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Gonzalo Oton

0000-0002-3299-6442

Project Officer (Photogrammetry and remote sensing; Earth sciences; Environmental sciences; Information and computing sciences)

Gonzalo Otón is a Project Officer at the European Commission's Joint Research Center (JRC), where he applies his expertise in remote sensing to forestry-related projects. As part of the LULUCF team, led by Giacomo Grassi, he plays a pivotal role in developing the spatially explicit version (GCBM) of the Carbon Budget Model (CBM) for the EU context. He also works closely with the team led by Alessandro Cescatti, contributing to research on canopy height, forest structure, biodiversity, and resilience.

Holding a Ph.D. in Geographic Information Technologies from the University of Alcalá, Gonzalo has a strong research background. His work includes contributions to the ESA's Fire_cci project, along with involvement in other significant European initiatives such as Copernicus and Multiply. He is well-versed in developing and analyzing burned area algorithms, notably contributing to the creation of products like FireCCILT11 and FireCCI51.

Publications

  • A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data
  • Analysis of Trends in the FireCCI Global Long Term Burned Area Product (1982–2018)
  • Monitoring the resilience of European forests from space and its relation to tree functional diversity
  • METHODS AND CHALLENGES IN TIMESERIES ANALYSIS OF VEGETATION IN THE GEOSPATIAL DOMAIN
  • 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”
  • Validation of low spatial resolution and no-dichotomy global long-term burned area product by Pareto boundary
  • Development of a consistent global long-term burned area product (1982–2018) based on AVHRR-LTDR data
  • Links between Climate Change Knowledge, Perception and Action: Impacts on Personal Carbon Footprint
  • Fire_CCI Long-Term Burned Area dataset: Version 1.1 (FireCCILT11)
  • ESA CCI ECV Fire Disturbance: D1.2 Algorithm Development Plan, version 2.1.
  • Fire_CCI Long-Term Burned Area dataset: Version 1.0 Beta (FireCCILT10)
  • ESA CCI ECV Fire Disturbance: D4.2.2 Product User Guide – AVHRR-LTDR, version 1.0.
  • ESA CCI ECV Fire Disturbance: O2.D3 Algorithm Theoretical Basis Document (ATBD) for AVHRR-LTDR data, version 1.0.
  • ESA CCI ECV Fire Disturbance: D2.2 End to End ECV Uncertainty Budget, version 2.1.
  • ESA CCI ECV Fire Disturbance: D3.3.4 Product User Guide - LTDR, version 1.1.
  • ESA CCI ECV Fire Disturbance: D3.2. Software Verification Report, version 2.4.
  • MULTIPLY. Validation of performance impact indicators.
  • ESA CCI ECV Fire Disturbance: O2.D2 Algorithm Theoretical Basis Document (ATBD) for AVHRR LTDR data, version 1.1.
  • Global detection of burned areas from AVHRR-LTDR images using RandomForest algorithms.
  • ESA CCI ECV Fire Disturbance: O2.D1 User Requirement Document and Product Specification Document for AVHRR, version 1.1.
  • Global generation of long-term burned area with AVHRR-LTDR data.
  • FireCCI50: a global burned area mapping algorithm based on MOD09GQ within Fire_cci project.
  • Selección de variables para la clasificación global de área qumada con Random Forest y datos AVHRR-LTDR.
  • Análisis global de áreas quemadas para la modelización climática: experiencias del proyecto Fire_cci.
  • Análisis contextual y temporal de tendencias para el producto FireCCILT10.
  • Detección global de áreas quemadas a partir de imágenes AVHRR-LTDR en una serie temporal larga (1982-2015).
  • Generation of a global burned area algorithm based on MODIS MOD09GQ images for Fire_cci project.
  • Extending time series of burned area estimations: from Terra - MODIS 250 m to Sentinel 3 - OLCI 300 m.
  • Adaptation of the FireCCI51 algorithm developed within the Fire_cci project to the Sentinel 3A and B OLCI sensor.
  • Development of a Global Burned Area Product for 1982-2018 Based on AVHRR-LTDR Images and the Burned Area Product FireCCI51.
  • Global Detection of Long-Term (1982–2017) Burned Area with AVHRR-LTDR Data
  • Correction: Otón, G., et al. Global Detection of Long-Term (1982–2017) Burned Area with AVHRR-LTDR Data. Remote Sensing 2019, 11, 2079
  • Assessing the relationship between forest structural diversity and resilience in a warming climate
  • A dataset on the structural diversity of European forests
  • Supplementary material to "A dataset on the structural diversity of European forests"
  • Predicting forest structural complexity in Europe through an integration of radar, optical data and machine learning

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