I6 - ECOSENSE – A smart DISTRIBUTED sensor Network for forests
- Event
- iCCC2026 - iCampus Cottbus Conference
2026-05-05 - 2026-05-07
Cottbus - Band
- Vorträge
- Chapter
- Bio- und Umweltsensorik
- Author(s)
- Prof. Dr. U. Wallrabe - University of Freiburg, IMTEK
- Pages
- 30 - 31
- DOI
- 10.5162/iCCC2026/5.2
- ISBN
- 978-3-910600-10-2
- Price
- free
Abstract
Global climate change threatens ecosystem functioning worldwide. Forest ecosystems are particularly important for carbon sequestration. However, recurrent stresses, such as heat waves, floods, and droughts, increasingly endanger forests worldwide. Many relevant processes occur at small scales and high spatial heterogeneity and their interactions can be key players to amplify or dampen a system’s response to stress. Currently, we are lacking the appropriate measuring, data and modelling tools allowing for comprehensive, real time quantification of relevant processes at high spatio-temporal coverage. the project research interdisciplinary Consequently, implements, and evaluates a versatile, distributed, autonomous, develops, intelligent sensor network based on novel microsensors tailored to the specific needs in harsh forest environments. All measurements need to happen in a minimally invasive manner in order not to disturb the ecosystem we are exploring. This requires consideration of size, weight, inertness, cleanliness, and robustness. The complex environment of a remote naturally structured forests with rain, wind, insects and mammals thereby poses its own challenges that the ECOSENSE sensors are facing. ECOSENSE The network comprises miniaturized sensors for 1) the quantification of gas and water fluxes, specifically isotope discriminated CO₂ and volatile organic compounds (VOC, here isoprene) at ppb concentrations, and 2) for the detection of typical stress markers, such as active, laser induced chlorophyll fluorescence. ECOSENSE explores these distributed sensors from leaf level including low power electronics, to medium sized laser spectrometers, up to drones and eddy covariance towers.