Google Earth Engine - GEE Tutorials

Land surface change happens all the time, and satellite sensors witness it. If a spectral index is chosen to match the type of change being sought, surface change can be inferred from changes in spectral index values. Over time, the progression of spectral values witnessed in each pixel tells a story of the processes of change, such as growth and disturbance. Time-series algorithms are designed to leverage many observations of spectral values over time to isolate and describe changes of interest, while ignoring uninteresting change or noise.

Interpreting Image Series - Interpreting Annual Time Series with LandTrendr Time-series analysis of change can be achieved by fitting the entire spectral trajectory using simple statistical models. These allow us to both simplify the time series and to extract useful information about the changes occurring. In this chapter, you will get an introduction to the use of LandTre...

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,....

Accelerated demand for natural resources has transformed the Amazon rainforest into a new economic frontier that generates commodities such as agricultural products, livestock, and more recently, minerals, especially gold (RAISG 2020). In Peru, illegal gold mining is a serious problem that affects local populations in the southeastern region of Madre de Dios (Yard et al. 2012, Asner and Tupayachi 2017, Alvarez-Berrios et al. 2021). According to Caballero et al. (2018), it has led to the deforestation of about 1,000 km2 of rainforest, affecting protected areas, Indigenous communities, and sustainable management areas. Gold mining is carried out throughout the year, even during the rainy season.

Human Applications - Monitoring Gold Mining Activity using SAR The expansion of gold mining has had a large impact on the rainforests of the Amazon over the last decades. To take just one example, it has affected both the biodiversity and the lives of local people in the Madre de Dios region of southeastern Peru. In this chapter, we will review a methodology de...

Urbanization involves replacement of natural landscapes with built-up structures such as buildings, roads, and parking lots. This land cover modification also changes the properties of the land surface. These changes can range from how much radiation is reflected and absorbed by the surface, to how the heat is dissipated from the surface (for example, removal of vegetation for urban development reduces evaporative cooling).

Human Applications - Heat Islands In this chapter, you will learn about urban heat islands and how they can be calculated from satellite measurements of thermal radiation from the Earth’s surface.Learning OutcomesUnderstanding how to derive land surface temperature.Understanding how to generate urban and rural references.Knowing h...

Urbanization has dramatically changed Earth’s surface. This chapter starts with a qualitative look at the impact urban expansion has on the landscape, covering three existing urban classification schemes that have been created by other remote sensing scientists. We look at how these classifications can be used to quantify urban areas, and close with instructions on how to perform per-pixel supervised image classification to map built-up land cover at any location on Earth and at any point in time using Landsat 7 imagery.

Human Applications - Urban Environments Urbanization has dramatically changed Earth’s surface. This chapter starts with a qualitative look at the impact urban expansion has on the landscape, covering three existing urban classification schemes that have been created by other remote sensing scientists. We look at how these classification...

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,....