Overview

Recent advances in remote sensing technology and the increasing availability of ecological data have transformed the field of ecological research. Remote sensing provides unprecedented insights into environmental changes and species distributions on a global scale. However, these advancements also present new challenges that must be addressed to fully leverage the potential of remote sensing data. These challenges include understanding and utilizing various coordinate systems,  and accurately modeling and analyzing ecological data over space and time.

In this course,  we will cover a range of topics and R packages designed to address these challenges. Participants will gain theoretical knowledge and practical skills necessary for advanced ecological remote sensing analyses.

Learning outcomes

 After attending the course, participants will be able to:

• Create and customize remote sensing packages in R, focusing on the imageRy package.
• Use ggridges and ecochange packages for advanced multitemporal ecological analyses.
• Apply the sdm, ecospat, and fuzzySim packages for species distribution modeling using remote sensing data.
• Analyze ecosystem variability in space and time with information theory and spectral distances using the rasterdiv package.
• Efficiently download and use global geographical data with the geodata package.

Daily schedule: 9.30 - 13:30 (Berlin time)

Session 1 – Introduction (Monday)

• Introduction to Ecological Remote Sensing: We will start with the basics of ecological remote sensing, covering key concepts and applications in environmental research.
• Reference Systems: This segment will introduce the main coordinate systems used in remote sensing, essential for accurate spatial analysis.
• The imageRy R Package: Learn how to develop and customize your own remote sensing package in R using the imageRy package, alongside an introduction to the versatile terra package.

Session 2 – R Eco-Packages (Tuesday)

• The ecochange R Package: Discover methods for measuring landscape changes over time and space using the ecochange package, essential for tracking ecological dynamics.
• The ggridges R Package: Utilize the ggridges package for performing multitemporal analysis, enabling the visualization and interpretation of ecological data across different time periods.
• The geodata R Package: Learn to download and manage geographical data from around the world using the geodata package, a crucial skill for global ecological studies.

Session 3 – Species Distribution Modelling with RS-Based Variables (Wednesday)

• The sdm R Package: Delve into species distribution modeling using remote sensing variables with the sdm package, crucial for predicting species habitats and distributions.
• The ecospat R Package: Explore different modeling techniques for species distributions with the ecospat package, enhancing your ability to analyze and interpret ecological niches.
• The fuzzySim R Package: Calculate fuzzy similarity in species distributions using the fuzzySim package, helping to understand species overlaps and ecological interactions.

Session 4 – Measuring Variability (Thursday)

• Measuring Ecosystem Variability: Gain insights into measuring ecosystem variability in space and time using information theory measures, providing a robust framework for ecological assessments.
• Measuring Variability via Spectral Distances: Learn techniques for measuring ecological variability using spectral distances, essential for analyzing remote sensing data.
• The rasterdiv R Package: Combine information theory and spectral distances to measure ecosystem variability with the rasterdiv package, offering a comprehensive approach to remote sensing analysis.

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.