利用机器学习估算可可种植园地上生物量

Sabrina Sankar, Marvin B. Lewis, Patrick Hosein
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引用次数: 1

摘要

大气中二氧化碳的迅速增加及其对气候变化和全球变暖的相关影响提高了监测碳固存水平的重要性。估算地上生物量(AGB)是监测森林地区碳固存的一种方法。使用直接方法对地上生物量进行量化既昂贵又耗时,而且在许多情况下不切实际。然而,遥感技术,如激光雷达(光探测和测距)捕获三维信息,可用于执行这种估计。特别是,激光雷达可以用来估计树在胸高(DBH)的直径,从中我们可以估计它的AGB。在这项研究中,我们使用激光雷达数据,以及各种机器学习(ML)算法(多元线性回归、随机森林、支持向量回归和回归树)来估计可可树的胸径。我们使用了各种特征选择方法来为我们的模型选择最重要的特征。表现最好的算法是Random Forest, R2值为0.83,RMSE值为0.062。该算法估计AGB值为28.75±2.34 Mg/ha (Megagram /公顷)。我们将这一结果与同一块可可地本地开发的异速生长方程得到的结果进行了比较。结果表明,我们的估计值比异速生长方程低14.7%。结果表明,使用ML与LiDAR测量进行AGB估计是非常有前途的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Above Ground Biomass Estimation of a Cocoa Plantation using Machine Learning
The rapid increase in carbon dioxide in the atmosphere and its associated effects on climate change and global warming has raised the importance of monitoring carbon sequestration levels. Estimating above ground biomass (AGB) is one way of monitoring carbon sequestration in forested areas. Quantifying above ground biomass using direct methods is costly, time-consuming and, in many cases, impractical. However, remote sensing technologies such as LiDAR (Light Detection And Ranging) captures three dimensional information which can be used to perform this estimation. In particular, LiDAR can be used to estimate the diameter of a tree at breast height (DBH) and from this we can estimate its AGB. For this research we used LiDAR data, along with various Machine Learning (ML) algorithms (Multiple Linear Regression, Random Forest, Support Vector Regression and Regression Tree) to estimate DBH of cocoa trees. Various feature selection methods were used to select the most significant features for our model. The best performing algorithm was Random Forest which achieved an R2 value of 0.83 and Root Mean Square Estimate (RMSE) value of 0.062. This algorithm then estimated an AGB value of 28.75 ± 2.34 Mg/ha (Megagram per hectare). We compared this result with that obtained from locally-developed allometric equations for the same cocoa plot. The comparison proved our estimate to be 14.7% lower than the allometric equation. The results demonstrated that using ML with LiDAR measurements for AGB estimation is quite promising.
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