COMPARATIVE ANALYSIS OF LAND USE CLASSIFICATION WITH RANDOM FOREST ALGORITHM BASED ON MACHINE LEARNING USING IMAGES FROM THE BRAZILIAN SATELLITE AMAZONIA-1 IN THE CERRADO OF MINAS GERAIS-BRAZIL

Authors

Keywords:

Geoprocessing, Environmental monitoring, Free orbital images, Machine learning, thematic quality control

Abstract

The Cerrado is the second largest biome in Brazil, with an area of ​​over 200 million hectares and distributed across thirteen states of the Federation. It is a forested area of ​​great importance for the biodiversity of Brazilian fauna and flora. However, the transformation of its vegetation has been accelerated in recent years, mainly due to the need for agricultural expansion and the search for pastures for cattle raising. In the specific case of the State of Minas Gerais, Brazil, the Cerrado Biome occupies over 54% of the territory and covers regions of great importance for agricultural productivity. In view of this, the research aims to perform a qualitative evaluation of thematic mapping based on the supervised classification of free orbital images (generated by the new Brazilian satellite Amazônia-1), which is considered a viable strategy for understanding the evolution of changes in land use and land cover. This research also analyzed the alerts for deforestation polygons generated by the Real-Time Deforestation Detection System (DETER) of the National Institute for Space Research (INPE), as well as the application of machine learning algorithms and techniques that can ensure the availability of this information with greater speed and reliability. The results achieved in terms of thematic quality, through the Kappa index, demonstrated great viability for the use of the methodologies used.

Author Biographies

Marcelo Antonio Nero, IGC-UFMG

IGC-UFMG

MSc, UFMG - Universidade Federal de Minas Gerais

Graduated in Military Sciences with an emphasis on Social Defense (2007) and Specialization in Public Security (2022), from the Military Police of the State of Minas Gerais; Environmental Engineering from Faculdade Santa Rita (2014); Postgraduate in Environmental Management and Law (2019) from the Research and Postgraduate Center of the Military Police Academy (CPP) and master's degree (2023) from the Postgraduate Program in Analysis and Modeling of Environmental Systems (AMSA) of the Institute of Geosciences (IGC) at the Federal University of Minas Gerais (UFMG). He is currently a doctoral candidate at PPGAMSA/IGC/UFMG. He is a senior officer (Major) of the Military Police of the State of Minas Gerais, where he serves as Head of the Intelligence and Planning and Operational Employment Section of the PMMG Environmental Policing Command. He is an advisor to the Environmental Policy Council of the State of Minas Gerais. He worked for 4 years in the area of #8203;#8203;Public Security Intelligence (Intelligence Activity Manager) with specialization in this activity. He has administrative experience in Logistics, Budget and Finance, as well as Human Resources Management. He has the Professional Merit, Military Merit and Ensign Tiradentes medals, awarded by the Military Police of Minas Gerais. He carried out operational activities as an Environmental Policing Platoon Command between 2009 and 2012.

PhD., UFMG - Universidade Federal de Minas Gerais

He holds a degree in Cartographic Engineering from the State University of Rio de Janeiro (1982), a Master's degree in Remote Sensing from the National Institute for Space Research (1994), and a PhD in Applied Computing from the National Institute for Space Research (2008). He was Vice Coordinator of the Postgraduate Program in Analysis and Modeling of Environmental Systems at UFMG (2016 to 2018). He was Head of the Cartography Department at UFMG for one term and Deputy Head of the Cartography Department for four terms. He is currently an associate professor in the Cartography Department at the Federal University of Minas Gerais. He has experience in the areas of Earth Sciences, with emphasis on Topography, Geodesy, Cartography, Remote Sensing, Geoprocessing, Digital Earth Image Processing, Radar Image Interferometry, Digital Terrain Models, Analysis and Modeling of Environmental Systems.

PhD., PPG em Ciências Geodésicas e Tecnologias da Geoinformação-UFPE

PhD in Geosciences from UFPE in the area of ​​Applied Geophysics. Post-Doctorate in Remote Sensing. Works in research of applied methods in orbital multispectral Remote Sensing, LiDAR, RPA, in the environmental area and Cartography.

References

AHMAD, W. M.; Amir, W.; GHAZALI, F. M. M.; YAQOOB, M. A. Basic Statistical Analysis Using RStudio Software. Penerbit USM, 2023. Available in: https://books.google.com.br/books?hl=pt-BR&lr=&id=YT3GEAAAQBAJ&oi=fnd&pg=PT9&dq=Basic+Statistical+Analysis+Using+RStudio+Software&ots=BKJHXGCkTd&sig=PmESLJpxj4cjqsGzHEX01D6ahTw. Accessed in: oct. 27th, 2024.

ALBERTINI, Cinzia et al. Exploring the use of random forest classifier with Sentinel-2 imagery in flooded area mapping. In: Geographical Information Science. Elsevier, 2024. p. 521-542. Available in: https://iopscience.iop.org/article/10.1088/1742-6596/2512/1/012012/pdf Accessed in: oct. 27th, 2024.

BARBOSA, I. M.; FERREIRA, M. G. V.; JÚNIOR, M. F. C. The Application of Machine learning to Amazonia-1 satellite power subsystem telemetry prediction. In: Journal of Physics: Conference Series. IOP Publishing, 2023. p. 012012. Available in: https://iopscience.iop.org/article/10.1088/1742-6596/2512/1/012012/pdf Accessed in: aug. 11st, 2023.

BOSCOLO, H. K. et al. Contributions of the Brazil’s National Institute for Space Research (INPE) to emergency response in the International Space and Major Disasters Charter. Discover Applied Sciences, v. 6, n. 4, p. 178, 2024. Available in: https://link.springer.com/content/pdf/10.1007/s42452-024-05831-3.pdf. Accessed in: oct. 25th, 2024.

BRAGAGNOLO, L Redes neurais totalmente convolucionais aplicadas à identificação de focos de desmatamento. 2021. Dissertação. Mestrado. 111p. Available in: https://rd.uffs.edu.br/bitstream/prefix/5231/1/BRAGAGNOLO.pdf Accessed in: oct. 27th, 2024.

BRASIL, I. B. G. E. Instituto Brasileiro de Geografia e Estatística. Área territorial brasileira 2022. Rio de Janeiro: IBGE, 2023.

BRASIL. Lei no 12.651, de 25 de maio de 2012. Available in: http://www.planalto.gov.br/ccivil_03/_Ato2011-2014/2012/Lei/L12651.htm. Accessed in: oct. 27th, 2024.

BREIMAN, L. Random forests. Machine learning, v. 45, p. 5-32, 2001. Available in: https://link.springer.com/content/pdf/10.1023/a:1010933404324.pdf. Accessed in: oct. 27th, 2024.

CONGALTON, R. G.; GREEN, K. Assessing the accuracy of remotely sensed data: principles and practices. CRC press, 2019. Available in: https://www.taylorfrancis.com/books/mono/10.1201/9780429052729/assessing-accuracy-remotely-sensed-data-russell-congalton-kass-green. Accessed in: oct. 27th, 2024.

CONGEDO, L. Semi-Automatic Classification Plugin: A Python tool for the download and processing of remote sensing images in QGIS. Journal of Open Source Software, v. 6, n. 64, p. 3172, 2021. Available in: https://doi.org/10.21105/joss.03172. Accessed in: jul. 27th, 2023.

DOMINGUEZ, David et al. Forecasting amazon rain-forest deforestation using a hybrid machine learning model. Sustainability, v. 14, n. 2, p. 691, 2022. Available in: https://www.mdpi.com/2071-1050/14/2/691/pdf Accessed in: oct. 27th, 2024.

ESA - European Space Agency. ESA SNAP GPT. Available in: https://step.esa.int/main/download/snap-download/. Accessed in: jul. 27th, 2023.

FARIA, P. D.; PARGA, J. P. F. A. Introdução à linguagem R. 2020. Available in: https://www.academia.edu/download/67777575/Introducao_R.pdf. Accessed in: jul. 27th, 2023.

FERNANDES, L. C.; ELMIRO, M. A. T.; NERO, M. A.; TEMBA, P. C. The use of remote sensing techniques by Modis (MCD45A1) images using to identify and evaluate burned areas in the metropolitan region of Belo Horizonte - MG, Brazil. Sustentabilidade em Debate, v. 11, p. 143-172, 2020. Available in: https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=21777675&AN=145748519&h=tQAxeyXDlvG1lzXgAyKIRzdp5YRK0KgYgdlm%2FyiJqc%2BWtFlbwubmN4CgEnpS6rOxYhgrdKvLLW0NZgm75hPkaw%3D%3D&crl=c Accessed in: oct. 27th, 2024.

FERNANDES, L. C. et al. Fire risk modeling using artificial neural networks. In: EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization. Springer International Publishing, 2019. p. 938-948. Available in: https://link.springer.com/chapter/10.1007/978-3-319-97773-7_81 Accessed in: oct. 27th, 2024.

FERNANDES, L. C.; NERO, M. A.; TEMBA, P. C.; SOARES FILHO, B. S. Modelagem de risco de incêndios florestais: uma visão geral. Sodebrás, v. 13, n. 147, p. 79-84, 2018. Available in: https://sodebras.com.br/edicoes/N147.pdf Accessed in: oct. 27th, 2024.

FERNANDO, W. Ashane M.; SENANAYAKE, I. P. Developing a two-decadal time-record of rice field maps using Landsat-derived multi-index image collections with a random forest classifier: A Google Earth Engine based approach. Information Processing in Agriculture, v. 11, n. 2, p. 260-275, 2024. Available in: https://www.sciencedirect.com/science/article/pii/S2214317323000197 Accessed in: oct. 27th, 2024.

FINEGOLD, Y.; ORTMANN, A.; LINDQUIST, E.; D’ANNUNZIO, R.; SANDKER, M. Map accuracy assessment and area estimation: a practical guide. Rome: Food and Agriculture Organization of the United Nations, 2016. Available in: https://www.sidalc.net/search/Record/dig-fao-it-20.500.14283-I5601E/Description. Accessed in: oct. 27th, 2024.

FLEISS, J. L.; COHEN, J.; EVERITT, B. S. Large sample standard errors of kappa and weighted kappa. Psychological Bulletin, v. 72, n. 5, p. 323–327, 1969. Available in: https://psycnet.apa.org/journals/bul/72/5/323.pdf?casa_token=WJwbEoVRIREAAAAA:SS-Gy1MKAeKEskOuG14s07MqnvCK6LuGSGC3KW1nYSXRTkFA7JD9xpnTURMgYcx7yE8poXUqkMshvp_COkSNXUwH. Accessed in: oct. 27th, 2024.

FOODY, G. M. Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sensing of Environment, v. 239, n. January, p. 111630, 2020. Available in: : https://www.sciencedirect.com/science/article/pii/S0034425719306509?casa_token=-wRo2TchTlsAAAAA:M6EWNjGuXexV1HibF4syHPmMu5JdESBXonMeLqWp3pnvOFOmvCb4Xai75nXJouWZiJRMnFQh8b0. Accessed in: oct. 27th, 2024.

GAO, Fan et al. A ConvNets-based approach for capturing the heterogeneity of spatial domain in parallel geoprocessing. International Journal of Digital Earth, v. 17, n. 1, p. 2398066, 2024. Available in: https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2398066. Accessed in: oct. 27th, 2024.

GARCÍA PEDREROS, Julián Guillermo; LAGÜELA LÓPEZ, Susana; RODRÍGUEZ MARTÍN, Manuel. Spatial Models of Solar and Terrestrial Radiation Budgets and Machine Learning: A Review. Remote Sensing, v. 16, n. 16, p. 2883, 2024. Available in: https://www.mdpi.com/2072-4292/16/16/2883. Accessed in: oct. 27th, 2024.

HAERY, Sana; MAHPOUR, Alireza; VAFAEINEJAD, Alireza. Forecasting urban travel demand with geo-AI: a combination of GIS and machine learning techniques utilizing uber data in New York City. Environmental Earth Sciences, v. 83, n. 20, p. 594, 2024. Available in: https://idp.springer.com/authorize/casa?redirect_uri=https://link.springer.com/article/10.1007/s12665-024-11900-y&casa_token=JlseVIRH5_8AAAAA:AYHEF2JsvmvbXjvd5QDbDG9H_1CoD-b_AL0OSeH3KmqFf_tOsIY7XJF5_FPCzsTAlTd3auIU8SVIq0KtxQ. Accessed in: oct. 27th, 2024.

INSTITUTO BRASILEIRO DE FLORESTAS - IBF. Bioma Cerrado. Available in: https://www.ibflorestas.org.br/bioma-cerrado. Accessed in: oct. 27th, 2024.

INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS (INPE). AMAZONIA 1: Descritivo da Missão e do Satélite. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2021. Available in: https://www.gov.br/inpe/pt-br/programas/amazonia1/repositorio-de-arquivos/a800000-ddd-001_v01-amazonia_1-descritivo_da_missao_e_do_satelite.pdf. Accessed in: oct. 27th, 2024.

INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS (INPE). Coordenação Geral de Observação da Terra – DETER. Available in: http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/deter/deter. Accessed in: aug. 11st, 2023.

KIYOHARA, B. H.; SANO, E. E. Avaliação de dados polarimétricos e de atributos de textura em imagens SAR para discriminar a floresta secundária em uma área de domínio de floresta amazônica. Ciência Florestal, v. 33, n. 2, p. e71235-e71235, 2023. Available in: https://periodicos.ufsm.br/cienciaflorestal/article/download/71235/61102 Accessed in: aug. 11st, 2023.

KOSTRZEWA, A. Geoprocessing of archival aerial photos and their scientific applications: A review. Reports on Geodesy and Geoinformatics, v. 118, n. 1, p. 1-16, 2024. Available in: https://intapi.sciendo.com/pdf/10.2478/rgg-2024-0010. Accessed in: oct. 27th, 2024.

LANDIS, J. R.; KOCH, G. G. The Measurament of Observer Agreement of Categorical Data. Biometrics, v. 33, n. 1, p. 159–174, 1977. Available in: https://pdfs.semanticscholar.org/7e73/43a5608fff1c68c5259db0c77b9193f1546d.pdf. Accessed in: oct. 27th, 2024.

LEMENKOVA, P. Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh. Water, v. 16, n. 8, p. 1141, 2024a. Available in: https://www.mdpi.com/2073-4441/16/8/1141?utm_source=releaseissue&utm_medium=email&utm_campaign=releaseissue_water&utm_term=doilink29&recipient=aguadoc@gmail.com&subject=Water%2C%20Volume%2016%2C%20Issue%208%20%28April-2%202024%29%20Table%20of%20Contents&campaign=ReleaseIssue Accessed in: oct. 27th, 2024.

LEMENKOVA, P. Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique. Coasts, v. 4, n. 1, p. 127-149, 2024b. Available in: https://www.mdpi.com/2073-4441/16/8/1141?utm_source=releaseissue&utm_medium=email&utm_campaign=releaseissue_water&utm_term=doilink29&recipient=aguadoc@gmail.com&subject=Water%2C%20Volume%2016%2C%20Issue%208%20%28April-2%202024%29%20Table%20of%20Contents&campaign=ReleaseIssue Accessed in: oct. 27th, 2024.

LILLESAND, T. M.; KIEFER, R. W. Remote Sensing and Image Interpretation, John Wiley & Sons, inc., 2015. Available in: https://books.google.com.br/books?id=AFHDCAAAQBAJ&printsec=frontcover&hl=pt-BR#v=onepage&q&f=false Accessed in: oct. 27th, 2024.

LLANO, X. C. AcATaMa - QGIS plugin for Accuracy Assessment of Thematic Maps, 2024, version 24.10-Noosa. Available in: https://plugins.qgis.org/plugins/AcATaMa/version/24.10/. Accessed in: oct. 24th, 2024.

MENDES, R. M.; MATRICARDI, E. A. T.; COUTO JUNIOR, A. F.; PASSO, D. P.; LUIZ, C. H. P.; MIGUEL, E. P. REGULARIDADE AMBIENTAL DAS ÁREAS DE RESERVA LEGAL DO CERRADO BRASILEIRO. Caminhos de Geografia, Uberlândia, v. 23, n. 85, p. 330–349, 2022. DOI: 10.14393/RCG238558017. Available in: https://seer.ufu.br/index.php/caminhosdegeografia/article/view/58017. Accessed in: oct. 27th, 2024.

MESSIAS, C. G. et al. Trabalho de campo em fitofisionomias florestais e não florestais de Roraima, no âmbito dos projetos PRODES e DETER. Revista do Departamento de Geografia, São Paulo, Brasil, v. 44, p. e217450, 2024. DOI:10.11606/eISSN.2236-2878.rdg.2024.217450. Available in: https://www.revistas.usp.br/rdg/article/view/217450. Accessed in: oct. 25th, 2024.

MICROSOFT, 2021. Microsoft Excel 365.

MONSERUD, R. A.; LEEMANS, R. Comparing global vegetation maps with the Kappa statistic. Ecological Modelling, v. 62, n. 4, p. 275–293, 1992. Available in: https://www.sciencedirect.com/science/article/pii/030438009290003W. Accessed in: oct. 25th, 2024.

MOTA JÚNIOR, E. R.; TRENTIN, C. B.; SILVA, E. B. da. Expansão de fronteiras agrícolas e transformações no uso e cobertura da terra em áreas circunvizinhas a terras indígenas no Sudeste Mato-grossense. Caminhos de Geografia, Uberlândia, v. 24, n. 91, p. 272–286, 2023. DOI: 10.14393/RCG249162476. Disable in: https://seer.ufu.br/index.php/caminhosdegeografia/article/view/62476. Accsseced in: aug. 11st, 2023.

MOURA, D. M. B. M. B; OLIVEIRA, I. J. Mapeamento da favorabilidade hídrica na Alta Bacia Hidrográfica do Rio Meia Ponte, como subsídio às políticas de segurança hídrica de Goiânia (GO). Revista Geográfica Acadêmica, [S. l.], v. 18, n. 1, p. 136–162, 2024. Available in: https://revista.ufrr.br/rga/article/view/8123. Accessed in: oct. 25th, 2024.

MUELLER, C. C.; MARTHA JÚNIOR, G. B. A agropecuária e o desenvolvimento socioeconômico recente do cerrado. Simpósio Nacional Cerrado, v. 9, p. 1-41, 2008. Available in: https://revista.ufrr.br/rga/article/view/8123. Accessed in: oct. 25th, 2024.

NASCIMENTO, R. F. F. et al. An assessment of the support vector machine for a CBERS-2 CCD image classification: a case study of a tropical reservoir in Brazil. Revista Brasileira de Cartografia, v. 65, p. 431-439, 2013. Available in: https://seer.ufu.br/index.php/revistabrasileiracartografia/article/download/44796/23807/185230. Accessed in: oct. 25th, 2024.

NERO, M. A.; FERNANDES, L. C.; FRANCO, D. S. M.; Temba P. C. Mapping Forest fire risk areas near conservation units in the metropolitan region of Belo Horizonte-MG and your application in the public politic. Sodebrás, v. 16, n. 191, p. 115-130, 2021. Available in: https://sodebras.com.br/edicoes/N191.pdf Accessed in: oct. 25th, 2024.

NORWAY'S INTERNATIONAL CLIMATE AND FOREST INITIATIVE SATELLITE DATA PROGRAM NICFI - Iniciativa Internacional do Clima e Florestas da Noruega. Available in: https://www.nicfi.no/. Accessed in: aug. 11st, 2023.

OLIVEIRA, A. C.; KÖRTING, T. S. A multi-temporal dataset for mapping burned areas in the Brazilian Cerrado using time series of remote sensing imagery. Big Earth Data, p. 1–32, 17 fev. 2025. Available in: https://doi.org/10.1080/20964471.2025.2463731 Accessed in: aug. 27th, 2025.

OLIVEIRA, A.; NERO, M. A. Application of fuzzy logic in prediction of fire in João Pessoa City-Brazil. In: Geo-Informatics in Resource Management and Sustainable Ecosystem: International Symposium, GRMSE 2013, Wuhan, China, November 8-10, 2013, Proceedings, Part II 1. Springer Berlin Heidelberg, 2013. p. 323-334. Available in: https://link.springer.com/chapter/10.1007/978-3-642-41908-9_33 Accessed in: aug. 11st, 2023.

OLIVEIRA, A. L. S.; NERO, M. A.; TAVARES JUNIOR, J. R.; CANDEIAS, A. L. B.; NÓBREGA, R. A. A. Comparação e validação da modelagem espacial de riscos de incêndios considerando diferentes métodos de predição. Boletim de Ciências Geodésicas, v. 23, p. 556-577, 2017. Available in: https://www.scielo.br/j/bcg/a/cCmZ54NR87nzKQNGCT7Qsxr/?lang=pt Accessed in: aug. 11st, 2023.

OLIVEIRA, W. L.; MORAIS, B. R.; NERO, M. A. Avaliação da eficiência dos classificadores de imagem de sensoriamento remoto com variação na resolução espacial. InterEspaço: Revista de Geografia e Interdisciplinaridade, v. 8, e202204, 2022. Available in: http://dx.doi.org/10.18764/2446-6549.e202204>. Accessed in: aug. 11st, 2023.

OLOFSSON, P.; FOODY, G. M.; HEROLD, M.; STEHMAN, S. V.; WOODCOCK, C. E.; WULDER, M. A. Good practices for estimating area and assessing accuracy of land change. Remote sensing of Environment, v. 148, p. 42-57, 2014. Available in: https://www.sciencedirect.com/science/article/pii/S0034425714000704?casa_token=QAa7O0_g24EAAAAA:Z-YCO5aq0_frB4Jw9M676RGg7jkAX7nY1YU8rDdjKdtYuUgHTDiQG_sdOFZZQmuBDXGMZsHZp-o. Accessed in: aug. 11st, 2023.

PAZ, A. M. M. O povo indígena Yanomami e a sua luta contra a vulnerabilidade. 2023. Tese de Doutorado. Available in: https://bibliotecadigital.fgv.br/dspace/bitstream/handle/10438/33843/TESE%20PARA%20ENVIO.pdf?sequence=1 Accessed in: aug. 11st, 2023.

PLANET TEAMl. Planet application program interface: In space for life on Earth. San Francisco, 2023. Available in: https://www.planet.com/products/basemap/ Accessed in: oct. 25th, 2023.

QGIS. QGIS versão “3.16.5 Hannover”. 2023. Available in: https://qgis.org/en/site/forusers/alldownloads.html. Accessed in: oct. 23th, 2023.

RAMOS, A. W. P.; AZEVEDO, U. R.; XAVIER, F. V.; NEVES, S. M.A. S.; GALVANIN, E. A. S. Padrões de desmatamento nos biomas Amazônia, Cerrado e Pantanal na Bacia Hidrográfica do Alto Paraguai, Mato Grosso - Brasil. Revista Geográfica Acadêmica, [S. l.], v. 17, n. 2, p. 136–158, 2023. Available in: https://revista.ufrr.br/rga/article/view/7907. Accessed in: oct. 25th, 2024.

R CORE TEAM. R: Uma linguagem e ambiente para computação estatística. Viena, Áustria: R Foundation for Statistical Computing. 2021.

RIBEIRO, José Felipe; WALTER, Bruno Machado Teles. As principais fitofisionomias do bioma Cerrado. Cerrado: ecologia e flora, v. 1, p. 151-212, 2008. Available in: https://www.webambiente.cnptia.embrapa.br/webambiente/wiki/lib/exe/fetch.php?media=webambiente:ribeirowalter2008.fitofisionomias.pdf Accessed in: oct. 27th, 2024.

RStudio Team. BostonRStudio: Integrated Development for R. RStudio, PBC, 2020. Available in: http://www.rstudio.com Accessed in: oct. 27th, 2024.

SANTOS, M. M. A Política externa do programa CBERS nos governos Dilma e Bolsonaro. Brazilian Journal of Latin American Studies, v. 21, n. 42, p. 129-150, 2022. Available in: https://doi.org/10.11606/issn.1676-6288.prolam.2022.192368. Accessed in: oct. 25th, 2024.

SANTOS, A. R. S.; PELUZIO, T. M. O.; SAITO, N. S. Spring 5.1.2 Passo a passo: Aplicações práticas. Alegre, ES: CAUFES, 2010. 153p. Available in: https://www.mundogeomatica.com/livros/spring5x/livrospring512passopassoaplicacaopratica.pdf Accessed in: oct. 25th, 2024.

SILVA JÚNIOR, J. A.; SILVA JÚNIOR, U. J.; PACHECO, A. P. Análise de acurácia para o mapeamento de áreas queimadas utilizando uma cena VIIRS 1 Km e classificação por Random Forest. Revista Brasileira de Geografia Física, v. 14, n. 6, p. 3225-3240, 2021. Available in: https://www.academia.edu/download/114166978/josicleda_2C_249396-198703-1-CE.pdf_filename_UTF-8josicleda_2C_249396-198703-1-CE.pdf. Accessed in: oct. 25th, 2024.

SILVA JÚNIOR, J. A. et al. Comparative Analysis between Remote Sensing Burned Area Products in Brazil: A Case Study in an Environmentally Unstable Watershed. Fire, v. 7, n. 7, p. 238, 2024. Available in: https://www.mdpi.com/2571-6255/7/7/238. Accessed in: oct. 25th, 2024.

Downloads

Published

01/08/2025

How to Cite

Nero, M. A., Oliveira, C. F. de, Elmiro, M. A. T., & Tavares Júnior, J. R. (2025). COMPARATIVE ANALYSIS OF LAND USE CLASSIFICATION WITH RANDOM FOREST ALGORITHM BASED ON MACHINE LEARNING USING IMAGES FROM THE BRAZILIAN SATELLITE AMAZONIA-1 IN THE CERRADO OF MINAS GERAIS-BRAZIL. REVISTA GEOGRÁFICA ACADÊMICA, 19(1), 164–186. Retrieved from http://revista.ufrr.br/rga/article/view/8519

Issue

Vol. 19 No. 1 (2025): Revista Geográfica Acadêmica

Section

Artigos

License

Copyright (c) 2025 REVISTA GEOGRÁFICA ACADÊMICA

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A RGA é detentora dos direitos autorais publicados, não sendo permitido que o mesmo conteúdo seja publicado em demais periódicos.