Global Forest Change

Introduction Below is a workflow used to create a forest change raster (2011-2019) for the Watershed Condition Class (WCC) analysis in USDA-USFS Region 1. The workflow leverages Google Earth Engine (GEE) (Gorelick et al. 2017) to get large remote sensing products fast and easy. This allows for reproducibility in the future as well as quick return times for clients, i.e. making large requests managable. The analysis uses the United States National Land Cover Database (NLCD) (Yang et al.

An Rmarkdown post

testing the excerpt field

RGEE and PRISM

This is the quick rundown on the rgee package that is pretty dope. The r-spatial folks have thrown down some sweet packages sf, stars, mapview (just to name a few) and now rgee. This is some pretty slick stuff. If your versed in JavaScript (JS) and wonder ‘should I waste my time with this API interface?’. Don’t worry. I’m not going to try and convince you to be a ‘R’ person just showing some of the basics and capabilities of using GEE R.

USGS, GEE and R. What?!?

Over the next month I’m going to be posting some quick examples from a few R packages that I’ve stumbled into. One is the dataRetrieval package by USGS. In short, it let’s you download any gauging station data across the country right into R and in a tidy format. How sweet is that? USGS also has other packages like the suite of smwr packages. These packages provide the user with some commonly used (but often tedious) functions used in hydrology, e.

Data Retrieval

This is a quick peek into the perks of using R and USGS package dataRetrieval. This package is pretty slick and has a lot of functions associated with it. We will just look into some basic functions like readNWISdv to get some USGS gauging data and then do some quick exploratory data analysis to using the tidyverse. First, load the packages (or install) from the library(). library(dataRetrieval) library(tidyverse) Now we can start bringing in some river data.