Johannes Heisig

STML

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Projekt:Open Earth Monitor
Raum:139
E-Mail:jheisig@uni-muenster.de
Telefon:+49 251 83-33199
Über:Open Earth Monitor & Cyberinfrastructure (OEMC)

OEMC is a EU-level project with 22 partners from research and industry. The goal is to accellerate the uptake of environmental data, especially big volumes of satellite observation. Our group contributes to several work packages with an emphasis on the data processing engine and its machine learning capabilities. Software tools will be demonstrated in use cases of environmental monitors.

PhD Project

My PhD project aims at quantifying wildfire hazard potential for Germany. In Central Europe, wildfires are a result of climatic extreme events (drought periods and heat waves), increasing probability of ignition and flammability of vegetation. While currently wildfire hazard is managable, future climate projections give reason for concern. Wildfire hazard is estimated using simulations of fire spread, influenced by climate, topography and burnable biomass (fuels). I use multi-spectral satellite imagery and LiDAR point clouds to derive forest structure, which allows to model how fire spreads through vegetation.

Education
  • M.Sc. Environmental Geography (University of Bayreuth)
  • B.Sc. Geography (Ludwig-Maximilians-University Munich)
  • PhD Geoinformatics (WWU Münster, ongoing)

Publications

Ziegler, Alice, Johannes Heisig, Marvin Ludwig, Chris Reudenbach, Hanna Meyer, and Thomas Nauss. 2023. “Using GEDI as Training Data for an Ongoing Mapping of Landscape-Scale Dynamics of the Plant Area Index.” Environmental Research Letters 18 (7): 075003. https://doi.org/10.1088/1748-9326/acde8f.

Heisig, Johannes, Edward Olson, and Edzer Pebesma. 2022. "Predicting Wildfire Fuels and Hazard in a Central European Temperate Forest Using Active and Passive Remote Sensing" Fire 5, no. 1: 29. https://doi.org/10.3390/fire5010029

Bonannella C, Hengl T, Heisig J, Parente L, Wright MN, Herold M, de Bruin S. 2022. Forest tree species distribution for Europe 2000–2020: mapping potential and realized distributions using spatiotemporal machine learning. PeerJ 10:e13728

Heisig, Johannes, and Cyrus Samimi. 2020. “Detecting Drought Effects on Tree Mortality in Forests of Franconia with Remote Sensing.” In EGU2020-15579, 17. Online. https://doi.org/10.5194/egusphere-egu2020-15579.