Mapping burned areas in Mediterranean countries using spectral mixture analysis from a uni‐temporal perspective

The main aim of this study was to evaluate the usefulness of spectral mixture analysis (SMA) for mapping forest areas burned by fires in the Mediterranean area using low and medium spatial resolution satellite sensor data. A methodology requiring only one single post‐fire image was used to carry out the study (uni‐temporal techniques). This methodology is based on the contextual classification of the fraction images obtained after applying SMA to the original post‐fire image. The results showed that the proposed method, using only one image acquired post‐fire, could accurately identify the burned surface area (Kappa coefficient>0.8). The spatial resolution of the satellite images had practically no influence on the accuracy of the burned area estimate but did affect the possibility of detecting areas inside the perimeter of the burned area which were only slightly damaged.     

A performance evaluation of a burned area object-based classification model when applied to topographically and non-topographically corrected TM imagery

Operational use of remote sensing as a tool for post-fire Mediterranean forest management has been limited by problems of classification accuracy arising from confusion between burned and non-burned land, especially within shaded areas. Object-oriented image analysis has been developed to overcome the limitations and weaknesses of traditional image processing methods for feature extraction from high spatial resolution images. The aim of this work was to evaluate the performance of an object-based classification model developed for burned area mapping, when applied to topographically and non-topographically corrected Landsat Thematic Mapper (TM) imagery for a site on the Greek island of Thasos. The image was atmospherically and geometrically corrected before object-based classification. The results were compared with the forest perimeter map generated by the Forest Service. The accuracy assessment using an error matrix indicated that the removal of topographic effects from the image before applying the object-based classification model resulted in only slightly more accurate mapping of the burned area (1.16% increase in accuracy). It was concluded that topographic correction is not essential prior to object-based classification of a burned Mediterranean landscape using TM data.     

The role of topographic correction in mapping recently burned Mediterranean forest areas from LANDSAT TM images

Operational use of remote sensing as a tool for post‐fire, Mediterranean forest management has been limited by problems of classification accuracy arising from confusion of burned and non‐burned areas. Frequently, this occurs as a result of slope illumination and shadowing effects caused by the complex topography encountered in many forested areas. Cloud shadows can also be a problem. The aim of this work was to investigate how image classification results could be improved by removing the illumination effects of topography from satellite images. This was achieved by applying supervised classification to both uncorrected and topographically corrected LANDSAT TM data for a site on the Greek island of Thasos. The classification methodology included atmospheric and geometric correction, field‐based training, seperability/contingency analysis and maximum likelihood processing. The classification scheme was determined on the basis of consultation with the Greek Forest Service. Overlay of the resulting class maps enabled comparison of the total burned area and its spatial extent using the two different approaches to processing. The results of each approach were compared with the forest perimeter map generated by the Forest Service using traditional survey methods. Accuracy assessment and error analysis clearly indicated that the removal of the topographic effect from the satellite image before its classification resulted in more accurate mapping of the burned area. It is concluded that operational use of satellite remote sensing for forest fire management depends on accurate, robust, widely available and proven techniques. Topographic correction should now be regarded as an essential element of any classification methodology which will be used for operational, post‐fire management of forests in complex Mediterranean landscapes.     

A simple method for Are growth mapping using AVHRR channel 3 data

AHVRR Channel 3 data have been used widely for forest fire detection and mapping. However, little attention has been paid to the use of these data for daily fire growth monitoring. A simple method for fire growth mapping using channel 3 data is presented. An 18000 hectare forest fire affecting the Mediterranean coast of Spain is used as a case study. Discrimination of burned area was performed on every image after multitemporal registration. A thermal threshold was established to mask out fire pixels in both diurnal and nocturnal images. GIS overlay techniques were used lo obtain a synthesis map of the daily evolution of the fire. This product can generate valuable input for fire behaviour programmes to improve our understanding of the factors affecting fire spread and fire severity.     

Agreement assessment of NOAA/AVHRR NDVI with Landsat TM NDVI for mapping burned forested areas

Meteorological satellites are appropriate for operational applications related to early warning, monitoring and damage assessment of forest fires. Environmental or resources satellites, with better spatial resolution than meteorological satellites, enable the delineation of the affected areas with a higher degree of accuracy. In this study, the agreement of two datasets, coming from National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Landsat TM, for the assessment of the burned area, was investigated. The study area comprises a forested area, burned during the forest fire of 21-24 July 1995 in Penteli, Attiki, Greece. Based on a colour composite image of Landsat TM a reference map of the burned area was produced. The scatterplot of the multitemporal Normalized Difference Vegetation Index (NDVI) images, from both Landsat TM and NOAA/AVHRR sensors, was used to detect the spectral changes due to the removal of vegetation. The extracted burned area was compared to the digitized reference map. The synthesis of the maps was carried out using overlay techniques in a Geographic Information System (GIS). It is illustrated that the NOAA/AVHRR NDVI accuracy is comparable to that from Landsat TM data. As a result NOAA/AVHRR data can, operationally, be used for mapping the extent of the burned areas.     

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.     

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。     

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.     

An automated two-step NDVI-based method for the production of low-cost historical burned area map records over large areas

The recognition and understanding of long-term fire-related processes and patterns, such as the possible connection between the increased frequency of wildfires and global warming, requires the study of historical data records. In this study, a methodology was proposed for the automated production of long historical burned area map records over large-scale regions. The methodology was based on remotely sensed, high temporal resolution, normalized difference vegetation index (NDVI) data that could be easily acquired at medium or low spatial resolution. The proposed methodology was used to map the burned areas of the wildfires that occurred over the Peloponnese peninsula, Greece, during the summer of 2007. The method was built upon the NDVI data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Système Pour l’Observation de la Terre (SPOT)-VEGETATION. The higher spatial resolution data of MODIS resulted in higher burned area user accuracy (91.10%) and kappa (0.85) values than the respective ones for VEGETATION (79.29% and 0.77). The majority of classification errors were located along the perimeter of the burned areas and were mainly attributed to spatial resolution limitations of the remotely sensed data. The commission errors located away from the fire perimeter were primarily attributed to topographically shaded areas and land-cover types spectrally similar to burned areas. The omission errors resulted primarily from the small size and elongated shape of remote burned areas. Owing to their geometry, they have a high proportion of mixed pixels, whose spectral properties failed to meet the strict set of criteria for core fire pixels. The benefits of the proposed methodology are maximized when applied to data of the highest available spatial resolution, such as those collected by MODIS and the Project for On-Board Autonomy – Vegetation (PROBA-V) and when land-cover types spectrally similar to burned areas are masked prior to its application.     

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.     

Detection of annual burned forest area using change metrics constructed from MODIS data in Manitoba,Canada

The monitoring of annual burned forest area is commonly used to evaluate forest fire carbon release and forest recovery and can provide information on the evolution of carbon sources and sinks. In this work, a new method for mapping annual burned area using four types of change metrics constructed from Moderate Resolution Imaging Spectroradiometer (MODIS) data for Manitoba, Canada, was developed for the 2003–2007 period. The proposed method included the following steps: (1) four types of change metrics constructed from MODIS composite data; (2) Stochastic Gradient Boosting algorithm; and (3) two thresholds to ascertain the final burned area map. Fire-event records from the Canadian National Fire Database (CNFDB) for Manitoba were used to train and validate the proposed algorithm. The predicted burned area was within 91.8% of the CNFDB results for all of the study years. The results indicate that the presented metrics could retain spectral information necessary to discriminate between burned and unburned forests while reducing the effects of clouds and other noise typically present in single-date imagery. A visual comparison to Thematic Mapper (TM) images further revealed that in some areas the mapping provided improvement to the CNFDB data set.     

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.     

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.     

A semi-automatic methodology to detect fire scars in shrubs and evergreen forests with Landsat MSS time series

This paper presents a semi-automatic methodology for fire scars mapping from a long time series of remote sensing data. Approximately, a hundred MSS images from different Landsat satellites were employed over an area of 32 100 km² in the north-east of the Iberian Peninsula. The analysed period was from 1975 to 1993. Results are a map series of fire history and frequencies. Omission errors are 23% for burned areas greater than 200 ha while commission errors are 8% for areas greater than 50 ha. Subsequent work based on the resultant fire scars will also help in describing fire regime and in monitoring post-fire regeneration dynamics.     

AVHRR-based mapping of fires in Russia: New products for fire management and carbon cycle studies

A new database of fire activity in Russia derived from 1-km resolution remote sensing imagery is presented and discussed. The procedure used to generate this burned-area product is described, including active-fire detection and burn-scar mapping approaches. Fire detection makes use of a probabilistic procedure using image data from the United States National Oceanic and Atmospheric Administration's (NOAA) advanced very high resolution radiometer (AVHRR) system. Using the combination of AVHRR data collected at the Krasnoyarsk, Russia, high-resolution picture transmission (HRPT) receiving station, and data from the NOAA Satellite Active Archive (SAA), fire maps are being created for all of Russia for 1995 to 1997 and all of Eastern Russia (east of the Ural Mountains) for 1995 to 2002. This mapping effort has resulted in the most complete set of historic fire maps available for Russia. An initial validation indicates that the burned-area estimates are conservative because the approaches do not detect smaller fires, and, in many cases, fire areas are slightly underestimated. Analyses using the fire database showed that an average of 7.7×10⁶ ha yr⁻¹ of fire occurred in Eastern Russia between 1996 and 2002 and that fire was widely dispersed in different regions. The satellite-based burned-area estimates area were two to five times greater than those contained in official government burned-area statistics. The data show that there is significant interannual variability in area burned, ranging between a low of 1.5×10⁶ ha in 1997 to a high of 12.1×10⁶ ha in 2002. Seasonal patterns of fire are similar to patterns seen in the North American boreal region, with large-fire seasons experiencing more late-season burning (in August and September) than during low-fire years. There was a distinct zonal distribution of fires in Russia; 65% of the area burned occurred in the taiga zone, which includes southern, middle, and northern taiga subzones, 20% in the steppe and forest steppe zones, 12% in the mixed forest zone, and 3% in the tundra and forest-tundra zones. Lands classified as forest experienced 55% of all burned area, while crops and pastures, swamps and bogs, and grass and shrubs land cover categories experienced 13% to 15% each. Finally, the utility of the products is discussed in the context of fire management and carbon cycling.     

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.     

A spectral unmixing approach for mapping burned areas in Mediterranean countries

The principal aim of this Letter is to evaluate the usefulness of Spectral Mixture Analysis (SMA) for estimating the area burned by forest fires in Mediterranean countries using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) unitemporal data. The results show that the method, using an image acquired just after the fire occurrence, is capable of discriminating burned area accurately (Kappa coefficient >0.76).     

Combining per-pixel and object-based classifications for mapping land cover over large areas

A plethora of national and regional applications need land-cover information covering large areas. Manual classification based on visual interpretation and digital per-pixel classification are the two most commonly applied methods for land-cover mapping over large areas using remote-sensing images, but both present several drawbacks. This paper tests a method with moderate spatial resolution images for deriving a product with a predefined minimum mapping unit (MMU) unconstrained by spatial resolution. The approach consists of a traditional supervised per-pixel classification followed by a post-classification processing that includes image segmentation and semantic map generalization. The approach was tested with AWiFS data collected over a region in Portugal to map 15 land-cover classes with 10 ha MMU. The map presents a thematic accuracy of 72.6 ± 3.7% at the 95% confidence level, which is approximately 10% higher than the per-pixel classification accuracy. The results show that segmentation of moderate-spatial resolution images and semantic map generalization can be used in an operational context to automatically produce land-cover maps with a predefined MMU over large areas.     

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.