Floresta e Ambiente
https://floram.org/article/doi/10.1590/2179-8087.028317
Floresta e Ambiente
Original Article Forest Management

Estimates of Deforestation Rates in Rural Properties in the Legal Amazon

Fabrício Assis Leal; Eder Pereira Miguel; Eraldo Aparecido Trondoli Matricardi

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Abstract

Abstract: This study aimed to assess the potential of artificial neural networks (ANN) as a tool to estimate deforestation rates in the municipality of São Félix do Xingu, PA, Brazil. The following input variables were used: deforestation rate until 2014, slope, altitude, Euclidean distance to roads and rivers, permanent preservation area (PPA), and property area. A total of 2,800 properties were used, of which 2,000 were used for training and 800 for validation of the networks. The input layer included nine neurons: six as quantitative variables and three as categorical variables. The output layer included a single neuron - the deforestation rate. The training results indicated high correlation (r = 0.92) and root mean square error (RMSE) of 12.4%. Validation of the model estimated RMSE = 12.9% and r = 0.91. The study results evidenced the high potential of ANN as a tool to estimate farm deforestation rates.

Keywords

deforestation, artificial intelligence, validation

References

Almeida LM, Ludermir TB. A multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks. Neurocomputing 2010; 73(7-9): 1438-1450. 10.1016/j.neucom.2009.11.007

Alvares CA, Stape JL, Sentelhas PC, Moraes Gonçalves JL, Sparovek G. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift 2013; 22(6): 711-728. 10.1127/0941-2948/2013/0507

Baldi P, Sadowski P. A theory of local learning, the learning channel, and the optimality of backpropagation. Neural Networks 2016; 83: 51-74. 10.1016/j.neunet.2016.07.006

Barber CP, Cochrane MA, Souza CM Jr, Laurance WF. Roads, deforestation, and the mitigating effect of protected areas in the Amazon. Biological Conservation 2014; 177: 203-209. 10.1016/j.biocon.2014.07.004

Binoti MLMS, Binoti DHB, Leite HG. Aplicação de redes neurais artificiais para estimação da altura de povoamentos equiâneos de eucalipto. Revista Árvore 2013; 37(4): 639-645. 10.1590/S0100-67622013000400007

Cartwright H. Using artificial intelligence in chemistry and biology: a practical guide. Boca Raton: CRC Press; 2008.

Chen WC, Tseng LY, Wu C-S. A unified evolutionary training scheme for single and ensemble of feedforward neural network. Neurocomputing 2014; 143: 347-361. 10.1016/j.neucom.2014.05.057

Cybenko G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems 1989; 2: 303-314. 10.1007/BF02551274

Draper NR, Smith H. Applied regression analysis. 3rd ed. New York: John Wiley e Sons; 1998.

Egrioglu EA, Yolcu U, Aladag CH, Bas E. Recurrent multiplicative neuron model artificial neural network for non-linear time series forecasting. Procedia: Social and Behavioral Sciences 2014; 109: 1094-1100. 10.1016/j.sbspro.2013.12.593

Gürüler H, Balli S, Yeniocak M, Göktaş O. Estimation the properties of particleboards manufactured from vine prunings stalks using artificial neural networks. Mugla Journal of Science and Technology 2015; 1(1): 24-33. 10.22531/muglajsci.209996

Haykin S. Redes neurais: princípios e prática. 2nd ed. Porto Alegre: Bookman; 2001.

Heaton J. Programming neural networks with Encog 3 in Java. 2nd ed. St. Louis: Heaton Research; 2011.

Heidari E, Sobati MA, Movahedirad S. Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN). Chemometrics and Intelligent Laboratory Systems 2016; 155: 73-85. 10.1016/j.chemolab.2016.03.031

Instituto Brasileiro de Geografia e Estatística - IBGE. Estimativas da população residente no brasil e unidades da federação, com data de referência em 1º de julho de 2015. Rio de Janeiro; 2015.

Instituto Nacional de Pesquisas Espaciais - INPE. Prodes digital. São José dos Campos; 2017.

Kawakubo FS, Morato RG, Luchiari A. Mapeamento do desmatamento em São Félix do Xingú utilizando composição colorida multitemporal de imagens frações sombra. Revista da ANPEGE 2013; 9(11): 119-133. 10.5418/RA2013.0911.0010

Köppen W. Das geographische system der Klimate. In: Köppen W, Geiger R, editors. Handbuch der Klimatologie. Berlin: Gebrüder Bornträger; 1936. Band 1. p. 1-44, part C.

Leal FA, Miguel EP, Matricardi EAT, Pereira RS. Redes neurais artificiais na estimativa de volume em um plantio de eucalipto em função de fotografias hemisféricas e número de árvores. Revista Brasileira de Biometria 2015; 33(2): 233-249.

Martins ER, Binoti MLMS, Leite HG, Binoti DHB, Dutra GC. Configuração de redes neurais artificiais para estimação do afilamento do fuste de árvores de eucalipto. Revista Brasileira de Ciências Agrárias 2016; 11(1): 33-38. 10.5039/agraria.v11i1a5354

Menezes CRC, Monteiro MA, Galvão IMF, editors. Zoneamento ecológico-econômico das zonas Leste e Calha Norte do estado do Pará. 3 vol. Belém: NGPR; 2010.

Miguel EP, Rezende AV, Leal FA, Matricardi EAT, Vale AT, Pereira RS. Redes neurais artificiais para a modelagem do volume de madeira e biomassa do cerradão com dados de satélite. Pesquisa Agropecuária Brasileira 2015; 50(9): 829-839. 10.1590/S0100-204X2015000900012

Oliveira-Esquerre KP, Mori M, Bruns RE. Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis. Brazilian Journal of Chemical Engineering 2002; 19(4): 365-370. 10.1590/S0104-66322002000400002

Richards PD, VanWey L. Farm-scale distribution of deforestation and remaining forest cover in Mato Grosso. Nature Climate Change 2016; 6: 418-425. 10.1038/nclimate2854

Riedmiller M, Braun H. A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE International Conference on Neural Networks; 1993; San Francisco. New York: IEEE; 1993. p. 586-591.

Schmink M, Wood CH. Conflitos sociais e a formação da Amazônia. Belém: Ed. UFPA; 2012.

Shapiro SS, Wilk M. An analysis of variance test for normality (complete samples). Biometrika 1965; 52(3-4): 591-611. 10.2307/2333709

Shiblee M, Chandra B, Kalra PK. Learning of geometric mean neuron model using resilient propagation algorithm. Expert Systems with Applications 2010; 37(12): 7449-7455. 10.1016/j.eswa.2010.04.018

Silveira CS, Silva VV. Dinâmicas de regeneração, degeneração e desmatamento da vegetação provocadas por fatores climáticos e geomorfológicos: uma análise geoecológica através de SIG. Revista Árvore 2010; 34(6): 1025-1034. 10.1590/S0100-67622010000600008

StatSoft. Statistica: data analysis software system, versão 7. Tulsa; 2007.

Wang S, Jiang Y, Chung F-L, Qian P. Feedforward kernel neural networks, generalized least learning machine, and its deep learning with application to image classification. Applied Soft Computing 2015; 37: 125-141. 10.1016/j.asoc.2015.07.040

Wilcoxon F. Individual comparisons by ranking methods. Biometrics Bulletin 1945; 1(6): 80-83.
 

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