In this module, we will discuss the following concepts:
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.
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.