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Fire events are increasing in frequency, duration, and severity worldwide. The combination of ash and uncovered land might increase the transportation of pollutants into the streams, potentially affecting the water supply systems. The intensifying fires in Brazil's Cerrado biome, responsible for 70% of the country's water supply, give rise to profound ecological, climatic, and socio-economic concerns that require urgent and effective mitigation strategies. However, little attention has been paid to the consequences of fire events on water resources in the region. In this study, the Fire Impact on Water Resources Index (FIWRI) is proposed and applied in six different water supply watersheds to analyse fire behaviour from 2003 to 2020 and its potential impact on inland water bodies. This is the first remote-sensing-based index for fire impact on water resources developed for the Brazilian territory, to support water management on a watershed scale and uses variables such as terrain slope, river proximity, and vegetation to classify fire events as having a low to high potential to contaminate water bodies. We observed that all six water supply watersheds suffered frequent fire events, with different FIWRI proportions, which ranged from High to Low FIWRI. The proposed index could be used in real-time fire monitoring alert systems in order to support water supply management. [ABSTRACT FROM AUTHOR]

Brazil has recently (2014) changed from a zero-fire policy to an Integrated Fire Management (IFM) program with the active use of prescribed burning (PB) in federal Protected Areas (PA) and Indigenous Territories (IT) of the Brazilian savanna (Cerrado). PB is commonly applied in the management of fire-prone ecosystems to mitigate large, high-intensity wildfires, the associated emissions, and high fire suppression costs. However, the effectiveness of such fire management in reducing large wildfires and emissions over Brazil remains mostly unevaluated. Here, we aim to fill the gap in the scientific evidence of the PB benefits by relying on the most up-to-date, satellitederived fire datasets of burned area (BA), fire size, duration, emissions, and intensity from 2003 to 2018. We focused on two Cerrado ITs with different sizes and hydrological regimes, Xerente and Araguaia, where IFM has been in place since 2015. To understand fire regime dynamics, we divided the study period into three phases according to the prevalent fire policy and the individual fire scars into four size classes. We considered two fire seasons: management fire season (MFS, which goes from rainy to mid-dry season, when PBs are undertaken) and wildfires season (WFS, when PBs are not performed and fires tend to grow out of control). Our results show that the implementation of the IFM program was responsible for a decrease of the areas affected by high fire recurrence in Xerente and Araguaia, when compared with the Zero Fire Phase (2008-2013). In both regions, PB effectively reduced the large wildfires occurrence, the number of medium and large scars, fire intensity, and emissions, changing the prevalent fire season from the WFS to the MFS. Such reductions are significant since WFS causes higher negative impacts on biodiversity conservation and higher greenhouse gas emissions. We conclude that the effect on wildfires can still be reduced if effective fire management policies, including PB, continue to be implemented during the coming decades. [ABSTRACT FROM AUTHOR]

Coarse spatial resolution of remote sensing imagery still hampers a comprehensive representation of long-term fire patterns at the regional level, in particular in areas characterized by small and sparse fire scars. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor launched in 2011 upgrades the spatial resolution (375 m) and gives continuity to the Earth long-term monitoring initiated by Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Therefore, aiming to assess VIIRS 375 m imagery capabilities to improve the accuracy and reliability of fire scars mapping over the Brazilian Cerrado, we developed a burned area detection algorithm (VIIRS-SVM) based on machine learning techniques. For this purpose, the (V, W) burnt index adjusted to VIIRS near-infrared and middle-infrared channels and the One-Class Support Vector Machine algorithm were used for burned area identification. The VIIRS-SVM algorithm was applied over the Brazilian Cerrado and evaluated against reference scars from 15 Landsat-8 scenes during the fire season of 2015, covering a large area with substantial variability in terms of fire scars characteristics. We also performed a comparison with the MCD64A1 collection-6 product over the validation sites. Relying on VIIRS 375 m imagery, the VIIRS-SVM algorithm allows an enhancement of 25% in discrimination of small and medium fire scars (25 to 1000 ha), when compared to the MODIS-derived product. Results have demonstrated that the enhancement of medium and small fire scars mapping over the Cerrado is possible using VIIRS sensor capabilities. [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]

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We used the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data (375 m spatial resolution) to automatically extract multispectral samples and train a One-Class Support Vector Machine for burned area mapping, and applied the resulting classification algorithm to 300-m spatial resolution imagery from the Project for On-Board Autonomy-Vegetation (PROBA-V). The active fire data were screened to prevent extraction of unrepresentative burned area samples and combined with surface reflectance bi-weekly composites to produce burned area maps. The procedure was applied over the Brazilian Cerrado savanna, validated with reference maps obtained from Landsat images and compared with the Collection 6 Moderate Resolution Imaging Spectrometer (MODIS) Burned Area product (MCD64A1) Results show that the algorithm developed improved the detection of small-sized scars and displayed results more similar to the reference data than MCD64A1. Unlike active fire-based region growing algorithms, the proposed approach allows for the detection and mapping of burn scars without active fires, thus eliminating a potential source of omission error. The burned area mapping approach presented here should facilitate the development of operational-automated burned area algorithms, and is very straightforward for implementation with other sensors. [ABSTRACT FROM AUTHOR]

Remote sensing from spaceborne sensors combining near- and middle-infrared information has proved to be an efficient means to monitor the effects of vegetation fires. Burn-sensitive spectral indices, such as the ( $V,W$ ) index system, have been developed and successfully applied for burned area discrimination. The ( $V,W$ ) index system provides useful capability to discriminate burned pixels, but the elaborate numerical computations involved are a major drawback in operational applications. This letter presents a simplified algorithm to compute the approximate values of indices $V$ and $W$ . The methodology developed is tested in a region located in the Brazilian Cerrado using remote-sensed data from the MODIS instrument. The simplification allows performing burned area discrimination with the same quality as the original algorithm. The methodology may be extended to other sensors and different combinations of bands and opens new perspectives to the generation of synergic long-term databases of burned area. [ABSTRACT FROM PUBLISHER]

The Brazilian Cerrado is significantly affected by anthropic fires every year, which makes the region an important source of pyrogenic emissions. This study aims at generating improved 1 km monthly burned area maps for Cerrado based on remote-sensed information. The algorithm relies on a burn-sensitive vegetation index based on MODIS daily values of near and middle infrared reflectance and makes use of active fire detection from multiple sensors. Validation is performed using reference burned area (BA) maps derived from Landsat imagery. Results are also compared with MODIS standard BA products. A monthly BA database for the Brazilian Cerrado is generated covering the period 2005-2014. Estimated value of BA is 1.3 times larger than the value derived from reference data, making the product suitable for applications in fire emission studies and ecosystem management. As expected the intra and inter-annual variability of estimated BA over the Brazilian Cerrado is in agreement with the regime of precipitation. This work represents the first step towards setting up a regional database of BA for Brazil to be developed in the framework of BrFLAS, an R and D project in the areas of fire emissions and ecosystem management planning. [ABSTRACT FROM AUTHOR]

A procedure is presented that allows using information from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor to improve the quality of emissivity maps for the Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (SEVIRI) currently in use as input to a generalized split window (SW) algorithm for land-surface temperature (LST) retrievals in the operational chain of the Satellite Application Facility on Land Surface Analysis (LSA SAF). Information from MODIS is incorporated by means of linear regression models expressing emissivity in SEVIRI thermal-infrared channels as a linear combination of emissivities in MODIS bands. The linear models are applied to the MODIS emissivity product MOD11C3, and a comparison is performed with the operational LSA-SAF product. Special attention is devoted to the semiarid and arid regions of North Africa where emissivity is highly variable. When compared with the new emissivity maps, the LSA-SAF product displays more uniform emissivity values over these regions, leading to higher retrievals for all channels (bias around 0.03) except for IR3.9 (bias from -0.05 to -0.08). The root-mean-square error (RMSE) varies from 0.06 to 0.09 (0.02 to 0.03) for IR3.9 (IR10.8 and IR12.0) and is about 0.06 for IR8.7. The impact on LST is assessed by comparing the retrievals from a SW algorithm using as input the following: 1) the SEVIRI emissivity LSA-SAF product and 2) SEVIRI emissivity maps from MOD11C3. The uncertainty in the LSA-SAF emissivity product results into LST values with bias ranging from -0.4 to -1.0 K and RMSE around 1.6 K. The new emissivity maps based on MODIS data may be an alternative to the standard LSA-SAF emissivity product over semiarid and arid areas, which cover 26% of the land surfaces within the SEVIRI full disk. [ABSTRACT FROM PUBLISHER]

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

Abstract: The ephemeral character of the radiative signal together with the presence of aerosols imposes severe limitations on the use of classical approaches, e.g. based on red and near-infrared, to discriminate between burned and unburned surfaces in tropical environments. Surface reflectance in the middle-infrared (MIR) has been used to circumvent these difficulties because the signal is virtually unaffected by the presence of aerosols associated to biomass burning. Retrieval of the MIR reflected component from the total signal is, however, a difficult problem because of the presence of a diversity of radiance sources, namely the surface reflected solar irradiance and the surface emitted radiance that may reach comparable magnitude during daytime. The method proposed by Kaufman and Remer (1994) to retrieve surface MIR reflectance presents the advantage of not requiring auxiliary datasets (e.g. atmospheric profiles) nor major computational means (e.g. for solving radiative transfer models). Nevertheless, the method was specifically designed to retrieve MIR reflectance over dense dark forests in the middle latitudes and, as shown in the present study, severe problems may arise when applying it beyond the range of validity, namely for burned area mapping in tropical environments. The present study consists of an assessment of the performance of the method for a wide range of atmospheric, geometric and surface conditions and of the usefulness of extracted surface reflectances for burned area discrimination. Results show that, in the case of tropical environments, there is a significant decrease in performance of the method for high values of land surface temperature, especially when associated with low sun elevation angles. Burned area discrimination is virtually impaired in such conditions, which are often present when using data from instruments on-board polar orbiters, namely MODIS in Aqua and Terra, to map burned surfaces over the Amazon forest and “cerrado” savanna regions. [Copyright &y& Elsevier]

• 21st century respiratory morbidity in Amazon is linked to changes in rainfall regime. • Drought exerts a primary control increasing respiratory diseases, except for Asthma. • Fire season acts as secondary influence mainly increasing the number of Asthma. • Drought-fire compounded effect boosts respiratory illness in elderly and children. Precipitation decline and vegetation fires in the Amazon region associated with climate change and deforestation expose local communities to hazardous air quality that may lead to damages in human health such as diseases of the respiratory system. Since 2000, drought incidence over the Amazon has been observed at a higher frequency than during the last century, and the effects of the drier climate and fires on human health remain uncertain. Understanding the roles played by precipitation decline and fire activity as well as associated changes of atmospheric parameters on the incidence of respiratory diseases hospitalizations (RDH) is a very complex task, and it is likely that the relative importance of those factors depends on the temporal and spatial variability used in the analysis. This work aims at serving as an updated reference that covers the impact of fire incidence on respiratory diseases in Porto Velho, the third most populated city of Brazilian Amazon, with emphasis on drought events from 2000 to 2016. For this purpose, we compare remote sensing data, meteorological variables and health indicators, that include RDH, in particular for children under five years old (RDH5) and people aged more than sixty (RDH60), as well as Asthma and Bronchitis hospitalizations. Here we show an increase of 27% in RDH (except for Asthma that decreased 75%) in drought years as described by the positive (negative for Asthma) relationship with rainfall. Rainfall and humidity exert a primary control in diseases of the respiratory system in the region. Hotspots, burned areas and smoke concentrate in August and September, and have a secondary influence of fire on RDH. Smoke production is strongly related to fire and temperature, which are associated with an increase in the number of hospitalizations during the fire season. RDH5 and RDH60 are respectively 11 and 22 times higher than RDH (for all ages), confirming the higher vulnerability for children and elderlies. Based on the observed data, we highlight the role of regional precipitation trends in driving respiratory hospitalizations, which are crucial ecological indicators for humans in the context of climate change. [ABSTRACT FROM AUTHOR]

• Compared to the latest report, the fraction of fires ignited by lighting was smaller. • Ignitions by lightning were more frequent amongst larger fire occurrences. • During the compound events of 2003 and 2017, lightning was a relevant fire cause. • Remote sensing data was able to reproduce the seasonality of lightning-ignited fires. Fire databases typically contain information regarding the location, timing, and duration of fire occurrences, as well as the cause (natural or human-induced). These databases allow for the analysis and understanding of the circumstances surrounding the ignition and propagation of wildfires, being highly relevant when addressing fire suppression and management plans, or for improvement of prevention policies. In Portugal, a large number of fires in the official database have unknown causes, which limits the understanding about the relevance of each fire cause, in particular the role of lightning on the ignition of fires over the country. The objective of this paper is twofold: first, we investigated the spatial-temporal variability of lightning-induced fires from 2003 to 2020. Then, we evaluate the feasibility of using remote sensing data as a surrogate for identifying fire causes, through validation with the fire database. Our results revealed that lightning-ignited fires represent an even smaller fraction of all fire events than mentioned in the latest official report about fire causes in the country, accounting for only 1 % of the fire occurrences and 4.6 % of the total burned area in the 18-year period under analysis. The months of June to September comprise 91 % of all lightning fires, which occur more frequently in the northern, remote regions of the country and at relatively high altitudes. Moreover, lightning ignitions originate fires larger than the global average and contributed significantly to the total area burned during the extreme 2003 and 2017 fire seasons. Its importance in these extreme years suggests that lightning can trigger large fires when in conjunction with compound events such as droughts and heatwaves. When compared with in-situ databases, the application of remote sensing data reproduced the seasonality of lightning-ignited fires, but failed to account for the contribution of smaller fires, which represent the majority of occurrences in the Portuguese fire database. Finally, we discuss the implications of our findings for the improved assessment of fire risks, causes, and impacts in Portugal. [ABSTRACT FROM AUTHOR]

Fire is an essential climate-related element in atmosphere-biosphere interactions. Global change will modify the frequency, intensity and occurrence of fire seasons, which are essential in the structure of fire-prone ecosystems. Therefore, knowledge of the fire dynamics is necessary to understand the impact of man-caused fires in the Brazilian Savannas ("Cerrado"). The burned area can be evaluated by satellite remote sensing observations to estimate fire impacts on a large scale spatially and temporally. New or reprocessed products incorporate algorithm refinements in order to improve the accuracy of fire mapping. However, there still is a lack of studies to quantify the uncertainties from available satellite products at biome level. Accordingly, the aim of this work was to evaluate the improvement of detection accuracy from MODerate resolution Imaging Spectroradiometer (MODIS) collection 6 BA product (MCD64 /C6) compared to previous collection 5.1 (C5.1) over 2 million km² in the Cerrado for the fire season of 2015, considering fire incidence and land use patterns. We used reference data derived from Landsat-8 OLI generated by National Institute of Space Research (INPE) to validate MCD64 collections and performed an intercomparison of C6 with independent active fires from the Visible Infrared Imaging Radiometer Suite (VIIRS) and recent land use patterns from IGBP-MCD12Q1 product. The overall accuracy assessment showed more satisfactory results for the regions with the highest total burned area, or with larger fire scar sizes. The southern Cerrado, with the highest intensity of land use, presented higher omission and commission errors (over 50%) due to the scar characteristics (small and fragmented), which are a challenge detection by MODIS ~500 m resolution. Our analysis showed that errors of both collections are associated more with underestimates (overestimates) of scar size than with complete omission (false detection), except for low fire severity. C6 performs better than C5.1, with a moderate increase in hits and a considerable reduction of omissions. Despite the improvements in the detection there are still challenges to be overcome regarding the MCD64 product for the Cerrado, such as small burnings in croplands. Keywords: MODIS, fire scars, active fires, commission error, omission error, land use, Cerrado.Acknowledgments: The study was funded by the Serrapilheira Institute (grant number Serra-1708-15159), by FAPESP/FCT Project Brazilian Fire-Land Atmosphere System (grants 1389/2014, 2015/01389-4) and by BNDES-Fundo Amazônia project Improvement of INPE´s Monitoring of Fires. CEF is funded by Fundação para a Ciência e a Tecnologia I.P. (FCT), Portugal (UID/AGR/00239/2013). [ABSTRACT FROM AUTHOR]

Remote sensing observations has improved the understanding of spatial and temporal fire patterns in Brazil in the last decades based on quantitative metrics such as severity, location, extension and duration. Nevertheless, large discrepancies and uncertainties persist in the currently burned area (BA) products in determining BA extension, location, and occurrence time. Visible Infrared Imaging Radiometer Suite (VIIRS) sensor was launched in 2011 to upgrade and to maintain the Earth long-term monitoring initiated by Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, but to our knowledge, none BA product has been developed using VIIRS data imagery. Accordingly, we present a BA mapping algorithm based on VIIRS imagery which includes two-steps. Firstly, monthly composites of (V, W) burned index are computed using spectral information of near infrared (NIR) and middle infrared (MIR) channels. Secondly, multispectral samples extracted by VIIRS active fires are used for training a One-Class Support Vector Machine (OC-SVM) classification that uses cumulative distribution functions criteria. The active fire data were screened to prevent extraction of unrepresentative BA samples and combined with burn index (V, W) monthly composites to produce BA scars. The procedure was applied over Brazilian savanna for 2015, a biome that has been increasingly affected by deforestation due to cropland and pasture expansion, consequently rising and changing the natural fire regime in region. Then, the developed algorithm was validated by reference scars obtained from Landsat imagery and compared with other BA product (e.g., MCD64A1). Results show that VIIRS BA product based on OC-SVM are able to map smaller areas more accurately than other products, including burned areas without active fires, due OC-SVM classification characterizes BA through active fire samples, thus eliminating a potential source of omission error.Keywords: burned area, VIIRS, SVM, Cerrado.Acknowledgments: The study was funded by the Serrapilheira Institute (grant number Serra-1708-15159), by FAPESP/FCT Project Brazilian Fire-Land Atmosphere System (grants 1389/2014, 2015/01389-4) [ABSTRACT FROM AUTHOR]

The Brazilian savanna (Cerrado) is considered the most floristically diverse savanna in the world, home to more than seven thousand species. The region is a mosaic of savannas, grasslands and forests whose unique biophysical and landscape attributes are on the basis of a recent ecoregional map, paving the way to improved region-based strategies for land management actions. However, as a fire-prone ecosystem, Cerrado owes much of its distribution and ecological properties to the fire regime and contributes to an important parcel of South America burned area. Accordingly, any attempt to use ecoregion geography as a guide for management strategies should take fire into account, as an essential variable. The main aim of this study is to complement the ecoregional map of the Cerrado with information related to the fire component. Using remotely sensed information, we identify patterns and trends of fire frequency, intensity, seasonality, extent and scar size, and combine this information for each ecoregion, relying on a simple classification that summarizes the main fire characteristics over the last two decades. Results show a marked north-south fire activity gradient, with increased contributions from MATOPIBA, the latest agricultural frontier. Five ecoregions alone account for two thirds of yearly burned area. More intense fires are found in the Arc of Deforestation and eastern ecoregions, while ecoregions in MATOPIBA display decreasing fire intensity. An innovative analysis of fire scars stratified by size class shows that infrequent large fires are responsible for the majority of burned area. These large fires display positive trends over many ecoregions, whereas smaller fires, albeit more frequent, have been decreasing in number. The final fire classification scheme shows well defined spatially-aggregated groups, where trends are found to be the key factor to evaluate fire within their regional contexts. Results presented here provide new insights to improve fire management strategies under a changing climate. [Display omitted] • There are very distinct regional fire behaviours in Cerrado. • Fire has a well-defined north-south gradient related to economic development. • Few large fires during the dry season are responsible for most of the burned area. • MATOPIBA, the latest agricultural frontier, shows increasing fire activity. • Adding fire characteristics on ecoregions allows improved local management policies. [ABSTRACT FROM AUTHOR]