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The formation of forest fire burned area, influenced by a variety of factors such as meteorology, topography, vegetation, and human intervention, is a dynamic process of fire line burning that develops from the point of ignition to the boundary of the burned area. Accurately simulating and predicting this dynamic process can provide a scientific basis for forest fire control and suppression decisions. In this study, five typical forest fires located in different regions of China were used as the study object. The straight path distances from the ignition point grid to each grid on fire line in Sentinel-2 imageries for each forest fire were used as the target variables. We obtained the values of 11 independent variables for each pathway, including wind speed component, Temperature, Relative Humidity, Elevation, Slope, Aspect, Degree of Relief, Normalized Difference Vegetation Index, Vegetation Type, Fire Duration, and Gross Domestic Product reflecting human intervention capacity for fires. The value of each target variable and that of its corresponding independent variable constituted a sample. Four machine learning models, such as Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained using 80% effective samples from four forest fires, and 20% used to verify the above models. The hyper-parameters of each model were optimized using grid search method. After analyzing the validation results of models which showed temperature as a non-significant variable, the training and validation process of models above was repeated after excluding temperature. The results show that RF is the optimal model with 49.55 m for root mean square error (RMSE), 29.19 m for mean absolute error (MAE) and 0.9823 for coefficient of determination (R2). This study used the RF model to construct the shape of burned areas by predicting lengths of all straight path distances from the ignition point to the fire line. The study can dynamically capture the development of forest fire scenes. [ABSTRACT FROM AUTHOR]

To study the identification of burned areas and post fire vegetation restoration in the Daxing′an Mountains region, based on Landsat TM remote sensing images from 2006 to 2020, Google Earth Engine was used to write code. The research background was the 2006 forest fire in the Nayuan Forest Farm in the Songling District of the Daxing′an Mountains region. The differential normalized burned ratio (dNBR) data was used to identify the burned areas, and the severity was classified into mild, moderate, severe, and extremely severe levels. Based on the Enhanced Vegetation Index (EVI) values of burned areas, methods such as univariate linear regression analysis, Mann-Kendall mutation test for climate diagnosis and Theil-Sen media trend analysis for treud analysis were used to analyze the vegetation restoration characteristics of burned areas from 2006 to 2020, and to explore the process of vegetation restoration in the Daxing′an Mountains region. The results showed that, 1) Based on dNBR, the burned areas in the study area was 2 488. 7 hm2, with 23. 5%, 9. 6%, 35. 2%, and 31. 7% of the areas affected by mild, moderate, severe, and extremely severe fires, respectively. Severe and extremely severe areas of excessive fire were distributed in the western and eastern parts of the burned area, and the severity of excessive fire gradually decreased from the central to the southern and northern parts. The EVI values decreased by about 30%, 40%, 58%, and 67% compared to before the fire, respectively. 2) The recovery rate of EVI in forest burned areas with different intensities showed extremely severe, severe, moderate, mild. During the vegetation restoration process, the EVI value of the burned areas gradually increased. Mild and moderate burned areas can recover 6-8 years after the fire, while the recovery of severely burned areas required 14 years. 3) During the restoration process of burned areas, there were fewer EVI mutation points in forested areas compared to grasslands, indicating stronger stability of forest ecosystems compared to irrigated grasslands. There were also certain differences in the mutation situation of forest burning sites with different intensities, and the mutation time point in the control area lagged behind the burning sites. [ABSTRACT FROM AUTHOR]

Forest fires have become a major concern in the northern parts of Morocco, particularly in the Tangier-Tetouan-Al Hoceima (TTA) region, causing significant damage to the environment and human lives. To address this pressing issue, this study proposes an approach that utilizes remote sensing (RS) and machine learning (ML) techniques to detect burned areas in the TTA region within the Google Earth Engine platform. The study focuses on burned areas resulting from forest fires in three specific locations in the TTA region that have experienced such fires in recent years, namely Tangier-Assilah in 2017, M'diq Fnideq in 2020, and Chefchaouen in 2021. In our study, we extensively explored multiple combinations of spectral indices, such as normalized burn ratio (dNBR), normalized difference vegetation index (dNDVI), soil-adjusted vegetation index (dSAVI), and burned area index (dBAI), in conjunction with Sentinel-2 (S2) satellite images. These combinations were employed within the Random Forest (RF) algorithm, allowing us to draw important conclusions. Initially, we assess the individual effectiveness of the dNBR index, which yields accuracy rates of 83%, 90%, and 82% for Tangier-Assilah, Chefchaouen, and M'diq Fnideq, respectively. Recognizing the need for improved outcomes, we expand our analysis by incorporating spectral indices and S2 bands. However, the results obtained from this expanded combination lack consistency and stability across different locations. While Tangier-Assilah and M'diq Fnideq experience accuracy improvements, reaching 95% and 88%, respectively, the inclusion of Sentinel bands has an adverse effect on Chefchaouen, resulting in a decreased accuracy of 87%. To achieve optimal accuracy, our focus shifted towards the combination of dNBR and the other spectral indices. The results were truly remarkable, with accuracy rates of 96%, 97%, and 97% achieved for Tangier-Assilah, Chefchaouen, and M'diq Fnideq, respectively. Our decision to prioritize the spectral indices was based on the feature importance method, which highlights the significance of each feature in the classification process. The practical implications of our study extend to fire management and prevention in the TTA region. The insights gained from our analysis can inform the development of effective policies and strategies to mitigate the impact of forest fires. By harnessing the potential of RS and ML techniques, along with the utilization of spectral indices, we pave the way for enhanced fire monitoring and response capabilities in the region. [ABSTRACT FROM AUTHOR]

This study uses a combined research approach based on remote-sensing and numerical modeling to quantify the effects of burned areas on the surface climate in the two Brazilian biomes most affected by fires: the tropical savanna and the Amazon rainforest. Our estimates indicate that between 2007 and 2020, approximately 6% of the savanna and 2% of the rainforest were burned on average. Non-parametric regressions based on 14-year climate model simulations indicate that latent heat flux decreases on average by approximately 0.17 W m−2 in the savanna and 0.60 W m−2 in the rainforest per each 1 km2 burned, with most of the impacts registered during the onset of the wet season. Sensible and ground heat fluxes are also impacted but at less intensity. Surface air is also warmer and drier, especially over rainforest burned sites. On average, fire reduced gross primary production in the savanna and rainforest by 12% and 10%, respectively, in our experiments. [ABSTRACT FROM AUTHOR]

In order to investigate the effects of vegetation restoration on soil aggregates and organic carbon in severe burning sites in Greater Khingan Mountains, this paper used larch plantations of different stand ages (11, 21 and 32 years) planted on heavy fire sites as the research object, and conducted a study on the stability of soil aggregates and organic carbon characteristics of soil aggregates by measuring the percentage content of each particle size and the organic carbon content of soil aggregates. The results showed that: 1)After vegetation restoration of burned areas, the mechanical stability and water stability of soil aggregates in larch plantations of different stand ages were dominated by the content of >0. 25 mm particle size, accounting for 85. 57%-89. 42% and 62. 86%-83. 19%, respectively. With the increase of stand age, the content of soil mechanical stability aggregates >2 mm particle size in 0-10 cm layer of larch plantation decreased significantly and the content of 0. 25-2 mm particle size increased significantly, while the water stability aggregates showed a trend that the content of >2 mm particle size decreased significantly and the content of 0. 5-2 mm particle size increased significantly (P < 0. 05). 2)32 years of larch plantation soil mechanical stability aggregates mean weight diameter (MWD) and geometric mean diameter (GMD) of the upper soil water-stable aggregates were significantly smaller than 11 years, and the MWD of the upper soil water-stable aggregates was consistent with the characteristics of the mechanically stable aggregates. Water-stable macroaggregate content (WSA 0. 25 ) gradually increased with the increase of restoration years, and the percentage of aggregate destruction (PAD) of the 32 years old larch plantation decreased significantly. 3)With the increase of forest age, the organic carbon content of soil aggregates in larch plantation forest increased, and the increase was more in the upper layer. The organic carbon contribution of large aggregates (>0. 25 mm fraction) dominated. The results could provide scientific basis for vegetation restoration of burned areas in Greater Khingan Mountains. [ABSTRACT FROM AUTHOR]

Wildfire is a kind of disaster that can damage living creatures and environment as well as cause small dust affecting to health of people. Wildfire can be naturally occurred and human's actions. This research aims to evaluate burned areas caused by wildfires in Omkoi Wildlife Sanctuary. The research was conducted by collecting data from Sentinel-2 Satellite for 4 years started from 2017 to 2020. Three formats of spectral indices, i.e., NBR, NDWI, and RBR were used for evaluating burned areas caused by wildfires. Validation was tested by using statistical methods. The results revealed that burned areas from 2017 to 2020 were 0.107 km², 1.160 km², 0.387 km², and 1.031 km2, respectively. The larges burned area was found in 2018 followed by 2020, 2019, and 2017. For validation, it was found that total accuracy of 2017 was 93.33 % with Kappa statistics of 0.87 whereas total accuracy of 2018 was 78.33 % with Kappa statistics of 0.57. In 2019, total accuracy was 81.67 % with Kappa statistics of 0.63. In 2020, total accuracy was 76.67 % with Kappa statistics of 0.53. [ABSTRACT FROM AUTHOR]

Forest fires are considered to be an important part of numerous terrestrial ecosystems and vegetation types, being also a significant factor of ecosystem disruption. In this sense, fires play an important role in the structure and function of the ecosystems. Biomes are characterized by a specific type of fire regime, which is a synergy of the climate conditions and the characteristics of the vegetation types dominating each biome. The assessment of burned areas and the identification of the fire regimes can be implemented with freely available low- to high-resolution satellite data as those of Landsat and Sentinel-2. Moreover, the biomes are characterized by the phenology, a useful component for vegetation monitoring, especially when time series of satellite images are used. Both the identification of fire regime by reconstructing the fire history and the monitoring of the post-fire evolution of burned areas were studied with remote sensing methods. Specifically, the present paper is a pilot study implemented in a Mediterranean biome, aimed at establishing the methodological framework to (i) define fire regimes, (ii) characterize the phenological pattern of the vegetation (pre-fire situation) of the fire-affected areas, and (iii) compare the phenology of the recovered fire-affected areas with the corresponding one of the pre-fire situation. At the global level, based on MODIS fire perimeters, we found that fires are occurring at 70% in the tropical and subtropical grasslands, savannas, and shrublands, followed by fires at tropical and subtropical moist broadleaf forests by 7% and by fires at deserts and xeric shrublands by 6.5%. [ABSTRACT FROM AUTHOR]

Nepal is highly vulnerable to climate change with increased fire occurrences and fire burned areas in recent years; therefore, we accessed the climatic drivers for its variability using fire burned areas product of Moderate Resolution Imaging Spectroradiometer (MODIS) from 2001 and 2020. The peak fire burned areas were observed in the spring season (~91%) from March to May, especially higher in the lowlands of the western and central parts. At the interannual timescale, low precipitation, humidity, soil moisture, and high temperature supported the existence of spring fire. Combining these factors induces drought conditions, enhancing evapotranspiration from vegetation and providing more combustible fuels. Furthermore, the El Niño phase in the central‐eastern Pacific Ocean is related to the weakened westerly moisture transport and moisture divergence that creates dry and warm conditions leading to increased fire activities. Thus, this study could be helpful for preparedness, management, and policy‐making to limit the multi‐dimensional losses in the ecosystem and society due to fire. [ABSTRACT FROM AUTHOR]

This research presents a methodology for assessing the extent of areas affected by wildfires, as well as the severity levels of burned areas using multispectral photographs from the Sentinel-2 satellite. As a case study on which the methodology will be applied are the burned areas caused by the wildfires that occurred in early August 2021 in the Pijanec-Maleshevo region of the Republic of N. Macedonia. The advantage of the proposed methodology is that it uses data with completely free access from the Sentinel-2 satellite. The processing and mapping of the results was done in the QGIS software which has open-source license. The obtained results indicate burned areas of 4220 hectares and four burn severity levels (low, moderate-low, moderate-high and high severity). A comparison with the European Forest Fire Information System (EFFIS), which uses multispectral photographs of the MODIS (Moderate Resolution Imaging Spectroradiometer sensor), shows that the proposed methodology improves the burn area estimate by 25 %. [ABSTRACT FROM AUTHOR]

• A deep learning approach for mapping and dating of burned areas is proposed. • The approach relies on VIIRS data and is tested over five distinct regions. • The proposed model improves estimates of partially burned pixels. • Attributed dates of burning are improved over existing products. • The procedure can be used for global near-real-time applications. Over the past decades, methods for burned areas mapping and dating from remote sensing imagery have been the object of extensive research. The limitations of current methods, together with the heavy pre-processing of input data they require, make them difficult to improve or apply to different satellite sensors. Here, we explore a deep learning approach based on daily sequences of multi-spectral images, as a promising and flexible technique that can be applicable to observations with various spatial and spectral resolutions. We test the proposed model for five regions around the globe using input data from VIIRS 750 m bands resampled to a 0.01° spatial resolution grid. The derived burned areas are validated against higher resolution reference maps and compared with the MCD64A1 Collection 6 and FireCCI51 global burned area datasets. We show that the proposed methodology achieves competitive results in the task of burned areas mapping, despite using lower spatial resolution observations than the two global datasets. Furthermore, we improve the task of burned areas dating for the considered regions of study when compared with state-of-the-art products. We also show that our model can be used to map burned areas for low burned fraction levels and that it can operate in near-real-time, converging to the final solution in only a few days. The obtained results are a strong indication of the advantage of deep learning approaches for the problem of mapping and dating of burned areas and provide several routes for future research. [ABSTRACT FROM AUTHOR]

In southeastern Mexico, forest fires threaten the biodiversity in tropical rainforests. The aim of this work was to analyze the spatial and temporal distribution of burned areas in Montes Azules Biosphere Reserve and its area of influence over three years: 1998, 2003 and 2015. The Normalized Burn Ratio (NBR) and the Burnt Area Index for MODIS (BAIM) were extracted from near-infrared and shortwave-infrared Landsat imagery; their accuracy was validated with ASTER images. As a result, 1,628 polygons of burned areas larger than 10 ha were delimited, covering 86,500 ha and representing 11.2 % of the study area: 7.7 % in 1998, 3.2 % in 2003 and 0.4 % in 2015. Burned areas of under 50 ha are of common occurrence each year, whereas the largest polygons (1,000 - 3,000 ha) are considered exceptional and are present only under the severe influence of El Niño weather phenomenon. The highest proportion of burned areas (54.7 %) affected agricultural lands, followed by secondary vegetation (34 %), tropical forests (7.6 %), temperate forests (2.1 %), savannah (1 %) and tular marshlands (0.6 %). Regarding burned areas, 20 % occurred within the protected area. The decreasing tendency of burned areas confirms their relationship with the intensity of ENSO. Cartographic representation of burned areas is an effective means of evaluating the effects of fire management programs and biodiversity conservation of tropical rainforests in protected areas. [ABSTRACT FROM AUTHOR]

The study of the relationship between fire and the dynamics of Brazilian landscapes is a major issue for a better understanding of ecological processes and the human impacts on landscape dynamics. We explore the dynamics of spatial-temporal variation in fire occurrence in Brazilian biomes during the last 18 years (2001-2018), based on the burned area product MCD64A1 v006 and the database of land use and land cover of Mapbiomas project. The Cerrado recorded 62.2% of the total burned in the analyzed period, followed by the Amazon biome, with 23.5%. Pampa, Mata Atlântica and Amazônia had a fire occurrence above -50% in areas with agropastoral land uses, while in the Cerrado, Caatinga and Pantanal biomes the fires occurred mainly on areas of natural vegetation. We confirmed the potential for combining the use of products derived from remote sensing for the analysis of the patterns of burned areas, generating relevant information regarding the anthropic influences in the alteration of fire regimes. [ABSTRACT FROM AUTHOR]

In this study, a low-cost unmanned aerial vehicle was used to obtain multi-spectral high-resolution imagery (1.4 cm px−1) from 2 microcatchments (3.3 ha) with burned Mediterranean shrubland and pine forests. This imagery was used to calculate the blue normalized differential vegetation index and to generate digital elevation models for estimating the sediment connectivity index. Both indices enabled an integrated approach for deciphering how hydrological and sediment connectivity interact with vegetation as well as soil conservation structures. The application of spatial analysis improves our understanding of the feedback between biological and geomorphological processes. Local spatial data analysis established a significant link between local geomorphological and biological factors, enabling a precise identification of homogeneous areas at micro-catchment scale and the minimal size of vegetation units reacting to geomorphology as natural groups at plot-scale where management strategies and efforts should be applied. Establishing this local relationship between sediment connectivity and vegetation patterns through new and interdisciplinary methodologies represents a new strategy for the assessment of ecosystem dynamics and management. Unlabelled Image • Very high-resolution multi-spectral imagery are used to generate HR-DEM models. • Relations between sediment connectivity and post-disturbance vegetation dynamics are mapped. • Spatial data established significant links between local bio-geomorphological factors. • Hotspot areas where management strategies should be applied are identified. [ABSTRACT FROM AUTHOR]

Research studies on the composition of basic vegetation in Sultan Thaha Saifuddin Grand Forest Park area have been conducted from July to August 2018 with multiple plot vegetation method.. The plots placement was done by purposive sampling at two different land cover locations. The plot size sampling was 10x10 meters, 8 plots were located at the burnt location and 5 plots at the location of the bulian natural habitat. The results of the study found 37 families, and 80 species, where the family that has the highest number of species is the Moraceae family which is as many as 6 species. Plant species found in burned areas were 48 species, in non-burnt areas (bulian habitat) 29 species and in post-burned and non-burned area as many as 3 species. [ABSTRACT FROM AUTHOR]

A discrete scattering model, based on the radiative-transfer theory, is used to simulate the backscattering of burned pine canopies at C-band. The model is first parameterized either with direct field measurements on a selected burned area in Greece or with proper estimations of the required variables, for which direct measurements were not possible. The simulated backscatter at VV polarization was compared against European Remote Sensing 2 (ERS-2) observations. The comparison was based on the observed backscattering of nine burned plots, during four different postfire acquisitions (n=36). In general, the model provides satisfying estimations of the backscattering with a root-mean-square error of 1.01 dB. The copolar signal for both HH and VV showed a mild decrease with increasing fire impacts and was considerably affected by the incidence angle. From the experiments performed in the simulated environment, it is concluded that the SAR copolar (C-band) backscatter varies with respect to certain fire impact levels. Other important acquisition- or stand-dependent variables (such as incidence angle and snag age) were also found to impact the relationship between backscatter and fire impacts. Finally, the backscattering variability on increasing volumetric soil moisture (VSM) and snag moisture was examined. The increase of VSM from 20% to 30% amplified the signal in both copolarized bands by 1.2-1.5 dB. This amplification was more apparent on VV polarization than in HH polarization. Instead, the HH signal proved to be more sensitive on the increase of snag moisture, which was tested under a stable dry soil. [ABSTRACT FROM PUBLISHER]

The objective of this study was to analyze the spatial and temporal distribution of burned areas in Rondônia State, Brazil during the years 2000 to 2011 and evaluate the burned area maps. A Linear Spectral Mixture Model (LSMM) was applied to MODIS surface reflectance images to originate the burned areas maps, which were validated with TM/Landsat 5 and ETM+/Landsat 7 images and field data acquired in August 2013. The validation presented a correlation ranging from 67% to 96% with an average value of 86%. The lower correlation values are related to the distinct spatial resolutions of the MODIS and TM/ETM+ sensors because small burn scars are not detected in MODIS images and higher spatial correlations are related to the presence of large fires, which are better identified in MODIS, increasing the accuracy of the mapping methodology. In addition, the 12-year burned area maps of Rondônia indicate that fires, as a general pattern, occur in areas that have already been converted to some land use, such as vegetal extraction, large animal livestock areas or diversified permanent crops. Furthermore, during the analyzed period, land use conversion associated with climatic events significantly influenced the occurrence of fire in Rondônia and amplified its impacts. [ABSTRACT FROM AUTHOR]

Fires modify the structure of vegetation communities, the carbon and water cycles, the soil's chemistry, and affect the climate system. Within this context, this work aimed to understand the distribution patterns of burned areas in Brazil, during the period of 2002 to 2010, taking into consideration each one of the six Brazilian biomes (Amazon, Caatinga, Cerrado, Atlantic Forest, Pampa and Pantanal) and the respective major land cover classes. Data from the MODIS MCD45A1 product (burned area), as well as thermal anomalies (MOD14 and MYD14) and precipitation (TRMM), were analyzed according to the 2002 Brazilian official land cover and land use map (PROBIO). The Brazilian savanna biome, known as Cerrado, presented the largest concentration of burned areas detected by MODIS (73%), followed by the Amazon (14%), Pantanal (6%), Atlantic Forest (4%), Caatinga (3%), and Pampa (0,06%) biomes. Indeed, in the years of 2007 and 2010, 90% and 92% of Brazil's burned areas were concentrated in the Cerrado and Amazon biomes, respectively. TRMM data indicated that during these two years there was a significant influence of La Niña, causing low rainfall in the Amazon, Cerrado, Caatinga, and Atlantic Forest biomes. Regarding the land cover classes, approximately 81% of the burned areas occurred over remnant vegetation areas. Although no unequivocal correlation can be established between burned areas and new land conversions, the conspicuous concentration of fire scars, particularly in Amazon-Cerrado transition (i.e., the Arc of Deforestation) is certainly not a simple coincidence. Such patterns and trends corroborate the need of improved territorial governance, in addition to the implementation of systematic fire warning and preventive systems. [ABSTRACT FROM AUTHOR]

Biomass burning releases a significant amount of trace gases and aerosols into the atmosphere and affects climate change, carbon cycle, and air quality. Accurate estimates of emissions depend strongly on the calculations of burned areas. Here, we present an algorithm that is used to derive burned areas by blending active fire observations from multiple satellites which are provided in the Hazard Mapping System (HMS). The HMS consolidates automated fire detections from Geostationary Operational Environmental Satellite (GOES) Imager, Advanced Very High Resolution Radiometer (AVHRR), and MODerate resolution Imaging Spectroradiometer (MODIS). Our goals are to derive burned areas in each GOES fire pixel across contiguous United States (CONUS) from 2004 to 2007 and to validate the estimates using Landsat Thematic Mapper/Enhanced Thematic Mapper plus (TM/ETM+) burn scars and National Fire Inventory data. The results show that annual fire events burn 0.4% (3.4\times \break 10^4\ \km^2) of total land across CONUS, which consists of 0.49% of total forests, 0.64% of savannas, 0.68% of shrublands, 0.40% of grasslands, and 0.30% of croplands. The large burned areas are dominantly distributed in the western CONUS, followed by the states in the southeast region and along the Mississippi Valley. Extensive validation shows that MODIS+AVHRR+GOES instruments greatly improve the determination of fire duration and fire detection rate compared to single instrument detections. The detection rate of small fire events (< 10 \ \km^2) from multiple instruments is 24% and 36% higher than that from MODIS and GOES, respectively. The error in the burned-area estimate is less than 30% in individual ecosystems, and it decreases exponentially with the increase of burn scar size. Overall, the accuracy of total burned area across CONUS is 98.9% when compared to TM/ETM+-based burn scars and 83% when compared to national inventory data. [ABSTRACT FROM AUTHOR]

A novel automatic burned area mapping algorithm for Mediterranean ecosystems based on Moderate-Resolution Imaging Spectroradiometer (MODIS) time series data is presented in this paper. This algorithm is based on a two-phase approach. The first phase detects the most severely burned areas, using spectral/temporal rules computed from dynamic temporal windows. The second phase improves the discrimination of burned areas around those “seed” burned pixels using contextual algorithms based on edge detectors. The use of those filters improved the performance of the contextual algorithm, by refining the discrimination of fire perimeters while restricting the segmentation process. The algorithm was validated over six Mediterranean regions during the fire season of 2003, where reference data was generated using Landsat TM/ETM+ images. Omission and commission errors were below 20%, with an overall Kappa value of 0.846. The validation based on regression scattergraphs of 5 \times 5 km grids showed good agreement as well (R^2 = 0.972). The standard MODIS burned area product (MCD45A1) showed lower accuracy than the proposed algorithm, with higher omission errors (38.6%) and lower Kappa (0.704) and R^2 (0.838) values. [ABSTRACT FROM AUTHOR]

This study analyses the relationship between fire incidence and some environmental factors, exploring the spatial non-stationarity of the phenomenon in sub-Saharan Africa. Geographically weighted regression (GWR) was used to study the above relationship. Environment covariates comprise land cover, anthropogenic and climatic variables. GWR was compared to ordinary least squares, and the hypothesis that GWR represents no improvement over the global model was tested. Local regression coefficients were mapped, interpreted and related with fire incidence. GWR revealed local patterns in parameter estimates and also reduced the spatial autocorrelation of model residuals. All the covariates were non-stationary and in terms of goodness of fit, the model replicates the data very well ( R = 87%). Vegetation has the most significant relationship with fire incidence, with climate variables being more important than anthropogenic variables in explaining variability of the response. Some coefficient estimates exhibit locally different signs, which would have gone undetected by a global approach. This study provides an improved understanding of spatial fire-environment relationships and shows that GWR is a valuable complement to global spatial analysis methods. When studying fire regimes, effects of spatial non-stationarity need to be incorporated in vegetation-fire modules to have better estimates of burned areas and to improve continental estimates of biomass burning and atmospheric emissions derived from vegetation fires. [ABSTRACT FROM AUTHOR]

Abstract: A transformation defined on the near-infrared (NIR) and middle-infrared (MIR) space is presented that allows deriving a new coordinate system appropriate for vegetation and burned area discrimination. The transformation is based on the difference between MIR and NIR in conjunction with the distance from a convergence point in the MIR/NIR space, representative of a totally burnt surface. One of the derived coordinates presents a small scatter for pixels associated to vegetated surfaces (strict scale) whereas the other one covers a wide range of values (large scale) that suggest its use as a proxy of water content of vegetation. The strict scale character of the first coordinate together with the large scale character of the second one make the coordinate system especially adequate to discriminate vegetated surfaces and rank them according to the water content, from green and dry to burned vegetation. The performance of the new coordinate system is then assessed against than traditional ratio or modified ratio indices (namely the Vegetation Index, the Burned Area Index and the Global Environmental Monitoring Index, modified to the MIR/NIR space) and it is shown that the new coordinate system provides better information than traditional indices, opening interesting perspectives for burned area discrimination and other applications like drought monitoring. [Copyright &y& Elsevier]

Abstract: Maps of burned area have been obtained from an automatic algorithm applied to a multitemporal series of Landsat TM/ETM+ images in two Mediterranean sites. The proposed algorithm is based on two phases: the first one intends to detect the more severely burned areas and minimize commission errors. The second phase improves burned patches delimitation using a hybrid contextual algorithm based on logistic regression analysis, and tries to minimize omission errors. The algorithm was calibrated using six study sites and it was validated for the whole territory of Portugal (89,000km2) and for Southern California (70,000km2). In the validation exercise, 65 TM/ETM+ scenes for Portugal and 35 for California were used, all from the 2003 fire season. A good agreement with the official burned area perimeters was shown, with kappa values close to 0.85 and low omission and commission errors (<16.5%). The proposed algorithm could be operationally used for historical mapping of burned areas from Landsat images, as well as from future medium resolution sensors, providing they acquire images in two bands of the Short Wave Infrared (1.5–2.2μm). [Copyright &y& Elsevier]

A number of remote sensing studies have evaluated the temporal trends of the normalized difference vegetation index (NDVI or vegetation greenness) in the North American boreal forest during the last two decades, often getting quite different results. To examine the effect that the use of different datasets might be having on the estimated trends, we compared the temporal trends of recently burned and unburned sites of boreal forest in central Canada calculated from two datasets: the Global Inventory, Monitoring, and Modeling Studies (GIMMS), which is the most commonly used 8 km dataset, and a new 1 km dataset developed by the Canadian Centre for Remote Sensing (CCRS). We compared the NDVI trends of both datasets along a fire severity gradient in order to evaluate the variance in regeneration rates. Temporal trends were calculated using the seasonal Mann–Kendall trend test, a rank-based, nonparametric test, which is robust against seasonality, nonnormality, heteroscedasticity, missing values, and serial dependence. The results showed contrasting NDVI trends between the CCRS and the GIMMS datasets. The CCRS dataset showed NDVI increases in all recently burned sites and in 50% of the unburned sites. Surprisingly, the GIMMS dataset did not capture the NDVI recovery in most burned sites and even showed NDVI declines in some burned sites one decade after fire. Between 50% and 75% of GIMMS pixels showed NDVI decreases in the unburned forest compared with <1% of CCRS pixels. Being the most broadly used dataset for monitoring ecosystem and carbon balance changes, the bias towards negative trends in the GIMMS dataset in the North American boreal forest has broad implications for the evaluation of vegetation and carbon dynamics in this region and globally. [ABSTRACT FROM AUTHOR]

Abstract: A time series of burned land areas was generated for a 23 year period (1984–2006) using 10-day composites of AVHRR data. The study area covers 1.6 million km2 of boreal forest in western Canada. The algorithm was intended to be consistent throughout the study period and region, and to avoid commission errors, so as to obtain a reliable sample of temporal trends in burned area in the region. The algorithm relies on temporal comparisons of several spectral indices (GEMI, BAI), as well as near infrared reflectance. It emphasizes the stability of the post-fire signal, to avoid false detections associated with cloud, cloud shadows, missed data and radiometric or geometric calibration between AVHRR sensors. Final results show a very consistent temporal adjustment to official statistics and fire perimeters, with very low commission error (<10%), but medium to high omission error (50%). Burned areas in the region were predominantly associated with coniferous forest cover, with the Taiga and Boreal Shield ecozones, in latitudes between 56 and 60°N, and predominantly at long distances from populated places. [Copyright &y& Elsevier]

The purpose of the present study was to determine the effect of a single nonprescribed fire on species community composition, woody and herbaceous species diversity, carrying capacity, density of dominating shrubs, and their resprouting capacity after fire. For short and long-term evaluations, two sites were chosen in a private extensive cattle ranch: (1) unburned areas and (2) burned areas. The forage total cover before fire (initial stage) was 54%; this cover decreased in burned areas to 13% (1986) and to 25% (2000), mainly due to a reduction in forage shrub cover. The cover of Atriplex lampa (zampa), the dominant forage shrub, decreased from 17.0 to 0.20% in 1986 after fire, and 15 years later this value remains constant (0.33%). The same area showed a carrying capacity, expressed in hectares per large stock unit (LSU), of 10 ha LSU-1 by 1986 and 17 ha LSU-1 by 2000, whereas in unburned areas it was 7 ha LSU-1, similar to prewildfire. All selected species that had been cut, simulating the action of fire, resprouted 1 year later, except zampa. We determined that the re-establishment of zampa plants in burned areas is extremely slow. Conditions as absence of adventitious bud/root crown and drought in following post-fire years may have determined the poor recovery of A. lampa. [ABSTRACT FROM AUTHOR]

This paper describes a methodology of using data acquired by the European Meteosat and the Japanese Geostationary Meteorological Satellite (GMS) geostationary satellites to detect burned areas in different tropical environments. The methodology is based on a multiple threshold approach applied to the thermal radiance and to a spectral index specific for burned surfaces. The Simple Index for Burned Areas (SIBA), also developed in this study, makes use of the information contained in the visible and thermal InfraRed (IR) band available on the geostationary satellites, whose main advantages are the high temporal resolution and the minimal level of pre-processing required. The results obtained with Meteosat data have been evaluated comparing them with NOAA–Advanced Very High Resolution Radiometer (AVHRR) data acquired over the Central Africa forest–savannah areas. For GMS imagery, AVHRR data acquired over the woodland–savannah areas of Northern Territory in Australia have been used. Despite the very low spatial and spectral resolution of the data, accuracy assessment showed at a regional and continental scale the resulting burned area maps could be a valuable source of information for the monitoring of the fire activity and for the assessment of fire impact on tropospheric chemistry. [Copyright &y& Elsevier]

Anthropogenic activities responsible for modifying climatic regimes and land use and land cover (LULC) have been altering fire behavior even in regions with natural occurrences, such as the Pantanal. This biome was highlighted in 2020 due to the record number of fire foci and burned areas registered. Thus, this study aimed to understand how changes in LULC and climate affect the spatial, temporal and magnitude dynamics of fire foci. The Earth Trends Modeler (ETM) was used to identify trends in spatiotemporal bases of environmental and climatic variables. No trend was identified in the historical series of precipitation data. However, an increasing trend was observed for evapotranspiration, normalized difference vegetation index (NDVI) and temperature. For soil moisture, a decreasing trend was observed. The comparison between the mean of the historical series and the year 2020 showed that the variables precipitation, temperature, soil moisture and evapotranspiration had atypical behavior. Such behavior may have contributed to creating a drier environment with available combustible material, leading to a record number of burned areas, about three million hectares (248%) higher than the historical average. The 2020 fire foci data were used in two types of spatial statistical analyses: Grouping, showing that 76% of the registered fire foci were at high risk of fire and; Hot and Cold Spots, indicating high concentrations of Hot Spots in the northern region of the Pantanal, close to Cerrado and Amazon biomes agricultural frontier. The results of the Land Change Modeler (LCM) tool evidenced a strong transition potential from the natural vegetation to agriculture and pasture in the eastern region of the Pantanal, indicating that this could be, in the future, a region of high concentration of fire foci and possibly high risk of fire. This tool also allowed the prediction of a scenario for 2030 that showed that if measures for environmental protection and combating fires are not adopted, in this year, 20% of the Pantanal areas will be for agricultural and pasture use. Finally, the results suggest that the advance of agriculture in the Pantanal and changes in climatic and environmental variables boosted the increase in fire foci and burned areas in the year 2020. [Display omitted] • Anthropogenic activities and climate change intensified the fires in 2020. • Agriculture and pasture expanded over natural vegetation, mainly in Pantanal eastern. • The highest concentrations of fire foci are adjacent to the agricultural frontier. • Climatic and environmental variables presented atypical behavior in 2020. [ABSTRACT FROM AUTHOR]

• Fire responds differently to concurrent and antecedent climatic conditions. • Humid conditions boost burned areas in arid ecoregions, largely in grasslands. • Fuel accumulation plays a key role in controlling fire regimes across ecoregions. • Moisture and vegetation productivity mediate the climate-fire relationships. • Climate explained 21.53% of the variation in the burned area on average. Climate widely influences the geographical patterns of wildfires in terrestrial ecosystems. Understanding climate-fire relationships is crucial in exploring the response of wildfires to climatic conditions in different ecogeographic environments. However, how climate affects regional wildfires and which factors dominate the spatial patterns of climate-fire relationships are still poorly understood. Using satellite-based burned area data and climate datasets of the Mongolian Plateau, we analyzed the concurrent and antecedent climate-fire relationships; evaluated the spatial change characteristics of the climate-fire relationships along gradients of moisture, fuel, topography, and human activity; and estimated the ability of climate to explain fire dynamics to identify key factors mediating the spatial patterns of climate-fire relationships at ecoregion scales. The results showed that concurrent hot and dry and antecedent warm climatic conditions widely facilitated larger burned areas, and humid conditions increased the amount of burned areas in most arid ecoregions by controlling fuel accumulation. The strength of the climate-fire relationships changed along the environmental factor gradients, and the nature of climate-fire relationships shifted when variation in drivers exceeded the gradient thresholds. Wildfires respond differently to climatic conditions, and ecoregion-based models revealed that climate explained 21.53% of the variation in burned areas on average. These findings suggested that under universal arid environments, climate across multiple time scales shapes distinct local fire regimes by controlling fuel flammability and fuel accumulation, and vegetation productivity and moisture mediate the spatial patterns of climate-fire relationships across ecoregions. This study provides knowledge of climate-fire interactions in mid-latitude regions at ecoregion scales and facilitates policies for regional fire suppression. [Display omitted] Illustration: Climate across multiple time scales shapes the geographic patterns of fire activity by controlling fuel flammability and fuel availability. The change characteristics of the climate-fire relationships along the environmental factor gradients were evaluated to identify key factors mediating the spatial patterns of climate-fire relationships at ecoregion scales. Climate explained 21.53% of the variation in burned areas on average, and vegetation productivity and moisture mediate the spatial patterns of climate-fire relationships across ecoregions. [ABSTRACT FROM AUTHOR]

Mapping burned areas using satellite imagery has become a subject of extensive research over the past decades. The availability of high-resolution satellite data allows burned area maps to be produced with great detail. However, their increasing spatial resolution is usually not matched by a similar increase in the temporal domain. Moreover, high-resolution data can be a computational challenge. Existing methods usually require downloading and processing massive volumes of data in order to produce the resulting maps. In this work we propose a method to make this procedure fast and yet accurate by leveraging the use of a coarse resolution burned area product, the computation capabilities of Google Earth Engine to pre-process and download Sentinel-2 10-m resolution data, and a deep learning model trained to map the multispectral satellite data into the burned area maps. For a 1500 ha fire our method can generate a 10-m resolution map in about 5 min, using a computer with an 8-core processor and 8 GB of RAM. An analysis of six important case studies located in Portugal, southern France and Greece shows the detailed computation time for each process and how the resulting maps compare to the input satellite data as well as to independent reference maps produced by Copernicus Emergency Management System. We also analyze the feature importance of each input band to the final burned area map, giving further insight about the differences among these events. [ABSTRACT FROM AUTHOR]

• There was a significant positive relationship between number of fires and mean annual temperature. • There was a significant negative relationship between number of fires and mean annual relative humidity. • There was a significant positive relationship between burned areas and mean annual wind velocity. • There was a significant negative relationship between burned areas and mean annual relative humidity. • The RF model created an accurate fire susceptibility map using climatic data. Fire is a natural disturbance in a wide range of ecosystem types around the world. Although the majority of fires are caused by anthropogenic ignitions, climate and weather are known to influence the risk of fire spread, regardless of ignition source. However, the specific relationships among these factors remain under-studied for many regions of the world. The objective of this study was to investigate the effect of climatic variables on forest and rangeland fire regimes in the Golestan Province of Iran in three recent decades. Specifically, we sought to determine: (a) the quantitative temporal relationships between climatic variables and fire occurrence; and (b) the spatial relationships between climatic variables and fire occurrence. To assess quantitative temporal relationships, we used correlation and regression analyses to identify and describe statistically significant relationships between climatic data (mean annual temperature, mean annual precipitation, mean annual relative humidity, and mean annual wind velocity) and fire data (number of fire and area burned), using climatic data from the Golestan Meteorological Administration and fire statistics from the Golestan Natural Resources Administration (GNRA). To determine the spatial relationships between climatic variables and fire occurrence, we combined geospatial data on climatic variables (mean annual temperature, mean annual rainfall, wind effect, and topographic wetness index (TWI)) obtained from ASTER-GDEM and active fires derived from MODIS fire products in a random forest (RF) model approach to construct a map of fire susceptibility at 30 m spatial resolution. Our results showed that there was a significant temporal relationship between the number of fires and mean annual temperature, mean annual relative humidity, and mean annual wind velocity. There were also significant temporal relationships between area burned and mean annual relative humidity and mean annual wind velocity. Based on the RF model, the mean annual rainfall had the highest relative importance in fire spatial occurrence. Validation of the fire susceptibility map using area under the curve (AUC) indicated that the RF model created an accurate fire susceptibility map (AUC: 0.823), suggesting that the map can be used as a spatio-temporal climatic alarm system for fire occurrence in the different climatic conditions of the Golestan Province. The results from this study will be important for helping perform climate-change analyses for investigating future temporal and spatial patterns of wildfire in the Golestan Province. [ABSTRACT FROM AUTHOR]

This paper presents a new method, based on clustering and thresholding, to automatically perform binary change detection in multitemporal spectral indices. The method is denoted as Buffer-From-Cluster Approach (BFCA). To estimate the distributions of changed and unchanged pixels, as needed for the purpose of a reliable thresholding of a spectral index, a clustering algorithm is preliminarily applied to identify image objects possibly corresponding to areas where significant changes occurred. Then, a buffer zone is created around the selected cluster to identify unchanged areas surrounding changed ones. The cluster and the buffer zone are jointly analyzed to estimate the distributions of changed and unchanged pixels and to verify that they can be distinguished from each other. Finally, the results of thresholding and clustering are combined to generate the binary change map. The BFCA has been conceived to map the extent of the areas affected by a natural disaster like wildfire. To validate the proposed method, burned area maps produced by applying the BFCA to spectral indices derived from Sentinel-2 data have been compared to maps produced by the Copernicus Emergency Management Service. For testing the multi-hazard detection capability, the same kind of exercise has been carried out for a flooding test case too. The positive results of the comparison have confirmed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Wildfires have major ecological, social and economic consequences. Information about the extent of burned areas is essential to assess these consequences and can be derived from remote sensing data. Over the last years, several methods have been developed to segment burned areas with satellite imagery. However, these methods mostly require extensive preprocessing, while deep learning techniques—which have successfully been applied to other segmentation tasks—have yet to be fully explored. In this work, we combine sensor-specific and methodological developments from the past few years and suggest an automatic processing chain, based on deep learning, for burned area segmentation using mono-temporal Sentinel-2 imagery. In particular, we created a new training and validation dataset, which is used to train a convolutional neural network based on a U-Net architecture. We performed several tests on the input data and reached optimal network performance using the spectral bands of the visual, near infrared and shortwave infrared domains. The final segmentation model achieved an overall accuracy of 0.98 and a kappa coefficient of 0.94. [ABSTRACT FROM AUTHOR]

Mount Kenya is one of Kenya's 'water towers', the headwaters for the country's major rivers including the Tana River and Ewaso Nyiro River, which provide water and hydroelectric power to the semiarid region. Fires affect water downstream, but are difficult to monitor given limited resources of local land management agencies. Satellite-based remote sensing has the potential to provide long term coverage of large remote areas on Mount Kenya, especially using the free Landsat data archive and moderate resolution imaging spectroradiometer (MODIS) fire products. In this study, we mapped burn scars on Mount Kenya using 30 m Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) derived dNBR (change in normalized burn ratio) and MODIS active fire detection and burned area data for fires occurring from 2004 to 2015. We also analyzed topographic position (elevation, slope, aspect) of these fires using an ASTER global digital elevation model (GDEM v2) satellite-derived 30 m digital elevation model (DEM). Results indicate that dNBR images calculated from data acquired about one year apart were able to identify large fires on Mount Kenya that match locations (and timing) of MODIS active fire points and burned areas from the same time period, but we were unable to detect smaller and/or older fires. [ABSTRACT FROM AUTHOR]