Potential and constraints of Unmanned Aerial Vehicle technology for the characterization of Mediterranean riparian forest

被引:166
作者
Dunford, R. [1 ,2 ]
Michel, K. [2 ]
Gagnage, M. [2 ]
Piegay, H. [2 ]
Tremelo, M. -L. [2 ]
机构
[1] Univ Durham, Dept Geog, Durham DH1 3LE, England
[2] Univ Lyon, CNRS, UMR 5600, F-69342 Lyon 07, France
基金
英国经济与社会研究理事会;
关键词
REMOTELY-SENSED DATA; IMAGERY; RIVER; CLASSIFICATION; PHOTOGRAPHY; BATHYMETRY; PATTERNS; BASIN;
D O I
10.1080/01431160903023025
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Unmanned Aerial Vehicle (UAV) technology provides potential for very high spatial resolution (<25 cm) mapping of relatively large areas at a user-defined re-survey frequency. In a riparian context, UAV technology provides a mechanism for riparian managers to (a) quantify riparian terrain and vegetation units and (b) identify standing dead wood and canopy mortality. In this study a paraglider UAV was used to survey 174 ha at 6.8-21.8 cm ground resolution. Pixel-based and object-oriented classification approaches were used at the scale of a single image and a channel mosaic. Significant potential was demonstrated: vegetation units were classified with an accuracy of kappa = 0.79 and standing dead wood units were identified with an average accuracy with respect to omission and commission errors of 80% and 65%, respectively. Work across multiple images identified that major constraints currently result from factors such as illumination conditions and sensor movement during flight, which create variations in spatial resolution and radiometry. It is expected that with further methodological refinement and more complex methods of automated radiometric correction UAV technology can provide the flexibility to rapidly produce very high resolution map products to aid riparian management.
引用
收藏
页码:4915 / 4935
页数:21
相关论文
共 33 条
  • [21] Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier-A Case of Yuyao, China
    Feng, Quanlong
    Liu, Jiantao
    Gong, Jianhua
    [J]. WATER, 2015, 7 (04) : 1437 - 1455
  • [22] Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest
    Mohan, Midhun
    Silva, Carlos Alberto
    Klauberg, Carine
    Jat, Prahlad
    Catts, Glenn
    Cardil, Adrian
    Hudak, Andrew Thomas
    Dia, Mahendra
    [J]. FORESTS, 2017, 8 (09):
  • [23] High-Throughput 3-D Monitoring of Agricultural-Tree Plantations with Unmanned Aerial Vehicle (UAV) Technology
    Torres-Sanchez, Jorge
    Lopez-Granados, Francisca
    Serrano, Nicolas
    Arquero, Octavio
    Pena, Jose M.
    [J]. PLOS ONE, 2015, 10 (06):
  • [24] Digital surface model applied to unmanned aerial vehicle based photogrammetry to assess potential biotic or abiotic effects on grapevine canopies
    Su Baofeng
    Xue Jinru
    Xie Chunyu
    Fang Yulin
    Song Yuyang
    Fuentes, Sigfredo
    [J]. INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING, 2016, 9 (06) : 119 - 130
  • [25] Discontinuity Characterization of Rock Masses through Terrestrial Laser Scanner and Unmanned Aerial Vehicle Techniques Aimed at Slope Stability Assessment
    Pagano, Marco
    Palma, Biagio
    Ruocco, Anna
    Parise, Mario
    [J]. APPLIED SCIENCES-BASEL, 2020, 10 (08):
  • [26] Extraction of soybean planting area based on feature fusion technology of multi-source low altitude unmanned aerial vehicle images
    Yang, Qi
    She, Bao
    Huang, Linsheng
    Yang, Yuying
    Zhang, Gan
    Zhang, Mai
    Hong, Qi
    Zhang, Dongyan
    [J]. ECOLOGICAL INFORMATICS, 2022, 70
  • [27] Potential Evaluation of High Spatial Resolution Multi-Spectral Images Based on Unmanned Aerial Vehicle in Accurate Recognition of Crop Types
    Li, Lei
    Zheng, Xingming
    Zhao, Kai
    Li, Xiaofeng
    Meng, Zhiguo
    Su, Chunhua
    [J]. JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2020, 48 (11) : 1471 - 1478
  • [28] Inversion and analysis of leaf area index (LAI) of urban park based on unmanned aerial vehicle (UAV) multispectral remote sensing and random forest (RF)
    Li, Yan
    Wang, Bocheng
    Zhao, Xuefei
    Zhang, Yichuan
    Qiao, Lifang
    [J]. PLOS ONE, 2025, 20 (03):
  • [29] Modeling of Alpine Grassland Cover Based on Unmanned Aerial Vehicle Technology and Multi-Factor Methods: A Case Study in the East of Tibetan Plateau, China
    Meng, Baoping
    Gao, Jinlong
    Liang, Tiangang
    Cui, Xia
    Ge, Jing
    Yin, Jianpeng
    Feng, Qisheng
    Xie, Hongjie
    [J]. REMOTE SENSING, 2018, 10 (02)
  • [30] Evaluation of Feature Selection Methods for Object-Based Land Cover Mapping of Unmanned Aerial Vehicle Imagery Using Random Forest and Support Vector Machine Classifiers
    Ma, Lei
    Fu, Tengyu
    Blaschke, Thomas
    Li, Manchun
    Tiede, Dirk
    Zhou, Zhenjin
    Ma, Xiaoxue
    Chen, Deliang
    [J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2017, 6 (02)