Vulimwa/Sagalla_Marungu_LULC

# Land Use Change Dashboard: Sagalla & Marungu This project analyzes land use and land cover (LULC) changes between 2018 and 2023 in Sagalla and Marungu wards, Taita Taveta, Kenya. It leverages Google Earth Engine, Sentinel-2 imagery, and machine learning classification to visualize and quantify landscape changes. ## Project Structure - `landuse_storymap_map.html` — Main dashboard with embedded interactive maps. - `map_2023.html` — Sentinel-2 2023 composite map. - `map_classified.html` — Classified land cover map for 2018 & 2023. - `map_swipe.html` — Swipe map for visual comparison between 2018 and 2023. - `sagalla.shp`, `.dbf`, `.prj`, etc. — Shapefile set defining the region of interest (ROI). - `README.md` — Project documentation (this file). ## How It Works 1. **Data Preparation** - The ROI is defined using the Sagalla shapefile. - Sentinel-2 imagery is pulled for 2018 and 2023, cloud-masked, and composited. 2. **Classification** - ESA WorldCover 2020 is used for training data. - A Random Forest classifier is trained and applied to both years. 3. **Visualization** - Three interactive maps are generated: - 2023 composite - Classified 2018 & 2023 - Swipe map (2018 vs 2023) - All maps are embedded in a single dashboard HTML for easy sharing and web deployment. ## Usage 1. Open `landuse_storymap_map.html` in your browser to view the dashboard. 2. Each map is interactive and can be explored directly in the browser. 3. For ArcGIS StoryMaps or other platforms, use the provided embed code: ```html ``` ## Requirements - Python 3.8+ - Packages: `geemap`, `earthengine-api`, `folium`, `pycrs` - Google Earth Engine account ## Export Notes - Ensure all map HTML files (`map_2023.html`, `map_classified.html`, `map_swipe.html`) are in the same directory as `landuse_storymap_map.html` for correct display. - For web deployment, upload all files to your web server or GitHub Pages. ## Credits - Developed by the S …