Análise do Modelo Linear de Mistura Espectral na Avaliação de Incêndios Florestais no Parque Nacional do Araguaia, Tocantins, Brasil: Imagens EO-1/Hyperion e Landsat-7/ETM+

Silva, Juarez Antonio da; Pacheco, Admilson da Penha

doi:10.11137/2020_4_440_450

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Briefly, the reflectance of burned surfaces is the result of a mixture of exposed soil, unburned vegetation, ash, and coal that are present on the surface after the fire event. In the NIR, vegetation combustion significantly influences the spectral signature of the burned target, generally decreasing the reflectance and consequently increasing the difference in relation to unburned conditions [71,72]. The importance of NIR for discrimination of burned areas is explored in Lizundia-Loiola et al [73].…”

Section: Sensitivity and Separability Of Detection Based On Spectral ...mentioning

confidence: 99%

Evaluation of the Ability of SLSTR (Sentinel-3B) and MODIS (Terra) Images to Detect Burned Areas Using Spatial-Temporal Attributes and SVM Classification

Silva

Pacheco

Ruiz-Armenteros

et al. 2022

Forests

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Forest fires are considered one of the major dangers and environmental issues across the world. In the Cerrado biome (Brazilian savannas), forest fires have several consequences, including increased temperature, decreased rainfall, genetic depletion of natural species, and increased risk of respiratory diseases. This study presents a methodology that uses data from the Sea and Land Surface Temperature Radiometer (SLSTR) sensor of the Sentinel-3B satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Terra satellite to analyze the thematic accuracy of burned area maps and their sensitivity under different spectral resolutions in a large area of 32,000 km2 in the Cerrado biome from 2019 to 2021. The methodology used training and the Support Vector Machine (SVM) classifier. To analyze the spectral peculiarities of each orbital platform, the Transformed Divergence (TD) index separability statistic was used. The results showed that for both sensors, the near-infrared (NIR) band has an essential role in the detection of the burned areas, presenting high separability. Overall, it was possible to observe that the spectral mixing problems, registration date, and the spatial resolution of 500 m were the main factors that led to commission errors ranging between 15% and 72% and omission errors between 51% and 86% for both sensors. This study showed the importance of multispectral sensors for monitoring forest fires. It was found, however, that the spectral resolution and burning date may gradually interfere with the detection process.

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Section: Sensitivity and Separability Of Detection Based On Spectral ...mentioning

confidence: 99%

Evaluation of the Ability of SLSTR (Sentinel-3B) and MODIS (Terra) Images to Detect Burned Areas Using Spatial-Temporal Attributes and SVM Classification

Silva

Pacheco

Ruiz-Armenteros

et al. 2022

Forests

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“…As already mentioned, we note the presence of features that presented spectral behavior that was similar to the burned area (for example, low reflectance values in the NIR), which can be caused by topography shadows and changes in land cover not associated with fires, such as very humid soils. Therefore, it is recommended to take special care in regions where these characteristics and events occur close to the area that is affected by the fire, in addition to controlling the photointerpretation with the size of the samples of interest, especially in applications with sensors of different spatial and spectral resolutions [128]. Thus, as a future study in the study area, assessing the separability for different classes of land use and the influence of sample size may be a good alternative.…”

Section: Separability Analysismentioning

confidence: 99%

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

et al. 2021

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Forest fires threaten the population's health, biomass, and biodiversity, intensifying the desertification processes and causing temporary damage to conservation areas. Remote sensing has been used to detect, map, and monitor areas that are affected by forest fires due to the fact that the different areas burned by a fire have similar spectral characteristics. This study analyzes the performance of the k-Nearest Neighbor (kNN) and Random Forest (RF) classifiers for the classification of an area that is affected by fires in central Portugal. For that, image data from Landsat-8, Sentinel-2, and Terra satellites and the peculiarities of each of these platforms with the support of Jeffries–Matusita (JM) separability statistics were analyzed. The event under study was a 93.40 km2 fire that occurred on 20 July 2019 and was located in the districts of Santarém and Castelo Branco. The results showed that the problems of spectral mixing, registration date, and those associated with the spatial resolution of the sensors were the main factors that led to commission errors with variation between 1% and 15.7% and omission errors between 8.8% and 20%. The classifiers, which performed well, were assessed using the receiver operating characteristic (ROC) curve method, generating maps that were compared based on the areas under the curves (AUC). All of the AUC were greater than 0.88 and the Overall Accuracy (OA) ranged from 89 to 93%. The classification methods that were based on the kNN and RF algorithms showed satisfactory results.

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Análise de acurácia do mapeamento do uso e ocupação do solo utilizando imagens Sentinel-2 e CBERS-4 no entorno de reservatórios

Silva Júnior,

Silva Junior,

Almeida

et al. 2024

CeN

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A detecção de mudanças na cobertura do solo ajuda os formuladores de políticas a entender a dinâmica das mudanças ambientais para garantir o desenvolvimento sustentável no bioma Caatinga. Assim, a identificação de características espaciais por Sensoriamento Remoto surgiu como um importante aspecto de pesquisa e, dessa forma, metodologia adequada e eficiente para o mapeamento de cobertura do solo necessárias é fator preponderante. Neste estudo, os dados do satélite Sentinel-2 e CBERS-4 capturados pelos sensores MultiSpectral Instrument (MSI) e a Câmera Pancromática e Multiespectral (PAN), respectivamente, foram usados para a classificação e análise de acurácia para cinco classes de cobertura da terra no entorno de Barragens localizados no município de Belo Jardim, Pernambuco. O algoritmo KNN (K-ésimo vizinho mais próximo) com um valor de k=1 foi utilizado para o treinamento e classificação das imagens. As recentes imagens de alta resolução do programa europeu WorldCover foram utilizadas como imagem de referência espacial e temática. Após a análise por Matrix Contingência entre os mapas de cobertura do solo e os dados de referência, foram obtidos uma acurácia global de 57,4% para o MSI e 54,5% para o produto PAN. Os resultados obtidos mostraram que o MSI apresentou mapas de cobertura da terra mais satisfatórias do que os dados PAN. Por outro lado, para a classe de vegetação arbustiva para o produto PAN apresentou r de 0,5 enquanto o MSI de 0,47. Características espaciais e espectrais das imagens foram os principais causadores das variabilidades encontradas nos coeficientes de acurácia temática.

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Análise do Modelo Linear de Mistura Espectral na Avaliação de Incêndios Florestais no Parque Nacional do Araguaia, Tocantins, Brasil: Imagens EO-1/Hyperion e Landsat-7/ETM+

Abstract: Anuário do Instituto de Geociências -UFRJ www.anuario.igeo.ufrj.br 4

Cited by 3 publications

References 18 publications

Evaluation of the Ability of SLSTR (Sentinel-3B) and MODIS (Terra) Images to Detect Burned Areas Using Spatial-Temporal Attributes and SVM Classification

Evaluation of the Ability of SLSTR (Sentinel-3B) and MODIS (Terra) Images to Detect Burned Areas Using Spatial-Temporal Attributes and SVM Classification

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

Análise de acurácia do mapeamento do uso e ocupação do solo utilizando imagens Sentinel-2 e CBERS-4 no entorno de reservatórios

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