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Climate changes have led to an increase in fire rates throughout the entire range of larch (Larix sibirica, L. gmelinii, and L. cajanderi). We have tested the hypothesis that natural fires are an important factor that contributes to the functional stability and dominance of larch forests in the continuous permafrost zone. We include satellite imageries, on-ground survey data, dendrochronological measurements, and ecoclimatic variables in our analysis. We find that warming in the 21st century has led to an increase in the intensity and frequency of fires and moderate and extreme (>10 000 ha) burned areas. The maximal burn areas and fire frequency are observed in the northern and southern parts of the permafrost zone, respectively. The frequency of fires and burned areas are inversely exponentially dependent on precipitation, soil and ground cover moisture, and atmospheric drought, and they increase exponentially with an increase in air temperature. In the zone of continuous permafrost, larch successfully regenerates in burned areas (up to 500 000+/ha of seedlings). In the zone of island permafrost (the southern part of the study area), the number of regeneration is 2–3 orders lower, and regeneration is represented mainly by hardwood species. The increasing fire frequency in the south of the larch range contributes to the partial transformation of the forested areas into grass and shrub communities. There is a high probability that larch will retreat from its southern range during the process of continuous permafrost thawing. Gross primary productivity (GPP) in burned areas quickly (3–15 years) recovers to the prefire level. In combination with increasing GPP trends, that indicates that larch forests retain their function as a carbon sink despite the increasing fire rate. Under conditions of an increase in fire rate, the firefighting strategy must be changed. It is necessary to realize (1) the impossibility of the total suppression of fires and (2) the ecological significance of fires in the larch forests in the cryolithozone, in which fires are the most important factor supporting the health and dominance of larch forests. Alongside that, periodic natural fires reduce the likelihood of catastrophic fires. It is necessary to focus firefighting efforts on the areas of the most important social, natural, and economic importance, while controlling burning outside these areas by monitoring methods. [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]

Fire occurrence has been increasing in the high Andean grasslands due to the presence of highly flammable elements and climate change. Understanding the effects of the fires is crucial for the conservation of these ecosystems. This study aimed to determine the effects of fires on the composition, diversity, fruiting of plant species, and physicochemical characteristics of soils in high Andean grasslands over two post-fire growing seasons. We assessed ten fires, five of which occurred in 2023 (one post-fire growing season) and the other five in 2022 (two post-fire growing seasons). Ten transects were established at each study site, five in burned areas and five in unburned areas. Species composition differed between burned and unburned areas across both post-fire growing seasons. Species diversity and evenness were higher in the burned areas compared to unburned areas after two post-fire growing seasons. In both post-fire growing seasons, dry mass was higher in unburned areas than in burned areas. Herbs showed a higher percentage of individuals in the reproductive stage in burned areas, but only after one post-fire growing season. In contrast, the presence of woody individuals with reproductive structures was higher in unburned areas across both post-fire growing seasons. Regarding soils, only some parameters (organic matter, Na+, CEC, pH, available P, and K+) were affected by fires, and the direction of these changes varied between ∼ 6 months and ∼ 18 months post-fires. Our study suggests that fire severity in this type of ecosystem is low, as most soil parameters were not significantly affected. The burning of grasses may lead to increased diversity, particularly due to the enhanced fruiting of herb species. However, the recovery of woody species and biomass requires more time, which is perceived negatively by the local population.

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.

Background: The cloud-penetrating and fog-penetrating capability of Synthetic Aperture Radar (SAR) give it the potential for application in forest fire progress monitoring; however, the low extraction accuracy and significant salt-and-pepper noise in SAR remote sensing mapping of the burned area are problems. Aims: This paper provides a method for accurately extracting the burned area based on fully exploiting the changes in multiple different dimensional feature parameters of dual-polarised SAR images before and after a fire. Methods: This paper describes forest fire progress monitoring using dual-polarisation SAR images combined with multi-scale segmentation and unsupervised classification. We first constructed polarisation feature and texture feature datasets using multi-scene Sentinel-1 images. A multi-scale segmentation algorithm was then used to generate objects to suppress the salt-and-pepper noise, followed by an unsupervised classification method to extract the burned area. Key results: The accuracy of burned area extraction in this paper is 91.67%, an improvement of 33.70% compared to the pixel-based classification results. Conclusions: Compared with the pixel-based method, our method effectively suppresses the salt-and-pepper noise and improves the SAR burned area extraction accuracy. Implications: The fire monitoring method using SAR images provides a reference for extracting the burned area under continuous cloud or smoke cover. This paper describes a method to monitor forest fire progress using dual-polarisation Synthetic Aperture Radar (SAR) images combined with multi-scale segmentation and unsupervised classification. We aimed to take full advantage of the many different dimensions of feature parameter changes caused by forest fires, relying on time-series dual-polarised SAR imagery to achieve burned area extraction and forest fire progress monitoring. [ABSTRACT FROM AUTHOR]

This study investigated the relationship between climate and biophysical variables in burned areas in Iran. The fire burned area (FBA) product (Fire CCI 5.1.1), land surface temperature (MOD11C3C), vegetation index (MOD13A1), and climate variables such as temperature, wind speed, relative humidity, and volumetric soil moisture from the ERA5 reanalysis dataset were used. Pearson correlation coefficient was used to determine the relationship between biophysical and climate variables and fire occurrence. The results show that FBA increased by 1.7 hectares/decade from 2001 to 2020. The high FBA in 2010 (the black summer of Iran) was due to high temperatures and significant heatwaves that led to extensive wildfires. Although anthropogenic activities are considered a significant cause of wildfires, several variables, including increased temperatures, less precipitation, relative humidity, and wind speed and direction, contribute to the extent and occurrence of wildfires. The country's FBA hotspot is in the Arasbaran region during the summer season. Temperature and relative humidity are the most significant variables influencing the occurrence of wildfires. The results show the vulnerability of Iran's forests and their high potential for fires. Considering the frequency of fire occurrences in Iran and the limited equipment, fire prevention plans should be carried out by applying proper management in high-risk regions. [ABSTRACT FROM AUTHOR]

Fire is a common natural disturbance in forest ecosystems and plays an important role in subsequent vegetation patterns. Based on the spatial sequence method, adopted as an alternative to the time successional sequence method, we selected burned areas in different locations in the Anning River Basin, which encompasses typical dry valleys. Quadrat surveys and quantitative classification were used to identify and classify the vegetation and distribution pattern and to carry out environmental interpretation during the natural restoration process after a forest fire. The results showed the following: (1) in the early stage of natural recovery after a forest fire disturbance, the vegetation community could be divided into seven community types, and Quercus guyavaefolia H. Leveille (Qg) was the dominant species in the community; (2) the vegetation samples could be divided into five ecological types, and the classification and distribution pattern of community types in this region changed most notably with altitude; and (3) a detrended correspondence analysis could be used to accurately classify vegetation community types, while a detrended canonical correspondence analysis could reveal the relationships between species and environmental factors. This study provides a scientific basis for guiding the restoration of ecosystem structural stability and biodiversity in burned areas. [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]

Copyright of Forest Research is the property of Forest Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Forest fires are one of the significant disturbances in forest ecosystems. It is essential to extract burned areas rapidly and accurately to formulate forest restoration strategies and plan restoration plans. In this work, we constructed decision trees and used a combination of differential normalized burn ratio (dNBR) index and OTSU threshold method to extract the heavily and mildly burned areas. The applicability of this method was evaluated with three fires in Muli County, Sichuan, China, and we concluded that the extraction accuracy of this method could reach 97.69% and 96.37% for small area forest fires, while the extraction accuracy was lower for large area fires, only 89.32%. In addition, the remote sensing environment index (RSEI) was used to evaluate the ecological environment changes. It analyzed the change of the RSEI level through the transition matrix, and all three fires showed that the changes in RSEI were stronger for heavily burned areas than for mildly burned areas, after the forest fire the ecological environment (RSEI) was reduced from good to moderate. These results realized the quantitative evaluation and dynamic evaluation of the ecological environment condition, providing an essential basis for the restoration, decision making and management of the affected forests. [ABSTRACT FROM AUTHOR]

Protected areas (PA) play an important role in minimizing the effects of soil erosion in watersheds. This study evaluated the performance of machine learning models, specifically support vector machine with linear kernel (SVMLinear), support vector machine with polynomial kernel (SVMPoly), and random forest (RF), on identifying indicators of soil erosion in 761 sub-watersheds and PA in northern Portugal, by using soil erosion by water in Europe, according to the revised universal soil loss equation (RUSLE2015), as target variable. The parameters analyzed were: soil erosion by water in Europe according to the revised universal soil loss equation (RUSLE2015), total burned area of the sub-watershed in the period of 1975-2020, fire recurrence, topographic wetness index (TWI), and the morphometric factors, namely area (A), perimeter (P), length (L), width (W), orientation (O), elongation ratio (Re), circularity ratio (Rc), compactness coefficient (Cc), form factor (Ff), shape factor (Sf), DEM, slope, and curvature. The median coefficient of determination (R2) for each model was RF (0.61), SVMpoly (0.68), and SVMLinear (0.54). Regarding the analyzed parameters, those that registered the greatest importance were A, P, L, W, curvature, and burned area, indicating that an analysis which considers morphometric factors, together with soil erosion data affected by water and soil moisture, is an important indicator in the analysis of soil erosion in watersheds. [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]

This study evaluated of the impact of wildfires, burned areas and affected species, for the first time, in Fez, Sefrou and Boulemane provinces located in the Middle Atlas Mountains of Morocco. Data collected from field surveys from 2000 to 2020 and GIS-based aggregation functions were used to produce spatial distribution maps and to show the evolution of wildfires and burned areas. Results show that wildfires were intense during summer periods, mainly in Sefrou and Boulemane where the rate of wildfires and burned areas were higher. Nine plant species were affected mostly, Pinus halepensis (43 wildfires) and Quercus rotundifolia (21 events). In Fez province, only two pre-urban forests were affected (51 wildfires), while in Boulemane (91 wildfires) and Sefrou (193 wildfires), the wildfires were severe and affected mountainous areas far from urban perimeters. Although this study demonstrated the impact of wildfires in the Middle Atlas, the biotic and abiotic factors might have a crucial role in this phenomenon and their impacts must be addressed carefully. This investigation offers new insight into the impact of wildfires in the forests of the Middle Atlas Mountains and provides valuable reference data to study other regions in the High Atlas and other plant species taking into account the threatened species.

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]

Protected areas (PA) play an important role in minimizing the effects of soil erosion in watersheds. This study evaluated the performance of machine learning models, specifically support vector machine with linear kernel (SVMLinear), support vector machine with polynomial kernel (SVMPoly), and random forest (RF), on identifying indicators of soil erosion in 761 sub-watersheds and PA in northern Portugal, by using soil erosion by water in Europe, according to the revised universal soil loss equation (RUSLE2015), as target variable. The parameters analyzed were: soil erosion by water in Europe according to the revised universal soil loss equation (RUSLE2015), total burned area of the sub-watershed in the period of 1975-2020, fire recurrence, topographic wetness index (TWI), and the morphometric factors, namely area (A), perimeter (P), length (L), width (W), orientation (O), elongation ratio (Re), circularity ratio (Rc), compactness coefficient (Cc), form factor (Ff), shape factor (Sf), DEM, slope, and curvature. The median coefficient of determination (R2) for each model was RF (0.61), SVMpoly (0.68), and SVMLinear (0.54). Regarding the analyzed parameters, those that registered the greatest importance were A, P, L, W, curvature, and burned area, indicating that an analysis which considers morphometric factors, together with soil erosion data affected by water and soil moisture, is an important indicator in the analysis of soil erosion in watersheds.

Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed an approach for representing synoptic- and diurnal-scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We disaggregated monthly GFED3 emissions during 2003–2009 to a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) active fire observations. Daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of burning in savannas. These patterns were consistent with earlier field and modeling work characterizing fire behavior dynamics in different ecosystems. On diurnal timescales, our analysis of the GOES WF_ABBA active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top-down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from multiple satellite sensors to improve daily emissions estimates.

Copyright of Ingeniería Solidaria is the property of Universidad Cooperativa de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this letter, we propose an approach based on the use of Sentinel-2 spectral indices and self-organizing map (SOM) to automatically map burned areas and burned severity. These analyses were performed on a test area in Chania, located in Crete, affected by a fire (around 200 ha) that occurred from July 13, 2018 to July 28, 2018. The investigated area is characterized by heterogeneous land cover types made up of natural and agricultural lands. To identify different levels of fire severity without using fixed thresholds, we applied SOM to the three spectral indices normalized difference vegetation index (NDVI), normalized burn ratio (NBR), and burned area index for sentinel (BAIS) used to enhance burned areas. This is a particular critical issue because fixed threshold values are generally not suitable for fragmented landscapes, vegetation types, and geographic regions different from those for which they were devised. To cope with this issue, the methodological approach herein proposed is based on three steps: 1) indices computation; 2) maps of the difference of the three indices computed using the data acquired from prefire and postfire occurrences; and 3) unsupervised classification obtained processing all the difference maps using the SOM. The obtained results were validated using an independent data set, which showed high correlation with satellite-based fire severity. [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 the tropics and subtropics, most fires are set by humans for a wide range of purposes. The total amount of burned area and fire emissions reflects a complex interaction between climate, human activities, and ecosystem processes. Here we used satellite-derived data sets of active fire detections, burned area, precipitation, and the fraction of absorbed photosynthetically active radiation (fAPAR) during 1998–2006 to investigate this interaction. The total number of active fire detections and burned area was highest in areas that had intermediate levels of both net primary production (NPP; 500–1000 g C m−2 year−1) and precipitation (1000–2000 mm year−1), with limits imposed by the length of the fire season in wetter ecosystems and by fuel availability in drier ecosystems. For wet tropical forest ecosystems we developed a metric called the fire-driven deforestation potential (FDP) that integrated information about the length and intensity of the dry season. FDP partly explained the spatial and interannual pattern of fire-driven deforestation across tropical forest regions. This climate-fire link in combination with higher precipitation rates in the interior of the Amazon suggests that a negative feedback on fire-driven deforestation may exist as the deforestation front moves inward. In Africa, compared to the Amazon, a smaller fraction of the tropical forest area had FDP values sufficiently low to prevent fire use. Tropical forests in mainland Asia were highly vulnerable to fire, whereas forest areas in equatorial Asia had, on average, the lowest FDP values. FDP and active fire detections substantially increased in forests of equatorial Asia, however, during El Niño periods. In contrast to these wet ecosystems we found a positive relationship between precipitation, fAPAR, NPP, and active fire detections in arid ecosystems. This relationship was strongest in northern Australia and arid regions in Africa. Highest levels of fire activity were observed in savanna ecosystems that were limited neither by fuel nor by the length of the fire season. However, relations between annual precipitation or drought extent and active fire detections were often poor here, hinting at the important role of other factors, including land managers, in controlling spatial and temporal variability of fire.

Forest fire is a common concern in Mediterranean watersheds. Fire-induced canopy mortality may cause the degradation of chemical–physical properties in the soil and influence hydrological processes within and across watersheds. However, the prediction of the pedological and hydrological effect of forest fires with heterogenous severities across entire watersheds remains a difficult task. A large forest fire occurred in 2017 in northern Italy providing the opportunity to test an integrated approach that exploits remote and in-situ data for assessing the impact of forest fires on the hydrological response of semi-natural watersheds. The approach is based on a combination of remotely-sensed information on burned areas and in-situ measurements of soil infiltration in burned areas. Such collected data were used to adapt a rainfall–runoff model over an experimental watershed to produce a comparative evaluation of flood peak and volume of runoff in pre- and post-fire conditions. The model is based on a semi-distributed approach that exploits the Soil Conservation Service Curve Number (SCS-CN) and lag-time methods for the estimation of hydrological losses and runoff propagation, respectively, across the watershed. The effects of fire on hydrological losses were modeled by adjusting the CN values for different fire severities. Direct infiltration measurements were carried out to better understand the effect of fire on soil infiltration capacity. We simulated the hydrological response of the burned watershed following one of the most severe storm events that had hit the area in the last few years. Fire had serious repercussions in regard to the hydrological response, increasing the flood peak and the runoff volume up to 125% and 75%, respectively. Soil infiltration capacity was seriously compromised by fire as well, reducing unsaturated hydraulic conductivity up to 75% compared with pre-fire conditions. These findings can provide insights into the impact of forest fires on the hydrological response of a whole watershed and improve the assessment of surface runoff alterations suffered by a watershed in post-fire conditions.

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]

In this letter, we performed investigations on the potentiality of the Sentinel-1, C-band synthetic-aperture radar (SAR), for the characterization and mapping of burned areas and fire severity. To this aim, we focused on fire occurred on July 13, 2017, in Metaponto (South of Italy). Both VH and VV polarizations were considered. Radar Burn Difference (RBD) and radar burn ratio (RBR) were computed between Sentinel-1 data acquired before and after the fire using both single- and time-averaged scenes (to reduce speckle noise effects). The most marked differences between burned and unburned areas were observed in the VH polarization of both RBD and RBR. The novelty of our approach is based on the use of three steps data processing devised to identify different levels of fire severity without using fixed thresholds. The burned areas are first: 1) highlighted using the ratio between multitemporal data set acquired before and after the fire occurrence; 2) further enhanced by Getis–Ord spatial statistic; and 3) finally, categorized using ISODATA unsupervised classification. The approach herein proposed pointed out that: 1) the time-averaged ratio of VH polarization of Sentinel-1 well perform in mapping burned area and 2) the use of Getis–Ord spatial statistic coupled with ISODATA unsupervised classification suitably captures the diverse levels of burned severity as confirmed by in situ assessment. [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]

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.

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.

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.

Characterizing biophysical changes in land change areas over large regions with short and noisy multivariate time series and multiple temporal parameters remains a challenging task. Most studies focus on detection rather than the characterization, i.e., the manner by which surface state variables are altered by the process of changes. In this study, a procedure is presented to extract and characterize simultaneous temporal changes in MODIS multivariate times series from three surface state variables the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST) and albedo (ALB). The analysis involves conducting a seasonal trend analysis (STA) to extract three seasonal shape parameters (Amplitude 0, Amplitude 1 and Amplitude 2) and using principal component analysis (PCA) to contrast trends in change and no-change areas. We illustrate the method by characterizing trends in burned and unburned pixels in Alaska over the 2001–2009 time period. Findings show consistent and meaningful extraction of temporal patterns related to fire disturbances. The first principal component (PC1) is characterized by a decrease in mean NDVI (Amplitude 0) with a concurrent increase in albedo (the mean and the annual amplitude) and an increase in LST annual variability (Amplitude 1). These results provide systematic empirical evidence of surface changes associated with one type of land change, fire disturbances, and suggest that STA with PCA may be used to characterize many other types of land transitions over large landscape areas using multivariate Earth observation time series.

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.

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In the framework of the landslide susceptibility assessment, the maps produced should include not only the landslide initiation areas, but also those areas potentially affected by the traveling mobilized material. To achieve this purpose, the susceptibility analysis must be separated in two distinct components: (1) The first one, which is also the most discussed in the literature, deals with the susceptibility to failure, and (2) the second component refers to the run-out modeling using the initiation areas as an input. Therefore, in this research we present a debris flow susceptibility assessment in a recently burned area in a mountain zone in central Portugal. The modeling of debris flow initiation areas is performed using two statistical methods: a bivariate (information value) and a multivariate (logistic regression). The independent validation of the results generated areas under the receiver operating characteristic curves between 0.91 and 0.98. The slope angle, plan curvature, soil thickness and lithology proved to be the most relevant predisposing factors for the debris flow initiation in recently burned areas. The run-out is simulated by applying two different methods: the empirical model Flow Path Assessment of Gravitational Hazards at a Regional Scale (Flow-R) and the hydrological algorithm D-infinity downslope influence (DI). The run-out modeling of the 36 initiation areas included in the debris flow inventory delivered a true positive rate of 83.5% for Flow-R and 80.5% for DI, reflecting a good performance of both models. Finally, the susceptibility map for the entire basin including both the initiation and the run-out areas in a scenario of a recent wildfire was produced by combining the four models mentioned above. [ABSTRACT FROM AUTHOR]

Background: Principal components analysis (PCA) is based conventially on the eigenvector decomposition (EVD). Mean-centering the input data prior to the eigenanalysis is treated as an integral part of the algorithm. It ensures that the first principal component is proportional to the maximum variance of the input data. Equivalent to EVD, but numerically more robust, is the singular value decomposition (SVD). Mean-centered data subjected to SVD, yield transformation coefficients identical to EVD. Nevertheless, mean-centering is optional in SVD. Avoiding to center the input data, results in generic first component that mainly reflects their mean. This may, however, detect more accurately distinct clusters in PCA-based change detection applications. Methods: In remote sensing, PCA transforms multi-spectral bands into a new coordinate system. The first, among the transformed components, contain the variance of unchanged landscape features. Succeeding components may contain an enhanced variance of changed features. Such is the case of burned surfaces appearing as distinct clusters in multitemporal composites. Conclusions: Within this framework, a non-centered SVD may increase the spectral separability of burned clusters among other features in some of the higher order principal components. [ABSTRACT FROM AUTHOR]