The Würzburg State Fire Service School is supporting the project as an associated partner.

In October 2025, a test of the forest fire early warning system using drones was conducted in Rotenburg an der Fulda. During the test, the performance of a special drone equipped with thermal imaging and optical sensors was demonstrated. The drone detected a fire being controlled by the volunteer fire department within seconds and transmitted the data to the relevant platform in real time.

Dr. Peter Hofmann from the Deggendorf Institute of Technology explained how the institute supports the project and how eCognition can effectively analyze forest structures and detect potential forest fire hazards. In addition to the recorded forest structure, consideration is given to aspects such as topography, season, weather, and much more. The remote sensing data obtained with the drone can be automatically evaluated using eCognition. The results of this evaluation can then also be used in forest management or for further spatial analyses.

Click here for more information on the KIWA Project.

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Let’s meet and talk on September 29 and 30, 2025 at the ‘Annual Meeting AK Remote Sensing 2025’ in Bochum.

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By clicking on the corresponding areas, the relevant information will automatically be displayed.

This feature enables you to align the information with your observations made during on-site visits.

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Landscape-scale modelling of pesticide exposure

Landscape-scale modelling of pesticide exposure and species effects (risk) requires corresponding geoinformation, however, base data is often not readily available in required resolution (≈10 m or higher)​. Hence, realistically applicable (availability, cost, quality, geographic coverage, collaboration) technology and workflows are needed for a range of risk assessment projects.

Project Goals

The goals of this project were:

1. Extract ‘riparian’ and ‘olives’ vegetation within a 100 m buffer to water bodies​ ​for different catchments in Spain and Greece using free high-resolution satellite data (10 m)
2. Precise extraction of olive field outlines in very high spatial resolution commercial satellite data (3 m)

Tama Group utilized provided catchment data to retrieve appropriate satellite scenes and performed the land cover classification based on time series information, including: cloud masking, atmospheric correction, pan-sharpening, layer calculations, feature importance calculation, object-based classification, accuracy check, and transferability tests. The most promising results were achieved combining object-based image analysis and convolutional neural networks.

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If a printout is required as a backup or in case of a malfunctioning mobile device, this can be easily accomplished. The map view can be printed as a PDF directly from the browser, ensuring a secure option.

Tip: Make sure the print format is set to A3 or A2. Otherwise, the entire card will not be visible in the image. When sending the PDF to the printer, select A4.

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A significant expanse of moorland is undergoing a process of renaturalization in this area. To track the success of the measures, we prepared boreholes with the project implementers from CO2-regio and with about 10 other participants at points that had been precisely measured beforehand. The drilling was performed manually, and the measuring stations were anchored in the boreholes.
In future, the measuring stations will provide valuable data on the water level in the area in order to track the success of the revitalization measures.
This is an auspicious beginning for a long-term project.
We have annotated the locations of the measuring stations on the TWC map (see above) and the TWC aerial photograph.

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Legen Sie einen Projektordner an, in dem Sie die passenden Kandidaten sammeln.
🌳 If you observe a tree that appears to be suitable, please set a Memo on the map.
 📷 Take a photo and link it to the designated folder. This feature simplifies the process of distinguishing between candidates, enhancing the clarity and organization of the application process.
🗺  GPS data for the marked trees is also viewable in the memo.

This feature provides a clear overview of the candidates eligible for funding and the distribution in your area.

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