Please wait a minute...
 
Remote Sensing for Natural Resources    2023, Vol. 35 Issue (3) : 1-9     DOI: 10.6046/zrzyyg.2022436
|
Current status of the acquisition and processing of airborne laser sounding data
CUI Ziwei1,2(), XU Wenxue2(), LIU Yanxiong2, GUO Yadong2, MENG Xiangqian1, JIANG Zhengkun2
1. College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
2. First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
Download: PDF(2378 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

As an essential branch of surveying and mapping science, underwater topographic surveys are closely related to human operations in oceans and lakes. For underwater topography detection in shallow-water areas, conventional acoustic methods face the hull stranding risk, and passive optical methods have low survey accuracy. The airborne laser sounding is a novel means for bathymetric surveys in shallow-water areas, and its application in offshore areas can fill the gap of underwater topography data in shallow-water areas. This study presents a brief introduction to the composition and principle of the airborne laser sounding system, followed by a description of laser sounding data acquisition. Furthermore, this study highlights the critical processing technologies for airborne laser sounding data, including waveform data processing, error correction, and point cloud data processing. Finally, this study summarizes the technical difficulties and developmental trends of airborne laser sounding.

Keywords airborne laser sounding      laser sounding data acquisition      waveform data processing      error correction      point cloud data processing     
ZTFLH:  TP79  
Issue Date: 19 September 2023
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Ziwei CUI
Wenxue XU
Yanxiong LIU
Yadong GUO
Xiangqian MENG
Zhengkun JIANG
Cite this article:   
Ziwei CUI,Wenxue XU,Yanxiong LIU, et al. Current status of the acquisition and processing of airborne laser sounding data[J]. Remote Sensing for Natural Resources, 2023, 35(3): 1-9.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022436     OR     https://www.gtzyyg.com/EN/Y2023/V35/I3/1
Fig.1  Schematic diagram of the airborne LiDAR bathymetry system
Fig.2  Schematic diagram of the principle of single-wavelength airborne LiDAR bathymetry
Fig.3  In-situ optical reflectance spectra of various substrates[17]
Fig.4  Flowchart of airborne LiDAR bathymetry data acquisition
Fig.5  Flowchart of airborne LiDAR bathymetry data processing
误差项 误差源
系统本身 量测时间误差 各部件工作时间并非完全同步,脉冲位置估计和非线性信号处理导致检测时间不准确
设备安置误差 系统扫描轴偏转、IMU角度不平行
外部环境 光速测量误差 不同介质中光速不同,但光速数值较大且在浅水测深,所以此影响较小
大气传输误差 在大气中传输因吸收或散射导致能量损耗
波浪潮汐变化误差 激光入射瞬间波浪和潮汐变化导致瞬时水深偏差
折射误差 大气与水密度不同产生折射现象,大气与水面间的折射现象影响较大,而在水体中的影响相对较小
水中散射误差 散射效应主要分为后向散射效应和前向散射效应
不规则地形起伏误差 水下地形不规则,具有不同坡度、曲率和粗糙度
其他因素 数据计算精度误差 处理数据时涉及到矩阵坐标变换等复杂过程,产生计算精度误差
Tab.1  Error analysis of airborne LiDAR bathymetry
[1] Harold K F. Multibeam bathymetric sonar:Sea beam and hydro chart[J]. Marine Geodesy, 1980, 4(2):77-93.
doi: 10.1080/15210608009379375 url: http://www.tandfonline.com/doi/abs/10.1080/15210608009379375
[2] 王文杰. 基于单波束多波束测深系统的海洋航道测量方法[J]. 电子测量技术, 2019, 42(8):45-51.
[2] Wang W J. Sea channel survey method considering single beam multi beam bathymetric system[J]. Electronic Measurement Technology, 2019, 42(8):45-51.
[3] 曹彬才. 遥感测深数据处理方法研究[D]. 郑州: 战略支援部队信息工程大学, 2017.
[3] Cao B C. A study of remotely-sensed data processing in bathymetry[D]. Zhengzhou: PLA Strategic Support Force Information Engineering University, 2017.
[4] 马毅, 张杰, 张靖宇, 等. 浅海水深光学遥感研究进展[J]. 海洋科学进展, 2018, 36(3):331-351.
[4] Ma Y, Zhang J, Zhang J Y, et al. Progress in shallow water depth mapping from optical remote sensing[J]. Advances in Marine Science, 2018, 36(3):331-351.
[5] Wright A E, Conlin D L, Shope S M. Assessing the accuracy of underwater photogrammetry for archaeology:A comparison of structure from motion photogrammetry and real time kinematic survey at the east key construction wreck[J]. Journal of Marine Science and Engineering, 2020, 8(11):849.
doi: 10.3390/jmse8110849 url: https://www.mdpi.com/2077-1312/8/11/849
[6] Muirhead K, Cracknell A P. Airborne LiDAR bathymetry[J]. International Journal of Remote Sensing, 1986, 7(5):597-614.
doi: 10.1080/01431168608954714 url: https://www.tandfonline.com/doi/full/10.1080/01431168608954714
[7] 陈文革, 黄铁侠, 卢益民. 机载海洋激光雷达发展综述[J]. 激光技术, 1998(3):21-26.
[7] Chen W G, Huang T X, Lu Y M. Survey of airborne oceanic LiDAR[J]. Laser Technology, 1998(3):21-26.
[8] 翟国君, 吴太旗, 欧阳永忠, 等. 机载激光测深技术研究进展[J]. 海洋测绘, 2012, 32(2):67-71.
[8] Zhai G J, Wu T Q, Ouyang Y Z, et al. The development of airborne laser bathymetry[J]. Hydrographic Surveying Charting, 2012, 32(2):67-71.
[9] 秦海明, 王成, 习晓环, 等. 机载激光雷达测深技术与应用研究进展[J]. 遥感技术与应用, 2016, 31(4):617-624.
[9] Qin H M, Wang C, Xi X H, et al. Development of airborne laser bathymetric technology and applications[J]. Remote Sensing Technology and Application, 2016, 31(4):617-624.
[10] 刘焱雄, 郭锴, 何秀凤, 等. 机载激光测深技术及其研究进展[J]. 武汉大学学报(信息科学版), 2017, 42(9):1185-1194.
[10] Liu Y X, Guo K, He X F, et al. Research progress of airborne laser bathymetry technology[J]. Geomatics and Information Science of Wuhan University, 2017, 42(9):1185-1194.
[11] 李奇, 王建超, 韩亚超, 等. 基于CZMIL Nova的中国海岸带机载激光雷达测深潜力分析[J]. 国土资源遥感, 2020, 32(1):184-190.doi:10.6046/gtzyyg.2020.01.25.
doi: 10.6046/gtzyyg.2020.01.25
[11] Li Q, Wang J C, Han Y C, et al. Potential evaluation of China’s coastal airborne LiDAR bathymetry based on CZMIL Nova[J]. Remote Sensing for Land and Resources, 2020, 32(1):184-190.doi:10.6046/gtzyyg.2020.01.25.
doi: 10.6046/gtzyyg.2020.01.25
[12] 国家市场监督管理总局, 中国国家标准化管理委员会. GB/T 39624—2020机载激光雷达水下地形测量技术规范[S]. 北京: 中国标准出版社, 2020.
[12] State Administration for Market Regulation, Standardization Administration. GB/T 39624—2020 Technical specification for underwater topographic survey of airborne LiDAR[S]. Beijing: China Standards Publishing House, 2020.
[13] 王丹菂. 机载激光测深回波信号探测技术研究[D]. 郑州: 战略支援部队信息工程大学, 2018.
[13] Wang D D. Research on signal detection for airborne LiDAR bathymetry[D]. Zhengzhou: PLA Strategic Support Force Information Engineering University, 2018.
[14] 徐广袖, 翟国君, 吴太旗, 等. 机载激光测深作业的关键技术问题[J]. 海洋测绘, 2019, 39(2):45-49.
[14] Xu G X, Zhai G J, Wu T Q, et al. The key technical issues on airborne LiDAR bathymetry operation[J]. Hydrographic Surveying and Charting, 2019, 39(2):45-49.
[15] 叶修松. 机载激光水深探测技术基础及数据处理方法研究[D]. 郑州: 战略支援部队信息工程大学, 2010.
[15] Ye X S. Research on principle and date processing methods of airborne laser bathymetric technique[D]. Zhengzhou: PLA Strategic Support Force Information Engineering University, 2010.
[16] 吴芳, 金鼎坚, 张宗贵, 等. 基于CZMIL测深技术的海陆一体地形测量初探[J]. 自然资源遥感, 2021, 33(4):173-180.doi:10.6046/zrzyyg.2021.10.07.
doi: 10.6046/zrzyyg.2021.10.07
[16] Wu F, Jin D J, Zhang Z G, et al. A preliminary study on land-sea integrated topographic surveying based on CZMIL bathymetric technique[J]. Remote Sensing for Natural Resources, 2021, 33(4):173-180.doi:10.6046/zrzyyg.2021.10.07.
doi: 10.6046/zrzyyg.2021.10.07
[17] 曾凯, 许占堂, 杨跃忠, 等. 浅海底质高光谱反射率测量系统的设计及应用[J]. 光谱学与光谱分析, 2020, 40(2):579-585.
[17] Zeng K, Xu Z T, Yang Y Z, et al. Design and application of reflectance measurement system for sea bottom in optically shallow water[J]. Spectroscopy and Spectral Analysis, 2020, 40(2):579-585.
[18] 金鼎坚, 吴芳, 于坤, 等. 机载激光雷达测深系统大规模应用测试与评估——以中国海岸带为例[J]. 红外与激光工程, 2020, 49(s2):9-23.
[18] Jin D J, Wu F, Yu K, et al. Large-scale application test and evaluation of an airborne LiDAR bathymetry system:A case study in China’s coastal zone[J]. Infrared and Laser Engineering, 2020, 49(s2):9-23.
[19] 颜炳玉. 激光对人体的损伤,激光产品的分级标准及安全防护措施[J]. 应用激光, 1987(4):172-176.
[19] Yan B Y. The damage of laser to human body and the methods of protection[J]. Applied Laser, 1987(4):172-176.
[20] 王成, 习晓环, 杨学博, 等. 激光雷达遥感导论[M]. 北京: 高等教育出版社, 2022:50.
[20] Wang C, Xi X H, Yang X B, et al. Introduction to LiDAR remote sensing[M]. Beijing: Higher Education Press, 2022:50.
[21] 王越. 机载激光浅海测深技术的现状和发展[J]. 测绘地理信息, 2014, 39(3):38-42.
[21] Wang Y. Current status and development of airborne laser bathymetry technology[J]. Journal of Geomatics, 2014, 39(3):38-42.
[22] 曲直, 卢秀山, 左建章, 等. 机载POS系统中惯性测量单元精度检校方法[J]. 测绘科学, 2011, 36(5):131-133.
[22] Qu Z, Lu X S, Zuo J Z, et al. Calibration method of IMU on airborne POS[J]. Science of Surveying and Mapping, 2011, 36(5):131-133.
[23] 黎东, 王成, 习晓环, 等. 共面约束的机载LiDAR IMU安置角误差自动检校方法[J]. 测绘科学, 2017, 42(9):98-103.
[23] Li D, Wang C, Xi X H, et al. An automatic bore-sight calibration method for airborne LiDAR based on co-planar constrains[J]. Science of Surveying and Mapping, 2017, 42(9):98-103.
[24] 王延存. 机载激光雷达全波形数据处理研究[D]. 青岛: 山东科技大学, 2019.
[24] Wang Y C. Research on full-waveform data processing of airborne LiDAR system[D]. Qingdao: Shandong University of Science and Technology, 2019.
[25] Jutzi B, Stilla U. Range Determination with waveform recording laser systems using a Wiener filter[J]. ISPRS Journal of Photogra-mmetry and Remote Sensing, 2006, 61(2):95-107.
[26] Biggs D S C, Anderws M. Acceleration of iterative image restoration algorithms[J]. Applied Optics, 1997, 36(8):1766-1775.
pmid: 18250863
[27] Lawson C L, Hanson R J. Solving least squares problems[M]. Englewood Cliffs,NJ: Prentice-Hall, 1974:50-60.
[28] Wagner W, Roncat A, Melzer T, et al. Waveform analysis techniques in airborne laser scanning[C]// International Archives of Photogrammetry and Remote Sensing, 2007, 36(3):413-418.
[29] 姚春华, 陈卫标, 臧华国, 等. 机载激光测深系统的最小可探测深度研究[J]. 光学学报, 2004(10):1406-1410.
[29] Yao C H, Chen W B, Zang H G, et al. Study of the capability of minimum depth using an airborne laser bathymetry[J]. Acta Optica Sinica, 2004(10):1406-1410.
[30] Wong H, Antoniou A. Characterization and decomposition of waveforms for laser 500 airborne system[J]. IEEE Transactions on Geo-science and Remote Sensing, 1991, 29 (6):912-921.
doi: 10.1109/36.101370 url: http://ieeexplore.ieee.org/document/101370/
[31] Abdallah H, Bailly J S, Baghdadi N N, et al. Potential of space-borne LiDAR sensors for global bathymetry in coastal and inland waters[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(1):202-216.
doi: 10.1109/JSTARS.4609443 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443
[32] 李凯, 张永生, 童晓冲, 等. 不同函数拟合水体后向散射波形对激光测深精度的影响[J]. 武汉大学学报(信息科学版), 2018, 43(4):548-554.
[32] Li K, Zhang Y S, Tong X C, et al. The impact of different fitting functions for water backscatter waveforms on the accuracy of laser sounding[J]. Geomatics and Information Science of Wuhan University, 2018, 43(4):548-554.
[33] 丁凯. 单波段机载测深激光雷达全波形数据处理算法及应用研究[D]. 深圳: 深圳大学, 2018.
[33] Ding K. Airborne LiDAR bathymetry full waveform data processing methods and application study[D]. Shenzhen: Shenzhen University, 2018.
[34] 郭锴, 刘焱雄, 徐文学, 等. 机载激光测深波形分解中LM与EM参数优化方法比较[J]. 测绘学报, 2020, 49(1):117-131.
doi: 10.11947/j.AGCS.2020.20180242
[34] Guo K, Liu Y X, Xu W X, et al. Comparison of LM and EM parameter optimization methods for airborne laser bathymetric full-waveform decomposition[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(1):117-131.
doi: 10.11947/j.AGCS.2020.20180242
[35] Shen X, Li Q, Wu G, et al. Decomposition of LiDAR waveforms by B-spline-based modeling[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 128(3):182-191.
doi: 10.1016/j.isprsjprs.2017.03.006 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271616303306
[36] 王丹菂, 徐青, 邢帅, 等. 一种由粗到精的机载激光测深信号检测方法[J]. 测绘学报, 2018, 47(8):1148-1159.
doi: 10.11947/j.AGCS.2018.20170466
[36] Wang D D, Xu Q, Xing S, et al. A coarse-to-fine signal detection method for airborne LiDAR bathymetry[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(8):1148-1159.
doi: 10.11947/j.AGCS.2018.20170466
[37] 亓超, 宿殿鹏, 王贤昆, 等. 基于分层异构模型的机载激光测深波形拟合算法[J]. 红外与激光工程, 2019, 48(2):114-121.
[37] Qi C, Su D P, Wang X K, et al. Fitting algorithm for airborne laser bathymetric waveforms based on layered heterogeneous model[J]. Infrared and Laser Engineering, 2019, 48(2):114-121.
[38] Roland S, Gottfried M, Martin P, et al. Design and evaluation of a full-wave surface and bottom-detection algorithm for LiDAR bathymetry of very shallow waters[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 150:1-10.
doi: 10.1016/j.isprsjprs.2019.02.002 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271619300358
[39] Yang F, Qi C, Su D, et al. An airborne LiDAR bathymetric waveform decomposition method in very shallow water:A case study around Yuanzhi Island in the South China Sea[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 109:102788.
doi: 10.1016/j.jag.2022.102788 url: https://linkinghub.elsevier.com/retrieve/pii/S0303243422001143
[40] Guenther G C, Larocque P E, Lillycropj W. Multiple surface channels in scanning hydrographic operational airborne LiDAR survey (SHOALS) airborne LiDAR[C]//Proceedings of 1994 International Society for Optical Engineering. Bergen,Norway: SPIE, 1994:422-431.
[41] 王丹菂, 邢帅, 徐青, 等. 单频机载激光测深海陆回波自动分类方法[J]. 测绘学报, 2022, 51(5):750-761.
doi: 10.11947/j.AGCS.2022.20200314
[41] Wang D D, Xing S, Xu Q, et al. Automatic sea-land waveform classification method for single-wavelength airborne LiDAR bathymetry[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(5):750-761.
doi: 10.11947/j.AGCS.2022.20200314
[42] Ji X, Tang Q, Xu W, et al. Island feature classification for single-wavelength airborne LiDAR bathymetry based on full-waveform parameters[J]. Applied Optics, 2021, 60(11):3055-3061.
doi: 10.1364/AO.420673 url: https://opg.optica.org/abstract.cfm?URI=ao-60-11-3055
[43] 胡善江, 贺岩, 陶邦一, 等. 基于深度学习的机载激光海洋测深海陆波形分类(英文)[J]. 红外与激光工程, 2019, 48(11):165-172.
[43] Hu S J, He Y, Tao B Y, et al. Classification of sea and land waveforms based on deep learning for airborne laser bathymetry[J]. Infrared and Laser Engineering, 2019, 48(11):165-172.
[44] Fernandez-Diaz J C, Glennie C L, Carter W E, et al. Early results of simultaneous terrain and shallow water bathymetry mapping using a single-wavelength airborne LiDAR sensor[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 7(2):623-635.
doi: 10.1109/JSTARS.4609443 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443
[45] Smeeckaert J, Clément M, David N. Classification of water surfaces using airborne topographic LiDAR data[C]// International Archives of Photogrammetry,Remote Sensing and Spatial Information Sciences (IAPRS), 2013, XL-1/W1:321-326.
[46] 王晓阳. 基于机载激光测深波形和点云数据的水陆分类研究[D]. 泰安: 山东农业大学, 2021.
[46] Wang X Y. Water-land classification based on waveform and point cloud data of airborne LiDAR bathymetry[D]. Taian: Shandong Agricultural University, 2021.
[47] 王鑫, 潘华志, 罗胜, 等. 机载激光雷达测深技术研究与进展[J]. 海洋测绘, 2019, 39(5):78-82.
[47] Wang X, Pan H Z, Luo S, et al. Bathymetric technology and research status of airborne LiDAR[J]. Hydrographic Surveying Charting, 2019, 39(5):78-82.
[48] Su D, Yang F, Ma Y, et al. Propagated uncertainty models arising from device,environment,and target for a small laser spot airborne LiDAR bathymetry and its verification in the South China Sea[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(5):3213-3231.
doi: 10.1109/TGRS.36 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=36
[49] Patrick W, Hans-Gerd M, Katja R, et al. Analysis and correction of ocean wave pattern induced systematic coordinate errors in airborne LiDAR bathymetry[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 128:314-325.
doi: 10.1016/j.isprsjprs.2017.04.008 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271616305809
[50] 赵建虎, 吴敬文, 赵兴磊, 等. 一种改进的机载激光测深深度偏差模型[J]. 武汉大学学报(信息科学版), 2019, 44(3):328-333.
[50] Zhao J H, Wu J W, Zhao X L, et al. A correction model for depth bias in airborne LiDAR bathymetry systems[J]. Geomatics and Information Science of Wuhan University, 2019, 44(3):328-333.
[51] 叶修松, 张传定, 王爱兵, 等. 机载激光水深测量误差分析[J]. 测绘科学技术学报, 2008, 25(6):400-402.
[51] Ye X S, Zhang C D, Wang A B, et al. Analysis of the airborne laser scanning bathymetry errors[J]. Journal of Geomatics Science and Technology, 2008, 25(6):400-402.
[52] Yang F, Su D, Ma Y, et al. Refraction correction of airborne LiDAR bathymetry based on sea surface profile and ray tracing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(11):6141-6149.
doi: 10.1109/TGRS.2017.2721442 url: http://ieeexplore.ieee.org/document/7987718/
[53] Xu W, Guo K, Liu Y, et al. Refraction error correction of airborne LiDAR bathymetry data considering sea surface waves[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102:102402.
doi: 10.1016/j.jag.2021.102402 url: https://linkinghub.elsevier.com/retrieve/pii/S0303243421001094
[54] 杨安秀. 基于多元地形特征的机载LiDAR点云抽稀算法研究[D]. 青岛: 山东科技大学, 2018.
[54] Yang A X. A point cloud thinning method of airborne LiDAR based on mutivariate terrain features[D]. Qingdao: Shandong University of Science and Technology, 2018.
[55] Zhang W, Qi J, Wan P, et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J]. Remote Sensing, 2016, 8(6):501.
doi: 10.3390/rs8060501 url: http://www.mdpi.com/2072-4292/8/6/501
[56] Yang A, Wu Z, Yang F, et al. Filtering of airborne LiDAR bathymetry based on bidirectional cloth simulation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 163:49-61.
doi: 10.1016/j.isprsjprs.2020.03.004 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271620300642
[57] 张凡, 徐文学, 唐玲, 等. 机载激光测深数据配准方法比较[J]. 南京信息工程大学学报(自然科学版), 2021, 13(6):678-685.
[57] Zhang F, Xu W X, Tang L, et al. Comparison of airborne LiDAR bathymetry data refistration methods[J]. Journal of Nanjing University of Information Science and Technology(Natural Science Edition), 2021, 13(6):678-685.
[58] Yang F, Su D, Zhang K, et al. Mosaicing of airborne LiDAR bathymetry strips based on Monte Carlo matching[J]. Marine Geophysical Research, 2017, 38(3):303-311.
doi: 10.1007/s11001-017-9309-4 url: http://link.springer.com/10.1007/s11001-017-9309-4
[59] Wang X, Yang F, Zhang H, et al. Registration of airborne LiDAR bathymetry and multibeam echo sounder point clouds[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 20(3):1-5.
[60] Ji X, Yang B, Tang Q, et al. A coarse-to-fine strip mosaicing model for airborne bathymetric LiDAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(10):8129-8142.
doi: 10.1109/TGRS.2021.3050789 url: https://ieeexplore.ieee.org/document/9332244/
[61] Xu W, Zhang F, Jiang T, et al. Feature curve-based registration for airborne LiDAR bathymetry point clouds[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 112:102883.
doi: 10.1016/j.jag.2022.102883 url: https://linkinghub.elsevier.com/retrieve/pii/S1569843222000851
[1] WANG Liying, MA Xuwei, YOU Ze, WANG Shichao, CAMARA Mahamadou. Spatial-spectral joint classification of airborne multispectral LiDAR point clouds based on the multivariate GMM[J]. Remote Sensing for Natural Resources, 2023, 35(3): 88-96.
[2] FENG Xiaogang, ZHAO Yi, LI Meng, ZHOU Zaihui, LI Fengxia, WANG Yuan, YANG Yongquan. Influence of urban rivers and their surrounding land on the surface thermal environment[J]. Remote Sensing for Natural Resources, 2023, 35(3): 264-273.
[3] WU Weichao, YE Fawang. Cloud detection of Sentinel-2 images for multiple backgrounds[J]. Remote Sensing for Natural Resources, 2023, 35(3): 124-133.
[4] DONG Ting, FU Weiqi, SHAO Pan, GAO Lipeng, WU Changdong. Detection of changes in SAR images based on an improved fully-connected conditional random field[J]. Remote Sensing for Natural Resources, 2023, 35(3): 134-144.
[5] LIN Jiahui, LIU Guang, FAN Jinghui, ZHAO Hongli, BAI Shibiao, PAN Hongyu. Extracting information about mining subsidence by combining an improved U-Net model and D-InSAR[J]. Remote Sensing for Natural Resources, 2023, 35(3): 145-152.
[6] GAO Chen, MA Dong, QU Man, QIAN Jianguo, YIN Haiquan, HOU Xiaozhen. Exploring the anomaly mechanism of borehole strain at the Huailai seismic station based on PS-InSAR[J]. Remote Sensing for Natural Resources, 2023, 35(3): 153-159.
[7] XI Lei, SHU Qingtai, SUN Yang, HUANG Jinjun, SONG Hanyue. Optimizing an ICESat2-based remote sensing estimation model for the leaf area index of mountain forests in southwestern China[J]. Remote Sensing for Natural Resources, 2023, 35(3): 160-169.
[8] WANG Jianqiang, ZOU Zhaohui, LIU Rongbo, LIU Zhisong. A method for extracting information on coastal aquacultural ponds from remote sensing images based on a U2-Net deep learning model[J]. Remote Sensing for Natural Resources, 2023, 35(3): 17-24.
[9] CHEN Haoyu, XIANG Lei, GAO He, MU Jinyi, SUO Xiaojing, HUA Bowei. Hyperspectral inversion of total nitrogen content in soils based on fractional order differential[J]. Remote Sensing for Natural Resources, 2023, 35(3): 170-178.
[10] HU Chenxia, ZOU Bin, LIANG Yu, HE Chencheng, LIN Zhijia. Spatio-temporal evolution of gross ecosystem product with high spatial resolution: A case study of Hunan Province during 2000—2020[J]. Remote Sensing for Natural Resources, 2023, 35(3): 179-189.
[11] YANG Yujin, YANG Fan, XU Zhenni, LI Zhu. Analysis and optimization of the spatio-temporal coordination between the ecological services and economic development in the Dongting Lake area[J]. Remote Sensing for Natural Resources, 2023, 35(3): 190-200.
[12] PARIHA Helili, ZAN Mei. Spatio-temporal changes and influencing factors of ecological environments in oasis cities of arid regions[J]. Remote Sensing for Natural Resources, 2023, 35(3): 201-211.
[13] WANG Yelan, YANG Xin, HAO Lina. Spatio-temporal changes in the normalized difference vegetation index of vegetation in the western Sichuan Plateau during 2001—2021 and their driving factors[J]. Remote Sensing for Natural Resources, 2023, 35(3): 212-220.
[14] LOU Yanhan, LIAO Jingjuan, CHEN Jiaming. Monitoring water level changes in the middle and lower reaches of the Yangtze River using Sentinel-3A satellite altimetry data[J]. Remote Sensing for Natural Resources, 2023, 35(3): 221-229.
[15] ZHOU Shisong, TANG Yuqi, CHENG Yuxiang, ZOU Bin, FENG Huihui. Spatial heterogeneity of the correlation between water quality and land use in the Chenjiang River basin, Chenzhou City[J]. Remote Sensing for Natural Resources, 2023, 35(3): 230-240.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech