Burned Area Detection Research Articles

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 km 2 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.

Comparison of multitemporal compositing methods for burnt area detection in Southeast Asian conditions

This study investigated the usability of different multitemporal compositing methods for burnt area detection purposes in the humid tropical conditions of insular Southeast Asia, characterised by persisting cloud cover, varying fire‐induced spectral changes and large amount of small burn scars. Six monthly composites of the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance images (MOD09) were built using six different algorithms. The performance and usability of the compositing algorithms were evaluated using three criteria: separability between burnt and unburnt areas, homogeneity and average sensor zenith angle. Maximum surface temperature method (band 31, 11.0 µm) produced the most homogeneous composites with preference to close‐to‐nadir observations. However, these composites showed unexpectedly low separability between burnt and unburnt areas, mainly due to low spatial resolution of band 31 (1000 m). Overall, taking into account the performance of the compositing methods and the large amount of small burnt areas in insular Southeast Asia, a minimum NIR (band 2, 0.86 µm) method, combined with pre‐compositing cloud and cloud shadow removal, was seen as the most suitable compositing method for burnt area detection in this region. Its strengths were 250 m spatial resolution in pixel selection and high burnt/unburnt separability combined with reasonably good performance on homogeneity and average sensor zenith angle.

Spatiotemporal problems with detecting and mapping mosaic fire regimes with coarse-resolution satellite data in savanna environments

Fire is a prominent disturbance factor and a major force of environmental change especially in the African savannas. The development of an accurate system to map and monitor fires on the African continent is a priority of numerous international research centers and programs. This effort has produced a flurry of research projects in recent years to detect and map areas affected by fires at the continental scale using coarse-resolution satellite imagery. The end product of these projects consists of weekly or monthly maps of burned area, several of which are available to the user community on the internet. It is argued here that the algorithms used to generate these products are designed to capture relatively large and contiguously burned areas and that the heterogeneous patterns of burn scars created by mosaic burning regimes pose a problem for current detection methodologies. Fine-scale burned area maps are generated using a series of Landsat ETM+imagery covering the 2002–2003 fire season for the study area in the savanna of southern Mali. These maps document a seasonal-mosaic pattern of burning in which burning begins early in the dry season and continues for several months ultimately affecting over 50% of the landscape. The majority of these fires burn relatively small areas producing a highly fragmented landscape pattern. A comparison of the fine scale maps with those from two widely available coarse-resolution products finds that the latter fail to detect approximately 90% of the burned area. A general argument is developed which suggests that the documented bias in the coarse resolution products is a function of low-resolution bias which derives from the fine-scale spatiotemporal pattern of burning not uncommon to savanna and other frequently burned environments. The study demonstrates how low-resolution bias can result in a significant underestimation of burned areas and/or a shift in the seasonal burned area profile in areas where mosaic burning occurs. The findings have implications for the development of broad-scale burned area detection algorithms as well as their applications to natural resource management and global environmental change research.

Burnt area detection at global scale using ATSR‐2: The GLOBSCAR products and their qualification

Quantifying the contribution of biomass burning to the global distribution of emissions of carbon into the atmosphere requires knowledge of the area burnt. The GLOBSCAR project was initiated in 2001 as part of the European Space Agency (ESA) Data User Programme, with the objective of producing global incremental monthly maps of burnt areas, using daytime data from year 2000 of the Along Track Scanning Radiometer (ATSR‐2) instrument onboard the ESA ERS‐2 satellite. The processing system combines the use of two algorithms representing different approaches to burnt area detection. The K1 algorithm is a contextual algorithm based on the geometrical characteristics of the burnt pixels in the near‐infrared (NIR, 0.87 microns)/thermal infrared (TIR, 11 microns) space, while the E1 algorithm consists of a series of fixed threshold tests applied to the data using information from four different spectral channels. The GLOBSCAR products are available from the GeoSuccess Web site (http://www.geosuccess.net) in ASCII and vector format. The products were validated using a variety of qualitative and quantitative tests against other field and remote sensing data. The GLOBSCAR results are presented per region and are compared to available statistics and other remote sensing products such as the ATSR‐2 World Fire Atlas (WFA) and the Global Burnt Area product derived from SPOT/VEGETATION (GBA‐2000). The potential and limitations of the GLOBSCAR products are then discussed. It is concluded that while the GLOBSCAR products represent a definite progress toward a better quantification of the areas burnt annually at the larger scales, coordinated improvements in remote sensing products are needed in order to better address the requirements of the user community. Future activities include the consolidation of the results through further qualification and the development of a multiyear multisensor product building upon the experience gained from both GLOBSCAR and GBA‐2000 projects.

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.

Infrared detection of active fires and burnt areas: theory and observations

We have investigated the problem of fire recognition and management using hyperspectral images remotely sensed by the multispectral infrared and visible imaging spectrometer (MIVIS). Twenty near infrared bands (between 1.99 and 2.48 μm) and ten thermal infrared (between 8.2 and 12.7 μm) MIVIS bands have been utilised. The main problems addressed in this work are the fire-front and burned-area recognition and the extraction of information useful for the management of the burned area. The paper shows data gathered by the sensor MIVIS over the fire, the data processing result and is completed with a brief theoretical discussion of the involved topics. Some hints are given about the diagnostic capabilities of other hyperspectral devices.

Radiometric analysis of SPOT-VEGETATION images for burnt area detection in Northern Australia

Radiometric analysis of SPOT-VEGETATION (VGT) images acquired over Australia was carried out as a basis for the development of an algorithm to map burnt areas in woodland savannas. We analysed the variability of daily ground reflectance and its relationship with illumination and viewing geometry. Finding that the geometrical effects can be parameterised by the phase angle (angle between the illumination and the viewing directions) and the viewing zenith angle (VZA), we fit a simple linear model to the observations. The results show that about 60–70% of the variability in the daily reflectance is caused by geometrical effects. The residual 30–40% of the variability is probably due to changes in vegetation condition, such as senescence, and residual atmospheric contamination. We tested temporal compositing as a practical method of reducing the variability in the reflectance whilst retaining the burnt area signal. We inspected the radiometric and geometrical effects of four different compositing criteria and showed that minimum near infrared (NIR) is the most appropriate for burnt area mapping over the study area. In order to analyse the sensitivity of the VGT spectral bands and derived indices to changes induced by fire, we extracted burnt area spectral signatures for different vegetation types. The persistence of the burnt signal, as observed with each band and index, was analysed. Among the bands, NIR is shown to be the most sensitive to fire occurrence. There is a clear drop in the reflectance immediately after the fire and it remains very low during subsequent weeks. On the other hand, the burnt signal in the short-wave infrared (SWIR) band is showed to be strongly dependent on the vegetation cover type and on the age of the burnt area. Among the indices, the Global Environment Monitoring Index (GEMI) is identified as the most suitable for detecting changes induced by fire on the vegetation cover.

Uncertainty propagation in models driven by remotely sensed data

Often, little importance is given to the problem of how uncertainty propagates in models driven by remotely sensed data, and what the effects of uncertainty might be on the output of these models. In this paper, a general procedure to support a characterisation of uncertainty in the generation of remote sensing (RS) products is proposed. The procedure can be used with models characterised by any degree of complexity and driven by any kind of data. It provides two useful tools to analyse models: uncertainty analysis (UA), which allows the assessment of the uncertainty associated with model output, and sensitivity analysis (SA), which is useful to determine how much each source of uncertainty contributes to model output uncertainty. Uncertainty modelling, i.e. finding suitable tools to represent uncertainty, is a key step in performing UA and SA. A general error model for quantitative raster data is described. Different applications of UA and SA are proposed, and, in the last part of the paper, an example of UA and SA on a model for burned area detection is discussed.

Compositing Criteria for Burned Area Assessment Using Multitemporal Low Resolution Satellite Data

Five different multitemporal image compositing criteria were compared for the specific aim of monitoring burned areas using AVHRR-GAC 5 km data. Two of them were based on a classical maximum value technique: maximum value composite of NDVI (MVCN) and of surface temperature (MVCT S). Three others were based on a minimum value technique: minimum value composite of Channel 1 (M iVCB1), of Channel 2 (M iVCB2) and of albedo (M iVCA). The compositing period used was 1 week, and four output channels suitable for identifying burned areas were generated: AVHRR Channel 2 (Ch2), normalized difference vegetation index (NDVI), surface temperature (T S), and global environment monitoring index (GEMI). The different compositing criteria were assessed based on their ability to retain burned areas and also on their choice of close to nadir pixels. The separability of burned and unburned areas for each of the compositing criteria was assessed for each of the individual output channels. The criterion that showed best results in terms of separability was M iVCA followed very closely by M iVCB2; the MVCN criterion clearly showed the least merit of all. An overall analysis of the satellite zenith angles showed that the M iVCB2 criterion selected the smallest angles or closer to nadir pixels followed by M iVCA, MVCN, MVCT s, and M iVCB1. Although impossible to choose a single best compositing criteria in absolute terms, M iVCA proved to be the best compromise because it chose near to nadir pixels and performed best in terms of separability between burned and unburned spectral signatures. Future work will determine how to combine different channels (e.g., GEMI, Ch2, T s, NDVI) for burned area detection, once the M iVCA compositing has been performed.

Remote Sensing of Biomass Burning in Tropical Regions: Sampling Issues and Multisensor Approach

Several global datasets on fire distribution are being generated from remotely sensed data to support research on the ecological impacts of biomass burning. This article examines the strengths and weaknesses of a number of approaches to the monitoring of biomass burning at a regional scale and suggests how to best combine the information content on fire distribution provided by different earth observation satellites. Remotely sensed data acquired over Central Africa from a variety of sensors (airborne video camera, SPOT XS, Landsat Thematic Mapper, NOAA Advanced Very High Resolution Radiometer, and ERS-1 Along Track Scanning Radiometer) were used to provide quantitative measurements of the spectral separability, and temporal and spatial sampling associated with the detection of burnt areas and active fires. Three main strategies to the monitoring of biomass burning were analyzed: detection of burnt areas at fine spatial resolution, detection of burnt areas at coarse spatial resolution and high temporal frequency, and detection of active fires at a coarse spatial resolution and high temporal frequency. In each case, we assess the detectability of the selected biomass burning indicator, the statistical representativity in time and space of the sample detected and whether the sample observations are an unbiased estimator of the total biomass burning events in the region. We conclude that while active fire detection remains important in defining the seasonality, timing, and interannual variations in biomass burning, the most reliable strategy for estimating biomass burning at a regional scale is a multisensor approach in which regional burnt area estimates from coarse spatial resolution data are calibrated on the basis of a sample of fine spatial resolution estimates of burnt areas, using a double sampling with regression estimator approach.