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.     

Detection of burned areas from mega-fires using daily and historical MODIS surface reflectance

The detection and mapping of burned areas from wildland fires is one of the most important approaches for evaluating the impacts of fire events. In this study, a novel burned area detection algorithm for rapid response applications using Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m surface reflectance data was developed. Spectra from bands 5 and 6, the composite indices of the Normalized Burn Ratio, and the Normalized Difference Vegetation Index were employed as indicators to discover burned pixels. Historical statistical data were used to provide pre-fire baseline information. Differences in the current (post-fire) and historical (pre-fire) data were input into a support vector machine classifier, and the fire-affected pixels were detected and mapped by the support vector machine classification process. Compared with the existing MODIS level 3 monthly burned area product MCD45, the new algorithm is able to generate burned area maps on a daily basis when new data become available, which is more applicable to rapid response scenarios when major fire incidents occur. The algorithm was tested in three mega-fire cases that occurred in the continental USA. The experimental results were validated against the fire perimeter database generated by the Geospatial Multi-Agency Coordination Group and were compared with the MCD45 product. The validation results indicated that the algorithm was effective in detecting burned areas caused by mega-fires.     

Changes in surface reflectance from wildfires on the Australian continent measured by MODIS

Atmospherically corrected Moderate Resolution Imaging Spectroradiometer (MODIS) data have been used to measure the changes in surface reflectance induced by fires. To account for observation geometry effects a kernel driven bi-directional reflectance factor model was applied. Whereas the blue, green, red and shortwave infrared bands show no consistent behaviour, the near-infrared bands almost always show a strong reduction in reflectance. An angular dependence of the change in reflectance was not found in this study. Different bio-geographical regions exhibit different spectral reflectance changes due to the different types of fuel being burnt (green/living versus dry/dead vegetation). This difference is also reflected in the seasonality of the green, red, near-infrared and shortwave infrared bands for the tropics. The conclusion of this study is that the near-infrared bands are the most suitable bands for an automatic burnt area mapping algorithm using optical, reflective remote sensing data. The results also suggest that satellite remote sensing might be able provide additional information about burning conditions which are strongly affecting greenhouse gas emissions.     

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.     

An in situ study of the effects of surface anisotropy on the remote sensing of burned savannah

This Letter presents field‐based evidence of the perturbing effects of surface anisotropy on the remote sensing of burned savannah. The analysis is based on bidirectional spectral reflectance data collected at different solar illumination angles and convolved to Moderate‐resolution Imaging Spectroradiometer (MODIS) reflective bands. Results from a grass savannah site show that burning reduces the anisotropy of the surface compared to its pre‐burn state. In contrast, at a shrub savannah site, burning reduces or increases surface anisotropy. Spectral indices defined from 1.240 µm and 2.130 µm reflectance, and 1.640 µm and 2.130 µm reflectance, provided stronger diurnal separation between burned and unburned areas than individual reflectance bands but do not eliminate anisotropic effects. The Normalized Difference Vegetation Index (NDVI) provides weak diurnal separation relative to these near‐ and mid‐infrared based indices. Implications of the findings are discussed for burned area mapping.     

Estimating burned area for Tropical Africa for the year 1990 with the NOAA-NASA Pathfinder AVHRR 8 km land dataset

The international scientific community recognizes the long-term monitoring of biomass burning as important for global climate change, vegetation disturbance and land cover change research on the Earth's surface. Although high spatial resolution satellite images may offer a more detailed view of land surfaces, their limited area coverage and temporal sampling have restricted their use to local research rather than global monitoring. Low spatial resolution images provide an invaluable source for the detection of burned areas in vegetation cover (scars) at global scale along time. However, the automated burned area mapping algorithm applicable at continental or global scale must be sufficiently robust to accommodate the global variation in burned scar signals. Here, the estimation of the percentage of a pixel area affected by a fire is crucial. In a first step, an empirical method is used which is based on a function between the change in Normalized Difference Vegetation Index (NDVI) and the surface area affected by fire. Next, a new statistical method, based on the Monte Carlo algorithm, is applied to compute probabilities of burned pixels percentages in different neighbourhood conditions.     

Spectral indices and fire behavior simulation for fire risk assessment in savanna ecosystems

The lack of information on the vegetation status before the use of fire as a management tool in protected areas leads to drastic destruction of the natural vegetation and biodiversity. This paper describes the use of spectral indices and simulation of savanna burning to assess risk of intensive fire propagation in a National Park (Niokolo Koba, Senegal, West Africa). Spectral parameters corresponding to thematic information (wetness, brightness, and greenness) were retrieved using an orthogonal transformation, the Tasseled Cap approach on LANDSAT-ETM images. Wetness and brightness indices were normalized (σ=1 and mean=0) and then combined in a simple semi-empirical algorithm of fire risk levels discrimination. These two indices are proven to reflect qualitatively both fuel moisture and its distribution, which constitute the most foreseen determinants of fire propagation in savanna areas. The fire risk assessment algorithm (FIRA algorithm) was used to produce a fire risk map at the beginning of the management fire implement period. In parallel, a fire area simulator (FARSITE) developed by USDA was used with randomly spaced fire sources to determine areas which can be potentially burned in the study site. These simulated burned areas and the FIRA algorithm results were cross-compared to a real fire scars dated at the end of the same burning period and to land cover map. A great consistency was found between the different sources of information. More than 85% of fire prone areas identified by the FIRA algorithm or simulated by FARSITE were located in trees-shrub, woodland, and shrub savannas. These cover types included also 95% of real fire scars. Almost 88% and 84% of real fire scars were found in the risk zones determined by the FIRA algorithm and the simulated burned areas by FARSITE, respectively. The method used is simple and suited for an operational use for management fire implementation in savanna ecosystems.     

秸秆焚烧遥感监测进展分析与展望

秸秆焚烧是生物质燃烧的重要组成部分,不仅导致秸秆资源浪费,而且还会对环境造成严重危害。传统秸秆焚烧监测方法以人工巡查为主,监测范围受限且人力物力资源耗费大。遥感技术作为新兴的地表信息监测手段,给秸秆焚烧大范围监测带来了发展契机。介绍了遥感技术在秸秆焚烧火点监测、过火面积估算和焚烧迹地监测3个方面的基本原理、监测方法和研究进展,并分析了遥感技术在秸秆焚烧监测应用中存在的不足。在此基础上,从多源数据融合互补、监测方法优化集成、监测信息深入挖掘和时空信息决策服务等4个方面对秸秆焚烧遥感监测的未来发展进行了展望。     

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.     

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。     

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.     

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.     

全球典型植被叶片光谱特征及其对LAI反演的影响分析

在全球范围长时间序列LAI遥感产品反演算法中,植被冠层反射率模型仅使用少量叶片光谱特征代表全球植被全年的典型植被光谱特征,叶片光谱的不确定性导致LAI遥感产品存在一定的误差。目前全球已经构建了多个典型植被叶片波谱数据集,这些数据集包含多个植被物种、不同空间地域及多时相叶片光谱数据,为定量分析叶片光谱特征提供了数据支持。主要利用LOPEX’93、ANGERS’03、中国典型地物波谱数据库和野外实测的叶片光谱数据,以黄边参数、红边参数和叶片光谱指数作为分析指标,探讨不同植被物种、不同气候区和不同物候期的叶片光谱特征差异,及其对植被冠层反射率、LAI反演的影响,为发展考虑现实叶片光谱差异的LAI反演算法提供研究基础。结果表明:植被叶片光谱存在多样性,叶片光谱特征差异主要影响MODIS传感器近红外波段和绿波段反射率值,其中,绿波段反射率值对叶片光谱变化最为敏感;在LAI反演算法中,如果只考虑植被类型而不考虑物种叶片光谱差异,可能会给LAI反演带来大于3的误差。     

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.     

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.     

AVHRR、VEGETATION和MODIS时间系列遥感数据产品现状与应用研究进展

全球时间系列卫星遥感产品自产生之日起就得到了高度关注,被广泛地应用于全球、洲际和区域的地表动态监测,并与气温、降水等气候变化表征参数结合起来,应用于全球变化分析。随着时间系列的逐渐延长和新兴传感器的不断涌现,时间系列遥感产品的内容和应用领域更是得到了极大扩展。主要介绍了:①当前国际上流行的可见光/近红外、短波红外和热红外时间系列卫星数据产品的发展现状,传感器主要包括AVHRR、VEGETATION和MODIS。早期以开发波段信息和植被指数等基础数据为主,当前大量专题产品的生产得到广泛开展;② 在数据产品的进一步处理和分析方面,重点介绍了时间系列重建、比较和延长、产品真实性检验的研究进展和发展趋势;③在数据产品应用方面,重点介绍了地表覆被特征的动态监测、物候和种植结构等信息提取、遥感产品在模型中的应用等方面的研究进展和发展趋势。     

An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah

We report on the numerical separation of burned and unburned vegetation classes using different bi-spectral spaces, based on the analysis of spectroradiometric data collected in situ and convolved to five spectral bands at red to mid-infrared (MIR) wavelengths. A combination of two MIR bands was found to have strong spectral separation of burned and unburned samples. Using these bands, a spectral index was formulated which is highly sensitive to spectral changes due to burning and relatively insensitive to intrinsic variability. Results have implications for the remote sensing of burned shrub-savannah using bands available on high- and low-spatial resolution sensors, in particular, Landsat TM and MODIS.     

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.     

Remote sensing of atmospheric biogenic volatile organic compounds (BVOCs) via satellite-based formaldehyde vertical column assessments

Global vegetation is intrinsically linked to atmospheric chemistry and climate, and better understanding vegetation–atmosphere interactions can allow scientists to not only predict future change patterns, but also to suggest future policies and adaptations to mediate vegetation feedbacks with atmospheric chemistry and climate. Improving global and regional estimates of biogenic volatile organic compound (BVOCs) emissions is of great interest for their biological and environmental effects and possible positive and negative feedbacks related to climate change and other vectors of global change. Multiple studies indicate that BVOCs are on the rise, and with near 20 years of global remote sensing of formaldehyde (HCHO), the immediate and dominant BVOC atmospheric oxidation product, the accurate and quantitative linkage of BVOCs with plant ecology, atmospheric chemistry, and climate change is of increasing relevance. The remote sensing of BVOCs, via HCHO in a three step process, suffers from an additive modelling error, but improvements in each of the steps have reduced this error by over a multiplication factor improvement compared to estimates without remote sensing. Differential optical absorption spectroscopy (DOAS) measurement of the HCHO slant columns from spectral absorption properties has been adapted to include the correction of numerous spectral artefacts and intricately refined for each of a series of sensors of increasing spectral and spatial resolution. Conversion of HCHO slant to HCHO vertical columns using air mass factors (AMFs) has been improved with the launch of new sensors and the incorporation of radiative transfer and chemical transport models (CTM). The critical process of linking HCHO to BVOC emissions and filtering non-biogenic emissions to explicitly quantify biogenic emissions has also greatly improved. This critical last step in down-scaling from global satellite coverage to local biogenic emissions now benefits from the increasing precision and near-explicitness of available CTMs as well as the increasing availability of global remote-sensing data sets needed to proportionally assign the HCHO column to different related biogenic (global plant functional type and land cover classifications), atmospheric (dust, aerosols, clouds, other trace gases), climate (temperature, wind, precipitation), and anthropogenic (fire, biomass burning) factors.