Abstract
Biomass burning is one of the critical components of the Earth system, significantly affecting atmospheric emissions and carbon budgets. Fires occurring in the interface between wildland and urban areas also have important socioeconomic effects, affecting people’s lives and resources. Even though fires are natural in many ecosystems, climate and societal changes have recently caused particularly severe fire seasons (Australia, California, Amazonia, Portugal…). Mitigating the negative impacts of fire requires further efforts to assess fire danger conditions. Satellite Earth observation provides considerable capabilities to evaluate the different variables involved in fire danger. Data obtained from remote sensors offer information on possible sources of fire ignition, on fuel status and abundance, and on the topography and the meteorological conditions that will affect fire spread. Satellite observations also provide near-real-time information on fire occurrence for early response teams. This article describes the different variables affecting fire danger and illustrates how satellite data can offer useful information to estimate these variables, focusing on global and continental fire danger systems.
Similar content being viewed by others
References
Abd-Elaal E-S, Mills JE, Ma X (2018) Numerical simulation of downburst wind flow over real topography. J Wind Eng Ind Aerodyn 172:85–95. https://doi.org/10.1016/j.jweia.2017.10.026
Abrams M (2016) ASTER global DEM version 3, and new ASTER water body dataset. Int Arch Photogramm Remote Sens Spatial Inf Sci XLI-B4:107–110. https://doi.org/10.5194/isprs-archives-XLI-B4-107-2016
Achard F, Eva HD, Mollicone D, Beuchle R (2008) The effect of climate anomalies and human ignition factor on wildfires in Russian boreal forests. Philos Trans R Soc Lond B Biol Sci 363:2331–2339. https://doi.org/10.1098/rstb.2007.2203
Albini FA (1976) Estimating wildfire behavior and effects, General Technical Report INT-30. USDA Forest Service, Intermountain Forest and Range Experiment Station, Odgen, UT
Albrecht RI, Goodman SJ, Petersen WA, Buechler DE, Bruning EC, Blakeslee RJ, Christian HJ (2011) The 13 years of TRMM lightning imaging sensor: from individual flash characteristics to decadal tendencies. In: XIV international conference on atmospheric electricity, Rio de Janeiro, Brasil.
Alonso-Benito A, Arroyo LA, Arbelo M, Hernández-Leal P (2016) Fusion of WorldView-2 and LiDAR data to map fuel types in the Canary Islands. Remote Sens 8:669
Andrews PL (2012) Modeling wind adjustment factor and midflame wind speed for Rothermel's surface fire spread model, Gen. Tech. Rep. RMRS-GTR-266. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO
Apke JM, Hilburn KA, Miller SD, Peterson DA (2020) Towards objective identification and tracking of convective outflow boundaries in next-generation geostationary satellite imagery. Atmos Meas Tech 13:1593–1608. https://doi.org/10.5194/amt-13-1593-2020
Archibald S et al (2018) Biological and geophysical feedbacks with fire in the Earth system. Environ Res Lett 13:033003. https://doi.org/10.1088/1748-9326/aa9ead
Archibald S, Scholes RJ, Roy DP, Roberts G, Boschetti L (2010) Southern African fire regimes as revealed by remote sensing. Int J Wildland Fire 19:861–878
Argañaraz JP, Landi MA, Bravo SJ, Gavier-Pizarro GI, Scavuzzo CM, Bellis LM (2016) Estimation of live fuel moisture content from MODIS images for fire danger assessment in Southern Gran Chaco. IEEE J Sel Top Appl Earth Observ Remote Sens 9:5339–5349. https://doi.org/10.1109/jstars.2016.2575366
Arino O et al. GlobCover: ESA service for global land cover from MERIS. In: International geoscience and remote sensing symposium, IGARSS 2007, Barcelona, Spain, 2007. IEEE- Inst Electrical Electronics Engineer Inc., pp 2412–2415. https://doi.org/10.1109/IGARSS.2007.4423328
Arroyo LA, Healey SP, Cohen WB, Cocero D, Manzanera JA (2006) Using object-oriented classification and high-resolution imagery to map fuel types in a Mediterranean region. J Geophys Res. https://doi.org/10.1029/2005JG000120,2006
Baccini A et al (2012) Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps Nat. Clim Change 2:182–185. https://doi.org/10.1038/nclimate1354
Bagnardi M, González PJ, Hooper A (2016) High-resolution digital elevation model from tri-stereo Pleiades-1 satellite imagery for lava flow volume estimates at Fogo Volcano. Geophys Res Lett 43:6267–6275. https://doi.org/10.1002/2016gl069457
Bajocco S, Dragoz E, Gitas I, Smiraglia D, Salvati L, Ricotta C (2015) Mapping forest fuels through vegetation phenology: the role of coarse-resolution satellite time-series. PLoS ONE 10:e0119811–e0119811. https://doi.org/10.1371/journal.pone.0119811
Balch JK, Bradley BA, Abatzoglou JT, Nagy RC, Fusco EJ, Mahood AL (2017) Human-started wildfires expand the fire niche across the United States. Proc Natl Acad Sci 114:2946. https://doi.org/10.1073/pnas.1617394114
Barroso Ramos-Neto M, Pivello VR (2000) Lightning fires in a brazilian savanna National Park: rethinking management strategies. Environ Manag 26:675–684. https://doi.org/10.1007/s002670010124
Berger C, Werner S, Wigley-Coetsee C, Smit I, Schmullius C (2019) Multi-temporal sentinel-1 data for wall-to-wall herbaceous biomass mapping in Kruger National Park, South Africa—first results. In: IGARSS 2019-2019 IEEE international geoscience and remote sensing symposium, 28 July–2 Aug. 2019, pp 7358–7360. https://doi.org/10.1109/igarss.2019.8898045
Bessho K et al (2016) An Introduction to Himawari-8/9— Japan’s new-generation geostationary. Meteorol Satellites J Meteorol Soc Jpn Ser II 94:151–183. https://doi.org/10.2151/jmsj.2016-009
Bistinas I, Oom D, Sá ACL, Harrison SP, Prentice IC, Pereira JMC (2013) Relationships between human population density and burned area at continental and global scales. PLoS ONE 8:e81188–e81188. https://doi.org/10.1371/journal.pone.0081188
Blakeslee RJ et al (2014) Lightning imaging sensor (LIS) for the international space station (ISS): missio n description and science goals. In: XV international conference on atmospheric electricity, Norman, Oklahoma
Bouvet A, Mermoz S, Le Toan T, Villard L, Mathieu R, Naidoo L, Asner GP (2018) An above-ground biomass map of African savannahs and woodlands at 25m resolution derived from ALOS PALSAR. Remote Sens Environ 206:156–173. https://doi.org/10.1016/j.rse.2017.12.030
Bowman DMJS et al (2011) The human dimension of fire regimes on Earth. J Biogeogr 38:2223–2236. https://doi.org/10.1111/j.1365-2699.2011.02595.x
Bowman DMJS et al (2009) Fire in the earth system. Science 324:481–484. https://doi.org/10.1126/science.1163886
Bradshaw LS, Deeming JE, Burgan RE, Cohen JD (1983) The 1978 National fire-danger rating system: technical documentation, GTR INT-169. USDA Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT
Burgan RE, Andrews PL, Bradshaw LS, Chase CH, Hartford RA, Latham DJ (1997) WFAS: wildland fire assessment system. Fire Manag Notes 57:14–17
Büttner G (2014) CORINE Land Cover and Land Cover Change Products. In: Manakos I, Braun M (eds) Land use and land cover mapping in Europe: practices & trends. Springer, Dordrecht, pp 55–74. https://doi.org/10.1007/978-94-007-7969-3_5
Büttner G, Kosztra B, Soukup T, Sousa A, Langanke T (2017) CLC2018 Technical Guidelines, Service Contract No 3436/R0-Copernicus/EEA.56665. European Topic Centre on Urban, Land and Soil Systems,
Camia A, Bovio G, Aguado I, Stach N (1999) Meteorological fire danger indices and remote sensing. In: Chuvieco E (ed) Remote sensing of large wildfires in the european mediterranean basin. Springer, Berlin, pp 39–59
Cano-Crespo A, Oliveira PJC, Boit A, Cardoso M, Thonicke K (2015) Forest edge burning in the Brazilian Amazon promoted by escaping fires from managed pastures. J Geophys Res Biogeosci 120:2095–2107. https://doi.org/10.1002/2015jg002914
Cardona OD et al. (2012) Determinants of risk: exposure and vulnerability. Cambridge, UK, and New York, NY, USA
Cecil DJ (2015) LIS/OTD gridded lightning climatology data collection, version2.3.2015. NASA EOSDIS globalhydrology resource center distributed active archive center, Huntsville, Alabama, U.S.A. https://dx.doi.org/10.5067/LIS/LIS-OTD/DATA311
Cecil DJ, Buechler DE, Blakeslee RJ (2014) Gridded lightning climatology from TRMM-LIS and OTD: dataset description. Atmos Res 135–136:404–414. https://doi.org/10.1016/j.atmosres.2012.06.028
Cohen JD, Deeming JE (1985) The National fire-danger rating system: basic equations, general technical report PSW-82. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, CA
Collins TW (2005) Households, forests, and fire hazard vulnerability in the American West: a case study of a California community. Environ Hazards 6:23–37
Conedera M, Cesti G, Pezzatti GB, Zumbrunnen T, Spinedi F (2006) Lightning-induced fires in the Alpine region: an increasing problem. In: Viegas DX (ed) V International conference on forest fire research, Coimbra, Portugal, 2006. Coimbra University Press
Costafreda-Aumedes S, Comas C, Vega-Garcia C (2017) Human-caused fire occurrence modelling in perspective: a review. Int J Wildland Fire 26:983–998. https://doi.org/10.1071/WF17026
Christian HJ et al (2003) Global frequency and distribution of lightning as observed from space by the Optical Transient Detector. J Geophys Res Atmos 108:ACL41–ACL415. https://doi.org/10.1029/2002jd002347
Christian HJ, Driscoll K, Goodman S, Blakeslee R, Mach DDB (1996) The optical transient detector (OTD). In: Proceedings of the 10th international conference on atmospheric electricity, Osaka, Japan, June 10–14 1996. pp 368–371
Chuvieco E et al (2014a) Integrating geospatial information into fire risk assessment. Int J Wildland Fire 23:606–619. https://doi.org/10.1071/WF12052
Chuvieco E, Allgöwer B, Salas J (2003) Integration of physical and human factors in fire danger assessment. In: Chuvieco E (ed) Wildland fire danger estimation and mapping, series in remote sensing. World Scientific Pub Co Inc, Singapore, pp 197–218. https://doi.org/10.1142/9789812791177_00074
Chuvieco E, Cocero D, Riaño D, Martin P, Martı́nez-Vega J, de la Riva J, Pérez F (2004) Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sens Environ 92:322–331. https://doi.org/10.1016/j.rse.2004.01.019
Chuvieco E, Justice C (2010) Relations between human factors and global fire activity. In: Chuvieco E, Li J, Yang X (eds) Advances in earth observation of global change. Springer, London, pp 187–199
Chuvieco E, Martínez S, Román MV, Hantson S, Pettinari ML (2014b) Integration of ecological and socio-economic factors to assess global vulnerability to wildfire. Global Ecol Biogeogr 23:245–258. https://doi.org/10.1111/geb.12095
Chuvieco E, Riaño D, Aguado I, Cocero D (2002) Estimation of fuel moisture content from multitemporal analysis of landsat thematic mapper reflectance data: applications in fire danger assessment. Int J Remote Sens 23:2145–2162. https://doi.org/10.1080/01431160110069818
Chuvieco E, Wagtendonk J, Riaño D, Yebra M, Ustin SL (2009) Estimation of fuel conditions for fire danger assessment. In: Chuvieco E (ed) Earth observation of wildland fires in Mediterranean ecosystems. Springer, Berlin, pp 83–96. https://doi.org/10.1007/978-3-642-01754-4
de Groot WJ, Flannigan MD, Cantin AS (2013) Climate change impacts on future boreal fire regimes. For Ecol Manag 294:35–44. https://doi.org/10.1016/j.foreco.2012.09.027
de Groot WJ et al (2006) Developing a global early warning system for wildland fire. In: Viegas DX (ed) V International conference on forest fire research, Coimbra, Portugal, 27–30 Nov 2006. p 12
Deeming JE, Burgan RE, Cohen JD (1977) The national fire-danger rating system—1978, General Technical Report INT-39. Ogden, Utah
Dell'Aglio DAG, Gargiulo M, Iodice A, Riccio D, Ruello G (2019) Active fire detection in multispectral super-resolved sentinel-2 images by means of sam-based approach. In: 2019 IEEE 5th international forum on research and technology for society and industry (RTSI), 9–12 Sept 2019. pp 124–127. https://doi.org/10.1109/rtsi.2019.8895538
Dowdy AJ, Mills GA, Finkele K, de Groot WJ (2009) Australian fire weather as represented by the McArthur forest fire danger index and the canadian forest fire weather index, CAWCR technical report No. 10. Centre for Australian Weather and Climate Research,
Doxsey-Whitfield E, MacManus K, Adamo SB, Pis-tolesi L, Squires J, Borkovska O, Baptista SR (2015) Taking advantage of the improved availability of census data: a first look at the gridded population of the world. Appl Geogr 1:226–234. https://doi.org/10.1080/23754931.2015.1014272
Dubayah R et al (2020) The Global Ecosystem Dynamics Investigation: high-resolution laser ranging of the Earth’s forests and topography. Sci Remote Sens 1:100002. https://doi.org/10.1016/j.srs.2020.100002
Esch T et al (2017) Breaking new ground in mapping human settlements from space—the global urban footprint ISPRS. J Photogramm 134:30–42. https://doi.org/10.1016/j.isprsjprs.2017.10.012
European Forest Fire Information System (2017) European Forest Map. JRC Contract No 384347 on the "Development of a European Fuel Map". European Commission
European Forest Fire Information System (2020) User Guide to EFFIS applications, version 2.5.
European Space Agency (2015) Sentinel-2 User Handbook, Issue 1, Rev 2.
Eva H, Lambin EF (2000) Fires and land-cover change in the tropics: a remote sensing analysis at the landscape scale. J Biogeogr 27:765–776
Evangeliou N et al (2019) Open fires in Greenland in summer 2017: transport, deposition and radiative effects of BC, OC and BrC emissions. Atmos Chem Phys 19:1393–1411. https://doi.org/10.5194/acp-19-1393-2019
FAO, GFMC (1999) 1999 Revision of the FAO Wildland Fire Management Terminology, by the Global Fire Monitoring Center (GFMC)
Farr TG et al (2007) The shuttle radar topography mission. Rev Geophys. https://doi.org/10.1029/2005rg000183
Fick SE, Hijmans RJ (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302–4315. https://doi.org/10.1002/joc.5086
Field RD et al (2015) Development of a global fire weather database. Nat Hazards Earth Syste Sci. https://doi.org/10.5194/nhess-15-1407-2015
Flannigan MD, Haar THV (1986) Forest fire monitoring using NOAA satellite AVHRR. Can J Forest Res 16:975–982. https://doi.org/10.1139/x86-171
Florinsky IV (2016) Chapter 3-digital elevation models. In: Florinsky IV (ed) Digital terrain analysis in soil science and geology, 2nd edn. Academic Presss, Cambridge, pp 77–108. https://doi.org/10.1016/B978-0-12-804632-6.00003-1
Franke J et al (2018) Fuel load mapping in the Brazilian Cerrado in support of integrated fire management. Remote Sens Environ 217:221–232. https://doi.org/10.1016/j.rse.2018.08.018
Freire S et al (2018) Enhanced data and methods for improving open and free global population grids: putting ‘leaving no one behind’ into practice. Int J Digital Earth. https://doi.org/10.1080/17538947.2018.1548656
Friedl MA, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A, Huang X (2010) MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens Environ 114:168–182. https://doi.org/10.1016/j.rse.2009.08.016
Ganteaume A, Camia A, Jappiot M, San-Miguel-Ayanz J, Long-Fournel M, Lampin C (2013) A review of the main driving factors of forest fire ignition over. Europe Environ Manag 51:651–662. https://doi.org/10.1007/s00267-012-9961-z
García M, Chuvieco E, Nieto H, Aguado I (2008) Combining AVHRR and meteorological data for estimating live fuel moisture content. Remote Sens Environ 112:3618–3627. https://doi.org/10.1016/j.rse.2008.05.002
García M, Popescu S, Riaño D, Zhao K, Neuenschwander A, Agca M, Chuvieco E (2012) Characterization of canopy fuels using ICESat/GLAS data. Remote Sens Environ 123:81–89. https://doi.org/10.1016/j.rse.2012.03.018
García M, Riaño D, Chuvieco E, Danson FM (2010) Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sens Environ 114:816–830. https://doi.org/10.1016/j.rse.2009.11.021
García M, Riaño D, Chuvieco E, Salas J, Danson FM (2011) Multispectral and LiDAR data fusion for fuel type mapping using Support Vector Machine and decision rules. Remote Sens Environ 115:1369–1379. https://doi.org/10.1016/j.rse.2011.01.017
García M, Saatchi S, Casas A, Koltunov A, Ustin SL, Ramirez C, Balzter H (2017) Extrapolating forest canopy fuel properties in the california rim fire by combining airborne LiDAR and landsat OLI data. Remote Sens 9:394
Giglio L, Boschetti L, Roy DP, Humber ML, Justice CO (2018) The Collection 6 MODIS burned area mapping algorithm and product. Remote Sens Environ 217:72–85. https://doi.org/10.1016/j.rse.2018.08.005
Giglio L, Descloitres J, Justice CO, Kaufman YJ (2003) An enhanced contextual fire detection algorithm for MODIS. Remote Sens Environ 87:273–282. https://doi.org/10.1016/S0034-4257(03)00184-6
Giglio L, Schroeder W, Justice CO (2016) The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens Environ 178:31–41. https://doi.org/10.1016/j.rse.2016.02.054
Goodman SJ et al (2013) The GOES-R geostationary lightning mapper (GLM). Atmos Res 125126:34–49. https://doi.org/10.1016/j.atmosres.2013.01.006
Hansen MC, DeFries RS, Townsend JRG, Carroll M, Dimiceli C, Sohlberg RA (2003) Global percent tree cover at a spatial resolution of 500 meters: first results of the MODIS vegetation continuous fields algorithm. Earth Interact 7:1–15
Hardy CC (2005) Wildland fire hazard and risk: problems, definitions, and context. Forest Ecol Manag 211:73–82. https://doi.org/10.1016/j.foreco.2005.01.029
Hawbaker TJ et al (2017) Mapping burned areas using dense time-series of landsat data. Remote Sens Environ 198:504–522. https://doi.org/10.1016/j.rse.2017.06.027
He J, Loboda TV, Jenkins L, Chen D (2019) Mapping fractional cover of major fuel type components across Alaskan tundra. Remote Sens Environ 232:111324. https://doi.org/10.1016/j.rse.2019.111324
Hermosilla T, Ruiz LA, Kazakova AN, Coops NC, Moskal LM (2014) Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. Int J Wildland Fire 23:224–233. https://doi.org/10.1071/WF13086
Hersbach H et al (2018) Operational global reanalysis: progress, future directions and synergies with NWP, ERA Report Series 27. European Centre for Medium Range Weather Forecasts, Reading, UK. https://doi.org/10.21957/tkic6g3wm
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. https://doi.org/10.1002/joc.1276
Hillger D et al (2013) First-Light Imagery from Suomi NPP VIIRS. Bull Am Meteor Soc 94:1019–1029. https://doi.org/10.1175/bams-d-12-00097.1
Holden ZA, Jolly WM (2011) Modeling topographic influences on fuel moisture and fire danger in complex terrain to improve wildland fire management decision support. For Ecol Manag 262:2133–2141. https://doi.org/10.1016/j.foreco.2011.08.002
Hościło A, Lewandowska A (2019) Mapping forest type and tree species on a regional scale using multi-temporal sentinel-2 data. Remote Sens 11:929
Hubacek M, Kovarik V, Kratochvil V (2016) Analysis of influence of terrain relief roughness on DEM accuracy generated from LIDAR in the Czech Republic territory. Int Arch Photogramm Remote Sens Spatial Inf Sci 4XLI-B4:25–30. https://doi.org/10.5194/isprs-archives-XLI-B4-25-2016
Huesca M, Riaño D, Ustin SL (2019) Spectral mapping methods applied to LiDAR data: application to fuel type mapping. Int J Appl Earth Obs Geoinf 74:159–168. https://doi.org/10.1016/j.jag.2018.08.020
Jackson PS, Hunt JCR (1975) Turbulent wind flow over a low hill. Q J R Meteorol Soc 101:929–955. https://doi.org/10.1002/qj.49710143015
Jia S, Kim SH, Nghiem SV, Kafatos M (2019) Estimating live fuel moisture using SMAP L-band radiometer soil moisture for Southern California, USA. Remote Sens 11:1575
Jones MW, Santín C, van der Werf GR, Doerr SH (2019) Global fire emissions buffered by the production of pyrogenic carbon. Nat Geosci 12:742–747. https://doi.org/10.1038/s41561-019-0403-x
Kanitz T et al (2019) Aeolus first light: first glimpse, vol 11180. In: International conference on space optics-ICSO 2018. SPIE
Keane RE, Burgan RE, van Wagtendonk J (2001) Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling. Int J Wildland Fire 10:301–319
Keetch JJ, Byram GM (1968) A drought index for forest fire control, Research Paper SE-38 (revised 1988). United States Department of Agriculture - Forest Service, Ashville, NC
Knorr W, Arneth A, Jiang L (2016) Demographic controls of future global fire risk. Nat Clim Change 6:781–785. https://doi.org/10.1038/nclimate2999
Konings AG, Rao K, Steele-Dunne SC (2019) Macro to micro: microwave remote sensing of plant water content for physiology and ecology. New Phytol 223:1166–1172. https://doi.org/10.1111/nph.15808
Lanorte A, Lasaponara R (2008) Fuel type characterization based on coarse resolution MODIS satellite data. Forest Biogeosci For 1:60–64. https://doi.org/10.3832/ifor0451-0010060
Lasaponara R, Lanorte A (2007) Remotely sensed characterization of forest fuel types by using satellite ASTER data. Int J Appl Earth Obs Geoinf 9:225–234. https://doi.org/10.1016/j.jag.2006.08.001
Latifovic R, Zhu Z-L, Cihlar J, Giri C, Olthof I (2004) Land cover mapping of North and Central America-Global Land Cover 2000. Remote Sens Environ 89:116–127. https://doi.org/10.1016/j.rse.2003.11.002
Lauk C, Erb K-H (2009) Biomass consumed in anthropogenic vegetation fires: global patterns and processes. Ecol Econ 69:301–309. https://doi.org/10.1016/j.ecolecon.2009.07.003
Lefsky MA (2010) A global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system. Geophys Res Lett 37:5. https://doi.org/10.1029/2010GL043622
Levine JS, Cofer WR, Cahoon DR, Winstead EL (1995) Biomass burning: a driver for global change. Environ Sci Technol 29:120A–125A. https://doi.org/10.1021/es00003a746
Leyk S et al (2019) The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use. Earth Syst Sci Data 11:1385–1409. https://doi.org/10.5194/essd-11-1385-2019
Lin Z, Chen F, Niu Z, Li B, Yu B, Jia H, Zhang M (2018) An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data. Remote Sens Environ 211:376–387. https://doi.org/10.1016/j.rse.2018.04.027
Liu L, Lim S, Shen X, Yebra M (2019) A hybrid method for segmenting individual trees from airborne lidar data. Comput Electron Agric 163:104871. https://doi.org/10.1016/j.compag.2019.104871
Lizundia-Loiola J, Otón G, Ramo R, Chuvieco E (2020) A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data. Remote Sens Environ 236:111493. https://doi.org/10.1016/j.rse.2019.111493
Lopez P (2016) A lightning parameterization for the ECMWF integrated forecasting system. Mon Weather Rev 144:3057–3075. https://doi.org/10.1175/mwr-d-16-0026.1
Lorenzini S, Bardazzi R, Giampietro MD, Feresin F, Taccola M, Cuevas LP (2012) Optical design of the lightning imager for MTG. In: Cugny B, Armandillo E, Karafolas N (eds) International conference on space optics 2012, Ajaccio, Corsica, France, 2012. SPIE. https://doi.org/10.1117/12.2309091
Luke RH, McArthur AG (1978) Bushfires in Australia. Australian Government Publishing Service, Canberra
Luo K, Quan X, He B, Yebra M (2019) Effects of live fuel moisture content on wildfire occurrence in fire-prone regions over Southwest China. Forests 10:887
Maliet E (2013) SPOT 6 and SPOT 7: offering SPOT data continuity. In: 64th International astronautical congress (IAC 2013), Beijing, China
Marino E, Ranz P, Tomé JL, Noriega MÁ, Esteban J, Madrigal J (2016) Generation of high-resolution fuel model maps from discrete airborne laser scanner and landsat-8 OLI: a low-cost and highly updated methodology for large areas. Remote Sens Environ 187:267–280. https://doi.org/10.1016/j.rse.2016.10.020
Markus T et al (2017) The ice, cloud, and land elevation satellite-2 (ICESat-2): science requirements, concept, and implementation. Remote Sens Environ 190:260–273. https://doi.org/10.1016/j.rse.2016.12.029
Martín Y, Zúñiga-Antón M, Rodrigues Mimbrero M (2019) Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain Geomatics. Nat Hazards Risk 10:385–411. https://doi.org/10.1080/19475705.2018.1526219
Martínez-Fernández J, Chuvieco E, Koutsias N (2013) Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression. Nat Hazards Earth Syst Sci 13:311–327. https://doi.org/10.5194/nhess-13-311-2013
Martínez J, Vega-Garcia C, Chuvieco E (2009) Human-caused wildfire risk rating for prevention planning in Spain. J Envinon Manag 90:1241–1252
McArthur AG (1967) Fire behaviour in eucalypt forests, Leaflet N. 107. Department of National Development, Forestry and Timber Bureau, Canberra
McCaffrey S (2004) Thinking of wildfire as a natural hazard. Soc Nat Resour 17:509–516. https://doi.org/10.1080/08941920490452445
Meldrum JR, Brenkert-Smith H, Champ PA, Falk L, Wilson P, Barth CM (2018) Wildland-urban interface residents’ relationships with wildfire: variation within and across communities. Soc Nat Resour 31:1132–1148. https://doi.org/10.1080/08941920.2018.1456592
Mendiguren G, Pilar Martín M, Nieto H, Pacheco-Labrador J, Jurdao S (2015) Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site. Biogeosciences 12:5523–5535. https://doi.org/10.5194/bg-12-5523-2015
Moritz MA et al (2014) Learning to coexist with wildfire. Nature 515:58–66. https://doi.org/10.1038/nature13946
Mutlu M, Popescu SC, Stripling C, Spencer T (2008) Mapping surface fuel models using lidar and multispectral data fusion for fire behavior. Remote Sens Environ 112:274–285. https://doi.org/10.1016/j.rse.2007.05.005
Myoung B, Kim HS, Nghiem VS, Jia S, Whitney K, Kafatos CM (2018) Estimating live fuel moisture from MODIS satellite data for wildfire danger assessment in Southern California USA. Remote Sens. https://doi.org/10.3390/rs10010087
Nadeau LB, McRae DJ, Jin J-Z (2005) Development of a national fuel-type map for Canada using fuzzy logic, information report NOR-X-406. Canadian Forest Service, Edmonton
Narine LL, Popescu SC, Malambo L (2019) Synergy of ICESat-2 and landsat for mapping forest aboveground biomass with deep learning. Remote Sens 11:1503
Nasir S, Iqbal IA, Ali Z, Shahzad A (2015) Accuracy assessment of digital elevation model generated from pleiades tri stereo-pair. In: 2015 7th international conference on recent advances in space technologies (RAST), 16–19 June 2015, pp 193–197. https://doi.org/10.1109/rast.2015.7208340
Nelson KJ, Long DG, Connot JA (2016) LANDFIRE 2010—Updates to the national dataset to support improved fire and natural resource management, 2016–1010. Reston, VA. https://doi.org/10.3133/ofr20161010
Neuenschwander AL, Magruder LA (2016) The potential impact of vertical sampling uncertainty on ICESat-2/ATLAS terrain and canopy height retrievals for multiple ecosystems. Remote Sens 8:1039
Noble IR, Gill AM, Bary GAV (1980) McArthur's fire-danger meters expressed as equations Australian. J Ecol 5:201–203. https://doi.org/10.1111/j.1442-9993.1980.tb01243.x
Oliva P, Schroeder W (2015) Assessment of VIIRS 375m active fire detection product for direct burned area mapping. Remote Sens Environ 160:144–155. https://doi.org/10.1016/j.rse.2015.01.010
Ottmar RD, Sandberg DV, Riccardi CL, Prichard SJ (2007) An overview of the fuel characteristic classification system—Quantifying, classifying, and creating fuelbeds for resource planning. Can J Forest Res 37:2383–2393
Pausas JG, Keeley JE (2009) A burning story: the role of fire in the history of life. Bioscience 59:593–601. https://doi.org/10.1525/bio.2009.59.7.10
Pesaresi M, Ehrlich D, Florczyk AJ, Freire S, Julea A, Kemper T, Syrris V (2016) The global human settlement layer from landsat imagery. In: 2016 IEEE international geoscience and remote sensing symposium (IGARSS), 10–15 July 2016, pp 7276–7279. https://doi.org/10.1109/igarss.2016.7730897
Pettinari ML, Chuvieco E (2017) Fire behavior simulation from global fuel and climatic information. Forests 8:179. https://doi.org/10.3390/f8060179
Pettinari ML, Chuvieco E (2016) Generation of a global fuel data set using the fuel characteristic classification system. Biogeosciences 13:2061–2076. https://doi.org/10.5194/bg-13-2061-2016
Pham HT, Marshall L, Johnson F, Sharma A (2018) A method for combining SRTM DEM and ASTER GDEM2 to improve topography estimation in regions without reference data. Remote Sens Environ 210:229–241. https://doi.org/10.1016/j.rse.2018.03.026
Popescu SC, Zhou T, Nelson R, Neuenschwander A, Sheridan R, Narine L, Walsh KM (2018) Photon counting LiDAR: an adaptive ground and canopy height retrieval algorithm for ICESat-2 data. Remote Sens Environ 208:154–170. https://doi.org/10.1016/j.rse.2018.02.019
Pyne SJ (1995) World fire. The culture of fire on earth. Henry Colt and Company Inc, New York
Pyne SJ, Andrews PL, Laven RD (1996) Introduction to wildland fire. Wiley, New York
Pyne SJ, Goldammer JG (1997) The culture of fire: an introduction to anthropogenic fire history. Sediment records of biomass burning and global change. Springer, Berlin, pp 71–114
Quan X, He B, Li X, Liao Z (2016) Retrieval of grassland live fuel moisture content by parameterizing radiative transfer model with interval estimated LAI. IEEE J Sel Top Appl Earth Observ Remote Sens 9:910–920. https://doi.org/10.1109/jstars.2015.2472415
Quan X, He B, Yebra M, Yin C, Liao Z, Li X (2017) Retrieval of forest fuel moisture content using a coupled radiative transfer model. Environ Model Softw 95:290–302. https://doi.org/10.1016/j.envsoft.2017.06.006
Quegan S et al (2019) The European space agency BIOMASS mission: measuring forest above-ground biomass from space. Remote Sens Environ 227:44–60. https://doi.org/10.1016/j.rse.2019.03.032
Quegan S et al. (2017) D6–Global Biomass Map: Algorithm Theoretical Basis Document.
Rienecker MM et al (2011) MERRA: NASA’s modern-era retrospective analysis for research and applications. J Clim 24:3624–3648. https://doi.org/10.1175/jcli-d-11-00015.1
Rodrigues M, de la Riva J, Fotheringham S (2014) Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weighted logistic regression. Appl Geogr 48:52–63. https://doi.org/10.1016/j.apgeog.2014.01.011
Rodrigues M, Jiménez A, de la Riva J (2016) Analysis of recent spatial–temporal evolution of human driving factors of wildfires in Spain. Nat Hazards 84:2049–2070. https://doi.org/10.1007/s11069-016-2533-4
Rollins MG (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. Int J Wildland Fire 18:235–249
Rosenqvist A, Shimada M, Ito N, Watanabe M (2007) ALOS PALSAR: a pathfinder mission for global-scale monitoring of the environment. IEEE Trans Geosci Remote 45:3307–3316. https://doi.org/10.1109/tgrs.2007.901027
Roteta E, Bastarrika A, Padilla M, Storm T, Chuvieco E (2019) Development of a sentinel-2 burned area algorithm: generation of a small fire database for sub-Saharan Africa. Remote Sens Environ 222:1–17. https://doi.org/10.1016/j.rse.2018.12.011
Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels, Research Paper INT-115. USDA Forest Service, Intermountain Forest and Range Experiment Station, Odgen, UT
Rothermel RC (1983) How to predict the spread and intensity of forest and range fires, INT-143. National Wildfire Coordinating Group, USDA Forest Service Intermountain Research Station, Boise, ID
Saatchi SS et al (2011) Benchmark map of forest carbon stocks in tropical regions across three continents. PNAS 108:9899–9904. https://doi.org/10.1073/pnas.1019576108
San-Miguel-Ayanz J et al. (2018) Basic Criteria to assess wildfire risk at the pan-European level. EUR 29500 EN. https://doi.org/10.2760/052345
San Miguel-Ayanz J et al. (2012) Comprehensive monitoring of wildfires in Europe: the European Forest Fire Information System (EFFIS). In: Tiefenbacher J (ed) Approaches to managing disaster—assessing hazards, emergencies and disaster impacts. InTech, Rijeka, Croatia, pp 87–108. https://doi.org/10.5772/28441
Sánchez Sánchez Y, Martínez-Graña A, Santos Francés F, Mateos Picado M (2018) Mapping wildfire ignition probability using sentinel 2 and LiDAR (Jerte Valley, Cáceres, Spain). Sensors 18:826
Santoro M (2018) GlobBiomass-global datasets of forest biomass. PANGAEA. https://doi.org/10.1594/pangaea.894711
Scott JH, Burgan RE (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel's Surface Fire Spread Model, RMRS-GTR-153. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO
Schenk T, Csatho B, van der Veen C, McCormick D (2014) Fusion of multi-sensor surface elevation data for improved characterization of rapidly changing outlet glaciers in Greenland. Remote Sens Environ 149:239–251. https://doi.org/10.1016/j.rse.2014.04.005
Schlobohm P, Brain J (2002) Gaining an understanding of the National Fire Danger Rating System, PMS 932. National Wildfire Coordinating Group, Fire Danger Working Team, Boise, ID
Schmetz J, Pili P, Tjemkes S, Just D, Kerkmann J, Rota S, Ratier A (2002) An introduction to meteosat second generatio (MSG). Bull Am Meteor Soc 83:977–992. https://doi.org/10.1175/1520-0477(2002)083
Schmit TJ, Griffith P, Gunshor MM, Daniels JM, Goodman SJ, Lebair WJ (2017) A closer look at the ABI on the GOES-R series. Bull Am Meteor Soc 98:681–698. https://doi.org/10.1175/bams-d-15-00230.1
Schneider FD, Ferraz AA, Hancock S, Duncanson LI, Dubayah RO, Pavlick RP, Schimel DS (2020) Towards mapping the diversity of canopy structure from space with GEDI. Environ Res Lett. https://doi.org/10.1088/1748-9326/ab9e99
Schroeder W, Oliva P, Giglio L, Csiszar IA (2014) The New VIIRS 375m active fire detection data product: algorithm description and initial assessment. Remote Sens Environ 143:85–96. https://doi.org/10.1016/j.rse.2013.12.008
Schroeder W, Oliva P, Giglio L, Quayle B, Lorenz E, Morelli F (2016) Active fire detection using Landsat-8/OLI data. Remote Sens Environ 185:210–220. https://doi.org/10.1016/j.rse.2015.08.032
Schunk C, Wastl C, Leuchner M, Schuster C, Menzel A (2013) Forest fire danger rating in complex topography—Results from a case study in the Bavarian Alps in autumn 2011. Nat Hazards Earth Syst Sci 13:2157–2167. https://doi.org/10.5194/nhess-13-2157-2013
Schutz BE, Zwally HJ, Shuman CA, Hancock D, DiMarzio JP (2005) Overview of the ICESat mission geophysical. Res Lett. https://doi.org/10.1029/2005gl024009
Shlisky A et al (2007) Fire, ecosystems and people: threats and strategiesfor global biodiversity conservation. The Nature Conservancy, Arlington, VA
Shu Q, Quan X, Yebra M, Liu X, Wang L, Zhang Y (2019) Evaluating the sentinel-2a satellite data for fuel moisture content retrieval. In: IGARSS 2019-2019 IEEE international geoscience and remote sensing symposium, 28 July–2 Aug 2019, pp 9416–9419. https://doi.org/10.1109/igarss.2019.8900104
Simard M, Pinto N, Fisher JB, Baccini A (2011) Mapping forest canopy height globally with spaceborne lidar. J Geophys Res 116:12. https://doi.org/10.1029/2011JG001708
SRTM (2015) The Shuttle Radar Topography Mission (SRTM) Collection User Guide. https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf.
Stefanidou A, Dragozi E, Stavrakoudis D, Gitas IZ (2018) Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery. Geocarto Int 33:1064–1083. https://doi.org/10.1080/10106049.2017.1333532
Stocks BJ, Lawson BD, Alexander ME, Van Wagner CE, McAlpine RS, Lynham TJ, Dubé DE (1989) Canadian forest fire danger rating system: an overview. For Chron 65:258–265
Stuhlmann R et al (2005) Plans for EUMETSAT’s third generation meteosat geostationary satellite programme. Adv Space Res 36:975–981. https://doi.org/10.1016/j.asr.2005.03.091
Syphard AD, Keeley JE, Pfaff AH, Ferschweiler K (2017) Human presence diminishes the importance of climate in driving fire activity across the United States. Proc Natl Acad Sci 114:13750–13755. https://doi.org/10.1073/pnas.1713885114
Syphard AD, Radeloff VC, Keeley JE, Hawbaker TJ, Clayton MK, Stewart SI, Hammer RB (2007) Human influence on California fire regimes. Ecol Appl 17:1388–1402
Tadono T, Nagai H, Ishida H, Oda F, Naito S, Minakawa K, Iwamoto H (2016) Generation of the 30 m-mesh global digital surface model. Int Arch Photogramm Remote Sens Spatial Inf Sci XLI-B4:157–162. https://doi.org/10.5194/isprs-archives-XLI-B4-157-2016
Tadono T, Takaku J, Ohgushi F, Doutsu M, Kobayashi K (2017) Updates of ‘AW3D30’ 30 M-MESH global digital surface model dataset. In: 2017 IEEE international geoscience and remote sensing symposium (IGARSS), 23–28 July 2017, pp 5656–5657. https://doi.org/10.1109/igarss.2017.8128290
Takaku J, Tadono T, Tsutsui K (2014) Generation of high resolution global DSM from ALOS PRISM. Int Arch Photogramm Remote Sens Spatial Inf Sci XL4:243–248. https://doi.org/10.5194/isprsarchives-XL-4-243-2014
Tatem AJ (2017) WorldPop, open data for spatial demography. Sci Data 4:170004. https://doi.org/10.1038/sdata.2017.4
Taylor PA, Mason PJ, Bradley EF (1987) Boundary-layer flow over low hills. Bound-Layer Meteorol 39:107–132. https://doi.org/10.1007/bf00121870
Thonicke K, Spessa A, Prentice IC, Harrison SP, Dong L, Carmona-Moreno C (2010) The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model. Biogeosciences 7:1991–2011. https://doi.org/10.5194/bg-7-1991-2010
Tyc G, Tulip J, Schulten D, Krischke M, Oxfort M (2005) The RapidEye mission design. Acta Astronaut 56:213–219. https://doi.org/10.1016/j.actaastro.2004.09.029
United States Geological Survey (2019) Landsat 8 (L8) Data Users Handbook, version 5.0, LSDS-1574.
van der Werf GR et al (2010) Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos Chem Phys 10:11707–11735. https://doi.org/10.5194/acp-10-11707-2010
Van Wagner CE (1987) Development and structure of the Canadian Forest Fire Weather Index System. Canadian Forestry Service, Ottawa
Van Wagtendonk JW, Root RR (2003) The use of multi-temporal landsat normalized difference vegetation index (NDVI) data for mapping fuel models in Yosemite National Park, USA. Int J Remote Sens 24:1639–1651. https://doi.org/10.1080/01431160210144679
Vegetation Continuous Field MOD44B, Collection 4, Version 3 (2007) University of Maryland, College Park, Maryland. https://www.glcf.umd.edu/data/vcf/. Accessed last accessed January 2012
Velden C et al (2005) Recent innovations in deriving tropospheric winds from meteorological satellites. Bull Am Meteor Soc 86:205–224. https://doi.org/10.1175/bams-86-2-205
Viegas D, Viegas M, Ferreira A (1992) Moisture content of fine forest fuels and fire occurrence in Central Portugal. Int J Wildland Fire 2:69–86. https://doi.org/10.1071/WF9920069
Vilar L, Camia A, San-Miguel-Ayanz J, Martín MP (2016) Modeling temporal changes in human-caused wildfires in Mediterranean Europe based on Land Use-Land Cover interfaces. For Ecol Manag 378:68–78. https://doi.org/10.1016/j.foreco.2016.07.020
Vitolo C, Di Giuseppe F, Krzeminski B, San-Miguel-Ayanz J (2019) A 1980–2018 global fire danger re-analysis dataset for the Canadian Fire Weather Indices. Sci Data 6:190032. https://doi.org/10.1038/sdata.2019.32
Wang L, Hunt ER, Qu JJ, Hao X, Daughtry CST (2013) Remote sensing of fuel moisture content from ratios of narrow-band vegetation water and dry-matter indices. Remote Sens Environ 129:103–110. https://doi.org/10.1016/j.rse.2012.10.027
Wang L, Quan X, He B, Yebra M, Xing M, Liu X (2019a) Assessment of the dual polarimetric sentinel-1A data for forest fuel moisture content estimation. Remote Sens 11:1568
Wang S et al (2019b) DEM generation from Worldview-2 stereo imagery and vertical accuracy assessment for its application in active tectonics. Geomorphology 336:107–118. https://doi.org/10.1016/j.geomorph.2019.03.016
Wessel B, Huber M, Wohlfart C, Marschalk U, Kosmann D, Roth A (2018) Accuracy assessment of the global TanDEM-X digital elevation model with GPS data ISPRS. J Photogramm 139:171–182. https://doi.org/10.1016/j.isprsjprs.2018.02.017
Whitney KL, Kim SH, Kafatos M (2019) Modeling live fuel moisture content with MODIS and VIIRS satellite data in Los Angeles County, California. In: American Geophysical Union, Fall Meeting 2019, San Francisco
Wooster M et al (2015) LSA SAF Meteosat FRP products - Part 1: algorithms, product contents, and analysis tmospheric. Chem Phys 15:13217–13239. https://doi.org/10.5194/acp-15-13217-2015
Yamaguchi Y, Kahle AB, Tsu H, Kawakami T, Pniel M (1998) Overview of advanced spaceborne thermal emission and reflection radiometer (ASTER). IEEE Trans Geosci Remote 36:1062–1071. https://doi.org/10.1109/36.700991
Yang J, Zhang Z, Wei C, Lu F, Guo Q (2017) Introducing the new generation of chinese geostationary weather satellites, Fengyun-4. Bull Am Meteor Soc 98:1637–1658. https://doi.org/10.1175/bams-d-16-0065.1
Yankovich EP, Yankovich KS, Baranovskiy NV, Bazarov AV, Sychev RS, Badmaev NB (2019) Mapping of vegetation cover using Sentinel-2 to estimate forest fire danger vol 11152. SPIE Remote Sensing. SPIE
Yebra M, Chuvieco E, Riaño D (2008) Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agric For Meteorol 148:523–536. https://doi.org/10.1016/j.agrformet.2007.12.005
Yebra M et al (2013) A global review of remote sensing of live fuel moisture content for fire danger assessment: moving towards operational products. Remote Sens Environ 136:455–468. https://doi.org/10.1016/j.rse.2013.05.029
Yebra M, Quan X, Riaño D, Rozas Larraondo P, van Dijk AIJM, Cary GJ (2018) A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing. Remote Sens Environ 212:260–272. https://doi.org/10.1016/j.rse.2018.04.053
Yebra M et al (2019) Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications. Sci Data 6:155. https://doi.org/10.1038/s41597-019-0164-9
Zhao J et al (2007) Spatial and temporal distributions of lightning activities in Northeast China from satellite observation and analysis for lightning fire. In: Gao W, Ustin SL (eds) Remote sensing and modeling of ecosystems for sustainability IV, vol 6679 San Diego, CA, p 66790M. https://doi.org/10.1117/12.729349
Zink M et al (2014) TanDEM-X: the new global DEM takes shape. IEEE Geosci Remote Sens Mag 2:8–23. https://doi.org/10.1109/mgrs.2014.2318895
Acknowledgements
The authors thank the International Space Science Institute (ISSI) for organizing the “Natural and man-made hazards monitoring by the Earth Observation missions: current status and scientific gaps” that led to this publication.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pettinari, M.L., Chuvieco, E. Fire Danger Observed from Space. Surv Geophys 41, 1437–1459 (2020). https://doi.org/10.1007/s10712-020-09610-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10712-020-09610-8