Estimating above‐ground burned biomass and CO2 emissions for tropical Africa for the year 1990 with the NOAA–NASA Pathfinder AVHRR 8 km land dataset

Biomass burning combusts Earth's vegetation (in forests, savannas and agricultural lands) and occurs over huge areas of the Earth's surface. Global estimates of biomass burning are thus required in order to provide exact figures of the gas fluxes derived from this source. In this paper we use coarse resolution images for estimating above‐ground burned biomass and CO₂ emissions for tropical Africa for the year 1990. The burned land cover areas have been derived from burn scar and land cover maps using the global daily National Oceanic and Atmospheric Administration–National Aeronautics and Space Administration (NOAA–NASA) Pathfinder AVHRR 8?km land dataset. A burned area estimation of (742±222)?Mha has been considered. Monthly maximum Normalized Difference Vegetation Index (NDVI) composites and biomass density measurements have been used for modelling the temporal behaviour of above‐ground biomass for the main seasonal vegetation classes in Africa (humid savanna, derived humid savanna, dry savanna grassland and broadleaf savanna). The amount of above‐ground burned biomass and therefore CO₂ emissions can be estimated from burned land cover area, above‐ground biomass density, burn efficiency and emission factor of trace gas by land cover class. A total of 6494 (3675–9312) Tg for CO₂ emissions was computed for tropical Africa for the year 1990.     

An active-fire based burned area mapping algorithm for the MODIS sensor

We present an automated method for mapping burned areas using 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) imagery coupled with 1-km MODIS active fire observations. The algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive vegetation index and a measure of temporal texture. Cumulative active fire maps are used to guide the selection of burned and unburned training samples. An accuracy assessment for three geographically diverse regions (central Siberia, the western United States, and southern Africa) was performed using high resolution burned area maps derived from Landsat imagery. Mapped burned areas were accurate to within approximately 10% in all regions except the high-tree-cover sub-region of southern Africa, where the MODIS burn maps underestimated the area burned by 41%. We estimate the minimum detectable burn size for reliable detection by our algorithm to be on the order of 120 ha.     

Modeling and sensitivity analysis of fire emissions in southern Africa during SAFARI 2000

This paper uses three recently generated southern African satellite burned area products for the month of September 2000 in a sensitivity study of regional biomass burning emissions for a number of trace gases and particulates. Differences in the extent and location of areas burned among products generated from Moderate Resolution Imaging Spectroradiometer (MODIS), Systeme Pour l'Observation de la Terre (SPOT-VEGETATION), and Along Track Scanning Radiometer (ATSR-2) data are significant and result in different emissions estimates for woodland and grassland land cover types. Due to the different emission profiles in woodlands and grasslands, favoring relatively more products of incomplete combustion in woodlands compared with products of complete combustion in grasslands in the late dry season, these changes are not proportional to the differences in the burned area amounts. The importance of accurate burned area information not just in terms of the total area but also in terms of its spatial distribution becomes apparent from our modeling results. This paper highlights the urgent need for satellite data producers to provide accuracy assessments associated with satellite-derived products. Preferably, these accuracy data will be spatially explicit, or defined in a way that can be applied in a spatially explicit modeling context, to enable emissions uncertainties to be defined with respect to different landscape units in support of greenhouse gas emissions reporting.     

Modeling and sensitivity analysis of fire emissions in southern Africa during SAFARI 2000

This paper uses three recently generated southern African satellite burned area products for the month of September 2000 in a sensitivity study of regional biomass burning emissions for a number of trace gases and particulates. Differences in the extent and location of areas burned among products generated from Moderate Resolution Imaging Spectroradiometer (MODIS), Systeme Pour l'Observation de la Terre (SPOT-VEGETATION), and Along Track Scanning Radiometer (ATSR-2) data are significant and result in different emissions estimates for woodland and grassland land cover types. Due to the different emission profiles in woodlands and grasslands, favoring relatively more products of incomplete combustion in woodlands compared with products of complete combustion in grasslands in the late dry season, these changes are not proportional to the differences in the burned area amounts. The importance of accurate burned area information not just in terms of the total area but also in terms of its spatial distribution becomes apparent from our modeling results. This paper highlights the urgent need for satellite data producers to provide accuracy assessments associated with satellite-derived products. Preferably, these accuracy data will be spatially explicit, or defined in a way that can be applied in a spatially explicit modeling context, to enable emissions uncertainties to be defined with respect to different landscape units in support of greenhouse gas emissions reporting.     

Comparison of burned area estimates derived from SPOT-VEGETATION and Landsat ETM+ data in Africa: Influence of spatial pattern and vegetation type

An algorithm for burned area mapping in Africa based on classification trees was developed using SPOT-VEGETATION (VGT) imagery. The derived 1 km spatial resolution burned area maps were compared with 30 m spatial resolution maps obtained with 13 Landsat ETM+ scenes, through linear regression analysis. The procedure quantifies the bias in burned area estimation present in the low spatial resolution burned area map. Good correspondence was observed for seven sites, with values of the coefficient of determination (R²) ranging from 0.787 to 0.983. Poorer agreement was observed in four sites (R² values between 0.257 and 0.417), and intermediate values of R² (0.670 and 0.613) were obtained for two sites. The observed variation in the level of agreement between the Landsat and VGT estimates of area burned results from differences in the spatial pattern and size distribution of burns in the different fire regimes encompassed by our analysis. Small and fragmented burned areas result in large underestimation at 1 km spatial resolution. When large and compact burned areas dominate the landscape, VGT estimates of burned area are accurate, although in certain situations there is some overestimation. Accuracy of VGT burned area estimates also depends on vegetation type. Results showed that in forest ecosystems VGT maps underestimate substantially the amount of burned area. The most accurate estimates were obtained for woodlands and grasslands. An overall linear regression fitted with the data from the 13 comparison sites revealed that there is a strong relationship between VGT and Landsat estimates of burned area, with a value of R² of 0.754 and a slope of 0.803. Our findings indicate that burned area mapping based on 1 km spatial resolution VGT data provides adequate regional information.     

Satellite-based mapping of Canadian boreal forest fires: Evaluation and comparison of algorithms

This paper evaluates annual fire maps that were produced from NOAA-14/AVHRR imagery using an algorithm described in a companion paper (Li et al., International Journal of Remote Sensing, 21, 3057-3069, 2000 (this issue)). Burned area masks covering the Canadian boreal forest were created by compositing the daily maps of fire hot spots over the summer and by examining Normalized Difference Vegetation Index (NDVI) changes after burning. Both masks were compared with fire polygons derived by Canadian fire agencies through aerial surveillance. It was found that the majority of fire events were captured by the satellite-based techniques, but burnt area was generally underestimated. The burn boundary formed by the fire pixels detected by satellite were in good agreement with the polygons boundaries within which, however, there were some fires missed by the satellite. The presence of clouds and low sampling frequency of satellite observation are the two major causes for the underestimation. While this problem is alleviated by taking advantage of NDVI changes, a simple combination of a hot spot technique with a NDVI method is not an ideal solution due to the introduction of new sources of uncertainty. In addition, the performance of the algorithm used in the International Geosphere-Biosphere Programme (IGBP) Data and Information System (IGBPDIS) for global fire detection was evaluated by comparing its results with ours and with the fire agency reports. It was found that the IGBP-DIS algorithm is capable of detecting the majority of fires over the boreal forest, but also includes many false fires over old burned scars created by fires taking place in previous years. A step-by-step comparison between the two algorithms revealed the causes of the problem and recommendations are made to rectify them.     

A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States

Improved wildland fire emission inventory methods are needed to support air quality forecasting and guide the development of air shed management strategies. Air quality forecasting requires dynamic fire emission estimates that are generated in a timely manner to support real-time operations. In the regulatory and planning realm, emission inventories are essential for quantitatively assessing the contribution of wildfire to air pollution. The development of wildland fire emission inventories depends on burned area as a critical input. This study presents a Moderate Resolution Imaging Spectroradiometer (MODIS) - direct broadcast (DB) burned area mapping algorithm designed to support air quality forecasting and emission inventory development. The algorithm combines active fire locations and single satellite scene burn scar detections to provide a rapid yet robust mapping of burned area. Using the U.S. Forest Service Fire Sciences Laboratory (FiSL) MODIS-DB receiving station in Missoula, Montana, the algorithm provided daily measurements of burned area for wildfire events in the western U.S. in 2006 and 2007. We evaluated the algorithm's fire detection rate and burned area mapping using fire perimeter data and burn scar information derived from high resolution satellite imagery. The FiSL MODIS-DB system detected 87% of all reference fires > 4 km², and 93% of all reference fires > 10 km². The burned area was highly correlated (R² = 0.93) with a high resolution imagery reference burn scar dataset, but exhibited a large over estimation of burned area (56%). The reference burn scar dataset was used to calibrate the algorithm response and quantify the uncertainty in the burned area measurement at the fire incident level. An objective, empirical error based approach was employed to quantify the uncertainty of our burned area measurement and provide a metric that is meaningful in context of remotely sensed burned area and emission inventories. The algorithm uncertainty is ± 36% for fires 50 km² in size, improving to ± 31% at a fire size of 100 km². Fires in this size range account for a substantial portion of burned area in the western U.S. (77% of burned area is due to fires > 50 km², and 66% results from fires > 100 km²). The dominance of these large wildfires in burned area, duration, and emissions makes these events a significant concern of air quality forecasters and regulators. With daily coverage at 1-km² spatial resolution, and a quantified measurement uncertainty, the burned area mapping algorithm presented in this paper is well suited for the development of wildfire emission inventories. Furthermore, the algorithm's DB implementation enables time sensitive burned area mapping to support operational air quality forecasting.     

Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS

Accurate production of regional burned area maps are necessary to reduce uncertainty in emission estimates from African savannah fires. Numerous methods have been developed that map burned and unburned surfaces. These methods are typically applied to coarse spatial resolution (1 km) data to produce regional estimates of the area burned, while higher spatial resolution (<30 m) data are used to assess their accuracy with little regard to the accuracy of the higher spatial resolution reference data. In this study we aimed to investigate whether Landsat Enhanced Thematic Mapper (ETM+)‐derived reference imagery can be more accurately produced using such spectrally informed methods. The efficacy of several spectral index methods to discriminate between burned and unburned surfaces over a series of spatial scales (ground, IKONOS, Landsat ETM+ and data from the MOderate Resolution Imaging Spectrometer, MODIS) were evaluated. The optimal Landsat ETM+ reference image of burned area was achieved using a charcoal fraction map derived by linear spectral unmixing (k = 1.00, a = 99.5%), where pixels were defined as burnt if the charcoal fraction per pixel exceeded 50%. Comparison of coincident Landsat ETM+ and IKONOS burned area maps of a neighbouring region in Mongu (Zambia) indicated that the charcoal fraction map method overestimated the area burned by 1.6%. This method was, however, unstable, with the optimal fixed threshold occurring at >65% at the MODIS scale, presumably because of the decrease in signal‐to‐noise ratio as compared to the Landsat scale. At the MODIS scale the Mid‐Infrared Bispectral Index (MIRBI) using a fixed threshold of >1.75 was determined to be the optimal regional burned area mapping index (slope = 0.99, r ² = 0.95, SE = 61.40, y = Landsat burned area, x = MODIS burned area). Application of MIRBI to the entire MODIS temporal series measured the burned area as 10 267 km² during the 2001 fire season. The char fraction map and the MIRBI methodologies, which both produced reasonable burned area maps within southern African savannah environments, should also be evaluated in woodland and forested environments.     

Determining the use of Sentinel-2A MSI for wildfire burning & severity detection

Accurate, reliable, and timely burn severity maps are necessary for planning, managing and rehabilitation after wildfires. This study aimed at assessing the ability of the Sentinel-2A satellite to detect burnt areas and separate burning severity levels. It also attempted to measure the spectral separability of the different bands and derived indices commonly used to detect burnt areas. A short investigation into the associated environmental variables present in the burnt landscape was also performed to explore the presence of any correlation. As a case study, a wildfire occurred in the Sierra de Gata region of the province of Caceres in North-Eastern Spain was used. A range of spectral indices was computed, including the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR). The potential added value of the three new Red Edge bands that come with the Sentinel-2A MSI sensor was also used. The slope, aspect, fractional vegetation cover and terrain roughness were all derived to produce environmental variables. The burning severity was tested using the Spectral Angle Mapper (SAM) classifier. European Environment Agency’s CORINE land cover map was also used to produce the land cover types found in the burned area. The Copernicus Emergency Management Service have produced a grading map for the fire using 0.5 m resolution Pleiades imagery, that was used as reference. Results showed a variable degree of correlation between the burning severity and the tested herein spectral indices. The visible part of the electromagnetic spectrum was not well suited to discern burned from unburned land cover. The NBRb12 (short-wave infrared 2 – SWIR2) produced the best results for detecting burnt areas. SAM resulted in a 73% overall accuracy in thematic mapping. None of the environmental variables appeared to have a significant impact on the burning severity. All in all, our study result showed that Sentinel-2 MSI sensor can be used to discern burnt areas and burning severity. However, further studies in different regions using the same dataset types and methods should be implemented before generalizing the results of the current study.     

Postfire soil burn severity mapping with hyperspectral image unmixing

Burn severity is mapped after wildfires to evaluate immediate and long-term fire effects on the landscape. Remotely sensed hyperspectral imagery has the potential to provide important information about fine-scale ground cover components that are indicative of burn severity after large wildland fires. Airborne hyperspectral imagery and ground data were collected after the 2002 Hayman Fire in Colorado to assess the application of high resolution imagery for burn severity mapping and to compare it to standard burn severity mapping methods. Mixture Tuned Matched Filtering (MTMF), a partial spectral unmixing algorithm, was used to identify the spectral abundance of ash, soil, and scorched and green vegetation in the burned area. The overall performance of the MTMF for predicting the ground cover components was satisfactory (r² = 0.21 to 0.48) based on a comparison to fractional ash, soil, and vegetation cover measured on ground validation plots. The relationship between Landsat-derived differenced Normalized Burn Ratio (dNBR) values and the ground data was also evaluated (r² = 0.20 to 0.58) and found to be comparable to the MTMF. However, the quantitative information provided by the fine-scale hyperspectral imagery makes it possible to more accurately assess the effects of the fire on the soil surface by identifying discrete ground cover characteristics. These surface effects, especially soil and ash cover and the lack of any remaining vegetative cover, directly relate to potential postfire watershed response processes.     

Temporal burn scar evolution in tallgrass prairie based on field spectroscopy

In situ field spectroscopy samples were used to simulate several Moderate Resolution Imaging Spectroradiometer (MODIS) bands and indices commonly used for burned area detection. Each band or index was tested for its ability to differentiate between burned and unburned tallgrass prairie during several time periods from spring (when burning took place) to late summer (peak biomass) with three analysis of variance tests. The normalized difference vegetation index (NDVI), global environmental monitoring index (GEMI), global environmental monitoring index – burn scar (GEMI-B), and normalized burn ratio (NBR) indices, as well as MODIS band 7 (longwave mid-infrared; LWMIR), showed virtually no promise for differentiating burned from unburned areas for more than several days after the burn. Others, including the burned area index (BAI), Mid-infrared burn index (MIRBI), and MODIS bands 3 (red), 4 (near-infrared; NIR), 5 (longwave near-infrared; LWNIR), and 6 (shortwave mid-infrared; SWMIR) were able to differentiate between burned and unburned areas well into the growing season – in some cases, even through its entire length. The performance of particular bands and indices often depended on grazing, vegetation phenology, ash/char/soil reflectance, and factors that influenced pre-burn biomass.     

Analysis of the relationship between spatial pattern and spectral detectability of areas burned in southern Africa using satellite data

Fires in Africa affect atmospheric emissions and carbon sequestration, landscape patterns, and regional and global climatic conditions. Studies of these effects require accurate estimation of the extent of measurable fire events. The goal of this study was to assess the influence of burned area spatial patterns on the spectral detectability of burned areas. Six Landsat‐7 ETM+ images from the southern Africa were used for burned area mapping and spatial pattern analysis, while contemporaneous MODIS 500 m spatial resolution images were used to measure the spectral detectability of burned areas. Using a 15 by 15 km sample quadrats analysis, we showed that above a burned area proportion threshold of approximately 0.5 the spectral detectability of burned areas increase due to the decrease in the number of mixed pixels. This was spatially related to the coalescence of burned patches and the decrease in the total burned area perimeter. Simple burned area shapes were found at the Botswana site, where the absence of tree cover and the presence of bright surfaces (soil and dry grass) enhanced the spectral contrast of the burned surfaces, thus enabling better estimates of burned area extent. At the Zambia and Congo sites, landscape fragmentation due to human activity and the presence of a tree vegetation layer, respectively, contribute to the presence of small burned area patches, which may remain undetectable using moderate spatial resolution satellite imagery, leading to less accurate burned area extent estimates.     

Ten years of forest cover change in the Sierra Nevada detected using Landsat satellite image analysis

The Sierra Nevada of California is a region where large wildfires have been suppressed for over a century. A detailed geographic record of recent vegetation regrowth and disturbance patterns in forests of the Sierra Nevada remains a gap that can be filled with remote-sensing data. Landsat Thematic Mapper imagery was analysed to detect 10 years of recent changes (between 2000 and 2009) in forest vegetation cover for areas burned by wildfires between years of 1995 and 1999 in the region. Results confirmed the prevalence of regrowing forest vegetation during the period 2000 and 2009 over 17% of the combined burned areas. Classification of these regrowing forest vegetation areas by the Landsat normalized burn ratio (NBR) showed that there was a marked increase in average disturbance index (ΔDI) values in the transitions from low to moderate to high burn severity classes. Within the five combined wildfire perimeters, 45% of the high burn severity area delineated by the RdNBR analysis was covered by regrowing forest detected between 2000 and 2009. In contrast, a notable fraction (12%) of the entire 5 km (unburned) buffer area outside the 1995–1999 fires perimeters showed decline in forest cover, and not nearly as many regrowing forest areas, covering only 3% of all the 1995–1999 buffer areas combined. Based on comparison of these results to ground-based survey data and high-resolution aerial images, the Landsat difference index (ΔDI) methodology can fulfil much of the need for consistent, low-cost monitoring of changes due to climate and biological factors in western forest regrowth following stand-replacing disturbances.     

A MODIS assessment of the summer 2007 extent burned in Greece

Devastating fires affected Greece in the summer 2007, with the loss of more than 60 human lives, the destruction of more than 100 villages and hundreds of square kilometres of forest burned. This Letter presents a map of the extent burned and the approximate day of burning in Greece mapped by the MODIS burned area product for 22 June to 30 August 2007 and the burned areas mapped independently by the European Forest Fires Information Service (EFFIS). The characteristics of the two datasets, and an evaluation of the areas burned comparing the MODIS and EFFIS data for the same temporal interval are described.     

Satellite-based detection of Canadian boreal forest fires: Development and application of the algorithm

This study presents a comprehensive investigation of fires across the Canadian boreal forest zone by means of satellite-based remote sensing. A firedetection algorithm was designed to monitor fires using daily Advanced Very High Resolution Radiometer (AVHRR) images. It exploits information from multichannel AVHRR measurements to determine the locations of fires on satellite pixels of about 1 km2 under clear sky or thin smoke cloud conditions. Daily fire maps were obtained showing most of the active fires across Canada (except those obscured by thick clouds). This was achieved by first compositing AVHRR scenes acquired over Canada on a given day and then applying the fire-detection algorithm. For the fire seasons of 1994-1998, about 800 NOAA/AVHRR daily mosaics were processed. The results provide valuable nation-wide information on fire activities in terms of their locations, burned area, starting and ending dates, as well as development. The total burned area as detected by satellite across Canada is estimated to be approximately 3.9, 4.9, 1.3, 0.4 and 2.4 million hectares in 1994, 1995, 1996, 1997 and 1998, respectively. The peak month of burning varies considerably from one year to another between June and August, as does the spatial distribution of fires. In general, conifer forests appear to be more vulnerable to burning and fires tend to grow larger than in deciduous forests.     

Burned area mapping with MERIS post-fire image

MERIS (Medium Resolution Imaging Spectrometer) offers a good balance between spectral, temporal and spatial resolution for mapping burned areas at a regional scale. In this article MERIS images were used to map fire-affected areas in the north-west of Spain, where extensive burning occurred in the summer of 2006. MERIS spectral indices and their ability to discriminate burned area signals have been assessed in this article. Additionally, the potentials of the spectral angle images (SAI) for mapping fire-affected areas were explored. SAI was used to measure the differences between pixels and reference spectra. The reference spectra were obtained from pure burned pixels in the image as well as from field spectral measurements. The MERIS burned area maps were then validated with visually digitized fire perimeters, produced from Advanced Wide Field Sensor, with 60 m pixel size. The Pareto boundary method was used to evaluate the errors from the error matrix, taking into account the spatial resolution of the sensor. This made it possible to discriminate between the errors caused by the spatial resolution and those caused by the limitations of the classification technique. Finally, the Euclidean distance between the errors and the Pareto boundary function was calculated in order to select the best result in an objective way. The η index, a component of the Global Environmental Monitoring Index, showed the best performance among the input indices, with distance values of 3.3 in the fires related to a reference fire polygon; followed by SAI computed from the spectrum obtained from the image with a distance value of 5.7.     

Mapping fire scars in a southern African savannah using Landsat imagery

The spectral, spatial and temporal characteristics of the Landsat data record make it appropriate for mapping fire scars. Twenty-two annual fire scar maps from 1972–2002 were produced from historical Landsat imagery for a semi-arid savannah landscape on the South Africa–Botswana border, centred over Madikwe Game Reserve (MGR) in South Africa. A principal components transformation (PCT) helped differentiate the spectral signal of fire scars in each image. A simple, nonparametric, supervised classification (parallelepiped) of the PCT data differentiated burned and unburned areas. During most years, fire occurrences and the percentage of area burned annually were lowest in Botswana, highest in MGR, and intermediate in South Africa outside MGR. These fire scar maps are aiding MGR managers, who are endeavouring to restore a more active fire regime following decades of fire exclusion.     

Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery

Uncertainties in burning efficiency (BE) estimates can lead to large errors in fire emission quantification (from 23% to 46%). One of the main causes of these errors is the spatial variability of fuel consumption within burned areas. This paper studies whether burn severity (BS) maps can be used to improve BE assessment. A burn severity map of two large fires in California was obtained by inverting a simulation model constrained by post-fire observations from Landsat TM imagery. Model output values of BS were validated against field measurements, obtaining a high correlation (R² = 0.85) and low errors (Root Mean Square Error, RMSE = 0.14) throughout a wide range of BS levels. The BS map obtained was then used to adjust BE reference values per vegetation type found in the area before the fire. The adjusted burning efficiency (BEadj) was compared to the burned biomass, which was estimated by subtracting vegetation indices from pre- and post-fire images. Results showed a high correlation for conifers (R² = 0.75) and hardwoods (R² = 0.73), and a moderate correlation (R² ∼ 0.5) for shrubs and grasslands. In general, for all vegetation types BEadj performed better (R² = 0.4-0.75) than literature-based BE (R² < 0.0001). This study demonstrates: (i) the consistency of the simulation model inversion for BS estimation in temperate ecosystems, and (ii) the improvement of BE estimation when the spatial variability of the combustion was quantified in terms of BS.     

Using SPOT-4 HRVIR and VEGETATION sensors to assess impact of tropical forest fires in Roraima,Brazil

Due to the El Niño phenomenon, the 1997-1998 dry season in Roraima (Brazil, Amazonia) was particularly pronounced. Consequently, vegetation fires spread widely and were monitored by many satellites in real time. Satellite images are currently being used to monitor vegetation fires either globally for climate studies or more regionally for impact assessment. After reviewing different satellite data used for impact assessment, this paper focuses on the contribution of SPOT-4's imagery provided by high resolution HRVIR and coarse resolution VEGETATION sensors. These sensors are described with emphasis on those characteristics of potential benefit for forest mapping and fire detection. Early images of Roraima from SPOT-4 are analysed and interpreted to delineate the areas already damaged by fire. VEGETATION images provide a first estimate of damaged areas on a regional scale and an indication of the main ecosystems affected. SPOT HRVIR is used to establish a much more precise classification of various ecosystems. Each vegetation class is associated with a biomass density. From the known burned areas, an estimate of burned biomass during the 1998 dry season is computed, as well as total carbon release. On an intensive study site of 20 400 km 2, 3060 km 2 of savannahs and crops and 6980 km 2 of forest have been burned; the corresponding carbon release is estimated as 210 000 t for croplands and savannahs and 23 M t for the evergreen seasonal forest. The estimated burnt surface areas derived from VEGETATION are then cross-validated with HRVIR and thus an attempt is made to extrapolate the burned biomass with the help of VEGETATION on a regional scale.     

Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data

Recent advances in instrument design have led to considerable improvements in wildfire mapping at regional and global scales. Global and regional active fire and burned area products are currently available from various satellite sensors. While only global products can provide consistent assessments of fire activity at the global, hemispherical or continental scales, the efficiency of their performance differs in various ecosystems. The available regional products are hard-coded to the specifics of a given ecosystem (e.g. boreal forest) and their mapping accuracy drops dramatically outside the intended area. We present a regionally adaptable semi-automated approach to mapping burned area using Moderate Resolution Imaging Spectroradiometer (MODIS) data. This is a flexible remote sensing/GIS-based algorithm which allows for easy modification of algorithm parameterization to adapt it to the regional specifics of fire occurrence in the biome or region of interest. The algorithm is based on Normalized Burned Ratio differencing (dNBR) and therefore retains the variability of spectral response of the area affected by fire and has the potential to be used beyond binary burned/unburned mapping for the first-order characterization of fire impacts from remotely sensed data. The algorithm inputs the MODIS Surface Reflectance 8-Day Composite product (MOD09A1) and the MODIS Active Fire product (MOD14) and outputs yearly maps of burned area with dNBR values and beginning and ending dates of mapping as the attributive information. Comparison of this product with high resolution burn scar information from Landsat ETM+ imagery and fire perimeter data shows high levels of accuracy in reporting burned area across different ecosystems. We evaluated algorithm performance in boreal forests of Central Siberia, Mediterranean-type ecosystems of California, and sagebrush steppe of the Great Basin region of the US. In each ecosystem the MODIS burned area estimates were within 15% of the estimates produced by the high resolution base with the R² between 0.87 and 0.99. In addition, the spatial accuracy of large burn scars in the boreal forests of Central Siberia was also high with Kappa values ranging between 0.76 and 0.79.