Prediction of Burned Areas Using the Random Forest Classifier in the Minas Gerais State-Reference-Cited by-同舟云学术

Prediction of Burned Areas Using the Random Forest Classifier in the Minas Gerais State

Published:2020 Issue:3 Volume:27 Page:
ISSN:2179-8087
Container-title:Floresta e Ambiente
language:
Short-container-title:Floresta Ambient.

Author:

Santos Eliana Elizabet dos¹^ORCID,Sena Nathalie Cruz¹^ORCID,Balestrin Diego¹^ORCID,Fernandes Filho Elpidio Inácio¹^ORCID,Costa Liovando Marciano da¹^ORCID,Zeferino Leiliane Bozzi¹^ORCID

Affiliation:

1. Universidade Federal de Viçosa, Brasil

Publisher

FapUNIFESP (SciELO)

Subject

Forestry

Link

http://www.scielo.br/pdf/floram/v27n3/2179-8087-floram-27-03-e20180115.pdf

Reference19 articles.

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2. Modelling of fire count data: fire disaster risk in Ghana;Boadi C;SpringerPlus,2015

3. Ecological models and Data in R;Bolker BM,2008

4. A stochastic forest fire growth model;Boychuk D;Environmental and Ecological Statistics,2009

5. Avaliação dos incêndios florestais ocorridos no Parque Nacional da Serra da Canastra - Minas Gerais;Magalhães SR;Cerne,2012

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2. Use of machine learning as a tool for determining fire management units in the brazilian atlantic forest;Anais da Academia Brasileira de Ciências;2023

3. Assessment of k-Nearest Neighbor and Random Forest Classifiers for Mapping Forest Fire Areas in Central Portugal Using Landsat-8, Sentinel-2, and Terra Imagery;Remote Sensing;2021-04-01

4. Fire in the Atlantic Rainforest: an analysis of 20 years of fire foci distribution and their social-ecological drivers;Geocarto International;2021-03-23