Comparison of stem volume estimates from terrestrial point clouds for mature Douglas-fir (Pseudotsuga menziessi (Mirb.) Franco)

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY Information Processing in Agriculture Pub Date : 2023-09-01 DOI:10.1016/j.inpa.2022.03.003
Rong Fang, Bogdan M. Strimbu
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引用次数: 0

Abstract

As a complement to traditional estimates of stem dimensions from numerical models, terrestrial light detection and ranging (Lidar) provides direct stem diameter and volume values using cylindrical models constructed from point clouds. This study used two approaches to estimate total stem volume using Lidar and compared them with two empirical equations, one used by the Forest Inventory Analysis in the Pacific Northwest (FIA-PNW) and one based on a taper equation. We fitted point clouds of 10 Douglas-fir with three sets of cylinder models that are distinguished by their segment length (i.e. 0.5 m, 1 m, and 2 m), then developed three taper equations based on the point-cloud-based diameter estimated previously. We estimated the total stem volume of the tree with eight models: six-point cloud-based (i.e. three taper and three cylinders) and two empirical. Finally, we used simulations to extrapolate the volume estimations of various methods for different diameters at breast height (DBH) classes. We found that all the point-cloud-based taper equations were similar in their performance (R2=0.94, RMSE = 4.6 cm) and produced mean volume estimates greater than mean estimates of the existing models. The cylinder models produced 11–16% greater mean volume estimates than the FIA-PNW estimate, with the 0.5 m segment length producing the greatest values, followed by the 1 m and 2 m segment length. The simulated data suggested that the mean volume estimates of a given DBH class are different when using different computation methods. ANOVA revealed a combined effect of the computation methods and the DBH class on the mean volume estimates. We conclude that the point-cloud-based taper equations, after being symmetrically calibrated, would be consistent with the regional stem volume estimates, whereas the cylinder models would be better in estimating individual stem volume. When constructing Lidar-based cylinder models in future applications, cylinder segment length would need to be adjusted to the length and DBH of the stem, as well as to the objectives of the research.

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从陆地点云估算成熟花旗松树干体积的比较
作为传统数值模型估算茎干尺寸的补充,地面光探测和测距(激光雷达)使用由点云构建的圆柱形模型提供直接的茎干直径和体积值。本研究使用两种方法利用激光雷达来估计总茎体积,并将其与两个经验方程进行比较,一个是太平洋西北地区森林清查分析(FIA-PNW)使用的,另一个是基于锥度方程的。我们用三组圆柱体模型拟合了10棵道格拉斯冷杉的点云,这些圆柱体模型由它们的段长度(即0.5 m, 1 m和2 m)区分,然后根据先前估计的基于点云的直径建立了三个锥度方程。我们估计了树的总茎体积与八个模型:六点云为基础(即三个锥度和三个圆柱体)和两个经验。最后,我们使用模拟来推断不同胸径(DBH)类别下各种方法的体积估计。我们发现,所有基于点云的锥度方程的性能相似(R2=0.94, RMSE = 4.6 cm),并且产生的平均体积估计值大于现有模型的平均估计值。圆柱体模型比FIA-PNW模型估计的平均体积高11-16%,其中0.5 m段长度产生的值最大,其次是1m和2m段长度。模拟数据表明,采用不同的计算方法,给定DBH类的平均体积估计值是不同的。方差分析揭示了计算方法和DBH类对平均体积估计的综合影响。我们得出的结论是,经过对称校准后,基于点云的锥度方程将与区域茎体积估计值一致,而圆柱体模型将更好地估计单个茎体积。在未来的应用中,当构建基于激光雷达的圆柱体模型时,圆柱体段的长度需要根据杆的长度和胸径以及研究目标进行调整。
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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