Biomass estimation models for four priority Prosopis species: Tools required for forestry management in overexploited arid ecosystems

Zarzosa, Pablo Salazar; Navarro-Cerrillo, Rafael M.; Cubbin, Elva Palacios Mc; Cruz, Gastón; Lopez, Manuel

doi:10.1016/j.jaridenv.2022.104904

Cited by 4 publications

(1 citation statement)

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“…Machine learning (ML) methods have been increasingly used for biomass estimation, as they can handle large datasets, complex relationships and nonlinear patterns in ecological systems. Algorithms such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forests (RF) and the Generalized Linear Model (GLM) are often used in these studies, seeking more accurate volume and biomass estimates [21,[39][40][41][42]. For example, researchers have used Random Forests to estimate aboveground biomass using airborne spectral indices [43], and also used Neural Networks and support-vector regression to estimate forest biomass using climate data [44].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning: Volume and Biomass Estimates of Commercial Trees in the Amazon Forest

et al. 2023

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Accurate estimation of the volume and above-ground biomass of exploitable trees by the practice of selective logging is essential for the elaboration of a sustainable management plan. The objective of this study is to develop machine learning models capable of estimating the volume and biomass of commercial trees in the Southwestern Amazon, based on dendrometric, climatic and topographic characteristics. The study was carried out in the municipality of Porto Acre, Acre state, Brazil. The volume and biomass of sample trees were determined using dendrometric, climatic and topographic variables. The Boruta algorithm was applied to select the best set of variables. Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forests (RF) and the Generalized Linear Model (GLM) were the machine learning methods evaluated. In general, the evaluated methods showed a satisfactory generalization power. The results showed that the volume and biomass predictions of commercial trees in the Amazon rainforest differed between the techniques (p < 0.05). ANNs showed the best performance in predicting the volume and biomass of commercial trees, with the highest ryŷ and the lowest RSME and MAE. Thus, machine learning methods such as SVM, ANN, RF and GLM are shown to be useful and efficient tools for estimating the volume and biomass of commercial trees in the Amazon rainforest. These methods can be useful tools to improve the accuracy of estimates in forest management plans.

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