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examples

unit00

Digital learning

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

Deliverables

We distinguish between unmarked and marked deliverables (“Studien- und Prüfungsleistung”). Both are required for passing the course but only the latter is m...

FAQ

This is a continuously updated collection of frequently asked questions. Course requirements What is the expected workload for this course? This course giv...

unit01

First Things First

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 ...

R and RStudio

Start to learn the essentials for working with R and RStudio.

Example: Vector Basics

Vectors are the basis for many data types in R. Creating a vector A vector is created using the c function. Here are some examples: my_vector_1 <- c(1,2...

Example: Data Frame Basics

Data frames are one of the most heavily used data structures in R. Creation of a data frame A data frame is created from scratch by supplying vectors to the...

Example: R Markdown with html output

This page shows how a compiled R markdown file looks like (in fact, all code examples in this course were compiled with R markdown). This is a header This ...

Assignment: Hello R

This worksheet introduces you to R, R scripts and R markdown. Your submission will be pushed to your class repository at GitHub. After completion you should ...

unit02

More basics

Become familiar with reading and writing data, computing summary statistics and visual data exploration. Get to know the relation between variables and build...

Tabulated Data I/O

Reading or writing tabulated data into or from a data frame is a quite common task in data analysis. You could use the read.table function for this. df <-...

Example: CSV I/O

Reading data from csv files Reading csv files is realized using the read.table function from R’s utils library. The function will return a data frame which ...

Example: Aggregation Statistics

Summarizing a data set The most straight forward function which returns some aggregated statistical information about a data set is summary. a <- c("A",...

Example: Visual Data Exploration

Visual data exploration should be one of the first steps in data analysis. In fact, it should start right after reading a data set. The following examples ar...

Example: Missing Values

Handling missing values is straight forward. Let’s start with a vector with one NA value at position 3. Please note that NA is not inside quotation marks sin...

Example: Date/Time

Coercing data types to date and/or time information is generally performed using as.Date or either as.POSIXct or as.POSIXlt. Let’s start with as.Date: as.Da...

Example: Sorting

Sorting vectors or lists Vectors can be sorted using the sort function. If you want to sort a list, you have to access the actual elements since sort require...

Example: Merging

When thinking about combining two data frames one has to distinguish between merging them by the values given in a specific column or consecutively putting t...

Practice your statistics(Assignment)

Do the following tasks on your own. Write down questions or difficulties you encountered which we can discuss afterwards. This will be a refresher for basic ...

Homework: Prepare R Spatial

If you have not done so already, get yourself an account for the Sentinel Hub of the ESA. You can register here for free. You will need this account ...

unit03

Spatial Data

Get a brute-force introduction to working with different kinds of spatial data in R.

Vector Data

Vector data consists of potentially linked points defined by coordinates that can form complex geometries with assigned attributes. Vector data model In a ...

Raster Data

Raster data consists of cells or pixels in a seamless grid system and usually contain only one value per cell or pixel. Raster data model Raster data is co...

Coordinate Reference Systems

An integral part of all spatial data is the coordinate reference system (CRS), which is used to define meaningful distances between data points irrespective ...

Example: Workflow in R

This example provides a schematic workflow for processing vector and raster data in R. Get raster data Firstly, we import some raster data into our working...

FAQ: sp vs. sf

Which R package should I learn to use? Short answer: currently both! Long answer: here Where can I find additional ressources about sf? The Introduction t...

Assignment: Spatial Data Processing

This exercise will lead you through some of the most basic and important steps for spatial data processing. Things you need for this worksheet R — the in...

unit04

Sensor Types

Active vs. Passive Conservation Technology Series Issue 4: SATELLITE REMOTE SENSING FOR CONSERVATION Passive Sensors The most common remote sensing...

Sensor Platforms

Footprint and Spatial Resolution The three most common satellite platforms for multispectral imagery are MODIS (Moderate Resolution Imaging Spectroradiomete...

Sentinel-2

What is Sentinel-2 Sentinel-2 is a pair of satellite platforms with multispectral sensor instruments (abbreviated MSI) on board. They cover almost the entir...

unit05

Example: LidR I/O and clipping

Since LiDAR data can have quit large file sizes and need some processing power, we only take a look at a small example area. This area was created with the s...

Exercise: Introduction to LidR

Data acquisition Please download “Lidar Data Lahntal” from the Ilias course directory “data” and save it in your project structure. It may be advisable to c...

Exercise: LiDAR products

DEM, DSM, CHM In this exersice we want to calculate three of the most common LiDAR products: the Digital Elevation Model, the Digital Surface Model, and the...

unit06

Exersice 1: Data preparation

In this exersice you prepare your mean vegetation height data from LiDAR and the Sentinel bands. Create a rasterstack containing the Sentinel-2 Bands 02,...

The Modelling Workflow

Predictors and Response. Source: Alice Ziegler Converting the rasterstack to a datafram...

Exercise2: Data visualisation

Before you build your model, it is a good idea to get a feeling about your data with some visualizations. Plot histogram of each predictor using the data...

Exercise3: My first model

Install the caret package Install the ranger package Install the tree package Decision trees Split your data into a training and testing set wit...

Exercise4: Hyperparameter tuning

Models in machine learning usually have multiple parameters which decide how the model looks like. A simple example is the slope and intercept of a linar mod...

Exercise5: Validation

Visual validation Use the Sentinel-2 data to predict the mean vegetation height for the entire Lahntal. Save the prediction as a tif file and have a lo...

Example: Putting it all together

Predictors and observations Example workflow: library(terra) library(ranger) library(caret) library(RStoolbox) library(viridis) # Prepared Sentinel 2 Sc...

A word about classifications

In this course, we focused on and made use of regression models. We used Random Forest to predict a continuous variable (the mean vegetation height) and also...

unit07

Projects

Apply your gained skills in your own work project.

unit08

unit09

worksheets