In the post titled Access, Download, Process and VIsualize sea surface height and geostrophic current from AVISO in R posted in my blog on Monday, Apr 15, 2019, I explained how we can download the satellite data like sea surface height from AVISO in R. I illustrate in detail getting the data using xtractomatic package (Mendelssohn 2018). Though xtractomatic package provide functions that allows us to get access to the ERDDAP server and get the data, but one big challenge is that the data comes is array and need an expensive computation process, especially if you deal with gridded data for a long term time series.
What is Raster Data? Raster or “gridded” data are data that are stored in pixels. In the spatial world, each pixel represents an area on the Earth’s surface. In this post will focus raster package and its key function for importing and manipulating raster objects. I expect that toward the end of the post, you will have a glimpse of this package and you will be able to:
Introduction Often in environmental studies we are interested in assessing the presence or absence of a long term trend. A widely applied is a parametric test for trend, which involves fitting a linear model that includes some measure of time as one of the predictor variables, and possibly allowing for serially correlated errors in the model. Instead of fitting the data to time series parametric test, Stephen Millard bundles several functions in EnvStats package that are non–parametric and agnostic in dealing with trend (Millard 2013).
