| 2 |
Akagi SK, Yokelson RJ, Wiedinmyer C, Alvarado MJ, Reid JS, Karl T, Crounse JD, Wennberg PO. Emission factors for open and domestic biomass burning for use in atmospheric models. Atmos Chem Phys, 2011, 11: 4039-4072,
DOI
|
| 3 |
Alberdi I, Cañellas I, Condes S. A long-scale biodiversity monitoring methodology for Spanish national forest inventory. Appl Álava Region Syst, 2014, 23: 93-110,
DOI
|
| 4 |
Araújo TM Jr, Carvalho AJ, Higuchi N Jr, Brasil ACP, Mesquita ALA. A tropical rainforest clearing experiment by biomass burning in the state of Pará, Brazil. Atmos Environ, 1999, 33: 1991-1998,
DOI
|
| 5 |
|
| 6 |
Barrett TM, Gray AN. Potential of a national monitoring program for forests to assess change in high-latitude ecosystems. Biol Conserv, 2011, 144: 1285-1294,
DOI
|
| 7 |
Bodí MB, Cerddà A, Mataix-Solera J, Doerr SH (2012) Efectos de los incendios forestales en la vegetación y el suelo en la cuenca mediterránea: Revisión bibliográfica. Bol la Asoc Geogr Esp 33–56. https://doi.org/10.21138/bage.2058
|
| 8 |
Cadena DA, Flores-Garnica JG, Flores-Rodríguez AG, Lomelí-Zavala ME. Efecto de incendios en la vegetación de sotobosque y propiedades químicas de suelo de bosques templados. Agro Productividad Agro Product, 2020, 13: 189-198
|
| 9 |
Carreiras JM, Pereira JM, Pereira JS. Estimation of tree canopy cover in evergreen oak woodlands using remote sensing. For Ecol Manage, 2006, 223: 45-53,
DOI
|
| 10 |
Castillo M, Pedernera P, Pena E. Incendios forestales y medio ambiente: una síntesis global. Rev Ambient y Desarro, 2003, 19: 44-53
|
| 11 |
Cervera T, Garrabou R, Tello E. Forestry policy and trends in the woodland areas of Catalonia from the 19th century until the present. Investig Hist Econ, 2015, 11: 116-127,
DOI
|
| 12 |
Chu T, Guo X, Takeda K. Temporal dependence of burn severity assessment in Siberian larch (Larix sibirica) forest of northern Mongolia using remotely sensed data. Int J Wildl Fire, 2016, 25: 685-698,
DOI
|
| 13 |
Chuvieco E, Riaño D, Danson FM, Martin P. Use of a radiative transfer model to simulate the postfire spectral response to burn severity. J Geophys Res Biogeosci, 2006, 111: 1-15,
DOI
|
| 14 |
Cocke AE, Fulé PZ, Crouse JE. Comparison of burn severity assessments using differenced normalized burn ratio and ground data. Int J Wildl Fire, 2005, 14: 189-198,
DOI
|
| 15 |
Collier S, Zhou S, Onasch TB, Jaffe DA, Kleinman L, Sedlacek AJ III, Briggs NL, Hee J, Fortner E, Shilling JE, Worsnop D, Yokelson RJ, Parworth C, Ge X, Xu J, Butterfield Z, Chand D, Dubey MK, Pekour MS, Springston S, Zhang Q. Regional influence of aerosol emissions from wildfires driven by combustion efficiency: insights from the BBOP campaign. Environ Sci Technol, 2016, 50: 8613-8622,
DOI
|
| 16 |
Conard SG, Solomon AM (2008) Chapter 5 Effects of Wildland Fire on Regional and Global Carbon Stocks in a Changing Environment. Dev. Environ. Sci.
|
| 17 |
Corona P, Chirici G, McRoberts RE, Winter S, Barbati A. Contribution of large-scale forest inventories to biodiversity assessment and monitoring. For Ecol Manage, 2011, 262: 2061-2069,
DOI
|
| 18 |
Davies GM, Domènech R, Gray A, Johnson PCD. Vegetation structure and fire weather influence variation in burn severity and fuel consumption during peatland wildfires. Biogeosciences, 2016, 13: 389-398,
DOI
|
| 19 |
De Santis A, Chuvieco E, Vaughan PJ. Short-term assessment of burn severity using the inversion of PROSPECT and GeoSail models. Remote Sens Environ, 2009, 113: 126-136,
DOI
|
| 20 |
De Santis A, Asner GP, Vaughan PJ, Knapp DE. Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery. Remote Sens Environ, 2010, 114: 1535-1545,
DOI
|
| 21 |
Deeming JE, Burgan RE, Cohen JD (1977) The national fire-danger rating system, 1978. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, vol 39
|
| 22 |
der Werf GR, Randerson JT, Giglio L, Collatz GJ, Mu M, Kasibhatla PS, van Leeuwen TT. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos Chem Phys, 2010, 10: 11707-11735,
DOI
|
| 23 |
|
| 24 |
Ellicott E, Vermote E, Giglio L, Roberts G. Estimating biomass consumed from fire using MODIS FRE. Geophys Res Lett, 2009, 36: 13,
DOI
|
| 25 |
Evtyugina M, Alves C, Calvo A, Nunes T, Tarelho L, Duarte M, Prozil SO, Evtuguin DV, Pio C. VOC emissions from residential combustion of Southern and mid-European woods. Atmos Environ, 2014, 83: 90-98,
DOI
|
| 26 |
Fady-Welterlen B. Is there really more biodiversity in Mediterranean forest ecosystems?. Taxon, 2005, 54: 905-910,
DOI
|
| 27 |
Fearnside PM, de Alencastro Graça PML, Rodrigues FJA. Burning of Amazonian rainforests: burning efficiency and charcoal formation in forest cleared for cattle pasture near Manaus, Brazil. For Ecol Manage, 2001, 146: 115-128,
DOI
|
| 28 |
Fernández-García V, Santamarta M, Fernández-Manso A, Quintano C, Marcos E, Calvo L. Burn severity metrics in fire-prone pine ecosystems along a climatic gradient using Landsat imagery. Remote Sens Environ, 2018, 206: 205-217,
DOI
|
| 29 |
Fernández-Manso A, Quintano C. Evaluating Landsat ETM+ emissivity-enhanced spectral indices for burn severity discrimination in Mediterranean forest ecosystems. Remote Sens Lett, 2015, 6: 302-310,
DOI
|
| 30 |
Ganteaume A, Camia A, Jappiot M, San-Miguel-Ayanz J, Long-Fournel M, Lampin C. A review of the main driving factors of forest fire ignition over Europe. Environ Manage, 2013, 51: 651-662,
DOI
|
| 31 |
Garcia M, Saatchi S, Casas A, Koltunov A, Ustin S, Ramirez C, Balzter H. Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data. J Geophys Res Biogeosci, 2017, 122: 340-353,
DOI
|
| 32 |
García-Llamas P, Suárez-Seoane S, Fernández-Guisuraga JM, Fernández-García V, Fernández-Manso A, Quintano C, Taboada A, Marcos E, Calvo L. Evaluation and comparison of Landsat 8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems. Int J Appl Earth Obs Geoinf, 2019, 80: 137-144,
DOI
|
| 33 |
Giglio L, Randerson JT, Van Der Werf GR. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). J Geophys Res Biogeosci, 2013, 118: 317-328,
DOI
|
| 34 |
González-Olabarria JR, Mola-Yudego B, Coll L. Different factors for different causes: analysis of the spatial aggregations of fire ignitions in Catalonia (Spain). Risk Anal, 2015, 35: 1197-1209,
DOI
|
| 35 |
Guo M, Xu J, Wang X, He H, Li J, Wu L. Estimating CO2 concentration during the growing season from MODIS and GOSAT in East Asia. Int J Remote Sens, 2015, 36: 4363-4383,
DOI
|
| 36 |
Guo M (2020) Remote Sensing of CO2 Emissions from Wildfires. Terr Ecosyst Biodivers (p 393–401
|
| 37 |
Hudak AT, Morgan P, Bobbitt MJ, Smith AM, Lewis SA, Lentile LB, McKinley RA. The relationship of multispectral satellite imagery to immediate fire effects. Fire Ecol, 2007, 3: 64-90,
DOI
|
| 38 |
Key CH, Benson NC (2006) Landscape assessment: remote sensing of severity, the Normalized Burn Ratio. In: Pages LA25--LA41 in DC Lutes. Fire effects monitoring and inventory system. USDA Forest Service, Rocky mountain research station, Fort Collins, Colorado, USA, FIREMON
|
| 39 |
Knorr W, Lehsten V, Arneth A. Determinants and predictability of global wildfire emissions. Atmos Chem Phys, 2012, 12: 6845-6861,
DOI
|
| 40 |
Köble R, Barbosa P, Seufert G (2008a) Estimating emissions from vegetation fires in Europe. 2000
|
| 41 |
Köble R, Barbosa P, Seufert G (2008b) Estimating emissions from vegetation fires in Europe. Atmos Environ, submitted for publication
|
| 42 |
Kukavskaya EA, Ivanova GA, Conard SG, McRae DJ, Ivanov VA. Biomass dynamics of central Siberian Scots pine forests following surface fires of varying severity. Int J Wildl Fire, 2014, 23: 872-886,
DOI
|
| 43 |
Leenhouts B. Assessment of biomass burning in the conterminous United States. Conserv Ecol, 1998, 2: 1
|
| 44 |
Liu JC, Pereira G, Uhl SA, Bravo MA, Bell ML. A systematic review of the physical health impacts from non-occupational exposure to wildfire smoke. Environ Res, 2015, 136: 120-132,
DOI
|
| 45 |
Lotufo DS, B. J, Machado NG, de Mello Taques, L. de S, M. D, M"utzenberg NLN, Biudes MS, . Índices Espectrais e Temperatura de Superfície em Áreas Queimadas no Parque Estadual do Araguaia em Mato Grosso. Rev Bras Geogr Física, 2020, 13: 2
|
| 46 |
Montero G, Ruiz-peinado R, Muñoz M (2005) Producción de biomasa y fijación de CO2 por los bosques españoles
|
| 47 |
Murphy BP, Prior LD, Cochrane MA, Williamson GJ, Bowman DM. Biomass consumption by surface fires across Earth’s most fire prone continent. Glob Chang Biol, 2019, 25: 254-268,
DOI
|
| 48 |
Oliva P, Chuvieco E (2011) Towards a dynamic burning efficiency factor. Adv Remote Sens GIS Appl For Fire Manag From local to Glob assessments 47
|
| 49 |
Oliva P (2013) FEMM -- Fire Effects Modeling and Mapping: An approach to estimate the spatial variability of burning efficiency. In: en Fernández, D. y Sabia, R. (Coords.): Remote sensing advances for system science the ESA science network: project, pp 93–102. Springer, Berlin, pp 2009–2011
|
| 50 |
Oliva P (2020) FEMM—fire effects modelling and mapping : an approach to estimate the spatial variability of burning efficiency FEMM—fire effects modelling and mapping : an approach to estimate the spatial variability of burning efficiency. https://doi.org/10.1007/978-3-642-32521-2
|
| 51 |
Ottmar RD, Miranda AI, Sandberg DV. Characterizing sources of emissions from wildland fires. Dev Environ Sci, 2009, 8: 61-78
|
| 52 |
Parks SA, Holsinger LM, Voss MA, Loehman RA, Robinson NP. Mean composite fire severity metrics computed with Google Earth Engine offer improved accuracy and expanded mapping potential. Remote Sens, 2018, 10: 879,
DOI
|
| 53 |
Pausas JG. Incendios forestales, 2012 Madrid Editorial CatarataCSIC
|
| 54 |
Righi CA, de Alencastro Graça LPM, Cerri CC, Feigl BJ, Fearnside PM. Biomass burning in Brazil’s Amazonian ``arc of deforestation’’: burning efficiency and charcoal formation in a fire after mechanized clearing at Feliz Natal, Mato Grosso. For Ecol Manage, 2009, 258: 2535-2546,
DOI
|
| 55 |
Rodrigues M, Alcasena F, Vega-García C. Modeling initial attack success of wildfire suppression in Catalonia, Spain. Sci Total Environ, 2019, 666: 915-927,
DOI
|
| 56 |
Ross AN, Wooster MJ, Boesch H, Parker R. First satellite measurements of carbon dioxide and methane emission ratios in wildfire plumes. Geophys Res Lett, 2013, 40: 4098-4102,
DOI
|
| 57 |
De Santis A, Asner GP, Vaughan PJ, Knapp DE. Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery. Remote Sens Environ, 2010, 114(7): 1535-1545,
DOI
|
| 58 |
Saulino L, Rita A, Migliozzi A, Maffei C, Allevato E, Garonna AP, Saracino A. Detecting burn severity across mediterranean forest types by coupling medium-spatial resolution satellite imagery and field data. Remote Sens, 2020, 12: 741,
DOI
|
| 59 |
Seiler W, Crutzen PJ. Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Clim Change, 1980, 2: 207-247,
DOI
|
| 60 |
Smith JE, Heath LS, Hoover CM. Carbon factors and models for forest carbon estimates for the 2005–2011 National Greenhouse Gas Inventories of the United States. For Ecol Manage, 2013, 307: 7-19,
DOI
|
| 61 |
Soverel NO, Perrakis DD, Coops NC. Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada. Remote Sens Environ, 2010, 114: 1896-1909,
DOI
|
| 62 |
Stambaugh MC, Hammer LD, Godfrey R. Performance of burn-severity metrics and classification in oak woodlands and grasslands. Remote Sens, 2015, 7: 10501-10522,
DOI
|
| 63 |
Stephens SL, Collins BM, Fettig CJ, Finney MA, Hoffman CM, Knapp EE, Wayman RB. Drought, tree mortality, and wildfire in forests adapted to frequent fire. Bioscience, 2018, 68: 77-88,
DOI
|
| 64 |
Turetsky MR, Kane ES, Harden JW, Ottmar RD, Manies KL, Hoy E, Kasischke ES. Recent acceleration of biomass burning and carbon losses in Alaskan forests and peatlands. Nat Geosci, 2011, 4: 27-31,
DOI
|
| 65 |
Urbanski S. Wildland fire emissions, carbon, and climate: Emission factors. For Ecol Manage, 2014, 317: 51-60,
DOI
|
| 66 |
Viegas DX, Ribeiro LM, Viegas MT, Pita LP, Rossa C. Impacts of fire on society: extreme fire propagation issues. Earth observation of wildland fires in mediterranean ecosystems, 2009 Berlin Springer 97-109,
DOI
|
| 67 |
Wang M, Son S, Shi W. Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data. Remote Sens Environ, 2009, 113: 635-644,
DOI
|
| 68 |
Warner TA, Skowronski NS, Gallagher MR. High spatial resolution burn severity mapping of the New Jersey Pine Barrens with WorldView-3 near-infrared and shortwave infrared imagery. Int J Remote Sens, 2017, 38: 598-616,
DOI
|
| 69 |
Whelan RJ. The ecology of fire, 1995 Cambridge Cambridge University Press
|
| 70 |
Wiedinmyer C, Quayle B, Geron C, Belote A, McKenzie D, Zhang X, O’Neill S, Wynne KK. Estimating emissions from fires in North America for air quality modeling. Atmos Environ, 2006, 40: 3419-3432,
DOI
|
| 71 |
Wiedinmyer C, Akagi SK, Yokelson RJ, Emmons LK, Al-Saadi JA, Orlando JJ, Soja AJ. The fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning. Geosci Model Dev, 2011, 4: 625-641,
DOI
|
| 72 |
Williamson GJ, Bowman DMJS, Price OF, Henderson SB, Johnston FH. A transdisciplinary approach to understanding the health effects of wildfire and prescribed fire smoke regimes. Environ Res Lett, 2016,
DOI
|
| 73 |
|
| 1 |
van der Werf GR, Randerson JT, Giglio L, van Leeuwen TT, Chen Y, Rogers BM, Mu M, van Marle MJE, Morton DC, Collatz GJ, Yokelson RJ, Kasibhatla PS. Global fire emissions estimates during 1997&2015 2017 Earth Syst Sci Data Discuss
DOI
|