Estimation of mortality and survival of individual trees after harvesting wood using artificial neural networks in the amazon rain forest

Reis, Leonardo Pequeno; Souza, Agostinho Lopes de; Reis, Pamella Carolline Marques dos Reis; Mazzei, Lucas; Soares, Carlos Pedro Boëchat; Torres, Carlos Moreira Miquelino Eleto; Silva, Liniker Fernandes da; Ruschel, Ademir Roberto; Rêgo, Lyvia Julienne Sousa; Leite, Hélio Garcia

doi:10.1016/j.ecoleng.2017.12.014

Cited by 46 publications

(21 citation statements)

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“…Commercial engineering development of low-cost eddy covariance networks, to attain state-wide spatial coverage across the US or for specific regions, for example, coupled with innovative features, including unmanned aerial vehicles (Berman et al, 2012;Metzger et al, 2012), shared data networks (Dai et al, 2018) and automated reporting is achievable offering a modernized alternative to estimation protocols. Advancements in blockchain accounting platforms (Düdder & Ross, 2017), artificial intelligence (Reis et al, 2018) and the internet of things (Subashini et al, 2018) can be readily integrated within eddy covariance networks but for the reasons we discuss here cannot be successful without direct measurement of CO 2 . Eddy covariance as an instrumental method has characteristic limitations and uncertainties (Nicolini et al, 2018) and faces engineering challenges for large-scale deployment as well as upscaling eddy covariance methods (Barba et al, 2018;Kumar et al, 2016;Ran et al, 2016;Warner et al, 2019;Wutzler et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Peer Review #3 of "California air resources board forest carbon protocol invalidates offsets (v0.1)"

2019

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The commercial asset value of sequestered forest carbon is based on protocols employed globally, however, their scientific basis has not been validated. We review and analyze commercial forest carbon protocols, claimed to have reduced net greenhouse gas emissions, issued by the California Air Resources Board and validated by the Climate Action Reserve (CARB-CAR). CARB-CAR forest carbon offsets, based on forest mensuration and model simulation, are compared to a global database of directly measured forest carbon sequestration, or net ecosystem exchange (NEE) of forest CO 2. NEE is a meteorologically based method integrating CO 2 fluxes between the atmosphere, forest and soils and is independent of the CARB-CAR methodology. Annual carbon accounting results for CAR681 are compared with NEE for the Ameriflux site, Howland Forest Maine, USA, (Ho-1), the only site where both methods were applied contemporaneously, invalidating CARB-CAR protocol offsets. We then test the null hypothesis that CARB-CAR project population data fall within global NEE population values for natural and managed forests measured in the field; net annual gC m-2 yr-1 are compared for both protocols. Irrespective of geography, biome and project type, the CARB-CAR population mean is significantly different from the NEE population mean at the 95% confidence interval, rejecting the null hypothesis. The CARB-CAR population exhibits standard deviation ~5x that of known interannual NEE ranges, is overcrediting biased, incapable of detecting forest transition to net positive CO 2 emissions, and exceeds the 5% CARB compliance limit for invalidation. Exclusion of CO 2 efflux via soil and ecosystem respiration precludes a valid net carbon accounting result for CARB-CAR and related protocols, consistent with our findings. Protocol invalidation risk extends to vendors and policy platforms such as the United Nations Program on Reducing Emissions from Deforestation and Forest Degradation (REDD+) and the Paris Agreement. We suggest that CARB-CAR and related protocols

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Section: Discussionmentioning

confidence: 99%

Peer Review #3 of "California air resources board forest carbon protocol invalidates offsets (v0.1)"

2019

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“…Applications of this computational intelligence technique in forestry have become very relevant because they have been achieving successful results. Examples include diameter distribution modeling (DIAMANTOPOULOU et al, 2015;REIS et al, 2018b), optimization of competition indices (RICHARDS; MCDONALD; AITKENHEAD, 2008), volume estimation (BINOTI et al, 2014), prediction of forest fires in the Brazilian Amazon (MAEDA et al, 2009), classification of successional stages in the Brazilian Amazon (KUPLICH, 2006), tree height estimation (DIAMANTOPOULOU, 2012), modeling individual tree mortality and survival in uneven-aged forests (CASTRO et al, 2015;ROCHA et al, 2018), production prognosis of evenaged stands (BINOTI et al, 2015), modeling of diameter growth and individual tree survival and mortality in the Brazilian Amazon (REIS et al, 2016;2018a), and estimation of physical and mechanical properties of wood species in the Amazon (REIS et al, 2018c).…”

Section: Introductionmentioning

confidence: 99%

Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest

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A modelagem do recrutamento em ﬂorestais tropicais é importante para estudos de sustentabilidade do manejo ﬂorestal, por dar subsídio adequado à recuperação do estoque de madeira. O objetivo do trabalho foi estimar o recrutamento após a colheita de madeira, empregando um modelo de rede neural artificial (RNA). A área de estudo está localizada na Floresta Nacional do Tapajós (55°00’ W, 2°45’ S), Pará. Em 64 ha da área de estudo, em 1979, foi realizada colheita intensiva de 72,5 m3 ha-1. Em 1981 foram instaladas, aleatoriamente, 36 parcelas permanentes de 50 m x 50 m. Essas parcelas foram mensuradas em 1982, 1983, 1985, 1987, 1992, 1997, 2007, 2010 e 2012. Para modelar o recrutamento foram consideradas as variáveis da subparcela-alvo e a sua vizinhança. As estimativas obtidas no treino e na generalização da RNA foram avaliadas pelas estatísticas: correlação () e raiz quadrada do erro quadrático médio (RQRQM), sendo obtido RQRQM 35,6% e 0,89. Foi possível modelar a tendência do recrutamento ao longo do tempo em ﬂorestas tropicais, após a colheita de madeira.

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“…A aplicabilidade das RNA's em relação à regressão para estudar povoamentos já foi destacada em espécies do gênero Eucalyptus (SILVA et al, 2009;BINOTI et al, 2015;LIMA et al, 2017;), Pinus (DIAMANTOPOULOU, 2005), Teca (VENDRUSCOLO et al, 2017), além de serem usadas no estudo em florestas nativas (REIS et al, 2016, REIS et al, 2018. Embora a técnica tenha eficiência comprovada, estudos envolvendo seu uso em plantios de mogno africano ainda são raros.…”

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Estimation of mortality and survival of individual trees after harvesting wood using artificial neural networks in the amazon rain forest

Cited by 46 publications

References 21 publications

Peer Review #3 of "California air resources board forest carbon protocol invalidates offsets (v0.1)"

Peer Review #3 of "California air resources board forest carbon protocol invalidates offsets (v0.1)"

Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest

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