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Current status and prospect of three-dimensional dynamic monitoring of natural resources based on LiDAR

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：ai10056@126.com
  • Introduction：李玉美，1988年生,女,助理研究员,研究方向为生态遥感应用。E-mail: ai10056@126.com
LI Yumei17,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China

  • Email：guo_qinghua@126.com
  • Introduction：郭庆华，1973年生，男，教授，研究方向为生态遥感应用。E-mail： guo_qinghua@126.com
GUO Qinghua2*,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China

WAN Bo3,  
  • Affiliation：

    Chinese Academy of Safety Sciences and Technology, Beijing 100012, China

QIN Hongnan4,  
  • Affiliation：

    Key Laboratory of Aquatic Botany and Watershed Ecology, Chinese Academy of Science, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China

WANG Dezhi5,  
  • Affiliation：

    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

    University of Chinese Academy of Sciences, Beijing 100049, China

XU Kexin67,  
  • Affiliation：

    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

    University of Chinese Academy of Sciences, Beijing 100049, China

SONG Shilin67,  
  • Affiliation：

    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

    University of Chinese Academy of Sciences, Beijing 100049, China

SUN Qianhui67,  
  • Affiliation：

    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

    University of Chinese Academy of Sciences, Beijing 100049, China

ZHAO Xiaoxia67,  
  • Affiliation：

    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

    University of Chinese Academy of Sciences, Beijing 100049, China

YANG Mohan67,  
  • Affiliation：

    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

    University of Chinese Academy of Sciences, Beijing 100049, China

WU Xiaoyong67,  
  • Affiliation：

    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

    University of Chinese Academy of Sciences, Beijing 100049, China

WEI Dengjie67,  
  • Affiliation：

    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

    University of Chinese Academy of Sciences, Beijing 100049, China

HU Tianyu67,  
  • Affiliation：

    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

    University of Chinese Academy of Sciences, Beijing 100049, China

SU Yanjun67

résumé

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.

mots-clés

remote sensing;lidar;natural resources;three-dimensional information;dynamic monitoring;data fusion

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    Armstrong C. 2013. Natural resources: the demands of equality. Journal of Social Philosophy, 44(4): 331-347
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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.
  18. 18.
    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
  19. 19.
    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
  20. 20.
    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
  21. 21.
    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
  22. 22.
    Chen Y Y and Lv X G. 2003. The wetland function and research Tendency of wetland science. Wetland Science, 1(1): 7-11
  23. 23.
    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
  24. 24.
    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
  25. 25.
    Dare P M. 2005. Shadow analysis in high-resolution satellite imagery of urban areas. Photogrammetric Engineering and Remote Sensing, 71(2): 169-177
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    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
  31. 31.
    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
  32. 32.
    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
  33. 33.
    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
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    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
  38. 38.
    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
  39. 39.
    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.
  40. 40.
    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
  41. 41.
    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
  42. 42.
    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
  43. 43.
    Guo C. 2014. Data Processing and Application of 3D Laser Scanning to Dam Subsidence Monitoring in Mining Area. Jilin: Jilin University
  44. 44.
    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
  45. 45.
    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
  46. 46.
    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
  47. 47.
    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
  48. 48.
    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
  49. 49.
    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
  50. 50.
    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
  51. 51.
    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]
  52. 52.
    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
  53. 53.
    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
  54. 54.
    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
  55. 55.
    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
  56. 56.
    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
  57. 57.
    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
  58. 58.
    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
  59. 59.
    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
  60. 60.
    Huang L and Zhang X L. 2006. Applications of Lidar and 3D remote sensing in forestry. World Forestry Research, 19(4): 11-17
  61. 61.
    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
  62. 62.
    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
  63. 63.
    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
  64. 64.
    Jin X L. 2004. The development of technique of marine geophysics. Journal of East China Institute of Technology, 27(1): 6-13
  65. 65.
    Jin X L. 2007. The development of research in marine geophysics and acoustic technology for submarine exploration. Progress in Geophysics, 22(4): 1243-1249
  66. 66.
    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
  67. 67.
    Johnson B D and Singh J. 2003. Building the national geobase for Canada. Photogrammetric Engineering and Remote Sensing, 69(10): 1169-1173
  68. 68.
    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
  69. 69.
    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
  70. 70.
    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
  71. 71.
    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
  72. 72.
    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
  73. 73.
    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
  74. 74.
    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
  75. 75.
    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
  76. 76.
    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]
  77. 77.
    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
  78. 78.
    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
  79. 79.
    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
  80. 80.
    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
  81. 81.
    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
  82. 82.
    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
  83. 83.
    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
  84. 84.
    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
  85. 85.
    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
  86. 86.
    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
  87. 87.
    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
  88. 88.
    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
  89. 89.
    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
  90. 90.
    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
  91. 91.
    Lyu G P. 2018. Application of LiDAR Technique to Extract Parameters of Vegetation and Topography in Mining Areas. Nanjing: Nanjing Forestry University
  92. 92.
    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
  93. 93.
    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
  94. 94.
    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.
  95. 95.
    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):
  96. 96.
    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
  97. 97.
    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
  98. 98.
    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
  99. 99.
    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
  100. 100.
    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
  101. 101.
    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
  102. 102.
    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
  103. 103.
    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
  104. 104.
    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
  105. 105.
    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.
  106. 106.
    Nelson R, Krabill W and MacLean G. 1984. Determining forest canopy characteristics using airborne laser data. Remote Sensing of Environment, 15(3): 201-212
  107. 107.
    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
  108. 108.
    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]
  109. 109.
    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
  110. 110.
    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
  111. 111.
    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
  112. 112.
    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
  113. 113.
    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
  114. 114.
    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
  115. 115.
    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
  116. 116.
    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
  117. 117.
    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
  118. 118.
    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
  119. 119.
    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
  120. 120.
    Shi Z Y. 2014. Terrestrial 3D laser Scanning Technology Application in Mining Subsidence. Xi’an: Xi’an University of Science and Technology
  121. 121.
    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
  122. 122.
    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
  123. 123.
    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
  124. 124.
    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
  125. 125.
    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
  126. 126.
    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
  127. 127.
    Streutker D R and Glenn N F. 2006. LiDAR measurement of sagebrush steppe vegetation heights. Remote Sensing of Environment, 102(1/2): 135-145
  128. 128.
    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
  129. 129.
    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
  130. 130.
    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
  131. 131.
    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
  132. 132.
    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
  133. 133.
    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
  134. 134.
    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
  135. 135.
    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
  136. 136.
    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
  137. 137.
    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
  138. 138.
    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
  139. 139.
    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
  140. 140.
    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
  141. 141.
    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
  142. 142.
    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
  143. 143.
    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
  144. 144.
    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
  145. 145.
    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
  146. 146.
    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
  147. 147.
    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
  148. 148.
    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
  149. 149.
    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
  150. 150.
    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
  151. 151.
    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
  152. 152.
    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
  153. 153.
    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
  154. 154.
    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
  155. 155.
    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
  156. 156.
    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
  157. 157.
    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
  158. 158.
    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
  159. 159.
    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.]
  160. 160.
    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
  161. 161.
    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
  162. 162.
    Zhang D and Zheng Y Q. 2013. Hyperspectral remote sensing and its development and application review. Optics and Optoelectronic Technology, 11(3): 67-73
  163. 163.
    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
  164. 164.
    Zhang H. 2015. Research on 3D Laser Scanning System for Slope Displacement of Open Pit Mine. An’shan: University of Science and Technology Liaoning
  165. 165.
    Zhang J X. 2010. Multi-source remote sensing data fusion: status and trends. International Journal of Image and Data Fusion, 1(1): 5-24
  166. 166.
    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):
  167. 167.
    I-IV
  168. 168.
    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
  169. 169.
    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
  170. 170.
    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
  171. 171.
    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
  172. 172.
    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
  173. 173.
    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
  174. 174.
    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
  175. 175.
    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
  176. 176.
    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
  177. 177.
    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
  178. 178.
    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
  179. 179.
    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
  180. 180.
    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
  181. 181.
    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
  182. 182.
    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

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