Burned Area Products Research Articles

Temporal Anomalies in Burned Area Trends: Satellite Estimations of the Amazonian 2019 Fire Crisis

Tropical forests are known for hosting about half of the global biodiversity, and therefore are considered to be a fundamental part of the Earth System. However, in the last decades, the anthropogenic pressure over these areas has been continuously increasing, mostly linked to agricultural expansion. This has created great international concern, which has crossed the limits of national policies. A clear example was the last crisis suffered this year (2019) in the Amazon, and in general, in tropical South America (SA), due to the increasing fire activity in the region, which is strongly linked to deforestation and forest degradation. International media extensively informed the world about fire activity based upon active fire data, which provided quick but incomplete information about the actual fire-affected areas. This short paper compares fire occurrence estimations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data of active fires and from burned area products for the first 10 months of 2019 in SA. Results show a significant increase in fire activity over the full-time series (2001–2018) in Bolivia, Paraguay and Venezuela, while Brazil shows a much higher BA than in 2018, but with values around the average burned area of the whole time series.

Spatio-temporal characteristics of forest fires in China between 2001 and 2017

The remotely sensed burned area (BA) products can provide continuous and spatiotemporally explicit characteristics of fire patches, which are critical data sources for understanding regional fire regimes. However, their accuracy remains to be improved. In this study, a global BA product (i.e., CCI_Fire) at 250 m resolution was integrated with global forest change (GFC) product at 30 m to generate a refined BA product, named CCI_GFC product, whose accuracy was evaluated through comparing the BA with pre-existing fire patches data. To reveal the characteristics of forest fire in China between 2001 and 2017, we conducted a grid analysis at 0.05°×0.05° spatial resolution based on the refined BA product and the spatial pattern of eco-regions at the macro scale. The results showed that the accuracy metrics including the recognition rate (RR), variance explained (R2), root mean squared error (RMSE), mean absolute percentage error (MAPE) of the CCI_GFC product (i.e., 83%, 0.91, 0.28, and 8.5% respectively) were all superior to the CCI_Fire product (i.e., 74%, 0.86, 0.36, and 11.8% respectively) and the MCD64A1 product (i.e., 35%, 0.78, 0.48, and 17.3% respectively). The total burned area of forest was approximately 12.11 million hm2 for the whole country from 2001 to 2017, while the annual burned area temporally decreased. Forest fires in China were dominated by the low-frequency [0<burned forests rate (BFR)≤40%] burns which occupied 79% of the total burned area. The medium-frequency (40%<BFR≤80%) and high-frequency (80%<BFR≤120%) burns accounted for 11% and 10% of the total burned area, respectively. The seasonality of forest fires differed substantially among eco-regions. The primary fire season of the temperate eco-region was spring (May) and autumn (October). Fire season in the tropical-subtropical eco-region started from the winter to the next spring which included the months from November to next March.

Accuracy and spatiotemporal distribution of fire in the Brazilian biomes from the MODIS burned-area products

The Moderate Resolution Imaging Spectroradiometer (MODIS) products are the most used in burned-area monitoring, on regional and global scales. This research aims to evaluate the accuracy of the MODIS burned-area and active-fire products to describe fire patterns in Brazil in the period 2001–2015. The accuracy analysis, in the year 2015, compared the MODIS products (MCD45/MCD64) and the burned areas extracted by the visual interpretation of the LANDSAT/Operational Land Imager (OLI) images from the confusion matrix. The accuracy analysis of the active-fire products (MOD14/MYD14) in the year 2015 used linear regression. We used the most accurate burned-area product (MCD64), in conjunction with environmental variables of land use and climate. The MCD45 product presented a high error of commission (&amp;gt;36.69%) and omission (&amp;gt;77.04%) for the whole country. The MCD64 product had fewer errors of omission (64.05%) compared with the MCD45 product, but increased errors of commission (45.85%). MCD64 data in 2001–2015 showed three fire domains in Brazil determined by the climatic pattern. Savanna and grassy areas in semi-humid zones are the most prone areas to fire, burning an average of 25% of their total area annually, with a fire return interval of 5–6 years.

A global wildfire dataset for the analysis of fire regimes and fire behaviour

Global fire monitoring systems are crucial to study fire behaviour, fire regimes and their impact at the global scale. Although global fire products based on the use of Earth Observation satellites exist, most remote sensing products only partially cover the requirements for these analyses. These data do not provide information like fire size, fire spread speed, how fires may evolve and joint into single event, or the number of fire events for a given area. This high level of abstraction is very valuable; it makes it possible to characterize fires by types (either size, spread, behaviour, etc.). Here, we present and test a data mining work flow to create a global database of single fires that allows for the characterization of fire types and fire regimes worldwide. This work describes the data produced by a data mining process using MODIS burnt area product Collection 6 (MCD64A1). The entire product has been computed until the present and is available under the umbrella of the Global Wildfire Information System (GWIS).

A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data

This paper presents the generation of a global burned area mapping algorithm using MODIS hotspots and near-infrared reflectance within ESA's Fire_cci project. The algorithm is based on a hybrid approach that combines MODIS highest resolution (250 m) near-infrared band and active fire information from thermal channels. The burned area is detected in two phases. In the first step, pixels with a high probability of being burned are selected in order to reduce commission errors. To do that, spatio-temporal active-fire clusters are created to determine adaptive thresholds. Finally, a contextual growing approach is applied from those pixels to the neighbouring area to fully detect the burned patch and reduce omission errors. The algorithm was used to obtain a time series of global burned area dataset (named FireCCI51), covering the 2001–2018 period. Validation based on 1200 sampled sites covering the period from 2003 to 2014 showed an average omission and commission errors of 67.1% and 54.4%. When using longer validation periods, the errors were found smaller (54.5% omission and 25.7% commission for the additional 1000 African sampled sites), which indicates that the product is negatively influenced by temporal reporting accuracy. The inter-comparison carried out with previous Fire_cci versions (FireCCI41 and FireCCI50), and NASA's standard burned area product (MCD64A1 c6) showed consistent spatial and temporal patterns. However, the new algorithm estimated an average BA of 4.63 Mkm2, with a maximum of 5.19 Mkm2 (2004) and a minimum of 3.94 Mkm2 (in 2001), increasing current burned area estimations. Besides, the new product was found more sensitive to detect smaller burned patches. This new product, called FireCCI51, is publicly available at: http://cci.esa.int/data, last accessed on September 2019.

Global validation of the collection 6 MODIS burned area product

This paper presents a Stage 3 validation of the recently released Collection 6 NASA MCD64A1 500 m global burned area product. The product is validated by comparison with Landsat 8 Operational Land Imager (OLI) image pairs acquired 16 days apart that were visually interpreted. These independent reference data were selected using a stratified random sampling approach that allows for probability sampling of Landsat data in both time and in space. A total of 558 Landsat 8 OLI image pairs (1116 images), acquired between March 1st, 2014 and March 19th , 2015, were selected and used to validate the MCD64A1 product. The areal accuracy of the MCD64A1 product was characterized at the 30 m resolution of the Landsat independent reference data using standard accuracy metrics derived from global and from biome specific confusion matrices. Because a probability based Stage 3 sampling protocol was followed, unbiased estimators of the accuracy metrics and associated standard errors could be used. Globally, the MCD64A1 product had an estimated 40.2% commission error and 72.6% omission error; the prevalence of omission errors is reflected by a negative estimated bias of the mapped global area burned relative to the Landsat independent reference data (−54.1%). Globally, the standard errors of the accuracy metrics were less than 6%. The lowest errors were observed in the boreal forest biome (27.0% omission and 23.9% estimated commission errors) where burned areas tend to be large and distinct, and remain on the landscape for long periods, and the highest errors were in the Tropical Forest, Temperate Forest, and Mediterranean biomes (estimated > 90% omission error and >50% commission error). The product accuracy was also characterized at coarser scale using metrics derived from the regression between the proportion of coarse resolution grid cells detected as burned by MCD64A1 and the proportion mapped in the Landsat 8 interpreted maps. The errors of omission and commission observed at 30 m resolution compensate to a considerable extent at coarser resolution, as indicated by the coefficient of determination (r2 > 0.70), slope (>0.79) and intercept (−0.0030) of the regression between the MCD64A1 product and the Landsat independent reference data in 3 km, 4 km, 5 km, and 6 km coarse resolution cells. The Boreal Forest, Desert and Xeric Shrublands, Temperate Savannah and Tropical Savannah biomes had higher r2 and slopes closer to unity than the Temperate Forest, Mediterranean, and Tropical Forest biomes. The analysis of the deviations between the proportion of area burned mapped by the MCD64A1 product and by the independent reference data, performed using 3 km × 3 km and 6 km × 6 km coarse resolution cells, indicates that the large negative bias in global area burned is primarily due to the systematic underestimation of smaller burned areas in the MCD64A1 product.

Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China

Previous studies have shown that Live Fuel Moisture Content (LFMC) is a crucial driver affecting wildfire occurrence worldwide, but the effect of LFMC in driving wildfire occurrence still remains unexplored over the southwest China ecosystem, an area historically vulnerable to wildfires. To this end, we took 10-years of LFMC dynamics retrieved from Moderate Resolution Imaging Spectrometer (MODIS) reflectance product using the physical Radiative Transfer Model (RTM) and the wildfire events extracted from the MODIS Burned Area (BA) product to explore the relations between LFMC and forest/grassland fire occurrence across the subtropical highland zone (Cwa) and humid subtropical zone (Cwb) over southwest China. The statistical results of pre-fire LFMC and cumulative burned area show that distinct pre-fire LFMC critical thresholds were identified for Cwa (151.3%, 123.1%, and 51.4% for forest, and 138.1%, 72.8%, and 13.1% for grassland) and Cwb (115.0% and 54.4% for forest, and 137.5%, 69.0%, and 10.6% for grassland) zones. Below these thresholds, the fire occurrence and the burned area increased significantly. Additionally, a significant decreasing trend on LFMC dynamics was found during the days prior to two large fire events, Qiubei forest fire and Lantern Mountain grassland fire that broke during the 2009/2010 and 2015/2016 fire seasons, respectively. The minimum LFMC values reached prior to the fires (49.8% and 17.3%) were close to the lowest critical LFMC thresholds we reported for forest (51.4%) and grassland (13.1%). Further LFMC trend analysis revealed that the regional median LFMC dynamics for the 2009/2010 and 2015/2016 fire seasons were also significantly lower than the 10-year LFMC of the region. Hence, this study demonstrated that the LFMC dynamics explained wildfire occurrence in these fire-prone regions over southwest China, allowing the possibility to develop a new operational wildfire danger forecasting model over this area by considering the satellite-derived LFMC product.

Global Detection of Long-Term (1982–2017) Burned Area with AVHRR-LTDR Data

This paper presents the first global burned area (BA) product derived from the land long term data record (LTDR), a long-term 0.05-degree resolution dataset generated from advanced very high resolution radiometer (AVHRR) images. Daily images were combined in monthly composites using the maximum temperature criterion to enhance the burned signal and eliminate clouds and artifacts. A synthetic BA index was created to improve the detection of the BA signal. This index included red and near infrared reflectance, surface temperature, two spectral indices, and their temporal differences. Monthly models were generated using the random forest classifier, using the twelve monthly composites of each year as the predictors. Training data were obtained from the NASA MCD64A1 collection 6 product (500 m spatial resolution) for eight years of the overlapping period (2001–2017). This included some years with low and high fire occurrence. Results were tested with the remaining eight years. Pixels classified as burned were converted to burned proportions using the MCD64A1 product. The final product (named FireCCILT10) estimated BA in 0.05-degree cells for the 1982 to 2017 period (excluding 1994, due to input data gaps). This product is the longest global BA currently available, extending almost 20 years back from the existing NASA and ESA BA products. BA estimations from the FireCCILT10 product were compared with those from the MCD64A1 product for continental regions, obtaining high correlation values (r2 &gt; 0.9), with better agreement in tropical regions rather than boreal regions. The annual average of BA of the time series was 3.12 Mkm2. Tropical Africa had the highest proportion of burnings, accounting for 74.37% of global BA. Spatial trends were found to be similar to existing global BA products, but temporal trends showed unstable annual variations, most likely linked to the changes in the AVHRR sensor and orbital decays of the NOAA satellites.

Theoretical uncertainties for global satellite-derived burned area estimates

Abstract. Quantitative information on the error properties of global satellite-derived burned area (BA) products is essential for evaluating the quality of these products, e.g. against modelled BA estimates. We estimate theoretical uncertainties for three widely used global satellite-derived BA products using a multiplicative triple collocation error model. The approach provides spatially unique uncertainties at 1∘ for the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 burned area product (MCD64), the MODIS Collection 5.1 (MCD45) product, and the European Space Agency (ESA) Climate Change Initiative Fire product version 5.0 (FireCCI50) for 2001–2013. The uncertainties on mean global burned area for three products are 3.76±0.15×106 km2 for MCD64, 3.70±0.17×106 km2 for FireCCI50, and 3.31±0.18×106 km2 for MCD45. These correspond to relative uncertainties of 4 %–5.5 % and also indicate previous uncertainty estimates to be underestimated. Relative uncertainties are 8 %–10 % in Africa and Australia, for example, and larger in regions with less annual burned area. The method provides uncertainties that are likely to be more consistent with modelling and data analysis studies due to their spatially explicit properties. These properties are also intended to allow spatially explicit validation of current burned area products.

Spectral mixture analysis in Google Earth Engine to model and delineate fire scars over a large extent and a long time-series in a rainforest-savanna transition zone

Fire is used worldwide to clear natural vegetation areas for economic activities and to manage the regeneration of already opened sites. In Brazil, fire has been traditionally used to convert natural vegetation areas to agricultural lands (slash and burn) and to manage pastures for livestock. We developed the Burned Area Spectral Mixture Analysis (BASMA) algorithm in Google Earth Engine, which is designed to process Landsat data to produce a multi-temporal fire scar database representing annual burned area for an extent of 362,000 km2 in the transition zone between the Amazon forest and the Cerrado biome. This region is considered a conservation hotspot, given its high deforestation rates over the last four decades. We digitally processed a 32-year time-series (1985 to 2017) of Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper+, and Landsat 8 Operational Land Imager data to map fire scars based on sub-pixel char fraction, aiming to generate a consistent burned area product at a finer scale and covering a longer period than those currently available for the region. Manually interpreted reference burned area polygons for each annual mosaic was used to guide the definition of the best fire scar endmember and its fraction threshold. To assess our BASMA-delineated fire scar, they were compared to independent datasets of manually delineated burned area produced by visual analysis of finer spatial resolution imagery, returning an average Dice Coefficient value of 0.86. Accuracy was also measured against the 30-meter Burned Area product available at the ‘Queimadas’ data portal. A total of 11,106,258 ha was mapped as having been affected by fire during the annual dry season over the 32 years, which represents 30.7% of the study region. Results showed a decreasing trend in the annual amount of burned area over the time-series. It reflects a similar pattern shown in the deforestation rate for the Legal Amazon, measured by the Brazilian National Institute for Space Research - INPE. Moreover, the Cerrado biome subset of the study region consistently showed higher burned area when compared to the Amazon forest subset. Our findings provide robust evidence that our approach is a consistent method to identify and delineate fire scars for large areas over a long time-series in a very efficient fashion, given the digital processing power of Google Earth Engine, which reduces the time necessary to analyze such big amount of data.

Landsat-8 and Sentinel-2 burned area mapping - A combined sensor multi-temporal change detection approach

The free-availability of global coverage Landsat-8 and Sentinel-2 data provides the opportunity for systematic generation of medium spatial resolution land products. This paper presents a combined Landsat-8 Sentinel-2 burned area mapping algorithm. The data handling integrates recent research on Landsat-8 and Sentinel-2 pre-processing to generate registered, surface nadir BRDF-adjusted reflectance (NBAR) sensor time series that are used as an input. The different sensor data are combined through a random forest change regression, trained with synthetic data built from laboratory and field spectra and using a spectral model of fire effects on reflectance. The random forest regression is applied independently at each gridded 30 m pixel location on a temporally rolling basis considering three months of sensor data to map the central month. Temporal consistency checks are used to reduce commission errors due to non-fire related spectral changes, and a region growing algorithm is used to reduce omission errors due to temporally sparse observations. In the resulting product, each 30 m pixel is labelled as burned, unburned or unmapped. At burned pixels the estimated day of burning, and a single value that provides an estimate of the product of the subpixel fraction burned (f) and the combustion completeness (cc), henceforth termed “f.cc”, and an associated quality measure, are defined. The algorithm is demonstrated using six months of every available Landsat-8 and Sentinel-2A acquisition over 10° × 10° of Southern Africa. Experiments comparing the mapped burned areas considering Sentinel-2A only and both Landsat-8 and Sentinel-2A data indicate a greater area burned and a smaller number of unmapped pixels when both sensors are used. The results are compared with contemporaneous NASA MODIS fire products to gain insights into their temporal and spatial reporting differences. Temporally, the Sentinel-2A and Landsat-8 30 m product reports the day of burning on average three days later than the MODIS 500 m burned area product, because of the lower revisit frequency of the Sentinel-2A and Landsat-8 observations. Spatially, the Sentinel-2A and Landsat-8 30 m product captures more detail than the MODIS 500 m burned area product, with systematically higher burned area estimates. Despite the areal differences, the spatial pattern of the two products is similar, as reflected by the correlation (r2 ~ 0.7) and slope (>0.8) of regressions of the proportions of area burned defined in coarse resolution cells between the two products, taking into account the 3 day temporal bias. A comparison with multi-date PLANET 3 m data, shows high visual agreement in the location and approximate day of burning and higher f.cc values in the interiors of distinct burns than at the edges, and lower values in areas containing mixes of burned and unburned 3 m pixels. The Landsat-8/Sentinel-2A burned area mapping results are validated by comparison with burned areas interpreted visually from two date Sentinel-2A data, over 30 km × 30 km boxes selected by systematic sampling. The results mapped less area burned than the interpreted maps, as reflected by a 0.24 omission error and a negative relative bias (−0.19), with a small 0.06 commission error. Regression between the proportions of area burned defined in 1.5 km cells provide a high correlation (r2 = 0.89) and slope close to unity (0.82) and indicate that the Landsat-8/Sentinel-2A results identified similar burned area spatial patterns as the interpreted maps.

A patch-based algorithm for global and daily burned area mapping

Increasing availability of dense time series of moderate spatial resolution satellite data for mapping global burned areas calls for mapping algorithms designed to easily integrate data at different spatial and temporal resolutions, irrespective of particular grid constraints. In this paper, we describe a novel hybrid approach for global burned area mapping that combines active fire data and time series of surface reflectance using graphs, which provide a flexible and efficient way of extracting spatiotemporal consistent patches.Our approach has three main steps. Firstly, we analyze burn-sensitive vegetation index time series to determine for each location a set of events, which are the dates for which the spectral-temporal signal indicates the possibility of a burn. Secondly, we explore the spatiotemporal distribution of all events and active fires to determine a subset of events with strong evidence of corresponding to burned areas. Those events are used as positive occurrences for training a one-class maximum entropy classifier and obtain, for each candidate event, a likelihood of it actually corresponding to a burn. Finally, we build a graph that combines all previous information, from which we extract spatiotemporal patches of densely connected events. Patches with strong evidence of burning determine the burned area map at any given time period.This research is part of the European Space Agency's Climate Change Initiative (ESA-CCI) and aims ultimately at generating Sentinel-3 daily global burned area products at 500 m spatial resolution. Towards that end, we test our approach with spatially and spectrally similar MODIS gridded surface reflectance (MOD/MYD09GA), as well as non-gridded active fire (MCD14ML) 2008 data and CCI global land cover maps. Using 105 independent Landsat fire reference perimeters to validate global results, we show that our algorithm applied to MOD/MYD09GA data (PT-M09) has very similar accuracy (44%) measured by the Dice coefficient compared with MCD64A1 v006 (45%). Moreover, PT-M09 exhibits a higher commission error but a lower omission error than MCD64A1. Due to their coarse resolution, this kind of product cannot capture very small burn areas. The bias relative to the reference burned area indicates that the algorithm presented in the current study underestimates burned area by 14% of the area actually burned according to reference data, which is lower than the underestimation in MCD64A1 (28%).We also analyzed the temporal accuracy of the patch based algorithm and concluded that the average time difference to active fires detection is 3.43 days, which is similar to MCD64A1 (3.75 days). Finally, we performed a sensitivity analysis which shows that total mapped burned areas varies only 3% when the main algorithm threshold that separates “burned” and “unburned” patches varies from quantile 45% to 55%.

Anthropogenic modifications to fire regimes in the wider Serengeti-Mara ecosystem.

Fire is a key driver in savannah systems and widely used as a land management tool. Intensifying human land uses are leading to rapid changes in the fire regimes, with consequences for ecosystem functioning and composition. We undertake a novel analysis describing spatial patterns in the fire regime of the Serengeti‐Mara ecosystem, document multidecadal temporal changes and investigate the factors underlying these patterns. We used MODIS active fire and burned area products from 2001 to 2014 to identify individual fires; summarizing four characteristics for each detected fire: size, ignition date, time since last fire and radiative power. Using satellite imagery, we estimated the rate of change in the density of livestock bomas as a proxy for livestock density. We used these metrics to model drivers of variation in the four fire characteristics, as well as total number of fires and total area burned. Fires in the Serengeti‐Mara show high spatial variability—with number of fires and ignition date mirroring mean annual precipitation. The short‐term effect of rainfall decreases fire size and intensity but cumulative rainfall over several years leads to increased standing grass biomass and fuel loads, and, therefore, in larger and hotter fires. Our study reveals dramatic changes over time, with a reduction in total number of fires and total area burned, to the point where some areas now experience virtually no fire. We suggest that increasing livestock numbers are driving this decline, presumably by inhibiting fire spread. These temporal patterns are part of a global decline in total area burned, especially in savannahs, and we caution that ecosystem functioning may have been compromised. Land managers and policy formulators need to factor in rapid fire regime modifications to achieve management objectives and maintain the ecological function of savannah ecosystems.

The influence of fire frequency on the structure and botanical composition of savanna ecosystems.

Savannas cover 60% of the land surface in Southern Africa, with fires and herbivory playing a key role in their ecology. The Limpopo National Park (LNP) is a 10,000 km2 conservation area in southern Mozambique and key to protecting savannas in the region. Fire is an important factor in LNP's landscapes, but little is known about its role in the park's ecology. In this study, we explored the interaction between fire frequency (FF), landscape type, and vegetation. To assess the FF, we analyzed ten years of the Moderate resolution Imaging Spectroradiometer (MODIS) burned area product (2003–2013). A stratified random sampling approach was used to assess biodiversity across three dominant landscapes (Nwambia Sandveld‐NS, Lebombo North‐LN, and Shrubveld Mopane on Calcrete‐C) and two FF levels (low—twice or less; and high—3 times or more, during 10 years). Six ha were sampled in each stratum, except for the LN versus high FF in which low accessibility allowed only 3 ha sampling. FF was higher in NS and LN landscapes, where 25% and 34% of the area, respectively, burned more than three times in 10 years. The landscape type was the main determinant of grass composition and biomass. However, in the sandy NS biomass was higher under high FF. The three landscapes supported three different tree/shrub communities, but FF resulted in compositional variations in NS and LN. Fire frequency had no marked influence on woody structural parameters (height, density, and phytomass). We concluded that the savannas in LNP are mainly driven by landscape type (geology), but FF may impose specific modifications. We recommend a fire laissez‐faire management system for most of the park and a long‐term monitoring system of vegetation to address vegetation changes related to fire. Fire management should be coordinated with the neighboring Kruger National Park, given its long history of fire management. Synthesis: This study revealed that grass and tree/shrub density, biomass, and composition in LNP are determined by the landscape type, but FF determines some important modifications. We conclude that at the current levels FF is not dramatically affecting the savanna ecosystem in the LNP (Figure 1). However, an increase in FF may drive key ecosystem changes in grass biomass and tree/shrub species composition, height, phytomass, and density.

Incorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping

Wildland fires result in a unique signal detectable by multispectral remote sensing and synthetic aperture radar (SAR). However, in many regions, such as Southeast Asia, persistent cloud cover and aerosols temporarily obstruct multispectral satellite observations of burned area, including the MODIS MCD64A1 Burned Area Product (BAP). Multiple days between cloud free pre- and post-burn MODIS observations result in burn date uncertainty. We incorporate cloud-penetrating, C-band SAR-with the MODIS MCD64A1 BAP in Southeast Asia, to exploit the strengths of each dataset to better estimate the burn date and reduce the potential burn date uncertainty range. We incorporate built-in quality control using MCD64A1 to reduce erroneous pixel updating. We test the method over part of Laos and Thailand during April 2016 and found average uncertainty reduction of 4.5 d, improving 15% of MCD64A1 pixels. A new BAP could improve monitoring temporal trends of wildland fires, air quality studies and monitoring post-fire vegetation dynamics.

A Comparison of Burned Area Time Series in the Alaskan Boreal Forests from Different Remote Sensing Products

Alaska’s boreal region stores large amounts of carbon both in its woodlands and in the grounds that sustain them. Any alteration to the fire system that has naturally regulated the region’s ecology for centuries poses a concern regarding global climate change. Satellite-based remote sensors are key to analyzing those spatial and temporal patterns of fire occurrence. This paper compiles four burned area (BA) time series based on remote sensing imagery for the Alaska region between 1982–2015: Burned Areas Boundaries Dataset-Monitoring Trends in Burn Severity (BABD-MTBS) derived from Landsat sensors, Fire Climate Change Initiative (Fire_CCI) (2001–2015) and Moderate-Resolution Imaging Spectroradiometer (MODIS) Direct Broadcast Monthly Burned Area Product (MCD64A1) (2000–2015) with MODIS data, and Burned Area-Long-Term Data Record (BA-LTDR) using Advanced Very High Resolution Radiometer LTDR (AVHRR-LTDR) dataset. All products were analyzed and compared against one another, and their accuracy was assessed through reference data obtained by the Alaskan Fire Service (AFS). The BABD-MTBS product, with the highest spatial resolution (30 m), shows the best overall estimation of BA (81%), however, for the years before 2000 (pre-MODIS era), the BA sensed by this product was only 44.3%, against the 55.5% obtained by the BA-LTDR product with a lower spatial resolution (5 km). In contrast, for the MODIS era (after 2000), BABD-MTBS virtually matches the reference data (98.5%), while the other three time series showed similar results of around 60%. Based on the theoretical limits of their corresponding Pareto boundaries, the lower resolution BA products could be improved, although those based on MODIS data are currently limited by the algorithm’s reliance on the active fire MODIS product, with a 1 km nominal spatial resolution. The large inter-annual variation found in the commission and omission errors in this study suggests that for a fair assessment of the accuracy of any BA product, all available reference data for space and time should be considered and should not be carried out by selective sampling.

Burned area and surface albedo products: Assessment of change consistency at global scale

This paper presents a framework for assessing the physical consistency between time-series of several satellite-based surface albedo and burned area products at global scale. The methodology evaluates the level of agreement of temporal change between these two Essential Climate Variables (ECVs) taking into account their uncertainties. Several configurations of surface albedo and burned area products, including the ones from MODIS Collection 6, Copernicus Global Land Service (C-GLS), Climate Change Initiative (CCI) and the GlobAlbedo, were analysed over the 2005–2011 period at 0.05°× 0.05° spatial resolution. The study evaluates the temporal and spatial coherence level in the ECV changes, and explores the product dependency impact on the fire-driven radiative forcings. The main findings indicate that the level of agreement between these two ECVs depends on the product combination but also on the albedo broadband. Whereas both GlobAlbedo and MODIS albedo changes are consistent both temporally and spatially when combined with any burned area product, the C-GLS albedo changes are characterised by lower levels of agreement. The results also indicate that surface albedo and burned area product changes are physically coherent for all biomes except grasslands and croplands, when using the MODIS burned area product. Using different combinations of burned area and surface albedo products, fire-induced radiative forcing estimates at the surface can differ, according to the biome, by 26% to 46%. The proposed framework identifies lack of coherency and consistency between the two ECVs. This novel approach allows us to address cross-ECV compatibility, and as such offers a valuable toolset to users.

Historical background and current developments for mapping burned area from satellite Earth observation

Fire has a diverse range of impacts on Earth's physical and social systems. Accurate and up to date information on areas affected by fire is critical to better understand drivers of fire activity, as well as its relevance for biogeochemical cycles, climate, air quality, and to aid fire management. Mapping burned areas was traditionally done from field sketches. With the launch of the first Earth observation satellites, remote sensing quickly became a more practical alternative to detect burned areas, as they provide timely regional and global coverage of fire occurrence. This review paper explores the physical basis to detect burned area from satellite observations, describes the historical trends of using satellite sensors to monitor burned areas, summarizes the most recent approaches to map burned areas and evaluates the existing burned area products (both at global and regional scales). Finally, it identifies potential future opportunities to further improve burned area detection from Earth observation satellites.

How well do global burned area products represent fire patterns in the Brazilian Savannas biome? An accuracy assessment of the MCD64 collections

Knowledge about the current fire dynamics in the Brazilian Savannas (Cerrado) relies heavily on satellite-derived burned area (BA) products applied at the biome level. Nevertheless, there is still a lack of studies analyzing the consistency of available available satellite products concerning BA location and extension for the region. Accordingly, we performed an accuracy assessment of the MODerate resolution Imaging Spectroradiometer (MODIS) collection 6 BA product (MCD64 /C6) over 222,768,000 ha encompassing the Brazilian Cerrado. We used reference data derived from Landsat-8 OLI to perform an intercomparison of MCD64/C6 with 1) the previous collection 5.1 (C5.1); 2) independent active fires from the Visible Infrared Imaging Radiometer Suite (VIIRS); and 3) recent land use patterns. The results of the comparison between C6 and C5.1 indicate that the new collection decreases the omission error in 90% of the analyzed area and increases the burn hits, providing improved BA estimates in 61% of the region. However, the MCD64 product increases the overall commission errors in 74% of the area. The MCD64/C6 product showed a high coefficient of correlation with active fires independently detected by VIIRS (τ = 0.74). For both MCD64 collections 5.1 and 6, the different accuracy assessment measures exhibited a marked performance deterioration from the north towards the south. The largest burn scars and total affected areas occur mainly across the northern Cerrado, explaining the better performance in that area. Conversely, greater inaccuracies were found in the southern Cerrado area, where natural vegetation has been converted into pasture and cropland, leading to fragmented landscapes and small fire patches. Finally, the BAs mapped by both collections were similar in location albeit divergent in the magnitude, with C6 detecting 21% more area than C5.1 during the year 2015.

30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.