Land Use/Cover Mapping and Trend Analysis Using Google Earth Engine

Dear Colleagues,

The integration of remote sensing and geospatial analysis through cloud-based platforms such as Google Earth Engine (GEE) has revolutionized our ability to monitor and understand land change dynamics at regional to global scales. By providing programmatic access through user-friendly JavaScript or Python APIs to petabytes of satellite imagery (e.g., MODIS, Landsat, and Sentinel) and geospatial datasets (e.g., geophysical, climate, and weather data), GEE empowers researchers with new opportunities to derive insights from multi-temporal and multi-source data.

This Special Issue aims to highlight the innovative applications of GEE in the realms of land use/cover mapping and relative trend analysis. Original research articles and review papers showcasing novel methodologies, case studies, or syntheses of GEE applications are welcome, and we encourage contributions that traverse a broad range of applications, including but not limited to:

-Vegetation mapping and pattern analysis;

-Trends analysis of vegetation biophysical parameters (e.g., LAI and chlorophyll content);

-Urban expansion and thermal environment monitoring;

-Coastal change detection;

-Ecological impact evaluation.

Dr. Yuanheng Sun
Dr. Huazhong Ren
Dr. Chengye Zhang
Guest Editors

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