MODIS Fire Products Research Articles

A two-decade analysis of the spatial and temporal variations in burned areas across Zimbabwe

Understanding wildfire dynamics in space and over time is critical for wildfire control and management. In this study, fire data from European Space Agency (ESA) MODIS fire product (ESA/CCI/FireCCI/5_1) with ≥ 70% confidence level was used to characterise spatial and temporal variation in fire frequency in Zimbabwe between 2001 and 2020. Results showed that burned area increased by 16% from 3,689 km2 in 2001 to 6,130 km2 in 2011 and decreased in subsequent years reaching its lowest in 2020 (1,161km2). Over, the 20-year period, an average of 40,086.56 km2 of land was burned annually across the country. In addition, results of the regression analysis based on Generalised Linear Model illustrated that soil moisture, wind speed and temperature significantly explained variation in burned area. Moreover, the four-year lagged annual rainfall was positively related with burned area suggesting that some parts in the country (southern and western) are characterised by limited herbaceous production thereby increasing the time required for the accumulation of sufficient fuel load. The study identified major fire hotspots in Zimbabwe through the integration of remotely sensed fire data within a spatially analytical framework. This can provide useful insights into fire evolution which can be used to guide wildfire control and management in fire prone ecosystems. Moreover, resource allocation for fire management and mitigation can be optimised through targeting areas most affected by wildfires especially during the dry season where wildfire activity is at its peak.

Wild, tamed, and domesticated: Three fire macroregimes for global pyrogeography in the Anthropocene.

Climate and natural vegetation dynamics are key drivers of global vegetation fire, but anthropogenic burning now prevails over vast areas of the planet. Fire regime classification and mapping may contribute towards improved understanding of relationships between those fire drivers. We used 15 years of daily active fire data from the MODIS fire product (MCD14ML, collection 6) to create global maps of six fire descriptors (incidence, size inequality, season length, interannual variability, intensity, and fire season modality). Using multiple correspondence analysis (MCA) and hierarchical agglomerative clustering, we identified three fire macroregimes: Wild, Tamed, and Domesticated, each of which splitting into prototypical and transitional regimes. Interpretation of the six fire regimes in terms of their main drivers relied on the global maps of anthromes and Köppen climate types. The analysis yielded a two-dimensional space where the principal dimension of variability is primarily defined by interannual variability in fire activity and fire season length, and the secondary axis is based mainly on fire incidence. The Wild fire macroregime occurs mostly in cold wildlands, where burning is sporadic and fire seasons are short. Tamed fires predominate in seasonally dry tropical rangelands and croplands with high fire incidence. Domesticated fires are characteristic of humid, warm temperate and tropical croplands and villages with low fire incidence. The Tamed and Domesticated fire macroregimes, representing managed burning, account for 86% of all active fires in our dataset and for 70% of the global burnable area. Fourteen percent of active fires were found in the cold wildlands, and in the rangelands and forests of steppe and desert climates of the Wild macroregime. These results highlight the extent of human control over global pyrogeography in the Anthropocene.

The temporal and spatial relationships between climatic parameters and fire occurrence in northeastern Iran

Fire is a natural disturbance in a wide range of ecosystem types around the world. Although the majority of fires are caused by anthropogenic ignitions, climate and weather are known to influence the risk of fire spread, regardless of ignition source. However, the specific relationships among these factors remain under-studied for many regions of the world. The objective of this study was to investigate the effect of climatic variables on forest and rangeland fire regimes in the Golestan Province of Iran in three recent decades. Specifically, we sought to determine: (a) the quantitative temporal relationships between climatic variables and fire occurrence; and (b) the spatial relationships between climatic variables and fire occurrence. To assess quantitative temporal relationships, we used correlation and regression analyses to identify and describe statistically significant relationships between climatic data (mean annual temperature, mean annual precipitation, mean annual relative humidity, and mean annual wind velocity) and fire data (number of fire and area burned), using climatic data from the Golestan Meteorological Administration and fire statistics from the Golestan Natural Resources Administration (GNRA). To determine the spatial relationships between climatic variables and fire occurrence, we combined geospatial data on climatic variables (mean annual temperature, mean annual rainfall, wind effect, and topographic wetness index (TWI)) obtained from ASTER-GDEM and active fires derived from MODIS fire products in a random forest (RF) model approach to construct a map of fire susceptibility at 30 m spatial resolution. Our results showed that there was a significant temporal relationship between the number of fires and mean annual temperature, mean annual relative humidity, and mean annual wind velocity. There were also significant temporal relationships between area burned and mean annual relative humidity and mean annual wind velocity. Based on the RF model, the mean annual rainfall had the highest relative importance in fire spatial occurrence. Validation of the fire susceptibility map using area under the curve (AUC) indicated that the RF model created an accurate fire susceptibility map (AUC: 0.823), suggesting that the map can be used as a spatio-temporal climatic alarm system for fire occurrence in the different climatic conditions of the Golestan Province. The results from this study will be important for helping perform climate-change analyses for investigating future temporal and spatial patterns of wildfire in the Golestan Province.

Impacts of ENSO events on forest fire weather of China

The ENSO events affect climate and fire danger of China. It would be helpful for improving fire danger forecast to understand the impacts of ENSO events on fire weather for various ecological zones in the country. We calculated the fire weather index (FWI) using the daily climatic dataset (V3.0) of international exchange weather stations in China during 1951-2016. The burned areas in forests for each ecological zone in 2001-2016 were derived from MODIS fire products. Temperature, precipitation, FWI and burned areas in fire season were estimated for each ecological zone by ENSO events (weak, medium, strong, and super strong El Niño events and weak, medium, and strong La Niña events). The results showed that there were 19 El Niño events and 14 La Niña events during 1950-2016. The average daily maximum temperature of the spring fire season increased significantly in the northwestern region with the influence of strong or super strong El Niño event, while the temperature reduced significantly in the medium El Niño event for mid-temperate semi-arid grassland. Precipitation in fire season generally increased in El Niño events in southern and southwestern forest regions. It would be reduced in most areas affected by the low and medium intensity La Niña event, but be increased during the strong La Nina event. The fire weather indices of southern forest regions decreased due to the weak El Niño event. The FWI of the northern forest regions increased with the strong or super strong El Niño event, and reduced in the southern and southwestern forest areas. There was a significant spatial difference on the FWI for some ecological zones with the impacts of the El Niño/La Niña events. The burned areas showed a consistent change trend with seasonal severity rating (SSR) during 2001-2016 when the SSR changed significantly for the regions of deciduous broad-leaved forest in humid/semihumid areas of warm temperate zone, broad-leaved forest in the middle north subtropical humid areas, and broad-leaved forest in tropical and subtropical humid areas. The burned areas in the rest regions were not affected by the ENSO events.

Satellite‐based top‐down Lagrangian approach to quantify aerosol emissions over California

AbstractAccurate forecasting of air quality demands better estimates of aerosol emissions. The accuracy of conventional bottom‐up approaches to estimate aerosol emissions depends on the degree to which various influencing parameters are estimated. The availability of satellite observations not only enhances the capability of determining various influencing parameters, but also provides alternate ways of assessing aerosol sources. The present study employs a Lagrangian approach to the Advection Diffusion Equation (ADE) to estimate the transported aerosols and hence the Aerosol Source Strength (ASS) using satellite‐measured Aerosol Optical Depth (AOD) and reanalysis wind data. This top‐down approach is based on the advection and diffusion of atmospheric aerosols considering wind circulation and atmospheric conditions rather than using indicative parameters. ASS was computed every 3 hr at a 0.25°×0.25° grid across California during July 2018. For the computation, AOD retrievals were obtained from the Geostationary Operational Environmental Satellite (GOES)‐16 with observations every 15 min. The data were resampled to the grid every 3 hr, and backward trajectories were run at every gridpoint to ascertain the initial aerosol concentration for the ADE. The final aerosol concentrations obtained from the ADE model were then compared with the observed AOD to obtain the ASS during that time period. The results are indicative of higher ASS around wildfire locations. The ASS values also show good correlation (R2=0.886) with Fire Radiative Power (FRP) obtained from Terra MODIS fire product. The method was further applied to investigate the spatial correlation of ASS with power plant density, which reveals a steady increase in ASS with power plant density (R2=0.82).

Using GIS to Develop a Model for Forest Fire Risk Mapping

Forests are the most beautiful natural resources around the world and play a pivotal role in preserving environmental balance. An important measure taken for managing and protecting forest areas as well as for decreasing the potential damages caused by the fire is the detection of regions susceptible to forest fire through forest fire risk mapping with different models and methods. In recent years, a geographic information system (GIS)-based multi-criteria decision analyses (MCDA) have been successfully applied in the production of forest fire risk maps. In this study, GIS-based analytical network process as MCDA method was employed in order to provide the fire risk map of Noshahr Forests (North Iran) using slope, slope aspect, altitude, land cover, normalized difference vegetation index, annual rainfall, annual temperature, distance to settlements, and distance to road as input data. Furthermore, to prepare the map of the distribution of occurred fires, MODIS fire product and wide-field observations were used. Thereafter, each of these subcriteria of the utilized factors was standardized according to their significance in a forest fire and then with the extracted coefficients in the analytical network process model merged in ArcGIS software. Finally, the fire risk map was generated. Evaluation of the results obtained using receiver operating characteristic curve indicated that the designed model has good accuracy with a value of under curve area of 0.783. According to the map prepared, 57.45% of the study area (1034.41 km2) is located in the high and very high-risk classes.

Bottom-up emission inventories of multiple air pollutants from open straw burning: A case study of Jiangsu province, Eastern China

Open straw burnings during harvest season generated large amounts of air pollutants in a short duration, resulting in significant visibility reduction, air quality deterioration and adverse impact on human health. In this study, a bottom-up approach of county level emission inventory from open straw burning was developed. A 1 km resolution inventories for Jiangsu province, Eastern China, was established as a case study. In order to increase temporal-spatial resolution, county level, seasonal data and local parameters were preferentially used. Per capita disposable income of rural residents was used to adjust burning proportion for different cities. Emissions during summer-harvest and autumn-harvest seasons were calculated separately. The inventory was further allocated based on both MODIS fire product and land use data of cropland. Total emissions of SO2, NOx, THC, NH3, OC, BC and primary PM2.5 from open straw burning in 2015 were 3.72, 15.25, 43.52, 2.65, 16.08, 2.08 and 44.70 Gg, respectively. From 2010 to 2015, provincial emissions decreased by 50.7% in average, and the difference between southern and northern Jiangsu has been even enlarged. Autumn-harvest crop straw contributed 63%–67% to NH3, OC and PM2.5, while summer-harvest crop straw contributed 60% to BC. Wheat and rice straws burning accounted for the majority of emissions at 80%–91% of the total for all 7 pollutants. Uncertainties ranged from a low of within 29% for BC to a high of within 40% for NH3. The EFs and the crop production of autumn-harvest rice and summer harvest wheat, are the key uncertainty sources.

Spatial and temporal patterns of fires in tropical savannas of Indonesia

This paper examines spatial and temporal patterns of large to medium‐sized fires across the Indonesian Archipelago using remote sensing, particularly the MODIS fire products, over a 14 years period (2000–13 inclusive) in conjunction with climatic and land data in a GIS. Results showed fires (burned area and number of fires) were detected the most in year 2002 and least in 2010. The extent of burning was correlated with the annual Southern Oscillation Index with most burning occurring during times of sustained negative SOI values, which generally means drier conditions across the region. Most of the detected fires occurred in the middle of the dry season and in drier eastern Indonesia. Across Indonesia, approximately 1.5 per cent of available open vegetation area burned, whereas only 0.5 per cent of forest area burned. Most open vegetation burning was detected in the savanna with an area equivalent to 11.1 per cent of its extent being burned over the 14 years. On Sumba Island, where the pronounced dry season extends for at least four months, open vegetation is the most common land cover type, of which 99 per cent burned by area over the study period making it the island with the most burned areas in the Indonesian Archipelago.

Accuracy assessment of MODIS active fire products in southern African savannah woodlands

AbstractSatellite remote sensing offers a cost‐effective method for monitoring fire occurrence in savannah systems, for proper fire management. However, the ability of satellite fire products to detect active fire is known to vary depending on the terrestrial ecosystems and sensor characteristics. In this study, the overall accuracy, kappa coefficient of agreement and true skill statistic (TSS) were used to assess the accuracy of two MODIS fire products (MOD14A1 and MCD14ML) to detect active fire at two savannah woodland sites dominated by Baikiaea plurijuga and Brachystegia spiciformis in Zimbabwe. In both sites, MOD14A1 with a coarse spatial resolution of 1 km had a poor index of agreement with ground fire data (kappa = 0, TSS = 0 and overall accuracy ≤ 0.4). By contrast, a moderate to strong agreement between MCD14ML and active fires measured on the ground was observed at both study sites (overall accuracy ≥ 0.7, kappa ≥ 0.6 and TSS ≥ 0.6; Table ). It was therefore concluded that MCD14ML, with a spatial resolution of 375 m, is a more suitable product for detecting active fires in both Baikiaea plurijuga and Brachystegia‐dominated savannah woodlands of southern Africa compared to MOD14A1.

Performance of Three MODIS Fire Products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a Mountainous Area of Northwest Yunnan, China, Characterized by Frequent Small Fires

An increasing number of end-users looking for ground data about fire activity in regions where accurate official datasets are not available adopt a free-of-charge global burned area (BA) and active fire (AF) products for applications at the local scale. One of the pressing requirements from the user community is an improved ability to detect small fires (less than 50 ha), whose impact on terrestrial environments is empirically known but poorly quantified, and is often excluded from global earth system models. The newest generation of BA algorithms combines the capabilities of both the BA and AF detection approaches, resulting in a general improvement of detection compared to their predecessors. Accuracy assessments of these products have been done in several ecosystems; but more complex ones, such as regions that are characterized by frequent small fires and steep terrain has never been assessed. This study contributes to the understanding of the performance of global BA and AF products with a first assessment of four selected datasets: MODIS-based MCD45A1; MCD64A1; MCD14ML; and, ESA’s Fire_CCI in a mountainous region of northwest Yunnan; P.R. China. Due to the medium to coarse resolution of the tested products and the reduced sizes of fires (often smaller than 50 ha) we used a polygon intersection assessment method where the number and locations of fire events extracted from each dataset were compared against a reference dataset that was compiled using Landsat scenes. The results for the two sample years (2006 and 2009) show that the older, non-hybrid products MCD45A1 and, MCD14ML were the best performers with Sørensen index (F1 score) reaching 0.42 and 0.26 in 2006, and 0.24 and 0.24 in 2009, respectively, while producer’s accuracies (PA) were 30% and 43% in 2006, and 16% and 47% in 2009, respectively. All of the four tested products obtained higher probabilities of detection when smaller fires were excluded from the assessment, with PAs for fires bigger than 50 ha being equal to 53% and 61% in 2006, 41% and 66% in 2009 for MCD45A1 and MCD14ML, respectively. Due to the technical limitations of the satellites’ sensors, a relatively low performance of the four products was expected. Surprisingly, the new hybrid algorithms produced worse results than the former two. Fires smaller than 50 ha were poorly detected by the products except for the only AF product. These findings are significant for the future design of improved algorithms aiming for increased detection of small fires in a greater diversity of ecosystems.

Fire Regimes and Their Drivers in the Upper Guinean Region of West Africa

The Upper Guinean region of West Africa exhibits strong geographic variation in land use, climate, vegetation, and human population and has experienced phenomenal biophysical and socio-economic changes in recent decades. All of these factors influence spatial heterogeneity and temporal trends in fires, but their combined effects on fire regimes are not well understood. The main objectives of this study were to characterize the spatial patterns and interrelationships of multiple fire regime components, identify recent trends in fire activity, and explore the relative influences of climate, topography, vegetation type, and human activity on fire regimes. Fire regime components, including active fire density, burned area, fire season length, and fire radiative power, were characterized using MODIS fire products from 2003 to 2015. Both active fire and burned area were most strongly associated with vegetation type, whereas fire season length was most strongly influenced by climate and topography variables, and fire radiative power was most strongly influenced by climate. These associations resulted in a gradient of increasing fire activity from forested coastal regions to the savanna-dominated interior, as well as large variations in burned area and fire season length within the savanna regions and high fire radiative power in the westernmost coastal regions. There were increasing trends in active fire detections in parts of the Western Guinean Lowland Forests ecoregion and decreasing trends in both active fire detections and burned area in savanna-dominated ecoregions. These results portend that ongoing regional landscape and socio-economic changes along with climate change will lead to further changes in the fire regimes in West Africa. Efforts to project future fire regimes and develop regional strategies for adaptation will need to encompass multiple components of the fire regime and consider multiple drivers, including land use as well as climate.

GHG AND AEROSOL EMISSION FROM FIRE PIXEL DURING CROP RESIDUE BURNING UNDER RICE AND WHEAT CROPPING SYSTEMS IN NORTH-WEST INDIA

Abstract. Emission of smoke and aerosol from open field burning of crop residue is a long-standing subject matter of atmospheric pollution. In this study, we proposed a new approach of estimating fuel load in the fire pixels and corresponding emissions of selected GHGs and aerosols i.e. CO2, CO, NO2, SO2, and total particulate matter (TPM) due to burning of crop residue under rice and wheat cropping systems in Punjab in north-west India from 2002 to 2012. In contrasts to the conventional method that uses RPR ratio to estimate the biomass, fuel load in the fire pixels was estimated as a function of enhanced vegetation index (EVI). MODIS fire products were used to detect the fire pixels during harvesting seasons of rice and wheat. Based on the field measurements, fuel load in the fire pixels were modelled as a function of average EVI using second order polynomial regression. Average EVI for rice and wheat crops that were extracted through Fourier transformation were computed from MODIS time series 16 day EVI composites. About 23 % of net shown area (NSA) during rice and 11 % during wheat harvesting seasons are affected by field burning. The computed average fuel loads are 11.32 t/ha (±17.4) during rice and 10.89 t/ha (±8.7) during wheat harvesting seasons. Calculated average total emissions of CO2, CO, NO2, SO2 and TPM were 8108.41, 657.85, 8.10, 4.10, and 133.21 Gg during rice straw burning and 6896.85, 625.09, 1.42, 1.77, and 57.55 Gg during wheat burning. Comparison of estimated values shows better agreement with the previous concurrent estimations. The method, however, shows its efficiency parallel to the conventional method of estimation of fuel load and related pollutant emissions.

Fire frequency, fire seasonality and fire intensity within the Okavango region derived from MODIS fire products

Fire frequency, fire seasonality and fire intensity within the Okavango region derived from MODIS fire products

Influence of open vegetation fires on black carbon and ozone variability in the southern Himalayas (NCO-P, 5079 m a.s.l.)

We analysed the variability of equivalent black carbon (BC) and ozone (O3) at the global WMO/GAW station Nepal Climate Observatory-Pyramid (NCO-P, 5079 m a.s.l.) in the southern Himalayas, for evaluating the possible contribution of open vegetation fires to the variability of these short-lived climate forcers/pollutants (SLCF/SLCP) in the Himalayan region.We found that 162 days (9% of the data-set) were characterised by acute pollution events with enhanced BC and O3 in respect to the climatological values. By using satellite observations (MODIS fire products and the USGS Land Use Cover Characterization) and air mass back-trajectories, we deduced that 56% of these events were likely to be affected by emissions from open fires along the Himalayas foothills, the Indian Subcontinent and the Northern Indo-Gangetic Plain.These results suggest that open fire emissions are likely to play an important role in modulating seasonal and inter-annual BC and O3 variability over south Himalayas.

Modeling of Emissions from Open Biomass Burning in Asia Using the BlueSky Framework

Open biomass burning (excluding biofuels) is an important contributor to air pollution in the Asian region. Estimation of emissions from fires, however, has been problematic, primarily because of uncertainty in the size and location of sources and in their temporal and spatial variability. Hence, more comprehensive tools to estimate wildfire emissions and that can characterize their temporal and spatial variability are needed. Furthermore, an emission processing system that can generate speciated, gridded, and temporally allocated emissions is needed to support air-quality modeling studies over Asia. For these reasons, a biomass-burning emissions modeling system based on satellite imagery was developed to better account for the spatial and temporal distributions of emissions. The BlueSky Framework, which was developed by the USDA Forest Service and US EPA, was used to develop the Asian biomass-burning emissions modeling system. The sub-models used for this study were the Fuel Characteristic Classification System (FCCS), CONSUME, and the Emissions Production Model (EPM). Our domain covers not only Asia but also Siberia and part of central Asia to assess the large boreal fires in the region. The MODIS fire products and vegetation map were used in this study. Using the developed modeling system, biomass-burning emissions were estimated during April and July 2008, and the results were compared with previous studies. Our results show good to fair agreement with those of GFEDv3 for most regions, ranging from 9.7% in East Asia to 52% in Siberia. The SMOKE modeling system was combined with this system to generate three-dimensional model-ready emissions employing the fire-plume rise algorithm. This study suggests a practicable and maintainable methodology for supporting Asian air-quality modeling studies and to help understand the impact of air-pollutant emissions on Asian air quality.

Emissions from Forest Fires in India - as assessment based on MODIS Fire and Global land cover products

Fires incur considerable economic losses and disturb the ecology, environment and livelihoods. Studies suggest that 90% of vegetation fires may be manmade in India and about 3.73 million hectare forest area is affected, leading to a loss of 4.40 billion annually. In the present study, MODIS (Moderate Resolution Imaging Spectro radiometer) data from Web Fire Mapper on active fire location for 20032010, burnt area product and land cover data at 1Km spatial resolution have been used to analyze spatial and inter annual variability. We have tried to relate vegetation fire incidences with emissions due to burning. Correspondence in the satellite detected burnt area product with the GLC 2000 land cover product has been used to identify the vulnerability of the forest types in India. Our estimate shows that tropical dry deciduous forest contributes maximum in CO2 emission, between a range of 49–90 Tg in the fire season (Feb –Jun) and corresponding fire counts ranging from 6000-13000 with substantial inter annual variation in the five years analysis. Variation in the fire induced emission is largely due to variability in climatic condition and length of burning period.

Mapping spatial and temporal patterns of Mediterranean wildfires from MODIS

Wildfires are part of the Mediterranean ecosystem, however, in Israel all wildfires are human caused, either intentionally or un-intentionally. In this study we aimed to develop and test a new method for mapping fire scars from MODIS imagery, to examine the temporal and spatial patterns of wildfires in Israel in the 2000s and to examine the factors controlling Israel's wildfire regime. To map the fires we used two ‘off-the-shelf’ MODIS fire products as our basis—the 1km MODIS Collection 5 fire hotspots, the 500m MCD45A1 burnt areas—and we created a new set of fire scar maps from the 250m MOD13Q1 product. We carried out a cross comparison of the three MODIS based wildfire scar maps and evaluated them independently against the wild fire scars mapped from 30m Landsat TM imagery. To examine the factors controlling wildfires we used GIS layers of rainfall, land use, and a Landsat-based national vegetation map. Wildfires occurred in areas where annual rainfall was above 250mm, mostly in areas with herbaceous vegetation. Wildfire frequency was especially high in the Golan Heights and in the foothills of the Judean mountains, and a high correspondence was found between military training zones and the spatial distribution of fire scars. The use of MODIS satellite images enabled us to map wildfires at a national scale due to the high temporal resolution of the sensor. Our MOD13Q1 based mapping of fire scars adequately mapped large (>1km2) fires with accuracies above 80%. Such large fires account for a large proportion of all fires, and pose the greatest threats. This database can aid managers in determining wildfire risks in space and in time.

Global Monitoring and Forecasting of Biomass-Burning Smoke: Description of and Lessons From the Fire Locating and Modeling of Burning Emissions (FLAMBE) Program

Recently, global biomass-burning research has grown from what was primarily a climate field to include a vibrant air quality observation and forecasting community. While new fire monitoring systems are based on fundamental Earth Systems Science (ESS) research, adaptation to the forecasting problem requires special procedures and simplifications. In a reciprocal manner, results from the air quality research community have contributed scientifically to basic ESS. To help exploit research and data products in climate, ESS, meteorology and air quality biomass burning communities, the joint Navy, NASA, NOAA, and University Fire Locating and Modeling of Burning Emissions (FLAMBE) program was formed in 1999. Based upon the operational NOAA/NESDIS Wild-Fire Automated Biomass Burning Algorithm (WF_ABBA) and the near real time University of Maryland/NASA MODIS fire products coupled to the operational Navy Aerosol Analysis and Prediction System (NAAPS) transport model, FLAMBE is a combined ESS and operational system to study the nature of smoke particle emissions and transport at the synoptic to continental scales. In this paper, we give an overview of the FLAMBE system and present fundamental metrics on emission and transport patterns of smoke. We also provide examples on regional smoke transport mechanisms and demonstrate that MODIS optical depth data assimilation provides significant variance reduction against observations. Using FLAMBE as a context, throughout the paper we discuss observability issues surrounding the biomass burning system and the subsequent propagation of error. Current indications are that regional particle emissions estimates still have integer factors of uncertainty.

Predictive fire occurrence modelling to improve burned area estimation at a regional scale: A case study in East Caprivi, Namibia

Fires threaten human lives, property and natural resources in Southern African savannas. Due to warming climate, fire occurrence may increase and fires become more intense. It is crucial, therefore, to understand the complexity of spatiotemporal and probabilistic characteristics of fires. This study scrutinizes spatiotemporal characteristics of fires and the role played by abiotic, biotic and anthropogenic factors for fire probability modelling in a semiarid Southern African savanna environment. The MODIS fire products: fire hot spots (MOD14A2 and MYD14A2) and burned area product MODIS (MCD45A1), and GIS derived data were used in analysis. Fire hot spots occurrence was first analysed, and spatial autocorrelation for fires investigated, using Moran's I correlograms. Fire probability models were created using generalized linear models (GLMs). Separate models were produced for abiotic, biotic, anthropogenic and combined factors and an autocovariate variable was tested for model improvement. The hierarchical partitioning method was used to determine independent effects of explanatory variables. The discriminating ability of models was evaluated using area under the curve (AUC) from the receiver operating characteristic (ROC) plot. The results showed that 19.2–24.4% of East Caprivi burned when detected using MODIS hot spots fire data and these fires were strongly spatially autocorrelated. Therefore, the autocovariate variable significantly improved fire probability models when added to them. For autologistic models, i.e. models accounting for spatial autocorrelation, discrimination was good to excellent (AUC 0.858–0.942). For models not counting spatial autocorrelation, prediction success was poor to moderate (AUC 0.542–0.745). The results of this study clearly showed that spatial autocorrelation has to be taken in to account in the fire probability model building process when using remotely sensed and GIS derived data. This study also showed that fire probability models accounting for spatial autocorrelation proved to be superior in regional scale burned area estimation when compared with MODIS burned area product (MCD45A1).

Forest fire detection using the normalized multi-band drought index (NMDI) with satellite measurements

This study focuses on investigating the ability of selected satellite-derived indices, the normalized multi-band drought index (NMDI), normalized difference water index (NDWI), and the normalized burn ratio (NBR), for detecting forest fires burning in southern Georgia, USA and southern Greece in 2007. Index performance is evaluated using MODIS fire products. Satellite images generated from each index are compared with the active fire map provided by the MODIS rapid response team. Performance measures extracted from the statistical analyses using the confusion matrices are used to verify the capacity of the indices for active fire detection. For each test case, NMDI has strong signals corresponding to active fires and pinpoints the active fire spots accurately. Both, performance evaluations by image comparison and statistical analyses, indicate that active fire detection using NMDI is quite accurate. NMDI reveals the highest overall performance and discrimination power compared to NDWI and NBR. The successful application of NMDI for detecting fires in different areas proves that NMDI is not site-specific and is expected to be applicable to different areas for active fire detection. Such a capacity can help monitor large-scale fire hazards and is therefore useful to carry out regional and global studies.