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• W4386530845 abstract "Chlorophyll is an important indicator of vegetation health status, accurate estimation of which is important for evaluating forest carbon sink. In this study, we estimated the chlorophyll content of coniferous forests, broad-leaved forests and mixed forest stands at stand and individual tree level by unmanned air vehicle (UAV) hyperspectral data combined with light detection and ranging (LiDAR) point clouds, which improved the non-destructive estimation accuracy of forest chlorophyll. We further comprehensively analyzed the spatial distribution of chlorophyll content at different scales. A total of 36 spectral characteristic variables related to chlorophyll content were screened by correlation analysis based on the fusion of UAV hyperspectral data and LiDAR point clouds combining with the empirical data from ground plots. We constructed multiple models for chlorophyll estimation by using statistical model, including multiple stepwise regression, BP neural network, BP neural network optimized by firefly algorithm, random forest and hybrid data-driven PROSPECT mechanism model. The optimal model was selected to estimate the chlorophyll content. The horizontal and vertical distribution of chlorophyll content at the stand level and individual tree level were analyzed. The results showed that the random forest model was superior to the models constructed by multiple stepwise regression, BP neural network and BP neural network optimized by firefly algorithm for chlorophyll estimation with R2 and RMSE of 0.59-0.64 and 3.79-5.83 μg·cm-2, respectively. The accuracy of the mechanism model was the highest, with R2 and RMSE of 0.97 and 3.40 μg·cm-2. The chlorophyll contents differed across stand types, with that in broad-leaved forest (25.25-31.60 μg·cm-2) being higher than mixed forest (13.52-23.93 μg·cm-2) and coniferous forest (6.40-13.71 μg·cm-2). There were significant differences in chlorophyll contents the in vertical direction among different stands. For individual tree of different species, the chlorophyll content inside the canopy was lower than that outside the canopy in the horizontal direction. In the vertical direction, there was no difference in chlorophyll content among different layers of Pinus sylvestris var. mongolica canopy. However, significant differences were observed among the upper, middle, and lower layers of Juglans mandshurica canopy. Using the fusion of hyperspectral image and LiDAR point cloud data, the mechanism model driven by hybrid data could effectively improve the accuracy and stability of chlorophyll content estimation at different scales.叶绿素是表征植被健康状况的重要指标,它的准确估计对森林碳汇评价研究至关重要。本研究通过无人机高光谱数据联合激光雷达点云估计针叶林、阔叶林和针阔混交林林分与单木水平的叶绿素含量,提升叶绿素无损估测精度,全面分析不同尺度叶绿素含量空间分布规律。在无人机高光谱数据与激光雷达点云融合的基础上,结合地面样地实测数据,通过相关性分析筛选与叶绿素含量相关的36个光谱特征变量,采用统计模型多元逐步回归、BP神经网络、萤火虫算法优化的BP神经网络、随机森林和混合数据驱动的机理模型PROSPECT模型构建多个叶绿素估算模型,选取最优模型估算森林叶绿素含量,分析其在林分和单木尺度上水平方向与垂直方向的空间分布规律。结果表明: 在统计模型中,随机森林(R2=0.59～0.64,RMSE=3.79～5.83 μg·cm-2)优于多元逐步回归、BP神经网络和萤火虫算法优化的BP神经网络构建的模型;机理模型验证精度最高(R2=0.97,RMSE=3.40 μg·cm-2)。不同林分类型叶绿素的含量存在较大差异,阔叶林叶绿素含量为25.25～31.60 μg·cm-2,高于针阔混交林(13.52～23.93 μg·cm-2)和针叶林(6.40～13.71 μg·cm-2),在垂直方向上,各林分间叶绿素含量存在显著差异。不同单木树种在水平方向上表现为冠层内部的叶绿素含量比冠层外部低,在垂直方向上,樟子松各冠层间叶绿素含量差异不显著,胡桃楸树冠上层与中、下层存在显著差异。采用融合的高光谱图像与激光雷达点云数据,基于混合数据驱动的机理模型可以有效提升不同尺度植被叶绿素含量估测的精度及稳定性。." @default.
• W4386530845 created "2023-09-09" @default.
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• W4386530845 date "2023-08-01" @default.
• W4386530845 modified "2023-10-16" @default.
• W4386530845 title "UAV hyperspectral combined with LiDAR to estimate chlorophyll content at the stand and individual tree scales." @default.
• W4386530845 doi "https://doi.org/10.13287/j.1001-9332.202308.004" @default.
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