1 Introduction

In this module, we will discuss the following concepts:

  1. The potential sources of data available in Google Earth Engine.
  2. Use cases for a sampling of datasets as shown through ecological examples.
  3. How Google Earth Engine can be used to access important metadata.

2 Background

To integrate remotely sensed into your research and analysis, it is very important to learn how to parse the large number of raster datasets available on Google Earth Engine. Understanding the how these data are categorized can help begin this process. If you have absolutely no idea where to start, there is a very helpful resource to peruse the general categories of rasters available in Google Earth Engine data catalog. Various levels of data cleaning and image pre-processing are performed on all the rasters available in Google Earth Engine but for this module we will focus on a broader exploration of finding the right dataset for some example ecological applications. More on pre-processing differences and analyses can be found in Module 5.

2.1 Data Category: Climate & Weather

These are rasters that convey information about temperature, precipitation, evapotranspiration, and other atmospheric and meteorological phenomena. These datasets are often used to help understand the ecological niche of a species or community based on a derived range of suitable habitat. It is important to understand that some of these are created from interpolation methods, which estimate values in the geographic space between monitoring sites, as opposed to many other rasters that provide only direct measurements.

Example Collection: CHIRPS
This dataset contains a variety of climate layers, including temperature and precipitation, as developed and monitored by the PRISM group at Oregon State University. A spatial understanding of climatological factors is invaluable when we attempt to understand the potential ecological niche of a species or group of species at the landscape level.

Monthly rainfall total for May, 2018 over central Africa using the CHIRPS dataset.

2.2 Data Category: Imagery

In this group, the rasters capture landscape characteristics as they pertain to passively collected solar energy, often providing enhanced information about the real world elements on the ground, including plants and soils. It is important to note that imagery rasters are usually split into a number of sub-images (bands) which correspond to particular ranges of light wavelengths. Many of the imagery datasets are used to create spectral indices that are accessible through pre-built collections (more on this in Module 8).

Example Collection: MODIS
The MODIS (Moderate Resolution Imaging Spectroradiometer) program collects data from two spaceborne sensors, Aqua and Terra, and a combination of the two. Numerous derived MODIS products are available on Google Earth Engine in addition to the surface reflectance data including snow cover, land surface temperature, leaf area index, and gross primary productivity. Though its spatial resolution is more coarse than some other imagery datasets, many MODIS products have a high temporal resolution, resulting in a dense time-series. The window between images over the same geographic area is as low as eight days.

MODIS-derived leaf area index predictions over the northern Japanese island of Hokkaido.

2.3 Data Category: Geophysical

The rasters in this group cover a wide range of map types. Elevation and topographic index maps are useful for defining the environmental constraints on species habitats, while land cover maps are convenient, pre-packaged layers that can be used as categorical data or comparisons to image classification you may generate yourself (more on this in Module 7).

Example Collection: NED
The National Elevation Dataset (NED) is a high-quality digital elevation model (DEM) that extends across the continental United States and portions of Alaska and Hawaii. The dataset is compiled from disparate elevation data from around the United States and edited to ensure a consistent spatial resolution, elevation units, and coordinate system. Elevation images like the NED can be used to create topographic indices, inform flood models, or classify geomorphological features.