PUBLICACIONES ÚLTIMOS AÑOS

2020

  • 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 Sensing of Environment, 236, 111493, https://doi.org/10.1016/j.rse.2019.111493

2019

  • M. Suárez-Muñoz, F.J. Bonet-García, J.A. Hódar, J. Herrero, M.A. Tanase, and L. Torres-Muros (2019). INSTAR: An Agent-Based Model that integrates existing knowledge to simulate the population dynamics of a forest pest. Ecological Modelling, vol 411 (1), 108764.
  • Belenguer-Plomer, M.A., Tanase, M.A., Fernandez-Carrillo, A., & Chuvieco, E. (2019). Burned area detection and mapping using Sentinel-1 backscatter coefficient and thermal anomalies. Remote Sensing of Environment, 233, 111345.
  • Carrillo, A. L. McCaw, M. Tanase (2019). Estimating prescribed fire severity in eucalypt forests using L-band SAR change detection. Remote Sensing of Environment,224, 133-144.
  • Cruz-López, M.I., Manzo-Delgado, L.d.L., Aguirre-Gómez, R., Chuvieco, E., & Equihua-Benítez, J.A. (2019). Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Ecoregions. Remote Sensing, 11, 1185.
  • Chuvieco, E., Mouillot, F., van der Werf, G.R., San Miguel, J., Tanasse, M., Koutsias, N., García, M., Yebra, M., Padilla, M., Gitas, I., Heil, A., Hawbaker, T.J., & Giglio, L. (2019). Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment, 225, 45-64.
  • Forkel, M., Andela, N., Harrison, S.P., Lasslop, G., van Marle, M., Chuvieco, E., Dorigo, W., Forrest, M., Hantson, S., Heil, A., Li, F., Melton, J., Sitch, S., Yue, C., & Arneth, A. (2019a). Emergent relationships on burned area in global satellite observations and fire-enabled vegetation models. Biogeosciences, 16, 47-76.
  • Forkel, M., Dorigo, W.A., Lasslop, G., Chuvieco, E., Hantson, S., Heil, A., Teubner, I., Thonicke, K., & Harrison, S., P. (2019b). Recent global and regional trends in burned area and their compensating environmental controls. Environmental Research Communications, 1 051005.
  • Otón, G., Ramo, R., Lizundia-Loiola, J., & Chuvieco, E. (2019). Global Detection of Long-Term (1982–2017) Burned Area with AVHRR-LTDR Data. Remote Sensing, 11, 2079, doi:2010.3390/rs11182079.
  • Roteta, E., Bastarrika, A., Storm, T., & Chuvieco, E. (2019). Development of a Sentinel-2 burned area algorithm: generation of a small fire database for northern hemisphere tropical Africa. Remote Sensing of Environment, 222, 1-17.
  • Turco, M., Herrera, S., Tourigny, E., Chuvieco, E., & Provenzale, A. A comparison of remotely-sensed and inventory datasets for burned area in Mediterranean Europe. International Journal of Applied Earth Observation and Geoinformation, 82.
  • Yebra, M., Scortechini, G., Badi, A., Beget, M.E., Boer, M.M., Bradstock, R., Chuvieco, E., Danson, F.M., Dennison, P., Resco de Dios, V., Di Bella, C.M., Forsyth, G., Frost, P., Garcia, M., Hamdi, A., He, B., Jolly, M., Kraaij, T., Martín, M.P., Mouillot, F., Newnham, G., Nolan, R.H., Pellizzaro, G., Qi, Y., Quan, X., Riaño, D., Roberts, D., Sow, M., & Ustin, S. (2019). Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications. Scientific Data, 6, 155.
  • Meyer, V., Saatchi, S., Ferraz, A., Xu, L., Duque, A., García, M., Chave, J. (2019). Forest degradation and biomass loss along the Chocó region of Colombia. Carbon Balance and Management, 14 (1), 2.
  • Huesca, M., Roth, K.L., García, M. & Ustin, S.L. (2019) Discrimination of Canopy Structural Types in the Sierra Nevada Mountains in Central California. Remote Sensing, 11, 1100.

2018

  • Chuvieco, E., Burgui, M., Da Silva, E.V., Hussein, K., & Alkaabi, K. (2018a). Factors Affecting Environmental Sustainability Habits of University Students: Intercomparison Analysis in Three Countries (Spain, Brazil and UAE). Journal of Cleaner Production, 198, 1372-1380.
  • Chuvieco, E., Lizundia-Loiola, J., Pettinari, M.L., Ramo, R., Padilla, M., Tansey, K., Mouillot, F., Laurent, P., Storm, T., Heil, A., & Plummer, S. (2018b). Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth Systems Science Data, 10, 2015-2031.
  • Ramo, R., García, M., Rodríguez, D., & Chuvieco, E. (2018). A data mining approach for global burned area mapping. International Journal of Applied Earth Observation and Geoinformation, 73, 39-51.
  • Melendo-Vega, J., Martín, M., P., Pacheco-Labrador, J., González-Cascón, R., Moreno, G., Pérez, F., Migliavacca, M., García, M., North, P., & RiañoD., (2018). Improving the Performance of 3-D Radiative Transfer Model FLIGHT to Simulate Optical Properties of a Tree-Grass Ecosystem. Remote Sensing, 10 (12).
  • Pourshamsi, M., García, M., Lavalle, M., & Balzter, H. (2018). A Machine-Learning approach to PolInSAR and LiDAR data fusion for improved tropical forest canopy height estimation using NASA AfriSAR campaign data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
  • Ferraz, A., Saatchi,S., Xu, L., Hagen, S., Chave, J., Yu, Y., Meyer, V., Garcia, M., Silva, C.A., Roswintiart, O., Samboko, A., Sist, P., Walker, S., Pearson, T. R. H., Wijaya, A., Sullivan, F.B., RutishauserE., Hoekman, D. and Ganguly, S. (2018). Carbon storage potential in degraded forests of Kalimantan, Indonesia. Environmental Research Letters, 13 (9).
  • Silva, C.A., Saatchi, S, García, M. Labrière, N., Klauberg, C., Ferraz, A., Meyer, A., Jeffery, K.J., Abernethy, K., White, L., Zhao, K., Lewis, S.L., Hudak, A.T. (2018). Comparison of small- and large-footprint LiDAR characterization of tropical forest aboveground structure and biomass: a case study from Central Gabon. IEEE Journal of selected topics in applied earth observations and remote sensing
  • García, M., Saatchi, S., Ustin, S. & Balzter, H. (2018). Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery. International Journal of Applied Earth Observation and Geoinformation, 66, 159-173, https://doi.org/10.1016/j.jag.2017.11.017.
  • M. Tanase, C. Aponte, S. Mermoz, A. Bouvet, T. Le Toan, and M. Heurich, (2018) Detection of windthrows and insect outbreaks by L-band SAR: a case study in the Bavarian Forest National Park, Remote Sensing of Environment, vol 209, pp. 700-711.
  • S. Hoffmann, T.M. Schmitt, A. Chiarucci, S.D.H. Irl, D. Rocchini, O.R. Vetaas, M. Tanase, and Carl Beierkuhnlein (2018) Beta diversity and spectral diversity represent different plant community patterns in a semi-natural system. Applied Vegetation Science, doi.org/10.1111/avsc.12403.
  • L. Zhu, P. Walker, N. Ye, C. Rudiger, J. Hacker, R. Panciera, M. Tanase, X. Wu, D. Gray, N. Stacy, A. Goh, H. Yardley, and J. Mead (2018) The Polarimetric L-band Imaging Synthetic Aperture Radar (PLIS): Description, Calibration, and Cross-Validation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11 (11), pp, 4513-4525, 10.1109/JSTARS.2018.2873218.
  • B.N. Tran, C. Aponte, M. Tanase, L.T. Bennett, (2018), Evaluation of spectral indices for fire severity assessment in Australian temperate forests. Remote Sensing, vol. 10 (11), pp. 1680, doi.org/10.3390/rs10111680.
  • B. Apostol, S. Chivulescu, A. Ciceu, M. Petrila, I.S. Pascu, E. N. Apostol, S. Leca, A. Lorent, M. Tanase, Ov. Badea, (2018), Data collection methods for forest inventory: a comparison between an integrated conventional equipment and terrestrial laser scanning. Annals of Forest Research, vol. 61 (2), pp. 189-202, 10.15287/afr.2018.1189.
  • Chuvieco, E., Lizundia-Loiola, J., Pettinari, M.L., Ramo, R., Padilla, M., Tansey, K., Mouillot, F., Laurent, P., Storm, T., & Heil, A. (2018). Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth System Science Data, 10, 2015-2031.
  • Emilio Chuvieco, Mario Burgui-Burgui, Edson Vicente Da Silva, Khalid Hussein, Khaula Alkaabi. (2018). «Factors affecting environmental sustainability habits of university students: Intercomparison analysis in three countries (Spain, Brazil and UAE)». Journal of Cleaner Production, 198, 1372-1380https://doi.org/10.1016/j.jclepro.2018.07.121
  • Hernández Carretero, A. M., Burgui, M., Velázquez de Castro, F., & Corrales Vázquez, J. M. (2018). ¿Responden los libros de texto a las demandas de la educación ambiental? Un análisis para la educación secundaria. Boletín de la Asociación de Geógrafos Españoles, 77, 80–110, doi: http://dx.doi.org/10.21138/bage.2535
  • Burgui Burgui, M., Ibarra Benlloch, P., & Echeverría Arnedo, M. T. (2018). Evolución de la calidad del paisaje a partir del desarrollo turístico en Cayo Santa María (Villa Clara, Cuba). Boletín de la Asociación de Geógrafos Españoles, 78, 444–473, doi: http://dx.doi.org/10.21138/bage.2720

2017

  • Garcia, M., Saatchi, S. Casas, A., Koltunov, A., Ustin, S.L., Ramirez, C. & Balzter, H. 2017. Extrapolating forest canopy fuel properties in the California Rim Fire by combining airborne LiDAR and Landsat OLI data. Remote Sensing 9(4), 394; doi:10.3390/rs9040394.
  • Garcia, M., Saatchi, S., Casas, A., Koltunov, A., Ustin, S., Ramirez, C., Garcia-Gutierrez, J. & Balzter, H. 2017. Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data. Journal of Geophysical Research: Biogeosciences. 10.1002/2015JG003315.
  • Garcia, M., Saatchi, S., Ferraz, A., Silva, C.A., Ustin, S., Koltunov, A. & Balzter, H. 2017. Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR. Carbon Balance and Management, 12:4. DOI 10.1186/s13021-017-0073-1.
  • Martínez, S., Chuvieco, E., Aguado, I., Salas, J. (2017) Severidad y regeneración en grandes incendios forestales: análisis a partir de series temporales de imágenes Landsat. Revista Española de Teledetección, nº 49 pp 17-32.
  • Nogueira, J., Ruffault, J., Chuvieco, E., Mouillot, F. (2017) Can We Go Beyond Burned Area in the Assessment of Global Remote Sensing Products with Fire Patch Metrics? Remote Sensing, 9(1), p. 7.
  • Padilla, M., Olofsson, P., Stehman, S. V., Tansey, K., Chuvieco, E. (2017) Stratification and sample allocation for reference burned area data. Remote Sensing of Environment (in press), https://doi.org/10.1016/j.rse.2017.06.041.
  • Pacheco-Angulo, C., Vilanova, E., Aguado, I., Mojardín, S., Martínez, S. (2017) Carbon emissions from deforestation and degradation in a Forest Reserve in Venezuela between 1990-2015. Forests, Vol, 8, p. 291.
  • Pettinari, M. L., Chuvieco, E. (2017) Fire Behavior Simulation from Global Fuel and Climatic Information. Forests, 8(6), p. 179, doi:10.3390/f8060179.
  • Ramo, R., Chuvieco, E. (2017) Developing a random forest algorithm for MODIS burned area classification. Remote Sensing, 9, 1193, doi:10.3390/rs9111193.
  • Silva, C.A., Hudak, A. T., Vierling, L.A., Klauberg, C., Garcia, M., Ferraz, A., Keller, M., Eitel, J. and Saatchi, S. (2017). Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest. Remote Sensing, 9, 1068; doi:10.3390/rs9101068.
  • Silva, C.A., Klauberg, C., Hudak, A.T., Vierling, L.A., Jaafar, W.S.W.M., Mohan, M., Garcia, M., Ferraz, A., Cardil, A. & Saatchi, S.. 2017. Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest. Forest, 8, 254; doi:10.3390/f8070254.
  • Zhao, K., Suarez, J.C., Garcia, M., Hu, T., Wang, C. & Londo, A. Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2017.09.007.
  • Viana-Soto A., Aguado I., Martínez S. (2017). Assessment of post-fire vegetation recovery using fire severity and geographical data in the Mediterranean region (Spain). Environments 4 (4), 90, https://doi.org/10.3390/environments4040090
  • Silva, C.A., Hudak, A. T., Vierling, L.A., Klauberg, C., García, M., Ferraz, A., Keller, M., Eitel, J. and Saatchi, S. (2017). Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest. Remote Sensing, 9, 1068, doi:10.3390/rs9101068.
  • Zhao, K., Suarez, J.C., García, M., Hu, T., Wang, C. & Londo, A. (2017). Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux. Remote Sensing of Environment, http://dx.doi.org/10.1016/j.rse.2017.09.007. [pdf]
  • Silva, C.A., Klauberg, C., Hudak, A.T., Vierling, L.A., Jaafar, W.S.W.M., Mohan, M., García, M., Ferraz, A., Cardil, A. & Saatchi, S.. 2017. Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest. Forest, 8, 254, doi:10.3390/f8070254.
  • L. He, Q. Qi, R. Panciera, M. Tanase, J. P. Walker, and Y. Hong (2017), An Extension of the Alpha Approximation Method for Soil Moisture Estimation Using Time-Series SAR Data Over Bare Soil Surfaces. Geoscience and Remote Sensing Letters, vol. 14 (8), pp. 1328-1332, doi:10.1109/LGRS.2017.2711006.
  • A.L. Montealegre, M.T. Lamelas, M. Tanase, J. de la Riva (2017) Forest fire severity estimation based on the LiDAR-PNOA data and the values of the Composite Burn Index, Revista de Teledeteccion, no. 49 pp.1-16.

2016

  • Alonso-Canas, I., Chuvieco, E. (2016) Desarrollo de un algoritmo global de área quemada para imágenes del sensor ENVISAT-MERIS. GeoFocus. Revista Internacional de Ciencia y Tecnología de la Información Geográfica, 17, pp. 3-25.
  • Casas, A., Garcia, M., Siegel, R., Koltunov, A., Ramirez, C. & Ustin, S. (2016) Burned forest characterization with Airborne Laser Scanning for wildlife habitat suitability assessment. Remote Sensing of Environment. 175, pp. 231 – 241.
  • Chuvieco, E. Fundamentals of Satellite Remote Sensing: An environmental approach (2nd Edition) (2016) CRC Press, Boca Raton (USA), 468 pp, ISBN 9781498728058.
  • Chuvieco, E., Yue, C., Heil, A., Mouillot, F., Alonso-Canas, I., Padilla, M., Pereira, J. M., Oom, D., Tansey, K. (2016) A new global burned area product for climate assessment of fire impacts. Global Ecology and Biogeography, 25(5), pp. 619-629, doi: 10.1111/geb.12440.
  • Hantson, S., Pueyo, S., Chuvieco, E. (2016) Global fire size distribution: from power law to log-normal. International Journal of Wildland Fire, 25, 403-412, http://dx.doi.org/10.1071/WF15108.
  • Huesca, M., Garcia, M., Roth, K., Casas, A. & Ustin, S. (2016) Canopy Structural Attributes Derived from AVIRIS Data in a Mixed Broadleaf/Conifer. Remote Sensing of Environment. 182, pp. 208 – 226.
  • Moreno, M. V., Chuvieco, E. (2016) Fire Regime Characteristics along Environmental Gradients in Spain. Forests, 7(11), 262, doi:10.3390/f7110262.
  • Pettinari, M. L., Chuvieco, E. (2016) Generation of a global fuel dataset using the Fuel Characteristic Classification System. Biogeosciences, 13, pp. 2061-2076, DOI: 10.5194/bg-13-2061-2016.
  • Rodríguez, V.M., Aguado, I., Aguilera, F., Bosque, J., Chuvieco, E., Escobar, F., Gómez Delgado, M., Salado, M. J., Salas, J. (2016) Competencias en Tecnologías de la Información Geográfica (TIG) en los estudios universitarios: reflexión y propuesta participativas. Cuadernos Geográficos, 55 (1), p. 360-382.
  • L. He, R. Panciera, M. Tanase, J. P. Walker, and Q. Qin, (2016), Soil moisture retrieval in agricultural fields using adaptive model-based polarimetric decomposition of SAR data. IEEE Transactions on Geoscience and Remote Sensing, vol. 54 (8), pp. 4445-4460, doi: 10.1109/TGRS.2016.2542214.
  • L.T. Bennett, M.J. Bruce, J. McHunter, M. Kohout, M.Tanase, C. Aponte, (2016), Mortality and recruitment of fire-tolerant eucalypts as influenced by wildfire severity and recent prescribed fire. Forest Ecology and Management, vol. 380, pp. 107-117, 10.1016/j.foreco.2016.08.047.

2015

  • Alonso-Canas, I., Chuvieco, E. (2015) Global burned area mapping from ENVISAT-MERIS and MODIS active fire data. Remote Sensing of Environment, 163, pp. 140-152, doi:10.1016/j.rse.2015.03.011.
  • Chuvieco, E. (2015) Mirar desde el Espacio: Aportaciones de la Teledetección a la Geografía. Boletín de la Real Sociedad Geográfica, 150, pp. 133-151, 2014-2015.
  • Eskandari, S., Chuvieco, E. (2015) Fire danger assessment in Iran based on geospatial information. International Journal of Applied Earth Observation and Geoinformation, 42(0), pp. 57-64, http://dx.doi.org/10.1016/j.jag.2015.05.006.
  • Garcia, M., Gajardo, J., Riaño, D., Zhao, K., Martin, P. & Ustin, S. (2015) Canopy clumping appraisal using terrestrial and airborne laser scanning. Remote Sensing of Environment. 161,pp. 78 – 88.
  • Garcia-Gutierrez, J., Mateos, D., Garcia, M. & Riquelme, J. (2015) An Evolutionary Majority Voting to Improve Support Vector Machines’ Classification of LIDAR and Imagery Data Fusion. Neurocomputing. 163, pp. 17 – 24.
  • Gómez, I., Martín, M.P. y Salas, J. (2015) Análisis del régimen de incendios forestales y su relación con los cambios de uso del suelo en la comunidad autónoma de Madrid (1989-2010). Geofocus, 16, 281-304. http://www.geofocus.org/index.php/geofocus/article/view/401.
  • Hantson, S., Lasslop, G., Kloster, S., Chuvieco, E. (2015) Anthropogenic effects on global mean fire size. International Journal of Wildland Fire, 24, 589-596, http://dx.doi.org/10.1071/WF14208.
  • Hantson, S., Pueyo, S., Chuvieco, E. (2015) Global fire size distribution is driven by human impact and climate. Global Ecology and Biogeography, doi: 10.1111/geb.12246, vol. 24, 77-86
  • Padilla, M., Stehman, S. V., Ramo, R., Corti, D., Hantson, S., Oliva, P., Alonso-Canas, I., Bradley, A., Tansey, K., Mota, B., Pereira, J. M., Chuvieco, E. (2015) Comparing the Accuracies of Remote Sensing Global Burned Area Products using Stratified Random Sampling and Estimation. Remote Sensing of Environment, 160, pp. 114-121, https://doi.org/10.1016/j.rse.2015.01.005.
  • Petinari, M. L., Chuvieco, E. (2015) Cartografía de combustible y potenciales de incendio en el Continente Africano utilizando FCCS. Revista de Teledetección (43), pp. 1-10, http://dx.doi.org/10.4995/raet.2015.2302.
  • Zhao, K., Garcia, M., Liu, S., Guo, Q., Chen, G., Zhang, X. & Zhou, Y. (2015) Measuring forest with ground-based terrestrial lidar: Maximum likelihood estimates of canopy profile, leaf area, and leaf angle distribution. Agricultural and Forest Meteorology. 209-210, pp. 100 – 113.
  • K. D. Fieber, I. J. Davenport, M. Tanase, J. M. Ferryman, R. J. Gurney, V. M. Becerra, J. P. Walker, J. M. Hacker, (2015), Validation of Canopy Height Profile methodology for small-footprint full-waveform airborne LiDAR data in a Discontinuous Canopy Environment. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 104 (6), pp.144-157, doi:10.1016/j.isprsjprs.2015.03.001.
  • M. Tanase, R. Panciera, K. Lowell, C. Aponte, (2015), Monitoring live fuel moisture in semiarid environments using L-band radar data. International Journal of Wildland Fire, vol. 24 (4), pp. 560-572, doi: 10.1071/WF14149
  • M. Tanase, R. Kennedy and C. Aponte, (2015), Fire severity estimation from space: A comparison of active and passive sensors and their synergy. International Journal of Wildland Fire, vol. 24 (8), pp.1062-1075, doi:10.1071/WF15059.
  • M. Tanase, I. Ismail, M. Santoro, O. Karyanto and K. Lowell, (2015), Detecting and quantifying forest change in support of REDD programs: the potential of existing global radar datasets. PLOS One, vol. 10 (6), pp.1-14, e0131079, doi; 10.1371/journal.pone.0131079.
  • M. Tanase, R. Kennedy and C. Aponte, (2015). Radar Burn Ratio for fire severity estimation at canopy level: an example for temperate forests. Remote Sensing of Environment, vol. 170, pp.14-31, doi: 10.1016/j.rse.2015.08.025
  • K. D. Fieber, I. J. Davenport, M. Tanase, J. M. Ferryman, R. J. Gurney, V. M. Becerra, J. P. Walker, J. M. Hacker, (2015), Validation of Canopy Height Profile methodology for small-footprint full-waveform airborne LiDAR data in a Discontinuous Canopy Environment. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 104 (6), pp.144-157, doi:10.1016/j.isprsjprs.2015.03.001.

2014

  • Bastarrika, A., Alvarado, M., Artano, K., Martinez, M.P., Mesanza, A., Torre, L., Ramo, R., Chuvieco, E. (2014) BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data. Remote Sensing, 6, 12360-12380.
  • Casas, A., Riaño, D., Ustin, S.l., Dennison, P. y Salas, J. (2014). Estimation of water-related biochemical and biophysical vegetation properties using multitemporal airborne hyperspectral data and its comparison to MODIS spectral responseIntegrating geospatial information into fire risk assessment. Remote Sensing of Environment, 148, 28-41 (http://dx.doi.org/10.1016/j.rse.2014.03.011).
  • de Tomás, A., Nieto, H., Guzinski, R., Salas, J., Sandholt, I. y Berliner, P. (2014). Validation and scale dependencies of the triangle method for the evaporative fraction estimation over heterogeneous areas. Remote Sensing of Environment, 152, 493-511 (http://dx.doi.org/10.1016/j.rse.2014.06.0281).
  • Chuvieco, E., Aguado, I., Jurdao, S., Pettinari, M.L., Yebra, M., Salas, J., Hantson, S., de la Riva, J., Ibarra, P., Rodrigues, M., Echeverría, M., Azqueta, D., Román, M.V., Bastarrika, A., Martínez, S., Recondo, C., Zapico, E. y Martínez-Vega, F.J. (2014). Integrating geospatial information into fire risk assessment. International Journal of Wildland Fire, 23, 606–619 (http://dx.doi.org/10.1071/WF12052).
  • Chuvieco, E., Martinez, S., Roman, M.V., Hantson, S. y Pettinari, L. (2014). Integration of ecological and socio-economic factors to assess global wildfire vulnerability. Global Ecology and Biogeography, 23, 245-258.
  • Hantson, S., Pueyo, S. y Chuvieco, E. (2014). Global fire size distribution is driven by human impact and climate. Global Ecology and Biogeography.
  • Jurdao, S., Yebra, M., Oliva, P. y Chuvieco, E. (2014). Laboratory Measurements of Plant Drying. Photogrammetric Engineering & Remote Sensing, 80, 451-459.
  • Moreno, M.V., Conedera, M., Chuvieco, E. y Pezzatti, G.B. (2014). Fire regime changes and major driving forces in Spain from 1968 to 2010. Environmental Science & Policy, 37, 11-22.
  • Mouillot, F., Schultz, M.G., Yue, C., Cadule, P., Tansey, K., Ciais, P. y Chuvieco, E. (2014). 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, 26, 64-79.
  • Pacheco, CE., Aguado, I. y Mollicone D (2014). Identification and characterization of deforestation hot spots in Venezuela using MODIS satellite images. Acta Amazonica, 44 (2), 185-196.
  • Padilla, M., Stehman, S.V. y Chuvieco, E. (2014). Validation of the 2008 MODIS-MCD45 global burned area product using stratified random sampling. Remote Sensing of Environment, 144, 187-196.
  • Padilla, M., Stehman, S.V., Litago, J. y Chuvieco, E. (2014). Assessing the Temporal Stability of the Accuracy of a Time Series of Burned Area Products. Remote Sensing, 6, 2050-2068.
  • Pettinari, M.L., Ottmar, R., Prichard, S., Andreu, A., Chuvieco E. (2014) Development and mapping of fuel characteristics and associated fire potentials for South America. International Journal of Wildland Fire, 23(5), 643-654 (http://dx.doi.org/10.1071/WF12137).
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