Análise de acurácia para o mapeamento de áreas queimadas utilizando uma cena VIIRS 1Km e classificação por Random Forest
DOI:
https://doi.org/10.26848/rbgf.v14.6.p3225-3240Resumo
A disponibilidade gratuita de dados de sensoriamento remoto em áreas atingidas por incêndios florestais em escala global oferece a oportunidade de geração sistemática de produtos terrestres de média resolução espacial, porém as conhecidas limitações de precisão é objeto de estudo em todo o mundo. Este artigo tem como objetivo analisar a acurácia da detecção de áreas queimadas utilizando o classificador Random Forest (RF) por meio de uma cena do sensor Radiômetro de Imagem Infravermelho Visível (VIIRS) (1Km) em quatro pontos da savana brasileira. Os resultados foram validados através dos produtos de referência espacial de áreas queimadas: Aq30m, Fire_cci e MCD64A1 por meio de uma abordagem estratificada possibilitando a amostragem dos dados no espaço e tempo. Os modelos de RF avaliados com seus parâmetros de entrada, em que, incluiu-se 400 árvores e um atributo, fornecendo uma taxa de erro abaixo de 4%. Os resultados mostraram que o mapeamento validado com o produto Aq30m apresentou importantes estimativas de Coeficiente de Sorensen-Dice enquanto a validação realizada entre os modelos globais, o MCD64A1 mostrou-se com maior exatidão (>50%) principalmente em feições de áreas queimadas de grandes proporções (> 200Km²). Em particular, a análise sugere que a validação de produtos de área queimada sempre deve estar ligada ao tempo mínimo da data dos dados de validação e o tamanho da área atingida pelo fogo. Os resultados mostram que esta abordagem é muito útil para ser usado para determinar áreas de floresta queimada.
Accuracy analysis for mapping burnt areas using a 1Km VIIRS scene and Random Forest classification
A B S T R A C T
The availability of remote sensing data with medium spatial resolution has offered several mapping possibilities for areas affected by forest fires on the Earth's surface. In this context, the analysis of sensor spatial accuracy limitations has been the subject of global research. The objective of this study was to analyze the mapping accuracy of the VIIRS sensor on board the NOAA satellite, using the Random Forest (RF) classifier for the detection of burned areas, in four points of the Chapada dos Veadeiros National Park - Goiás, inserted in the Brazilian savanna. The methodology consisted in validating the classification using the Sorensen-Dice coefficient (SD) in a stratified approach, using as reference the products: Aq30m, Fire_cci and MCD64A1. As a result, the RF models, included 400 trees and one attribute, with an error of less than 4%. Among the global models, the MCD64A1 presented a significant accuracy, greater than 50%, especially in features of burned areas greater than 200Km². Thus, the data suggest that the quality of accuracy of the validation process of mapping products for burned areas is associated with the minimum time interval of availability of validation data and the size of the area affected by fire. Based on this, the results show effectiveness in using the RF algorithm on medium spatial resolution images for fire detection in seasonally dry forests, such as the Cerrado.
Keywords: Cerrado, fires, Random Forest.
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