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

International Journal of Applied Earth Observation and Geoinformation

Volumn 26, Issue 1, 2014, Pages 64-79

  Mouillot, Florent ^a   Schultz, Martin G ^b   Yue, Chao ^c   Cadule, Patricia ^c   Tansey, Kevin ^d   Ciais, Philippe ^c   Chuvieco, Emilio ^e  

^a CENTRE D ECOLOGIE FONCTIONNELLE ET EVOLUTIVE   (France)

^b FORSCHUNGSZENTRUM JÜLICH   (Germany)

^c DIF   (France)

^d UNIVERSITY OF LEICESTER   (United Kingdom)

^e UNIVERSITY OF ALCALÁ   (Spain)

Author keywords

Global burned area; Remote sensing; Review

Indexed keywords

BIOMASS BURNING; BIOME; CARBON EMISSION; CLIMATE MODELING; ECOSYSTEM MANAGEMENT; LAND MANAGEMENT; MODIS; REMOTE SENSING; SENSOR; SPATIAL RESOLUTION; VEGETATION COVER;

EID: 84897586157     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2013.05.014     Document Type: Review

References (150)

1. Akagi, S.K., Yokelson, R.J., Wiedinmyer, C., Alvarado, M.J., Reid, J.S., Karl, T., Crounse,J.D., Wennberg, P.O., 2011. Emission factors for open and domestic biomassburning for use in atmospheric models. Atmospheric Chemistry and Physics 11,4039-4072.
2. Anderson, K.R., Englefield, P., Little, J.M., Reuter, G., 2009. An approach to operationalforest fire growth predictions for Canada. International Journal of Wildland Fire18 (8), 893-905.
3. Andreae, M.O., Merlet, P., 2001. Emission of trace gases and aerosols from biomassburning. Global Biogeochemical Cycles 15, 955-966.
4. Arca, B., 2007. Evaluation of FARSITE simulator in Mesditeranean Maquis. Interna-tional Journal of Wildland Fire 16 (5), 563-572.
5. Archibald, S., Roy, D.P., van Wilgen, B.W., Scholes, R.J., 2009. What limits fire? Anexamination of drivers of burnt area in Southern Africa. Global Change Biology15 (3), 613-630.
6. Ardakani, A.S., Zoej, M.J.V., Mohammadzadeh, A., Mansourian, A., 2011. Spatial andtemporal analysis of fires detected by MODIS data in Northern Iran from 2001 to2008. IEEE Journal of Selected Topics in Applied Earth Observations and RemoteSensing 4 (1), 216-225.
7. Arora, V.K., 2003. Simulating energy and carbon fluxes over winter wheat usingcoupled land surface and terrestrial ecosystem models. Agricultural and ForestMeteorology 118, 21-47.
8. Arora, V.K., Boer, G.J., 2005. Fire as an interactive component of dynamicvegetation models. Journal of Geophysical Research 110, G02008,http://dx.doi.org/10.1029/2005JG000042.
9. Bachelet, D., Lenihan, J.M., Daly, C., Neilson, R.P., Ojima, D.S., Parton, W.J., 2001. MC1: a dynamic vegetation model for estimating the distribution of vegeta-tion and associated ecosystem fluxes of carbon, nutrients, and water. In: PacificNorthwest Station General Technical Report. PNW-GTR-508, U. S. D. A. For. Serv.,Washington, DC.
10. Barbosa, P.M., Stroppiana, D., Gregoire, J.M., 1999. An assessment of vegetation fire inAfrica (1981-1991): burned areas, burned biomass, and atmospheric emissions.Global Biogeochemical Cycles 13, 933-950.
11. Bartalev, S.A., Egorov, V.A., Loupian, E.A., Uvarov, I.A., 2007. Multi-year circumpolarassessment of the area burnt in boreal ecosystems using SPOT-VEGETATION.International Journal of Remote Sensing 28 (6), 1397-1404.
12. Bartholome, E., Belward, A.S., 2005. GLC2000: a new approach to global land covermapping from Earth observation data. International Journal of Remote Sensing26, 1959-1977.
13. Bartsch, A., Balzter, H., George, C., 2009. The influence of regional surface soil mois-ture anomalies on forest fires in Siberia observed from satellites. EnvironmentalResearch Letters 4 (4), 45021.
14. Biswajeet, P., Hamid, A., 2010. Forest fire detection and monitoring using high tem-poral MODIS and NOAA AVHRR satellite images in Peninsular Malaysia. DisasterAdvances 3 (2), 18-23.
15. Bond, W.J., Midgley, G.F., Woodward, F.I., 2003. The importance of low atmosphericCO2and fire in promoting the spread of grasslands and savannas. Global ChangeBiology 9, 973-982.
16. Boschetti, L., Eva, H.D., Brivio, P.A., Gregoire, J.M., 2004. Lessons to be learned from thecomparison of three satellite-derived biomass burning products. GeophysicalResearch Letters 31 (21), L21501.
17. Boschetti, L., Roy, D.P., 2009. Strategies for the fusion of satellite fire radiative powerwith burned area data for fire radiative energy derivation. Journal of GeophysicalResearch: Atmosphere 114, D20302.
18. Brivio, P.A., Maggi, M., Binaghi, E., Gallo, I., 2003. Mapping burned surfaces insub-Saharan Africa based on multi-temporal neural classification. InternationalJournal of Remote Sensing 24 (20), 4003-4018.
19. Bromley, L., 2010. Relating violence to MODIS fire detections in Darfur Sudan. Inter-national Journal of Remote Sensing 31 (9), 2277-2292.
20. Carmona-Moreno, C., Belward, A., Malingreau, J.-P., Hartley, A., Garcia-Alegre, M.,Antonovskiy, M., Buchshtaber, V., Pivovarov, V., 2005. Characterizing interann-ual variations in global fire calendar using data from Earth observing satellites.Global Change Biology 11, 1537-1555.
21. Chang, D., Song, Y., 2009. Comparison of L3JRC and MODIS global burned area prod-ucts from 2000 to 2007. Journal of Geophysical Research: Atmosphere 114,D16106.
22. Chen, J., Chen, W., Liu, J., Cihlar, J., Gray, S., 2000. Annual carbon balance of Canada'sforests during 1895-1996. Global Biochemical Cycles 14, 839-849.
23. Chuvieco, E., Congalton, R.G., 1988. Mapping and inventory of forest fires from digitalprocessing of TM data. Geocarto International 4, 41-53.
24. Chuvieco, E., Aguado, I., Yebra, M., Nieto, H., Salas, J., Martin, M.P., Vilar, L., Mar-tinez, J., Martin, S., Ibarra, P., de la Riva, J., Baeza, J., Rodriguez, F., Molina, J.R.,Herrera, M.A., Zamora, R., 2010. Development of a framework for fire risk assess-ment using remote sensing and geographic information system technologies. Ecological Modelling 221 (1), 46-58.
25. Ciais, P., Moore, B., Steffen, W., Hood, M., Quegan, S., Cihlar, J., Raupach, M., Rasool, I.,Doney, S., Heinze, C., Sabine, C., Hibbard, K., Schulze, E.D., Heimann, M., Chédin,A., Monfray, P., Watson, A., LeQuéré, C., Tans, P., Dolman, H., Valentini, R., Arino,O., Townshend, J., Seufert, G., Field, C., Igrashi, T., Goodale, C., Nobre, A., Inoue,G., Crisp, D., Baldocchi, D., Tschirley, J., Denning, S., Cramer, W., Francey, R.,Wickland, D., 2004. Integrated Global Carbon Observation Theme: A strategyto realise a coordinated system of integrated global carbon cycle observations.In: Report to the Integrated Global Observing System Partnership (IGOS-P).http://ioc.unesco.org/igospartners/docs/theme reports/IGOS carbon.
26. zipCiais, P., Dolman, A.J., Dargaville, R., Barrie, L., Bombelli, A., Butler, J., Canadell, P.,Moriyama, T., 2010. Geo Carbon Strategy Geo Secretariat Geneva. FAO, Rome,48 pp.
27. Cox, P.M., 2001. Description of the TRIFFID dynamic global vegetation model. In:Tech. Note 24. Hadley Cent, Bracknell, UK, 16 pp.
28. Crutzen, P.J., Heidt, L.E., Krasnec, J.P., Pollock, W.H., Seiler, W., 1979. Biomass burningas a source of atmospheric gases CO, H2, N2O, CH3Cl and COS. Nature 282 (5736),253-256.
29. Crutzen, P.J., Andreae, M.O., 1990. Biomass burning in the tropics: impact on atmo-spheric chemistry and biogeochemical cycles. Science 250, 1669-1677.
30. Dai, Y., Zeng, X.B., Dickenson, R.E., Baker, I., Bonan, G.B., Bosilovich, M.G., Denning,A.S., Dirmeyer, P.A., Houser, P.R., 2003. The common land model. Bulletin of theAmerican Meteorological Society 84, 1013-1023.
31. Davies, D.K., Ilavajhala, S., Wong, M.M., Justice, C.O., 2009. Fire information forresource management system: archiving and distributing MODIS active firedata. IEEE Transactions in Geosciences and Remote Sensing 47 (1), 72-79.
32. De Klerk, H., 2008. A pragmatic assessment of the usefulness of the MODIS (Terra andAqua) 1-km active fire (MOD14A2 and MYD14A2) products for mapping fires inthe fynbos biome. International Journal of Wildland Fire 17 (2), 166-178.
33. Dempewolf, J., Trigg, S., DeFries, R.S., Eby, S., 2007. Burned-area mapping of theSerengeti-Mara region using MODIS reflectance data. IEEE Geoscience RemoteSensing Letters 4 (2), 312-316.
34. Devineau, J.L., Fournier, A., Nignan, S., 2010. Savanna fire regimes assessment withMODIS fire data: their relationship to land cover and plant species distribu-tion in western Burkina Faso (West Africa). Journal of Arid Environment 74 (9),1092-1101.
35. Diagne, M., Drame, M., Ferrao, C., Marchetti, P.G., Pinto, S., Rivolta, G., 2010. Mul-tisource data integration for fire risk management: the local test of a globalapproach. IEEE Geosciences and Remote Sensing Letters 7 (1), 93-97.
36. Duguy, B., Alloza, J.A., Roder, A., Vallejo, R., Pastor, F., 2007. Modelling the effectsof landscape fuel treatment on fire growth and behaviour in a Mediter-ranean landscape (eastern Spain). International Journal of Wildland Fire 16 (5),619-632.
37. Duncan, B.N., Bey, I., Chin, M., Mickley, L.J., Fairlie, T.D., Martin, R.V., Matsueda, H.,2003a. Indonesian wildfires of 1997: impact on tropospheric chemistry. Journalof Geophysical Research: Atmosphere 108 (D15), 4458.
38. Duncan, B.N., Martin, R.V., Staudt, A.C., Yevich, R., Logan, J.A., 2003b. Interannualand seasonal variability of biomass burning emissions constrained by satelliteobservations. Journal of Geophysical Research: Atmosphere 108 (D2), 4100.
39. Dwyer, E., Pinnock, S., Gregoire, J.M., Pereira, J.M.C., 2000. Global spatial and tem-poral distribution of vegetation fire as determined from satellite observations.International Journal of Remote Sensing 21, 1289-1302.
40. Eckmann, T.C., Roberts, D.A., Still, C.J., 2008. Using multiple endmember spec-tral mixture analysis to retrieve subpixel fire properties from MODIS. RemoteSensing of Environment 112 (10), 3773-3783.
41. Ellicott, E., Vermote, E., Giglio, L., Roberts, G., 2009. Estimating biomass consumedfrom fire using MODIS FRE. Geophysical Research Letters 36, L13401.
42. Eva, H., Lambin, E.F., 1998a. Burnt area mapping in Central Africa using ATSR data.International Journal of Remote Sensing 19, 3473-3497.
43. Eva, H., Lambin, E.F., 1998b. Remote sensing of biomass burning in tropical regions- sampling issues and multisensor approach. Remote Sensing of Environment64, 292-315.
44. FAO, 2001. Global Forest Fire Assessment, 1990-2000. FAO, Rome, Italy.
45. Fernandez, A., Illera, P., Casanova, J.L., 1997. Automatic mapping of surfaces affectedby forest fires in Spain using AVHRR NDVI composite image data. Remote Sensingof Environment 60 (2), 153-162.
46. Field, C.B., Randerson, J.T., Malmstrom, C.M., 1995. Global net primary production -combining ecology and remote sensing. Remote Sensing of Environment 51 (1),74-88.
47. Field, C.B., Fung, I.Y., 1999. Biogeochemical Cycles - the not so big US carbon sink.Science 285 (5427), 544-545.
48. Fraser, R.H., Hall, R.J., La Dry, R., Lynham, T., Raymond, D., Lee, B., Li, Z., 2004. Vali-dation and calibration of Canada-wide coarse resolution satellite burned areamaps. Photogrammetric Engineering and Remote Sensing 70 (4), 451-460.
49. Fraser, R.H., Li, Z., Landry, R., 2000. SPOT vegetation for characterizing boreal forestfires. International Journal of Remote Sensing 21 (18), 3525-3532.
50. Frei, A., Tedesco, M., Lee, S., Foster, J., Hall, D.K., Kelly, R., Robinson, D.A., 2012 a.review of global satellite derived snow products. Advances in Space Research50 (8), 1007-1029.
51. Furyaev, V.V., 1985. The use of aerospace imagery to examine and assess the conse-quences of forest fires. Soviet Journal of Remote Sensing 4, 773-782.
52. GCOS., 2010. Implementation Plan for the Global Observing System for Cli-mate in Support of the UNFCCC (2010 Update) (GCOS-138). August 2010,http://www.wmo.int/pages/prog/gcos/Publications/gcos-138.pdf (last retrieval23/07/2012).
53. GCOS., 2011. Systematic Observation Requirements for Satellite-Based Productsfor Climate 2011 Update (GCOS-154). Supplemental details to the satellite-based component of the "Implementation Plan for the Global ObservingSystem for Climate in Support of the UNFCCC (2010 Update)". December 2011,http://www.wmo.int/pages/prog/gcos/Publications/gcos-154.pdf (last retrieval23/07/2012).
54. GTOS, 2009a. Assessment of the Status of the Development of the Stan-dards for the Terrestrial Essential Climate Variables - Fire DisturbanceRome, ftp://ftp.fao.org/docrep/fao/012/i1239e/i1239e00.pdf (last retrieval23/07/2012).
55. Generoso, S., Breon, F.M., Balkanski, Y., Boucher, O., Schulz, M., 2003. Improving theseasonal cycle and interannual variations of biomass burning aerosol sources.Atmospheric Chemistry and Physics 3, 1211-1222.
56. Gerard, F., Plummer, S., Wadsworth, R., Sanfeliu, A.F., Lliffe, L., Baltzer, H., Wyatt, B.,2003. Forest fire scar detection in the boreal forest with mulmtitemporal SPOT-VEGETATION data. IEEE Transactions on Geosciences and Remote Sensing 41(11), 2575-2585.
57. George, C., Rowland, C., Gerard, F., Balzter, H., 2006. Restrospective mapping of burntareas in Central Siberia using a modification of the normalised difference waterindex. Remote Sensing of Environement 104 (3), 346-359.
58. Giglio, L., Loboda, T., Roy, D.P., Quayle, B., Justice, C.O., 2009. An active-fire basedburned area mapping algorithm for the MODIS sensor. Remote Sensing of Envi-ronment 113 (2), 408-420.
59. Giglio, L., Randerson, J.T., van der Werf, G.R., Kasibhatla, P.S., Collatz, G.J., Mor-ton, D.C., DeFries, R.S., 2010. Assessing variability and long-term trends inburned area by merging multiple satellite fire products. Biogeosciences 7 (3),1171-1186.
60. Giglio, L., van der Werf, G.R., Randerson, J.T., Collatz, G.J., Kasibhatla, P., 2006. Globalestimation of burned area using MODIS active fire observations. AtmosphericChemistry and Physics 6, 957-974.
61. GTOS T13, Fire Disturbance, 2009b. Assessment of the status of the develop-ment of the standards for the Terrestrial Essential Climate Variables, Rome,http://www.fao.org/gtos/doc/ECVs/T13/T13.pdf
62. Hao, W.M., Liu, M.H., 1994. Spatial and temporal distribution of tropical biomassburning. Global Biogeochemical Cycles 8 (4), 495-503.
63. Harwell, E.E., 2000. Remote sensibilities: discourses of technology and the makingof Indonesia's natural disaster. Development and Change 31 (1), 307-340.
64. Hawbaker, T.J., Radeloff, V.C., Syphard, A.D., Zhu, Z.L., Stewart, S.I., 2008. Detectionrates of the MODIS active fire product in the United States. Remote Sensing ofEnvironment 112 (5), 2656-2664.
65. Herold, M., Mayaux, P., Woodcock, C., Baccini, A., Schmullius, C., 2008. Some chal-lenges in global land cover mapping: an assessment of agreement and accuracyin existing 1 km datasets. Remote Sensing of Environment 12 (5), 2538-2556.
66. Isaev, A.S., Korovin, G.N., Bartalev, S.A., Ershov, D.V., Janetos, A., Kasischke, E.S.,Shugart, H.H., French, N.H.F., Orlick, B.E., Murphy, T.L., 2002. Using remotesensing to assess Russian forest fire carbon emissions. Climatic Change 55 (1-2),235-249.
67. Justice, C.O., et al., 2011. White Paper on a NASA Fire ESDR. http://landportal.gsfc.nasa.gov/Documents/ESDR/Fire Justice whitepaper.pdf
68. Kaiser, J.W., Suttie, M., Flemming, J., Morcrette, J.-J., Boucher, O., Schultz, M.G., 2009. Global real-time fire emission estimates based on space-borne fire radiativepower observations. In: Proceedings of the Internal Radiation Symposium, SanDiego, CA, USA, August 2008.
69. Kasischke, E.S., French, N.H.F., 1995. Locating and estimating the aeral extent ofwildfires in Alaskan boreal forests using multiple season AVHRR NDVI compositedata. Remote Sensing of Environment 51 (2), 263-275.
70. Kasischke, E.S., Loboda, T., Giglio, L., French, N.H.F., Hoy, E.E., de Jong, B., Riano,D., 2011. Quantifying burned area for North American forests: implicationsfor direct reduction of carbon stocks. Journal of Geophysical Research: Biogeo-sciences 116, G04003.
71. Keane, R.E., Cary, G.J., Davies, I.D., Flannigan, M.D., Gardner, R.H., LAvorel, S., Lenihan,J.M., Li, C., Rupp, T.S., 2004. A classification of landscape fire succession models:spatial simulations of fire and vegetation dynamics. Ecological Modelling 179(1), 3-27.
72. Keane, R.E., Arno, S.F., Brown, J.K., 1989 September. USDA ForestService Intermoun-tain Research Station General Technical Report INT-266FIRESUM - An ecologicalProcess Model for Fire Succession in Western Conifer Forests.
73. Korontzi, S., Roy, D.P., Justice, C.O., Ward, D.E., 2004. Modeling and sensitivity anal-ysis of fire emissions in southern Africa during SAFARI 2000. Remote Sensing ofEnvironment 92 (2), 255-275.
74. Knorr, W., Lehsten, V., Arneth, A., 2012. Determinants and predictability ofglobal wildfire emissions. Atmospheric Chemistry and Physics 12 (15),6845-6861.
75. Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein,P., Ciais, P., Sitch, S., Prentice, I.C., 2005. A dynamic global vegetation model forstudies of the coupled atmosphere-biosphere system. Global BiogeochemicalCycles 19, GB1015.
76. Kucharik, C.J., Foley, J.A., Delire, C., Fisher, V.A., Coe, M.T., Lenters, J.D., Young-Molling,C., Ramankutty, N., 2000. Testing the performance of a dynamic global ecosys-tem model: water balance, carbon balance, and vegetation structure. GlobalBiogeochemical Cycles 14, 795-825.
77. Kushida, K., 2010. Detection of active wildland fires using multitemporal MODISimages. IEEE Geosciences and Remote Sensing Letters 7 (2), 301-305.
78. Lawrence, P.J., Chase, T.N., 2007. Representing a new MODIS consistent land surfacein the Community Land Model (CLM 3.0). Journal of Geophysical Research 112,G01023.
79. Le Page, Y., Pereira, J.M.C., Trigo, R., da Camara, C., Oom, D., Mota, B., 2008.Global fire activity patterns (1996-2006) and climatic influence: an analy-sis using the World Fire Atlas. Atmospheric Chemistry and Physics 8 (7),1911-1924.
80. Lenihan, J.M., Neilson, R.P., 1998. Simulating broad-scale fire severity in a dynamicglobal vegetation model. Northwest Sciences 72, 91-103.
81. Levis, S., Bonan, G.B., Vertenstein, M., Oleson, K.W., 2004. The Community LandModel's dynamic global vegetation model (CLM-DGVM): Technical descriptionand user's guide. In: NCAR Tech. Note TN-459 IA, Terrestrial Sciences Section, Boulder, Colorado.
82. Loboda, T., O'Neal, K.J., Csiszar, I., 2007. Regionally adaptable dNBR-based algorithmfor burned area mapping from MODIS data. Remote Sensing of Environment 109(4), 429-442.
83. Loboda, T.V., Csiszar, I.A., 2007. Reconstruction of fire spread within wildland fireevents in Northern Eurasia from the MODIS active fire product. Global andPlanetary Change 56, 258-273.
84. Loboda, T.V., Hoy, E.E., Giglio, L., Kasischke, E.S., 2011. Mapping burned area in Alaskausing MODIS data: a data limitations-driven modification to the regional burnedarea algorithm. International Journal of Wildland Fire 20 (4), 487-496.
85. McCallum, I., Obersteiner, M., et al., 2006. A spatial comparison of four satellitederived 1 km global land cover datasets. International Journal of Applied EarthObservation and Geoinformation 8 (4), 246-255.
86. Meiyappan, P., Jain, A.K., 2012. Three distinct global estimates of historical land coverchange and land use conversions for over 200 years. Frontiers of Earth Sciences6 (2), 122-139.
87. Miettinen, J., Langner, A., Siegert, F., 2007. Burnt area estimation for the year 2005 inBorneo using multi-resolution satellite imagery. International Journal of Wild-land Fire 16 (1), 45-53.
88. Mieville, A., Granier, C., Liousse, C., Guillaume, B., Mouillot, F., Lamarque, J.F., Gre-goire, J.M., Petron, G., 2010. Emissions of gases and particles from biomassburning during the 20th century using satellite data and an historical recon-struction. Atmospheric Environment 44 (11), 1469-1477.
89. Milne, A.K., 1986. The use of remote sensing in mapping and monitoring vegeta-tional change associated with bushfire events in Eastern Australia. GeocartoInternational 1, 25-32.
90. Moorcroft, P.R., Hurtt, G.C., Pacala, S.W., 2001. A method for scaling vegetationdynamics: the ecosystem demography model (ED). Ecological Monographs 71,557-586.
91. Moreira, F., Viedma, O., Arianoutsou, M., Curt, T., Koutsias, N., Rigolot, E., Bar-bati, A., Corona, P., Vaz, P., Xanthopoulos, G., Mouillot, F., Bilgili, E., 2011. Landscape-wildfire interactions in southern Europe: implications for landscapemanagement. Journal of Environmental Management 92 (10), 2389-2402.
92. Morton, D.C., DeFries, R.S., Nagol, J., Souza, C.M., Kasischke, E.S., Hurtt, G.C., Dubayah,R., 2011. Mapping canopy damage from understory fires in Amazon forests usingannual time series of Landsat and MODIS data. Remote Sensing of Environment115 (7), 1706-1720.
93. Mouillot, F., Field, C.B., 2005. Fire history and the global carbon budget: a 1 × 1 degreefire history reconstruction for the 20th century. Global Change Biology 11 (3),398-420.
94. Mouillot, F., Narasimha, A., Balkanski, Y., Lamarque, J.F., Field, C.B., 2006. Global car-bon emissions from biomass burning in the 20th century. Geophysical ResearchLetters 33 (1), L01801.
95. NASA., 2011. White Paper on a NASA Fire ESDR, http://landportal.gsfc.nasa.gov/Documents/ESDR/FireJustice whitepaper.pdf. last retrieval 23/07/2012.
96. Oleson, K.W., Lawrence, D.M., Bonan, G.B., Flanner, M.G., Kluzek, E., Lawrence, P.J.,Levis, S., Swenson, S.C., Thornton, P.E., Dai, A., Decker, M., Dickinson, R., Feddema,J., Heald, C.L., Hoffman, F., Lamarque, J.F., Mahowald, N., Niu, G.-Y., Qian, T., Ran-derson, J., Running, S., Sakaguchi, K., Slater, A., Stockli, R., Wang, A., Yang, Z.-L.,Zeng, X., Zeng, X., 2010. Technical description of version 4.0 of the CommunityLand Model, NCAR Tech. Note NCAR/TN-478+STR, Terrestrial Sciences Section,Boulder, Colorado.
97. Palacios-Orueta, A., Chuvieco, E., Parra, A., Carmona-Moreno, C., 2005. Biomass burn-ing emissions: a review of models using remote sensing data. EnvironmentalMonitoring and Assessment 104 (1-3), 189-209.
98. Pausas, J.G., Bradstock, R.A., Keith, D.A., Keeley, J.E., 2004. Plant functional traits inrelation to fire in crown-fire ecosystems. Ecology 85, 1085-1100.
99. Peng, G.X., Shen, W., Hu, D.Y., Li, J., Chen, Y.H., 2008. Method to identify forest firebased on smoke plumes mask by using MODIS data. Journal of Infrared andMillimeter Waves 27 (3), 185-189.
100. Pereira, J.M.C., 2003. Remote sensing of burned area in tropical savannas. Interna-tional Journal of Wildland Fire 12, 259-270.
101. Pereira, J.M.C., Vasconcelos, M.J.P., Sousa, A.M., 2000. A rule based system for burnedarea mapping in temperate and tropical regions using NOAA/AVHRR imagery.In: Innes, J.L., Beniston, M., Verstraete, M.M. (Eds.), Biomass Burning and Its Inter-Relationship With The Climate System. Kluwer Academic Publishers, Dordrecht,pp. 215-232.
102. Pfeifer, M., Disney, M., Quaife, T., Marchant, R., 2012. Terrestrial ecosystems fromspace: a review of earth observation products for macroecology applications.Global Ecology and Biogeography 21, 603-624.
103. Plummer, S., Arino, O., Ranera, F., Tansey, K., Chen, J., Dedieu, G., Eva, H., et al., 2007. An update on the Globcarbon initiative: multi-sensor estimation of global bio-physical products for global terrestrial carbon studies. In: Proceedings Paper,http://eprints.ucl.ac.uk/179082
104. Plummer, S., Olivier, A., Muriel, S., Will, S., 2006. Establishing a earth observationproduct service for the terrestrial carbon community: the globcarbon initia-tive. Mitigation and Adaptation Strategies for Global Change 11 (1), 97-111,http://dx.doi.org/10.1007/s11027-006-1012-8.
105. Potter, C.S., Randerson, J.T., Field, C.B., Matson, P.A., Vitousek, P.M., Mooney,H.A., Klooster, S.A., 1993. Terrestrial ecosystem production: a process modelbased on global satellite and surface data. Global Biogeochemical Cycles 7 (4),811-841.
106. Potter, C.S., Davidson, E.A., Klooster, S.A., Nepstad, D.C., DeNegreiros, G.H., Brooks,V., 1998. Regional application of an ecosystem production model for studies ofbiogeochemistry in Brazilian Amazonia. Global Change Biology 4 (3), 315-333.
107. Pu, R., Gong, P., Li, Z., Scarborough, J., 2004. A dynamic algorithm for wildfire mappingwith NOAA/AVHRR data. International Journal of Wildland Fire 13 (3), 275-285.
108. Pu, R.L., Li, Z.Q., Gong, P., Csiszar, I., Fraser, R., Hao, W.M., Kondragunta, S., Weng,F.Z., 2007. Development and analysis of a 12-year daily 1-km forest fire datasetacross North America from NOAA/AVHRR data. Remote Sensing of Environment108 (2), 198-208.
109. Punia, M., Nautiyal, V.P., Kant, Y., 2008. Identifying biomass burned patches of agri-culture residue using satellite remote sensing data. Current Sciences 94 (9),1185-1190.
110. Radke, L.F., Stith, J.L., Hegg, D.A., Hobbs, P.V., 1978. Airborne studies of particles andgases from forest fires. Journal of Air Pollution Control Association 28 (1), 30-34.
111. Redo, D., Milington, A.C., Hinderry, D., 2011. Deforestation dynamics and policychanges in Bolivia's post neoliberal era. Land Use Policy 28 (1), 227-241.
112. Reid, J.S., Hyer, E.J., Prins, E.M., Westphal, D.L., Zhang, J.L., Wang, J., Christopher, S.A.,Curtis, C.A., Schmidt, C.C., Eleutorio, D.P., Richardson, K.A., Hoffman, J.P., 2009.Global monitoring and forecasting of biomass burning smoke: description of andlessons from the fire locating and modelling of burning emissions (FLMABE) pro-gram. IEEE Journal of Selected Topics in Applied Earth Observation and RemoteSensing 2 (3), 144-162.
113. Ressl, R., Lopez, G., Cruz, I., Colditz, R.R., Schmidt, M., Ressl, S., Jimenez, R., 2009. Oper-ational active fire mapping and burnt area identification applicable to MexicanNature protection areas using MODIS and NOWW AVHRR direct readout data.Remote Sensing of Environment 113 (6), 1113-1126.
114. Robbins, A.M.J., Eckelmann, C.M., Quinones, M., 2008. Forest fires in the insularCaribbean. Ambio 37, 528-534.
115. Roy, D.P., Giglio, L., Kendall, J.D., Justice, C.O., 1999. Multi-temporal active-fire basedburn scar detection algorithm. International Journal of Remote Sensing 20 (5), 1031-1038.
116. Roy, D.P., Boschetti, L., Justice, C.O., Ju, J., 2008. The collection 5 MODIS burned areaproduct - global evaluation by comparison with the MODIS active fire product.Remote Sensing of Environment 112 (9), 3690-3707.
117. Roy, D.P., Jin, Y., Lewis, P.E., Justice, C.O., 2005. Prototyping a global algorithm forsystematic fire-affected area mapping using MODIS time series data. RemoteSensing of Environment 97 (2), 137-162.
118. Roy, D.P., Trigg, S.N., Bhima, R., Brockett, B.H., Dube, O.P., Frost, P., Govender, N.,Landmann, T., Le Roux, J., Lepono, T., Macuacua, J., Mbow, C., Mhwandangara,K.L., Mosepele, B., Mutanga, O., Neo-Mahupeleng, G., Norman, M., Virgilo, S.,2006. The utility of satellite fire product accuracy information - perspectivesand recommendations from the southern Africa fire network. IEEE Transactionsin Geosciences and Remote Sensing 44 (7), 1928-1930.
119. Running, S.W., Hunt Jr., E.R., 1993. Generalization of a forest ecosystem processmodel for other biomes, BIOME-BGC, and an application for global-scale mod-els. In: Ehleringer, J.R., Field, C.B. (Eds.), Scaling Physiological Processes: Leaf toGlobe. Academic Press Inc., New York, pp. 141-158.
120. Sa, A.C.L., Pereira, J.M.C., Gardner, R.H., 2007. Analysis of the relationship betweenspatial pattern and spectral detectability of areas burned in southern Africa usingsatellite data. International Journal of Remote Sensing 28 (16), 3583-3601.
121. Sato, H., Itoh, A., Kohyama, T., 2007. SEIB-DGVM: a new Dynamic Global VegetationModel using a spatially explicit individual-based approach. Ecological Modelling 200, 279-307.
122. Schroeder, W., Ruminski, M., Csiszar, I., Giglio, L., Prins, E., Schmidt, C., Morisette, J.,2008. Validation analyses of an operational fire monitoring product: the hazardmapping system. International Journal of Remote Sensing 29 (20), 6059-6066.
123. Schultz, M.G., Heil, A., Hoelzemann, J.J., Spessa, A., Thonicke, K., Goldammer, J.G.,Held, A.C., Pereira, J.M.C., Van Het Bolscher, M., 2008. Global wildland fire emis-sions from 1960 to 2000. Global Biogeochemical Cycles 22 (2), GB2002.
124. Schultz, M.G., 2002. On the use of ATSR fire count data to estimate the seasonal andinterannual variability of vegetation fire emissions. Atmospheric Chemistry andPhysics 2, 387-395.
125. Seiler, W., Crutzen, P.J., 1980. Estimates of gross and net fluxes of carbon betweenthe biosphere and the atmosphere from biomass burning. Climatic Change 2,207-247.
126. Simon, M., Plummer, S., Fierens, F., Hoelzemann, J.J., Arino, O., 2004. Burnt area detec-tion at global scale using ATSR-2: the GLOBSCAR products and their qualification.Journal of Geophysical Research: Atmosphere 109 (D14), S02.
127. Sitch, S., Smith, B., Prentice, I.C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J.O.,levis, S., Lucht, W., Sykes, M.T., Thonicke, K., Venevsky, S., 2003. Evaluation ofecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJdynamic global vegetation model. Global Change Biology 9, 161-185.
128. Smith, R.B., Woodgate, P.W., 1985. Appraisal of fire damage and inventory for tim-ber salvage by remote sensing in mountain ash forests in Victoria. AustralianForestry 48, 252-263.
129. Smith, B., Prentice, I.C., Sykes, M.T., 2001. Representation of vegetation dynamics inthe modeling of terrestrial ecosystems: comparing two contrasting approacheswithin European climate space. Global Ecology and Biogeography 10, 621-637.
130. Stroppiana, D., Gregoire, J.M., Pereira, J.M.C., 2002. The use of SPOT-VEGETATIONdata in a classification tree approach for burbnt area mapping in Australiasavanna. International Journal of Remote Sensing 24 (10), 2131-2151.
131. Sukhinin, A.I., French, N.H.F., Kasischke, E.S., Hewson, J.H., Soja, A.J., Csiszar, I.A.,Hyer, E.J., Loboda, T., Conrad, S.G., Romasko, V.I., Pavlichenko, E.A., Miskiv, S.I.,Slinkina, O.A., 2004. AVHRR-based mapping of fires in Russia: new products forfire management and carbon cycle studies. Remote Sensing of Environment 93,546-564.
132. Tansey, K., Beston, J., Hoscilo, A., Page, S.E., Hernandez, C.U.P., 2008a. Relationshipbetween MODIS fire hot spot count and burned area in a degraded tropical peatswamp forest in Central Kalimantan Indonesia. Journal of Geophysical Research:Atmosphere 113 (D23), D23112.
133. Tansey, K., Jean-Marie, G., Defourny, P., Leigh, R., Pekel, J.-F., van Bogaert, E.,Bartholomé, E., 2008b. A new, global, multi-annual (2000-2007) burnt areaproduct at 1 km resolution. Geophysical Research Letters 35 (1), GL031567.
134. Tansey, K., Grégoire, J.-M., Stroppiana, D., Sousa, A., Silva, J., José, M.C., Pereira, LuigiBoschetti, et al., 2004. Vegetation burning in the year 2000: global burned areaestimates from SPOT VEGETATION data. Journal of Geophysical Research 109,22.
135. Thonicke, K., Spessa, A., Prentice, I.C., Harrison, S.P., Dong, L., Carmona-Moreno, C.,2010. The influence of vegetation, fire spread and fire behaviour on biomassburning and trace gas emissions: results from a process-based model. Biogeo-sciences 7 (6), 1991-2011.
136. Thonicke, K., Venevsky, S., Sitch, S., Cramer, W., 2001. The role of fire disturbancefor global vegetation dynamics: Coupling fire into a Dynamic Global VegetationModel. Global Ecology and Biogeography 10, 661-677.
137. Townshend, J.R., Latham, J., Arino, O., Balstad, R., Belward, A., Conant, R., Elvidge, C.,Feuquay, J., El Hadani, D., Herold, M., Janetos, A., Justice, C.O., Jiyuan, L., Loveland,T., Nachtergaele, F., Ojima, D., Maiden, M., Palazzo, F., Schmullius, C., Sessa, R.,Singh, A., Tschirley, J., Yamamoto, H., 2008. Integrated global observations of theland: an IGOS-P theme. In: IGOL Report No. 8. GTOS 54.
138. Trigg, S.N., Roy, D.P., 2007. A focus group study of factors that promote and con-strain the use of satellite-derived fire products by resource managers in southernAfrica. Journal of Environmental Management 82 (1), 95-110.
139. Van der Werf, G.R., Randerson, J.T., Collatz, G.J., Giglio, L., 2003. Carbon emissionsfrom fires in tropical and subtropical ecosystems. Global Change Biology 9 (4),547-562.
140. Van der Werf, G.R., Morton, D.C., DeFries, R.S., Giglio, L., Randerson, J.T., Collatz,G.J., Kasibhatla, P.S., 2009. Estimates of fire emissions from an active deforesta-tion region in the southern Amazon based on satellite data and biogeochemicalmodelling. Biogeosciences 6 (2), 235-249.
141. Van der Werf, G.R., Randerson, J.T., Giglio, L., Collatz, G.J., Kasibhatla, P.S., Arellano,A.F., 2006. Interannual variability in global biomass burning emissions from1997 to 2004. Atmospheric Chemistry and Physics 6, 3423-3441.
142. Van der Werf, G.R., Randerson, J.T., Giglio, L., Collatz, G.J., Kasibhatla, P.S., Morton,D.C., DeFries, R.S., 2010. The improved Global Fire Emissions Database (GFED)version 3: contribution of savanna, forest, deforestation, and peat fires to theglobal fire emissions budget. In: Geophysical Research Abstracts 12, Vienna,Austria., pp. 13010.
143. Venevsky, S., Thonicke, K., Sitch, S., Cramer, W., 2002. Simulating fire regimes inhuman-dominated ecosystems: Iberian Peninsula case study. Global ChangeBiology 8, 984-998.
144. Wang, W.L., Dungan, J., Hashimoto, H., Michaelis, A.R., Milesi, C., Ichii, K., Nemani,R.R., 2011. Diagnosing and assessing uncertainties of terrestrial ecosystem mod-els in a multimodel ensemble experiment: 2. Carbon balance. Global ChangeBiology 17 (3), 1367-1378.
145. Woodward, F.I., Lomas, M.R., 2004. Vegetation dynamics-simulation responses toclimatic change. Biological Reviews 79, 643-670.
146. Woodward, F.I., Smith, T.M., Emanuel, W.R., 1995. A global land primary productivityand phytogeography model. Global Biogeochemical Cycles 9, 471-490.
147. Wright, S.J., Sanchez-Azofeifa, G.A., Portillo-Quintero, C., Davies, D., 2007. Povertyand corruption compromise tropical forest reserves. Ecological Applications 17(5), 1259-1266.
148. Zeng, X.D., 2010. Evaluating the dependence of vegetation on climate in animproved dynamic global vegetation model. Advances in Atmospheric Sciences27, 977-991.
149. Zhang, X.Y., Kondragunta, S., Schmidt, C., Kogan, F., 2008. Near real time monitor-ing of biomass burning particulate emissions (PM2.5) across contiguous UnitedStates using multiple satellite instruments. Atmospheric Environment 42 (29), 6959-6972.
150. Zhang, Y.H., Wooster, M.J., Tutubalina, O., Perry, G.L.W., 2003. Monthly burned areaand forest fire carbon emission estimates for the Russian Federation from SPOTGVT. Remote Sensing of Environment 87 (1), 1-15.