Remote Sensing Technology and Application, Volume. 39, Issue 2, 413(2024)

Application Tendency of Unmanned Aerial Vehicle for Geographical Research based on Bibliometric

Zhongxu BAO1,2,3、*, Runhe SHI1,2,3, and Yaohuan HUANG4,5
Author Affiliations
  • 1Key Laboratory of Geographic Information Science,Ministry of Education,East China Normal University,Shanghai 200241,China
  • 2School of Geographic Sciences,East China Normal University,Shanghai 200241,China
  • 3Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities,Ministry of Natural Resources,Shanghai 200241,China
  • 4State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
  • 5College of Resource and Environment,University of Chinese Academy of Sciences,Beijing 100049,China
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    References(77)

    [1] WANG Ke, FU Yiran, PENG Xiangyang et al. Overview of UAV low altitude remote sensing technology and application in typical industries. Bulletin of Surveying and Mapping, 2017, 79-83.

    [2] LIAO Xiaohan, XIAO Qing, ZHANG Hao. UAV remote sensing: Popularization and expand application development trend. Journal of Remote Sensing, 23, 1046-1052(2019).

    [3] AN Yuan, ZHANG Ling. Applied research summary on bibliome trics in library and information field. Library, 63-68(2014).

    [4] REN quan-e. Bibliometrics research retrospect and prospect in China for 40 years based on knowledge map. Journal of Information and Management, 5, 16-31(2020).

    [5] ZHUANG Chen. Public security perspective on drone involvement in counter-terrorism. Legal and Economy, 30, 46-53(2021).

    [6] RAPARELLI E, BAJOCCO S. A bibliometric analysis on the use of unmanned aerial vehicles in agricultural and forestry studies. International Journal of Remote Sensing, 1-14(2019).

    [7] MUKHERJEE A, MISRA S, RAGHUWANSHI N S. A survey of unmanned aerial sensing solutions in precision agriculture. Journal of Network and Computer Applications, 148, 1-24(2019).

    [8] LI Hongjun, HE Xiongkui, SONG Jianli et al. Comparison of global R&D of agricultural unmanned aerial vehicle, based on bibliometrics. Journal of China Agricultural University, 26, 154-167(2021).

    [9] SINGH A P, YERUDKAR A, MARIANI V et al. A bibliometric review of the use of unmanned aerial vehicles in precision agriculture and precision viticulture for sensing applications. Remote Sensing, 14, 1604(2022).

    [10] WANG Junli, REN Shiqi, ZHANG Zhonghua et al. Research progress on unmanned aerial vehicle for ecological remote sensing monitoring based on bibliometric assessment. Tropical Geography, 39, 616-624(2019).

    [11] MÜNSTER S. Digital heritage as a scholarly field—topics, researchers, and perspectives from a bibliometric point of view. Journal on Computing and Cultural Heritage (JOCCH), 12, 1-27(2019).

    [12] LIU Yuhao. Current status and challenges of UAV in construction project management based on bibliometric analysis. Modern Business Trade Industry, 41, 201-202(2020).

    [13] LIAO Xiaohan, HUANG Yaohuan, XU Chenchen. Views on the study of low-altitude airspace resources for UAV applications. Acta Geographica Sinica, 76, 2607-2620(2021).

    [14] LIAO Xiaohan. The mutual development of geographical research and uav application: Preface to the album "progress in geographical science and UAV application". Progress in Geography, 40, 1439-1440(2021).

    [15] PIÉGAY H, ARNAUD F, BELLETTI B et al. Remotely sensed rivers in the anthropocene: State of the art and prospects. Earth Surface Processes and Landforms, 45, 157-188(2019).

    [16] ZHANG X, HUAN L I, GONG Z et al. Method for UAV-based 3D topography reconstruction of tidal creeks. Journal of Geographical Sciences, 1852-1872(2021).

    [17] YUE J, YANG G, TIAN Q et al. Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 226-244(2019).

    [18] PÁDUA L, GUIMARES N et al. Post-Fire forestry recovery monitoring using high-resolution multispectral imagery from unmanned aerial vehicles, 301-305(2019).

    [19] GARRETT B, ANDERSON K. Drone methodologies: taking flight in human and physical geography. Transactions of the Institute of British Geographers, 43, 341-359(2018).

    [20] PANG Jingan. Research methodology of scientometrics(1999).

    [21] CHEN H, HO Y S. Highly cited articles in biomass research: a bibliometric analysis. Renewable and Sustainable Energy Reviews, 49, 12-20(2015).

    [22] LI W, ZHAO Y. Bibliometric analysis of global environmental assessment research in a 20-year period. Environmental Impact Assessment Review, 50, 158-166(2015).

    [23] ECK N V, WALTMAN L. Software survey:VOSviewer,a com-puter program for bibliometric mapping. Scientometrics, 84, 523-538(2010).

    [24] WALTMAN L, VAN E N J, NOYONS E C M et al. A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4, 629-635(2010).

    [25] SMITH M W, CARRIVICK J L, QUINCEY D J. Structure from motion photogrammetry in physical geography. Progress in Physical Geography, 40, 247-275(2015).

    [26] LEJOT J, DELACOURT C, PIÉGAY H et al. Very high spatial resolution imagery for channel bathymetry and topography from an unmanned mapping controlled platform. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 32, 1705-1725(2007).

    [27] HODSON A, ANESIO A M et al. A glacier respires: Quantifying the distribution and respiration CO2 flux of cryoconite across an entire arctic supraglacial ecosystem. Journal of Geophysical Research Biogeosciences, 112(2015).

    [28] SCHIEFER F, KATTENBORN T, FRICK A et al. Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 170, 205-215(2020).

    [29] HAO Z, LIN L, POST C J et al. Automated tree-crown and height detection in a young forest plantation using Mask Region-based Convolutional Neural Network (Mask R-CNN). ISPRS Journal of Photogrammetry and Remote Sensing, 178, 112-123(2021).

    [30] LIU T, ABD-ELRAHMAN A, MORTON J et al. Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system. GIScience & Remote Sensing, 55, 243-264(2018).

    [31] HALLA C, BLÖTHE J H, BALDIS C T et al. Ice content and interannual water storage changes of an active rock glacier in the dry andes of Argentina. The Cryosphere, 15, 1187-1213(2021).

    [32] SILVA O L, BEZERRA F H R, MAIA R P et al. Karst landforms revealed at various scales using Lidar and uav in semi-arid Brazil: Consideration on karstification processes and methodological constraints. Geomorphology, 295, 611-630(2017).

    [33] HERAVI A. Application of deep learning on UAV-based aerial images for flood detection. Smart Cities, 4, 1220-1242(2021).

    [34] GAO Y, HAO M, Wang Y et al. Multi-Scale coal fire detection based on an improved active contour model from Landsat-8 satellite and UAV images. ISPRS International Journal of Geo-Information, 10, 449(2021).

    [35] XU X Q, LU J S, ZHANG N et al. Inversion of rice canopy chlorophyll content and leaf area index based on coupling of radiative transfer and Bayesian network models. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 185-196(2019).

    [36] WU Gang, ZHOU Bin, YANG Liankang. Review and outlook of domestic and international civil drone industry development. Economic Research Guide, 160-162(2016).

    [37] HUANG Aifeng, DENG Kexu. Civilian UAV development status and key technologies, 24-30(2012).

    [38] YUAN Liqun, SHAN Hangying, YANG Zhongqing et al. The application and prospect of composite materials in UAV. Fiber Class, 30-36(2017).

    [39] KIM J, LEE D W, CHO K et al. Development of an electro-optical system for small UAV. Aerospace Science and Technology, 14, 505-511(2010).

    [40] LI Deren, LI Ming. Research advance and application project of unmanned aerial vehicle remote sensing system. Geomatics and Information Science of Wuhan University, 39, 505-513(2014).

    [41] WAN Jianhua, WANG Zhao, LIU Shanwei et al. 1:500 large scale surveying application for consumer multi-rotor unmanned aerial vehicles. Remote Sensing Technology and Application, 34, 1048-1053(2019).

    [42] AGAPIOU A. Vegetation extraction using visible-bands from openly licensed unmanned aerial vehicle imagery. Drones, 4, 27(2020).

    [43] LÓPEZ G F, TORRES S J, CASTRO A et al. Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery. Agronomy for Sustainable Development, 36, 67-78(2016).

    [44] ALBETIS J, DUTHOIT S, GUTTLER F et al. Detection of flavescence dorée grapevine disease using Unmanned Aerial Vehicle(UAV) multispectral imagery. Remote Sensing, 9, 308(2017).

    [45] ZAMANALLAH M, VERGARA O, ARAUS J L et al. Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods, 11, 35(2015).

    [46] SHIN J I, SEO W W, KIM T et al. Using UAV multispectral images for classification of forest burn severity—A case study of the 2019 Gangneung forest fire. Forests, 10, 1025(2019).

    [47] YANG B, HAWTHORNE T L, TORRES H et al. Using object-oriented classification for coastal management in the east central coast of Florida: A quantitative comparison between UAV, satellite, and aerial data. Drones, 3, 60(2019).

    [48] YUE J, YANG G, LI C et al. Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sensing, 9, 708(2017).

    [49] VANEGAS F, BRATANOV D, POWELL K et al. A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data. Sensors, 18, 260(2018).

    [50] LU J, LI W, YU M et al. Estimation of rice plant potassium accumulation based on non-negative matrix factorization using hyperspectral reflectance. Precision Agriculture, 22, 51-74(2021).

    [51] WEI L, HUANG C, ZHONG Y et al. Inland waters suspended solids concentration retrieval based on PSO-LSSVM for UAV-Borne hyperspectral remote sensing imagery. Remote Sensing, 11, 1455(2019).

    [52] WANG Z, ZHOU J, LIU S et al. A land surface temperature retrieval method for UAV broadband thermal imager data. IEEE Geoscience and Remote Sensing Letters, 1-5(2022).

    [53] AMBROSIA V G, WEGENER S, ZAJKOWSKI T et al. The Ikhana Unmanned Airborne System (UAS) western states fire imaging missions: From concept to reality (2006–2010). Geocarto International, 26, 85-101(2011).

    [54] ALESSANDRO M, RITA B, ANDREA B et al. Estimation of water stress in grapevines using proximal and remote sensing methods. Remote Sensing, 10, 114(2018).

    [55] LI F, YANG W, LIU X et al. Using high-resolution UAV-borne thermal infrared imagery to detect coal fires in Majiliang mine, Datong coalfield, Northern China. Remote Sensing Letters, 9, 71-80(2018).

    [56] LIU He, GU Lingjia, REN Ruizhi. Research progress of forest parameter acquisition based on UAV remote sensing technology. Remote Sensing Technology and Application, 36, 489-501(2021).

    [57] SOLAZZO D, SANKEY J B, SANKEY T T et al. Mapping and measuring aeolian sand dunes with photogrammetry and LiDAR from Unmanned Aerial Vehicles(UAV) and multispectral satellite imagery on the Paria Plateau, AZ, USA. Geomorphology, 174-185(2018).

    [58] KADHIM I, ABED F M. The potential of LiDAR and UAV-Photogrammetric data analysis to interpret archaeological sites: A case study of Chun Castle in South-West England. International Journal of Geo-Information, 10, 1-21(2021).

    [59] LEWIS Q W, EDMONDS D A, YANITES B J. Integrated UAS and LiDAR reveals the importance of land cover and flood magnitude on the formation of incipient chute holes and chute cutoff development. Earth Surface Processes and Landforms, 45, 1441-1455(2020).

    [60] AZOUZ A, LI Z. Improved phase gradient autofocus algorithm based on segments of variable lengths and minimum-entropy phase correction(2014).

    [61] AGUASCA A, ACEVO-HERRERA R, BROQUETAS A et al. ARBRES: light-weight CW/FM SAR sensors for small UAVs. Sensors, 13, 3204-3216(2013).

    [62] ZHANG Jixian, LIU Fei, WANG Jian. Review of the light-weighted and small UAV system for aerial photography and remote sensing. National Remote Sensing Bulletin, 25, 708-724(2021).

    [63] YAN Lei, LIAO Xiaohan, ZHOU Chenghu et al. The impact of UAV remote sensing technology on the industrial development of China: A review. Journal of Geo-information Science, 21, 476-495(2019).

    [64] LIAO Xiaohan, ZHOU Chenghu, SU Fenzhen et al. The mass innovation era of UAV remote sensing. Journal of Geo-information Science, 18, 1439-1447(2016).

    [65] LI Xin, YUAN Linwang, PEI Tao et al. Information geography discipline system and development strategy key points. Acta Geographica Sinica, 76, 2094-2103(2021).

    [66] HU Jianbo, ZHANG Jian. Unmanned aerial vehicle remote sensing in ecology:Advances and prospect. Acta Ecologica Sinica, 38, 20-30(2018).

    [67] FAN Deqin, ZHAO Xuesheng, ZHU Wenquan et al. Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data. Progress in Geography, 35, 304-319(2016).

    [68] HUANG Tieqing, ZHAO Tao, ZHAI Jinliang et al. Taking advantage of spatial information technology to serve Wenchuan earthquake relief secisions. Remote Sensing Technology and Application, 102, 486-492(2008).

    [69] LIU K, SHEN X, CAO L et al. Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 465-482(2018).

    [70] HE Lei, MIAO Fang, TANG Shuya et al. Unmanned aerial unmanned aerial vehicle remote sensing sensing image and Three-Dimension-AI visualization applied in WenChuan earthquake. Computing Techniques for Geophysical and Geochemical Exploration, 32, 206-112(2010).

    [71] QIN Qiming, CHEN Jin, ZHANG Yongguang et al. A discussion on some frontier directions of quantitative remote sensing. Remote Sensing for Land and Resources, 32, 8-15(2020).

    [72] ZHAO S, WANG Q, LI Y et al. An overview of satellite remote sensing technology used in China’s environmental protection. Earth Science Informatics, 10, 137-148(2017).

    [73] MCCABE M F, RODELL M, ALSDORF D E et al. The future of earth observation in hydrology. Hydrology and Earth System Sciences, 21, 3879-3914(2017).

    [74] TANG Guoqiang, LONG Di, WAN Wei et al. An overview and outlook of global water remote sensing technology and applications. Scientia Sinica(Technological), 45, 1013-1023(2015).

    [75] JIANG Ying, YANG Ninghui, LIU Xiaoming et al. A study of the regional distribution of China's national natural science foundation. Science of Science and Management of S. & T, 24, 5-10(2003).

    [76] XIA Yuan. The functions, funding and personnel of the French National Centre for Scientific Research. Science and Technology Policy and Development Strategy, 2004, 7.

    [77] TIAN Miao, FANG Yibing, CHEN Yue et al. The scientific and technological revolution and the modernization of Italy. 田淼, 等(2020).

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    Zhongxu BAO, Runhe SHI, Yaohuan HUANG. Application Tendency of Unmanned Aerial Vehicle for Geographical Research based on Bibliometric[J]. Remote Sensing Technology and Application, 2024, 39(2): 413

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    Paper Information

    Category: Research Articles

    Received: Oct. 2, 2022

    Accepted: --

    Published Online: Aug. 13, 2024

    The Author Email: Zhongxu BAO (51213901065@stu.ecnu.edu.cn)

    DOI:10.11873/j.issn.1004-0323.2024.2.0413

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