Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997-2004) and MODIS (2000-2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image-derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars < 50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997-2002. MODIS data were suitable for mapping medium (50-500 ha) and large (> 500 ha) burn scars that accounted for the majority of all fire-damaged forests in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 km²) were an order of magnitude higher than during the 1997-1998 El Niño event (124 km² and 39 km², respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by applying the BDR algorithm over larger areas and longer image time series.     

Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product

Burned area is a critical input to the algorithms of biomass burning emissions and understanding variability in fire activity due to climate change but it is difficult to estimate. This study presents a robust algorithm to reconstruct the patterns in burned areas across Contiguous United States (CONUS) in diurnal, seasonal, and interannual scales from 2000-2006. Specifically, burned areas in individual fire pixels are empirically calculated using diurnal variations in instantaneous fire sizes from the Geostationary Operational Environmental Satellites (GOES) WF_ABBA (Wildfire Automated Biomass Burning Algorithm) fire product. GOES burned areas exhibit diurnal variability with a temporal scale of half hours. The cumulative burned area during 9:00-16:00 local solar time accounts for 65%-81% of the total daily burned area. The diurnal variability is strongest in croplands compared to shrublands, grasslands, savannas, and forests. Analysis on a seasonal scale indicates that over 56% of burning occurs during summer (June-August). On average, the total annual burned area during the last seven years is 2.12 × 10⁴ ± 0.41 × 10⁴ km². The algorithm developed in this study can be applied to obtain burned area from the detections of GOES active fires at near real time, which can greatly improve the estimates of biomass burning emissions needed for predicting air quality.     

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.     

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.     

Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: Balancing omission and commission errors

Maps of burned area have been obtained from an automatic algorithm applied to a multitemporal series of Landsat TM/ETM+ images in two Mediterranean sites. The proposed algorithm is based on two phases: the first one intends to detect the more severely burned areas and minimize commission errors. The second phase improves burned patches delimitation using a hybrid contextual algorithm based on logistic regression analysis, and tries to minimize omission errors. The algorithm was calibrated using six study sites and it was validated for the whole territory of Portugal (89,000 km²) and for Southern California (70,000 km²). In the validation exercise, 65 TM/ETM+ scenes for Portugal and 35 for California were used, all from the 2003 fire season. A good agreement with the official burned area perimeters was shown, with kappa values close to 0.85 and low omission and commission errors (< 16.5%). The proposed algorithm could be operationally used for historical mapping of burned areas from Landsat images, as well as from future medium resolution sensors, providing they acquire images in two bands of the Short Wave Infrared (1.5-2.2 μm).     

Satellite‐derived 2003 wildfires in southern Siberia and their potential influence on carbon sequestration

The burned area, fuel type, crown fire percentage, and carbon release of the southern Siberia 2003 wildfire were analysed using AVHRR, MODIS, MERIS, ASTER images and a carbon release model. More than 200 000 km² were burned from 14 March to 8 August 2003, of which 71.4% was forest, 9.5% humid grassland, and 2.15% bogs or marshes. During 1996 to 2003, 32.2% of the forested area and 23.36% of the total area was burned, and 13.9% of the total area was affected by fire at least twice. Direct carbon emission from this 2003 fire was around 400640 Tg. The 2003 Siberian fires could well have contributed to the high increase of the atmospheric CO₂ and CO concentration in 2003. The increasing human pressure coupled with intensive fire severity, recurrent fire frequency, and increasing occurrence of summer droughts will reduce the carbon sequestration potential of this important carbon pool.     

Active fire detection and characterization with the advanced spaceborne thermal emission and reflection radiometer (ASTER)

We present an automated fire detection algorithm for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor capable of mapping actively burning fires at 30-m spatial resolution. For daytime scenes, our approach uses near infrared and short-wave infrared reflectance imagery. For nighttime scenes a simple short wave infrared radiance threshold is applied. Based on a statistical analysis of 100 ASTER scenes, we established omission and commission error rates for nine different regions. In most regions the probability of detection was between 0.8 and 0.9. Probabilities of false alarm varied between 9 × 10^− 8 (India) and 2 × 10^− 5 (USA/Canada). In most cases, the majority of false fire pixels were linked to clusters of true fire pixels, suggesting that most false fire pixels occur along ambiguous fire boundaries. We next consider fire characterization, and formulate an empirical method for estimating fire radiative power (FRP), a measure of fire intensity, using three ASTER thermal infrared channels. We performed a preliminary evaluation of our retrieval approach using four prescribed fires which were active at the time of the Terra overpass for which limited ground-truth data were collected. Retrieved FRP was accurate to within 20%, with the exception of one fire partially obscured by heavy soot.     

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.     

Detecting a moorland wildfire scar in the Peak District,UK, using synthetic aperture radar from ERS-2 and Envisat ASAR

Wildfires occur annually in UK moorland environments, especially in drought years. They can be severely damaging to the ecosystem when they burn deep into the peat, killing ground-nesting birds and releasing CO₂ into the atmosphere. Synthetic aperture radar (SAR) was evaluated for detecting the 18 April 2003 Bleaklow wildfire scar (7.4 km²). SAR’s ability to penetrate cloud is advantageous in this inherently overcast area. SAR can provide fire scar boundary information which is otherwise labour intensive to collect in the field using a global positioning system (GPS). This article evaluates the potential of SAR intensity and InSAR coherence to detect a large peat moorland wildfire scar in the Peak District of northern England. A time-series of pre-fire and post-fire ERS-2 and advanced synthetic aperture radar (ASAR) Single Look Complex (SLC) data were pre-processed using SARScape 4.2 to produce georeferenced greyscale images. SAR intensity and InSAR coherence values were analysed against Coordinate Information on the Environment (CORINE) land‐cover classes and precipitation data. SAR intensity detected burnt peat well after a precipitation event and for previous fire events within the CORINE peat bog class. For the 18 April 2003 fire event, intensity increased to 0.84 dB post-fire inside the fire scar for the peat bog class. InSAR coherence peaked post-fire for moors and heathland and natural grassland classes inside the fire scar, but peat bog exposed from previous fires was less responsive. Overall, SAR was found to be effective for detecting the Bleaklow moorland wildfire scar and monitoring wildfire scar persistence in a degraded peat landscape up to 71 days later. Heavy precipitation amplified the SAR fire scar signal, with precipitation after wildfires being typical in UK moorlands. Further work is required to disentangle the effects of fire size, topography, and less generalized land‐cover classes on SAR intensity and InSAR coherence for detecting fire scars in degraded peat moorlands.     

Global characterization of biomass-burning patterns using satellite measurements of fire radiative energy

Remote sensing is the most practical means of measuring energy release from large open-air biomass burning. Satellite measurement of fire radiative energy (FRE) release rate or power (FRP) enables distinction between fires of different strengths. Based on a 1-km resolution fire data acquired globally by the MODerate-resolution Imaging Spectro-radiometer (MODIS) sensor aboard the Terra and Aqua satellites from 2000 to 2006, instantaneous FRP values ranged between 0.02 MW and 1866 MW, with global daily means ranging between 20 and 40 MW. Regionally, at the Aqua-MODIS afternoon overpass, the mean FRP values for Alaska, Western US, Western Australia, Quebec and the rest of Canada are significantly higher than these global means, with Quebec having the overall highest value of 85 MW. Analysis of regional mean FRP per unit area of land (FRP flux) shows that at peak fire season in certain regions, fires can be responsible for up to 0.2 W/m² at peak time of day. Zambia has the highest regional monthly mean FRP flux of ~ 0.045 W/m² at peak time of day and season, while the Middle East has the lowest value of ~ 0.0005 W/m². A simple scheme based on FRP has been devised to classify fires into five categories, to facilitate fire rating by strength, similar to earthquakes and hurricanes. The scheme uses MODIS measurements of FRP at 1-km resolution as follows: category 1 (< 100 MW), category 2 (100 to < 500 MW), category 3 (500 to < 1000 MW), category 4 (1000 to < 1500 MW), category 5 (≥ 1500 MW). In most regions of the world, over 90% of fires fall into category 1, while only less than 1% fall into each of categories 3 to 5, although these proportions may differ significantly from day to day and by season. The frequency of occurrence of the larger fires is region specific, and could not be explained by ecosystem type alone. Time-series analysis of the proportions of higher category fires based on MODIS-measured FRP from 2002 to 2006 does not show any noticeable trend because of the short time period.     

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.     

Integration of geostationary FRP and polar-orbiter burned area datasets for an enhanced biomass burning inventory

Earth Observation (EO) sensors play an important role in quantifying biomass burning related fuel consumption and carbon emissions, and capturing their spatial and temporal dynamics. Typically, biomass burning emissions inventories are developed by exploiting either burned area (BA) or active fire (AF) measures of fire radiative energy (FRE). These approaches have both advantages and limitations. For example, methods based on burned area data typically require hard-to-obtain estimates of fuel load and combustion completeness, and the accuracy of the BA algorithm may deteriorate for small fires or those in densely forested terrain. Conversely, ‘raw’ FRE-based methods are typically low-biassed due to the non-detection of low intensity fires, and are also hindered by cloud cover. Here we develop a methodology integrating these two EO data types to deliver a high temporal resolution emissions inventory, maximising the benefit of each data type without requiring additional information. We focus on Africa, the most fire affected continent, and combine daily FRE observations provided by Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) with BA measures delivered by Moderate Resolution Imaging Spectroradiometer (MODIS). For individual fires detected by both types of data, we estimate fuel consumption per unit area (FC_A: g·m^− 2) via the ratio of FRE-derived total fuel consumption (FC_T) to BA. These values are then extrapolated to fires that were mapped using the BA data but which remained undetected in the SEVIRI AF product, thus correcting for the ‘low spatial resolution bias’ inherent in geostationary AF datasets. Calculated daily continental scale FC_T for Africa varies between 0.3 and 20 Tg for the period February 2004-January 2005. We estimate annual continental FC_T to be 1418 Tg, far closer to the 2272 Tg provided by the widely used Global Fire Emissions Database (version 3; GFEDv3) than is obtained when using ‘raw’ FRE data alone. This synergistic approach has substantially narrowed the gap between GFEDv3 and FRE-derived emissions inventories, whilst the geostationary FRP observations offer the advantage that the daily emissions estimates can be distributed more accurately over the diurnal fire cycle if required for linking to atmospheric transport models.     

Modeling fire severity in black spruce stands in the Alaskan boreal forest using spectral and non-spectral geospatial data

Biomass burning in the Alaskan interior is already a major disturbance and source of carbon emissions, and is likely to increase in response to the warming and drying predicted for the future climate. In addition to quantifying changes to the spatial and temporal patterns of burned areas, observing variations in severity is the key to studying the impact of changes to the fire regime on carbon cycling, energy budgets, and post-fire succession. Remote sensing indices of fire severity have not consistently been well-correlated with in situ observations of important severity characteristics in Alaskan black spruce stands, including depth of burning of the surface organic layer. The incorporation of ancillary data such as in situ observations and GIS layers with spectral data from Landsat TM/ETM+ greatly improved efforts to map the reduction of the organic layer in burned black spruce stands. Using a regression tree approach, the R² of the organic layer depth reduction models was 0.60 and 0.55 (p < 0.01) for relative and absolute depth reduction, respectively. All of the independent variables used by the regression tree to estimate burn depth can be obtained independently of field observations. Implementation of a gradient boosting algorithm improved the R² to 0.80 and 0.79 (p < 0.01) for absolute and relative organic layer depth reduction, respectively. Independent variables used in the regression tree model of burn depth included topographic position, remote sensing indices related to soil and vegetation characteristics, timing of the fire event, and meteorological data. Post-fire organic layer depth characteristics are determined for a large (> 200,000 ha) fire to identify areas that are potentially vulnerable to a shift in post-fire succession. This application showed that 12% of this fire event experienced fire severe enough to support a change in post-fire succession. We conclude that non-parametric models and ancillary data are useful in the modeling of the surface organic layer fire depth. Because quantitative differences in post-fire surface characteristics do not directly influence spectral properties, these modeling techniques provide better information than the use of remote sensing data alone.     

基于ENVISAT-MERIS数据的过火区制图方法研究

森林或草原在发生火灾后,过火区内的植被层在近红外波段的反射率通常要比健康植被低,利用光学遥感数据的近红外波段和红光波段可以探测出植被层的反射率在大气上界的明显变化。对过火区域的提取是利用卫星数据进行测算森林或草原火灾过火面积的关键技术之一。根据实验区内近年来发生的多次重特大森林或草原火灾,在对ENVISAT\|MERIS数据中典型地物光谱特征进行分析的基础上,分别采用图像处理方法、植被指数法和面向对象的图像分析方法对过火区制图方法进行对比研究。研究结果表明,通过面向对象的图像分析方法获得的过火区域,可以较好地适用于过火区面积的估测,该方法是一项实现定量提取过火区域的行之有效的方法。     

Remote sensing approaches for reconstructing fire perimeters and burn severity mosaics in desert spring ecosystems

Desert spring ecosystems provide water resources essential for sustaining wildlife, plants, and humans inhabiting arid regions of the world. Disturbance processes in desert spring ecosystems are likely important but have not been well studied. Documentation of historic wildfires in these often remote areas has been inconsistent and proxy records are often not available. Remote sensing methods have been used in other environments to gain information about fires that have occurred over recent decades, but these methods have not been tested in desert spring environments. The differenced normalized burn ratio (dNBR) is the most commonly used method for delineating fire perimeters and burn severity mosaics, although another method, differenced linear spectral unmixing (dSMA), may produce more accurate results in heterogeneous desert spring ecosystems due to its ability to detect changes at the sub-pixel scale. This study compared dNBR and dSMA using field observations of burn presence and fire severity for two recent wildfires. The dNBR method outperformed dSMA, but required some post-processing manipulation to reduce errors of commission. The dNBR classification correctly indentified burned areas with 86% accuracy (3% omission error, 19% commission error) and classified fire severity with 76% accuracy. Misclassification errors were most common in dune and mesquite bosque/meadow land cover types (mean misclassification rate = 36%). Nine of the fifteen wildfires reported to have occurred in the study site were successfully identified, with five of the unidentified fires having reported sizes of less than one hectare. Additional refinement of remote sensing methods is necessary to better distinguish small (< 5 ha) burned areas from areas of change resulting from soil moisture fluctuation and other short-term shifts in background conditions.     

Satellite mapping of CO emission from forest fires in Northwest America using MOPITT measurements

We present a study on MOPITT (Measurements Of Pollution In The Troposphere) detection of CO emission from large forest fires in the year 2000 in the northwest United States. Fire data used are from the space-borne Advanced Very High Resolution Radiometer (AVHRR) at 1-km resolution. The study shows that MOPITT can reliably detect CO plumes from forest fires whenever there are >30 AVHRR hotspots in a 0.25° × 0.25° grid, which is comparable to the pixel area of MOPITT in the region. The spatial CO pattern during the fire events is found to be consistent with the location and density of AVHRR hotspots and wind direction. While the increase of CO abundance inside the study area is closely correlated to the AVHRR-derived hotspot number in general (R > 0.75), the non-linearity of fire emission with fuel consumption is also observed. MOPITT can also capture the temporal variation in CO emission from forest fires through 3-day composites so it may offer an opportunity to enhance our knowledge of temporal fire emission over large areas. The CO emission is quantitatively estimated with a one-box model. The result is compared with a bottom-up approach using surface data including burnt area, biomass density, and fire emission factors. If mean emission factors for the region are used, the bottom-up approach results in total emission estimates which are 10%-50% lower than the MOPITT-based estimate. In spite of the limitations and uncertainties addressed in this study, MOPITT data may provide a useful constraint on uncertain ground-based fire emission estimates.     

Reconstructing long time series of burned areas in arid grasslands of southern Russia by satellite remote sensing

Fire is an important natural disturbance process in many ecosystems, but humans can irrevocably change natural fire regimes. Quantifying long-term change in fire regimes is important to understand the driving forces of changes in fire dynamics, and the implications of fire regime changes for ecosystem ecology. However, assessing fire regime changes is challenging, especially in grasslands because of high intra- and inter-annual variation of the vegetation and temporally sparse satellite data in many regions of the world. The breakdown of the Soviet Union in 1991 caused substantial socioeconomic changes and a decrease in grazing pressure in Russia's arid grasslands, but how this affected grassland fires is unknown. Our research goal was to assess annual burned area in the grasslands of southern Russia before and after the breakdown. Our study area covers 19,000 km² in the Republic of Kalmykia in southern Russia in the arid grasslands of the Caspian plains. We estimated annual burned area from 1985 to 2007 by classifying AVHRR data using decision tree algorithm, and validated the results with RESURS, Landsat and MODIS data. Our results showed a substantial increase in burned area, from almost none in the 1980s to more than 20% of the total study area burned in both 2006 and 2007. Burned area started to increase around 1998 and has continued to increase, albeit with high fluctuations among years. We suggest that it took several years after livestock numbers decreased in the beginning of the 1990s for vegetation to recover, to build up enough fuel, and to reach a threshold of connectivity that could sustain large fires. Our burned area detection algorithm was effective, and captured burned areas even with incomplete annual AVHRR data. Validation results showed 68% producer's and 56% user's accuracy. Lack of frequent AVHRR data is a common problem and our burned area detection approach may also be suitable in other parts of the world with comparable ecosystems and similar AVHRR data limitations. In our case, AVHRR data were the only satellite imagery available far enough back in time to reveal marked increases in fire regimes in southern Russia before and after the breakdown of the Soviet Union.     

The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product

The results of the first consecutive 12 months of the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) global burned area product are presented. Total annual and monthly area burned statistics and missing data statistics are reported at global and continental scale and with respect to different land cover classes. Globally the total area burned labeled by the MODIS burned area product is 3.66 × 10⁶ km² for July 2001 to June 2002 while the MODIS active fire product detected for the same period a total of 2.78 × 10⁶ km², i.e., 24% less than the area labeled by the burned area product. A spatio-temporal correlation analysis of the two MODIS fire products stratified globally for pre-fire leaf area index (LAI) and percent tree cover ranges indicate that for low percent tree cover and LAI, the MODIS burned area product defines a greater proportion of the landscape as burned than the active fire product; and with increasing tree cover (> 60%) and LAI (> 5) the MODIS active fire product defines a relatively greater proportion. This pattern is generally observed in product comparisons stratified with respect to land cover. Globally, the burned area product reports a smaller amount of area burned than the active fire product in croplands and evergreen forest and deciduous needleleaf forest classes, comparable areas for mixed and deciduous broadleaf forest classes, and a greater amount of area burned for the non-forest classes. The reasons for these product differences are discussed in terms of environmental spatio-temporal fire characteristics and remote sensing factors, and highlight the planning needs for MODIS burned area product validation.     

Testing the potential of multi-spectral remote sensing for retrospectively estimating fire severity in African Savannahs

The remote sensing of fire severity is a noted goal in studies of forest and grassland wildfires. Experiments were conducted to discover and evaluate potential relationships between the characteristics of African savannah fires and post-fire surface spectral reflectance in the visible to shortwave infrared spectral region. Nine instrumented experimental fires were conducted in semi-arid woodland savannah of Chobe National Park (Botswana), where fire temperature (T_max) and duration (dt) were recorded using thermocouples positioned at different heights and locations. These variables, along with measures of fireline intensity (FLI), integrated temperature with time (T_sum) and biomass (and carbon/nitrogen) volatilised were compared to post-fire surface spectral reflectance. Statistically significant relationships were observed between (i) the fireline intensity and total nitrogen volatilised (r² = 0.54, n = 36, p < 0.001), (ii) integrated temperature (T_sum−μ) and total biomass combusted (r² = 0.72, n = 32, p < 0.001), and (iii) fire duration as measured at the top-of-grass sward thermocouple (dt_T) and total biomass combusted (r² = 0.74, n = 34, p < 0.001) and total nitrogen volatilised (r² = 0.73, n = 34, p < 0.001). The post-fire surface spectral reflectance was found to be related to dt and T_sum via a quadratic relationship that varied with wavelength. The use of visible and shortwave infrared band ratios produced statistically significant linear relationships with fire duration as measured by the top thermocouple (dt_T) (r² = 0.76, n = 34, p < 0.001) and the mean of T_sum (r² = 0.82, n = 34, p < 0.001). The results identify fire duration as a versatile measure that relates directly to the fire severity, and also illustrate the potential of spectrally-based fire severity measures. However, the results also point to difficulties when applying such spectrally-based techniques to Earth Observation satellite imagery, due to the small-scale variability noted on the ground. Results also indicate the potential for surface spectral reflectance to increase following higher severity fires, due to the laying down of high albedo white mineral ash. Most current techniques for mapping burned area rely on the general assumption that surface albedo decreases following a fire, and so if the image spatial resolution was high enough such methods may fail. Determination of the effect of spatial resolution on a sensor's ability to detect white ash was investigated using a validated optical mixture modelling approach. The most appropriate mixing model to use (linear or non-linear) was assessed using laboratory experiments. A linear mixing model was shown most appropriate, with results suggesting that sensors having spatial resolutions significantly higher than those of Landsat ETM+ will be required if patches of white ash are to be used to provide EO-derived information on the spatial variation of fire severity.     

Testing a MODIS Global Disturbance Index across North America

Large-scale ecosystem disturbances (LSEDs) have major impacts on the global carbon cycle as large pulses of CO₂ and other trace gases from terrestrial biomass loss are emitted to the atmosphere during disturbance events. The high temporal and spatial variability of the atmospheric emissions combined with the lack of a proven methodology to monitor LSEDs at the global scale make the timing, location and extent of vegetation disturbance a significant uncertainty in understanding the global carbon cycle. The MODIS Global Disturbance Index (MGDI) algorithm is designed for large-scale, regular, disturbance mapping using Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and Aqua/MODIS Enhanced Vegetation Index (EVI) data. The MGDI uses annual maximum composite LST data to detect fundamental changes in land-surface energy partitioning, while avoiding the high natural variability associated with tracking LST at daily, weekly, or seasonal time frames. Here we apply the full Aqua/MODIS dataset through 2006 to the improved MGDI algorithm across the woody ecosystems of North America and test the algorithm by comparison with confirmed, historical wildfire events and the windfall areas of documented major hurricanes. The MGDI accurately detects the location and extent of wildfire throughout North America and detects high and moderate severity impacts in the windfall area of major hurricanes. We also find detections associated with clear-cut logging and land-clearing on the forest-agricultural interface. The MGDI indicates that 1.5% (195,580 km²) of the woody ecosystems within North America was disturbed in 2005 and 0.5% (67,451 km²) was disturbed in 2006. The interannual variability is supported by wildfire detections and official burned area statistics.