Marked Assignment: Project I - Tree Delineation
This worksheet introduces your first GI-project which is focusing a reliable algorithm for tree delineation in the Marburg Open Forest (MOF)
Things you need for this worksheet
- R — the interpreter can be installed on any operation system.
- RStudio — we recommend to use R Studio for (interactive) programming with R.
- Git environment for your operating system. For Windows users with little experience on the command line we recommend GitHub Desktop, or the RStudio build in Git client.
- Data sets provided by the course instructors.
- Many other things depending on your specific workflow and analysis design.
The big picture
The whole project will be wrapped up in a paper that is comprising the methodology and interpretation of:
- Tree crown segmentation (GIS, parallel time frame )
- Prediction of tree species (RS course)
- Forest structure and tree competition (GIS)
This cross-linking of the courses has an important implication on the planing of this project. One cannot specify the actual data set requirements at this stage and but one must iteratively adjust the requirements based on insights and knowledge gained in the parallel work. Hence, this dual project workflow acts quite similar to other knowledge gaps one might have at the beginning of a project, e.g related to computing images based on focal operations. Acting under uncertainty is a key competence on job and in science - just keep it in mind.
All information about the tree species classification will be supplied in the parallel RS course. To get an idea on tree delineation concepts in general, the Master thesis of Möller Finn on comparing different segmentation algorithms can be a starting point.
Project for tree delineation
Please identify and if necessary clean up a basic LiDAR data set. The data set should provide information on
- basic data set covering the MOF
- derived data sets as a canopy height model (CHM).
Please use the upcoming spotlight units as a sort of guideline and a source of inspiration for your work.