The detailed characterization of savanna ecosystems is a particularly challenging task due to their high spatial complexity and structural dynamics. Traditional inventory approaches have distinct limitations in this regard as they rely on manual assessments of easy-to-measure tree and grass attributes (e.g. tree diameter or sward height), along with time-consuming, logistically difficult and error-prone destructive sampling. Much more detailed and accurate three-dimensional measurements of savanna vegetation structure and composition are key to reducing errors in biomass estimates and carbon dynamics and to better understanding the role of savannas in global ecosystem and climate change processes.
In this context, terrestrial laser scanning (TLS) represents a new measurement technology to capture the 3D environment with very high accuracy and precision. However, despite this promising development, TLS in savanna ecosystems is also a very young field of research with a lot of open issues that need to be addressed scientifically before the method can be used in an operational context. The successful PhD candidate will focus on the development of accurate and robust procedures for extracting savanna vegetation parameters (e.g., tree diameter and biomass, crown cover and size, LAI, sward height etc.) from TLS point clouds. The latter will be measured during the dry and wet season at the savanna and shrubland test sites of the ARS AfricaE and EMSAfrica research projects. Further emphasis will be put on (1) a comparison between the developed methods and traditional inventory approaches as well as (2) the combination of the derived TLS data products with coarser resolution SAR and optical satellite imagery (Sentinel-1/2) for upscaling and large area vegetation mapping. The outlined research agenda will be conducted in close collaboration with all German and South African project partners.
- Master’s degree in remote sensing, GIScience, informatics, mathematics, geography, ecology or related fields
- Strong interest in TLS and/or LiDAR remote sensing for environmental applications
- Excellent skills in computer programming (e.g., R, Python, MATLAB) and statistics
- Experience in point cloud (e.g., SimpleTree, LAStools, Quick Terrain Modeler), digital image processing (e.g., ENVI, Geomatica, eCognition) and GIS (ArcMap, QGIS)
- Motivation and willingness to conduct field work
- Team spirit, flexibility, and ability to work independently
- Scientific curiosity and willingness to work on a doctoral thesis
- Full written and verbal fluency in English, some knowledge of or at least willingness to learn German
Host institute: Friedrich-Schiller University of Jena, Germany
Supervision: Prof. Dr. Christiane Schmullius (FSU), Dr. Christian Berger (FSU)
Contact: Prof. Dr. Christiane Schmullius, firstname.lastname@example.org