Prof. Dr. Miguel Mahecha

Prof. Dr. Miguel Mahecha

Professor

Umweltdatenwissenschaften und Fernerkundung
Institutsgebäude
Talstraße 35, Room 2-06
04103 Leipzig

Phone: +49 341 97 - 32801

Prof. Dr. Miguel Mahecha

Prof. Dr. Miguel Mahecha

Institute Head

Institute for Earth System Science and Remote Sensing
Institutsgebäude
Talstraße 35
04103 Leipzig

Abstract

Miguel Mahecha is Professor of Environmental Data Science and Remote Sensing at the University of Leipzig and the Remote Sensing Centre for Earth System Research. His main scientific interest is understanding ecosystem responses to climate extremes and human-environment relationships during climate extremes. He also works on understanding macro-ecological dynamics and ecosystem functioning. His research is based on data-driven methods and high-dimensional Earth observations. In recent years, he has co-developed the concept of the Earth System Data Cube, which combines empirical methods with theoretical understanding to understand complex interactions in the Earth system. Miguel Mahecha is a member of the German Centre for Integrative Biodiversity Research (iDiv) and PI in the Centre for Scalable Data Analytics and Artificial Intelligence. He is a Fellow of the European Laboratory for Learning and Intelligent Systems.

Professional career

  • since 03/2020
    Full Professor for Modelling Approaches in Remote Sensing
  • 12/2012 - 03/2020
    Leader of the research group: Emprical Inference in the Earth System, Max Planck Institute for Biogeochemistry, Jena
  • 10/2009 - 11/2012
    PosDoc, Max Planck Institute for Biogeochemistry, Jena
  • 06/2006 - 09/2009
    PhD studies, ETH Zürich
  • 10/2000 - 04/2006
    Studies of Geoecology, Bayreuth University
  • Responses of ecosystems to climate extremes
  • Biogeography and ecosystem functioning
  • Earth System Data cubes
  • Earth System Data science methods
  • Digital Forest - A real-time forest monitoring system for climate change impacts
    Mahecha, Miguel
    Duration: 03/2021 – 08/2024
    Funded by: Stiftungen Inland
    Involved organisational units of Leipzig University: Umweltdatenwissenschaften und Fernerkundung
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  • NFDI4BioDiversity: Biodiversity, Ecology & Environmental Data
    Mahecha, Miguel
    Duration: 01/2021 – 12/2026
    Funded by: DFG Deutsche Forschungsgemeinschaft
    Involved organisational units of Leipzig University: Umweltdatenwissenschaften und Fernerkundung; Spezielle Botanik und funktionelle Biodiversität
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  • Extracting spatiotemporal macroecological patterns using plant occurrence data crowd-sourced via Flora Incognita (Flexpool)
    Mahecha, Miguel
    Duration: 10/2020 – 09/2022
    Funded by: DFG Deutsche Forschungsgemeinschaft
    Involved organisational units of Leipzig University: Umweltdatenwissenschaften und Fernerkundung
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  • XAIDA - eXtreme events: Artificial Intelligence for Detection and Attribution
    Mahecha, Miguel
    Duration: 09/2021 – 08/2025
    Funded by: EU Europäische Union
    Involved organisational units of Leipzig University: Umweltdatenwissenschaften und Fernerkundung
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  • Intensive field measurement s "Leipziger Auwaldkran" & "Großpösna"
    Mahecha, Miguel
    Duration: 09/2021 – 12/2022
    Funded by: SMWK Staatsministerium für Wissenschaft, Kultur und Tourismus
    Involved organisational units of Leipzig University: Umweltdatenwissenschaften und Fernerkundung
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more projects

  • Mahecha, M.; Bastos, A.; Bohn, F. J. et al.
    Biodiversity loss and climate extremes - study the feedbacks
    Nature. 2022. pp. 30–32.
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  • Migliavacca, M.; Musavi, T.; Mahecha, M. et al.
    The three major axes of terrestrial ecosystem function
    Nature. 2021. pp. 468–472.
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  • Wolf, S.; Mahecha, M.; Sabatini, F. M.; Wirth, C.; Bruelheide, H.; Kattge, J.; Moreno-Martínez, Á.; Mora, K.; Kattenborn, T. J.
    Citizen science plant observations encode global trait patterns
    Nature Ecology & Evolution. 2022.
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  • Bodesheim, P.; Babst, F.; Frank, D. C.; Hartl, C.; Zang, C. S.; Jung, M.; Reichstein, M.; Mahecha, M.
    Predicting spatiotemporal variability in radial tree growth at the continental scale with machine learning
    Environmental Data Science. 2022.
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  • Cortés-Andrés, J.; Camps-Valls, G.; Sippel, S.; Székely, E.; Sejdinovic, D.; Diaz, E.; Perez-Suay, A.; Li, Z.; Mahecha, M.; Reichstein, M.
    Physics-aware nonparametric regression models for Earth data analysis
    Environmental Research Letters. 2022. 17 (5). p. 054034.
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more publications

Current research questions in physical geography

  • 12-GGR-B-PG01B: Fundamentals of Physical Geography/Geoecology II - Climate and Water

    In this module we invite first year students to get familiar with basic concepts of climate geography and hydrology. Based on this foundation, we explore global patterns of terrestrial vegetation dynamics. 

  • 12-GGR-B-PG09.VL01: Landscape analysis, Resource protection, Risks and Impact Research

    The lecture "Landscape Analysis, Resource Protection, Risks and Impact Research" focuses this year on the consequences of climate change in the different sectors, as well as the impacts of human influences on different types of geoecosystems. After an introduction to the current state of the climate crisis, consequences for different parts of the Earth system will be discussed at different scales.

  • 12-GGR-B-PG06-S2: Current Research in Physical Geography

    We will identify and analyse current global trends and transformation processes of terrestrial ecosystems. The starting point is an introduction to statistical properties of trends, their determinations, and possible problems. The aim is then to assess causes of global trends (e.g. "global greening", "loss of biodiversity", "droughts" etc.) and consequences for ecosystem functioning. At the end, interactions of global trends are analysed and synthesised.

  • 12-GGR-B-PG06-S3: Research Methods in Physical Geography

    The seminar "Research Methods in Physical Geography" accompanies the seminar "Current Research in Physical Geography". Based on the identified trends, a (possibly joint) research paper will be designed. This work is used to teach the "Code of Good Scientific Practice", forms of scientific publications, literature research, the review process, and even the basics of scientific writing.

  • 12-GGR-M-GFP2: Geographical Information Systems - Models and Analyses

  • 12-GGR-M-GFA1: Geodata analysis in economic and social geography

  • 12-GEO-M-AG02: Earth System Components