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

Author

Listed:
  • Samuel José Silva Soares da Rocha

    (Departamento de Ciências Florestais, Universidade Federal de Lavras, Lavras 37200-900, MG, Brazil)

  • Flora Magdaline Benitez Romero

    (Instituto Nacional de Pesquisas da Amazônia—INPA, Manaus 69067-375, AM, Brazil)

  • Carlos Moreira Miquelino Eleto Torres

    (Departamento de Engenharia Florestal, Universidade Federal de Viçosa, Viçosa 36570-900, MG, Brazil)

  • Laércio Antônio Gonçalves Jacovine

    (Departamento de Engenharia Florestal, Universidade Federal de Viçosa, Viçosa 36570-900, MG, Brazil)

  • Sabina Cerruto Ribeiro

    (Centro de Ciências Biológicas e da Natureza, Universidade Federal do Acre (UFAC), Campus Universitário BR 364, Km 04, Distrito Industrial, Rio Branco 69920-900, AC, Brazil)

  • Paulo Henrique Villanova

    (Departamento de Engenharia Florestal, Universidade Federal de Viçosa, Viçosa 36570-900, MG, Brazil)

  • Bruno Leão Said Schettini

    (Departamento de Engenharia Florestal, Universidade Federal de Viçosa, Viçosa 36570-900, MG, Brazil)

  • Vicente Toledo Machado de Morais Junior

    (Brandt Meio Ambiente LTDA, Alameda do Ingá, 89, Vale do Sereno, Nova Lima 34006-042, MG, Brazil)

  • Leonardo Pequeno Reis

    (Instituto de Desenvolvimento Sustentável Mamirauá, Tefé 69553-225, AM, Brazil)

  • Maria Paula Miranda Xavier Rufino

    (Departamento de Engenharia Florestal, Universidade Federal de Viçosa, Viçosa 36570-900, MG, Brazil)

  • Indira Bifano Comini

    (Departamento de Engenharia Florestal, Universidade Federal de Viçosa, Viçosa 36570-900, MG, Brazil)

  • Ivaldo da Silva Tavares Júnior

    (Departamento de Engenharia Florestal, Universidade Federal de Viçosa, Viçosa 36570-900, MG, Brazil)

  • Águida Beatriz Traváglia Viana

    (Departamento de Engenharia Florestal, Universidade Federal de Viçosa, Viçosa 36570-900, MG, Brazil)

Abstract

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 r yŷ 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.

Suggested Citation

  • Samuel José Silva Soares da Rocha & Flora Magdaline Benitez Romero & Carlos Moreira Miquelino Eleto Torres & Laércio Antônio Gonçalves Jacovine & Sabina Cerruto Ribeiro & Paulo Henrique Villanova & Br, 2023. "Machine Learning: Volume and Biomass Estimates of Commercial Trees in the Amazon Forest," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9452-:d:1169406
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    References listed on IDEAS

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    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
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