Identifying crown areas in an undulating area planted with eucalyptus using unmanned aerial vehicle near-infrared imagery |
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| Authors: | Jun Kang Kun Jia Zheng Niu Muhammad Shakir Hailang Qiao |
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| Institution: | 1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing, China;2. College of Resources and Environment, University of Chinese Academy of Science, Beijing, China;3. State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, China |
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| Abstract: | In this letter, we propose an identification method of tree crown areas for imagery captured by a near-infrared camera on board an unmanned aerial vehicle platform over an undulating Eucalyptus planting area in Guangdong Province, China. The method extracts crown areas by applying mathematical morphology, unsupervised segmentation based on J-value segmentation, local spatial statistics, and Iterative Self-Organizing Data Analysis Technique Algorithm. Two morphology filters and four segmentation scales were compared between densely and sparsely planted plots as well as sunlit and shaded plots. The opening operation by the window size of 9×9 pixel and segmentation by the seed area sized 65×65 pixel achieved the best performance with overall accuracy of 91%, 93%, 89% and 91% in densely sunlit, sparsely sunlit, densely shaded and sparsely shaded plots. |
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