Active Fire Locations Research Articles

Spatial and Temporal Distribution Characteristics of Active Fires in China Using Remotely Sensed Data

Based on the FIRMS MODIS active fire location data in the Chinese mainland from 2001 to 2018, the GIS fishing net (1 km × 1 km) was used to analyze the spatiotemporal distribution characteristics of active fire occurrence probability and intensity, and a GWLR fire risk assessment model was established to explore its influencing factors. The results show that active fires in the Chinese mainland are mainly low intensity. They are mainly distributed in the area where the annual average temperature is 14–19 °C, the precipitation is 400–800 mm, the surface temperature is 15–20 °C, the altitude is 1000–3000 m, the slope is <15°, and the NDVI value is >0.6. The GWLR fire risk assessment model was constructed to divide mainland China into five fire risk zones. NDVI, temperature, elevation, and slope have significant spatial effects on the occurrence of active fires in the Chinese mainland. Eight fire risk influencing factor areas were divided by calculation, and differentiated fire prevention suggestions are put forward.

An automated and optimized geo-computation approach for spatial fire risk modelling using geo-web service orchestration

Forest fire detection through space-based observations using thermal sensors is in operation since more than one decades. These active fire alerts are generated by using products from MODIS and SNPP–VIIRS satellites and are limited up to identification of active fire locations based on recorded temperature in satellite sensors. However, relation of fire location with forest type map, terrain profile and weather data are required for planning and monitoring of vulnerable areas. An attempt is made to develop online Spatial Decision Support System (SDSS) and spatial model for forest fire risk mapping. The developed SDSS is based on Geospatial Web Service Orchestration (GWSO) workflow which enables forest fire risk alerts and advisories as Web Map Service, Geo SMS and a Geoportal application. User can use this SDSS with minimum technical skills and limited computation resources to generate daily forest fire risk map for fire monitoring and management.

A New Predictive Framework for Amazon Forest Fire Smoke Dispersion over South America

AbstractAerosol particles from forest fire events in the Amazon can be effectively transported to urban areas in southeastern South America, thus affecting the air quality over those regions. A combination of observational data and a comprehensive air quality modeling system capable of anticipating acute air pollution episodes is therefore required. A new predictive framework for Amazon forest fire smoke dispersion over South America has been developed based on the Weather Research and Forecasting with Chemistry community (WRF-Chem) model. Two experiments of 48-h simulations over South America were performed by using this system at 20-km horizontal resolution, on a daily basis, during August and September of 2018 and 2019. The experiment in 2019 included the very strong 3-week forest fire event, when the São Paulo metropolitan area, located in southeastern South America, was plunged into darkness on August 19. The model results were satisfactorily compared against satellite-based data products and in situ measurements collected from air quality monitoring sites. The system is executed daily immediately after the CPTEC Satellite Division releases the latest active fire locations data and provides 48-h forecasts of regional distributions of chemical species such as CO, PM2.5, and O3. The new modeling system will be used as a benchmark within the framework of the Chemistry of the Atmosphere–Field Experiment in Brazil (CAFE-Brazil) project, which will take place in 2022 over the Amazon.

IoT and deep learning-inspired multi-model framework for monitoring Active Fire Locations in Agricultural Activities

This paper proposes an Internet of Things (IoT) and deep learning-inspired multi-model system for detection, dissemination, and monitoring of Active Fire Locations(AFL) in agricultural activities. The IoT module of the proposed system works on the fusion of IoT sensors-based detectors and deep learning-based detectors. Fuzzy logic is used for the fusion of various sensors and providing real-time detection and location of AFL. The deep learning detector implements IP camera-based MobilenetV2 architecture for accurate and long-distance detections trained on a novel self-created dataset. The proposed framework also provides a software module for monitoring and tracking of various AFL. The software comes with several features like automatic extraction of fire locations from remote sensing sites, assigning active fire locations to multiple stakeholders, extracting farmers' names indulged in burning, automatic sending a notification to government agencies, and provisions for citizens centric participation. The results of the proposed framework are quite encouraging.

Validation of MODIS and AVHRR Fire Detections in Australia

The Sentinel Bushfire Monitoring System is an internet-based mapping tool which provides timely spatial information to fire agencies across Australia. The mapping system allows users to identify active fire locations that pose a potential risk to communities and property. Sentinel at Geoscience Australia currently provides hotspot information derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors on a continent-wide and daily basis enabling the fire community and general public to locate active fires. There has been little validation undertaken of the Sentinel since the system began operating in November 2002. Validation datasets have been collected for this work during the 2003-2007 fire seasons. Five study areas were selected to validate the detection capabilities of the MODIS and AVHRR hotspot product with fire activity that was mapped using high resolution earth observation imagery. The objective is to evaluate the reliability with which hotspots identified in MODIS and AVHRR thermal data can be used to identify fires. This consists of comparing the accuracy of AVHRR versus MODIS and quantifying the accuracy of both products. This objective is achieved by characterising errors through a stratified random sampling technique establishing a relationship between the ‘fire’ and ‘no fire’ condition, and error assessment using multi- source reference datasets over coincident MODIS and AVHRR pixels. The validation framework comprised two key approaches including validation of AVHRR hotspots in relation to MODIS hotspots and validation of both MODIS and AVHRR hotspots using multi-sensor earth observation imagery datasets. The study identified sources of errors associated with the Sentinel hotspots which could be used to improve the performance of hotspot algorithms and provide user-friendly information for the users. Statistical analysis revealed that overall commission errors of MODIS and AVHRR hotspots over the 5% sample data were 15% and 68% respectively, and overall omission errors of MODIS and AVHRR hotspots were 17% and 23% respectively. An important outcome of this study is the production of a database of fire locations derived from high-resolution imagery, which can serve as a resource for future validation efforts as detection algorithms evolve and sensors change.

Validation of MODIS and AVHRR Fire Detections in Australia

The Sentinel Bushfire Monitoring System is an internet-based mapping tool which provides timely spatial information to fire agencies across Australia. The mapping system allows users to identify active fire locations that pose a potential risk to communities and property. Sentinel at Geoscience Australia currently provides hotspot information derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors on a continent-wide and daily basis enabling the fire community and general public to locate active fires. There has been little validation undertaken of the Sentinel since the system began operating in November 2002. Validation datasets have been collected for this work during the 2003-2007 fire seasons. Five study areas were selected to validate the detection capabilities of the MODIS and AVHRR hotspot product with fire activity that was mapped using high resolution earth observation imagery. The objective is to evaluate the reliability with which hotspots identified in MODIS and AVHRR thermal data can be used to identify fires. This consists of comparing the accuracy of AVHRR versus MODIS and quantifying the accuracy of both products. This objective is achieved by characterising errors through a stratified random sampling technique establishing a relationship between the ‘fire’ and ‘no fire’ condition, and error assessment using multi- source reference datasets over coincident MODIS and AVHRR pixels. The validation framework comprised two key approaches including validation of AVHRR hotspots in relation to MODIS hotspots and validation of both MODIS and AVHRR hotspots using multi-sensor earth observation imagery datasets. The study identified sources of errors associated with the Sentinel hotspots which could be used to improve the performance of hotspot algorithms and provide user-friendly information for the users. Statistical analysis revealed that overall commission errors of MODIS and AVHRR hotspots over the 5% sample data were 15% and 68% respectively, and overall omission errors of MODIS and AVHRR hotspots were 17% and 23% respectively. An important outcome of this study is the production of a database of fire locations derived from high-resolution imagery, which can serve as a resource for future validation efforts as detection algorithms evolve and sensors change.

Understanding agricultural fire dynamics in the southern Yucatán Peninsular Region using the MODIS (C6) active fire product

ABSTRACT Smallholder farmers commonly use fire for land clearing and agricultural maintenance in the Yucatán Peninsula of Mexico. The southern Yucatán Peninsular Region (SYPR) has experienced increasing fire frequency since the 1960s due to expanding smallholder agriculture and recurrent droughts. Beginning in January 2019, a government-subsidized programme named Sembrando Vida encouraged further smallholder agricultural development throughout Mexico, resulting in increased burning for cropland preparation. This study uses the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 1 km Active Fire product to characterize agricultural burning trends in the SYPR between 2010 and 2019, focusing on the impacts of the Sembrando Vida programme. Comparison of MODIS Active Fire locations to visible agricultural burns in coincident Landsat imagery showed a 29.5% (21/71) agreement. MODIS Active Fire time series data exhibited a biennial trend in fire frequency that was caused by interannual rainfall variability. An inverse relationship was found between total rainfall in the burning season and annual fire frequency. A combination of low rainfall and the implementation of Sembrando Vida in 2019 caused a 98% increase in state-authorized burn permitting compared to the 2015–2018 average, and MODIS Active Fire data exhibited a 33% increase in agricultural burns in 2019 compared to previous years that had >1000 fires.

The DLR FireBIRD Small Satellite Mission: Evaluation of Infrared Data for Wildfire Assessment

Wildfires significantly influence ecosystem patterns and processes on a global scale. In many cases, they pose a threat to human lives and property. Through greenhouse gas emissions, wildfires also directly contribute to climate change. The monitoring of such events and the analysis of acquired data is crucial for understanding wildfire and ecosystem interactions. The FireBIRD small satellite mission, operated by the German Aerospace Center (DLR), was specifically designed for the detection of wildfires. It features a higher spatial resolution than available with other Earth-observation systems. In addition to the detection of active fire locations, the system also allows the derivation of fire intensity by means of the Fire Radiative Power (FRP). This indicator can be used as a basis to derive the amount of emitted pollutant, which makes it valuable for climate studies. With the FireBIRD mission facing its end of life in 2021, this study retrospectively evaluates the performance of the system through an inter-comparison with data from two satellite missions of the National Aeronautics and Space Administration (NASA) and discusses the potential of such a system. The comparison is performed regarding both geometrical and radiometric aspects, the latter focusing on the FRP. This study uses and compares two different methods to derive the FRP from FireBIRD data. The data are analyzed regarding six major fire incidents in different regions of the world. The FireBIRD results are in accordance with the reference data, showing a geometrical overlapping rate of 83% and 84% regarding MODIS (Moderate-resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) overpasses in close temporal proximity. Furthermore, the results show a positive bias in FRP of about 11% compared to MODIS.

Characterizing Vegetation Fire dynamics in Myanmar and South Asian Countries

Fire is among the major disasters leading to biodiversity loss and climate change. Analysing high temporal resolution data over space and time can provide critical inputs for environmental management. The present study attempted to analyse fire occurrences to determine the distribution of vegetation fire hotspots in Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan, Sri Lanka, and Myanmar. The spatial statistics and space–time pattern mining tools are used for investigating fire trends that occurred across vegetated landscapes over a period of 16 years. This study found a total of 2,352,063 active vegetation fire locations using daily MODIS data from 2003 to 2018. Mann–Kendall test has identified the statistically significant trend of fire hotspots across forests, scrub, grasslands, agriculture, and plantations. The emerging hotspot analysis detected considerable spatial variation across fire hotspots and identified the geographical clusters of fire hotspots across the South Asian countries and Myanmar. The fires showed variations with an increasing or decreasing trend or no statistically significant trend in different land cover types across the eight countries. The emerging hotspot analysis found the dominance of sporadic hotspots and persistent hotspots.

Short-run environmental effects of COVID-19: Evidence from forest fires.

The outbreak of the 2019 novel coronavirus disease (COVID-19) has raised questions about changes in economic production and subsequent effects on the environment. This article employs satellite data on real-time active fire locations in Nepal to evaluate the short-term environmental effects of COVID-19. Using plausibly exogenous variation in the number of reported COVID-19 cases across the country, this study finds that the incidence of COVID-19 led to a strong negative effect on the incidence of human-induced forest fires. Results indicate that an additional reported case of COVID-19 resulted in a 4.54% decrease in the number of forest fire incidents and a 11.36% reduction in fire radiative power associated with these events. Findings also show that districts with smaller areas of community-managed forests per capita experienced a 8.11% decrease in the number of forest fire incidents. Restrictions on movement of people across districts in response to the pandemic likely reduced the incidence of forest fire events in Nepal. These short-run estimates of environmental benefits, which do not account for negative consequences of the virus outbreak on health and labor market outcomes, partially offset the social cost of pandemics in the developing world.

Identification and characterization of spatio-temporal hotspots of forest fires in South Asia.

Forest fire is considered as one of the major threats to global biodiversity and a significant source of greenhouse gas emissions. Rising temperatures, weather conditions, and topography promote the incidences of fire due to human ignition in South Asia. Because of its synoptic, multi-spectral, and multi-temporal nature, remote sensing data can be a state of art technology for forest fire management. This study focuses on the spatio-temporal patterns of forest fires and identifying hotspots using the novel geospatial technique "emerging hotspot analysis tool" in South Asia. Daily MODIS active fire locations data of 15years (2003-2017) has been aggregated in order to characterize fire frequency, fire density, and hotspots. A total of 522,348 active fire points have been used to analyze risk of fires across the forest types. Maximum number of forest fires in South Asia was occurring during the January to May. Spatial analysis identified areas of frequent burning and high fire density in South Asian countries. In South Asia, 51% of forest grid cells were affected by fires in 15years. Highest number of fire incidences was recorded in tropical moist deciduous forest and tropical dry deciduous forest. The emerging hotspots analysis indicates prevalence of sporadic hotspots, followed by historical hotspots, consecutive hotspots, and persistent hotspots in South Asia. Of the seven South Asian countries, Bangladesh has highest emerging hotspot area (34.2%) in forests, followed by 32.2% in India and 29.5% in Nepal. Study results offer critical insights in delineation of fire vulnerable forest landscapes which will stand as a valuable input for strengthening management of fires in South Asia.

MONITORING OF ACTIVE FIRE EVENTS DUE TO PADDY RESIDUE BURNING IN INDO-GANGETIC PLAINS USING THERMAL REMOTE SENSING

Abstract. In India, stubble burning is frequently practiced in the dominant rice-growing states of the Indo-Gangetic Plains, primarily in Punjab, Haryana and Uttar Pradesh. We attempted monitoring and mapping of the active fire events in real time for all the 3 states by acquiring thermal datasets from 3 different sensors i.e. Visible Infrared Imaging Radiometer Suite (VIIRS) at 375 m aboard Sumo-NPP, Moderate-Resolution Imaging Spectro-Radiometer (MODIS) at 1000 m aboard Terra & Aqua, and Advanced Very High Resolution Radiometer (AVHRR) at 1,100 m aboard NOAA 18/19 & MetOp 1/2, made available by IARI Satellite Ground Station during Kharif crop harvest season (October–November) 2018. The standard algorithm of detection of the temperature of a pixel at (4.0 µm and/or 10 to 12 µm) and its difference from the temperature of surrounding pixels was employed for day and night passes. Analysis of active fire locations detected from VIIRS, MODIS and AVHRR between 01st October and 30th November 2018 suggests continuing practice of stubble burning with a total of 75563 burning events distributed as 59695, 9232 and 6636 in Punjab, Haryana and Uttar Pradesh, respectively. District-wise monitoring showed a significant upsurge of fire events during the study period in the south-western and eastern districts of Punjab. Intense fire was witnessed in northern districts of Haryana and western districts of Uttar Pradesh. Comparative analysis shows that the burning events in the current year up till 30th November 2018 are 85% of the events detected in 2017 and about 59.10% of the events detected in 2016. It implies that the burning of rice stubble in the current year has reduced significantly over the past years due to various scheme implemented by the State and Central government.

Prototype Downscaling Algorithm for MODIS Satellite 1 km Daytime Active Fire Detections

This work presents development of an algorithm to reduce the spatial uncertainty of active fire locations within the 1 km MODerate resolution Imaging Spectroradiometer (MODIS Aqua and Terra) daytime detection footprint. The algorithm is developed using the finer 500 m reflective bands by leveraging on the increase in 2.13 μm shortwave infrared reflectance due to the burning components as compared to the non-burning neighborhood components. Active fire presence probability class for each of the 500 m pixels within the 1 km footprint is assigned by locally adaptive contextual tests against its surrounding neighborhood pixels. Accuracy is assessed using gas flares and wildfires in conjunction with available high-resolution imagery. Proof of concept results using MODIS observations over two sites show that under clear sky conditions, over 84% of the 500 m locations that had active fires were correctly assigned to high to medium probabilities, and correspondingly low to poor probabilities were assigned to locations with no visible flaming fronts. Factors limiting the algorithm performance include fire size/temperature distributions, cloud and smoke obscuration, sensor point spread functions, and geolocation errors. Despite these limitations, the resulting finer spatial scale of active fire detections will not only help first responders and managers to locate actively burning fire fronts more precisely but will also be useful for the fire science community.

Assessing uncertainty and demonstrating potential for estimating fire rate of spread at landscape scales based on time sequential airborne thermal infrared imaging

ABSTRACTAn important property of wildfire behaviour is rate of spread (ROS). The objectives of this study are to evaluate the uncertainty of landscape-scale ROS estimates derived from repetitive airborne thermal infrared (ATIR) georeferenced imagery and the utility of such estimates for understanding fire behaviour and controls on spread rates. Time-sequential ATIR image data were collected for the Cedar, Detwiler, and Rey Fires, which burned in California during summers of 2016 and 2017. We analyse error, uncertainty, and precision of ROS estimates associated with co-location accuracy, delineation of active fire front positions, and generation of fire spread vectors. The major sources of uncertainty influencing accuracy of ROS estimates are co-registration accuracy of sequential image pairs and procedures for delineating active fire front locations and spread vectors between them; none of these were found to be substantial. Median ROS estimates are 11 m min−1 for the Cedar Fire and 8 m min−1 for the Detwiler Fire, both of which burned through mixed shrub and tree areas of the Sierra Nevada foothills and were estimated for downslope spread events. Of the three study fires, the fastest spread rates (average spread of 25 m min−1 with maximum of 39 m min−1) are estimated for the Rey Fire, which burned on variable directional slopes through chaparral shrubland vegetation.

Global operational land imager Landsat-8 reflectance-based active fire detection algorithm

ABSTRACTA global operational land imager (GOLI) Landsat-8 daytime active fire detection algorithm is presented. It utilizes established contextual active fire detection approaches but takes advantage of the significant increase in fire reflectance in Landsat-8 band 7 (2.20 μm) relative to band 4 (0.66 μm). The detection thresholds are fixed and based on a statistical examination of 39 million non-burning Landsat-8 pixels. Multi-temporal tests based on band 7 reflectance and relative changes in normalized difference vegetation index in the previous six months are used to reduce commissions errors. The probabilities of active fire detection for the GOLI and two recent Landsat-8 active fire detection algorithms are simulated to provide insights into their performance with respect to the fire size and temperature. The algorithms are applied to 11 Landsat-8 images that encompass a range of burning conditions and environments. Commission and omission errors are assessed by visual interpretation of detected active fire locations and by examination of the Landsat-8 images and higher spatial resolution Google Earth imagery. The GOLI algorithm has lower omission and comparable commission errors than the recent Landsat-8 active fire detection algorithms. The GOLI algorithm has demonstrable potential for global application and is suitable for implementation with other Landsat-like reflective wavelength sensors.

Spatio-Temporal Characterization of Agriculture Residue Burning in Punjab and Haryana, India, Using MODIS and Suomi NPP VIIRS Data

Agriculture residue (in the form of stubble) burning is commonly practised in the northern states of India along the Indian part of the Indo-Gangetic Plains (IGP), predominantly in the states of Punjab and Haryana. In the present study, we characterize spatio-temporal patterns of stubble burning in Punjab and Haryana states of India, using active fire data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi National Polar-orbiting Partnership - Visible Infrared Imaging Radiometer Suite (Suomi NPP-VIIRS) for kharif cropping season (September- November) 2014. Analysis of active fire locations derived from MODIS and VIIRS during September-November 2014 suggest intense practice of stubble burning in the study area with total fire detections going up to 15,222 (MODIS) and 15,568 (VIIRS). Comparative analysis of MODIS and VIIRS active fire detections suggested that VIIRS is more sensitive with higher detection capability. Further, grid-based (5 × 5 km) analysis of fire patterns, viz. total fire detections, fire frequency and total fire intensity using temporal ( kharif season for 2004-2014) MODIS active fire locations suggest intense burning activity in the central and southern districts of Punjab during the study period. Temporal analysis of MODIS active fire locations (2004-2014) suggests a transition of decrease-to-increase in the total number of fire detections during October to November respectively, during 2004 through 2014.

Seasonal, interannual, and long-term variabilities in biomass burning activity over South Asia.

The seasonal, interannual, and long-term variations in biomass burning activity and related emissions are not well studied over South Asia. In this regard, active fire location retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), the retrievals of aerosol optical depth (AOD) from MODIS Terra, and tropospheric column NO2 from Ozone Monitoring Instrument (OMI) are used to understand the effects of biomass burning on the tropospheric pollution loadings over South Asia during 2003-2013. Biomass burning emission estimates from Global Fire Emission Database (GFED) and Global Fire Assimilation System (GFAS) are also used to quantify uncertainties and regional discrepancies in the emissions of carbon monoxide (CO), nitrogen oxide (NOx), and black carbon (BC) due to biomass burning in South Asia. In the Asian continent, the frequency of fire activity is highest over Southeast Asia, followed by South Asia and East Asia. The biomass burning activity in South Asia shows a distinct seasonal cycle that peaks during February-May with some differences among four (north, central, northeast, and south) regions in India. The annual biomass burning activity in north, central, and south regions shows an increasing tendency, particularly after 2008, while a decrease is seen in northeast region during 2003-2013. The increase in fire counts over the north and central regions contributes 24 % of the net enhancement in fire counts over South Asia. MODIS AOD and OMI tropospheric column NO2 retrievals are classified into high and low fire activity periods and show that biomass burning leads to significant enhancement in tropospheric pollution loading over both the cropland and forest regions. The enhancement is much higher (110-176 %) over the forest region compared to the cropland (34-62 %) region. Further efforts are required to understand the implications of biomass burning on the regional air quality and climate of South Asia.

VISIR-SAT – A PROSPECTIVE MICRO-SATELLITE BASED MULTI-SPECTRAL THERMAL MISSION FOR LAND APPLICATIONS

Abstract. Current space-borne thermal infrared satellite systems aimed at land surface remote sensing retain some significant deficiencies, in particular in terms of spatial resolution, spectral coverage, number of imaging bands and temperature-emissivity separation. The proposed VISible-to-thermal IR micro-SATellite (VISIR-SAT) mission addresses many of these limitations, providing multi-spectral imaging data with medium-to-high spatial resolution (80m GSD from 800 km altitude) in the thermal infrared (up to 6 TIR bands, between 8 and 11μm) and in the mid infrared (1 or 2 MIR bands, at 4μm). These MIR/TIR bands will be co-registered with simultaneously acquired high spatial resolution (less than 30 m GSP) visible and near infrared multi-spectral imaging data. To enhance the spatial resolution of the MIR/TIR multi-spectral imagery during daytime, data fusion methods will be applied, such as the Multi-sensor Multi-resolution Technique (MMT), already successfully tested over agricultural terrain. This image processing technique will make generation of Land Surface Temperature (LST) EO products with a spatial resolution of 30 x 30 m2 possible. For high temperature phenomena such as vegetation- and peat-fires, the Fire Disturbance Essential Climate Variables (ECV) “Active fire location” and “Fire Radiative Power” will be retrieved with less than 100 m spatial resolution. Together with the effective fire temperature and the spatial extent even for small fire events the innovative system characteristics of VISIR-SAT go beyond existing and planned IR missions. The comprehensive and physically high-accuracy products from VISIR-SAT (e.g. for fire monitoring) may synergistically complement the high temperature observations of Sentinel-3 SLSTR in a unique way. Additionally, VISIR-SAT offers a very agile sensor system, which will be able to conduct intelligent and flexible pointing of the sensor’s line-of-sight with the aim to provide global coverage of cloud free imagery every 5–10 days with only one satellite (using near real time cloud cover information). VISIRSAT may be flown in convoy with Sentinel-3 and/or Sentinel-2.

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.

Spatial modelling of wildland fire danger for risk analysis and conflict resolution in Malaysia: linking Fire Danger Rating Systems (FDRS) with Wildfire Threat Rating Systems (WTRS)

This study provides a comprehensive framework to mitigate or prevent forest and land (or wildland) fire disaster in Malaysia. This system supports emergency response and preparedness for wildland fire by means of integrated modelling, monitoring and mapping of fire danger. In this framework, multi-sensor applications for monitoring fire danger and fire activity are linked with decision-aid models in a Geographic Information System (GIS) environment to generate information required for wildland fire management. Using a customized version of the spatial Fire Management System software, components of the Malaysia Fire Danger Rating System (FDRS) are calculated to provide fire weather, fire behaviour and wildfire threat information. Wildfire threat ratings (WTRs) are assessed on the basis of fire occurrence risk, potential fire behaviour, suppression capability and values at risk. Outputs from the Malaysia FDRS were integrated with hotspots extracted from remote sensing data to generate combined maps of active fire locations, fire danger, potential fire behaviour and uncontrolled wildland fire (or wildfire) threat. In case of wildfire, remotely sensed data were also used to generate wildfire affected area and emergency response maps for emergency management. The system architecture and application models for wildfire analysis, which aid decision-making components for wildfire mitigation and relief, are described. These include early warning of fire, risk analysis, damage assessment and emergency response analysis. This article provides the first documentation of a national, operational system linking fire danger rating with socio-economic values, as defined by the WTR models, to guide fire and rescue decision-making during wildfire events. It is finally shown how the proposed system can reduce the risk offire management disputes in Malaysia by directing the conflict to a more favourable resolution.