7 Open Issues Need Help Last updated: Jul 1, 2025

Open Issues Need Help

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ndti thresholds for mean about 2 months ago

AI Summary: Determine optimal Normalized Difference Turbidity Index (NDTI) thresholds for identifying high and low turbidity in five specified locations (Paunganisu, Rewa Delta, Gulf Province, Funafuti, and two additional locations with high and low turbidity). Document findings with screenshots in a slide presentation by July 21st.

Complexity: 4/5
documentation enhancement help wanted

water quality monitoring

Jupyter Notebook

AI Summary: Create a new folder and Jupyter notebooks within the existing 'coastal' folder for chlorophyll analysis. The structure and naming conventions should mirror the existing 'ndti' folder and notebooks. This is a request for assistance from @Sosi19301.

Complexity: 2/5
documentation good first issue help wanted

water quality monitoring

Jupyter Notebook

AI Summary: Regularly update the water quality monitoring project with daily PRs focusing on turbidity and chlorophyll data. This allows for timely feedback and iterative development.

Complexity: 2/5
documentation good first issue help wanted

water quality monitoring

Jupyter Notebook

AI Summary: The task involves reminding the team to use the odc-stac library instead of rasterio for loading satellite data in a water quality monitoring project focused on turbidity and chlorophyll. This change is based on a successful implementation in a similar project (Satellite Derived Bathymetry), with provided code examples for data preparation, model training, prediction, and a simplified processing function.

Complexity: 2/5
documentation good first issue

water quality monitoring

Jupyter Notebook

AI Summary: Determine optimal Normalized Difference Turbidity Index (NDTI) thresholds for identifying low, medium, and high turbidity levels in volcanic regions within the MACBLUE area. This involves testing various thresholds and potentially adjusting formulas to find a universally applicable solution, supported by visual examples (screenshots).

Complexity: 4/5
documentation help wanted question

water quality monitoring

Jupyter Notebook

AI Summary: Mask clouds from satellite imagery using the Scene Classification Layer (SCL) and then determine optimal thresholds for the Normalized Difference Turbidity Index (NDTI) using raw NDTI values (not standard deviation) across multiple scenes. This involves creating a pull request (PR) for the cloud masking notebook and subsequently finding suitable NDTI thresholds.

Complexity: 4/5
documentation enhancement help wanted question

water quality monitoring

Jupyter Notebook

AI Summary: Test water quality turbidity thresholds at specified locations (Suva, Yanuca, Leluvia, Bootless Bay, Gulf Province PNG, Paunganisu Vanuatu, Tulagi Solomons) and record the results using a specific format (as shown in the provided image) and paste the snippets into a shared Google Doc. Backup coverage is required if team members are unavailable.

Complexity: 2/5
documentation enhancement help wanted

water quality monitoring

Jupyter Notebook