Course Units

How-to Upscaling

This course is intended as a blended learning module, although the provided introductions, explanations and examples might be useful for self-study only, too.

01 Getting started

Go through a brute force introduction into R, R Markdown, the RStudio IDE, version management with Git and GitHub’s classroom functionality to get ready for solving the upcoming assignment problems and submitting your solutions.

02 Kilimanjaro SES

Get a brief introduction to the Kilimanjaro Social-Ecological System project funded by DFG.

03 Remote Sensing basics

Learn and recall the essentials of Remote Sensing.

04 LiDAR Remote Sensing

LiDAR or Light Detection And Ranging is a very promising remote sensing method that uses lasers to record distances which when combined with complimentary data, can be used to generate 3D models of a surface. With its 3D approach, we can measure canopy height and density along with many environmental parameters. In remote areas as well as areas with steep slopes, airborne LiDAR has proved to be very effective in monitoring vegetation structure (Getzin et al 2017). In this unit, we explore the potential of airborne LiDAR data to understand the forest structure and derive different LiDAR metrics useful for upscaling.

05 Hyperspectral Remote Sensing

Latest advances in remote sensing has led the way to the development of hyperspectral sensors, which has multiple applications in the field of forestry, geology, agriculture, etc. Hyperspectral remote sensing combines the principles of remote sensing and spectroscopy to generate images consisting of several contiguous bands with relatively narrow bandwidths (5-10 nm). These narrow bands help scientists and researchers investigate different properties of materials on the ground.

06 Set up your research project

This unit is all about your final research project. Think about your research question and create a project outline as basis for your project. To finish this course, it is mandatory to submit the final project.

07 Upscaling workflow

This unit is about learning the essentials of upscaling using machine learning. In machine learning, algorithms are used to fit a model to a dataset through training or learning. In this unit, we will explore such models and understand the different steps of upscaling.