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.
The goals of this project were:
- 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)
- 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.
Fig.: LULC classification within 100m buffer to water bodies
Fig.: Detailed view of extracted olive field outlines
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