Quantifying vegetation indices using terrestrial laser scanning: methodological complexities and ecological insights from a Mediterranean forest
Quantifying vegetation indices using terrestrial laser scanning: methodological complexities and ecological insights from a Mediterranean forest
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用地面激光扫描量化植被指数：方法的复杂性和来自地中海森林的生态学见解
DOI：
10.5194/bg-20-2769-2023
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发表时间：
2023-07
期刊：
影响因子：
4.9
通讯作者：
W. Flynn;H. Owen;S. Grieve;E. Lines
中科院分区：
文献类型：
--
作者：
W. Flynn;H. Owen;S. Grieve;E. Lines
抽象。准确测量植被密度指标，包括植物、木材和叶面积指数（派、阿威和LAI），是监测和模拟森林碳储存和吸收的关键。传统的无源传感器方法，如数字半球摄影（DHP），不能分离树叶和木材材料，也不能单独的树木，并且在处理过程中需要许多假设。地面激光扫描（TLS）数据提供了新的机会，以提高对树木和冠层结构的理解。已经开发了多种方法来从TLS数据中获得派和LAI，但对于最佳方法几乎没有共识，也没有将方法作为标准基准。使用TLS数据收集在33个地块包含2472棵树的5个物种在地中海森林，我们比较了三种TLS方法（激光雷达脉冲，二维强度图像和基于体素），以获得派和比较与共定位DHP。然后，我们在单个树点云中分离叶和木材，以计算木材与总植物面积的比率（α），这是一个用于校正LAI估计中非光合物质的度量。我们使用单个树木TLS点云来估计α随物种、树高和林分密度的变化。我们发现激光雷达脉冲方法与DHP最接近，但它仅限于单次扫描数据，因此无法确定单个树木的属性，包括α。基于体素的方法显示了生态研究的希望，因为它可以应用于单个树点云。使用基于体素的方法，我们表明，物种解释一些变化α，但是，高度和地块密度是更好的预测。我们的研究结果强调了TLS数据的价值，以提高对树的形式和功能的基本理解，以及在新方法迅速发展的时候严格测试TLS数据处理方法的重要性。新的算法需要与传统的方法和现有的算法进行比较，使用共同的参考数据。虽然很有希望，但我们的研究结果表明，从TLS数据中获得的指标尚未可靠地校准和验证，以取代传统的方法来大规模监测派和LAI。
Abstract. Accurate measurement of vegetation density metrics including plant, wood and leaf area indices (PAI, WAI and LAI) is key to monitoring and modelling carbon storage and uptake in forests. Traditional passive sensor approaches, such as digital hemispherical photography (DHP), cannot separate leaf and wood material, nor individual trees, and require many assumptions in processing. Terrestrial laser scanning (TLS) data offer new opportunities to improve understanding of tree and canopy structure. Multiple methods have been developed to derive PAI and LAI from TLS data, but there is little consensus on the best approach, nor are methods benchmarked as standard. Using TLS data collected in 33 plots containing 2472 trees of 5 species in Mediterranean forests, we compare three TLS methods (lidar pulse, 2D intensity image and voxel-based) to derive PAI and compare with co-located DHP. We then separate leaf and wood in individual tree point clouds to calculate the ratio of wood to total plant area (α), a metric to correct for non-photosynthetic material in LAI estimates. We use individual tree TLS point clouds to estimate how α varies with species, tree height and stand density. We find the lidar pulse method agrees most closely with DHP, but it is limited to single-scan data, so it cannot determine individual tree properties, including α. The voxel-based method shows promise for ecological studies as it can be applied to individual tree point clouds. Using the voxel-based method, we show that species explain some variation in α; however, height and plot density were better predictors. Our findings highlight the value of TLS data to improve fundamental understanding of tree form and function as well as the importance of rigorous testing of TLS data processing methods at a time when new approaches are being rapidly developed. New algorithms need to be compared against traditional methods and existing algorithms, using common reference data. Whilst promising, our results show that metrics derived from TLS data are not yet reliably calibrated and validated to the extent they are ready to replace traditional approaches for large-scale monitoring of PAI and LAI.