Mariano Garcia-Alonso

Mariano Garcia AlonsoResearch Associate

Contact Details


I obtained a degree in Geodesy and Cartography (MSc. Eq.) from the University of Alcala (Spain), an MSc in GIS and Remote Sensing from the University of Greenwich (England) and hold a PhD degree from the University of Alcala (Spain) in Geographical Information Technologies. Since my very first class in Geographical Information Systems and Remote Sensing as an undergraduate student, I understood the potential and importance of these technologies to better understand our environment and its spatio-temporal dynamics.

Research interests

My research focuses on the development of methods to derive improved information on 3D forest structure from passive and active (LiDAR and SAR) remote sensing data. 3D forest structure is a critical element of the carbon and carbon cycle, the light regime, the habitat, the biodiversity and even the recreational, scenic and cultural value of our forest. It also affects the measurements collected by remote sensors. A central aspect of my research is the up-scaling of detailed information provided by ground and airborne sensors at the landscape scale, to the regional and global scales by data provided by satellite-borne sensors. I strongly believe that the full potential of remote sensing lies in the integration of data collected from different sensors, maximizing their synergy. Thus, the integration of the structural information provided by active sensors (LiDAR and SAR) and the ability of multispectral and hyperspectral sensors to provide information on species composition based on their spectral response, enables us to derive accurate spatially explicit information on biophysical variables. In this regards, I have investigated the integration of passive and active sensors using machine learning methods to improve fuel type mapping and land cover classification.

I am particularly interested in the role of forest fires in the structure and functioning of different terrestrial ecosystems for which fires are a major disturbance factor. Although it is a natural process of many ecosystems, climate change is inducing changes in the intensity and frequency of forest fires, that is, in their regime. Knowing the amount and distribution of fuels is critical to predict the fire behavior and effects, and to perform appropriate fire planning and management activities. To improve the spatial information available on forest fuels, I have developed methods to quantify canopy structural parameters at multiple scales from LiDAR data collected using terrestrial, airborne and satellite sensors. I have also developed methods to evaluate the condition of fuels, specifically their moisture content, based on multispectral data.

Current projects


Research Results

The results of my research have been published in several peer-review journals and presented in different conferences. Find a link to my publications here:

Peer-reviewed journals

  1. 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. [pdf]
  2. 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. [pdf]
  3. 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. [pdf]
  4. 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 [pdf].
  5. 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. [pdf].
  6. 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. [pdf].
  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. [pdf]
  8. 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. [pdf]
  9. 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.[pdf]
  10. 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.[pdf]
  11. Garcia, M., Popescu, S., Riaño, D., Zhao, K., Neuenschwander, A., Agca, M. 2012. Characterization of canopy fuels using ICESat/GLAS data. Remote Sensing of Environment. 115, pp. 1369 - 1379. [pdf]
  12. Nieto, H., Aguado, I., Garcia, M. & Chuvieco, E. 2012. Lightning-caused fires in Central Spain: development of a probability model of occurrence for two Spanish regions.Agricultural and Forest Meteorology. 162-163, pp. 35 - 43. [pdf]
  13. Garcia, M., Riaño, D., Chuvieco, E., Salas, F.J. & Danson, F.M., 2011. Multispectral and LiDAR data fusion for fuel models mapping using support vector machine and decision rules. Remote Sensing of Environment. [pdf]
  14. Garcia, M., Danson, F.M., Riaño, D., Chuvieco, E. Ramirez, F.A. & Bandugula, V. 2011. Terrestrial laser scanning to estimate plot-level forest canopy fuel properties.International Journal of Applied Earth Observation and Geoinformation. 13, pp. 636 - 645. [pdf]
  15. Garcia, M., Riaño, D., Chuvieco, E. & Danson, F.M. 2010 Estimating biomass carbon stocks for a Mediterranean forest in central Spain using height and intensity LiDAR data. Remote Sensing of Environment. 114 - 4, pp. 816 - 830. [pdf].
  16. Garcia, M., Prado, E., Riaño, D., Chuvieco, E. & Danson, F.M. 2009. Ajuste planimétrico de datos LiDAR para la estimación de características dasométricas en el parque natural del Alto Tajo.Geofocus. Revista Internacional de Ciencia y Tecnología de la Información Geográfica. pp. 184 - 208. Geofocus. [pdf].
  17. Garcia, M. Chuvieco, E., Nieto, H. & Aguado, I. 2008 Combining AVHRR and meteorological data for estimating live fuel moisture content. Remote Sensing of Environment. 112, pp. 3618 - 3627. [pdf].
  18. Garcia, M. & Chuvieco, E. 2004. Assessment of the potential of SAC-C/MMRS imagery for mapping burned areas in Spain. Remote Sensing of Environment. 92, pp. 414 - 423. [pdf].


Book chapters

  1. Gajardo, J., Garcia, M. & Riaño, D. 2013. Applications of ALS in forest fuel assessment and fire prevention. Forestry Applications of Airborne Laser Scanning.pp. 439 - 462. Springer, [link]
  2. Baldauf, T. & Garcia, M. 2016. Image Processing of Radar and Lidar in Tropical Forestry. Tropical Forestry Handbook. pp. 635 -661. [link]

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