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WaddenSee Dashboard

01

Chooising desired maps

02

Selecting environmental variables

Selecting relevant biodiversity maps is a fundamental decision in biodiversity research and conservation efforts. These datasets serve as the foundation for understanding and addressing critical issues according to the biodiversity

The selection of the environmental variables can be dependent on the availability of thedatasets and the use case. Examples of environmental variables are bathymetry and salinity.

03

Training the AI Model

Based on the selected biodiversity map and the environmental variables, a Deep Learning Model
can be trained for the WaddenSee

Generating your maps: The generated model can predict similar biodiversity maps when the same environmental variables
are given as input for any point in time

01

Biodiversity maps

Abundance Map
Benthic Biomass
Seagrass
Birddensity
Bivalve Density
Species Richness
Other/finetune

02

Inputs

Satellite Image
Med. Grain Size
Salinity
Abiotic Factors
Near IR
Button
Button
Bathymetry
Silt Content
Exposure Time
RGB
Button
Button
Button
Add custom dataset

Model

03

Upload own Deep Learning Model
Use WaddenSee DL

Abundance Map

Abundance maps in the Wadden Sea reveal specific species' distribution, shedding light on their preferred habitats and migration patterns. These maps quantify population densities, pinpointing biodiversity hotspots, and areas with high or low species numbers. Seasonal variations in species abundance are depicted, aiding understanding of ecosystem dynamics. Identifying biodiversity hotspots showcases regions of thriving species. Comparing abundance maps over time helps assess the impact of environmental changes on the Wadden Sea's ecosystems, including climate change and human activities. Furthermore, these maps guide conservation priorities by identifying areas that require protection and management to preserve biodiversity.

Click here for more information.

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Coordinates: ....

Data used: ...

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