tharlestsa / ndvi-cog

The NDVI Time-Series Analyzer is a Python script that calculates and visualizes the Normalized Difference Vegetation Index (NDVI) for given geospatial points over a specified time range using Sentinel-2 L2A imagery.

NDVI Time-Series Analyzer

The NDVI Time-Series Analyzer is a Python script that calculates and visualizes the Normalized Difference Vegetation Index (NDVI) for given geospatial points over a specified time range using Sentinel-2 L2A imagery.

Dependencies

• os
• numpy
• pandas
• geopandas
• plotly.express
• scipy
• datetime
• satsearch
• shapely
• rio_tiler
• loguru
• joblib

Setup

1. Ensure you have all the dependencies installed.
2. Clone/download the repository.

How to Use

1. Prepare a GeoJSON file named points.geojson in the root directory. This should contain the geospatial points you want to analyze.
2. Adjust the start_year and end_year variables at the end of the script to set your desired date range.
3. Run the script.

Features

• get_pixel_value(urls, lon, lat, datetime): Retrieves the pixel value for specified bands at the given coordinates.

• process_period(id, start_year, end_year, geometry): Processes a specified date range for a given point, fetching and analyzing satellite data.

• plot_ndvi(df): Calculates, smoothes, and visualizes the NDVI values over time.

• execute(start_year, end_year, geojson_file): Main execution function that reads in the GeoJSON data and triggers the data processing and visualization.

Constants Configuration

• STAC_API_URL: URL for the STAC API.
• COLLECTION: Name of the satellite data collection.
• CLOUD_COVER_LIMIT: Maximum acceptable cloud cover percentage for satellite data.

Notes

• This script currently only supports points in the GeoJSON file.
• Ensure that the points.geojson file has a column named 'ID'.
• Data fetching and processing may take some time depending on the date range and number of points.