Ten years of global burned area products from spaceborne remote sensing - A review: Analysis of user needs and recommendations for future developments

Abstract Early global estimates of carbon emissions from biomass burning were based on empirical assumptions of fire return interval in different biomes in the 1980s. Since then, significant improvements of spaceborne remote sensing sensors have resulted in an increasing number of derived products characterizing the detection of active fire or the subsequent burned area (GFED, MODIS MCD45A1, L3JRC, Globcarbon, GBS, GLOBSCAR, GBA2000). When coupled with global land cover and vegetation models allowing for spatially explicit fuel biomass estimates, the use of these products helps to yield important information about the spatial and the temporal variability of emission estimates. The availability of multi-year products (>10 years) leads to a better understanding of uncertainties in addition to increasing accuracy. We surveyed a wide range of users of global fire data products whilst also undertaking a review of the latest scientific literature. Two user groups were identified, the first being global climate and vegetation modellers and the second being regional land managers. Based on this review, we present here the current needs covering the range of end-users. We identified the increasing use of BA products since the year 2000 with an increasing use of MODIS as a reference dataset. Scientific topics using these BA products have increased in diversity and area of application, from global fire emissions (for which BA products were initially developed) to regional studies with increasing use for ecosystem management planning. There is a significant need from the atmospheric science community for low spatial resolution (gridded, 1/2 degree cell) and long time series data characterized with supplementary information concerning the accuracy in timing of the fire and reductions of omission/commission errors. There is also a strong need for precisely characterizing the perimeter and contour of the fire scar for better assimilation with land cover maps and fire intensity. Computer and earth observation facilities remain a significant gap between ideal accuracies and the realistic ones, which must be fully quantified and comprehensive for an actual use in global fire emissions or regional land management studies.

[1]  Anuradha Eaturu,et al.  Biophysical and anthropogenic controls of forest fires in the Deccan Plateau, India. , 2008, Journal of environmental management.

[2]  Ajith Kaduwela,et al.  Pollution influences on atmospheric composition and chemistry at high northern latitudes: Boreal and California forest fire emissions , 2010 .

[3]  R. Kahn,et al.  An investigation of methods for injecting emissions from boreal wildfires using WRF-Chem during ARCTAS , 2010 .

[4]  A. Stohl,et al.  Tracing biomass burning aerosol from South America to Troll Research Station, Antarctica , 2009 .

[5]  Yu Song,et al.  A new emission inventory for nonagricultural open fires in Asia from 2000 to 2009 , 2010 .

[6]  H. Eva,et al.  Burnt area mapping in Central Africa using ATSR data , 1998 .

[7]  R. Colditz,et al.  Operational active fire mapping and burnt area identification applicable to Mexican Nature Protection Areas using MODIS and NOAA-AVHRR direct readout data , 2009 .

[8]  D. Roy,et al.  A focus group study of factors that promote and constrain the use of satellite-derived fire products by resource managers in southern Africa. , 2007, Journal of environmental management.

[9]  J. Qu,et al.  Forest fire detection using the normalized multi-band drought index (NMDI) with satellite measurements , 2008 .

[10]  Heiko Balzter,et al.  Forest fire scar detection in the boreal forest with multitemporal SPOT-VEGETATION data , 2003, IEEE Trans. Geosci. Remote. Sens..

[11]  Sandy P. Harrison,et al.  The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model , 2010 .

[12]  R. Trigo,et al.  Global fire activity patterns (1996–2006) and climatic influence: an analysis using the World Fire Atlas , 2007 .

[13]  Josef Cihlar,et al.  Annual carbon balance of Canada's forests during 1895–1996 , 2000 .

[14]  R. DeFries,et al.  Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data , 2011 .

[15]  D. Roy,et al.  Strategies for the fusion of satellite fire radiative power with burned area data for fire radiative energy derivation , 2009 .

[16]  C. Field,et al.  Fire history and the global carbon budget: a 1°× 1° fire history reconstruction for the 20th century , 2005 .

[17]  E. A. Loupian,et al.  Multi‐year circumpolar assessment of the area burnt in boreal ecosystems using SPOT‐VEGETATION , 2007 .

[18]  C. Justice,et al.  Global characterization of fire activity: toward defining fire regimes from Earth observation data , 2008 .

[19]  Hisashi Sato,et al.  SEIB–DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individual-based approach , 2007 .

[20]  T. Loboda,et al.  Reconstruction of fire spread within wildland fire events in Northern Eurasia from the MODIS active fire product , 2004 .

[21]  Mariana Vertenstein,et al.  The Community Land Model's Dynamic Global Vegetation Model (CLM-DGVM): Technical description and user's guide , 2004 .

[22]  J. Randerson,et al.  Terrestrial ecosystem production: A process model based on global satellite and surface data , 1993 .

[23]  H. D. Klerk.  A pragmatic assessment of the usefulness of the MODIS (Terra and Aqua) 1-km active fire (MOD14A2 and MYD14A2) products for mapping fires in the fynbos biome , 2008 .

[24]  J. Randerson,et al.  Continental-Scale Partitioning of Fire Emissions During the 1997 to 2001 El Niño/La Niña Period , 2003, Science.

[25]  김종환,et al.  aerosol optical properties , 2008 .

[26]  Xiaodong Zeng,et al.  Evaluating the dependence of vegetation on climate in an improved dynamic global vegetation model , 2010 .

[27]  Andrew C. Millington,et al.  Deforestation dynamics and policy changes in Bolivia's post-neoliberal era , 2011 .

[28]  Lorraine A. Remer,et al.  Reversal of trend of biomass burning in the Amazon , 2007 .

[29]  Wei Min Hao,et al.  Spatial and temporal distribution of tropical biomass burning , 1994 .

[30]  José M. C. Pereira,et al.  The use of SPOT VEGETATION data in a classification tree approach for burnt area mapping in Australian savanna , 2003 .

[31]  Atul K. Jain.  Global estimation of CO emissions using three sets of satellite data for burned area , 2007 .

[32]  Li Jing,et al.  Forest fire risk assessment combining remote sensing and meteorological information , 2007 .

[33]  J. Kendall,et al.  A multi-year active fire dataset for the tropics derived from the TRMM VIRS , 2003 .

[34]  Heiko Balzter,et al.  The influence of regional surface soil moisture anomalies on forest fires in Siberia observed from satellites , 2009 .

[35]  P. V. Velthoven,et al.  Updated African biomass burning emission inventories in the framework of the AMMA-IDAF program, with an evaluation of combustion aerosols , 2010 .

[36]  D. Roy,et al.  Defining a fire year for reporting and analysis of global interannual fire variability , 2008 .

[37]  C. Field,et al.  The Not-So-Big U.S. Carbon Sink , 1999, Science.

[38]  C. Justice,et al.  The spatial and temporal distribution of crop residue burning in the contiguous United States. , 2009, The Science of the total environment.

[39]  D. Roy,et al.  The Southern Africa Fire Network (SAFNet) regional burned‐area product‐validation protocol , 2005 .

[40]  Emilio Chuvieco,et al.  Remote Sensing of Forest Fires , 2000 .

[41]  F. Achard,et al.  The effect of climate anomalies and human ignition factor on wildfires in Russian boreal forests , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[42]  Michael T. Coe,et al.  Testing the performance of a dynamic global ecosystem model: Water balance, carbon balance, and vegetation structure , 2000 .

[43]  Shihyan Lee,et al.  A review of global satellite-derived snow products , 2012 .

[44]  J. Randerson,et al.  Forecasting Fire Season Severity in South America Using Sea Surface Temperature Anomalies , 2011, Science.

[45]  Charles B. Yackulic,et al.  Expansion of sugarcane production in São Paulo, Brazil: Implications for fire occurrence and respiratory health , 2009 .

[46]  Vivek K. Arora,et al.  Simulating energy and carbon fluxes over winter wheat using coupled land surface and terrestrial ecosystem models , 2003 .

[47]  T. Loboda.  Modeling fire danger in data-poor regions: a case study from the Russian Far East. , 2009 .

[48]  Ruiliang Pu,et al.  A dynamic algorithm for wildfire mapping with NOAA/AVHRR data , 2004 .

[49]  Robert H. Fraser,et al.  Validation and Calibration of Canada-Wide Coarse-Resolution Satellite Burned-Area Maps , 2004 .

[50]  R. B. Smith,et al.  Appraisal of fire damage and inventory for timber salvage by remote sensing in mountain ash forests in Victoria , 1985 .

[51]  Johannes W. Kaiser,et al.  Global Real‐time Fire Emission Estimates Based on Space‐borne Fire Radiative Power Observations , 2009 .

[52]  R. Dickinson,et al.  The Common Land Model , 2003 .

[53]  Benjamin Smith,et al.  Estimating carbon emissions from African wildfires , 2008 .

[54]  Ruiliang Pu,et al.  Development and analysis of a 12-year daily 1-km forest fire dataset across North America from NOAA/AVHRR data , 2007 .

[55]  F. I. Woodward,et al.  The importance of low atmospheric CO2 and fire in promoting the spread of grasslands and savannas , 2003 .

[56]  Will Steffen,et al.  Establishing A Earth Observation Product Service For The Terrestrial Carbon Community: The Globcarbon Initiative , 2006 .

[57]  Carlos C. DaCamara,et al.  Dating Fire Events on End of Season Maps of Burnt Scars , 2008 .

[58]  H. Balzter,et al.  Retrospective mapping of burnt areas in Central Siberia using a modification of the normalised difference water index , 2006 .

[59]  E. Chuvieco,et al.  Mapping and inventory of forest fires from digital processing of tm data , 1988 .

[60]  L. Giglio,et al.  Mapping burned area in Alaska using MODIS data: a data limitations-driven modification to the regional burned area algorithm , 2011 .

[61]  Griša Močnik,et al.  Characterization of pollution events in the East Baltic region affected by regional biomass fire emissions , 2010 .

[62]  S. Pacala,et al.  A METHOD FOR SCALING VEGETATION DYNAMICS: THE ECOSYSTEM DEMOGRAPHY MODEL (ED) , 2001 .

[63]  Martin Herold,et al.  Some challenges in global land cover mapping : An assessment of agreement and accuracy in existing 1 km datasets , 2008 .

[64]  Christopher B. Field,et al.  The Not-So-Big U.S. Carbon Sink , 1999, Science.

[65]  D. Riaño,et al.  Quantifying burned area for North American forests: Implications for direct reduction of carbon stocks , 2011 .

[66]  Olivier Boucher,et al.  Improving the seasonal cycle and interannual variations of biomass burning aerosol sources , 2003 .

[67]  Yoram J. Kaufman,et al.  An Enhanced Contextual Fire Detection Algorithm for MODIS , 2003 .

[68]  Tapada da Ajuda,et al.  Analysis of the relationship between spatial pattern and spectral detectability of areas burned in southern Africa using satellite data , 2007 .

[69]  R. Martin,et al.  Interannual and seasonal variability of biomass burning emissions constrained by satellite observations , 2003 .

[70]  J. Morisette,et al.  Validation analyses of an operational fire monitoring product: The Hazard Mapping System , 2008 .

[71]  C. Justice,et al.  Using MODIS to evaluate heterogeneity of biomass burning in southern African savannahs: a case study in Etosha , 2005 .

[72]  J. Pereira.  Remote sensing of burned areas in tropical savannas , 2003 .

[73]  Daniel A. Jaffe,et al.  Causes of high O3 in the lower free troposphere over the Pacific Northwest as observed at the Mt. Bachelor Observatory , 2011 .

[74]  E. Kasischke,et al.  AVHRR-based mapping of fires in Russia: New products for fire management and carbon cycle studies , 2004 .

[75]  E. Hyer,et al.  Baseline uncertainties in biomass burning emission models resulting from spatial error in satellite active fire location data , 2009 .

[76]  D. Roy,et al.  Global assessment of the temporal reporting accuracy and precision of the MODIS burned area product , 2010 .

[77]  A. K. Milne,et al.  The use of remote sensing in mapping and monitoring vegetational change associated with bushfire events in Eastern Australia , 1986 .

[78]  Xiangao Xia,et al.  Aerosol optical properties from the Atmospheric Radiation Measurement Mobile Facility at Shouxian, China , 2010 .

[79]  Isabelle Pignatel,et al.  Lessons to be learned: French top accounting journals' contribution to climate change , 2010 .

[80]  Stephen Sitch,et al.  Simulating fire regimes in human‐dominated ecosystems: Iberian Peninsula case study , 2002 .

[81]  F. Woodward,et al.  Vegetation dynamics – simulating responses to climatic change , 2004, Biological reviews of the Cambridge Philosophical Society.

[82]  Sundar A. Christopher,et al.  Global Monitoring and Forecasting of Biomass-Burning Smoke: Description of and Lessons From the Fire Locating and Modeling of Burning Emissions (FLAMBE) Program , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[83]  E. Lambin,et al.  A Multilevel Analysis of the Impact of Land Use on Interannual Land-Cover Change in East Africa , 2007, Ecosystems.

[84]  J. Randerson,et al.  Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009) , 2010 .

[85]  Wamburu W. Kihato,et al.  AN ANALYSIS OF THE RELATIONSHIP BETWEEN , 2013 .

[86]  J. D. Laat,et al.  An aerosol boomerang: Rapid around-the-world transport of smoke from the December 2006 Australian forest fires observed from space , 2009 .

[87]  Pierpaolo Duce,et al.  Evaluation of FARSITE simulator in Mediterranean maquis , 2007 .

[88]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[89]  Paulo M. Barbosa,et al.  Remote sensing of forest fires in Southern Europe using IRS-WiFS and MODIS data , 2002, Remote Sensing.

[90]  Wolfgang Knorr,et al.  Determinants and predictability of global wildfire emissions , 2012 .

[91]  H. Eva,et al.  Remote Sensing of Biomass Burning in Tropical Regions: Sampling Issues and Multisensor Approach , 1998 .

[92]  D. Chang,et al.  Estimates of biomass burning emissions in tropical Asia based on satellite-derived data , 2009 .

[93]  Yimin Ji,et al.  An overview of the TRMM/TSDIS fire algorithm and product , 2002 .

[94]  C. O. Justicea,et al.  The MODIS fire products , 2002 .

[95]  D. Roy,et al.  Modeling and sensitivity analysis of fire emissions in southern Africa during SAFARI 2000 , 2004 .

[96]  Yu Song,et al.  Comparison of L3JRC and MODIS global burned area products from 2000 to 2007 , 2009 .

[97]  Olivier Arino,et al.  Assessment of the status of the development of standards for the Terrestrial Essential Climate Variables , 2008 .

[98]  Mika Siljander,et al.  Predictive fire occurrence modelling to improve burned area estimation at a regional scale: A case study in East Caprivi, Namibia , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[99]  D. Morton,et al.  Modeling fire-driven deforestation potential in Amazonia under current and projected climate conditions , 2010 .

[100]  O. Arino,et al.  Verification of the consistency of POLDER Aerosol Index over land with ATSR‐2/ERS‐2 fire product , 2000 .

[101]  N. Pankratova,et al.  Estimates of carbon monoxide emissions from wildfires in northern Eurasia for airquality assessment and climate modeling , 2010 .

[102]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[103]  Vivek K. Arora,et al.  Fire as an interactive component of dynamic vegetation models , 2005 .

[104]  F. Moreira,et al.  Landscape--wildfire interactions in southern Europe: implications for landscape management. , 2011, Journal of environmental management.

[105]  D. Roy,et al.  Burned area mapping using multi-temporal moderate spatial resolution data—a bi-directional reflectance model-based expectation approach , 2002 .

[106]  B. Fischer.  Lessons Learned. , 2016, Schizophrenia bulletin.

[107]  Wisdom M. Dlamini,et al.  A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland , 2010, Environ. Model. Softw..

[108]  P. Crutzen,et al.  Biomass Burning in the Tropics: Impact on Atmospheric Chemistry and Biogeochemical Cycles , 1990, Science.

[109]  J. Lamarque,et al.  Emissions of gases and particles from biomass burning during the 20th century using satellite data and an historical reconstruction , 2009 .

[110]  J. Randerson,et al.  Daily and 3‐hourly variability in global fire emissions and consequences for atmospheric model predictions of carbon monoxide , 2011 .

[111]  J. Randerson,et al.  Climate controls on the variability of fires in the tropics and subtropics , 2008 .

[112]  T. Chase,et al.  Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0) , 2007 .

[113]  D. Roy,et al.  The MODIS fire products , 2002 .

[114]  Mian Chin,et al.  Indonesian wildfires of 1997: Impact on tropospheric chemistry , 2003 .

[115]  J. Pereira,et al.  Global wildland fire emissions from 1960 to 2000 , 2008 .

[116]  Trissevgeni Stavrakou,et al.  Global emissions of non-methane hydrocarbons deduced from SCIAMACHY formaldehyde columns through 2003-2006 , 2009 .

[117]  Keiji Kushida,et al.  Detection of Active Wildland Fires Using Multitemporal MODIS Images , 2010, IEEE Geoscience and Remote Sensing Letters.

[118]  J. Randerson,et al.  Assessing variability and long-term trends in burned area by merging multiple satellite fire products , 2009 .

[119]  Ruth S. DeFries,et al.  Burned-Area Mapping of the Serengeti–Mara Region Using MODIS Reflectance Data , 2007, IEEE Geoscience and Remote Sensing Letters.

[120]  E. Chuvieco,et al.  Generation of long time series of burn area maps of the boreal forest from NOAA–AVHRR composite data , 2008 .

[121]  V. Lehsten,et al.  Modelling burned area in Africa , 2010 .

[122]  Lars Bromley,et al.  Relating violence to MODIS fire detections in Darfur, Sudan , 2010 .

[123]  J. Penner,et al.  Global estimates of biomass burning emissions based on satellite imagery for the year 2000 , 2004 .

[124]  Jean-Louis Devineau,et al.  Savanna fire regimes assessment with MODIS fire data: Their relationship to land cover and plant species distribution in western Burkina Faso (West Africa) , 2010 .

[125]  S. Panigrahy,et al.  Characterisation of Residue Burning from Agricultural System in India using Space Based Observations , 2011 .

[126]  Gareth Roberts,et al.  Fire Detection and Fire Characterization Over Africa Using Meteosat SEVIRI , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[127]  A. Ito,et al.  Seasonal and interannual variations in CO and BC emissions from open biomass burning in Southern Africa during 1998–2005 , 2007 .

[128]  S. Running,et al.  Generalization of a forest ecosystem process model for other biomes, Biome-BGC, and an application for global-scale models. Scaling processes between leaf and landscape levels , 1993 .

[129]  D. E. Harrison,et al.  Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (2010 Update) , 2010 .

[130]  D. Roberts,et al.  Using multiple endmember spectral mixture analysis to retrieve subpixel fire properties from MODIS , 2008 .

[131]  Ruth S. DeFries,et al.  The improved Global Fire Emissions Database (GFED) version 3: contribution of savanna, forest, deforestation, and peat fires to the global fire emissions budget , 2010 .

[132]  I. D. Smedt,et al.  Evaluating the performance of pyrogenic and biogenic emission inventories against one decade of space-based formaldehyde columns , 2008 .

[133]  J. Pereira,et al.  Vegetation burning in the year 2000: Global burned area estimates from SPOT VEGETATION data , 2004 .

[134]  S. K. Akagi,et al.  Emission factors for open and domestic biomass burning for use in atmospheric models , 2010 .

[135]  J. Randerson,et al.  Global net primary production: Combining ecology and remote sensing , 1995 .

[136]  M. Chin,et al.  Comparison of global inventories of CO emissions from biomass burning derived from remotely sensed data , 2010 .

[137]  H. In,et al.  Estimation of the aerosol optical thickness distribution in the Northeast Asian forest fire episode in May 2003: Possible missing emissions , 2010 .

[138]  M. Heimann,et al.  Assessment of the regional atmospheric impact of wildfire emissions based on CO observations at the ZOTTO tall tower station in central Siberia , 2011 .

[139]  William J. Conover,et al.  Modeling and sensitivity analysis , 1998 .

[140]  D. Roy,et al.  The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product , 2008 .

[141]  K. Tansey,et al.  Relationship between MODIS fire hot spot count and burned area in a degraded tropical peat swamp forest in Central Kalimantan, Indonesia , 2008 .

[142]  Javier Gallego,et al.  A sampling method for the retrospective validation of global burned area products , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[143]  Benjamin Smith,et al.  Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space , 2008 .

[144]  D. Roy.  Multi-temporal active-fire based burn scar detection algorithm , 1999 .

[145]  S. Wright,et al.  Poverty and corruption compromise tropical forest reserves. , 2007, Ecological applications : a publication of the Ecological Society of America.

[146]  Soo Chin Liew,et al.  Influence of peatland and land cover distribution on fire regimes in insular Southeast Asia , 2011 .

[147]  P. Crutzen,et al.  Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning , 1980 .

[148]  Emily Harwell,et al.  Remote Sensibilities: Discourses of Technology and the Making of Indonesia’s Natural Disaster , 2000 .

[149]  R. Beer,et al.  Tropospheric Emission Spectrometer (TES) , 2003 .

[150]  G. Reuter,et al.  An approach to operational forest fire growth predictions for Canada , 2009 .

[151]  D. Fuller,et al.  The Enso-Fire Dynamic in Insular Southeast Asia , 2004 .

[152]  C. J. Tucker,et al.  Remote sensing of fires with the TRMM VIRS , 2000 .

[153]  Achim Röder,et al.  Modelling the effects of landscape fuel treatments on fire growth and behaviour in a Mediterranean landscape (eastern Spain) , 2007 .

[154]  J. Randerson,et al.  Carbon emissions from fires in tropical and subtropical ecosystems , 2003 .

[155]  M. Schultz,et al.  Atmospheric Chemistry and Physics , 2001 .

[156]  J. Grégoire,et al.  Lessons to be learned from the comparison of three satellite‐derived biomass burning products , 2004 .

[157]  Jennifer L. Dungan,et al.  Diagnosing and assessing uncertainties of terrestrial ecosystem models in a multimodel ensemble experiment: 2. Carbon balance , 2011 .

[158]  E. Kasischke,et al.  Locating and estimating the areal extent of wildfires in alaskan boreal forests using multiple-season AVHRR NDVI composite data , 1995 .

[159]  J. Randerson,et al.  Interannual variability in global biomass burning emissions from 1997 to 2004 , 2006 .

[160]  Susan I. Stewart,et al.  Detection rates of the MODIS active fire product in the United States , 2008 .

[161]  A. Robbins,et al.  Forest Fires in the Insular Caribbean , 2008, Ambio.

[162]  José M. C. Pereira,et al.  A Rule-Based System for Burned Area Mapping in Temperate and Tropical Regions Using NOAA/AVHRR Imagery , 2000 .

[163]  S. Plummer,et al.  Burnt area detection at global scale using ATSR‐2: The GLOBSCAR products and their qualification , 2004 .

[164]  J. Randerson,et al.  Estimates of fire emissions from an active deforestation region in the southern Amazon based on satellite data and biogeochemical modelling , 2008 .

[165]  Milap Punia,et al.  Identifying biomass burned patches of agriculture residue using satellite remote sensing data , 2008 .

[166]  Ignazio Gallo,et al.  Mapping burned surfaces in Sub-Saharan Africa based on multi-temporal neural classification , 2003 .

[167]  Peter M. Cox,et al.  Description of the "TRIFFID" Dynamic Global Vegetation Model , 2001 .

[168]  Christopher B. Field,et al.  Global carbon emissions from biomass burning in the 20th century , 2006 .

[169]  C. Potter,et al.  Regional application of an ecosystem production model for studies of biogeochemistry in Brazilian Amazonia , 1998 .

[170]  G. Peng.  METHOD TO IDENTIFY FOREST FIRE BASED ONSMOKE PLUMES MASK BY USING MODIS DATA: METHOD TO IDENTIFY FOREST FIRE BASED ONSMOKE PLUMES MASK BY USING MODIS DATA , 2008 .

[171]  D. Roy,et al.  Modeling and sensitivity analysis of fire emissions in southern Africa during SAFARI 2000 , 2004 .

[172]  E. Vermote,et al.  Estimating biomass consumed from fire using MODIS FRE , 2009 .

[173]  David P. Roy,et al.  Southern Africa Validation of the MODIS, L3JRC, and GlobCarbon Burned-Area Products , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[174]  Jose M. Cardoso Pereira,et al.  An assessment of vegetation fire in Africa (1981–1991): Burned areas, burned biomass, and atmospheric emissions , 1999 .

[175]  F. Siegert,et al.  Spatiotemporal fire occurrence in Borneo over a period of 10 years , 2009 .

[176]  A. Belward,et al.  Characterizing interannual variations in global fire calendar using data from Earth observing satellites , 2005 .

[177]  F. Gonzalez-Alonso,et al.  Modis reflectance and active fire data for burn mapping and assessment at regional level , 2010 .

[178]  D. Roy,et al.  Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data , 2005 .

[179]  T. Loboda,et al.  Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data , 2007 .

[180]  X. Pons,et al.  REMOTE SENSING OF FOREST FIRES , 2001 .

[181]  P. Crutzen,et al.  Biomass burning as a source of atmospheric gases CO, H2, N2O, NO, CH3Cl and COS , 1979, Nature.

[182]  G. Perry,et al.  Monthly burned area and forest fire carbon emission estimates for the Russian Federation from SPOT VGT , 2003 .

[183]  Atul K. Jain,et al.  Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years , 2012, Frontiers of Earth Science.

[184]  F. Siegert,et al.  Burnt area estimation for the year 2005 in Borneo using multi-resolution satellite imagery , 2007 .

[185]  S. Ly,et al.  What limits fire? An examination of drivers of burnt area in Southern Africa , 2008 .

[186]  Thomas M. Smith,et al.  A global land primary productivity and phytogeography model , 1995 .

[187]  Li Jing,et al.  Method to identify forest fire based on smoke plumes mask by using MODIS data , 2008 .

[188]  N. Coops,et al.  Predicting satellite-derived patterns of large-scale disturbances in forests of the Pacific Northwest Region in response to recent climatic variation , 2011 .

[189]  Onisimo Mutanga,et al.  The utility of satellite fire product accuracy Information-perspectives and recommendations from the Southern Africa fire network , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[190]  Anthony C. Janetos,et al.  Using Remote Sensing to Assess Russian Forest Fire Carbon Emissions , 2002 .

[191]  Ali Mansourian,et al.  Spatial and Temporal Analysis of Fires Detected by MODIS Data in Northern Iran From 2001 to 2008 , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[192]  M. Andreae,et al.  Emission of trace gases and aerosols from biomass burning , 2001 .

[193]  Xiaoyang Zhang,et al.  Near real time monitoring of biomass burning particulate emissions (PM2.5) across contiguous United States using multiple satellite instruments , 2008 .

[194]  S. Running,et al.  8 – Generalization of a Forest Ecosystem Process Model for Other Biomes, BIOME-BGC, and an Application for Global-Scale Models , 1993 .

[195]  T. Loboda,et al.  Assessing the risk of ignition in the Russian far east within a modeling framework of fire threat. , 2007, Ecological applications : a publication of the Ecological Society of America.

[196]  Peter V. Hobbs,et al.  Airborne Studies of Particles and Gases from Forest Fires , 1978 .

[197]  E. Chuvieco,et al.  Biomass Burning Emissions: A Review of Models Using Remote-Sensing Data , 2005, Environmental monitoring and assessment.

[198]  S. Sitch,et al.  The role of fire disturbance for global vegetation dynamics: coupling fire into a Dynamic Global Vegetation Model , 2008 .

[199]  A. Fernández,et al.  Automatic mapping of surfaces affected by forest fires in Spain using AVHRR NDVI composite image data , 1997 .

[200]  E. Chuvieco,et al.  Development of a framework for fire risk assessment using remote sensing and geographic information system technologies , 2010 .

[201]  P. G. Marchetti,et al.  Multisource Data Integration for Fire Risk Management: The Local Test of a Global Approach , 2010, IEEE Geoscience and Remote Sensing Letters.

[202]  A. Vivchar,et al.  Wildfires in Russia in 2000–2008: estimates of burnt areas using the satellite MODIS MCD45 data , 2011 .

[203]  Karl Rihaczek,et al.  1. WHAT IS DATA MINING? , 2019, Data Mining for the Social Sciences.

[204]  J. Randerson,et al.  The use of ATSR active fire counts for estimating relative patterns of biomass burning – a study from the boreal forest region , 2003 .

[205]  M. Disney,et al.  Terrestrial ecosystems from space: a review of earth observation products for macroecology applications , 2012 .

[206]  Diane K. Davies,et al.  Fire Information for Resource Management System: Archiving and Distributing MODIS Active Fire Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[207]  Israel Gómez,et al.  Prototyping an artificial neural network for burned area mapping on a regional scale in Mediterranean areas using MODIS images , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[208]  S. Nilsson,et al.  A spatial comparison of four satellite derived 1 km global land cover datasets , 2006 .

[209]  Ian McCallum,et al.  An Update on the globcarbon initiative : multi-sensor estimation of global biophysical products for global terrestrial carbon studies , 2007 .

[210]  Jon E. Keeley,et al.  PLANT FUNCTIONAL TRAITS IN RELATION TO FIRE IN CROWN-FIRE ECOSYSTEMS , 2004 .

[211]  E. Dwyer,et al.  Global spatial and temporal distribution of vegetation fire as determined from satellite observations , 2000 .

[212]  J. Randerson,et al.  Technical Description of version 4.0 of the Community Land Model (CLM) , 2010 .

[213]  Florian Siegert,et al.  Satellite‐derived 2003 wildfires in southern Siberia and their potential influence on carbon sequestration , 2009 .

[214]  Ernest Hilsenrath,et al.  Integrated Global Observation Strategy - Ozone and Atmospheric Chemistry Project , 2000 .

[215]  Robert H. Fraser,et al.  SPOT VEGETATION for characterizing boreal forest fires , 2000 .

[216]  J. Grégoire,et al.  A new, global, multi‐annual (2000–2007) burnt area product at 1 km resolution , 2008 .

[217]  O. Boucher,et al.  Emissions from open biomass burning in India: Integrating the inventory approach with high‐resolution Moderate Resolution Imaging Spectroradiometer (MODIS) active‐fire and land cover data , 2006 .

[218]  F. Ratle,et al.  Detection of clusters using space–time scan statistics , 2009 .

[219]  M. Moritz,et al.  Global Pyrogeography: the Current and Future Distribution of Wildfire , 2009, PloS one.

[220]  Yu Song,et al.  Spatiotemporal variation in nonagricultural open fire emissions in China from 2000 to 2007 , 2009 .

[221]  Olivier Arino,et al.  ESA activities related to forest fires: ATSR World Fire Atlas (WFA), GlobCarbon, and RISK-EOS , 2007, SPIE Remote Sensing.

[222]  J. Randerson,et al.  Global estimation of burned area using MODIS active fire observations , 2005 .

[223]  Robert E. Keane,et al.  A classification of landscape fire succession models: spatial simulations of fire and vegetation dynamics , 2004 .

[224]  I. C. Prentice,et al.  Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .

[225]  D. Roy,et al.  An active-fire based burned area mapping algorithm for the MODIS sensor , 2009 .

[226]  Syahrir Syahrir,et al.  THE ANALYSIS OF RELATIONSHIP BETWEEN , 2016 .