Overview

The course addresses challenges in ecological remote sensing, specifically focusing on planetary changes. It is designed for scholars who have no prior experience with R. We will start from the basics in R and guide attendees through the process of coding remote sensing analysis step-by-step.

By the end of the course, participants will gain proficiency in coding their own analysis workflows and effectively reporting their findings using LaTeX. The final part of the course will cover the fundamentals of LaTeX scripting for both articles and presentations.

Learning outcomes

- Opening and managing a GitHub account to store your code

- Basic coding in R: from scratch until remote sensing data analysis

- Basic intro to LaTeX for coding texts and output reporting

Daily schedule: 15:00 - 19:00 (Berlin time)

** Session 1 – Introduction (Monday)

- The roots of ecological remote sensing

- Reference systems: introduction to the main coordinate systems

- Setting up, importing and visualizing remote sensing data in R (with Landsat data)
[R package: terra]

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** Session 3 – Remote sensing data exploration 2 (Wednesday)

- Vegetation indices: the case of Amazon forest
[R packages: terra, RStoolbox]

- Multitemporal analysis of remote sensing data: the case of Greenland ice melt (with Copernicus data)
[R package: terra, ggplot2, viridis]

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** Session 4 – Remote sensing data analysis (Thuersday)

- Image classification: introduction to unsupervised image classification
[R package: RStoolbox]

- Multivariate analysis: PCA
[R package: RStoolbox]

- Spatial variability and image diversity (with Sentinel data): the case of the Similaun Glacier
[R packages: terra, rasterdiv]

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** Session 5 – Reporting 1 (Friday)

- knitr: reports in R
[R package: knitr]

- LaTeX basics: articles in LaTeX

- LaTeX basics: beamer presentations in LaTeX

Cancellation Policy:

> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.

Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.