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Tomislav Hengl
PeerJ Author
1,055 Points

Contributions by role

Author 810
Preprint Author 245

Contributions by subject area

Biogeography
Computational Biology
Plant Science
Data Mining and Machine Learning
Spatial and Geographic Information Science
Soil Science
Computational Science
Ecology
Ecosystem Science
Data Science
Ethical Issues
Legal Issues
Science Policy

Tomislav Hengl

PeerJ Author

Summary

Dr Tom Hengl is the Technical Director at OpenGeoHub and EnvirometriX Ltd with backgrounds in predictive soil mapping, geostatistics, GIS and remote sensing. He is an active developer of Machine Learning Algorithms for processing soil and environmental data mainly in R and open source GIS. Tom has over 20 years of experience in research for mapping and modelling environmental data at regional and global scales, and has published over 60 journal articles and several textbooks in the field of geo-information science, soil mapping and spatial statistics (Google Scholar h-index of 30 with >500 citations per year). He has been elected vice chair of the International Society for Geomorphometry (geomorphometry.org) in the period 2011–2015; he also initiated the OpenGeoHub Summer schools (training courses using Open Source Software tools) that has been running for already 12 years at various places from Europe to North America and Australia. Tom's current special interests are developing Machine Learning methods for spatial and spatiotemporal data primarily for the purpose of automated mapping / interpolation.

Biogeography Environmental Sciences Soil Science Spatial & Geographic Information Science Spatial & Geographic Information Systems

Work details

Technical Director

OpenGeoHub Foundation
May 2018
Global datasets
https://opengeohub.org/team-list

Technical director / Senior researcher

Envirometrix Ltd
February 2018
Machine learning and geocomputing

Identities

@tomhengl

Websites

  • Google Scholar
  • Homepage

PeerJ Contributions

  • Articles 8
  • Preprints 3
  • Questions 1
December 4, 2024
A computational framework for processing time-series of earth observation data based on discrete convolution: global-scale historical Landsat cloud-free aggregates at 30 m spatial resolution
Davide Consoli, Leandro Parente, Rolf Simoes, Murat Şahin, Xuemeng Tian, Martijn Witjes, Lindsey Sloat, Tomislav Hengl
https://doi.org/10.7717/peerj.18585 PubMed 39650555
March 13, 2024
Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution
Julia Hackländer, Leandro Parente, Yu-Feng Ho, Tomislav Hengl, Rolf Simoes, Davide Consoli, Murat Şahin, Xuemeng Tian, Martin Jung, Martin Herold, Gregory Duveiller, Melanie Weynants, Ichsani Wheeler
https://doi.org/10.7717/peerj.16972 PubMed 38495753
June 23, 2023
Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation
Carmelo Bonannella, Tomislav Hengl, Leandro Parente, Sytze de Bruin
https://doi.org/10.7717/peerj.15593 PubMed 37377791
June 6, 2023
Ecodatacube.eu: analysis-ready open environmental data cube for Europe
Martijn Witjes, Leandro Parente, Josip Križan, Tomislav Hengl, Luka Antonić
https://doi.org/10.7717/peerj.15478 PubMed 37304863
July 25, 2022
Forest tree species distribution for Europe 2000–2020: mapping potential and realized distributions using spatiotemporal machine learning
Carmelo Bonannella, Tomislav Hengl, Johannes Heisig, Leandro Parente, Marvin N. Wright, Martin Herold, Sytze de Bruin
https://doi.org/10.7717/peerj.13728 PubMed 35910765
July 21, 2022
A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat
Martijn Witjes, Leandro Parente, Chris J. van Diemen, Tomislav Hengl, Martin Landa, Lukáš Brodský, Lena Halounova, Josip Križan, Luka Antonić, Codrina Maria Ilie, Vasile Craciunescu, Milan Kilibarda, Ognjen Antonijević, Luka Glušica
https://doi.org/10.7717/peerj.13573 PubMed 35891647
August 29, 2018
Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables
Tomislav Hengl, Madlene Nussbaum, Marvin N. Wright, Gerard B.M. Heuvelink, Benedikt Gräler
https://doi.org/10.7717/peerj.5518 PubMed 30186691
August 22, 2018
Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential
Tomislav Hengl, Markus G. Walsh, Jonathan Sanderman, Ichsani Wheeler, Sandy P. Harrison, Iain C. Prentice
https://doi.org/10.7717/peerj.5457 PubMed 30155360
October 19, 2018 - Version: 2
A brief introduction to Open Data, Open Source Software and Collective Intelligence for environmental data creators and users
Tomislav Hengl, Ichsani Wheeler, Robert A MacMillan
https://doi.org/10.7287/peerj.preprints.27127v2
August 6, 2018 - Version: 3
Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables
Tomislav Hengl, Madlene Nussbaum, Marvin N Wright, Gerard B.M. Heuvelink, Benedikt Gräler
https://doi.org/10.7287/peerj.preprints.26693v3
July 27, 2018 - Version: 2
Global mapping of potential natural vegetation: an assessment of Machine Learning algorithms for estimating land potential
Tomislav Hengl, Markus G Walsh, Jonathan Sanderman, Ichsani Wheeler, Sandy P Harrison, Iain C Prentice
https://doi.org/10.7287/peerj.preprints.26811v2

1 Question

0
Could the authors provide also names of the 340 researchers (and how/why were they selected)?
about Citations and the h index of soil researchers and journals in the Web of Science, Scopus, and Google Scholar