Monitoring Water Quality in Coral Reefs and Tourism Impacts in Indonesia Using Earth Observation
Coral reefs are vital ecosystems that provide ecological, economic, and cultural benefits—but they are under growing pressure from land-use change and marine tourism. This MSc project explores the use of Earth Observation technologies to monitor how tourism-related activities affect coastal water quality and coral reef health within Marine Protected Areas (MPAs) in Indonesia. The project leverages Earth Observation imagery such as historical satellite data and high-resolution drone imagery to identify patterns of environmental change and assess the feasibility of remote sensing methods for routine monitoring in tropical marine settings. This thesis research will be conducted in collaboration with the INREEF project (https://inreef.org/). The exact topic will be discussed at the time of selection, but could go in the direction of the objectives below.
This topic will be supervised together with:
- Riza Aitiando Pasaribu (PhD hosted WUR)
- Lisa Becking (WUR)
- Analyze long-term trends in land use, water quality indicators (e.g. chlorophyll-a, SST, TSS, DO, SSS), and coral cover using satellite data.
- Evaluate the effectiveness and accuracy of drone-based multispectral imagery for detecting water quality changes.
- Investigate the potential of remote sensing to identify tourism hotspots and associated environmental pressures.
Methodology:
The methodology is dependent on the selected objective. An example is as follows. The student will use open-access satellite imagery (e.g. Landsat) to derive time series of land use and water quality parameters around selected MPAs (Maluku Province, Bali Province, Papua Barat Daya Province) in Indonesia for 20 years. Field-collected or archived drone imagery with multispectral sensors will be used to map current conditions at high spatial resolution. Note that this topic doesn't require field work, but will be working with data collected through INREEF. Image classification techniques (e.g. SVM, CNN) and object detection may be applied to detect infrastructure and tourism indicators.
Harasyn, M. L., Chan, W. S., Ausen, E. L. & Barber, D. G. Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning. Drone Systems and Applications 10, 77–96 (2022).
Shahvaran, A. R., Pour, H. K., Binding, C. & Van Cappellen, P. Mapping satellite-derived chlorophyll-a concentrations from 2013 to 2023 in Western Lake Ontario using Landsat 8 and 9 imagery. The Science of the Total Environment 968, 178881 (2025).
Windle, A. E. & Silsbe, G. M. Evaluation of Unoccupied Aircraft System (UAS) remote sensing reflectance retrievals for water quality monitoring in coastal waters. Frontiers in Environmental Science 9, (2021).
Through the integration of different interests from local stakeholders regarding tourism impacts and different pressures on coral reefs.