Claudia A Silva | Universidade de Brasília - UnB (original) (raw)

Papers by Claudia A Silva

Research paper thumbnail of Análise Qualitativa do Desmatamento na Floresta Amazonica

Anuário do Instituto de Geociências

A mitigação de mudanças climáticas e preservação de ecossistemas depende da redução do desmatamen... more A mitigação de mudanças climáticas e preservação de ecossistemas depende da redução do desmatamento e degradação de florestas tropicais. O objetivo deste estudo foi analisar imagens de radar, ópticas e termais para identificar desmatamentos por corte raso no período de 2016 a 2018 em uma área localizada no arco de desmatamento da Amazônia. Foram utilizadas imagens de radar em bandas X (satélite COSMO-SkyMed) e C (satélite SENTINEL-1A), índices de vegetação por diferença normalizada (NDVI), índices de umidade por diferença normalizada (NDMI) e temperaturas da superfície terrestre (LST) (satélite Landsat-8). As áreas com evidências de antropismo mapeadas com base nas imagens do satélite COSMO-SkyMed no município de Novo Progresso (PA), período de 2016 a 2018, foram utilizadas como máscara inicial. Imagens de radar identificaram, com boa precisão relativa, as épocas e as áreas de desmatamento. NDVI e NDMI evidenciaram, respectivamente, quedas nas atividades fotossintéticas e nos níveis de biomassa nas áreas de desmatamento identificadas. Já a LST foi mais elevada nas áreas de rebrota em relação à vegetação densa. A análise do potencial de imagens de radar, ópticos e termais mostrou elevada relevância na detecção de desmatamento por corte raso em ambiente florestal úmido.

Research paper thumbnail of Fire Occurrences and Greenhouse Gas Emissions from Deforestation in the Brazilian Amazon

Remote Sensing MDPI, 2021

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Research paper thumbnail of Near-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks

European Journal of Remote Sensing, 2022

Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective ... more Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective in the rainy season. This study identify clear-cut deforested areas through Neural Network (NN) algorithm based on C-band, VV-and VH-polarized, Sentinel-1 images. Statistical parameters of backscatter coefficients (mean, standard deviation, and the difference between maximum and minimum values-MMD) were computed from 30 Sentinel-1 images, from 2019, used as input parameters of the NN classifier. The samples were manually selected, including forested and deforested areas. After deforestation, mean backscatter signals decreased on the average of 2 dB for VV and 2.3 dB for VH from May to September-October. A Multi-Layer Perceptron (MLP) network was used for detecting near-real time forest disturbances larger than 2 ha. Case studies were performed for both polarizations considered the following input sets to the MLP: mean; mean and standard deviation; mean and MMD; and mean, standard deviation, and MMD. For the 2019 dataset, the latter showed the best performance of the NN algorithm with accuracy and F1 score of 99%. Automatic extraction using 2018 Sentinel-1 images reached accuracy and F1 score of 89% with the MapBiomas reference data and accuracy of 81% and F1 score of 79% with the PRODES reference data.

Research paper thumbnail of Cláudia Arantes Silva Nome em citações bibliográficas

(2021)-Aplicação de Dados Orbitais Multi-sensores para Identificação do Desmatamento na Floresta ... more (2021)-Aplicação de Dados Orbitais Multi-sensores para Identificação do Desmatamento na Floresta Amazônica. Tem experiência na área de Geociências, com ênfase em Geoprocessamento e Análise Ambiental, processamento de imagens radar e oticas; modelagem geológica aplicada à área do petróleo; geofísica de exploração com métodos potenciais aplicada à mineração. Atuou principalmente nos seguintes temas: desmatamento da floresta Amazônica, modelagem geológica de reservatório aplicada à indústria do petróleo, exploração geofísica e de campo aplicada à mineração. (Texto informado pelo autor) Identificação Endereço Formação acadêmica/titulação

Research paper thumbnail of Análise Qualitativa do Desmatamento na Floresta Amazônica a partir de Sensores SAR, Óptico e Termal

A mitigacao de mudancas climaticas e preservacao de ecossistemas depende da reducao do desmatamen... more A mitigacao de mudancas climaticas e preservacao de ecossistemas depende da reducao do desmatamento e degradacao de florestas tropicais. O objetivo deste estudo foi analisar imagens de radar, opticas e termais para identificar desmatamentos por corte raso no periodo de 2016 a 2018 em uma area localizada no arco de desmatamento da Amazonia. Foram utilizadas imagens de radar em bandas X (satelite COSMO-SkyMed) e C (satelite SENTINEL-1A), indices de vegetacao por diferenca normalizada (NDVI), indices de umidade por diferenca normalizada (NDMI) e temperaturas da superficie terrestre (LST) (satelite Landsat-8). As areas com evidencias de antropismo mapeadas com base nas imagens do satelite COSMO-SkyMed no municipio de Novo Progresso (PA), periodo de 2016 a 2018, foram utilizadas como mascara inicial. Imagens de radar identificaram, com boa precisao relativa, as epocas e as areas de desmatamento. NDVI e NDMI evidenciaram, respectivamente, quedas nas atividades fotossinteticas e nos niveis...

Research paper thumbnail of Near-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks

European Journal of Remote Sensing, 2022

Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective ... more Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective in the rainy season. This study identify clear-cut deforested areas through Neural Network (NN) algorithm based on C-band, VV-and VH-polarized, Sentinel-1 images. Statistical parameters of backscatter coefficients (mean, standard deviation, and the difference between maximum and minimum values-MMD) were computed from 30 Sentinel-1 images, from 2019, used as input parameters of the NN classifier. The samples were manually selected, including forested and deforested areas. After deforestation, mean backscatter signals decreased on the average of 2 dB for VV and 2.3 dB for VH from May to September-October. A Multi-Layer Perceptron (MLP) network was used for detecting near-real time forest disturbances larger than 2 ha. Case studies were performed for both polarizations considered the following input sets to the MLP: mean; mean and standard deviation; mean and MMD; and mean, standard deviation, and MMD. For the 2019 dataset, the latter showed the best performance of the NN algorithm with accuracy and F1 score of 99%. Automatic extraction using 2018 Sentinel-1 images reached accuracy and F1 score of 89% with the MapBiomas reference data and accuracy of 81% and F1 score of 79% with the PRODES reference data.

Research paper thumbnail of Fire Occurrences and Greenhouse Gas Emissions from Deforestation in the Brazilian Amazon

Remote Sensing

This work presents the dynamics of fire occurrences, greenhouse gas (GHG) emissions, forest clear... more This work presents the dynamics of fire occurrences, greenhouse gas (GHG) emissions, forest clearing, and degradation in the Brazilian Amazon during the period 2006–2019, which includes the approval of the new Brazilian Forest Code in 2012. The study was carried out in the Brazilian Amazon, Pará State, and the municipality of Novo Progresso (Pará State). The analysis was based on deforestation and fire hotspot datasets issued by the Brazilian Institute for Space Research (INPE), which is produced based on optical and thermal sensors onboard different satellites. Deforestation data was also used to assess GHG emissions from the slash-and-burn practices. The work showed a good correlation between the occurrence of fires in the newly deforested area in the municipality of Novo Progresso and the slash-and-burn practices. The same trend was observed in the Pará State, suggesting a common practice along the deforestation arch. The study indicated positive coefficients of determination of ...

Research paper thumbnail of Fire Occurrences and Greenhouse Gas Emissions from Deforestation in the Brazilian Amazon

RemoteSensing, 2021

This work presents the dynamics of fire occurrences, greenhouse gas (GHG) emissions, forest cleari... more This work presents the dynamics of fire occurrences, greenhouse gas (GHG) emissions, forest clearing, and degradation in the Brazilian Amazon during the period 2006–2019, which includes the approval of the new Brazilian Forest Code in 2012. The study was carried out in the Brazilian Amazon, Pará State, and the municipality of Novo Progresso (Pará State). The analysis was based on deforestation and fire hotspot datasets issued by the Brazilian Institute for Space Research (INPE), which is produced based on optical and thermal sensors onboard different satellites. Deforestation data was also used to assess GHG emissions from the slash-and-burn practices. The work showed a good correlation between the occurrence of fires in the newly deforested area in the municipality of Novo Progresso and the slash-and-burn practices. The same trend was observed in the Pará State, suggesting a common practice along the deforestation arch. The study indicated positive coefficients of determination of 0.72 and 0.66 between deforestation and fire occurrences for the municipality of Novo Progresso and Pará State, respectively. The increased number of fire occurrences in the primary forest suggests possible ecosystem degradation. Deforestation reported for 2019 surpassed 10,000 km2, which is 48% higher than the previous ten years, with an average of 6760 km2. The steady increase of deforestation in the Brazilian Amazon after 2012 has been a worldwide concern because of the forest loss itself as well as the massive GHG emitted in the Brazilian Amazon. We estimated 295 million tons of net CO2, which is equivalent to 16.4% of the combined emissions of CO2 and CH4 emitted by Brazil in 2019. The correlation of deforestation and fire occurrences reported from satellite images confirmed the slash-and-burn practice and the secondary effect of deforestation, i.e., degradation of primary forest surrounding the deforested areas. Hotspots’ location was deemed to be an important tool to verify forest degradation. The incidence of hotspots in forest area is from 5% to 20% of newly slashed-and-burned areas, which confirms the strong impact of deforestation on ecosystem degradation due to fire occurrences over the Brazilian Amazon.

Research paper thumbnail of Near-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks

European Journal of Remote Sensing, 2022

Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective ... more Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective in the rainy season. This study identify clear-cut deforested areas through Neural Network (NN) algorithm based on C-band, VV-and VH-polarized, Sentinel-1 images. Statistical parameters of backscatter coefficients (mean, standard deviation, and the difference between maximum and minimum values-MMD) were computed from 30 Sentinel-1 images, from 2019, used as input parameters of the NN classifier. The samples were manually selected, including forested and deforested areas. After deforestation, mean backscatter signals decreased on the average of 2 dB for VV and 2.3 dB for VH from May to September-October. A Multi-Layer Perceptron (MLP) network was used for detecting near-real time forest disturbances larger than 2 ha. Case studies were performed for both polarizations considered the following input sets to the MLP: mean; mean and standard deviation; mean and MMD; and mean, standard deviation, and MMD. For the 2019 dataset, the latter showed the best performance of the NN algorithm with accuracy and F1 score of 99%. Automatic extraction using 2018 Sentinel-1 images reached accuracy and F1 score of 89% with the MapBiomas reference data and accuracy of 81% and F1 score of 79% with the PRODES reference data.

Research paper thumbnail of Análise Qualitativa do Desmatamento na Floresta Amazonica

Anuário do Instituto de Geociências - UFRJ, 2019

A mitigação de mudanças climáticas e preservação de ecossistemas depende da redução do desmatamen... more A mitigação de mudanças climáticas e preservação de ecossistemas depende da redução do desmatamento e degradação de florestas tropicais. O objetivo deste estudo foi analisar imagens de radar, ópticas e termais para identificar desmatamentos por corte raso no período de 2016 a 2018 em uma área localizada no arco de desmatamento da Amazônia. Foram utilizadas imagens de radar em bandas X (satélite COSMO-SkyMed) e C (satélite SENTINEL-1A), índices de vegetação por diferença normalizada (NDVI), índices de umidade por diferença normalizada (NDMI) e temperaturas da superfície terrestre (LST) (satélite Landsat-8). As áreas com evidências de antropismo mapeadas com base nas imagens do satélite COSMO-SkyMed no município de Novo Progresso (PA), período de 2016 a 2018, foram utilizadas como máscara inicial. Imagens de radar identificaram, com boa precisão relativa, as épocas e as áreas de desmatamento. NDVI e NDMI evidenciaram, respectivamente, quedas nas atividades fotossintéticas e nos níveis de biomassa nas áreas de desmatamento identificadas. Já a LST foi mais elevada nas áreas de rebrota em relação à vegetação densa. A análise do potencial de imagens de radar, ópticos e termais mostrou elevada relevância na detecção de desmatamento por corte raso em ambiente florestal úmido.

Research paper thumbnail of Análise Qualitativa do Desmatamento na Floresta Amazonica

Anuário do Instituto de Geociências

A mitigação de mudanças climáticas e preservação de ecossistemas depende da redução do desmatamen... more A mitigação de mudanças climáticas e preservação de ecossistemas depende da redução do desmatamento e degradação de florestas tropicais. O objetivo deste estudo foi analisar imagens de radar, ópticas e termais para identificar desmatamentos por corte raso no período de 2016 a 2018 em uma área localizada no arco de desmatamento da Amazônia. Foram utilizadas imagens de radar em bandas X (satélite COSMO-SkyMed) e C (satélite SENTINEL-1A), índices de vegetação por diferença normalizada (NDVI), índices de umidade por diferença normalizada (NDMI) e temperaturas da superfície terrestre (LST) (satélite Landsat-8). As áreas com evidências de antropismo mapeadas com base nas imagens do satélite COSMO-SkyMed no município de Novo Progresso (PA), período de 2016 a 2018, foram utilizadas como máscara inicial. Imagens de radar identificaram, com boa precisão relativa, as épocas e as áreas de desmatamento. NDVI e NDMI evidenciaram, respectivamente, quedas nas atividades fotossintéticas e nos níveis de biomassa nas áreas de desmatamento identificadas. Já a LST foi mais elevada nas áreas de rebrota em relação à vegetação densa. A análise do potencial de imagens de radar, ópticos e termais mostrou elevada relevância na detecção de desmatamento por corte raso em ambiente florestal úmido.

Research paper thumbnail of Fire Occurrences and Greenhouse Gas Emissions from Deforestation in the Brazilian Amazon

Remote Sensing MDPI, 2021

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Research paper thumbnail of Near-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks

European Journal of Remote Sensing, 2022

Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective ... more Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective in the rainy season. This study identify clear-cut deforested areas through Neural Network (NN) algorithm based on C-band, VV-and VH-polarized, Sentinel-1 images. Statistical parameters of backscatter coefficients (mean, standard deviation, and the difference between maximum and minimum values-MMD) were computed from 30 Sentinel-1 images, from 2019, used as input parameters of the NN classifier. The samples were manually selected, including forested and deforested areas. After deforestation, mean backscatter signals decreased on the average of 2 dB for VV and 2.3 dB for VH from May to September-October. A Multi-Layer Perceptron (MLP) network was used for detecting near-real time forest disturbances larger than 2 ha. Case studies were performed for both polarizations considered the following input sets to the MLP: mean; mean and standard deviation; mean and MMD; and mean, standard deviation, and MMD. For the 2019 dataset, the latter showed the best performance of the NN algorithm with accuracy and F1 score of 99%. Automatic extraction using 2018 Sentinel-1 images reached accuracy and F1 score of 89% with the MapBiomas reference data and accuracy of 81% and F1 score of 79% with the PRODES reference data.

Research paper thumbnail of Cláudia Arantes Silva Nome em citações bibliográficas

(2021)-Aplicação de Dados Orbitais Multi-sensores para Identificação do Desmatamento na Floresta ... more (2021)-Aplicação de Dados Orbitais Multi-sensores para Identificação do Desmatamento na Floresta Amazônica. Tem experiência na área de Geociências, com ênfase em Geoprocessamento e Análise Ambiental, processamento de imagens radar e oticas; modelagem geológica aplicada à área do petróleo; geofísica de exploração com métodos potenciais aplicada à mineração. Atuou principalmente nos seguintes temas: desmatamento da floresta Amazônica, modelagem geológica de reservatório aplicada à indústria do petróleo, exploração geofísica e de campo aplicada à mineração. (Texto informado pelo autor) Identificação Endereço Formação acadêmica/titulação

Research paper thumbnail of Análise Qualitativa do Desmatamento na Floresta Amazônica a partir de Sensores SAR, Óptico e Termal

A mitigacao de mudancas climaticas e preservacao de ecossistemas depende da reducao do desmatamen... more A mitigacao de mudancas climaticas e preservacao de ecossistemas depende da reducao do desmatamento e degradacao de florestas tropicais. O objetivo deste estudo foi analisar imagens de radar, opticas e termais para identificar desmatamentos por corte raso no periodo de 2016 a 2018 em uma area localizada no arco de desmatamento da Amazonia. Foram utilizadas imagens de radar em bandas X (satelite COSMO-SkyMed) e C (satelite SENTINEL-1A), indices de vegetacao por diferenca normalizada (NDVI), indices de umidade por diferenca normalizada (NDMI) e temperaturas da superficie terrestre (LST) (satelite Landsat-8). As areas com evidencias de antropismo mapeadas com base nas imagens do satelite COSMO-SkyMed no municipio de Novo Progresso (PA), periodo de 2016 a 2018, foram utilizadas como mascara inicial. Imagens de radar identificaram, com boa precisao relativa, as epocas e as areas de desmatamento. NDVI e NDMI evidenciaram, respectivamente, quedas nas atividades fotossinteticas e nos niveis...

Research paper thumbnail of Near-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks

European Journal of Remote Sensing, 2022

Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective ... more Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective in the rainy season. This study identify clear-cut deforested areas through Neural Network (NN) algorithm based on C-band, VV-and VH-polarized, Sentinel-1 images. Statistical parameters of backscatter coefficients (mean, standard deviation, and the difference between maximum and minimum values-MMD) were computed from 30 Sentinel-1 images, from 2019, used as input parameters of the NN classifier. The samples were manually selected, including forested and deforested areas. After deforestation, mean backscatter signals decreased on the average of 2 dB for VV and 2.3 dB for VH from May to September-October. A Multi-Layer Perceptron (MLP) network was used for detecting near-real time forest disturbances larger than 2 ha. Case studies were performed for both polarizations considered the following input sets to the MLP: mean; mean and standard deviation; mean and MMD; and mean, standard deviation, and MMD. For the 2019 dataset, the latter showed the best performance of the NN algorithm with accuracy and F1 score of 99%. Automatic extraction using 2018 Sentinel-1 images reached accuracy and F1 score of 89% with the MapBiomas reference data and accuracy of 81% and F1 score of 79% with the PRODES reference data.

Research paper thumbnail of Fire Occurrences and Greenhouse Gas Emissions from Deforestation in the Brazilian Amazon

Remote Sensing

This work presents the dynamics of fire occurrences, greenhouse gas (GHG) emissions, forest clear... more This work presents the dynamics of fire occurrences, greenhouse gas (GHG) emissions, forest clearing, and degradation in the Brazilian Amazon during the period 2006–2019, which includes the approval of the new Brazilian Forest Code in 2012. The study was carried out in the Brazilian Amazon, Pará State, and the municipality of Novo Progresso (Pará State). The analysis was based on deforestation and fire hotspot datasets issued by the Brazilian Institute for Space Research (INPE), which is produced based on optical and thermal sensors onboard different satellites. Deforestation data was also used to assess GHG emissions from the slash-and-burn practices. The work showed a good correlation between the occurrence of fires in the newly deforested area in the municipality of Novo Progresso and the slash-and-burn practices. The same trend was observed in the Pará State, suggesting a common practice along the deforestation arch. The study indicated positive coefficients of determination of ...

Research paper thumbnail of Fire Occurrences and Greenhouse Gas Emissions from Deforestation in the Brazilian Amazon

RemoteSensing, 2021

This work presents the dynamics of fire occurrences, greenhouse gas (GHG) emissions, forest cleari... more This work presents the dynamics of fire occurrences, greenhouse gas (GHG) emissions, forest clearing, and degradation in the Brazilian Amazon during the period 2006–2019, which includes the approval of the new Brazilian Forest Code in 2012. The study was carried out in the Brazilian Amazon, Pará State, and the municipality of Novo Progresso (Pará State). The analysis was based on deforestation and fire hotspot datasets issued by the Brazilian Institute for Space Research (INPE), which is produced based on optical and thermal sensors onboard different satellites. Deforestation data was also used to assess GHG emissions from the slash-and-burn practices. The work showed a good correlation between the occurrence of fires in the newly deforested area in the municipality of Novo Progresso and the slash-and-burn practices. The same trend was observed in the Pará State, suggesting a common practice along the deforestation arch. The study indicated positive coefficients of determination of 0.72 and 0.66 between deforestation and fire occurrences for the municipality of Novo Progresso and Pará State, respectively. The increased number of fire occurrences in the primary forest suggests possible ecosystem degradation. Deforestation reported for 2019 surpassed 10,000 km2, which is 48% higher than the previous ten years, with an average of 6760 km2. The steady increase of deforestation in the Brazilian Amazon after 2012 has been a worldwide concern because of the forest loss itself as well as the massive GHG emitted in the Brazilian Amazon. We estimated 295 million tons of net CO2, which is equivalent to 16.4% of the combined emissions of CO2 and CH4 emitted by Brazil in 2019. The correlation of deforestation and fire occurrences reported from satellite images confirmed the slash-and-burn practice and the secondary effect of deforestation, i.e., degradation of primary forest surrounding the deforested areas. Hotspots’ location was deemed to be an important tool to verify forest degradation. The incidence of hotspots in forest area is from 5% to 20% of newly slashed-and-burned areas, which confirms the strong impact of deforestation on ecosystem degradation due to fire occurrences over the Brazilian Amazon.

Research paper thumbnail of Near-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks

European Journal of Remote Sensing, 2022

Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective ... more Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective in the rainy season. This study identify clear-cut deforested areas through Neural Network (NN) algorithm based on C-band, VV-and VH-polarized, Sentinel-1 images. Statistical parameters of backscatter coefficients (mean, standard deviation, and the difference between maximum and minimum values-MMD) were computed from 30 Sentinel-1 images, from 2019, used as input parameters of the NN classifier. The samples were manually selected, including forested and deforested areas. After deforestation, mean backscatter signals decreased on the average of 2 dB for VV and 2.3 dB for VH from May to September-October. A Multi-Layer Perceptron (MLP) network was used for detecting near-real time forest disturbances larger than 2 ha. Case studies were performed for both polarizations considered the following input sets to the MLP: mean; mean and standard deviation; mean and MMD; and mean, standard deviation, and MMD. For the 2019 dataset, the latter showed the best performance of the NN algorithm with accuracy and F1 score of 99%. Automatic extraction using 2018 Sentinel-1 images reached accuracy and F1 score of 89% with the MapBiomas reference data and accuracy of 81% and F1 score of 79% with the PRODES reference data.

Research paper thumbnail of Análise Qualitativa do Desmatamento na Floresta Amazonica

Anuário do Instituto de Geociências - UFRJ, 2019

A mitigação de mudanças climáticas e preservação de ecossistemas depende da redução do desmatamen... more A mitigação de mudanças climáticas e preservação de ecossistemas depende da redução do desmatamento e degradação de florestas tropicais. O objetivo deste estudo foi analisar imagens de radar, ópticas e termais para identificar desmatamentos por corte raso no período de 2016 a 2018 em uma área localizada no arco de desmatamento da Amazônia. Foram utilizadas imagens de radar em bandas X (satélite COSMO-SkyMed) e C (satélite SENTINEL-1A), índices de vegetação por diferença normalizada (NDVI), índices de umidade por diferença normalizada (NDMI) e temperaturas da superfície terrestre (LST) (satélite Landsat-8). As áreas com evidências de antropismo mapeadas com base nas imagens do satélite COSMO-SkyMed no município de Novo Progresso (PA), período de 2016 a 2018, foram utilizadas como máscara inicial. Imagens de radar identificaram, com boa precisão relativa, as épocas e as áreas de desmatamento. NDVI e NDMI evidenciaram, respectivamente, quedas nas atividades fotossintéticas e nos níveis de biomassa nas áreas de desmatamento identificadas. Já a LST foi mais elevada nas áreas de rebrota em relação à vegetação densa. A análise do potencial de imagens de radar, ópticos e termais mostrou elevada relevância na detecção de desmatamento por corte raso em ambiente florestal úmido.