用. 测绘通报, (7): 152-155, 158 [DOI: 10.13474/j.cnki.11-2246.2020.0234http://dx.doi.org/10.13474/j.cnki.11-2246.2020.0234]
Jin X L. 2004. The development of technique of marine geophysics. Journal of East China Institute of Technology, 27(1): 6-13
金翔龙. 2004. 海洋地球物理技术的发展. 东华理工学院学报, 27(1): 6-13 [DOI: 10.3969/j.issn.1674-3504.2004.01.002http://dx.doi.org/10.3969/j.issn.1674-3504.2004.01.002]
Jin X L. 2007. The development of research in marine geophysics and acoustic technology for submarine exploration. Progress in Geophysics, 22(4): 1243-1249
金翔龙. 2007. 海洋地球物理研究与海底探测声学技术的发展. 地球物理学进展, 22(4): 1243-1249 [DOI: 10.3969/j.issn.1004-2903.2007.04.034http://dx.doi.org/10.3969/j.issn.1004-2903.2007.04.034]
Jin Y X, Yang X C, Qiu J J, Li J Y, Gao T, Wu Q, Zhao F, Ma H L, Yu H D and Xu B. 2014. Remote sensing-based biomass estimation and its spatio-temporal variations in temperate grassland, Northern China. Remote Sensing, 6(2): 1496-1513 [DOI: 10.3390/rs6021496http://dx.doi.org/10.3390/rs6021496]
Johnson B D and Singh J. 2003. Building the national geobase for Canada. Photogrammetric Engineering and Remote Sensing, 69(10): 1169-1173 [DOI: 10.14358/PERS.69.10.1169http://dx.doi.org/10.14358/PERS.69.10.1169]
Kim Y and Kim Y. 2014. Improved classification accuracy based on the output-level fusion of high-resolution satellite images and airborne LiDAR data in urban area. IEEE Geoscience and Remote Sensing Letters, 11(3): 636-640 [DOI: 10.1109/LGRS.2013.2273397http://dx.doi.org/10.1109/LGRS.2013.2273397]
Kirchner P B, Bales R C, Molotch N P, Flanagan J and Guo Q H. 2014. LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California. Hydrology and Earth System Sciences Discussions, 18(10): 4261-4275 [DOI: 10.5194/hess-18-4261-2014http://dx.doi.org/10.5194/hess-18-4261-2014]
Kwok R and Cunningham G F. 2015. Variability of Arctic sea ice thickness and volume from CryoSat-2. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2045): 20140157 [DOI: 10.1098/rsta.2014.0157http://dx.doi.org/10.1098/rsta.2014.0157]
Kwok R and Markus T. 2018. Potential basin-scale estimates of Arctic snow depth with sea ice freeboards from CryoSat-2 and ICESat-2: an exploratory analysis. Advances in Space Research, 62(6): 1243-1250 [DOI: 10.1016/j.asr.2017.09.007http://dx.doi.org/10.1016/j.asr.2017.09.007]
Laxon S W, Giles K A, Ridout A L, Wingham D J, Willatt R, Cullen R, Kwok R, Schweiger A, Zhang J L, Haas C, Hendricks S, Krishfield R, Kurtz N, Farrell S and Davidson M. 2013. CryoSat-2 estimates of Arctic sea ice thickness and volume. Geophysical Research Letters, 40(4): 732-737 [DOI: 10.1002/grl.50193http://dx.doi.org/10.1002/grl.50193]
Lefsky M A. 2010. A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System. Geophysical Research Letters, 37(15): L15401 [DOI: 10.1029/2010GL043622http://dx.doi.org/10.1029/2010GL043622]
Li A H, Dhakal S, Glenn N F, Spaete L P, Shinneman D J, Pilliod D S, Arkle R S and McIlroy S K. 2017. Lidar aboveground vegetation biomass estimates in Shrublands: prediction, uncertainties and application to coarser scales. Remote Sensing, 9(9): 903 [DOI: 10.3390/rs9090903http://dx.doi.org/10.3390/rs9090903]
Li H, Xie H J, Kern S, Wan W, Ozsoy B, Ackley S and Hong Y. 2018a. Spatio-temporal variability of Antarctic sea-ice thickness and volume obtained from ICESat data using an innovative algorithm. Remote Sensing of Environment, 219: 44-61 [DOI: 10.1016/j.rse.2018.09.031http://dx.doi.org/10.1016/j.rse.2018.09.031]
Li H , Gao J , Hu Q , Li Y, Tian J, Liao C, Ma W and Xu Y. 2019. Assessing revegetation effectiveness on an extremely degraded grassland, southern Qinghai-Tibetan Plateau, using terrestrial LiDAR and field data. Agriculture, Ecosystems & Environment,282:13-22[10.1016/j.agee.2019.05.013]
Li L, Guo Q H, Tao S L, Kelly M and Xu G C. 2015. Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass. ISPRS Journal of Photogrammetry and Remote Sensing, 102: 198-208 [DOI: 10.1016/j.isprsjprs.2015.02.007http://dx.doi.org/10.1016/j.isprsjprs.2015.02.007]
Li W K, Guo Q H, Jakubowski M K and Kelly M. 2012. A new method for segmenting individual trees from the lidar point cloud. Photogrammetric Engineering and Remote Sensing, 78(1): 75-84 [DOI: 10.14358/PERS.78.1.75http://dx.doi.org/10.14358/PERS.78.1.75]
Li W, Niu Z, Shang R, Qin Y C, Wang L and Chen H Y. 2020a. High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data. International Journal of Applied Earth Observation and Geoinformation, 92: 102163 [DOI: 10.1016/j.jag.2020.102163http://dx.doi.org/10.1016/j.jag.2020.102163]
Li W F, Kong R and Wang R C. 2008. Research on Evaluation Target System of Important Mineral Resources in China. Natural Resource Economics of China, 21(7): 26-28
李文芳, 孔锐, 王仁财. 2008. 我国重要矿产资源评价指标体系研究. 中国国土资源经济, 21(7): 26-28 [DOI: 10.3969/j.issn.1672-6995.2008.07.010http://dx.doi.org/10.3969/j.issn.1672-6995.2008.07.010]
Li Y M, Su Y J, Hu T Y, Xu G C and Guo Q H. 2018b. Retrieving 2-D leaf angle distributions for deciduous trees from terrestrial laser scanner data. IEEE Transactions on Geoscience and Remote Sensing, 56(8): 4945-4955 [DOI: 10.1109/TGRS.2018.2843382http://dx.doi.org/10.1109/TGRS.2018.2843382]
Li Y M, Su Y J, Zhao X X, Yang M H, Hu T Y, Zhang J, Liu J, Liu M and Guo Q H. 2020b. Retrieval of tree branch architecture attributes from terrestrial laser scan data using a Laplacian algorithm. Agricultural and Forest Meteorology, 284: 107874 [DOI: 10.1016/j.agrformet.2019.107874http://dx.doi.org/10.1016/j.agrformet.2019.107874]
Liu B Y, Li R Q, Yang Q and Kong X J. 2019a. Estimation of global detection depth of spaceborne oceanographic lidar in blue-green spectral region. Infrared and Laser Engineering, 48(1): 106006
Liu H, Li J, Tang Q H, Zhou X H, Liu J Y, Shi S C, Huang B Z, Xu W X and Fu Y G. 2020. Object-based island hierarchical land cover classification using unmanned aerial vehicle multitype data. Journal of Applied Remote Sensing, 14(3): 034514 [DOI: 10.1117/1.JRS.14.034514http://dx.doi.org/10.1117/1.JRS.14.034514]
Liu Q, Cui X M, Liu W L, Li W J and Liu P F. 2015. Application of 3D laser scanning technology to the reclamation of coal gangue dump. Engineering of Surveying and Mapping, 24(10): 67-70
刘强, 崔希民, 刘文龙, 李文杰, 刘鹏飞. 2015. 三维激光扫描技术在煤矸石山复垦中的应用. 测绘工程, 24(10): 67-70 [DOI: 10.3969/j.issn.1006-7949.2015.10.016http://dx.doi.org/10.3969/j.issn.1006-7949.2015.10.016]
Liu X Q, Chen Y M, Li S Y, Cheng L and Li M C. 2019b. Hierarchical classification of urban ALS data by using geometry and intensity information. Sensors, 19(20): 4583 [DOI: 10.3390/s19204583http://dx.doi.org/10.3390/s19204583]
Liu Y X, Guo K, He X F, Xu W X and Feng Y K. 2017. Research progress of airborne laser bathymetry technology. Geomatics and Information Science of Wuhan University, 42(9): 1185-1194
刘焱雄, 郭锴, 何秀凤, 徐文学, 冯义楷. 2017. 机载激光测深技术及其研究进展. 武汉大学学报(信息科学版), 42(9): 1185-1194 [DOI: 10.13203/j.whugis20150779http://dx.doi.org/10.13203/j.whugis20150779]
Liu Z P, Liu D, Xu P T, Wu L, Zhou Y D, Han B, Liu Q, Song Q J, Mao Z H, Zhang Y P, Cui X Y and Chen P. 2019. Retrieval of seawater optical properties with an oceanic lidar. Journal of Remote Sensing, 23(5): 944-951
刘志鹏, 刘东, 徐沛拓, 吴兰, 周雨迪, 韩冰, 刘群, 宋庆君, 毛志华, 张与鹏, 崔晓宇, 陈鹏. 2019. 海洋激光雷达反演水体光学参数. 遥感学报, 23(5): 944-951 [DOI: 10.11834/jrs.20198354http://dx.doi.org/10.11834/jrs.20198354]
Luan X N, Li J W, Guo J J and Zheng R R. 2014. Ocean Lidar for fishery resources survey and ecological environment monitoring. Acta Laser Biology Sinica, 23(6): 534-541
栾晓宁, 李菁文, 郭金家, 郑荣儿. 2014. 海洋激光雷达在渔业资源调查和生态环境监测中的应用. 激光生物学报, 23(6): 534-541 [DOI: 10.3969/j.issn.1007-7146.2014.06.005http://dx.doi.org/10.3969/j.issn.1007-7146.2014.06.005]
Luo L P, Zhai Q P, Su Y J, Ma Q, Kelly M and Guo Q H. 2018. Simple method for direct crown base height estimation of individual conifer trees using airborne LiDAR data. Optics Express, 26(10): A562-A578 [DOI: 10.1364/OE.26.00A562http://dx.doi.org/10.1364/OE.26.00A562]
Lyu G P. 2018. Application of LiDAR Technique to Extract Parameters of Vegetation and Topography in Mining Areas. Nanjing: Nanjing Forestry University
吕国屏. 2018. 基于地基激光雷达的矿山植被生态参数提取研究. 南京: 南京林业大学
Lymburner L, Bunting P, Lucas R, Scarth P, Alam I, Phillips C, Ticehurst C and Held A. 2020. Mapping the multi-decadal mangrove dynamics of the Australian coastline. Remote Sensing of Environment, 238: 111185 [DOI: 10.1016/j.rse.2019.05.004http://dx.doi.org/10.1016/j.rse.2019.05.004]
Ma Q, Su Y J, Tao S L and Guo Q H. 2018. Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains, California. International Journal of Digital Earth, 11(5): 485-503 [DOI: 10.1080/17538947.2017.1336578http://dx.doi.org/10.1080/17538947.2017.1336578]
Mallet C, Soergel U and Bretar F. 2008. Analysis of full-waveform lidar data for classification of urban areas//The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. (Beijing, China: [s.
n.]: 85-92
Maltamo M, Peuhkurinen J, Malinen J, Vauhkonen J, Packalén P and Tokola T. 2009. Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data. Silva Fennica, 43(3):
Article ID 203 [DOI: 10.14214/sf.203http://dx.doi.org/10.14214/sf.203]
Mandlburger G, Hauer C, Wieser M and Pfeifer N. 2015. Topo-bathymetric LiDAR for monitoring river morphodynamics and instream habitats—a case study at the Pielach River. Remote Sensing, 7(5): 6160-6195 [DOI: 10.3390/rs70506160http://dx.doi.org/10.3390/rs70506160]
Mao D H, Wang Z M, Du B J, Li L, Tian Y L, Jia M M, Zeng Y, Song K S, Jiang M and Wang Y Q. 2020. National wetland mapping in China: a new product resulting from object-based and hierarchical classification of Landsat 8 OLI images. ISPRS Journal of Photogrammetry and Remote Sensing, 164: 11-25
Marcinkowska-Ochtyra A, Jarocinska A, Bzdega K and Tokarska-Guzik B. 2018. Classification of expansive grassland species in different growth stages based on hyperspectral and LiDAR data. Remote Sensing, 10(12): 2019 [DOI: 10.3390/rs10122019http://dx.doi.org/10.3390/rs10122019]
Martin J L, Maris V and Simberloff D S. 2016. The need to respect nature and its limits challenges society and conservation science. Proceedings of the National Academy of Sciences of the United States of America, 113(22): 6105-6112 [DOI: 10.1073/pnas.1525003113http://dx.doi.org/10.1073/pnas.1525003113]
Matikainen L, Karila K, Litkey P, Ahokas E and Hyyppä J. 2020. Combining single photon and multispectral airborne laser scanning for land cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 164: 200-216 [DOI: 10.1016/j.isprsjprs.2020.04.021http://dx.doi.org/10.1016/j.isprsjprs.2020.04.021]
Means J E, Acker S A, Fitt B J, Renslow M, Emerson L and Hendrix C J. 2000. Predicting forest stand characteristics with airborne scanning lidar. Photogrammetric Engineering and Remote Sensing, 66(11): 1367-1371
Mitchell J J, Glenn N F, Sankey T T, Derryberry D R, Anderson M O and Hruska R C. 2011. Small-footprint lidar estimations of sagebrush canopy characteristics. Photogrammetric Engineering and Remote Sensing, 77(5): 521-530 [DOI: 10.14358/PERS.77.5.521http://dx.doi.org/10.14358/PERS.77.5.521]
Moeslund J E, Zlinszky A, Ejrnæs R, Brunbjerg A K, Bøcher P K, Svenning J C and Normand S. 2019. Light detection and ranging explains diversity of plants, fungi, lichens, and bryophytes across multiple habitats and large geographic extent. Ecological Applications, 29(5): e01907 [DOI: 10.1002/eap.1907http://dx.doi.org/10.1002/eap.1907]
Moorthy I, Miller J R, Berni J A J, Zarco-Tejada P, Hu B X and Chen J. 2011. Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data. Agricultural and Forest Meteorology, 151(2): 204-214
Næsset E, Bollandsås O M, Gobakken T, Gregoire T G and Ståhl G. 2013. Model-assisted estimation of change in forest biomass over an 11 year period in a sample survey supported by airborne LiDAR: a case study with post-stratification to provide “activity data”. Remote Sensing of Environment, 128: 299-314.
Nelson R, Krabill W and MacLean G. 1984. Determining forest canopy characteristics using airborne laser data. Remote Sensing of Environment, 15(3): 201-212 [DOI: 10.1016/0034-4257(84)90031-2http://dx.doi.org/10.1016/0034-4257(84)90031-2]
Olsoy P J, Glenn N F, Clark P E and Derryberry D R. 2014. Aboveground total and green biomass of dryland shrub derived from terrestrial laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 88: 166-173 [DOI: 10.1016/j.isprsjprs.2013.12.006http://dx.doi.org/10.1016/j.isprsjprs.2013.12.006]
Owers, C J, Rogers K and Woodroffe C D. 2018. Terrestrial laser scanning to quantify above–ground biomass of structurally complex coastal wetland vegetation. Estuarine, Coastal and Shelf Science, 204:164–176 [10.1016/j.ecss.2018.02.027]
Pearse G D, Morgenroth J, Watt M S and Dash J P. 2017. Optimising prediction of forest leaf area index from discrete airborne lidar. Remote Sensing of Environment, 200: 220-239 [DOI: 10.1016/j.rse.2017.08.002http://dx.doi.org/10.1016/j.rse.2017.08.002]
Pittman S J, Costa B M and Battista T A. 2009. Using lidar bathymetry and boosted regression trees to predict the diversity and abundance of fish and corals. Journal of Coastal Research, 10053: 27-38 [DOI: 10.2112/SI53-004.1http://dx.doi.org/10.2112/SI53-004.1]
Priestnall G, Jaafar J and Duncan A. 2000. Extracting urban features from LiDAR digital surface models. Computers, Environment and Urban Systems, 24(2): 65-78
Prince A, Franssen J, Lapierre J F and Maranger R. 2020. High-resolution broad-scale mapping of soil parent material using object-based image analysis (OBIA) of LiDAR elevation data. Catena, 188: 104422 [DOI: 10.1016/j.catena.2019.104422http://dx.doi.org/10.1016/j.catena.2019.104422]
Qiu N, Xu S J, Qiu P H, Song Y, Niu A Y and Xu G E. 2017. Species diversity and spatial distribution pattern of mangrove in Nansha Wetland Park, Guangzhou, Guangdong Province, China. Ecology and Environment Sciences, 26(1): 27-35
邱霓, 徐颂军, 邱彭华, 宋焱, 牛安逸, 许观嫦. 2017. 南沙湿地公园红树林物种多样性与空间分布格局. 生态环境学报, 26(1): 27-35 [DOI: 10.16258/j.cnki.1674-5906.2017.01.005http://dx.doi.org/10.16258/j.cnki.1674-5906.2017.01.005]
Quang Minh N and La H P. 2011. Land cover classification using LiDAR intensity data and neural network. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 29(4): 429-438 [DOI: 10.7848/ksgpc.2011.29.4.429http://dx.doi.org/10.7848/ksgpc.2011.29.4.429]
Reese D C, O'Malley R T, Brodeur R D and Churnside J H. 2011. Epipelagic fish distributions in relation to thermal fronts in a coastal upwelling system using high-resolution remote-sensing techniques. ICES Journal of Marine Science, 68(9): 1865-1874 [DOI: 10.1093/icesjms/fsr107http://dx.doi.org/10.1093/icesjms/fsr107]
Reineman B D, Lenain L, Castel D and Melville W K. 2009. A portable airborne scanning Lidar system for ocean and coastal applications. Journal of Atmospheric and Oceanic Technology, 26(12): 2626-2641 [DOI: 10.1175/2009JTECHO703.1http://dx.doi.org/10.1175/2009JTECHO703.1]
Rizeei H M and Pradhan B. 2019. Urban mapping accuracy enhancement in high-rise built-up areas deployed by 3D-orthorectification correction from WorldView-3 and LiDAR imageries. Remote Sensing, 11(6): 692 [DOI: 10.3390/rs11060692http://dx.doi.org/10.3390/rs11060692]
Schultz-Fellenz E S, Coppersmith R T, Sussman A J, Swanson E M and Cooley J A. 2018. Detecting surface changes from an underground explosion in granite using unmanned aerial system photogrammetry. Pure and Applied Geophysics, 175: 3159-3177 [DOI: 10.1007/s00024-017-1649-0http://dx.doi.org/10.1007/s00024-017-1649-0]
Schulze-Brüninghoff D, Hensgen F, Wachendorf M and Astor T. 2019. Methods for LiDAR-based estimation of extensive grassland biomass. Computers and Electronics in Agriculture, 156: 693-699 [DOI: 10.1007/s11356-019-04520-8http://dx.doi.org/10.1007/s11356-019-04520-8]
Shi Z Y. 2014. Terrestrial 3D laser Scanning Technology Application in Mining Subsidence. Xi’an: Xi’an University of Science and Technology
施展宇. 2014. 地面三维激光扫描技术在开采沉陷应用研究. 西安: 西安科技大学
Shu S, Liu H X, Frappart F, Huang Y, Wang S J, Hinkel K M, Beck R A, Yu B L, Jones B M, Arp C D, Wang L and Ye Z X. 2018. Estimation of snow accumulation over frozen Arctic lakes using repeat ICESat laser altimetry observations–A case study in northern Alaska. Remote Sensing of Environment, 216: 529-543 [DOI: 10.1016/j.rse.2018.07.018http://dx.doi.org/10.1016/j.rse.2018.07.018]
Simard M, Fatoyinbo L, Smetanka C, Rivera-Monroy V H, Castañeda-Moya E, Thomas N and van Der Stocken T. 2019. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nature Geoscience, 12: 40-45 [DOI: 10.1038/s41561-018-0279-1http://dx.doi.org/10.1038/s41561-018-0279-1]
Simard M, Zhang K Q, Rivera-Monroy V H, Ross M S, Ruiz P L, Castañeda-Moya E, Twilley R R and Rodriguez E. 2006. Mapping height and biomass of mangrove forests in everglades national park with SRTM elevation data. Photogrammetric Engineering and Remote Sensing, 72(3): 299-311 [DOI: 10.14358/PERS.72.3.299http://dx.doi.org/10.14358/PERS.72.3.299]
Sithole G and Vosselman G. 2004. Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 59(1/2): 85-101 [DOI: 10.1016/j.isprsjprs.2004.05.004http://dx.doi.org/10.1016/j.isprsjprs.2004.05.004]
Smith B, Fricker H A, Holschuh N, Gardner A S, Adusumilli S, Brunt K M, Csatho B, Harbeck K, Huth A, Neumann T, Nilsson J and Siegfried M R. 2019. Land ice height-retrieval algorithm for NASA's ICESat-2 photon-counting laser altimeter. Remote Sensing of Environment, 233: 111352 [DOI: 10.1016/j.rse.2019.111352http://dx.doi.org/10.1016/j.rse.2019.111352]
Somvanshi S S, Bhalla O, Kunwar P, Singh M and Singh P. 2020. Monitoring spatial LULC changes and its growth prediction based on statistical models and earth observation datasets of Gautam Budh Nagar, Uttar Pradesh, India. Environment, Development and Sustainability, 22: 1073-1091 [DOI: 10.1007/s10668-018-0234-8http://dx.doi.org/10.1007/s10668-018-0234-8]
Streutker D R and Glenn N F. 2006. LiDAR measurement of sagebrush steppe vegetation heights. Remote Sensing of Environment, 102(1/2): 135-145 [DOI: 10.1016/j.rse.2006.02.011http://dx.doi.org/10.1016/j.rse.2006.02.011]
Su Q, Yu Y and Chen W B. 2020. Analysis of land use change based on the results of the third land survey——Taking Yongchang county as an example. Huabei Land and Resources, (3): 112-114
苏琦, 于洋, 陈文葆. 2020. 基于第三次国土调查成果的土地利用变化分析——以永昌县为例. 华北自然资源, (3): 112-114
Su Y J, Guo Q H, Xue B L, Hu T Y, Alvarez O, Tao S L and Fang J Y. 2016. Spatial distribution of forest aboveground biomass in China: estimation through combination of spaceborne lidar, optical imagery, and forest inventory data. Remote Sensing of Environment, 173: 187-199 [DOI: 10.1016/j.rse.2015.12.002http://dx.doi.org/10.1016/j.rse.2015.12.002]
Sun G, Ranson K J, Kharuk V I and Kovacs K. 2003. Validation of surface height from shuttle radar topography mission using shuttle laser altimeter. Remote Sensing of Environment, 88(4): 401-411 [DOI: 10.1016/j.rse.2003.09.001http://dx.doi.org/10.1016/j.rse.2003.09.001]
Tang H, Brolly M, Zhao F, Strahler A H, Schaaf C L, Ganguly S, Zhang G and Dubayah R. 2014. Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: a case study in Sierra National Forest, CA. Remote Sensing of Environment, 143: 131-141 [DOI: 10.1016/j.rse.2013.12.007http://dx.doi.org/10.1016/j.rse.2013.12.007]
Tao S L, Guo Q H, Xu S W, Su Y J, Li Y M and Wu F F. 2015. A geometric method for wood-leaf separation using terrestrial and simulated Lidar data. Photogrammetric Engineering and Remote Sensing, 81(10): 767-776 [DOI: 10.14358/PERS.81.10.767http://dx.doi.org/10.14358/PERS.81.10.767]
Tian J Y, Wang L, Li X J, Shi C and Gong H L. 2017. Differentiating tree and shrub LAI in a mixed forest with ICESat/GLAS Spaceborne LiDAR. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(1): 87-94 [DOI: 10.1109/JSTARS.2016.2557074http://dx.doi.org/10.1109/JSTARS.2016.2557074]
van Leeuwen M and Nieuwenhuis M. 2010. Retrieval of forest structural parameters using LIDAR remote sensing. European Journal of Forest Research, 129(4): 749-770 [DOI: 10.1007/s10342-010-0381-4http://dx.doi.org/10.1007/s10342-010-0381-4]
Verpoorter C, Kutser T, Seekell D A and Tranvik L J. 2014. A global inventory of lakes based on high-resolution satellite imagery. Geophysical Research Letters, 41(18): 6396-6402 [DOI: 10.1002/2014GL060641http://dx.doi.org/10.1002/2014GL060641]
Vicari M B, Disney M, Wilkes P, Burt A, Calders K and Woodgate W. 2019. Leaf and wood classification framework for terrestrial LiDAR point clouds. Methods in Ecology and Evolution, 10(5): 680-694 [DOI: 10.1111/2041-210X.13144http://dx.doi.org/10.1111/2041-210X.13144]
Volchko Y, Norrman J, Ericsson L O, Nilsson K L, Markstedt A, Oberg M, Mossmark F, Bobylev N and Tengborg P. 2020. Subsurface planning: towards a common understanding of the subsurface as a multifunctional resource. Land Use Policy, 90: 104316
Vörösmarty C J, McIntyre P B, Gessner M O, Dudgeon D, Prusevich A, Green P, Glidden S, Bunn S E, Sullivan C A, Liermann C R and Davies P M. 2010. Global threats to human water security and river biodiversity. Nature, 467(7315): 555-561 [DOI: 10.1038/nature09440http://dx.doi.org/10.1038/nature09440]
Wallace L, Hillman S, Reinke K and Hally B. 2017. Non-destructive estimation of above-ground surface and near-surface biomass using 3D terrestrial remote sensing techniques. Methods in Ecology and Evolution, 8(11): 1607-1616 [DOI: 10.1111/2041-210X.12759http://dx.doi.org/10.1111/2041-210X.12759]
Wallace L, Lucieer A, Malenovský Z, Turner D and Vopěnka P. 2016. Assessment of forest structure using two UAV techniques: a comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests, 7(3): 62 [DOI: 10.3390/f7030062http://dx.doi.org/10.3390/f7030062]
Wan L M, Lin Y Y, Zhang H S, Wang F, Liu M F and Lin H. 2020. GF-5 hyperspectral data for species mapping of mangrove in Mai Po, Hong Kong. Remote Sensing, 12(4): 656 [DOI: 10.3390/rs1204 0656http://dx.doi.org/10.3390/rs12040656]
Wang C K and Philpot W D. 2007. Using airborne bathymetric lidar to detect bottom type variation in shallow waters. Remote Sensing of Environment, 106(1): 123-135 [DOI: 10.1016/j.rse.2006.08.003http://dx.doi.org/10.1016/j.rse.2006.08.003]
Wang D Z, Wan B, Liu J, Su Y J, Guo Q H, Qiu P H and Wu X C. 2020. Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery. International Journal of Applied Earth Observation and Geoinformation, 85: 101986 [DOI: 10.1016/j.jag.2019.101986http://dx.doi.org/10.1016/j.jag.2019.101986]
Wang D L, Xin X P, Shao Q Q, Brolly M, Zhu Z L and Chen J. 2017. Modeling aboveground biomass in Hulunber grassland ecosystem by using unmanned aerial vehicle discrete lidar. Sensors, 17(1): 180 [DOI: 10.3390/s17010180http://dx.doi.org/10.3390/s17010180]
Wang J Y, Shu R, Huang G H and Xue Y Q. 2013. Laser imaging detection and ranging technologies and systems development. Bulletin of Chinese Academy of Sciences, 28(S): 67-76
王建宇, 舒嵘, 黄庚华, 薛永祺. 2013. 激光成像雷达技术和系统研制. 中国科学院院刊, 28(增刊): 67-76
Wang Z H, Bai M and Li H J. 2019. Direction analysis on service for natural resource investigation and monitoring using geospatial big data. Geomatics World, 26(1): 1-5
王占宏, 白穆, 李宏建. 2019. 地理空间大数据服务自然资源调查监测的方向分析. 地理信息世界, 26(1): 1-5 [DOI: 10.3969/j.issn.1672-1586.2019.01.001http://dx.doi.org/10.3969/j.issn.1672-1586.2019.01.001]
Wang X W, Gong P, Zhao Y Y, Xu Y, Cheng X, Niu Z G, Luo Z C, Huang H B, Sun F D and Li X W. 2013. Water-level changes in China’s large lakes determined from ICESat/GLAS data. Remote Sensing of Environment, 132: 131-144 [DOI: 10.1016/j.rse.2013.01.005http://dx.doi.org/10.1016/j.rse.2013.01.005]
Wang Z W, Liu S X, Dai J W, You S C, Shen J P and Li F. 2015. Analysis of new constructed land monitoring by using Gaofen-2 satellite imagery. Spacecraft Engineering, 24(6): 134-139
王忠武, 刘顺喜, 戴建旺, 尤淑撑, 沈均平, 李芬. 2015. 高分二号卫星数据在新增建设用地监测中的应用分析. 航天器工程, 24(6): 134-139 [DOI: 10.3969/j.issn.1673-8748.2015.06.020http://dx.doi.org/10.3969/j.issn.1673-8748.2015.06.020]
Watt M S, Pearse G D, Dash J P, Melia N and Leonardo E M C. 2019. Application of remote sensing technologies to identify impacts of nutritional deficiencies on forests. ISPRS Journal of Photogrammetry and Remote Sensing, 149: 226-241 [DOI: 10.1016/j.isprsjprs.2019.01.009http://dx.doi.org/10.1016/j.isprsjprs.2019.01.009]
Wei W, Li Z Y and Tan B X. 2010. A review of application of hyperspectral remote sensing to wetland study. World Forestry Research, 23(3): 18-23
韦玮, 李增元, 谭炳香. 2010. 高光谱遥感技术在湿地研究中的应用. 世界林业研究, 23(3): 18-23
Wijesingha J, Moeckel T, Hensgen F and Wachendorf M. 2019. Evaluation of 3D point cloud-based models for the prediction of grassland biomass. International Journal of Applied Earth Observation and Geoinformation, 78: 352-359 [DOI: 10.1016/j.jag.2018.10.006http://dx.doi.org/10.1016/j.jag.2018.10.006]
Wu D, Meng Y, Zhan K and Ma F. 2018. A LIDAR SLAM based on point-line features for underground mining vehicle//2018 Chinese Automation Congress. Xi'an, China: IEEE: 2879-2883 [DOI: 10.1109/CAC.2018.8623075http://dx.doi.org/10.1109/CAC.2018.8623075]
Wu H B. 2019. Studies on changes in water level and storage of Bosten Lake based on satellite-borne radar altimetry data. Journal of Water Resources and Water Engineering, 30(3): 9-16, 23
吴红波. 2019. 基于星载雷达测高资料估计博斯腾湖水位—水量变化研究. 水资源与水工程学报, 30(3): 9-16, 23 [DOI: 10.11705/j.issn.1672-643X.2019.03.02http://dx.doi.org/10.11705/j.issn.1672-643X.2019.03.02]
Wu Q, Zhong R F, Zhao W J, Song K and Du L M. 2019. Land-cover classification using GF-2 images and airborne lidar data based on Random Forest. International Journal of Remote Sensing, 40(5/6): 2410-2426 [DOI: 10.1080/01431161.2018.1483090http://dx.doi.org/10.1080/01431161.2018.1483090]
Xie Z L, Chen Y L, Lu D S, Li G Y and Chen E X. 2019. Classification of land cover, forest, and tree species classes with ZiYuan-3 multispectral and stereo data. Remote Sensing, 11(2): 164 [DOI: 10.3390/rs11020164http://dx.doi.org/10.3390/rs11020164]
Xu K X, Su Y J, Liu J, Hu T Y, Jin S C, Ma Q, Zhai Q P, Wang R, Zhang J, Li Y M, Liu H Y and Guo Q H. 2020. Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data. Ecological Indicators, 108: 105747 [DOI: 10.1016/j.ecolind.2019.105747http://dx.doi.org/10.1016/j.ecolind.2019.105747]
Yan B Y and Cui L. 2018. A unified natural resource survey plan based on land resource survey. Modern Agricultural Science and Technology, (22): 291-292
闫保银, 崔立. 2018. 基于土地资源调查的自然资源统一调查方案. 现代农业科技, (22): 291-292
Yin D M and Wang L. 2019. Individual mangrove tree measurement using UAV-based LiDAR data: possibilities and challenges. Remote Sensing of Environment, 223: 34-49 [DOI: 10.1016/j.rse.2018.12.034http://dx.doi.org/10.1016/j.rse.2018.12.034]
Yu A W, Krainak M A, Harding D J, Abshire J B, Sun X L, Cavanaugh J, Valett S and Ramos-Izquierdo L. 2010. Airborne lidar simulator for the lidar surface topography (LIST) mission//25th International Laser Radar Conference.St. Petersburg, Russia: [s.n.]
Yu Y, Ma Y, Li H, Huang J, Fang Y, Liang K and Zhou B. 2014. Simulation of simultaneously obtaining ocean temperature and salinity using dual-wavelength Brillouin lidar. Laser Physics Letters, 11(3): 036001 [DOI: 10.1088/1612-2011/11/3/036001http://dx.doi.org/10.1088/1612-2011/11/3/036001]
Zhang C Y, Smith M and Fang C Y. 2018. Evaluation of Goddard's LiDAR, hyperspectral, and thermal data products for mapping urban land-cover types. GIScience and Remote Sensing, 55(1): 90-109 [DOI: 10.1080/15481603.2017.1364837http://dx.doi.org/10.1080/15481603.2017.1364837]
Zhang D and Zheng Y Q. 2013. Hyperspectral remote sensing and its development and application review. Optics and Optoelectronic Technology, 11(3): 67-73
张达, 郑玉权. 2013. 高光谱遥感的发展与应用. 光学与光电技术, 11(3): 67-73
Zhang F, Guo J J, Li Z G and Luan X N. 2019. Preliminary testing of Chlorophyll-a concentration on offshore surface based on oceanographic lidar. Laser and Optoelectronics Progress, 56(5): 117-124
张锋, 郭金家, 李志刚, 栾晓宁. 2019. 基于海洋激光雷达的近海表层叶绿素a浓度测量初步测试. 激光与光电子学进展, 56(5): 117-124 [DOI: 10.3788/LOP56.051201http://dx.doi.org/10.3788/LOP56.051201]
Zhang H. 2015. Research on 3D Laser Scanning System for Slope Displacement of Open Pit Mine. An’shan: University of Science and Technology Liaoning
张贺. 2015. 露天矿边坡位移三维激光扫描监测技术研究. 鞍山: 辽宁科技大学
Zhang J X. 2010. Multi-source remote sensing data fusion: status and trends. International Journal of Image and Data Fusion, 1(1): 5-24 [DOI: 10.1080/19479830903561035http://dx.doi.org/10.1080/19479830903561035]
Zhang J D, Tian L and Zhao H. 2008. Preliminary study on working methods of mine geological environment monitoring in our country. Hydrogeology and Engineering Geology, 35(2):
I-IV) (张进德, 田磊, 赵慧. 2008. 我国矿山地质环境监测工作方法初探. 水文地质工程地质, 35(2):
I-IV [DOI: 10.3969/j.issn.1000-3665.2008.02.027http://dx.doi.org/10.3969/j.issn.1000-3665.2008.02.027]
Zhang K Q, Chen S C, Whitman D, Shyu M L, Yan J H and Zhang C C. 2003. A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Transactions on Geoscience and Remote Sensing, 41(4): 872-882 [DOI: 10.1109/TGRS.2003.810682http://dx.doi.org/10.1109/TGRS.2003.810682]
Zhang L, Ouyang Y Z and Teng H Z. 2017. Applications of aerial photogrammetry and airborne LiDAR in remote sensing monitoring of coastal zones. Hydrographic Surveying and Charting, 37(6): 62-65
张靓, 欧阳永忠, 滕惠忠. 2017. 航测与机载LiDAR技术在海岸带遥感中的应用. 海洋测绘, 37(6): 62-65 [DOI: 10.3969/j.issn.1671-3044.2017.06.016http://dx.doi.org/10.3969/j.issn.1671-3044.2017.06.016]
Zhang S W, Yan F Q, Yu L X, Bu K, Yang J C and Chan L P. 2013. Application of remote sensing technology to wetland research. Scientia Geographica Sinica, 33(11): 1406-1412
张树文, 颜凤芹, 于灵雪, 卜坤, 杨久春, 常丽萍. 2013. 湿地遥感研究进展. 地理科学, 33(11): 1406-1412 [DOI: 10.13249/j.cnki.sgs.2013.011.1406http://dx.doi.org/10.13249/j.cnki.sgs.2013.011.1406]
Zhang W W, Jin Y, Bao J M and Jiang W B. 2019. The development history of and thinking on marine ecological environment monitoring in China. World Environment, (3): 30-32
张微微, 金媛, 包吉明, 姜文博. 2019. 中国海洋生态环境监测发展历程与思考. 世界环境, (3): 30-32
Zhang X H, Lou Q S and Zhang C Y. 2010. Coastal 3-dimensional landscape simulation based on airborne Lidar. Journal of Tropical Oceanography, 29(5): 44-48
张晓浩, 娄全胜, 张春雨. 2010. 基于机载激光雷达的海岸带三维景观仿真模拟. 热带海洋学报, 29(5): 44-48 [DOI: 10.3969/j.issn.1009-5470.2010.05.007http://dx.doi.org/10.3969/j.issn.1009-5470.2010.05.007]
Zhang Z X, Wang X, Wen Q K, Zhao X L, Liu F, Zuo L J, Hu S G, Xu J Y, Yi L and Liu B. 2016. Research progress of remote sensing application in land resources. Journal of Remote Sensing, 20(5): 1243-1258
张增祥, 汪潇, 温庆可, 赵晓丽, 刘芳, 左丽君, 胡顺光, 徐进勇, 易玲, 刘斌. 2016. 土地资源遥感应用研究进展. 遥感学报, 20(5): 1243-1258 [DOI: 10.11834/jrs.20166149http://dx.doi.org/10.11834/jrs.20166149]
Zhao K G, Suarez J C, Garcia M, Hu T X, Wang C and Londo A. 2018. Utility of multitemporal lidar for forest and carbon monitoring: tree growth, biomass dynamics, and carbon flux. Remote Sensing of Environment, 204: 883-897 [DOI: 10.1016/j.rse.2017.09.007http://dx.doi.org/10.1016/j.rse.2017.09.007]
Zhao X Q, Guo Q H, Su Y J and Xue B L. 2016. Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas. ISPRS Journal of Photogrammetry and Remote Sensing, 117: 79-91 [DOI: 10.1016/j.isprsjprs.2016.03.016http://dx.doi.org/10.1016/j.isprsjprs.2016.03.016]
Zhen Y, Xie J F, Zhu H, Liu R, Yang C C and Zhai H R. 2019. Land cover classification method considering the contribution of waveform characteristic parameters and the pooling scale. Journal of Applied Remote Sensing, 13(4): 044529 [DOI: 10.1117/1.JRS.13.044529http://dx.doi.org/10.1117/1.JRS.13.044529]
Zheng Z S, Ma Q, Jin S C, Su Y J, Guo Q H and Bales R C. 2019. Canopy and terrain interactions affecting snowpack spatial patterns in the Sierra Nevada of California. Water Resources Research, 55(11): 8721-8739 [DOI: 10.1029/2018WR023758http://dx.doi.org/10.1029/2018WR023758]
Zhou W Q, Huang G L, Troy A and Cadenasso M L. 2009. Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: a comparison study. Remote Sensing of Environment, 113(8): 1769-1777 [DOI: 10.1016/j.rse.2009.04.007http://dx.doi.org/10.1016/j.rse.2009.04.007]
Zhou Y S, Hu J, Li Z W, Li J, Zhao R and Ding X L. 2019. Quantifying glacier mass change and its contribution to lake growths in central Kunlun during 2000-2015 from multi-source remote sensing data. Journal of Hydrology, 570: 38-50
Zhu Y H, Liu K, Liu L, Wang S G and Liu H X. 2015. Retrieval of mangrove aboveground biomass at the individual species level with WorldView-2 images. Remote Sensing, 7(9): 12192-12214 [DOI: 10.3390/rs70912192http://dx.doi.org/10.3390/rs70912192]
Zlinszky A, Deák B, Kania A, Schroiff A and Pfeifer N. 2016. Biodiversity mapping via natura 2000 conservation status and EBV assessment using airborne laser scanning in alkali grasslands. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B8: 1293-1299 [DOI: 10.5194/isprs-archives-XLI-B8-1293-2016http://dx.doi.org/10.5194/isprs-archives-XLI-B8-1293-2016]
Zlinszky A, Schroiff A, Kania A, Deák B, Mücke W, Vári A, Székely B and Pfeifer N. 2014. Categorizing grassland vegetation with full-waveform airborne laser scanning: a feasibility study for detecting natura 2000 habitat types. Remote Sensing, 6(9): 8056-8087 [DOI: 10.3390/rs6098056http://dx.doi.org/10.3390/rs6098056]
关联资源
相关作者
相关机构
京公网安备11010802024621
