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    柯涛

    • 博士生导师 硕士生导师
    • 性别:男
    • 毕业院校:武汉大学
    • 学历:研究生毕业
    • 在职信息:在职
    • 所在单位:遥感信息工程学院
    • 学科: 摄影测量与遥感
    • 办公地点:信息学部教学实验大楼
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    Recognition of ecological vegetation fairy circles in intertidal salt marshes from UAV LiDAR point clouds

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    发表刊物:International Journal of Applied Earth Observation and Geoinformation

    关键字:accuracy assessment;aerial survey;algorithm;detection method;holistic approach;image classification;intertidal environment;lidar;mudflat;orthophoto;reflectance;saltmarsh;unmanned vehicleve;getation cover;Fairy circles;Intensity correction;Point cloud classification;Salt marsh vegetation;UAV LiDAR

    摘要:Fairy circles (FC) are a type of spatial self-organized patterns that widely exist in various vegetation ecosystems and the accurate detection and quantitative characterization of these mysterious circles remain a technical challenge. In this study, vegetation FC in intertidal salt marshes are recognized from the derived reflectance information (backscattered intensity) and geometric quantities of light detection and ranging (LiDAR) carried on unmanned aerial vehicle (UAV). The specular effect on the UAV LiDAR intensity data over nadir regions of wet salt marshes is eliminated using the laser radar equation and Phong model where the absent distances and incidence angles are approximately retrieved on the basis of geometric and temporal relations in data collection. The FC are progressively recovered through three interconnected procedures. First, the retrieved reflectance information is used to discriminate the mudflat and vegetation points. Second, a spatial connectivity clustering algorithm is utilized on the extracted vegetation points to form individual spatially disconnected clusters. Finally, FC and regular vegetation are successfully recognized by jointly using the salient, size, and circularity features of the generated clusters. A multi-echo UAV LiDAR system is used for data collection at an intertidal salt marsh to assess the feasibility and prospects of the proposed method. Taking the manual detection results from the orthophoto generated by images of a UAV camera system as a reference, the missing detection rate, false detection rate, and area detection error of the proposed method are 6%, 9%, and 5%, respectively. Results suggest that UAV LiDAR is an extremely promising technique to characterize the geometric properties (e.g., location, size, and quantity) of FC from a holistic perspective.

    合写作者:Tao Ke,Shuai Liu,Weiguo Zhang,Jianru Yang,Xiangjie Zhu

    通讯作者:Kai Tan

    卷号:114:103029

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    发表时间:2022-11-30

    收录刊物:SCI