Ungraded Assignment

Design a project aiming in the computation of a comprehensive image (raster data stack) collection for species prediction, and heterogeneity mapping.

Skills and Competences

At the end of your ungraded assignment project you should be able to:

• process optical raster data to compile a comprehensive dataset for your area of interest
• compute artificial images which highlight spectral, structural or spatial properties using the cpabilities of R and more (e.g. OTB)
• design and implement medium complex workflows related to raster based image computations
• work with collaborative project and software development tools
• sufficiently document your code and analysis workflow for re-usability and scientific review
• compute land-cover classifications using R packages like caret to build a model for the prediction of the selected tree species classes
• discuss the principal logic and required steps of training a machine learning model,
• understand the importance of appropriate training site selection strategies,
• discuss strategies on which predictor variables to include in a classification model,
• communicate your work in a poster presentation.

Comprehensive dataset for Classification purposes

Please compile a comprehensive dataset based on spectral and structural information derived from both multi-channel aerial and LiDAR datasets which is suitable for the parallel and upcoming project work mentioned above. The dataset should provide information on:

• spectral properties
• structural properties
• spatial patterns

Tree species Classification

Please design and perform a classification workflow to identify the tree species in MOF on pixel resolution using a ML approach. You should provide:

• tree species classification for Marburg Open Forest
• training and validation strategy