Contrasting seismic risk for Santiago, Chile, from near-field and distant earthquake sources

More than half of all the people in the world now live in dense urban centres. The rapid expansion of cities, particularly in low-income nations, has enabled the economic and social development of millions of people. However, many of these cities are located near active tectonic faults that have not produced an earthquake in recent memory, raising the risk of losing hard-earned progress through a devastating earthquake. In this paper we explore the possible impact that earthquakes can have on the city of Santiago in Chile from various potential near-field and distant earthquake sources. We use high-resolution stereo satellite imagery and imagery-derived digital elevation models to accurately map the trace of the San Ramón Fault, a recently recognised active fault located along the eastern margins of the city. We use scenario-based seismic-risk analysis to compare and contrast the estimated damage and losses to the city from several potential earthquake sources and one past event, comprising (i) rupture of the San Ramón Fault, (ii) a hypothesised buried shallow fault beneath the centre of the city, (iii) a deep intra-slab fault, and (iv) the 2010 Mw 8.8 Maule earthquake. We find that there is a strong magnitude–distance trade-off in terms of damage and losses to the city, with smaller magnitude earthquakes in the magnitude range of 6–7.5 on more local faults producing 9 to 17 times more damage to the city and estimated fatalities compared to the great magnitude 8+ earthquakes located offshore in the subduction zone. Our calculations for this part of Chile show that unreinforced-masonry structures are the most vulnerable to these types of earthquake shaking. We identify particularly vulnerable districts, such as Ñuñoa, Santiago, and Providencia, where targeted retrofitting campaigns would be most effective at reducing potential economic and human losses. Due to the potency of near-field earthquake sources demonstrated here, our work highlights the importance of also identifying and considering proximal minor active faults for cities in seismic zones globally in addition to the more major and distant large fault zones that are typically focussed on in the assessment of hazard

Estimation of real evapotranspiration (ETR) and potential evapotranspiration (ETP) in the southwest of the Buenos Aires Province (Argentina) using MODIS images

[EN] Using regression analysis between actual evapotranspiration (ETR) and potential evapotranspiration (ETP) values obtained in seven meteorological observatories and remote sensing derived data from MODIS images (Surface temperature and Normalized Difference Vegetation Index - NDVI) models for estimating ETR and ETP in the southwest of the Buenos Aires Province (Argentina) were developed for the 2000–2014 period. Both models were satisfactorily evaluated in the meteorological observatories used. A regression model was adjusted for ETR with a determination coefficient of 0,6959. Regression model was nonlinear in the case of the ETP variable with a determination coefficient of 0,8409. The individual regression analysis for each meteorological observatories explicate the behavior of the regression for the total data set of ETR and ETP. According to these results, the utility of remote sensing in determination of ETR and ETP in areas without meteorological data was confirmed.[ES] Se han elaborado modelos para el cálculo de evapotranspiración real (ETR) y de evapotranspiración poten-cial (ETP) en base a un análisis de regresión múltiple entre dichos parámetros estimados en siete estaciones meteoro-lógicas y dos variables derivadas de imágenes satelitales MODIS: Temperatura de Superficie (TS) e Índice Normalizado de Diferencia de Vegetación (Normalized Difference Vegetation Index -NDVI). Dichos modelos permitieron estimar ETR y ETP en el sudoeste de la provincia de Buenos Aires (Argentina) en base al análisis del período 2000/2014. Ambos fueron calibrados satisfactoriamente en cada una de las estaciones meteorológicas utilizadas. Se ajustó un modelo de regresión múltiple lineal a la variable ETR, con un coeficiente de determinación de 0,6959. En el caso de la variable ETP el modelo de regresión ajustado fue no lineal y su coeficiente de determinación de 0,8409. El análisis de regresión individual de cada una de las estaciones meteorológicas permitió explicar el comportamiento de la regresión basada en el conjunto completo de datos, tanto para la variable ETR como para la variable ETP. Los resultados refuerzan la ventaja de la teledetección en la estimación de ETR y ETP en zonas en donde no se dispone de datos meteorológicos.Marini, F.; Santamaría, M.; Oricchio, P.; Di Bella, CM.; Basualdo, A. (2017). Estimación de evapotranspiración real (ETR) y de evapotranspiración potencial (ETP) en el sudoeste bonaerense (Argentina) a partir de imágenes MODIS. Revista de Teledetección. (48):29-41. doi:10.4995/raet.2017.6743.SWORD294148Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998). A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212-213, 198-212. doi:10.1016/s0022-1694(98)00253-4Bastiaanssen, W. G. . (2000). SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of Hydrology, 229(1-2), 87-100. doi:10.1016/s0022-1694(99)00202-4Burnham, K.P., Anderson, D.R. 2002. Model Selection and Multimodel Inference - A Practical InformationTheoretic Approach. New York: Springer-Verlag.CASELLES, V., DELEGIDO, J., SOBRINO, J. A., & HURTADO, E. (1992). Evaluation of the maximum evapotranspiration over the La Mancha region, Spain, using NO A A AVHRR data. International Journal of Remote Sensing, 13(5), 939-946. doi:10.1080/01431169208904167Caselles, V., Artigao, M. M., Hurtado, E., Coll, C., & Brasa, A. (1998). Mapping Actual Evapotranspiration by Combining Landsat TM and NOAA-AVHRR Images: Application to the Barrax Area, Albacete, Spain. Remote Sensing of Environment, 63(1), 1-10. doi:10.1016/s0034-4257(97)00108-9Casta-eda-Ibá-ez, C., Martínez-Menes, M., PascualRamírez, F., Flores-Magdaleno, H., FernándezReynoso, D. y Esparza-Govea, S. 2015. Estimación de coeficientes de cultivo mediante sensores remotos en el distrito de riego río Yaqui, Sonora, México. Agrociencia, 49, 221-232.Castellví, F., & Snyder, R. L. (2010). A New Procedure Based on Surface Renewal Analysis to Estimate Sensible Heat Flux: A Case Study over Grapevines. Journal of Hydrometeorology, 11(2), 496-508. doi:10.1175/2009jhm1151.1Cihlar, J., St.-Laurent, L., & Dyer, J. A. (1991). Relation between the normalized difference vegetation index and ecological variables. Remote Sensing of Environment, 35(2-3), 279-298. doi:10.1016/0034-4257(91)90018-2Di Bella, C. M., Rebella, C. M., & Paruelo, J. M. (2000). Evapotranspiration estimates using NOAA AVHRR imagery in the Pampa region of Argentina. 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Estimation of grassland biophysical parameters in a “dehesa” ecosystem from field spectroscopy and airborne hyperspectral imagery

[EN] The aim of this paper is the estimation of biophysical vegetation parameters from its optical properties. The variables Fuel Moisture Content (FMC), Canopy Water Content (CWC), Leaf Area Index (LAI), dry matter (Cm) and AboveGround Biomass (AGB) were estimated in the laboratory from vegetation samples collected simultaneously with the acquisition of spectral data from the Compact Airborne Spectrographic Imager (CASI) sensor and the field spectroradiometer ASD FieldSpec® 3. Spectral vegetation indices found in the literature were computed from hyperspectral data. Their linear relationships with the biophysical variables measured in the field were analysed. Results show consistent relationships between analysed biophysical parameters and spectral indices, mainly those using SWIR and red-egde bands which reveal the importance of these spectral regions for the estimation of biophysical variables in herbaceous covers. Determination coefficients (R2) above 0.91 and RRMSE of 21.4% have been obtained for the spectral indexes calculated whit ASD data, and 0.91 R2 and RRMSE of 15.5% for the spectral indexes calculated whit CASI data.[ES] Este trabajo aborda la estimación de variables biofísicas de un pastizal de dehesa a partir de información óptica generada por sensores próximos y remotos. Las variables de contenido de humedad del combustible (FMC), contenido de agua del dosel (CWC), índice de área foliar (LAI), materia seca (Cm) y biomasa superficial (AGB) fueron estimadas en laboratorio a partir de muestras de vegetación tomadas simultáneamente a la adquisición de datos hiperespectrales del sensor Compact Airbone Spectrographic Imager (CASI) y del espectro-radiómetro de campo ASD FieldSpec®3. A partir de la información espectral se han calculado diversos índices extraídos de la literatura y se han analizado las relaciones lineales existentes con las variables biofísicas medidas en campo. Los resultados muestran relaciones consistentes entre las variables biofísicas y los índices espectrales, especialmente en el caso de los índices basados en bandas del infrarrojo medio de onda corta (SWIR) y del red-edge, poniendo de manifiesto la importancia de estas regiones en la estimación de variables biofísicas en cubiertas de pastizal. Se han obteniendo coeficientes de determinación (R2) superiores a 0,91 y un error cuadrático medio relativo (RRMSE) de 21,4%, para los índices espectra-les calculados con datos ASD; yR2 de 0,91 y RRMSE de 15,5% para los índices espectrales calculados con datos CASI.Este trabajo se ha realizado en el contexto de los proyectos BIOSPEC (CGL2008-02301/CLI) financiado por el Ministerio e Innovación y FLUχPEC (CGL2012-34383) financiado por el Ministerio de Economía y Competitividad. Agradecemos al Ministerio de Educación, Cultura y Deporte la financiación recibida a través del programa de becas FPU del investigador predoctoral José Ramón Melendo. Nuestro agradecimiento al personal de SpecLab-CSIC, Universidad de Alcalá e Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria que ha participado en la recogida y procesamiento de datos.Melendo-Vega, JR.; Martín, MP.; Vilar Del Hoyo, L.; Pacheco-Labrador, J.; Echavarría, P.; Martínez-Vega, J. (2017). Estimación de variables biofísicas del pastizal en un ecosistema de dehesa a partir de espectro-radiometría de campo e imágenes hiperespectrales aeroportadas. Revista de Teledetección. (48):13-28. https://doi.org/10.4995/raet.2017.7481SWORD132848Haboudane, D. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3), 337-352. doi:10.1016/j.rse.2003.12.013Hardisky, M.A., Klemas, V., Smart, R.M. 1983. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrametry Engineering and Remote Sensing, 49, 77-83Hernández-Clemente, R., Navarro-Cerrillo, R. M., Suárez, L., Morales, F., & Zarco-Tejada, P. J. (2011). Assessing structural effects on PRI for stress detection in conifer forests. Remote Sensing of Environment, 115(9), 2360-2375. doi:10.1016/j.rse.2011.04.036Herrmann, I., Pimstein, A., Karnieli, A., Cohen, Y., Alchanatis, V., & Bonfil, D. J. (2011). LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sensing of Environment, 115(8), 2141-2151. doi:10.1016/j.rse.2011.04.018Hilker, T., Coops, N. C., Hall, F. G., Black, T. A., Wulder, M. A., Nesic, Z., & Krishnan, P. (2008). Separating physiologically and directionally induced changes in PRI using BRDF models. Remote Sensing of Environment, 112(6), 2777-2788. doi:10.1016/j.rse.2008.01.011Hill, M.J., Hanan, N.P., Hoffmann, W., Scholes, R., Prince, S., Ferwerda, J., Lucas, R.M., Baker, I., Arneth, A., Higgings, S.I., Barret, D.J., Disney, M., Hutley, L. 2011. Remote sensing and modeling of savannas: The state of the dis-union. 34th International Symposium on Remote Sensing of Environment. Sydney, 1-6.HongRui, R., GuangSheng, Z., Feng, Z., XinShi, Z. 2011. Evaluating cellulose absorption index (CAI) for non-photosynthetic biomass estimation in the desert steppe of Inner Mongolia. Chinese Science Bulletin, 57, 1716-1722.Huete, A. . (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309. doi:10.1016/0034-4257(88)90106-xKuusk, A. (1995). A fast, invertible canopy reflectance model. Remote Sensing of Environment, 51(3), 342-350. doi:10.1016/0034-4257(94)00059-vLee, K.-S., Cohen, W. B., Kennedy, R. E., Maiersperger, T. K., & Gower, S. T. (2004). Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sensing of Environment, 91(3-4), 508-520. doi:10.1016/j.rse.2004.04.010Li, W., Niu, Z., Liang, X., Li, Z., Huang, N., Gao, S., … Muhammad, S. (2015). Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling. International Journal of Applied Earth Observation and Geoinformation, 41, 88-98. doi:10.1016/j.jag.2015.04.020Liu, J., Miller, J.R., Haboudane, D., Pattey, E. 2004. Exploring the relationship between red edge parameters and crop variables for precision agriculture. 2004 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Anchorage, 1276-1279.Mahalanobis, P.C. 1936. On the generalised distance in statistics. Proceedings National Institute of Science, India, 49-55Nagler, P. L., Inoue, Y., Glenn, E. ., Russ, A. ., & Daughtry, C. S. . (2003). Cellulose absorption index (CAI) to quantify mixed soil–plant litter scenes. Remote Sensing of Environment, 87(2-3), 310-325. doi:10.1016/j.rse.2003.06.001Pacheco-Labrador, J., González-Cascón, R., Martín, M. P., & Riaño, D. (2014). Understanding the optical responses of leaf nitrogen in Mediterranean Holm oak (Quercus ilex) using field spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 26, 105-118. doi:10.1016/j.jag.2013.05.013Perez-Priego, O., Guan, J., Rossini, M., Fava, F., Wutzler, T., Moreno, G., … Migliavacca, M. (2015). Sun-induced chlorophyll fluorescence and photochemical reflectance index improve remote-sensing gross primary production estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem. Biogeosciences, 12(21), 6351-6367. doi:10.5194/bg-12-6351-2015Pinty, B., & Verstraete, M. M. (1992). GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio, 101(1), 15-20. doi:10.1007/bf00031911Privette, J. ., Myneni, R. ., Knyazikhin, Y., Mukelabai, M., Roberts, G., Tian, Y., … Leblanc, S. . (2002). Early spatial and temporal validation of MODIS LAI product in the Southern Africa Kalahari. Remote Sensing of Environment, 83(1-2), 232-243. doi:10.1016/s0034-4257(02)00075-5Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119-126. doi:10.1016/0034-4257(94)90134-1Riano, D., Vaughan, P., Chuvieco, E., Zarco-Tejada, P. J., & Ustin, S. L. (2005). Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: analysis at leaf and canopy level. IEEE Transactions on Geoscience and Remote Sensing, 43(4), 819-826. doi:10.1109/tgrs.2005.843316Richter, K., Atzberger, C., Hank, T. B., & Mauser, W. (2012). Derivation of biophysical variables from Earth observation data: validation and statistical measures. Journal of Applied Remote Sensing, 6(1), 063557-1. doi:10.1117/1.jrs.6.063557Rouse, J.W., Hass, R.H., Schell, J.A., Deering, D.W. 1974. Monitoring Vegetation Systems in the Great Plains whit ERTS. Proceeding, 3rd Earth Resource Technology Satellite (ERTS) Symposium, NASA, Washington DC, 1, 48-62SCHMIDTLEIN, S. (2004). Mapping of continuous floristic gradients in grasslands using hyperspectral imagery. Remote Sensing of Environment, 92(1), 126-138. doi:10.1016/j.rse.2004.05.004Serrano, L., Peñuelas, J., & Ustin, S. L. (2002). Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data. Remote Sensing of Environment, 81(2-3), 355-364. doi:10.1016/s0034-4257(02)00011-1SHAPIRO, S. S., & WILK, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3-4), 591-611. doi:10.1093/biomet/52.3-4.591Smith, G. M., & Milton, E. J. (1999). The use of the empirical line method to calibrate remotely sensed data to reflectance. International Journal of Remote Sensing, 20(13), 2653-2662. doi:10.1080/014311699211994Wieneke, S., Ahrends, H., Damm, A., Pinto, F., Stadler, A., Rossini, M., & Rascher, U. (2016). 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The Isolation of Typhoid or Paratyphoid Bacilll from Urines.

The socio-economic factors are of key importance during all phases of wildfire management that include prevention, suppression and restoration. However, modeling these factors, at the proper spatial and temporal scale to understand fire regimes is still challenging. This study analyses socio-economic drivers of wildfire occurrence in central Spain. This site represents a good example of how human activities play a key role over wildfires in the European Mediterranean basin. Generalized Linear Models (GLM) and machine learning Maximum Entropy models (Maxent) predicted wildfire occurrence in the 1980s and also in the 2000s to identify changes between each period in the socio-economic drivers affecting wildfire occurrence. GLM base their estimation on wildfire presence-absence observations whereas Maxent on wildfire presence-only. According to indicators like sensitivity or commission error Maxent outperformed GLM in both periods. It achieved a sensitivity of 38.9% and a commission error of 43.9% for the 1980s, and 67.3% and 17.9% for the 2000s. Instead, GLM obtained 23.33, 64.97, 9.41 and 18.34%, respectively. However GLM performed steadier than Maxent in terms of the overall fit. Both models explained wildfires from predictors such as population density and Wildland Urban Interface (WUI), but differed in their relative contribution. As a result of the urban sprawl and an abandonment of rural areas, predictors like WUI and distance to roads increased their contribution to both models in the 2000s, whereas Forest-Grassland Interface (FGI) influence decreased. This study demonstrates that human component can be modelled with a spatio-temporal dimension to integrate it into wildfire risk assessment

Novel CYP4F22 mutations associated with autosomal recessive congenital ichthyosis (ARCI). Study of the CYP4F22 c.1303C>T founder mutation

Mutations in CYP4F22 cause autosomal recessive congenital ichthyosis (ARCI). However, less than 10% of all ARCI patients carry a mutation in CYP4F22. In order to identify the molecular basis of ARCI among our patients (a cohort of ninety-two Spanish individuals) we performed a mutational analysis using direct Sanger sequencing in combination with a multigene targeted NGS panel. From these, eight ARCI families (three of them with Moroccan origin) were found to carry five different CYP4F22 mutations, of which two were novel. Computational analysis showed that the mutations found were present in highly conserved residues of the protein and may affect its structure and function. Seven of the eight families were carriers of a highly recurrent CYP4F22 variant, c.1303C>T; p.(His435Tyr). A 12Mb haplotype was reconstructed in all c.1303C>T carriers by genotyping ten microsatellite markers flanking the CYP4F22 gene. A prevalent 2.52Mb haplotype was observed among Spanish carrier patients suggesting a recent common ancestor. A smaller core haplotype of 1.2Mb was shared by Spanish and Moroccan families. Different approaches were applied to estimate the time to the most recent common ancestor (TMRCA) of carrier patients with Spanish origin. The age of the mutation was calculated by using DMLE and BDMC2. The algorithms estimated that the c.1303C>T variant arose approximately 2925 to 4925 years ago, while Spanish carrier families derived from a common ancestor who lived in the XIII century. The present study reports five CYP4F22 mutations, two of them novel, increasing the number of CYP4F22 mutations currently listed. Additionally, our results suggest that the recurrent c.1303C>T change has a founder effect in Spanish population and c.1303C>T carrier families originated from a single ancestor with probable African ancestry

Biogeographical origin and timing of the founder ichthyosis TGM1 c.1187G > A mutation in an isolated Ecuadorian population

An unusually high frequency of the lamellar ichthyosis TGM1 mutation, c.1187G > A, has been observed in the Ecuadorian province of Manabi. Recently, the same mutation has been detected in a Galician patient (Northwest of Spain). By analyzing patterns of genetic variation around this mutation in Ecuadorian patients and population matched controls, we were able to estimate the age of c.1187G > A and the time to their most recent common ancestor (TMRCA) of c.1187G > A Ecuadorian carriers. While the estimated mutation age is 41 generations ago (~1,025 years ago [ya]), the TMRCA of Ecuadorian c.1187G > A carrier haplotypes dates to just 17 generations (~425 ya). Probabilistic-based inferences of local ancestry allowed us to infer a most likely European origin of a few (16% to 30%) Ecuadorian haplotypes carrying this mutation. In addition, inferences on demographic historical changes based on c.1187G > A Ecuadorian carrier haplotypes estimated an exponential population growth starting ~20 generations, compatible with a recent founder effect occurring in Manabi. Two main hypotheses can be considered for the origin of c.1187G > A: (i) the mutation could have arisen in Spain >1,000 ya (being Galicia the possible homeland) and then carried to Ecuador by Spaniards in colonial times ~400 ya, and (ii) two independent mutational events originated this mutation in Ecuador and Galicia. The geographic and cultural characteristics of Manabi could have favored a founder effect that explains the high prevalence of TGM1 c.1187G > A in this region

Propuesta de un sistema espacialmente explícito para evaluar el peligro de incendios

Los incendios forestales suponen un factor muy destacado en la transformación ambiental de buena parte de los ecosistemas terrestres. Tienen impactos globales, afectando a la superficie forestal y a las emisiones de gases de efecto invernadero, y efectos locales, relacionados con la degradación de suelos, erosión, modificación de la dinámica de la vegetación y pérdida de recursos y de vidas humanas. La prevención de incendios resulta cada vez más crítica, para paliar los efectos negativos de los mismos. Se presentan en este trabajo las variables de entrada y el esquema de integración para estimar el peligro de ocurrencia de incendios que se desarrolló en el marco del proyecto Firemap. Se generó información de diversas fuentes, que hacen referencia a variables socio-económicas, así como al estado de los combustibles y las características del territorio, utilizando sistemas de información geográfica (SIG) e imágenes de satélite. Todas las variables se cartografiaron a una resolución espacial de un 1 km2 y se integraron en un servidor web, que estuvo operativo para su evaluación durante el verano de 2007. Se presenta la comparación entre la variación temporal de los índices generados y la ocurrencia observada en la Comunidad de Madrid, una de las regiones del estudio.Forest fires are a major factor of environmental transformation in several ecosystems. Fires have global impacts, affecting forested areas and having an important impact in greenhouse gas emissions. Additionally, fires have local impacts, associated to soil degradation, soil erosion, vegetation dynamics, and lost of lives and properties. Fire prevention is critical to reduce the negative impacts of fire. This paper presents the input variables and the integration scheme developed within the Firemap project (funded under the Spanish Ministry of Science and Technology) to map wildland fire occurrence probability. The project first generated fire risk variables related to several factors of fire ignition or propagation. They were generated from a wide variety of sources using geographic information systems and remote sensing technologies. All variables were mapped at 1 sq km spatial resolution, and were integrated into single indices. The risk system included the development of a dedicated web-mapping server to facilitate the access to the end-users. This service was tested in the summer of 2007 for semi-operational use. The paper presents the first validation results of the danger index, by comparing temporal trends of the different danger components to the fire occurrence in the Madrid region, one of the test sites

Challenge 8: Digital Citizenship

Digital relations are deeply transforming our lives : from the nature of political participation to the relationship between digital and non-digital environments ; from the reorganization of the public sphere to the ethics of responsibility, transparency or inclusiveness. We are witnessing fundamental changes in the infrastructures of democracy and the emergence of new forms of digital citizenship.Peer reviewe

Propuesta de un sistema espacialmente explícito para evaluar el peligro de incendios

Los incendios forestales suponen un factor muy destacado en la transformación ambiental de buena parte de los ecosistemas terrestres. Tienen impactos globales, afectando a la superficie forestal y a las emisiones de gases de efecto invernadero, y efectos locales, relacionados con la degradación de suelos, erosión, modificación de la dinámica de la vegetación y pérdida de recursos y de vidas humanas. La prevención de incendios resulta cada vez más crítica, para paliar los efectos negativos de los mismos. Se presentan en este trabajo las variables de entrada y el esquema de integración para estimar el peligro de ocurrencia de incendios que se desarrolló en el marco del proyecto Firemap. Se generó información de diversas fuentes, que hacen referencia a variables socio-económicas, así como al estado de los combustibles y las características del territorio, utilizando sistemas de información geográfica (SIG) e imágenes de satélite. Todas las variables se cartografiaron a una resolución espacial de un 1 km2 y se integraron en un servidor web, que estuvo operativo para su evaluación durante el verano de 2007. Se presenta la comparación entre la variación temporal de los índices generados y la ocurrencia observada en la Comunidad de Madrid, una de las regiones del estudio.Forest fires are a major factor of environmental transformation in several ecosystems. Fires have global impacts, affecting forested areas and having an important impact in greenhouse gas emissions. Additionally, fires have local impacts, associated to soil degradation, soil erosion, vegetation dynamics, and lost of lives and properties. Fire prevention is critical to reduce the negative impacts of fire. This paper presents the input variables and the integration scheme developed within the Firemap project (funded under the Spanish Ministry of Science and Technology) to map wildland fire occurrence probability. The project first generated fire risk variables related to several factors of fire ignition or propagation. They were generated from a wide variety of sources using geographic information systems and remote sensing technologies. All variables were mapped at 1 sq km spatial resolution, and were integrated into single indices. The risk system included the development of a dedicated web-mapping server to facilitate the access to the end-users. This service was tested in the summer of 2007 for semi-operational use. The paper presents the first validation results of the danger index, by comparing temporal trends of the different danger components to the fire occurrence in the Madrid region, one of the test sites

Development of a framework for fire risk assessment using remote sensing and geographic information system technologies

Forest fires play a critical role in landscape transformation, vegetation succession, soil degradation and air quality. Improvements in fire risk estimation are vital to reduce the negative impacts of fire, either by lessen burn severity or intensity through fuel management, or by aiding the natural vegetation recovery using post-fire treatments. This paper presents the methods to generate the input variables and the risk integration developed within the Firemap project (funded under the Spanish Ministry of Science and Technology) to map wildland fire risk for several regions of Spain. After defining the conceptual scheme for fire risk assessment, the paper describes the methods used to generate the risk parameters, and presents proposals for their integration into synthetic risk indices. The generation of the input variables was based on an extensive use of geographic information system and remote sensing technologies, since the project was intended to provide a spatial and temporal assessment of risk conditions. All variables were mapped at 1 km2 spatial resolution, and were integrated into a web-mapping service system. This service was active in the summer of 2007 for semi-operational testing of end-users. The paper also presents the first validation results of the danger index, by comparing temporal trends of different danger components and fire occurrence in the different study regions.The Firemap project was funded by the Spanish Ministry of Science and Education (CGL2004-060490C04-01/CLI) through the Environment and Climate programPeer reviewe