University of Cambridge
Cambridge CB2 3EA
DSc Agriculture & Forest Science. University of Eastern Finland
PhD Remote Sensing. Technical University of Madrid
MSc Environmental Sciences. Technical University of Madrid
MSc Biology. University of Navarra
Dr Rubén Valbuena has participated in many projects involving habitat mapping, environmental indicators, and remote sensing involving both passive satellite optical and airborne Lidar. Most recently, he developed Pan-European indicators of forest structure at the European Forest Institute (International Organization, 2010-2014). Other projects include Priority Habitat mapping in Wales and remote sensing-assisted forest inventory at the Forest Research Agency of the Forestry Commission (UK, 2008-2009), and determining baseline indicators for Strategic Environmental Assessment in Madrid (2006-2008). Overall, he has gained extensive experience in the field of remote sensing applied to forest inventory, REDD+, and ecological purposes.
Project LORENZLIDAR: Classification of Forest Structural Types with LiDAR remote sensing applied to study tree size-density scaling theories. Marie S. Curie Fellowship H202-MSCA-IF-2014: 658180
Mine is a multidisciplinary research involving a wide range of topics mainly in the fields of Forest Ecology and Remote Sensing, including tree size scaling theories, forest structure, competition and dominance, modelling, fusion of remote sensors and integration of information in forest inventory projects, especially those using lidar.
Unsuitability of diversity indices for forest structure characterization. After considering many possibilities for describing the structural complexity of forests, all of which were available in the scientific literature, Valbuena et al. (2012 Forest Ecology and Management) demonstrated that indices based on theory of information (e.g. diversity indices) were unsuited for such purposes, which contradicted many previous publications by taking a deeper insight in mathematical theory of diversity and equitability ordering.
The Lorenz curve method for forest structure characterization. Once it was clear that approaches for describing tree size inequality of forests had to be based on the Lorenz curve, Dr Valbuena had a number of important results which keep summing up evidence supporting this method. The use of Lorenz curve and Gini Coefficient was fairly new to the study of tree assemblages. Diverse studies proved that: (i) the value of GC = 0.5 corresponds to the maximum entropy for the Gini Coefficient of tree sizes, and therefore serves as a boundary between even tree-sized and uneven tree-sized forests (Valbuena et al., 2012 Forest Ecology and Management); (ii) the inflexion point of the Lorenz curve represents the quadratic mean diameter, and therefore is a key in describing the relative dominance between overstorey and understorey (Valbuena et al., 2013 Canadian Journal of Forest Research)
Pan-European indicators of forest structure from airborne laser scanning. In the advent of National Programmes covering entire countries with lidar surveys, I grew this original idea that simple methods yielding concise indicators of forest structure would make trans-national comparison plausible. The work started from the European Forest Institute (EFI) has evolved into promising results that indeed few metrics from lidar can be used to explain forest characteristics with no involvement of statistical approaches (Valbuena et al., 2017 Remote Sensing of Environment).
Fusion of lidar and image data using back-projection outperforms orthorectification. Previous research demonstrated the back-projection method to be better suited than many approaches for orthorectification in remote sensing data fusion (Valbuena et al., 2011 Remote Sensing of Environment), despite of the latter being more prevalent in the industry. Back-projection consists in calculating the positions that individual lidar returns would have in the camera at the time of exposure, by rendering them mathematically.
This interactive web app illustrates the synergic capabilities of lidar and multispectral sensors. The lidar detects the development of understorey under the dominant canopy, whereas the presence of a standing dead tree is emphasized by the infrared information from the multispectral sensor.
Valbuena R., Maltamo M. Mehtätalo L., & Packalen P. (2017) Key Structural Features of Boreal Forests may be Detected Directly from Airborne Lidar without Field Training Data. Remote Sensing of Environment. DOI
Bottalico F., Chirici G., Giannini R., Mele S., Mura M., Puxeddu M., McRoberts R.E., Valbuena R. & Travaglini D. (2017) Modeling Mediterranean Forest Structure Using Airborne Laser Scanning Data. International Journal of Applied Earth Observation and Geoinformation 57: 145-153. DOI
Heiskanen J., Jinxiu L., Valbuena R., Aynekulu E., Packalen P. & Pellikka P. (2017) Remote Sensing Approach for Spatial Planning of Land Management Interventions in West African Savannas. Journal of Arid Environments 140: 29-41. DOI
Valbuena R., Eerikäinen K., Packalen P. & Maltamo M. (2016) Gini Coefficient Predictions from Airborne Lidar Remote Sensing Display the Effect of Management Intensity on Forest Structure. Ecological Indicators 60: 574-585. DOI
de Almeida D.R.A., Nelson B.W., Schiettia, J., Görgens E.B., Resende A.F., Stark S.C. & Valbuena R. (2016) Contrasting Fire Susceptibility and Fire Damage Between Seasonally Flooded Forest and Upland Forest in the Central Amazon Using Portable Terrestrial Profiling Lidar. Remote Sensing of Environment 184: 153-160. DOI
Valbuena R., Heiskanen J., Aynekulu E., Pitkänen S. & Packalen P. (2016) Sensitivity of Above-Ground Biomass Estimates to Height-Diameter Modelling in Mixed-Species West African Woodlands. PLOS ONE 11(7): e0158198. DOI
Vihervaara P., Mononen L., Auvinen A.P., Virkkala R., Lü Y., Pippuri I., Packalen P., Valbuena R., Valkama J. (2015) How to Integrate Remotely Sensed Data and Biodiversity for Ecosystem Assessments at Landscape Scale. Landscape Ecology 30 (3): 501-516. DOI
Verkerk P.J., Levers C., Kuemmerle T., Lindner M., Valbuena R., Verburg P.H. & Zudin S. (2015) Mapping Wood Production in European Forests. Forest Ecology and Management 357: 228-238. DOI
Valbuena R. (2014) Integrating Airborne Laser Scanning with Data from Global Navigation Satellite Systems and Optical Sensors. In: Maltamo M., Næsset E. & Vauhkonen J. (Eds.) Forestry Applications of Airborne Laser Scanning. Concepts and Case Studies. Managing Forest Ecosystems Series 27. pp. 63-88. Springer
Valbuena R., Packalen P., García-Abril A., Mehtätalo L. & Maltamo M. (2013) Characterizing Forest Structural Types and Shelterwood Dynamics from Lorenz-based Indicators Predicted by Airborne Laser Scanning. Canadian Journal of Forest Research 43: 1063-1074. DOI
Valbuena R., Packalén P., Martín-Fernández S. & Maltamo M. (2012) Diversity and Equitability Ordering Profiles Applied to the Study of Forest Structure. Forest Ecology and Management 276: 185-195. DOI
Valbuena R., Mauro F., Arjonilla F.J. & Manzanera J.A. (2011) Comparing Airborne Laser Scanning-Imagery Fusion Methods Based on Geometric Accuracy in Forested Areas. Remote Sensing of Environment 115(8): 1942-1956. DOI
Visit my GitHub repository to access my codes for replicating remote sensing-assisted carbon accounting tasks, mixed-effects modelling, etc.