2022

• Arrogante-Funes, F., Aguado, I., Chuvieco, E. (2022). Global assessment and mapping of ecological vulnerability to wildfires. Natural Hazards and Earth System Sciences. 22 (9) 2981-3003.  https://doi.org/10.5194/nhess-22-2981-2022. 

• Chávez-Durán, A.A., Olvera-Vargas, M., Figueroa-Rangel, B., García, M., Aguado, I., Ruiz-Corral, J.A. (2022) Homogeneous response areas for forest fuel management using geospatial data, K-means, and random forest classification. Forests 13 (12), 1970. https://doi.org/10.3390/f13121970

• Chuvieco, E., Roteta, E., Sali, M., Stroppiana, D., Boettcher, M., Kirches, G., Storm, T., Khairoun, A., Pettinari, M.L., Franquesa, M., Albergel, C. (2022). Building a small fire database for Sub-Saharan Africa from Sentinel-2 high-resolution images. Science of The Total Environment, 157139. https://doi.org/10.1016/j.scitotenv.2022.157139

• Franquesa, M., Lizundia-Loiola, J., Stehman, S.V. & Chuvieco, E. (2022). Using long temporal reference units to assess the spatial accuracy of global satellite derived burned area products. Remote Sensing of Environment, 269, 112823. https://doi.org/10.1016/j.rse.2021.112823 

• Franquesa, M., Rodríguez-Montellano, A. M., Chuvieco, E., Aguado, I.  (2022). Reference data accuracy impacts burned área product validation: the role of the expert analyst. Remote Sensing 14(17), 4354. https://doi.org/10.3390/rs14174354g

• Franquesa, M., Stehman, S.V., Chuvieco, E. (2022). Assessment and characterization of sources of error impacting the accuracy of global burned area products. Remote Sensing of Environment, 280, 113214. https://doi.org/10.1016/j.rse.2022.113214.

• García, M.; Pettinari, M.L.; Chuvieco, E.; Salas, J.; Mouillot, F.; Chen, W.; Aguado, I. Characterizing Global Fire Regimes from Satellite-Derived Products. Forests 2022, 13, 699. https://doi.org/10.3390/f13050699.

• Hegglin, M.I., Bastos, A., Bovensmann, H., Buchwitz, M., Fawcett, D., Ghent, D., Kulk, G., Sathyendranath, S., Shepherd, T.G., Quegan, S., Rothlisberger, R., Briggs, S., Buontempo, C., Cazenave, A., Chuvieco, E., Ciais, P., Crisp, D., Engelen, R., Fadnavis, S., Herold, M., Horwath, M., Jonsson, O., Kpaka, G., Merchant, C.J., Mielke, C., Nagler, T., Paul, F., Popp, T., Quaife, T., Rayner, N.A., Robert, C., Schroder, M., Sitch, S., Venturini, S., van der Schalie, R., van der Vliet, M., Wigneron, J.P., Woolway, R.I. (2022). Space-based Earth observation in support of the UNFCCC Paris Agreement. Frontiers in Environmental Science, 10:941490. https://doi.org/10.3389/fenvs.2022.941490

• Leite, R.V., Silva, C.A., Broadbent, E.N., Liesenberg, C.H.A.V., Almeida, D.R.A., Mohan, M., Godinho, S., Cardil, A., Hamamura, C., Faria, B.L., Brancalion, P.H.S., Hirsch, A., Marcatti, G.E. Dalla Corte, A.P., Almeyda Zambrano, A.M., Teixeira Da Costa, M.B., Trondoli Matricardi, E.A., Da Silva, A.L., Ruggeri Ré L., Goya, Y., Valbuena, R., Furtado De Mendonça, B.A.., Silva Junior, C.H.L., Aragão, L.E.O.C., García, M., Liang, J., Merrick, T., Hudak, A.T., Xiao, J., Hancock, S., Duncason, L., Pinheiro Ferreira, M., Valle, D., Saatchi, S.,  Klauberg, C., (2022). Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data, Remote Sensing of Environment, 268, 112764. https://doi.org/10.1016/j.rse.2021.112764.   

• Lizundia-Loiola, J., Franquesa, M., Khairoun, A., Chuvieco, E. (2022). Global burned area mapping from Sentinel-3 Synergy and VIIRS active fires. Remote Sensing of Environment, 282, 113298. https://doi.org/10.1016/j.rse.2022.113298

• Munizaga, J., Garcia, M., Ureta, F., Novoa, V., Rojas, O., & Rojas, C. (2022). Mapping Coastal Wetlands Using Satellite Imagery and Machine Learning in a Highly Urbanized Landscape. Sustainability, 14(9), 5700; https://doi.org/10.3390/su14095700

• Otón, G., Pereira, J.M.C., Silva, J.M.N., Chuvieco, E. (2022) Reply to Giglio et al. Comment on “Otón et al. Analysis of Trends in the FireCCI Global Long Term Burned Area Product (1982–2018). Fire 2021, 4, 74”, Fire 5(3), 56, https://doi.org/10.3390/fire5030056

• Stroppiana, D., Sali, M., Busetto, L., Boschetti, M., Ranghetti, L., Franquesa, M., Pettinari, M.L., Chuvieco, E. (2022). Sentinel-2 sampling design and reference fire perimeters to assess accuracy of Burned Area products over Sub-Saharan Africa for the year 2019. ISPRS Journal of Photogrammetry and Remote Sensing, 191, 223-234. https://doi.org/10.1016/j.isprsjprs.2022.07.015.

• Sun, M., Cui, L., Park, J., Garcia, M., Zhou, Y.Y., Silva, C.A., He, L., Zhang, H., Zhao, K.G. (2022). Evaluation of NASA’s GEDI Lidar Observations for Estimating Biomass in Temperate and Tropical Forests. Forests 2022, 13, 1686. https://doi.org/10.3390/f13101686.

• Tijerin-Trivino, J., Moreno-Fernandez, D., Zavala, M.A., Astigarraga, J., & Garcia, M. (2022). Identifying Forest Structural Types along an Aridity Gradient in Peninsular Spain: Integrating Low-Density LiDAR, Forest Inventory, and Aridity Index. Remote Sensing, 14(1), 235; https://doi.org/10.3390/rs14010235

• Viana-Soto, A., García, M., Aguado, I., Salas, J. (2022). Assessing post-fire forest structure recovery by combining LiDAR data and Landsat time series in Mediterranean pine forests. International Journal of Applied Earth Observation and Geoinformation. 108. https://doi.org/10.1016/j.jag.2022.102754 

• Viana-Soto, A., Okujeni, A., Pflugmacher, D., García, M., Aguado, I. y Hostert, P. (2022). Quantifying post-fire shifts in woody-vegetation cover composition in Mediterranean pine forests using Landsat time series and regression-based unmixing. Remote Sensing of Environment. 281.113239.  https://doi.org/10.1016/j.rse.2022.113239

2021

1. Aragoneses, E., Chuvieco, E. (2021). Generation and Mapping of Fuel Types for Fire Risk Assessment. Fire, 4(3), 59, https://doi.org/10.3390/fire4030059. 

2. Belenguer-Plomer, M.A., Tanase, M.A., Chuvieco, E., Bovolo, F. (2021). CNN-based burned area mapping using radar and optical data. Remote Sensing of Environment, 260, 112468. https://doi.org/10.1016/j.rse.2021.112468 

3. Borlaf-Mena I., Badea Ov., Tanase M.A. (2021) Assessing the Utility of Sentinel-1 Coherence Time Series for Temperate and Tropical Forest Mapping, Remote Sensing, 13(23), 4814, https://doi.org/10.3390/rs13234814. 

4. Burton J., Bennett L., Kasel S., Nitschke C., Tanase M., Faiman T., Parker L., Fedrigo M., and Aponte C. (2021) Fire, drought and productivity as drivers of dead wood biomass in eucalypt forests of south-eastern Australia, Forest Ecology and Management, vol. 482, https://doi.org/10.1016/j.foreco.2020.118859 

5. Chuvieco, E., Burgui-Burgui, M., Orellano, A., Otón, G., Ruíz-Benito, P. (2021). Links between Climate Change Knowledge, Perception and Action: Impacts on Personal Carbon Footprint. Sustainability, 13, 8088. https://doi.org/10.3390/su13148088 

6. Chuvieco, E., Pettinari, M.L., Koutsias, N., Forkel, M., Hantson, S., & Turco, M. (2021). Human and climate drivers of global biomass burning variability. Science of the Total Environment, 779, 146361. https://doi.org/10.1016/j.scitotenv.2021.146361 

7. García-Duro J., Ciceu A., Chivulescu S., Badea Ov., Tanase M., Aponte C. (2021) Shifts in forest species composition and abundance under climate change scenarios in Romanian temperate forests, Forests, 12 (11), 1434, https://doi.org/10.3390/f12111434.  

8. Lizundia-Loiola, J., Franquesa, M., Boettcher, M., Kirches, G., Pettinari, M.L., Chuvieco, E. (2021). Implementation of the Burned Area Component of the Copernicus Climate Change Service: From MODIS to OLCI Data. Remote Sensing, 13, 4295. https://doi.org/10.3390/rs13214295 

9. Moreno-Fernández, D., Viana-Soto, A., Camarero, J. J., Zavala, M. A., Tijerín, J., García, M. (2021). Using spectral indices as early warning signals of forest dieback: The case of drought-prone Pinus pinaster forests. Science of The Total Environment, 148578. https://doi.org/10.1016/j.scitotenv.2021.148578  

10. Otón, G., Lizundia-Loiola, J., Pettinari, M.L., Chuvieco, E. (2021). Development of a consistent global long-term burned area product (1982–2018) based on AVHRR-LTDR data. International Journal of Applied Earth Observation and Geoinformation, 103, 102473. https://doi.org/10.1016/j.jag.2021.102473 

11. Otón, G., Pereira, J.M.C., Silva, J.M.N., Chuvieco, E. (2021). Analysis of Trends in the FireCCI Global Long Term Burned Area Product (1982–2018). Fire, 4(4), 74. https://doi.org/10.3390/fire4040074  

12. Pourshamsi, M., Xia, J, Yokoya, N., Garcia, M., Lavalle, M., Pottier, E.. Balzter, H. (2021), Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning, ISPRS Journal of Photogrammetry and Remote Sensing, 172, 79-94. doi.org/10.1016/j.isprsjprs.2020.11.008 

13. Ramo, R., Roteta, E., Bistinas, I., van Wees, D., Bastarrika, A., Chuvieco, E., van der Werf, G.R. (2021). African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data. Proceedings of the National Academy of Sciences, 118, e2011160118. https://doi.org/10.1073/pnas.2011160118 

14. Rodes, M.; Torres, P; García, M. (2021) Assessing tree decay in an urban park using PlanetScope images: the case of Cerro Almodovar Park. Proceedings Volume 11864, Remote Sensing Technologies and Applications in Urban Environments VI; 118640L https://doi.org/10.1117/12.2600081 

15. Roteta, E., Bastarrika, A., Ibisate, A., Chuvieco, E. (2021). A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine. Remote Sensing, 13, 4298. https://doi.org/10.3390/rs13214298 

16. Roteta, E., Bastarrika, A., Franquesa, M., Chuvieco, E. (2021). Landsat and Sentinel-2 Based Burned Area Mapping Tools in Google Earth Engine. Remote Sensing, 13, 816. https://doi.org/10.3390/rs13040816 

17. Tanase M.A., Borlaf-Mena I., Santoro M., Aponte C., Marin Gh., Apostol B., Badea Ov. (2021) Growing stock volume retrieval in the Carpathians from single and multi-frequency radar backscatter, Forests 12 (7), 944, https://doi.org/10.3390/f12070944 

18. Torres, P., Rodes-Blanco, M., Viana-Soto, A., Nieto, H., & García, M. (2021). The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis. Forests, 12(8), 1134. https://doi.org/10.3390/f12081134 

19. Zheng, B., Ciais, P., Chevallier, F., Chuvieco, E., Chen, Y., & Yang, H. (2021). Increasing forest fire emissions despite the decline in global burned area. Science Advances, 7, eabh2646. https://doi.org/10.1126/sciadv.abh2646  

2020

1. Aponte C., Kasel S., Nitschke C., Tanase M., Vickers H., Parker L., Fedrigo M., Kohout M., Ruiz-Benito P., Zavala M.A., Bennett L.T. (2020) Structural diversity underpins carbon storage in Australian temperate forests, Global Ecology and Biogeography, https://doi.org/10.1111/geb.13038  

2. Bispo, P.C.; Rodríguez-Veiga, P.; Zimbres, B.; do Couto de Miranda, S.; Henrique Giusti Cezare, C.; Fleming, S.; Baldacchino, F.; Louis, V.; Rains, D.; Garcia, M.; Del Bon Espírito-Santo, F.; Roitman, I.; Pacheco-Pascagaza, A.M.; Gou, Y.; Roberts, J.; Barrett, K.; Ferreira, L.G.; Shimbo, J.Z.; Alencar, A.; Bustamante, M.; Woodhouse, I.H.; Eyji Sano, E.; Ometto, J.P.; Tansey, K.; Balzter, H. (2020). Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach. Remote Sensing, 12, 2685. https://doi.org/10.3390/rs12172685 

3. Borlaf-Mena I., Santoro M., Villard L., Badea Ov., Tanase M. A. (2020) Investigating the Impact of Digital Elevation Models on Sentinel-1 Backscatter and Coherence Observations, Remote Sensing, Vol 12 (18), 3016, https://doi.org/10.3390/rs12183016  

4. Bowman, D.M., Williamson, G., Yebra, M., Lizundia-Loiola, J., Pettinari, M.L., Shah, S., Bradstock, R., Chuvieco, E. (2020). Wildfires: Australia needs a national monitoring agency. Nature, 584, 188-191. https://doi.org/10.1038/d41586-020-02306-4 

5. Burgui-Burgui, M., Chuvieco, E. (2020). Beyond Carbon Footprint Calculators. New Approaches for Linking Consumer Behaviour and Climate Action. Sustainability, 12, 6529. https://doi.org/10.3390/su12166529 

6. Chuvieco, E. (2020). Fundamentals of Satellite Remote Sensing: An Environmental Approach. 3rd Ed. Boca Raton (FL): CRC Press. 

7. Chuvieco, E., Aguado, I., Salas, J., García, M., Yebra, M., & Oliva, P. (2020). Satellite Remote Sensing Contributions to Wildland Fire Science and Management. Current Forestry Reports, https://doi.org/10.1007/s40725-40020-00116-40725  

8. Franquesa, M., Vanderhoof, M.K., Stavrakoudis, D., Gitas, I.Z., Roteta, E., Padilla, M., Chuvieco, E. (2020). Development of a standard database of reference sites for validating global burned area products. Earth Syst. Sci. Data 12, 3229-3246. https://doi.org/10.5194/essd-12-3229-2020  

9. Gajardo, J.; Riaño, D.; García, M.; Salas, J.; Martín, M.P. (2020) Estimation of Canopy Gap Fraction from Terrestrial Laser Scanner Using an Angular Grid to Take Advantage of the Full Data Spatial Resolution. Remote Sens. 12, 1596. https://doi.org/10.3390/rs12101596 

10. García, M., North, P., Viana-Soto, A., Stavros, N. E., Rosette, J., Martín, M. P., Franquesa, M., González-Cascón, R., Riaño, D., Becerra, J. & Zhao, K. (2020). Evaluating the potential of LiDAR data for fire damage assessment: A radiative transfer model approach. Remote Sensing of Environment, 247, 111893. https://doi.org/10.1016/j.rse.2020.111893 

11. García, M.; Riaño, D.; Yebra, M.; Salas, J.; Cardil, A.; Monedero, S.; Ramirez, J.; Martín, M.P.; Vilar, L.; Gajardo, J.; Ustin, S. (2020) A Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models. Remote Sens. 12, 1714. https://doi.org/10.3390/rs12111714 

12. Kavvada, A., Metternicht, G., Kerblat, F., Mudau, N., Haldorson, M., Laldaparsad, S., Friedl, L., Held, A., Chuvieco, E. (2020). Towards delivering on the sustainable development goals using earth observations. Remote Sensing of Environment, 247, https://doi.org/10.1016/j.rse.2020.111930. 

13. Lizundia-Loiola, J., Pettinari, M.L., Chuvieco, E. (2020). Temporal Anomalies in Burned Area Trends: Satellite Estimations of the Amazonian 2019 Fire Crisis. Remote Sensing, 12, 151. https://doi.org/10.3390/rs12010151 

14. 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   

15. Moura, Y., Balzter, H., Galvao, L., Dalagnol, R., Espirito Santo, F., Santos, E. Garcia, M., Bispo, P., Oliveira, R., & Shimabukuro, Y. (2020) Carbon Dynamics in a Human-Modified Tropical Forest: a Case Study using Multi-Temporal LiDAR Data. Remote Sensing, 12, 430; https://doi:10.3390/rs12030430   

16. Orellano, A., Valor, C., Chuvieco, E. (2020) The Influence of Religion on Sustainable Consumption: A Systematic Review and Future Research Agenda. Sustainability, 12, 7901: https://doi.org/7910.3390/su12197901  

17. Pascu I.S., Dobre A.C., Badea Ov., Tanase M.A. (2020) Retrieval of Forest Structural Parameters from Terrestrial Laser Scanning: A Romanian case study, Forests, Vol. 11 (4), pp. 392, https://doi.org/10.3390/f11040392  

18. Pettinari, M.L., Chuvieco, E. (2020). Fire Danger Observed from Space. Surveys in Geophysics, 41, 1437-1459. https://doi.org/10.1007/s10712-020-09610-8  

19. Popp, T., Hegglin, M.I., Hollmann, R., Ardhuin, F., Bartsch, A., Bastos, A., Bennett, V., Boutin, J., Brockmann, C., Buchwitz, M., Chuvieco, E., Ciais, P., Dorigo, W., Ghent, D., Jones, R., Lavergne, T., Merchant, C.J., Meyssignac, B., Paul, F., Quegan, S., Sathyendranath, S., Scanlon, T., Schröder, M., Simis, S.G.H., & Willén, U. (2020). Consistency of Satellite Climate Data Records for Earth System Monitoring. Bulletin of the American Meteorological Society, 101, E1948-E1971. https://doi.org/10.1175/BAMS-D-19-0127.1 

20. Rex, F.E.; Silva, C.A.; Dalla Corte, A.P.; Klauberg, C.; Mohan, M.; Cardil, A.; Silva, V.S.; Almeida, D.R.A.; García, M.; Broadbent, E.N.; Valbuena, R.; Stoddart, J.; Merrick, T.; Hudak, A.T. (2020) Comparison of Statistical Modelling Approaches for Estimating Tropical Forest Aboveground Biomass Stock and Reporting Their Changes in Low-Intensity Logging Areas Using Multi-Temporal LiDAR Data. Remote Sens. 12, 1498. https://doi.org/10.3390/rs12091498  

21. Tanase, M.A., Belenguer-Plomer, M.A., Roteta, E., Bastarrika, A., Wheeler, J., Fernández-Carrillo, Á., Tansey, K., Wiedemann, W., Navratil, P., Lohberger, S., Siegert, F., Chuvieco, E. (2020). Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa. Remote Sensing, 12, 334. https://doi.org/10.3390/rs12020334 

22. Tran B.N., Aponte C., Tanase M., Bennett L.T. (2020) High-severity wildfires in temperate Australian forests have increased in extent and aggregation in recent decades. PLOS One 15(11): e0242484, https://doi.org/10.1371/journal.pone.0242484  

23. Viana-Soto, A., Aguado, I., Salas, J., García, M. (2020). Identifying post-fire recovery trajectories and driving factors using landsat time series in fire-prone mediterranean pine forests. Remote Sensing, 12(9), 1499. https://doi.org/10.3390/rs12091499  

2019

1. Belenguer-Plomer, M.A., Chuvieco, E., Tanase, M.A. (2019). Temporal Decorrelation of C-Band Backscatter Coefficient in Mediterranean Burned Areas. Remote Sensing, 11, 2661. https://doi.org/10.3390/rs11222661 

2. Belenguer-Plomer M., Tanase M.A., Fernandez-Carillo A., Chuvieco E. (2019) Burned area detection and mapping using Sentinel-1 backscatter coefficient and thermal anomalies, Remote Sensing of Environment, Vol 233, 111345. https://doi.org/10.1016/j.rse.2019.111345 

3. Borlaf I., Gomez A., Tanase M. (2019) Methods for tree cover extraction from high resolution orthophotos and airborne LiDAR scanning in Spanish dehesas, Revista de Teledeteccion. Vol. 53, pp. 17-32. https://doi.org/10.4995/raet.2019.11320 

4. Carillo A., McCaw L., Tanase M. (2019) Estimating prescribed fire severity in eucalypt forests using L-band SAR change detection, Remote Sensing of Environment, vol. 224, pp.133-144. https://doi.org/10.1016/j.rse.2019.02.005 

5. Chuvieco, E., Mouillot, F., van der Werf, G.R., San Miguel, J., Tanase, 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. https://doi.org/10.1016/j.rse.2019.02.013 

6. 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. https://doi.org/10.3390/rs11101185 

7. Ferraz, A., Saatchi S.S., Xu L., Hagen S., Chave J., Yu Y., Meyer V., Garcia M., Silva C., Roswintiarti O., Samboko A., Sist P., Walker S.M., Pearson T., Wijaya A., Sullivan F.B., Rutishauser E., Hoekman D., Ganguly S. (2019) Aboveground Biomass, Landcover, and Degradation, Kalimantan Forests, Indonesia, 2014. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1645 

8. 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. (2019). Emergent relationships on burned area in global satellite observations and fire-enabled vegetation models. Biogeosciences, 16, 47-76. https://doi.org/10.5194/bg-2018-427  

9. Forkel, M., Dorigo, W.A., Lasslop, G., Chuvieco, E., Hantson, S., Heil, A., Teubner, I., Thonicke, K., Harrison, S., P. (2019). Recent global and regional trends in burned area and their compensating environmental controls. Environmental Research Communications, 1 051005. https://doi.org/10.1088/2515-7620/ab25d2 

10. Gomez C., Alejandro P., Hermosilla T., Montes F., Pascual C., Ruiz L.A., Taboada F. A., Tanase M.A., Valbuena R. (2019) Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities, Forest Systems, Vol. 28, No. 1, pp. 33. https://doi.org/10.5424/fs/2019281-14221  

11. Hoffmann S., Schmitt T.M., Chiarucci A., Irl S.D.H., Rocchini D., Vetaas O.R., Tanase M., Beierkuhnlein C. (2019) Remote sensing of β‐diversity: Evidence from plant communities in a semi‐natural system, Applied Vegetation Science, vol. 22, pp. 13-26; https://doi.org/10.1111/avsc.12403  

12. 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. https://doi.org/10.3390/rs11091100 

13. 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. https://doi.org/10.1186/s13021-019-0117-9  

14. 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, https://doi.org/2010.3390/rs11182079  

15. Pascu I.S., Dobre A.C., Badea Ov., Tanase M. (2019) Estimating forest stand structure attributes from Terrestrial Laser Scans, Science of the Total Environment, vol. 691, 205-215. https://doi.org/10.1016/j.scitotenv.2019.06.536 

16. 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. https://doi.org/10.1016/j.rse.2018.12.011 

17. Suárez-Muñoz M., Bonet-García F.J., Hódar J.A., Herrero J., Tanase M.A., Torres-Muros L. (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. https://doi.org/10.1016/j.ecolmodel.2019.108764 

18. Tanase M., Villard L., Silaghi D., Apostol B., Petrila M., Chivulescu S., Leca S., Borlaf Mena I., Pascu I., Dobre A.C., Pitar D., Guiman Gh., Lorent A., Anghelus C., Ciceu A., Nedea G., Stanculeanu R., Popescu Fl., Aponte C., Badea Ov. (2019) Synthetic aperture radar sensitivity to forest changes: a simulations-based study for the Romanian forests, Science of the Total Environment, vol. 689, pp. 1104-1114. https://doi.org/10.1016/j.scitotenv.2019.06.494 

19. 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, 101887. https://doi.org/10.1016/j.jag.2019.05.020 

20. 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. https://doi.org/10.1038/s41597-019-0164-9  

21. Zhao, K., Ryu, Y., Tongxi, H. García, M., Li, Y., Liu, Z., Londo, A., Wang, C. (2019) How to better estimate leaf area index and leaf angle distribution from digital hemispherical photography? Switching to a binary nonlinear regression paradigm. Methods in Ecology and Evolution, 10:1864-1874. https://doi.org/10.1111/2041-210X.13273 

2018

1. Apostol B., Chivulescu S., Ciceu A., Petrila M., Pascu I.S., Apostol E. N., Leca S., Lorent A., Tanase M., Badea Ov. (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, https://doi.org/10.15287/afr.2018.1189 

2. 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, http://doi.org/10.21138/bage.2720 

3. Chuvieco, E., Burgui, M., Da Silva, E.V., Hussein, K., Alkaabi, K. (2018). Factors Affecting Environmental Sustainability Habits of University Students: Intercomparison Analysis in Three Countries (Spain, Brazil and UAE). Journal of Cleaner Production, 198, 1372-1380. https://doi.org/10.1016/j.jclepro.2018.07.121 

4. 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. https://doi.org/10.5194/essd-10-2015-2018  

5. 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., Ganguly, S. (2018). Carbon storage potential in degraded forests of Kalimantan, Indonesia. Environmental Research Letters, 13 (9).  https://doi.org/10.1088/1748-9326/aad782 

6. 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 

7. 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, http://doi.org/10.21138/bage.2535  

8. 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). https://doi.org/10.3390/rs10122061 

9. 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. https://doi.org/10.1109/JSTARS.2018.2868119  

10. 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. https://doi.org/10.1016/j.jag.2018.05.027 

11. 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. https://doi.org/10.1109/JSTARS.2018.2816962  

12. Tanase M., Aponte C., Mermoz S., Bouvet A., Le Toan T., Heurich M., (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. https://doi.org/10.1016/j.rse.2018.03.009 

13. Tran B.N., Aponte C., Tanase M., Bennett L.T. (2018), Evaluation of spectral indices for fire severity assessment in Australian temperate forests. Remote Sensing, vol. 10 (11), pp. 1680, https://doi.org/10.3390/rs10111680  

14. Zhao, K., Suarez, J.C., Garcia, M., Hu, T., Wang, C., Londo, A. (2018) Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux. Remote Sensing of Environment, http://doi.org/10.1016/j.rse.2017.09.007  

15.  Zhu L., Walker P., Ye N., Rudiger C., Hacker J., Panciera R., Tanase M., Wu X., Gray D., Stacy N., Goh A., Yardley H., Mead J. (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, https://doi.org/10.1109/JSTARS.2018.2873218  

2017

1. 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, https://doi.org/10.3390/rs9040394  

2. 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, https://doi.org/10.1002/2015JG003315  

3. 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, https://doi.org/10.1186/s13021-017-0073-1  

4. He L., Qi Q., Panciera R., Tanase M., Walker J. P., Hong Y. (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, https://doi.org/10.1109/LGRS.2017.2711006  

5. 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.   https://doi.org/10.4995/raet.2017.7182  

6. Montealegre A.L., Lamelas M.T., Tanase M.A., de la Riva J. (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. https://doi.org/10.4995/raet.2017.7371 

7. 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. https://doi.org/10.3390/rs9010007 

8. 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, https://doi.org/10.1016/j.rse.2017.06.041  

9. 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. 

10. Pettinari, M. L., Chuvieco, E. (2017) Fire Behavior Simulation from Global Fuel and Climatic Information. Forests, 8(6), p. 179, https://doi.org/10.3390/f8060179  

11. Ramo, R., Chuvieco, E. (2017) Developing a random forest algorithm for MODIS burned area classification. Remote Sens, 9, 1193, https://doi.org/10.3390/rs9111193.  

12. Silva, C.A., Hudak, A. T., Vierling, L.A., Klauberg, C., Garcia, M., Ferraz, A., Keller, M., Eitel, J., 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, https://doi.org/10.3390/rs9101068  

13. 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, https://doi.org/10.3390/f8070254  

14. 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  

2016

1. Alonso-Canas, I., Chuvieco, E. (2016) Desarrollo de un algoritmo global de área quemada para imágenes del sensor ENVISAT-MERIS. Revista Internacional de Ciencia y Tecnología de la Información Geográfica, 17, pp. 3-25. https://geofocus.org/index.php/geofocus/article/view/423/350  

2. 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. https://doi.org/10.1016/j.rse.2015.12.044 

3. Chuvieco, E. Fundamentals of Satellite Remote Sensing: An environmental approach (2nd Edition) (2016) CRC Press, Boca Raton (USA), 468 pp, ISBN 9781498728058. 

4. Chuvieco, E., Burgui, M., Gallego, I. (2016). Impact of Religious Beliefs on Environmental Indicators. Is Christianity More Aggressive Than Other Religions? Worldviews, 20, 251–271. https://doi.org/10.1163/15685357-02003004 

5. 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, http://doi.org/10.1111/geb.12440  

6. 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://doi.org/10.1071/WF15108  

7. 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. https://doi.org/10.1016/j.rse.2016.04.020 

8. Moreno, M. V., Chuvieco, E. (2016) Fire Regime Characteristics along Environmental Gradients in Spain. Forests, 7(11), 262, http://doi.org/10.3390/f7110262  

9. Pettinari, M. L., Chuvieco, E. (2016) Generation of a global fuel dataset using the Fuel Characteristic Classification System. Biogeosciences, 13, pp. 2061-2076, http://doi.org/10.5194/bg-13-2061-2016  

10. 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. ISSN 0210-5462. 

2015

1. 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, http://doi.org/10.1016/j.rse.2015.03.011  

2. 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. ISSN 0210-8577. 

3. 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://doi.org/10.1016/j.jag.2015.05.006  

4. 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. https://doi.org/10.1016/j.rse.2015.01.030 

5. 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. https://doi.org/10.1016/j.neucom.2014.08.086 

6. Gómez, I., Martín, M.P., 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  

7. 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://doi.org/10.1071/WF14208 

8. Hantson, S., Pueyo, S., Chuvieco, E. (2015) Global fire size distribution is driven by human impact and climate. Global Ecology and Biogeography vol. 24, 77-86, http://doi.org/10.1111/geb.12246  

9. 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  

10. Pettinari, 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://doi.org/10.4995/raet.2015.2302  

11. 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. https://doi.org/10.1016/j.agrformet.2015.03.008 

2014

1. 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, http://doi.org/.3390/rs61212360  

2. Casas, A., Riaño, D., Ustin, S.l., Dennison, P., 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://doi.org/10.1016/j.rse.2014.03.011  

3. 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., Martínez-Vega, F.J. (2014). Integrating geospatial information into fire risk assessment. International Journal of Wildland Fire, 23, 606–619, https://doi.org/10.1071/WF12052    

4. Chuvieco, E., Martinez, S., Roman, M.V., Hantson, S., Pettinari, M.L. (2014) Integration of ecological and socio-economic factors to assess global wildfire vulnerability. Global Ecology and Biogeography, 23, 245-258. https://doi.org/10.1111/geb.12095   

5. de Tomás, A., Nieto, H., Guzinski, R., Salas, J., Sandholt, I., 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://doi.org/10.1016/j.rse.2014.06.0281  

6. Hantson, S., Pueyo, S., Chuvieco, E. (2014). Global fire size distribution is driven by human impact and climate. Global Ecology and Biogeography. https://doi.org/10.1111/geb.12246 

7. Jurdao, S., Yebra, M., Oliva, P., Chuvieco, E. (2014) Laboratory Measurements of Plant Drying. Photogrammetric Engineering & Remote Sensing, 80, 451-459. https://doi.org/10.14358/PERS.80.5.451 

8. Moreno, M.V., Conedera, M., Chuvieco, E., Pezzatti, G.B. (2014) Fire regime changes and major driving forces in Spain from 1968 to 2010. Environmental Science & Policy, 37, 11-22. https://doi.org/10.1016/j.envsci.2013.08.005 

9. Mouillot, F., Schultz, M.G., Yue, C., Cadule, P., Tansey, K., Ciais, P., 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. https://doi.org/10.1016/j.jag.2013.05.014 

10. Pacheco, C.E., Aguado, I., Mollicone D. (2014) Identification and characterization of deforestation hot spots in Venezuela using MODIS satellite images. Acta Amazonica, 44 (2), 185-196. 

11. Padilla, M., Stehman, S.V., Chuvieco, E. (2014) Validation of the 2008 MODIS-MCD45 global burned area product using stratified random sampling. Remote Sensing of Environment, 144, 187-196. https://doi.org/10.1016/j.rse.2014.01.008 

12. Padilla, M., Stehman, S.V., Litago, J., Chuvieco, E. (2014) Assessing the Temporal Stability of the Accuracy of a Time Series of Burned Area Products. Remote Sensing, 6, 2050-2068. https://doi.org/10.3390/rs6032050 

13. 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://doi.org/10.1071/WF12137. 

14. Van Leeuwen, T.T., van der Werf, G.R., Hoffmann, A.A., Detmers, R.G., Rücker, G., French N.H.F., Archibald, S., Carvalho Jr., J.A., Cook, G.D., de Groot, W.J., Hély, C., Kasischke, E.S., Kloster, S., McCarty, J.L., Pettinari, M.L., Savadogo, P., Alvarado, E.C., Boschetti, L., Manuri, S., Meyer, C.P., Siegert, F., Trollope L.A., y Trollope, W.S.W. (2014) Biomass burning fuel consumption rates: a field measurement database. Biogeosciences, http://doi.org/10.5194/bg-11-7305-2014