• ISSN 1004-0323     CN 62-1099/TP
• 联合主办：中国科学院遥感联合中心
• 中国科学院兰州文献情报中心
• 中国科学院国家空间科学中心
 遥感技术与应用  2021, Vol. 36 Issue (3): 533-543    DOI: 10.11873/j.issn.1004-0323.2021.3.0533
 森林遥感专栏

Detection of Lodging Landscape Trees in Typhoon Disaster based on Unmanned Aerial Vehicle Remote Sensing
Hongyan Liao(),Xiaocheng Zhou(),Hongyu Huang
Key Laboratory of Spatial Data Mining &Information Sharing of Ministry of Education，National Engineering Research Centre of Geospatial Information Technology，Fuzhou University，Fuzhou 350116，China
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Abstract:

Fuzhou University was taken as an experimental area， this paper presented a fast extraction method of lodging landscape trees in typhoon disaster based on unmanned aerial vehicle remote sensing image， which can provide reference for the assessment of typhoon disaster losses and post-disaster reconstruction of the landscape department. Firstly， unmanned aerial vehicle remote sensing technology was used to obtain Pre and Post images during typhoon passing with a resolution higher than 10cm. After processing， Digital Orthophoto Map（DOM）and Digital Surface Model（DSM）were obtained. Then gaussian high pass filtering algorithm was used to highlight the edge information of tree trunk. And the best feature subset was selected by contrast filtering segmentation algorithm combined with maximum Relevance Minimum Redundancy（mRMR）feature selection algorithm. In addition， the tree trunk and non-tree trunk were detected according to the threshold value and Random Forest（RF）classification method respectively. At last， the tree trunk of lodging tree was simplified into skeleton line by using skeletonization algorithm， and the single tree trunk was extracted by using octo-neighborhood tracking method. The results show that a total of 71 lodging trees were detected using threshold classification based on single-phase UVA images， with an accuracy of 76.06% in the experimental area. However， the accuracy of lodging tree extraction improved by 12.73% based on RF classification，and the missed detection reached 25.39%. In order to compare the detection efficiency of lodging trees based on single-phase and two-phase images， combined the difference value of DSM in the two phases， threshold and RF classification were used respectively， with an accuracy of 89.66% and 87.30%， a commission of 17.46% and 12.70%．Research suggests that the single-phase image features can basically detect the lodging trees， and the multi-phase images analysis can effectively improve the detection accuracy of the lodging trees， providing an effective reference for the detection of the lodging trees under different data sources. According to the research， the UAV remote sensing technology can realize the rapid estimation of the number of lodging trees after typhoon.

Key words: UAV Remote Sensing    Typhoon disaster    Lodging tree    Feature selection    Hazard assessment

 ZTFLH: TP79