Remote sensing applications for mapping large wildfires based on machine learning and time series in Northwestern Portugal

Mapping large wildfires (LW) is essential for environmental applications and enhances the understanding of the dynamics of affected areas. Remote sensing techniques supported by machine learning and time series have been increasingly used in studies addressing this issue and have shown potential for this type of analysis. The main aim of this article is to develop a methodology for mapping LW in northwestern Portugal using a machine learning algorithm and time series from Landsat images. For the burnt area classification, we initially used the Fourier harmonic model to define outliers in the time series that represented pixels of possible burnt areas and, then, we applied the random forest classifier for the LW classification. The results indicate that the harmonic analysis provided estimates with the actual observed values of the NBR index; thus, the pixels classified by random forest were only those that were masked, collaborated in the processing, and reduced possible spectral confusion between targets with similar behaviour. The burnt area maps revealed that ~23.5% of the territory was burnt at least once from 2001 to 2020. The temporal variability of the burnt area indicated that, on average, 6.504 hectares were affected by LW within the 20 years. The annual burnt area varied over the years, with the minimum annual area detected in 2014 (679.5 hectares) and the maximum mapped area detected in 2005 (73,025.1 hectares). We concluded that the process of defining the mask with the outliers considerably reduced the universe of pixels to be classified within each image, which leaves the training of the classifier focused on separating the set of pixels into two groups with very similar spectral characteristics, thus contributing so that the separation of groups with similar spectral behaviour was performed automatically and without great sampling effort. The method showed satisfactory accuracy results with little omission for burnt areas.This research was funded by Portuguese funds through Fundação para a Ciência e a Tecnologia, I.P., within the scope of the research project “EcoFire—O valor económico dos incêndios florestais como suporte ao comportamento preventivo”, reference PCIF/AGT/0153/2018

Avaliação da regeneração da vegetação pós-incêndio no Parque Nacional da Chapada Diamantina do Brasil através de sensoriamento remoto

Understanding fire dynamics in vegetation is essential for assessing the impacts caused by wildfire action, especially because biomass burning in ecosystems has been indicated as one of the main factors that impact climate and biodiversity. A current alternative to detecting fire via satellite data is cloud processing platforms such as Google Earth Engine (GEE). Given this context, this work aims to assess the degree of vegetation regrowth after a wildfire in an area included in the Chapada Diamantina National Park (Bahia - Brazil) based on applying the Normalized Burn Ratio (NBR) in Landsat Surface Reflectance Tier 1 data sets. The images were accessed and processed on the GEE platform. The NBR index was more sensitive to the pre-and post-fire displacements of the pixels affected by the fires between the Landsat NIR and SWIR image bands. We found that the NBR mean values decreased immediately after the fire occurrence in the entire study area. Then, following the wildfire, the NBR mean values returned to conditions similar to those that preceded the fire. We can conclude that the plant biomass had already recovered considerably nine months after the fire when checking the NBR values. Therefore, this study points out the need to better understand the wildfire dynamics in the Chapada Diamantina National Park region and the impact associated with these events, with respect to fire ecology.A compreensão da dinâmica do fogo na vegetação é essencial para avaliar os impactes causados pela ação dos incêndios florestais, especialmente porque a queima de biomassa nos ecossistemas tem sido indicada como um dos principais fatores que impactam o clima e a biodiversidade. Uma alternativa atual para detetar incêndios através de dados de satélite são as plataformas de processamento em nuvens, como o Google Earth Engine (GEE). Dado este contexto, o presente trabalho visa avaliar o grau de recuperação da vegetação após um evento de incêndio numa área incluída no Parque Nacional da Chapada Diamantina (Bahia - Brasil) com base na aplicação da Razão de Queimada Normalizada (NBR) em conjuntos de dados Landsat Surface Reflectance Tier 1. As imagens foram acessadas e processadas na plataforma GEE. O índice NBR revelou-se mais sensível aos deslocamentos pré e pós-fogo dos pixels afetados pelos incêndios entre as bandas de imagem Landsat NIR e SWIR. Verificou-se que os valores médios do NBR diminuíram imediatamente após a ocorrência do incêndio em toda a área de estudo. Após o incêndio, os valores médios do NBR foram apontando no sentido do retorno a condições similares àquelas que o precederam, indicando os valores de NBR que a biomassa vegetal, nove meses após o incêndio, já apresentava uma considerável recuperação. Neste sentido, este estudo demonstra a necessidade de se conhecer melhor a dinâmica dos incêndios na região do Parque Nacional da Chapada Diamantina e os impactes associado a estes eventos, no que respeita à ecologia do fogo

Avaliação da regeneração da vegetação pós-incêndio no Parque Nacional da Chapada Diamantina do Brasil através de sensoriamento remoto

Understanding fire dynamics in vegetation is essential for assessing the impacts caused by wildfire action, especially because biomass burning in ecosystems has been indicated as one of the main factors that impact climate and biodiversity. A current alternative to detecting fire via satellite data is cloud processing platforms such as Google Earth Engine (GEE). Given this context, this work aims to assess the degree of vegetation regrowth after a wildfire in an area included in the Chapada Diamantina National Park (Bahia - Brazil) based on applying the Normalized Burn Ratio (NBR) in Landsat Surface Reflectance Tier 1 data sets. The images were accessed and processed on the GEE platform. The NBR index was more sensitive to the pre- and post-fire displacements of the pixels affected by the fires between the Landsat NIR and SWIR image bands. We found that the NBR mean values decreased immediately after the fire occurrence in the entire study area. Then, following the wildfire, the NBR mean values returned to conditions similar to those that preceded the fire. We can conclude that the plant biomass had already recovered considerably nine months after the fire when checking the NBR values. Therefore, this study points out the need to better understand the wildfire dynamics in the Chapada Diamantina National Park region and the impact associated with these events, with respect to fire ecology.A compreensão da dinâmica do fogo na vegetação é essencial para avaliar os impactes causados pela ação dos incêndios florestais, especialmente porque a queima de biomassa nos ecossistemas tem sido indicada como um dos principais fatores que impactam o clima e a biodiversidade. Uma alternativa atual para detetar incêndios através de dados de satélite são as plataformas de processamento em nuvens, como o Google Earth Engine (GEE). Dado este contexto, o presente trabalho visa avaliar o grau de recuperação da vegetação após um evento de incêndio numa área incluída no Parque Nacional da Chapada Diamantina (Bahia - Brasil) com base na aplicação da Razão de Queimada Normalizada (NBR) em conjuntos de dados Landsat Surface Reflectance Tier 1. As imagens foram acessadas e processadas na plataforma GEE. O índice NBR revelou-se mais sensível aos deslocamentos pré e pós-fogo dos pixels afetados pelos incêndios entre as bandas de imagem Landsat NIR e SWIR. Verificou-se que os valores médios do NBR diminuíram imediatamente após a ocorrência do incêndio em toda a área de estudo. Após o incêndio, os valores médios do NBR foram apontando no sentido do retorno a condições similares àquelas que o precederam, indicando os valores de NBR que a biomassa vegetal, nove meses após o incêndio, já apresentava uma considerável recuperação. Neste sentido, este estudo demonstra a necessidade de se conhecer melhor a dinâmica dos incêndios na região do Parque Nacional da Chapada Diamantina e os impactes associados a estes eventos, no que respeita à ecologia do fogo

Long-term Landsat-based monthly burned area dataset for the Brazilian biomes using Deep Learning

Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil

Remote Sensing Applications for Mapping Large Wildfires Based on Machine Learning and Time Series in Northwestern Portugal

Mapping large wildfires (LW) is essential for environmental applications and enhances the understanding of the dynamics of affected areas. Remote sensing techniques supported by machine learning and time series have been increasingly used in studies addressing this issue and have shown potential for this type of analysis. The main aim of this article is to develop a methodology for mapping LW in northwestern Portugal using a machine learning algorithm and time series from Landsat images. For the burnt area classification, we initially used the Fourier harmonic model to define outliers in the time series that represented pixels of possible burnt areas and, then, we applied the random forest classifier for the LW classification. The results indicate that the harmonic analysis provided estimates with the actual observed values of the NBR index; thus, the pixels classified by random forest were only those that were masked, collaborated in the processing, and reduced possible spectral confusion between targets with similar behaviour. The burnt area maps revealed that ~23.5% of the territory was burnt at least once from 2001 to 2020. The temporal variability of the burnt area indicated that, on average, 6.504 hectares were affected by LW within the 20 years. The annual burnt area varied over the years, with the minimum annual area detected in 2014 (679.5 hectares) and the maximum mapped area detected in 2005 (73,025.1 hectares). We concluded that the process of defining the mask with the outliers considerably reduced the universe of pixels to be classified within each image, which leaves the training of the classifier focused on separating the set of pixels into two groups with very similar spectral characteristics, thus contributing so that the separation of groups with similar spectral behaviour was performed automatically and without great sampling effort. The method showed satisfactory accuracy results with little omission for burnt areas

Land Use and Land Cover Classification in the Northern Region of Mozambique Based on Landsat Time Series and Machine Learning

Accurate land use and land cover (LULC) mapping is essential for scientific and decision-making purposes. The objective of this paper was to map LULC classes in the northern region of Mozambique between 2011 and 2020 based on Landsat time series processed by the Random Forest classifier in the Google Earth Engine platform. The feature selection method was used to reduce redundant data. The final maps comprised five LULC classes (non-vegetated areas, built-up areas, croplands, open evergreen and deciduous forests, and dense vegetation) with an overall accuracy ranging from 80.5% to 88.7%. LULC change detection between 2011 and 2020 revealed that non-vegetated areas had increased by 0.7%, built-up by 2.0%, and dense vegetation by 1.3%. On the other hand, open evergreen and deciduous forests had decreased by 4.1% and croplands by 0.01%. The approach used in this paper improves the current systematic mapping approach in Mozambique by minimizing the methodological gaps and reducing the temporal amplitude, thus supporting regional territorial development policies

Bibliometric Analysis of Land Degradation Studies in Drylands Using Remote Sensing Data: A 40-Year Review

Drylands are vast and face threats from climate change and human activities. Traditional reviews cannot capture interdisciplinary knowledge, but bibliometric analysis provides valuable insights. Our study conducted bibliometric research of scientific production on climate change and land degradation in drylands using remote sensing. We examined 1527 Scopus-indexed publications to identify geographic and thematic hotspots, extracting leading authors, journals, and institutions. China leads in publications, followed by the US, Germany, and Australia. The US has the highest citation count. Collaboration networks involve the US, China, and European countries. There has been an exponential increase in remote sensing of land degradation in drylands (RSLDD) publications since 2011. Key journals include “International Journal of Remote Sensing” and “Remote Sensing of Environment”. The analysis highlights the growing interest in the field, driven by Australia, the US, and China. Key areas of study are vegetation dynamics and land use change. Future perspectives for this scientific field involve promoting collaboration and exploring emerging technologies for comprehensive land degradation and desertification research

Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning

Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil

Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning

Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil