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LORENZLIDAR Classification of Forest Structural Types with LiDAR Remote Sensing Applied to Study Tree Size-Density Scaling Theories.
Funded under H2020-EU.1.3.2. - MSCA-IF-2014-EF - Marie Skłodowska-Curie Individual Fellowships.
Project ID: 658180. Total cost: €195 454,80
Project coordinator: Professor David Coomes
Research fellow: Dr Rubén Valbuena


The main goal of this research is to develop an objective methodology for monitoring forest structural complexity by airborne laser scanning (ALS) remote sensing. Most European countries are currently acquiring low-density national ALS data, by scanning with LiDAR sensors onboard airborne platforms, in the process to obtain full-country coverage and making it publicly available. These datasets are taken in relatively homogeneous conditions, therefore providing with a chance to develop Pan-European indicators and automated unsupervised methods not requiring field data. With the intention of producing a methodology that could be replicated in practice by any forest practitioner, publicly available ALS data from national land surveys of Member States will be used, and unsupervised methods not requiring field data will be developed. The laser partly penetrates the forest canopy, therefore providing an opportunity to study the establishment of natural regeneration in the understory layers. The analysis will be based on the study of the Lorenz curve, a method for which the applicant has obtained promising preliminary results and which the present proposal plans to generalize for more forest ecosystem types and low-density National laser datasets. The diameter distributions will be evaluated with regard to their agreement to metabolic ecology and demographic equilibrium theories. The development of a mathematical framework linking Lorenz ordering to diameter-density scaling relationships will provide with a method for automated ecological evaluation of forests by means of ALS remote sensing. In practice this means that competition and forest disturbance conditions are different at different forest areas, and we suggest that the Lorenz method for ALS can provide indicators for these conditions. The application will be on a replicable method for forest stratification into structural types from ALS data acquired in national programmes.
Most relevant publications:
Other related collaborative publications:
  • Nunes M.H., Jucker T., Swinfield T., Asner G., Vaughn N., Valbuena R., Svátek M., Kvasnica J., Both S., Elias D.M.O., Riutta T., Malhi Y. & Coomes D.A. (2019) Microclimate conditions driven by edge effects and topography control forest canopy losses during El Niño in Borneo

  • Almeida DRA, Stark SC, Shao G, Schietti J, Nelson BW, Silva CA, Gorgens EB, Valbuena R, Papa DA & Brancalion PHS (2019) Optimizing the remote detection of tropical rainforest structure with airborne lidar: leaf area profile sensitivity to pulse density and spatial resolution

  • Hernando A., Puerto L., Mola-Yudego B., Manzanera J.A., García-Abril A., Maltamo M. & Valbuena R. (2018) Estimation of forest biomass components through airborne LiDAR and multispectral sensors. iForests

  • Valbuena R., Hernando A., Manzanera J.A., Görgens E.B., Almeida D.R.A., & García-Abril A. (2017) Evaluating observed versus predicted forest biomass: R-squared, index of agreement or maximal information coefficient?. European Journal of Remote Sensing

  • Almeida DRA, Stark SC, Schietti J, Camargo JLC, Amazonas NT, Görgens EB, Rosa DM, Smith MN, Valbuena R, Saleska S, Nelson BW, Mesquita R, Laurance WF, Lovejoy TE & Brancalion PHS (2018) Revealing persistent impacts of more than 20 years of tropical rainforest fragmentation on canopy structure and forest function with Lidar remote sensing

  • Valbuena R., Hernando A., Manzanera J.A., Martínez-Falero E., García-Abril A., & Mola-Yudego, B. (2017) Most Similar Neighbour Imputation of Forest Attributes Using Metrics Derived from Combined Airborne LIDAR and Multispectral Sensors. International Journal of Digital Earth 10.1080/17538947.2017.1387183 DOI