Floodplain and wetland mapping of the Okavango Delta, Botswana
Laurel Larsen, Professor
Geography
Applications for Fall 2024 are closed for this project.
The Okavango Delta in Botswana is one of the world’s largest and most significant wetlands. Land-use changes in its headwaters and climate change are altering its patterns of inundation, with potential consequences for wildlife and human livelihoods. Due to the vastness and remoteness of the Okavango, however, its changing hydrologic and geomorphic dynamics have not been well quantified.
Through a partnership with the US Army Corps of Engineers, the Environmental Systems Dynamics Laboratory has access to airborne LiDAR imagery of the entire Okavango Delta. We are using this high-resolution topographic and vegetation data in conjunction with multispectral imagery to delineate the floodplain, fluvial landforms, and distinct vegetation communities throughout the Okavango Delta. We will develop a machine learning-based classification system to create GIS layers that can be used to develop new hydrologic models and serve as a baseline against which future change can be compared.
Role: The URAP student who is recruited to work on this project will work in collaboration with the PI, a postdoctoral scholar, and potentially a PhD student to map the Okavango Delta and make the data products available. Specific tasks are anticipated to include:
- Curating existing vegetation and geomorphic maps of the Okavango Delta and satellite products that could be used in the analysis and integrating them into the data processing workflow
- Using existing maps and 360-degree photographic data from several National Geographic transects of the Okavango Delta to develop a labeled map of wetland/landform types that can be used for supervised classification
- Performing quality assurance and quality control of the lidar data and integrating it into the data processing pipeline.
Learning outcomes include:
- Become familiar with environmental and water resource issues associated with the Okavango Delta
- Gain experience working with large, computationally intensive spatial datasets
- Learn how supervised and unsupervised classification of remote sensing imagery works
- Hone spatial data processing skills
Qualifications: Desired skills and background of the URAP student:
- Applicants should have experience (via class or previous research) with remote sensing and/or GIS and should have some experience with programming.
- Google Earth Engine experience is desired but not required.
- Applicants should have some background in and interest in environmental science.
Hours: to be negotiated
Related website: http://esdlberkeley.com
Mathematical and Physical Sciences