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Forest and land fires cause substantial economic, social, and environmental devastation. Interagency forest and land fire management has succeeded in decreasing the impact of these fires, particularly in Indonesia. Having comprehensive information on fire locations and frequencies will benefit national forest and land fire management programmes. This study describes nearly a decade of satellite-based burned area (BA) monitoring conducted by the Indonesian government. We discuss (1) the history of BA mapping in Indonesia, (2) the most recent techniques for producing monthly BA maps, (3) an evaluation of product accuracy, (4) advantages and disadvantages, and (5) recommendations for future research. The most recent approach combines manual and digital classification, primarily using Landsat images, but has been supplemented with Sentinel data since 2020. The digital analysis, named the normalized burn ratio difference index threshold, was used to distinguish between burned and unburned pixels, guiding the interpreter’s manual digitization. BA confidence levels were determined using active fire products and ground truth data. We engaged provincial and local agency stakeholders to verify the products and provide quality assurance. We also assessed product performance by examining high-resolution images captured at three different locations to ascertain the relative advantages and disadvantages, which varied depending on each region’s fire regime. Small fires and cloud cover reduced the accuracy of the national monthly BA product. However, the omission error decreased by 65% when all fires throughout the entire year were considered. The inclusion of all available Sentinel-2 (A and B) images yielded higher accuracy than using only Landsat 8 data. We conclude that Indonesia’s cloud coverage constraint requires additional observational data to obtain clear-sky imagery when using optical sensors, in addition to the adjusted technique. We urge the introduction of reliable digital classifications for all Indonesian fire regimes to simplify resource deployment and reduce manual labour operations. [ABSTRACT FROM AUTHOR]

Accurate burn severity mapping is essential for understanding the impacts of wildfires on vegetation dynamics in arid savannas. The frequent wildfires in these biomes often cause topkill, where the vegetation experiences above-ground combustion but the below-ground root structures survive, allowing for subsequent regrowth post-burn. Investigating post-fire regrowth is crucial for maintaining ecological balance, elucidating fire regimes, and enhancing the knowledge base of land managers regarding vegetation response. This study examined the relationship between bush burn severity and woody vegetation post-burn coppicing/regeneration events in the Kalahari Desert of Botswana. Utilizing UAV-derived RGB imagery combined with a Random Forest (RF) classification algorithm, we aimed to enhance the precision of burn severity mapping at a fine spatial resolution. Our research focused on a 1 km2 plot within the Modisa Wildlife Reserve, extensively burnt by the Kgalagadi Transfrontier Fire of 2021. The UAV imagery, captured at various intervals post-burn, provided detailed orthomosaics and canopy height models, facilitating precise land cover classification and burn severity assessment. The RF model achieved an overall accuracy of 79.71% and effectively identified key burn severity indicators, including green vegetation, charred grass, and ash deposits. Our analysis revealed a >50% probability of woody vegetation regrowth in high-severity burn areas six months post-burn, highlighting the resilience of these ecosystems. This study demonstrates the efficacy of low-cost UAV photogrammetry for fine-scale burn severity assessment and provides valuable insights into post-fire vegetation recovery, thereby aiding land management and conservation efforts in savannas. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal 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.)

Satellite remote sensing is a critical tool for continental and synoptic monitoring and mapping of savannah wildfires. Satellite active fire products, which report on the time and location of a fire and may further characterise fire by estimating fire radiative power (FRP), provide valuable utility for savannah fire management and carbon accounting. These applications require that satellite measurements are of high accuracy, which can only be determined through validation. However, acquiring reference data for validation that is a representative of the fire conditions at the time of satellite image capture is challenging, due to rapid changes in fire behaviour and the inherent safety considerations of collecting field data during fire events. This review explores traditional and contemporary methods used to assess the accuracy and consistency of fire detections and FRP derived from satellite data in savannah ecosystems, with a focus on the approaches and challenges in collecting suitable reference data for a phenomenon as dynamic, ephemeral, and hazardous as wildfire. From this synthesis, we present generalised frameworks for the validation and intercomparison of satellite active fire products within savannah ecosystems. This article reviews traditional and contemporary methods that have been used to assess the accuracy of satellite active fire products and the consistency between products. We provide through this review generalised frameworks that advise on good practice for validating satellite fire products within the savannah biome. This article belongs to the Collection Savanna Burning. [ABSTRACT FROM AUTHOR]

This study assessed the spatio-temporal variation of the degree of curing (DoC) in the Golden Gate Highlands National Park (GGHNP) from 2016 to 2020. The estimation of the degree of grass curing was carried out with the high spatial resolution fused remotely sensed data of MODIS and Sentinel-2 employing Index-then-Blend (IB) data fusion methods on Google Earth Engine (GEE) to compute Grassland Curing Index (GCI) using soil and vegetation moisture content indices-based grassland curing algorithm. Grassland curing indices were computed on Google Earth Engine and converted into Grassland Curing Maps (GCMs), which were then synthesized into fire danger maps. The study showed that all selected indices have the highest DoC in the month of September, with the minor time variation observed in MODIS and Sentinel 2-derived GCI estimated using Shortwave Infrared Water Stress (GCI_SIWSI) in the months of August and March, respectively. The analysis further revealed that GCI_SIWSI (44%) derived from Sentinel-2 data yielded the highest area of extremely high fire danger, followed by GCI estimated using Normalized Difference Moisture Index (GCI_NDMI) (25%) derived from fused data, MODIS-derived GCI_SIWSI and fused-derived GCI estimated using Global Vegetation Monitoring Index (GCI_GVMI) (both 16%). The GCI_SIWSI derived from Sentinel-2 data also showed that more than 95% of the entire landmass of the study area was within a high to extremely high fire danger zone, making the park's vegetation susceptible to fire. Among all the four selected indices correlated against fire point, only SIWSI revealed a positive relationship across MODIS, Sentinel-2 and Fused data. It is also evident from the study that GCMs derived from Fused data outperformed MODIS and Sentinel-2 with the highest R2 and F values of 0.65 and 380, respectively. These results indicate that GCI derived from fused remotely sensed data is a promising GCI for accurately assessing the DoC over mountainous grassland environments. The study paved the way for increasing the spatial resolution for estimating the DoC for fire and fuel management of parks and other fire agencies within mountainous grassland environments. [ABSTRACT FROM AUTHOR]

Wildfires are an intrinsic and vital driving factor in the Miombo ecosystem. Understanding fire regimes in Miombo is crucial for its ecological sustainability. Miombo is dominant in Central Mozambique, having one of the highest fire incidences in the country. This study evaluated the spatio-temporal patterns of fire regimes (intensity, seasonality, frequency and fire return interval) in the LevasFlor Forest Concession (LFC), Central Mozambique using remotely sensed data from 2001 to 2022. We conducted hotspot spatial statistics using the Getis-Ord Gi* method to assess fire distribution and patterns. The results revealed that 88% of the study area was burnt at least once from 2001 to 2022, with an average burned area of 9733 ha/year (21% of LFC's total area). Fires were more likely to occur (74.4%) in open and deciduous Miombo types. A total of 84% of the studied area, burned in a range of 4 to 22 years of fire return interval (FRI) over the 21 assessed. Only 16% of the area was affected by high to very high FRI (1 to 4 years), with an average FRI of 4.43 years. Generally, fires are more frequent and intense in September and October. These results highlight the usefulness of remote sensing in evaluating long-term spatiotemporal fire trends for effective fire management strategies and control measures in African savanna ecosystems. [ABSTRACT FROM AUTHOR]

Forest fires cause extensive damage to ecosystems, biodiversity, and human property, posing significant challenges for emergency response and resource management. The accurate and timely delineation of forest fire perimeters is crucial for mitigating these impacts. In this study, methods for delineating forest fire perimeters using near-real-time (NRT) remote sensing data are evaluated. Specifically, the performance of various algorithms—buffer, concave, convex, and combination methods—using VIIRS and MODIS datasets is assessed. It was found that increasing concave α values improves the matching percentage with reference areas but also increases the commission error (CE), indicating overestimation. The results demonstrate that combination methods generally achieve higher matching percentages, but also higher CEs. These findings highlight the trade-off between improved perimeter accuracy and the risk of overestimation. The insights gained are significant for optimizing sensor data alignment techniques, thereby enhancing rapid response, resource allocation, and evacuation planning in fire management. This research is the first to employ multiple algorithms in both individual and synergistic approaches with NRT or ultra-real-time (URT) active fire data, providing a critical foundation for future studies aimed at improving the accuracy and timeliness of forest fire perimeter assessments. Such advancements are essential for effective disaster management and mitigation strategies. [ABSTRACT FROM AUTHOR]

Introduction: Forest fires are increasing in terms of number, size, and extent which have a growing influence on the achievement of the Sustainable Development Goals (SDGs). The economy and ecology of Northeast India have been seriously impacted by forest fires in many places, it is important to comprehend the region's spatiotemporal distribution, severity, and future projections for forest fires in light of climate change. Methods: Geographical information systems (GIS) integrating with remote sensing (RS) were used to understand the role of different parameters in all four bioclimatic zones of the region. Results: and discussion: Most of the fires were restricted to pre-monsoon season (93 %), alone 62 % in March. The forest fire in the present scenario was highest in the Lawngtlai district, followed by Dhalai and Ri-Bhoi. The Lawngtlai and Dhalai districts are at the highest risk (greater than 70 %) for future forest fires. Categorically, among the protected areas, Lengteng WLS has the highest (86.6 %) future forest fire risk followed by Tawi WLS (86.5 %), Ngengpui WLS (84.9 %), and Pualreng WLS (84.6 %). Conclusion: The results suggest that underground biomass in the lower elevated forest needs to be managed effectively at the onset of the fire season to reduce the occurrence of forest fires. There is a need for a well-defined framework supported by geospatial technology to predict, identify, and prioritize the fire potential zone with synergic strategies supported by the local community to mitigate the fire impact on the forests.

The adjoining Luengue-Luiana and Mavinga national parks in southeastern Angola are two of the largest though least actively managed protected areas in Africa. The parks are home to a diversity of wildlife and are managed as a single unit. The area is characterised as a fire-dependent savanna, where fires may be naturally ignited (such as by lightning), but where the fires each year are frequently set by people. As a result of the lack of resource investment, uncontrolled fires dominate the region, potentially negatively affecting biodiversity. The past fire regimes (2001–2021) of the two parks were investigated with use of the MODIS and VIIRS remote-sensing instrument. Extremely short fire return intervals dominated the landscape, with almost half (47.8%) of the area experiencing a subsequent fire within a year of the previous fire, and an additional 30% of the area burning within two years after a preceding fire. Only 2.4% of the landscape was shown to remain fire-free for a period of ten years or more. Overall, the fire intensities were highest from August to October, with the largest areas burnt between August and September. Fire frequency, seasonality and intensity were influenced by the land-cover type, and a comparison of the different satellite products was made. This information is the first of its kind for the region and is intended to provide a basis from which fire management activities can be coordinated. [ABSTRACT FROM AUTHOR]

Prescribed burning is a key management strategy within fire-adapted systems, and improved monitoring approaches are needed to evaluate its effectiveness in achieving social-ecological outcomes. Remote sensing provides opportunities to analyse the impacts of prescribed burning, yet a comprehensive understanding of the applications of remote sensing for prescribed burn research is lacking. We conduct a literature review of 120 peer-reviewed publications to synthesise the research aims, methodologies, limitations and future directions of remote sensing for the analysis of prescribed fire. Studies evaluating management outcomes found prescribed burning effective for wildfire risk reduction, yet few analysed co-benefits or trade-offs with other management goals. Most studies use passive, spaceborne, low spatial resolution sensors, characterised in the literature as consistent and accessible data sources but limited in detecting small, low-severity and short-duration fires characteristic of prescribed burns. In contrast, active remote sensing approaches including LiDAR are less frequently employed, but show promise for highly accurate, spatially explicit 3D vegetation and fuel load mapping. Remote sensing advances toward higher spatial resolution, more frequent revisit, denser spectral sampling and more data across the electromagnetic spectrum are critical to advancing prescribed fire research, addressing current methodological gaps, and improving fuels and fire management capacity. Remote sensing is frequently used in fire ecology and management, but its applications for prescribed burning are not well established. We review studies that use remote sensing for prescribed burn research, finding that wildfire remote sensing approaches are not optimised for small, short-duration and low-severity burns typical of prescribed fire. [ABSTRACT FROM AUTHOR]

Given the increasing prevalence of wildland fires in the Western US, there is a critical need to develop tools to understand and accurately predict burn severity. We develop a novel machine learning model to predict post-fire burn severity using pre-fire remotely sensed data. Hydrological, ecological, and topographical variables collected from four regions of California — the site of the Kincade fire (2019), the CZU Lightning Complex fire (2020), the Windy fire (2021), and the KNP Fire (2021) — are used as predictors of the differenced normalized burn ratio. We hypothesize that a Super Learner (SL) algorithm that accounts for spatial autocorrelation using Vecchia's Gaussian approximation will accurately model burn severity. We use a cross-validation study to show that the spatial SL model can predict burn severity with reasonable classification accuracy, including high burn severity events. After fitting and verifying the performance of the SL model, we use interpretable machine learning tools to determine the main drivers of severe burn damage, including greenness, elevation, and fire weather variables. These findings provide actionable insights that enable communities to strategize interventions, such as early fire detection systems, pre-fire season vegetation clearing activities, and resource allocation during emergency responses. When implemented, this model has the potential to minimize the loss of human life, property, resources, and ecosystems in California. [ABSTRACT FROM AUTHOR]

The modification of fire regimes and their impact on vegetation recovery, soil properties, and fuel structure are current key research areas that attempt to identify the thresholds of vegetation's susceptibility to wildfires. This study aimed to evaluate the vulnerability of Mediterranean pine forests (Pinus halepensis Mill. and Pinus pinaster Aiton) to wildfires, analyzing two major forest fires that occurred in Yeste (Spain) in 1994 and 2017, affecting over 14,000 and 3200 hectares, respectively. Four recovery regions were identified based on fire severity—calculated using the delta Normalized Burn Ratio (dNBR) index—and recurrence: areas with high severity in 2017 but not in 1994 (UB94-HS17), areas with high severity in 1994 but not in 2017 (HS94-UB17), areas with high severity in both fires (HS94-HS17), and areas unaffected by either fire (UB94-UB17). The analysis focused on examining the recovery patterns of three spectral indices—the Normalized Difference Vegetation Index (NDVI), Normalized Moisture Index (NDMI), and Normalized Burn Ratio (NBR)—using the Google Earth Engine platform from 1990 to 2023. Additionally, the Relative Recovery Indicator (RRI), the Ratio of Eighty Percent (R80P), and the Year-on-Year average (YrYr) metrics were computed to assess the spectral recovery rates by region. These three spectral indices showed similar dynamic responses to fire. However, the Mann–Kendall and unit root statistical tests revealed that the NDVI and NDMI exhibited distinct trends, particularly in areas with recurrence (HS94-HS17). The NDVI outperformed the NBR and NDMI in distinguishing variations among regions. These results suggest accelerated vegetation spectral regrowth in the short term. The Vegetation Recovery Capacity After Fire (VRAF) index showed values from low to moderate, while the Vulnerability to Fire (V2FIRE) index exhibited values from medium to high across all recovery regions. These findings enhance our understanding of how vegetation recovers from fire and how vulnerable it is to fire. [ABSTRACT FROM AUTHOR]

Copyright of Fire Ecology is the property of Springer Nature 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.)

We present a novel statistical methodology for analyzing shifts in spatio-temporal fire occurrence patterns within the Brazilian Pantanal, utilizing remote sensing data. Our approach employs a Log-Gaussian Cox Process to model the spatiotemporal dynamics of fire occurrence, deconstructing the intensity function into components of trend, seasonality, cycle, covariates, and time-varying spatial effects components. The results indicate a negative correlation between rainfall and fire intensity, with lower precipitation associated with heightened fire intensity. Forest formations exhibit a positive effect on fire intensity, whereas agricultural land use shows no significant impact. Savannas and grasslands, typical fire-dependent ecosystems, demonstrate a positive relationship with fire intensity. Human-induced fires, often used for agricultural purposes, contribute to an increase in both fire frequency and intensity, particularly in grassland areas. Trend analysis reveals fluctuating fire activity over time, with notable peaks in 2018–2021. [ABSTRACT FROM AUTHOR]

Forest fires pose a significant threat worldwide, with Algeria being no exception. In 2020 alone, Algeria witnessed devastating forest fires, affecting over 16,000 hectares of land, a phenomenon largely attributed to the impacts of climate change. Understanding the severity of these fires is crucial for effective management and mitigation efforts. This study focuses on the Akfadou forest and its surrounding areas in Algeria, aiming to develop a robust method for mapping fire severity. We employed a comprehensive approach that integrates satellite imagery analysis, machine learning techniques, and geographic information systems (GIS) to assess fire severity. By evaluating various remote sensing attributes from the Sentinel-2 and Planetscope satellites, we compared different methodologies for fire severity classification. Specifically, we examined the effectiveness of reflectance indices-based metrics such as Relative Burn Ratio (RBR) and Difference Burned Area Index for Sentinel-2 (dBIAS2), alongside machine learning algorithms including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), implemented in ArcGIS Pro 3.1.0. Our analysis revealed promising results, particularly in identifying high-severity fire areas. By comparing the output of our methods with ground truth data, we demonstrated the robust performance of our approach, with both SVM and CNN achieving accuracy scores exceeding 0.84. An innovative aspect of our study involved semi-automating the process of training sample labeling using spectral indices rasters and masks. This approach optimizes raster selection for distinct fire severity classes, ensuring accuracy and efficiency in classification. This research contributes to the broader understanding of forest fire dynamics and provides valuable insights for fire management and environmental monitoring efforts in Algeria and similar regions. By accurately mapping fire severity, we can better assess the impacts of climate change and land use changes, facilitating proactive measures to mitigate future fire incidents. [ABSTRACT FROM AUTHOR]

Copyright of Fire Ecology is the property of Springer Nature 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.)

Conifer forest resilience may be threatened by increasing wildfire activity and compound disturbances in western North America. Fire refugia enhance forest resilience, yet may decline over time due to delayed mortality—a process that remains poorly understood at landscape and regional scales. To address this uncertainty, we used high‐resolution satellite imagery (5‐m pixel) to map and quantify delayed mortality of conifer tree cover between 1 and 5 years postfire, across 30 large wildfires that burned within three montane ecoregions in the western United States. We used statistical models to explore the influence of burn severity, topography, soils, and climate moisture deficit on delayed mortality. We estimate that delayed mortality reduced live conifer tree cover by 5%–25% at the fire perimeter scale and 12%–15% at the ecoregion scale. Remotely sensed burn severity (1‐year postfire) was the strongest predictor of delayed mortality, indicating patch‐level fire effects are a strong proxy for fire injury severity among surviving trees that eventually perish. Delayed mortality rates were further influenced by long‐term average and short‐term postfire climate moisture deficits, illustrating the impact of drought on fire‐injured tree survival. Our work demonstrates that delayed mortality in conifer forests of the western United States can be remotely quantified at a fine grain and landscape scale, is a spatially extensive phenomenon, is driven by fire–climate–environment interactions, and has important ecological implications. [ABSTRACT FROM AUTHOR]

Copyright of Fire Ecology is the property of Springer Nature 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.)

Wildfires are important natural drivers of forest stands dynamics, strongly affecting their natural regeneration and providing important ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns in the Middle Volga region of the Russian Federation from 2000 to 2022, using remote sensing time series data and considering the influence of climatic factors on forest fires. To assess the temporal trends, the Mann–Kendall nonparametric statistical test and Theil–Sen's slope estimator were applied using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment revealed a high overall accuracy (>84%) and F-score value (>82%) for forest burnt area detection, evaluated against 581 reference test sites. The results indicate that fire occurrences in the region were predominantly irregular, with the highest frequency recorded as 7.3 over the 22-year period. The total forest BA was estimated to be around 280 thousand hectares, accounting for 1.7% of the land surface area or 4.0% of the total forested area in the Middle Volga region. Coniferous forest stands were found to be the most fire-prone ecosystems, contributing to 59.0% of the total BA, while deciduous stands accounted for 25.1%. Insignificant fire occurrences were observed in young forests and shrub lands. On a seasonal scale, temperature was found to have a greater impact on BA compared with precipitation and wind speed. [ABSTRACT FROM AUTHOR]

Grasslands are key to the Earth's system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in the grassland biome. Integrated fire-risk assessment systems provide an integral approach to fire prevention and mitigate the negative impacts of fire. However, fire risk-assessment is extremely challenging, owing to the myriad of factors that influence fire ignition and behaviour. Most fire danger systems do not consider fire causes; therefore, they are inadequate in validating the estimation of fire danger. Thus, fire danger assessment models should comprise the potential causes of fire. Understanding the key drivers of fire occurrence is key to the sustainable management of South Africa's grassland ecosystems. Therefore, this study explored six statistical and machine learning models—the frequency ratio (FR), weight of evidence (WoE), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) in Google Earth Engine (GEE) to assess fire danger in an Afromontane grassland protected area (PA). The area under the receiver operating characteristic curve results (ROC/AUC) revealed that DT showed the highest precision on model fit and success rate, while the WoE was used to record the highest prediction rate (AUC = 0.74). The WoE model showed that 53% of the study area is susceptible to fire. The land surface temperature (LST) and vegetation condition index (VCI) were the most influential factors. Corresponding analysis suggested that the fire regime of the study area is fuel-dominated. Thus, fire danger management strategies within the Golden Gate Highlands National Park (GGHNP) should include fuel management aiming at correctly weighing the effects of fuel in fire ignition and spread. [ABSTRACT FROM AUTHOR]

Wildfires are a common disturbance factor worldwide, especially over the last decade due to global climate change. Monitoring postfire forest regrowth provides fundamental information needed to enhance the management and support of ecosystem recovery after fires. The purpose of this study is to propose an algorithm for postfire forest regrowth monitoring using tasseled-cap-derived indices. A complex approach is used for its implementation, for which a model is developed based on three components—Disturbance Index (DI), Vector of Instantaneous Condition (VIC), and Direction Angle (DA). The final product—postfire regrowth (PFIR)—allows for a quantitative assessment of the intensity of regrowth. The proposed methodology is based on the linear orthogonal transformation of multispectral satellite images—tasseled cap transformation (TCT)—that increases the degree of identification of the three main components that change during a fire—soil, vegetation, and water/moisture—and implies a higher accuracy of the assessments. The results provide a thematic raster representing the intensity of the regrowth classes, which are defined after the PFIR threshold values are determined (HRI—high regrowth intensity; MRI—moderate regrowth intensity; and LRI—low regrowth intensity). The accuracy assessment procedure is conducted using very-high-resolution (VHR) aerial and satellite data from World View (WV) sensors, as well as multispectral Sentinel 2A images. Three different forest test sites affected by fire in Bulgaria are examined. The results show that the classified thematic raster maps are distinguished by a good performance in monitoring the regrowth dynamics, with an average overall accuracy of 62.1% for all three test sites, ranging from 73.9% to 48.4% for the individual forests. [ABSTRACT FROM AUTHOR]

Woody encroachment (WE) and agricultural expansion are widespread in tropical savannas, where they threaten biodiversity and ecosystem function. In Africa's largest savanna, the miombo woodlands, cropland expansion is expected to cause extensive habitat loss over the next 30 years. Meanwhile, widespread WE is altering the remaining untransformed vegetation. Quantifying the extent of both processes in the Angolan miombo woodlands (~570,000 km2) has been challenging due to limited infrastructure, a history of conflict, and widespread landmines. Here, we analyze spectral satellite imagery to investigate the extent of WE and cropland expansion in the Angolan miombo woodlands since 1990. We asses WE using two complementary metrics: multi‐decade canopy greenness trends and conversion from grassland to woodland. We also examine whether WE trends are driven by landscape fragmentation and decreasing fire frequency. We found that from 1990 to 2020, 34.1% of the Angolan miombo woodlands experienced significant WE or was converted to cropland, while open grassy vegetation declined by 62%. WE advanced rapidly even in areas experiencing extraordinarily high burn frequencies and was not adequately explained by changing temperature or precipitation. WE was concentrated far from the agricultural frontier, in remote areas with low population densities. These results challenge the hypothesis that human‐altered fire regimes are the primary driver of WE in mesic savannas. The results will help decision‐makers conserve the miombo woodlands' biodiversity and ecosystem services, by highlighting that strategies to slow habitat loss must address WE and cropland expansion together. [ABSTRACT FROM AUTHOR]

The "Forest Fires Prediction and Detection" edition highlights the importance of research on fires worldwide. In recent years, the increased frequency of fires caused by climate change has rendered the planet uninhabitable. Several works have been prepared and published in an effort to raise awareness among civil society and government bodies about the importance of developing new technologies for monitoring areas prone to mega-fires. This special issue includes nine important works from various countries. The goal is to better understand the impacts on the world's most diverse regions, ecosystems, and forest phytophysiognomies. New geotechnologies and fire models were used, both of which are important and could be used in the future to improve short- and long-term planning in firefighting. [ABSTRACT FROM AUTHOR]

Wildfires have become increasingly prevalent and destructive in forest ecosystems worldwide, necessitating a comprehensive understanding of post-fire recovery dynamics for effective conservation and management. Remote sensing technology, coupled with vegetation indices such as Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Green Red Vegetation Index (GRVI), and Red Vegetation Index (RVI), offers a powerful means to investigate these processes. In this study, we utilize remote sensing techniques to conduct a comparative analysis of secondary ecological succession following wildfires in three distinct forest types (Coniferous, Sclerophyll, and Mixed) of a forest affected by fire near Moguer, Spain. Through the acquisition and analysis of multispectral satellite imagery, we monitored changes in vegetation health and recovery across the region of interest. The NBR index allowed us to assess the severity and extent of wildfire damage, while NDVI quantified vegetation greenness and regrowth. GRVI and RVI provided insights into subtle variations in vegetation composition and health. We identified distinct temporal and spatial patterns in post-fire recovery among the different forest types by applying these indices for the period between 2017 and 2021. Our findings underscore the significance of understanding the diverse responses of these ecosystems to wildfires. While common recovery patterns emerged, such as an initial decrease in NDVI followed by regeneration, variations were observed in the timing and magnitude of recovery. These distinctions are attributed to differences in species composition, fire adaptations, and ecological processes specific to each forest type. In conclusion, the utilization of NBR, NDVI, GRVI, and RVI indices allows for a more nuanced evaluation of post-fire recovery dynamics. [ABSTRACT FROM AUTHOR]

Addressing post-fire impacts largely depends on burn "severity." A singular severity classification that encompasses the holistic effects of fire on all ecosystem processes does not currently exist. Lumping vegetation burn severity and soil burn severity into one metric, or using them interchangeably, can induce large inaccuracies and uncertainties in the intended ecosystem response to forcing. Often, burn "severity" reflects fire impacts on vegetation, which can be measured through remote sensing. Vegetation burn severity is likely more apropos for ecological research, whereas soil burn severity is more relevant for hydrological analyses. This paper reviews different remotely sensed vegetation severity products currently (mis)used for hydrological modeling, provides examples of when vegetation burn severity may (not) match soil burn severity, and summarizes the potential synergistic future of remote sensing with in situ severity metrics. While the focus in this paper is on the western United States, the lessons and principles apply universally. [ABSTRACT FROM AUTHOR]

The influence of forest treatments on wildfire effects is challenging to interpret. This is, in part, because the impact forest treatments have on wildfire can be slight and variable across many factors. Effectiveness of a treatment also depends on the metric considered. We present and define human–fire interaction, fire behavior, and ecological metrics of forest treatment effects on wildfire and discuss important considerations and recommendations for evaluating treatments. We demonstrate these concepts using a case study from the Cameron Peak Fire in Colorado, USA. Pre-fire forest treatments generally, but not always, experienced reduced burn severity, particularly when surface fuels were reduced. Treatments in the Cameron Peak Fire have also been documented as increasing tree survivorship, aiding suppression efforts, promoting firefighter safety, and influencing fire spread. However, the impacts of pre-fire management on primary landscape-scale objectives, like watershed protection, are unknown. Discussions about the influence of pre-fire treatments on fire effects must define the indicator(s) being assessed, as the same treatment may be considered successful under one measure but not others. Thus, it is critical to bring a common language and understanding to conversations about treatment effects and advance efforts to evaluate the range of treatment effects, thus supporting treatment planning. [ABSTRACT FROM AUTHOR]

The measurement of plant flammability is crucial for understanding fire behavior in natural and managed ecosystems. As climate change and land use patterns alter fire regimes globally, accurate assessments of plant flammability are increasingly important for fire management, ecological research, and conservation efforts. This article reviews the current state of techniques used to measure plant flammability, including laboratory and field methods, and evaluates their effectiveness and limitations. We also explore emerging technologies and methodologies that could enhance flammability assessments, such as remote sensing and advanced fire modeling. The prospective advancements in this field promise improved fire risk prediction, better management strategies, and enhanced understanding of fire ecology [ABSTRACT FROM AUTHOR]

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Accurate fuel mapping is crucial for effectively determining wildfire risk and implementing management strategies. The primary challenge in fuel type mapping lies in the need to develop accurate and efficient methods for identifying and categorizing the various combustible materials present in an area, often on a large scale. In response to this need, this paper presents a comprehensive approach that combines remote sensing data and Convolutional Neural Network (CNN) to discriminate between fire behavior fuel models. In particular, a CNN-based classification approach that leverages Sentinel-2 imagery is exploited to accurately classify fuel types into seven preliminary main classes (broadleaf, conifers, shrubs, grass, bare soil, urban areas, and water bodies). To further refine the fuel mapping results, subclasses were generated from the seven principles by using biomass and bioclimatic maps. These additional maps provide complementary information about vegetation density and climatic conditions, respectively. By incorporating this information, we align our fuel type classification with the widely used Standard Scott and Burgan (2005) fuel classification system. The results are highly promising, showcasing excellent CNN training performance with all three metrics—accuracy, recall, and F1 score—achieving an impressive 0.99%. Notably, the network exhibits exceptional accuracy in a test case conducted in the southern region of Sardinia, successfully identifying Burnable classes in previously unseen pixels: broadleaf at 0.99%, conifer at 0.79%, shrub at 0.76%, and grass at 0.84%. The proposed approach presents a valuable tool for enhancing fire management, contributing to more effective wildfire prevention and mitigation efforts. Thus, this tool could be leveraged by fire management agencies, policymakers, and researchers to improve the determination of wildfire risk and management. [ABSTRACT FROM AUTHOR]

Seismic lines are the dominant anthropogenic disturbance in the boreal forest of the Canadian province of Alberta, fragmenting over 1900 km2 of peatland areas and accounting for more than 80% of all anthropogenic disturbance in this region. The goal of this study is to determine whether the wildland fires that burn across seismic lines in peatlands result in the regeneration of woody vegetation within the ecotonal areas adjacent to seismic lines. We use a combination of seismic line and vegetation structural characteristics derived from multi-spectral airborne lidar across a post-fire peatland chronosequence. We found an increasing encroachment of shrubs and trees into seismic lines after many years since a fire, especially in fens, relative to unburned peatlands. Fens typically had shorter woody vegetation regeneration (average = 3.3 m ± 0.9 m, standard deviation) adjacent to seismic lines compared to bogs (average = 3.8 m ± 1.0 m, standard deviation), despite enhanced shrubification closer to seismic lines. The incoming solar radiation and seismic line age since the establishment of seismic line(s) were the factors most strongly correlated with enhanced shrubification, suggesting that the increased light and time since a disturbance are driving these vegetation changes. Shrub encroachment closer to seismic lines tends to occur within fens, indicating that these may be more sensitive to drying conditions and vegetation regeneration after several years post-fire/post-seismic line disturbance. [ABSTRACT FROM AUTHOR]

Tropical savannas and grasslands are the most frequently burned biome in the world, and fire has an important role in sustaining ecosystem processes. Modern management of fires in savannas has roots in traditions stretching back centuries, and nowadays earth observation data is incorporated extensively in fire management practices. In tropical savannas in particular strongly seasonal monsoonal climates allow relatively low severity prescribed burning in the early part of the dry season (EDS) with the goal of preventing more destructive late dry season (LDS) fires. In many regional contexts it is common that a specific, fixed date is used officially to indicate when the window of safe burning has expired and the EDS transitions to the LDS, based on the experience of local or regional fire management authorities. This approach, while practical, neglects inter-annual variability in meteorological conditions and timing of onset of more dangerous fire weather. In this study, we pro-pose a remote sensing-based method for determining when this EDS window expires for five savanna-dominated continental-scale regions. By taking ad- vantage of the fact that conditions allowing night-time burning occur later in the dry season, we use day and night-time active fire detections from the MODerate Resolution Imaging Spectroradiometer (MODIS) instruments to set a flexible date of transition between the EDS and LDS. The vast majority of tropical savannas have very variable (std. dev. ≈ 20–40 days) transition dates, though this is somewhat modulated by fire frequency. Fuel connectivity rather than fuel condition appears to be a strong driving factor behind this variability. We find that especially national parks and protected areas have a high proportion of potentially more severe burning in the LDS, though areas with well-established EDS burning programmes are reducing this impact.

The frequency and intensity of summer droughts and heat waves in Western Europe have been increasing, raising concerns about the emergence of fire hazard in less fire-prone areas. This exposure of old-growth forests hosting unadapted tree species may cause disproportionately large biomass losses compared to those observed in frequently burned Mediterranean ecosystems. Therefore, analyzing fire seasons from the perspective of exposed burned areas alone is insufficient; we must also consider impacts on biomass loss. In this study, we focus on the exceptional 2022 summer fire season in France and use very high-resolution (10 m) satellite data to calculate the burned area, tree height at the national level, and subsequent ecological impact based on biomass loss during fires. Our high-resolution semi-automated detection estimated 42 520 ha of burned area, compared to the 66 393 ha estimated by the European automated remote sensing detection system (EFFIS), including 48 330 ha actually occurring in forests. We show that Mediterranean forests had a lower biomass loss than in previous years, whereas there was a drastic increase in burned area and biomass loss over the Atlantic pine forests and temperate forests. High biomass losses in the Atlantic pine forests were driven by the large burned area (28 600 ha in 2022 vs. 494 hayr-1 in 2006–2021 period) but mitigated by a low exposed tree biomass mostly located on intensive management areas. Conversely, biomass loss in temperate forests was abnormally high due to both a 15-fold increase in burned area compared to previous years (3300 ha in 2022 vs. 216 ha in the 2006–2021 period) and a high tree biomass of the forests which burned. Overall, the biomass loss (i.e., wood biomass dry weight) was 0.25 Mt in Mediterranean forests and shrublands, 1.74 Mt in the Atlantic pine forest, and 0.57 Mt in temperate forests, amounting to a total loss of 2.553 Mt, equivalent to a 17 % increase of the average natural mortality of all French forests, as reported by the national inventory. A comparison of biomass loss between our estimates and global biomass/burned areas data indicates that higher resolution improves the identification of small fire patches, reduces the commission errors with a more accurate delineation of the perimeter of each fire, and increases the biomass affected. This study paves the way for the development of low-latency, high-accuracy assessment of biomass losses and fire patch contours to deliver a more informative impact-based characterization of each fire year. [ABSTRACT FROM AUTHOR]

Wildfires are key not only to landscape transformation and vegetation succession, but also to socio-ecological values loss. Fire risk mapping can help to manage the most vulnerable and relevant ecosystems impacted by wildfires. However, few studies provide accessible daily dynamic results at different spatio-temporal scales. We develop a fire risk model for Sicily (Italy), an iconic case of the Mediterranean Basin, integrating a fire hazard model with an exposure and vulnerability analysis under present and future conditions. The integrated model is data-driven but can run dynamically at a daily time step, providing spatially and temporally explicit results through the k.LAB (Knowledge Laboratory) software. This software provides an environment for input data integration, combining methods and data such as geographic information systems, remote sensing and Bayesian network algorithms. All data and models are semantically annotated, open and downloadable in agreement with the FAIR principles (findable, accessible, interoperable and reusable). The fire risk analysis reveals that 45 % of vulnerable areas of Sicily have a high probability of fire occurrence in 2050. The risk model outputs also include qualitative risk indexes, which can make the results more understandable for non-technical stakeholders. We argue that this approach is well suited to aiding in landscape and fire risk management, under both current and climate change conditions. [ABSTRACT FROM AUTHOR]

Fire regimes are expected to change with climate change, resulting in a crucial need to understand the specific ways in which variable fire regimes impact important contributors to ecosystem functioning, such as mound‐building termites. Termite mounds and fire are both important agents of savanna ecosystem heterogeneity and functioning, but there is little understanding of how they interact across savanna types.We used very high‐resolution LiDAR remote sensing to measure the size and distribution of termite mounds across approximately 1300 ha of experimental burn plots in four South African savanna landscapes representing a wide range of fire treatments differing in seasonality and frequency of burning.In nutrient‐poor granitic savannas, fire had no impact on termite mound size, densities and spatial distributions. In nutrient‐rich basaltic savannas with high mammalian herbivore abundance and intermediate rainfall, very frequent fires caused a decrease in termite mound size, whereas in arid nutrient‐rich basaltic savannas, fires that occurred at intermediate frequencies and in transitional seasons (i.e. late dry season and late wet season) decreased the degree of spatial overdispersal exhibited by mounds.Overall, our results suggest that termite mounds are resistant to variation in fire seasonality and frequency, likely indicating that ecosystem services provided by mound‐building termites will be unaffected by changing fire regimes. However, consideration of changes to termite mound size and distribution could be necessary for land managers in specific savanna types, such as nutrient‐rich soils with high mammalian herbivore abundance. [ABSTRACT FROM AUTHOR]

The controlled use of fires to reduce combustible materials in prescribed burning helps to prevent the occurrence of forest fires. In recent decades, these fires have mainly been caused by anthropogenic activities. The study area is located in the Pantanal biome. In 2020, the greatest drought in 60 years happened in the Pantanal. The fire affected almost one third of the biome. The objective of this study is to evaluate the effect of prescribed burnings carried out in 2021 on three macrohabitats (M1: natural grassland flooded with a proliferation of Combretum spp., M2: natural grassland of seasonal swamps, and M3: natural grassland flooded with a proliferation of Vochysia divergens) inside the SESC Pantanal Private Natural Heritage Reserve. Multispectral and thermal data analyses were conducted with remotely piloted aircraft systems in 1 ha plots in three periods of the dry season with early, mid, and late burning. The land use and land cover classification indicate that the predominant vegetation type in these areas is seasonally flooded grassland, with percentages above 73%, except in zone three, which has a more diverse composition and structure, with the presence of arboreal specimens of V. divergem Pohl. The pattern of the thermal range showed differentiation pre- and post-burning. The burned area index indicated that fire was more efficient in the first two macrohabitats because they are natural grasslands, reducing the grass species in the burnings. Early and mid prescribed burnings are a good option to reduce the continuous accumulation of dry forest biomass fuel material and help to promote landscape heterogeneity. The use of multispectral sensor data with high spatial/spectral resolution can show the effects of fires, using highly detailed scales for technical decision making. [ABSTRACT FROM AUTHOR]

Burn severity mapping facilitates post-fire management and restoration and predicts surface erosion and landslide risk. Different severity levels are usually distinguished by fixed threshold values with remote sensing techniques. Since the climate, ecosystem, geology, and morphology control the destruction level of forest fires, site-specific class thresholds should be considered to discriminate severity classes precisely. Therefore, the purpose of this study is to produce an accurate burn severity map using spectral indices with site-specific thresholds for unburned, low, moderate and high severity classes. In this context, pre- and post-fire Landsat 8 images were used to produce bi-temporal burn severity indices such as normalized burn ratio (NBR), normalized burned index (NBI), normalized difference vegetation index (NDVI), and green optimized soil adjusted vegetation index (GOSAVI). An alternative classification method based on a statistical distribution-based clustering approach was employed on the differential indices to determine severity class thresholds. The proposed thresholds were validated by the composite burn severity index (CBI) ratings of the field sampling points. The overall classification accuracy was found to be between 50% and 92.5%. In addition, the results were compared with the thresholds published in the literature. Consequently, this methodology can be used as adaptive thresholding in similar ecological and morphological zone to determine the burn severity classes. [ABSTRACT FROM AUTHOR]

Background. Fires in the boreal forest occur with natural frequencies and patterns. Burned area (BA) is an essential variable in assessing the impact of climate change in boreal regions. Aims. Spatial wildfire occurrence data since the 1950s are available for North America. However, there are no reliable data for Eurasia, mainly for Siberia, during the 1980s and 1990s. Methods. A Bayesiannetwork algorithm was applied to the Long-Term Data Record (LTDR) Version 5 to generate a BA DataSet (BA-LTDR-DS) for the Boreal region from 1982 to 2020, validated using official reference data and compared with the MODIS MCD64A1 product. Key results. A high correlation (>93%) with all the reference BA datasets was found. BA-LTDR-DS data grouped by decades estimated a linear increase in BA of 4.47 million ha/decade. This trend provides evidence of how global warming affects fire activity in these boreal forests. Conclusions. BA-LTDR-DS constitutes a unique data source for the pre-MODIS era, and becomes a reliable source when other products with higher spatial/spectral resolution are not available. Implications. The BA-LTDR-DS dataset constitutes the longest time series developed for the boreal region at this spatial resolution. BA-LTDR-DS could be used as input in global climate models, helping improve wildfire prediction capabilities and understand the interactions between fire, climate and vegetation dynamics. [ABSTRACT FROM AUTHOR]

Alto Minho (in northwestern Iberia) is one of the European regions most affected by fires. Many of these fires originate from rangeland management of Atlantic heathlands, and, while being illegal, often are not actively suppressed. In this study, pastoral fires (autumn-to-spring fires unrecorded by authorities), spring wildfires, and summer wildfires were independently mapped and dated from remote sensing. Alto Minho burned at a mean annual rate of 5.0% of the territory between 2001 and 2020. Pastoral burning totalled 40,788 hectares during the period, accounting for 20% of the total burnt area. Rangeland burning occurs mostly from December to April, the rainiest months that guarantee the conditions for pasture renewal and fire self-extinction. The mean fire return interval of pastoral burning is slightly higher than that of wildfires (13 years vs. 11 years), except in part of the inner mountains where it dominates fire activity. Pastoral fires are more frequent and largely prevail over wildfires in the parishes with higher livestock quantities. Conversely, the largest wildfires and higher summer burnt areas correspond with very low livestock and nearly non-existing pastoral fires. Traditional fire knowledge should not be overlooked by fire management, as it contributes to more sustainable fire regimes and ecosystems. [ABSTRACT FROM AUTHOR]

Natural disasters are defined as negative events that affect the lives of all living things on earth with their various effects. Fires which are one of these natural disasters, are the partial or complete burning of vegetated areas caused by natural or man-made causes. Today, monitoring and managing fires with Remote Sensing (RS) and Geographical Information Systems (GIS) tools are considered an integral part of and after their occurrence. RS and GIS are regarded as extremely useful and modern methods for data analysis, querying, and visualization in disaster management. The aim of this study is to determine the local and general patterns of the fires in the European continent by examining them with spatial statistical methods and making a spatial analysis. In addition, it aims to examine the fires that occurred over time with temporal pattern analysis. In the study, Global Moran's I, Getis-Ord Gi* Hot Spot Analysis, Anselin Local Moran's I Cluster and Outlier, and Emerging Hot Spot Analysis for Space-Time Pattern Analysis were performed on the dataset created with the fires that occurred in the European continent between 2000 and 2020. According to the analysis, the region that is exposed to the most intense fire and has the highest fire risk has been determined as the northern region of Portugal. The Balkans, the south of Italy, and the Sicilian peninsula are also dense and risky regions in terms of fires. [ABSTRACT FROM AUTHOR]

Background: Understanding the spatio-temporal dynamics associated with a wildfire event is essential for projecting a clear profile of its potential ecological influences. Aims: To develop a reliable framework to extract fire footprints from MODIS-based burn products to facilitate the understanding of fire event evolution. Methods: This study integrated the Jenks natural breaks classification method and the density-based spatial clustering of applications with noise (DBSCAN) algorithm to extract the fire footprints in Daxing'anling region of China between 2001 and 2006 from MCD64A1 burned area data. Key results: The results showed that the fire footprints extracted by the model gained an overall accuracy of 80% in spatial and temporal domains after an intensive validation by using the historical fire records provided by the local agency. The agreement of burned area between the extracted fire patches and the historical fire records for those matched fire points was characterised by an overall determination coefficient R 2 at 0.91. Conclusions: The proposed framework serves as an efficient and convenient wildfire management tool for areas requiring large-scale and long-term wildfire monitoring. Implications: The current framework can be used to create a reliable large-scale fire event database by providing an important alternative for the improvement of field investigation. We propose an integrated framework that combines temporal classification and spatial clustering to extract fire footprints. This framework will provide more complete descriptive information than ground-based survey fire records from publicly available remote-sensing-based burn pixel products, which will assist in formulating a targeted and informed wildfire management plan and accurately account for fire-induced carbon emissions. [ABSTRACT FROM AUTHOR]

Background: Fire research and management applications, such as fire behaviour analysis and emissions modelling, require consistent, highly resolved spatiotemporal information on wildfire growth progression. Aims: We developed a new fire mapping method that uses quality-assured sub-daily active fire/thermal anomaly satellite retrievals (2003–2020 MODIS and 2012–2020 VIIRS data) to develop a high-resolution wildfire growth dataset, including growth areas, perimeters, and cross-referenced fire information from agency reports. Methods: Satellite fire detections were buffered using a historical pixel-to-fire size relationship, then grouped spatiotemporally into individual fire events. Sub-daily and daily growth areas and perimeters were calculated for each fire event. After assembly, fire event characteristics including location, size, and date, were merged with agency records to create a cross-referenced dataset. Key results: Our satellite-based total fire size shows excellent agreement with agency records for MODIS (R 2 = 0.95) and VIIRS (R 2 = 0.97) in California. VIIRS-based estimates show improvement over MODIS for fires with areas less than 4047 ha (10 000 acres). To our knowledge, this is the finest resolution quality-assured fire growth dataset available. Conclusions and Implications: The novel spatiotemporal resolution and methodological consistency of our dataset can enable advances in fire behaviour and fire weather research and model development efforts, smoke modelling, and near real-time fire monitoring. We developed a fire mapping method that uses active fire data from the MODIS and VIIRS instruments on board satellites to develop a high-resolution wildfire growth database. The satellite-derived fire events show excellent agreement with agency records (R 2 ≥ 0.95). This method can enable many fire-weather modelling and real-time fire applications. [ABSTRACT FROM AUTHOR]

The frequency and intensity of summer droughts and heat waves in Western Europe have been increasing, raising concerns about the emergence of fire hazard in less fire prone areas. This exposure of old-growth forests hosting unadapted tree species may cause disproportionately large biomass losses compared to those observed in frequently burned Mediterranean ecosystems. Therefore, analyzing fire seasons from the perspective of exposed burned areas alone is insufficient, we must also consider impacts on biomass loss. In this study, we focus on the exceptional 2022 summer fire season in France and use very high-resolution (10 m) satellite data to calculate the burned area, tree height at the national level, and the subsequent ecological impact based on biomass loss during fires. Our high resolution semi-automated detection estimated 42,520 ha of burned area, compared to the 66,393 ha estimated by the European automated remote sensing detection system (EFFIS), including 48,330 ha actually occurring in forests. We show that Mediterranean forests had a lower biomass loss than in previous years, whereas there was a drastic increase in burned area and biomass loss over the Atlantic pine forests and temperate forests. High biomass losses in the Atlantic pine forests were driven by the large burned area (28,600 ha in 2022 vs. 494 ha yr−1 in 2006–2021 period) but mitigated by a low exposed tree biomass mostly located on intensive management areas. Conversely, biomass loss in temperate forests was abnormally high due to both a 15-fold increase in burned area compared to previous years (3,300 ha in 2022 vs. 216 ha in the 2006–2021 period) and a high tree biomass of the forests which burned. Overall, the biomass loss (i.e. wood biomass dry weight) was 0.25 Mt in Mediterranean forests and shrublands, 1.74 Mt in the Atlantic pine forest, and 0.57 Mt in temperate forests, amounting to a total loss of 2.553 Mt, equivalent to a 17 % increase of the average natural mortality of all French forests, as reported by the national inventory. A comparison of biomass loss between our estimates and global biomass/burned areas data indicates that higher resolution improves the identification of small fire patches, reduces the commission errors with a more accurate delineation of the perimeter of each fire, and increases the biomass affected. This study paves the way for the development of low-latency, high-accuracy assessment of biomass losses and fire patch contours to deliver a more informative impact-based characterization of each fire year. [ABSTRACT FROM AUTHOR]

As a tropical nation with ~40% forested land area and 290 protected areas in the Indo-Burma Biodiversity Hotspot, Vietnam holds an important part of global forests. Despite a complex history of multiple colonial rules, war, rapid economic development and societal growth, Vietnam was one of a few Southeast Asian countries to reverse deforestation trends and sustain net forest cover gain since the 1990s. However, a considerable amount of Vietnam's forest gain has been from plantation forestry, as Vietnam's policies have promoted economic development. In the Central Highlands region of Vietnam, widespread forest degradation and deforestation has occurred recently in some areas due to plantation forestry and other factors, including fire-linked deforestation, but protected areas here have been largely effective in their conservation goals. We studied deforestation, wildfires, and the contribution of fire-linked deforestation from 2001 to 2020 in an area near the Da Lat Plateau of the Central Highlands of Vietnam. We stratified our study area to distinguish legally protected areas and those in the surrounding landscape matrix without formal protection. Using satellite-derived data, we investigated four questions: (1) Have regional deforestation trends continued in parts of the Central Highlands from 2001 to 2020? (2) Based on remotely sensed fire detections, how has fire affected the Central Highlands and what proportion of deforestation is spatiotemporally linked to fire? (3) Were annual deforestation and burned area lower in protected areas relative to the surrounding land matrix? (4) Was the proportion of fire-linked deforestation lower in protected areas than in the matrix? To answer these questions, we integrated the Global Forest Change and FIRED VIETNAM datasets. We found that 3794 fires burned 8.7% of the total study area and 13.6% of the area became deforested between 2001 and 2020. While nearly half of fires were linked to deforestation, fire-linked deforestation accounted for only a small part of forest loss. Across the entire study area, 54% of fire-linked deforestation occurred in natural forests and 46% was in plantation forests. Fire ignitions in the study area were strongly linked to the regional dry season, November to March, and instrumental climate data from 1971 to 2020 showed statistically significant increasing trends in minimum, mean, and maximum temperatures. However, the total area burned did not have a significant increasing trend. Regional trends in deforestation continued in Vietnam's Central Highlands from 2001 to 2020, and nearly half of all detected fires can be spatially and temporally linked to forest loss. However, protected areas in the region effectively conserved forests relative to the surrounding landscape. [ABSTRACT FROM AUTHOR]

In many regions, the frequency and extent of wildfires has increased in recent years, a trend which is expected to continue. Hence, there is a need for effective fire management strategies. Such strategies need to be based on accurate and complete data on vegetation condition and post-fire effects, collected in the field as well as by remote sensing approaches. Unfortunately, wildfire databases are often incomplete in terms of their spatial and temporal coverage, as well as the documentation of fire outcomes. The aim of this study was to devise a methodology to construct a comprehensive national wildfire database. We developed and tested this methodology for Israel, as a case study. The methodology combines data on wildfires in Israel from two sources: remote sensing and field data collected by governmental agencies, representing the period between 2015 and 2022. The resulting database contains 2276 wildfires larger than 10 ha with information (where and when available) on their occurrence date, fire duration, ignition cause, and more. The methodology presented here provides a blueprint for developing large-scale and comprehensive fire databases elsewhere, and facilitates future studies on wildfire risk by providing a robust, unified database of the fire history of Israel from 2015 onwards. [ABSTRACT FROM AUTHOR]

Mega wildfires are one of the environmental disasters worldwide. This study evaluates the pre-fire species diversity and the indirect effects, including habitat loss for one of the largest wildfires in Manavgat (Antalya-Turkey) in 2021, with a two-step methodology. Here, (1) burnt areas in the Manavgat district (2021) were detected with remote sensing data from Sentinel-2A by delta Normalized Burn Ratio calculation for a selected area in Google Earth Engine, and (2) mammals' habitat vector data of International Union for Conservation of Nature (IUCN) Red List were integrated into Habitat and Biodiversity modelling of Terrset to analyze the alpha, beta, gamma diversity and Range Restriction Index for the wildfire region. In the total 4210 km2 study area, 696 km2 of the area was damaged by different fire severity; also, there were 56 mammal species' habitats here. These species include bats (i.e. Nyctalus leisleri), felids (i.e. Felis chaus), rodents (i.e. Rattus norvegicus) and large mammals (i.e. Ursus arctos). 88 % of these species are in IUCN's Least Concern category. The remaining classes are Near Threatened (3.7 %) and Vulnerable (7.4 %). This study also indicated that the burnt area's species richness does not totally consist of endemic species. Therefore, pre-fire species richness analyses of this study can be a base for further studies about the species' post-fire activity and occupancy. Furthermore, the 2021 mega wildfires show us the necessity of wildfire monitoring and risk studies in the entire Mediterranean ecosystem to evaluate the risks to the Sustainable Development Goals. Therefore, post-fire spatial data, fire migration monitorization, and recording of the species' activities should be performed temporally. In this way, the ability of wildlife's recovering, and the direct and indirect effects of the fire will be examined for ecosystems in the long term. [ABSTRACT FROM AUTHOR]

Wildfire occurrence has increased sharply in the last two decades in the Peruvian Andes. There is, however, little research on wildfires and their impacts. This study explores the conditions conducive to wildfire during 2020. MODIS images were collected to estimate the development of vegetation. In addition, ground-based monthly and satellite-based daily precipitation data were collected. Daily precipitation regularity was evaluated using a concentration index (CI), while monthly precipitation was used to estimate the Standard Precipitation Index (SPI). We used also the Global Vegetation Moisture Index (GVMI), which is a useful indicator of vegetation dynamics based on vegetation moisture. Our results do not indicate a direct link between rainfall regularity (lowest CI values) and development of vegetation. Although the SPI drought analysis using seasonal rainfall indicated nearly normal conditions during 2019–2020, analysis of dry-day frequency (DDF) suggests that the dry period played an important role between September and November 2020, producing conditions similar to the droughts of 2005, 2010 and 2016. GVMI also showed below-average values from April to November. We corroborate the usefulness of DDF for monitoring the potential increase in wildfire conditions. A controlled burn policy could offer a more useful way to reduce the impacts of wildfire. Wildfire occurrence has increased sharply in the last two decades in the Peruvian Andes. Drought analysis using seasonal rainfall indicated fairly normal conditions during 2020, but dry-day frequency (DDF) analysis suggests that a dry period played an important role between September and November 2020, producing severe drought conditions. [ABSTRACT FROM AUTHOR]

Abstract. Volcanic islands are special ecosystems for studying biogeographical and evolutionary processes. Occasional disturbance events, such as eruptions, tsunami or big fires, can represent major drivers of such processes leading to biotic sterilisation or major changes in island biotas. In this study, through remotely sensed data, we investigated the intensity an the extent of a large fire event that occurred on the small volcanic island of Stromboli (Aeolian archipelago, Italy) on 25-26 May 2022, to assess the short-term effect of fire damages on local plant communities. For this purpose, two different spectrally sensitive indices, i. e. the differential Normalised Burned Index (dNBR) and the Normalised Difference Vegetation Index (NDVI), were used. The dNBR was also used to quantify the extent of early-stage vegetation recovery, dominated by Saccharum biflorum Forssk. (Poaceae), a rhizomatous C4 perennial grass of paleotropical origin. The burned area was estimated to have an extension of around 337.83 ha, corresponding to 27.7% of the island surface and to 49.8% of Stromboli’s vegetated area. On the one hand, this event considerably damaged the native plant communities, hosting many species of high biogeographic interest. On the other hand, Saccharum biflorum clearly benefited from arson. In fact, it showed a very high vegetative performance after burning, being able to exert unchallenged dominance in the early stages of the post-fire succession, reaching within a few months stem density values that are only slightly lower than those of the unburned stands. Our results confirm the complex and probably synergic impact of different human disturbances (recurrent fires, introduction of invasive alien plants) on the structure and the functioning of natural ecosystems on small volcanic islands. The natural dynamics of such ecosystems is dependent on the complex relation between successional processes and the intensity and frequency of natural or anthropogenic disturbance, which can regulate mid- and long-term response of Saccharum. In fact, although the expansion of Saccharum proves to be surprisingly fast, its decline may also be relatively rapid as well, if local vegetation is no more affected by fire. After the abandonment of the agricultural practices in the highest portion of the island, the rewilding process could lead to the replacement of the large beds dominated by this invasive grass by native woody vegetation within a few decades. [ABSTRACT FROM AUTHOR]