Course Setup


Software

We use openly available and platform independent (Windows, Linux, Mac OS) software packages throughout this course. Please install the latest versions of:


Data

All data used in the course is openly accessible. Mostly, we´ll be working with Landsat images, which you can access through the USGS Earth Explorer. We provide download links to the datasets for each session. It will be helpful if you organize your data in a course directory on your local machine. GCG students might want to use drive O:/Student_Data/your_name/EO/, note however that this is only accessible via VPN connection. We will refer to this folder as course.dir throughout this course. Create subdirectories for each session, e.g. course.dir/S01/ and separate data, code and course materials in additional sub-directories (e.g. /data, /code, /docs).


Assignments

The weekly assignments are defined in the respective session. Each session comprises several tasks that involve scripting in R. Course participants must submit completed assignments, documented as R scripts, in moodle to pass. Weekly submission deadlines are Monday, 23:59. Please name the script of your work group as SXX_name1_name2.R. Please structure your script for every assignment as follows:

#############################################################################
# MSc Earth Observation Assignment [Session number]
# [Your Name]
#############################################################################

# Load packages, use install.packages('packagename') to install if needed
library(raster)

# In case you run into memory issues, uncomment this setting of the raster 
# options to store large computations in temp files on disk:
# rasterOptions(maxmemory = 1e12)

# Define the folder that contains your data...
data.dir <- 'course.dir/S01/data/'

#############################################################################
# 1)    
#############################################################################

# Comments for task 1
data <- read.table('dataset_1.csv')
result <- afunction(data)
print(result)

#############################################################################
# 2)    
#############################################################################

# ...

Readings

The first sessions of the course contain reading materials, such as peer-reviewed papers and technical reports. You will find the reading materials for the next session at the end of each session. We highlight aspects to focus upon to streamline the reading process. As an introduction, please read Wulder et al. (2019): Current status of Landsat program, science, and applications.



Copyright © 2020 Humboldt-Universität zu Berlin. Department of Geography.