| 基于无人机多源遥感数据海岛植被叶面积指数协同反演研究 |
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| 引用本文： | 吴剑,陈鹏,傅世锋,陈庆辉,潘翔,宋志晓.基于无人机多源遥感数据海岛植被叶面积指数协同反演研究[J].海洋技术,2019,38(6):1-8. |
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| 作者姓名： | 吴剑 陈鹏 傅世锋 陈庆辉 潘翔 宋志晓 |
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| 作者单位： | 自然资源部第三海洋研究所海洋环境管理与可持续发展研究中心,福建厦门361005;自然资源部第三海洋研究所海洋环境管理与可持续发展研究中心,福建厦门361005;自然资源部第三海洋研究所海洋环境管理与可持续发展研究中心,福建厦门361005;自然资源部第三海洋研究所海洋环境管理与可持续发展研究中心,福建厦门361005;自然资源部第三海洋研究所海洋环境管理与可持续发展研究中心,福建厦门361005;自然资源部第三海洋研究所海洋环境管理与可持续发展研究中心,福建厦门361005 |
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| 基金项目： | 国家重点研发计划;福建省自然科学基金;基本科研业务费项目 |
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| 摘 要： | 无人机多源遥感数据的获取、融合以及应用是当今研究的热点和难点。文中以城洲岛为例,针对海岛特殊的地理生态环境,获取无人机多源遥感数据。结合无人机多光谱遥感数据定量分析各遥感植被指数与植被叶面积指数(Leaf Area Index, LAI)的响应关系,构建单因子遥感反演模型;基于无人机激光LiDAR点云提取海岛植被冠层高度模型(Canopy Height Model,CHM),并将其作为自变量引入到多源统计回归分析中,从而构建多源遥感数据协同反演模型,对区域尺度下海岛叶面积指数(LAI)进行估算,开展验证和精度评价。结果显示,加入植被冠层高度因子的协同反演模型的判定系数R2为0.92,绝对平均误差系数为12.29%,预测精度要优于单因子反演模型(判定次数R2为0.86,绝对平均误差系数19.95%)。研究表明,加入了植被冠层高度因子的协同反演模型能在一定程度上提高乔木植被LAI的预测精度。实践证明,无人机多源遥感技术在生态学定量研究中具有巨大的潜力和广阔的应用前景。
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| 关 键 词： | 无人机多源遥感 冠层高度模型 协同识别分类和反演 激光雷达 城洲岛 |
The study on cooperative regression of Island Leaf Area Index (LAI) with UAV multiple remote sensing data |
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| Wu Jian,Chen Peng,Fu Shifeng,Chen Qinghui,Pan Xiang and Song Zhixiao.The study on cooperative regression of Island Leaf Area Index (LAI) with UAV multiple remote sensing data[J].Ocean Technology,2019,38(6):1-8. |
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| Authors: | Wu Jian Chen Peng Fu Shifeng Chen Qinghui Pan Xiang and Song Zhixiao |
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| Institution: | The Research Centre of Marine Environmental Management and Sustainable Development, Third Institute of Oceanography, Ministry of Nature Resources, Xiamen 361005, China,The Research Centre of Marine Environmental Management and Sustainable Development, Third Institute of Oceanography, Ministry of Nature Resources, Xiamen 361005, China,The Research Centre of Marine Environmental Management and Sustainable Development, Third Institute of Oceanography, Ministry of Nature Resources, Xiamen 361005, China,The Research Centre of Marine Environmental Management and Sustainable Development, Third Institute of Oceanography, Ministry of Nature Resources, Xiamen 361005, China,The Research Centre of Marine Environmental Management and Sustainable Development, Third Institute of Oceanography, Ministry of Nature Resources, Xiamen 361005, China and The Research Centre of Marine Environmental Management and Sustainable Development, Third Institute of Oceanography, Ministry of Nature Resources, Xiamen 361005, China |
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| Abstract: | Nowadays, the multiple-data achieving, fusing and application of UAV (Unmanned Aerial Vehicle) platform has been a hot and difficult spot. This study does research on vegetation cooperative regression on island LAI by combining the optical and LiDAR remote sensing data from UAV at a regional scale in Cheng Zhou Island. Firstly, the quantitative relationships between LAI and vegetation indices are analized based on the UAV multispectral remote sensing data, and then the Canopy Height Model (CHM) is extracted from the UAV LiDAR point cloud data. After that, the cooperative regression model of Island Leaf Area Index is built to estimate the LAI index. By the accuray evaluation , the result depicts that the cooperative regression model with CHM parameter achieves more scientific and accurate result with the R2 of 0.92(Absolute average error coefficient is 12.29%) as compared to 0.86(Absolute average error coefficient is 19.95%)of single factor retrieve model. It is concluded that the UAV multiple remotely-sensed technology will have an extensive application foreground in research field of ecological quantitative analysis. |
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| Keywords: | UAV Multiple Remote Sensing Canopy Height Model (CHM) Cooperative Recognition and Regression LIDAR Chen Zhou island |
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