Google Earth Engine - GEE Tutorials

Deforestation and forest degradation are large sources of carbon emissions and negatively impact biodiversity, food security, and human well-being. The ability to quickly and accurately detect forest disturbance events is essential for preventing future forest loss and mitigating the negative effects. Combining optical and radar data has the potential to achieve faster detection of forest disturbance than using an individual system. The main challenge is the methodological approach for fusing the different datasets. The Fusion Near Real-Time (FNRT) algorithm (Tang et al. 2022) is a monitoring algorithm for tropical forest disturbance that combines data from Landsat, Sentinel-2, and Sentinel-1.

Terrestrial Applications part 1 - Deforestation Viewed from Multiple Sensors Combining data from multiple sensors is the best way to increase data density and hence detect change faster. The purpose of this chapter is to demonstrate a simple method of combining Landsat, Sentinel-2, and Sentinel-1 data for monitoring tropical forest disturbance. You will learn how to import,....

The following tutorial details how to use observations from the Gravity Recovery and Climate Experiment (GRACE) to evaluate changes in groundwater storage for a large river basin. Here, you will learn how to apply remote sensing estimates of total water storage anomalies, land surface model output, and in situ observations to resolve groundwater storage changes in California’s Central Valley. The following method has been applied to study water storage changes around the world, and can be ported to quantify groundwater storage change for major river basins.

Aquatic and Hydrological Applications - Groundwater monitoring with GRACE The following tutorial details how to use observations from the Gravity Recovery and Climate Experiment (GRACE) to evaluate changes in groundwater storage for a large river basin. Here, you will learn how to apply remote sensing estimates of total water storage anomalies, land surface model output,....