forest ecosystems<\/strong><\/a>, leaving enough time for action to be taken before the changes reach the stage of being irreversible.<\/p>\nThe German Research Foundation (DFG) has approved the Collaborative Research Centre (CRC) 1537, \u2018ECOSENSE.\u2019 The DFG intends to fund ECOSENSE from 1 July 2022. The research team will receive approximately \u20ac10.5m over four years for its interdisciplinary, detailed research focusing on ecosystem processes in forests.<\/p>\n
The ECOSENSE team: A groundbreaking collaboration<\/h2>\n
The team led by CRC spokespersons Professor Dr Ulrike Wallrabe, Professor of Microactuators at the Institute of Microsystems Engineering, Professor Dr Christiane Werner, Professor of Ecosystem Physiology at the Institute of Earth and Environmental Sciences at the University of Freiburg, would like to be able to detect and predict changes in the forest ecosystem more precisely and quickly. These ecosystem changes are taking place as a consequence of climate change. <\/p>\n
The research group is composed of scientists from various research areas, including Freiburg University researchers from the Faculty of Environment and Natural Resources, scientists from the Institute for Microsystems Engineering (IMTEK), and researchers from the Institute for Sustainable Technical Systems (INATECH).<\/p>\n
\u201cThis means that two large departments are equally involved in this project,\u201d explained Wallrabe. As part of the CRC, the Freiburg researchers are collaborating with the Institute for Microstructure Technology and the Institute for Meteorology and Climate Research at the Karlsruhe Institute of Technology (KIT).<\/p>\n
Ulrike Wallrabe and Christiane Werner commented: \u201cThe ECOSENSE toolkit will enable rapid assessment of any ecosystem in the future; even in remote areas.\u201d<\/p>\n
Sensor network to predict changes in the forest ecosystem in real-time<\/h2>\n
The CRC is developing an autonomous, intelligent sensor network technology centred around microsensors. Tailored to harsh forest environments, these will measure the spatio-temporal dynamics of ecosystem states and fluxes in a natural, complex-structured forest in a minimally invasive manner.<\/p>\n
\u201cThe measurement data will be transferred in real-time to a sophisticated database and will be immediately available for process analysis, deep learning, and improved simulation models for short- and medium-term predictions,\u201d Wallrabe explained. \u201cCurrently, there is a lack of suitable measurement, data and modelling tools for comprehensive quantification of change processes in real-time at the highest spatio-temporal resolution. That is where we come in and develop mobile, easily deployable systems.\u201d<\/p>\n
How climate change has impacted complex forest ecosystems is largely unexplored<\/h2>\n
\u201cClimate change is threatening forest ecosystems worldwide, which serve an important regulatory function in the climate system as carbon reservoirs. The impacts on complex forest ecosystems with their multiple processes and interactions between soil, plant, and atmosphere are largely unexplored. Future changes are therefore hardly predictable,\u201d Werner said.<\/p>\n
\u201cImproved process understanding of carbon and water cycles is imperative for accurate predictions of climate change impacts on our forests.\u201d<\/p>\n
Werner and Wallrabe concluded: \u201cThe ECOSENSE toolkit, validated under controlled climate stress experiments and in our ECOSENSE forest, will enable a rapid assessment of any ecosystem in the future; even in remote areas.\u201d<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
A research team from Albert-Ludwigs-Universit\u00e4t Freiburg has developed sensor network technology to predict changes in the forest ecosystem A collaborative team, including scientists from the University of Freiburg and the Karlsruhe Institute of Technology, is developing innovative sensor network technology for harsh forest environments. It is intended that the sensor network will help to predict changes in\u2026<\/p>\n","protected":false},"author":2,"featured_media":12168,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_scj_primary_category_id":60,"_scj_featured":false,"_scj_featured_from":"","_scj_featured_until":"","_scj_featured_order":0,"_scj_visibility_class":"current","_scj_layout_family":"standard","_scj_article_style":"","_scj_display_overrides":[],"_scj_intro_image_id":12168,"_scj_intro_alt":"Sensor network technology: To predict changes in the forest ecosystem","_scj_intro_caption":"","_scj_intro_class":"","_scj_intro_float":"","_scj_full_image_id":12168,"_scj_full_alt":"Sensor network technology: To predict changes in the forest ecosystem","_scj_full_caption":"","_scj_full_class":"","_scj_full_float":"","_scj_media_type":"","_scj_media_provider":"","_scj_media_external_id":"","_scj_media_url":"","_scj_media_poster_id":0,"_scj_media_width":0,"_scj_media_height":0,"_scj_media_aspect_ratio":"","_scj_media_description":"","_scj_gallery_items":[],"_scj_related_post_ids":[],"_scj_additional_authors":[],"scj_layout_family":"standard","scj_media_provider":"","scj_media_url":"","scj_full_caption":"","footnotes":""},"categories":[60],"tags":[],"class_list":["post-12169","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sustainability"],"yoast_head":"\n
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