Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest

It is challenging to detect burn severity and vegetation recovery because of the relatively long time period required to capture the ecosystem characteristics. Multitemporal remote sensing data can provide multitemporal observations before, during and after a wildfire, and can improve the change detection accuracy. The goal of this study is to examine the correlations between multitemporal spectral indices and field-observed burn severity, and to provide a practical method to estimate burn severity and vegetation recovery. The study site is the Jasper Fire area in the Black Hills National Forest, South Dakota, that burned during August and September 2000. Six multitemporal Landsat images acquired from 2000 (pre-fire), 2001 (post-fire), 2002, 2003, 2005 and 2007 were used to assess burn severity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized burn ratio (NBR), integrated forest index (IFI) and the differences of these indices between the pre-fire and post-fire years were computed and analysed with 66 field-based composite burn index (CBI) plots collected in 2002. Results showed that differences of NDVI and differences of EVI between the pre-fire year and the first two years post-fire were highly correlated with the CBI scores. The correlations were low beyond the second year post-fire. Differences of NBR had good correlation with CBI scores in all study years. Differences of IFI had low correlation with CBI in the first year post-fire and had good correlation in later years. A CBI map of the burnt area was produced using regression tree models and the multitemporal images. The dynamics of four spectral indices from 2000 to 2007 indicated that both NBR and IFI are valuable for monitoring long-term vegetation recovery. The high burn severity areas had a much slower recovery than the moderate and low burn areas.     

Assessing the feasibility of sub-pixel burned area mapping in miombo woodlands of northern Mozambique using MODIS imagery

The goal of this study was to evaluate the feasibility of sub-pixel burned area detection in the miombo woodlands of northern Mozambique, using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). Multitemporal Landsat-7 ETM+ data were acquired to produce a high spatial resolution map of areas burned between mid-August and late September 2000, and a field campaign was conducted in early November 2000 to gather ground truth data. Mapping of burned areas was performed with an ensemble of classification trees and yielded a kappa value of 0.896. This map was subsequently degraded to a spatial resolution of 500 m, to produce an estimate of burned area fraction, at the MODIS pixel size. Correlation analysis between the sub-pixel burned area fraction map and the MODIS reflective channels 1-7 yielded low but statistically significant correlations for all channels. The better correlations were obtained for MODIS channels 2 (0.86 µm), 5 (1.24 µm) and 6 (1.64 µm). A regression tree was constructed to predict sub-pixel burned area fraction as a function of those MODIS channels. The resulting tree has nine terminal nodes and an overall root mean square error of 0.252. The regression tree analysis confirmed that MODIS channels 2, 5, and 6 are the best predictors of burned area fraction. It may be possible to improve these results considering, as an alternative to individual channels, some appropriate spectral indices used to enhance the burnt scar signal, and by including MODIS thermal data in the analysis. It may also be possible to improve the accuracy of sub-pixel burned area fraction using MODIS imagery by allowing the regression tree to automatically create linear combinations of individual channels, and by using ensembles of trees.     

Detection of fire impact and vegetation recovery over tropical peat swamp forest by satellite data and ground-based NDVI instrument

Multitemporal Principal Component Analysis (MPCA) was used for processing Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper plus (ETM+) satellite images. MPCA was able to merge spectral data corresponding to TM-1996 (pre-fire in 1997), ETM-2000 (post-fire 1997 and pre-fire 2002) and ETM-2003 (post-fire in 2002), which was crucial for detecting the fire impact and vegetation recovery. Results indicate that the burnt areas of 1997 and 2002 were 89,086 ha (16.5%) and 31,859 ha (5.9%), respectively, within the study area of 540,000 ha. Satellite Pour 1’Observation de la Terre (SPOT)-VEGETATION 10-day Maximum Value Composite (MVC) data were also used and compared with Normalized Difference Vegetation Index (NDVI) from ground-based NDVI. Our research demonstrates the strong relationship between Landsat- TM/ETM+, SPOT-VEGETATION data and ground-based NDVI in identifying land-cover changes and vegetation recovery over the tropical peat swamp forest area in Central Kalimantan, Indonesia that is affected by forest fires that occurred in 1997 and 2002.     

Monitoring changes in land cover induced by atmospheric pollution in the Kola Peninsula,Russia, using Landsat-MSS data

We present the results of hybrid unsupervised-supervised classification of a series of Landsat-MSS images, spanning the period from 1978 to 1992, to study the impact of SO2 emissions from the nickel smelter at Monchegorsk (67 55 N, 32 50 E) in the Kola Peninsula, Russia, on adjacent boreal forest and upland (lichen-dominated) tundra vegetation. Ground truth data were collected from a 2500 km2 area during airborne and surface field campaigns in 1994 and 1995, and used to classify the 1992 image into 56 different surface types, including a characterisation of the level of vegetation damage. The pre-1992 images could not be classified by transferring the spectral signatures from the 1992 image, mainly as a result of phenological differences. Instead, they were classified using spatial context and a set of observationally-derived botanical rules governing the types of allowable land-cover change. A comparison of the classified images was performed by further combining the land-cover classes into groups representing forest areas with varying proportions of canopy damage, and upland tundra areas with varying degrees of lichen cover and damage level. Quantitative comparative results were obtained for a 22 225km2 area common to all images after 1978. Although damage levels were already significant by 1980, our results show that most of the increase in vegetation damage since 1980 has in fact occurred since 1989. We attribute this to a change in the local meteorological conditions. The method developed in this paper has the merit of revealing areas of upland tundra vegetation showing early effects of SO2-induced damage. It should thus have widespread applicability to the delineation and monitoring of areas of industrially affected arctic vegetation, especially in the former Soviet Union, where many such areas occur, often in regions where access is severely limited. However, it is likely that field validation will continue to be required, since the mechanism by which increasing damage levels are depicted in MSS images of lichen tundra areas remains unclear.     

Automatic mapping of surfaces affected by forest fires in Spain using AVHRR NDVI composite image data

In this work, we describe the statistical techniques used to analyze images from the National Oceanic and Atmospheric Administration's advanced very high resolution radiometer for the calculation and mapping of surfaces affected by large forest fires in Spain in 1993 and 1994. Maximum value normalized difference vegetation index (NDVI) composites (MVCs) were generated for every ten-day period over the two years of the study. Two techniques, one regression analysis and the other differencing, were applied to the NDVI-MVCs both before and after each fire event to determine detection thresholds of change and to delineate and objectively evaluate the burned surfaces. The comparison between the single-fires burned areas predicted by the techniques and that provided by the Spanish Forestry Service (ground based) showed that the regression algorithm was more reliable, giving rise to virtually no bias (−0.9%) and a root mean square error (RMS) of 20.3%, both calculated as a percentage of the mean burned area of the whole sample. The technique of differencing provided worse results with a 3.2% bias and a 23.5% RMS error. Likewise, a comparison between. the perimeters of the large fires supplied by official data (GPS-based) and those obtained by the regression method confirmed the validity of the technique not only for calculating fire size, but also for mapping of large forest fires.     

The detection and mapping of Alaskan wildfires using a spaceborne imaging radar system

The study presented here focuses on using a spaceborne imaging radar, ERS-1, for mapping and estimating areal extent of fires which occurred in the interior region of Alaska. Fire scars are typically 3 to 6 dB brighter than adjacent unburned forests in the ERS-1 imagery. The enhanced backscatter from burned areas was found to be a result of high soil moisture and exposed rough ground surfaces. Fire scars from 1979 to 1992 are viewed in seasonal ERS-1 synthetic aperture radar (SAR) data obtained from 1991 to 1994. Three circumstances which influence the detectability of fire scars in the ERS-1 imagery are identified and examined; seasonality of fire scar appearances, fires occurring in mountainous regions, and fires occurring in wetland areas. Area estimates of the burned regions in the ERS-1 imagery are calculated through the use of a Geographic Information System (GIS) database. The results of this analysis are compared to fire records maintained by the Alaska Fire Service (AFS) and to estimates obtained through a similar study using the Advanced Very High Resolution Radiometer (AVHRR) sensor.     

Landsat digital analysis of the initial recovery of burned tundra at Kokolik River, Alaska

During late July and early August 1977, a wildfire burned 48km² of tundra in northwestern Alaska near the Kokolik River. The environmental effects of the fire were studied in the field and from aircraft and Landsat data. Three categories of burn severity were delineated using an August 1977 Landsat scene acquired shortly after the fire stopped. Measurable reflectance increased in all three categories by the following year as determined from a Landsat image acquired in August 1978. Regrowth of vegetation in the year following the fire was measured using Landsat digital data and compared with field measurements from selected portions of the burned area. Live vascular plant cover doubled in one of the severely burned portions of the area and increased 33% in a lightly burned portion as determined from field measurements. Landsat-derived measurements showed an increase of 62.5% in reflectance for the severely burned areas, and 53% for the lightly burned areas, which is attributed to regrowth of vegetation. Within the most severely burned portion, 9.6 out of a total of 13.3 km² showed minimal recovery based on the Landsat-derived spectral data. Within the lightly burned portion, 5.9 out of a total of 13.5 km² showed the same range of spectral values as did the control areas. Prefire terrain and vegetation conditions were found to influence burn severity. The drier high-relief areas generally burned more completely than lower-lying wet areas. Satellite data acquired after the fire confirmed this for much of the burned area.     

Comparing the spectral signal of burned surfaces between Landsat 7 ETM+ and Landsat 8 OLI sensors

There is a long history of the use of Landsat data in burned land mapping mainly due to certain characteristics of the Landsat imagery including the spatial, spectral, and temporal data resolution, the low cost (Landsat data are now freely available), and the existence of an almost 35-year historical archive (excluding Landsat 1–3). Landsat 8 (Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)) was launched on 11 February 2013 and it captures data in three new bands along with two additional thermal bands. However, is the spectral signal of burned surfaces in satellite remote-sensing data of Landsat series consistent and robust enough to allow the successful application of the techniques developed so far for Landsat 8? In this article, we compare the spectral signal of burned surfaces between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 OLI sensors using five case studies that correspond to five large fire events in different biophysical environments in Greece, for which both Landsat 7 ETM+ and Landsat 8 OLI data were available. From the comparative analysis using histogram data plots of burned (post-fire image) and vegetated (pre-fire image) areas, spectral signature plots and separability indices of certain land-cover types, estimated using the same sampling areas over both satellite images, a general consistency was observed between the two sensors. Slight differences between the sensors were attributed to differences in the acquisition dates and were related to the type of vegetation rather than the sensors used to record the satellite images. Neither sensor provided improved discrimination over the other.     

Comparison of the use of WiFS and LISS images to estimate the area burned in a large forest fire

We evaluate the utility of medium spatial resolution images from the Wide Field Sensor (WiFS) for the estimation of the area burned in a large fire. The performance of methodologies using these images is compared with similar methodologies using high spatial resolution image from the Linear Imaging and Self Scanning Sensor (LISS-III) and other ancillary data. Both sensors are located onboard the Indian Remote Sensing Satellite 1C (IRS-1C). The post-fire LISS image was analysed by means of Matched Filtering (MF) techniques. Two WiFS images (pre- and post-fire) were analysed using MF techniques and also by means of changes in the Normalized Difference Vegetation Index (NDVI). Ground data were used to classify the three thematic images obtained in several post-fire classes. The results show a greater proportion of transition areas between burned and unburned places and a slightly larger area burned estimation in the WiFS than in the LISS analysis. Nevertheless, the results obtained, and the comparisons with ground data, indicate that medium spatial resolution images' estimation of the area burned is a useful tool at regional and national scales.     

Assessing the feasibility of a global model for multi-temporal burned area mapping using SPOT-VEGETATION data

The possibility of using the Syst@me Probatoire de l'Observation de la Terre (SPOT)-VEGETATION (VGT) data for global burned area mapping with a single algorithm was investigated. Using VGT images from south-eastern Africa, the Iberian Peninsula and south-eastern Siberia/north-eastern China, we analysed the variability of the spectral signature of burned areas and its relationship with land cover, and performed the selection of the best variables for burned area mapping. The results show that in grasslands and croplands, near-infrared (NIR) and short-wave infrared (SWIR) reflectance always decreases as a result of fire. In forests and woodlands, there may occur a simultaneous decrease of SWIR and NIR or an increase of SWIR and a decrease of NIR. Burning of green vegetation (high values of the Normalized Difference Vegetation Index (NDVI)) tends to result in an increase of the SWIR. The best variables for burned area mapping are different in each region. Only the NIR allows a good discrimination of burned areas in all study areas. We derived a logistic regression model for multi-temporal burned area mapping in tropical, temperate and boreal regions, which handles the spectral variability of burned areas dependent on the type of vegetation. The results underline the feasibility of a single model for global burned area mapping.     

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.     

Assessment of multitemporal compositing techniques of MODIS and AVHRR images for burned land mapping

The performance of several criteria to generate multitemporal composites of daily Moderate Resolution Imaging Spectrometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) images for burned land mapping was tested using data acquired over the Iberian Peninsula in 2001, 2003 and 2004. The experiment was based on four tests that assessed the discriminability between burned and unburned areas, the presence of artifacts (clouds and clouds shadows), the verticality of the sensor viewing angle, and the spatial coherency of the composite images. Seven different compositing techniques were tested, based on maximizing normalized difference vegetation index (NDVI) and brightness/surface temperature, and minimizing reflectance and sensor zenith angles. The composite criterions that provide the most accurate images for burned land mapping were based on maximizing brightness/surface temperatures, either as the only criterion or in conjunction with minimizing sensor zenith angle or near infrared (NIR) reflectance. These composites present high discrimination capacity between burned and unburned areas, remove most clouds and cloud shadows, offer high spatial coherency and present middle-to-low sensor zenith angles. Traditional compositing criterion based on maximizing NDVI values provided poor results in most tests. Finally, standard NASA MODIS composite provides close to nadir observation angles, and good spatial coherency, but it offered lower discrimination between burned and unburned areas that those composites based on thermal data.     

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.     

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.     

Multidate MESMA for monitoring vegetation growth forms in southern California shrublands

Identifying habitats that should be protected from further disturbance or conversion and isolating high-risk areas is a focus of community habitat plans in southern California shrublands. Larger wildfires are occurring at shorter intervals in recent decades, contributing to degradation and conversion of shrubland vegetation. Multitemporal remote-sensing approaches can bridge the gap between vegetation mapping and field sampling in habitats where frequent quantification and mapping of vegetation growth forms over large extents is required. The objective of this study is to examine the reliability and stability of a multiple endmember spectral mixture analysis (MESMA) approach with moderate spatial resolution imagery for monitoring changes in growth form fractional cover in shrubland habitats. Estimates from visual interpretation of high spatial resolution image were used as reference data for validating MESMA-derived maps and as basis for providing complementary monitoring protocols that may be accurate and cost-effective across multiple scales. Growth form proportions modelled in burned and unburned management areas compare well with expected fractional cover in mature and regenerating shrublands. In the management areas recovering from fire, herbaceous cover fraction exceeded 0.40 for all three study dates, suggesting that large portions of those management areas may already be invaded. From 2008 to 2011 overall herbaceous cover fraction in shrubland area increased by 2%. Herbaceous cover fraction was modelled with an overall mean absolute error (MAE) of 0.08, a smaller percentage than the percentage of herbaceous cover change recorded in areas recovering from fire (increase in herbaceous cover fraction from 0.09 to 0.13). This MESMA approach would be effective for quantifying changes in fractional cover that exceed 0.10, providing a way to delineate and quantify herbaceous invasions and expansions following disturbance or succession.     

Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination

A new spectral index named Burned Area Index (BAI), specifically designed for burned land discrimination in the red-near-infrared spectral domain, was tested on multitemporal sets of Landsat Thematic Mapper (TM) and NOAA Advanced Very High Resolution Radiometer (AVHRR) images. The utility of BAI for burned land discrimination was assessed against other widely used spectral vegetation indices: Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Global Environmental Monitoring Index (GEMI). BAI provided the highest discrimination ability among the indices tested. It also showed a high variability within scorched areas, which reduced the average normalized distances with respect to other indices. A source of potential confusion between burned land areas and low-reflectance targets, such as water bodies and cloud shadows, was identified. Since BAI was designed to emphasize the charcoal signal in post-fire images, this index was highly dependent on the temporal permanence of charcoal after fires.     

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.     

Influence of fire severity on plant regeneration by means of remote sensing imagery

In this paper we analyse the interactions between fire severity (plant damage) and plant regeneration after fire by means of remote sensing imagery and a field fire severity map. A severity map was constructed over a large fire (2692 ha) occurring in July 1994 in the Barcelona province (north-east of Spain). Seven severity classes were assigned to the apparent plant damage as a function of burning intensity. Several Landsat TM and MSS images from dates immediately before and after the fire were employed to monitor plant regeneration processes as well as to evaluate the relationship with fire severity observed in situ . Plant regeneration was monitored using NDVI measurements (average class values standardized with neighbour unburned control plots). Pre-fire NDVI measurements were extracted for every plant cover category (7), field fire severity class (7), and spatial cross-tabulation of both layers (33) and compared to post-fire values. NDVI decline due to fire was positively correlated with field fire severity class. Results show different patterns of recovery for each dominant species, severity class and combination of both factors. For all cases a significant negative correlation was found between damage and regeneration ability. This work leads to a better understanding of the influence of severity, a major fire regime parameter on plant regeneration, and may aid to manage restoration on areas burned under different fire severity levels.     

Effect of spatial resolution on the accuracy of satellite-based fire scar detection in the northwest of the Iberian Peninsula

In this work, an empirical study was carried out to evaluate the impact of the spatial resolution of satellite images on the accuracy and uncertainty of burned area detection using classification techniques based on neuro-fuzzy (NF) models. The study area was situated in the northwest of the Iberian Peninsula, where in the summer of 2006, a large number of fires occurred, razing a surface area of more than 100,000 ha. A set of 12 zones containing a burned area in their central part were selected. Landsat Thematic Mapper (TM), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer Local Area Coverage (AVHRR-LAC), and Advanced Very High Resolution Radiometer Land Long Term Data Record (AVHRR-LTDR) images with a spatial resolution of 30, 250, 1100 m, and 0.05° (～5000 m), respectively, obtained on 20 August 2006, were used. An NF classifier at pixel level for every image was constructed, taking into account only the spectrum bands (red and near-infrared (NIR)) common to all of them. The results in the study region suggest that burned areas of ～1200 ha could be detected with a mean relative error less than 30% only in the MODIS image. In the case of the LAC and LTDR images, a minimum burned area size of >1800 ha and >3600 ha, respectively, is required to find similar errors. Burned areas greater >3600 ha can be detected in MODIS imagery with a mean relative error of ～15%. A regression model of commission and omission error intervals compared with spatial resolution is presented. The conclusion is that in regard to the conditions of the study area, both error intervals increase symmetrically and linearly with the logarithm of the pixel size. The results also suggest that red and NIR spectrum bands could be used to detect burned area in post-fire images in Iberia, but with a relative error depending on burned area size for different spatial resolutions.