A Web GIS platform for spatiotemporal ecological analysis of the Cangzhou section of the Beijing–Hangzhou Grand Canal, China.

This project integrates Google Earth Engine (GEE), remote sensing ecological index (RSEI), and Web GIS visualization technologies to support long-term ecological monitoring and ecological restoration assessment from 2000 to 2025.

RSEI-based ecological quality evaluation

NDVI, WET, NDBSI, and LST visualization

Multi-buffer spatial analysis (100m / 300m / 1000m)

Interactive Leaflet Web GIS map

Dynamic ECharts-based temporal analysis

Ecological restoration trend analysis

Static deployment with GitHub Pages

This project uses Landsat remote sensing imagery and ecological indicators processed through Google Earth Engine (GEE).

RSEI (Remote Sensing Ecological Index)

NDVI (Normalized Difference Vegetation Index)

WET (Wetness Component)

NDBSI (Normalized Difference Built-up and Soil Index)

LST (Land Surface Temperature)

2000–2025

Canal buffer zones:

JavaScript

Leaflet

ECharts

PapaParse

GeoJSON

GitHub Pages

Google Earth Engine (GEE)

This project can be used for:

Ecological restoration assessment

Remote sensing analysis

Spatiotemporal environmental monitoring

River ecology analysis

Web GIS visualization

GIS and remote sensing education

Undergraduate graduation project demonstrations

This project demonstrates how remote sensing data, ecological evaluation models, and GIS technologies can be integrated into an interactive web-based visualization system for ecological analysis.

The system focuses on the ecological restoration process of the Beijing–Hangzhou Grand Canal (Cangzhou section) and provides a lightweight Web GIS solution for ecological monitoring and spatial analysis.

MIT License