RStoolbox: An R package for remote sensing data analysis
Konstantin Müller, Jakob Schwalb‐Willmann, Martin Wegmann, Benjamin Leutner
Abstract
Abstract The role of Satellite Remote Sensing in monitoring the Earth's surface is more important than ever, as it allows us to see changes in space, time, and across the electromagnetic spectrum. Therefore, it is crucial to not only gather data but also to analyse, visualize and present the findings. rstoolbox package offers a suite of functions for (a) preprocessing, (b) analysis and (c) visualization of (multi‐band) remote sensing data, implementing state‐of‐the‐art methods such as unsupervised and supervised classification, or spectral unmixing or change vector analysis. Thereby, rstoolbox enables various levels of users, from students to experts, to process and scientifically analyse different kinds of remote sensing data within a single programming environment. To best integrate in pre‐existing workflows, rstoolbox is based on well‐established data types for representing spatial data in and inherits well‐known packages popular within the spatial data science and remote sensing research communities. To showcase the simple usage of rstoolbox we provide multiple examples with sample data provided directly within the package.