Examining seagrass ecosystems health in tropical waters with the application of remote sensing techniques: Experiences from Zanzibar, Tanzania

Idrissa Yussuf Hamad

Research output: Book/anthology/dissertation/reportPh.D. thesis


Effective assessment of the seagrass ecosystems’ health relies on finding answers to critical ecological questions such as: how seagrass meadows are distributed, what is their species diversity; to what extent natural environmental conditions (pressures) affect their growth and distribution, and how human-related pressures mediate these conditions to determine the overall distribution and ecological health of the seagrass meadows. Hence, methods that enhance the acquisition of seagrass conditions and use these data to answer these important ecological questions on seagrass health are indispensable. In this Ph.D., I applied remote sensing and in situ data collection to examine the health of seagrass ecosystems in the tropical waters of Zanzibar. The research questions I addressed were A) which seagrass-related health conditions can be obtained through drone observations? B) what is the potential of integrating drone-based seagrass data with environmental data for modeling seagrass distribution? C) is it possible to apply drone imagery to develop a useful seagrass ecological health index for assessing the seagrass ecological status and link these to human-related pressures finally D) to what extent can drone and in situ data on seagrass health be applied for tracing sources of pollution?
The pursuit of these research questions is presented as key outputs arranged as papers I-IV: In Paper I, I demonstrated the utility of drone imagery to map and characterize shallow water seagrass-dominated meadows covering Unguja Island, the main Island of Zanzibar. Covering a total area of 7 km2 in different study areas of the Island, the drone imagery enabled us to obtain rich information on the important seagrass health elements such as area coverage (habitat extent), percent cover (density), and species composition, fragmentation and locating seaweed farming. While measures of epiphytic cover, sea urchin density, and dead leaves were obtained through in situ observations. In Paper II, we used drone observations to develop (calibrate) and validate a habitat suitability model (HSM) to predict the potential seagrass cover around the entire Island of Unguja, in Zanzibar). With this Paper, we produced a high-accuracy modeled seagrass potential cover map within 0- 20 m estimating about 412 km2 of potential seagrass area. In Paper III, we applied the information derived from drone imagery and in situ data to develop the seagrass ecological health index (SEHI). In this process, we computed multi-parameter indices of seagrass ecosystem health, including parameters of seagrass abundance, taxonomic composition (diversity), eutrophication, leave health, and fragmentation of seagrass meadows. We assessed SEHI with natural and human pressures. We found SEHI to respond to the important human pressures of sea urchin density and to natural pressures, particularly tidal exposure. We also found a strong contribution of nutrient loading, seaweed farming, and fishing intensity influencing the SEHI. Additionally, in Paper IV as co-author, we assessed the potential exposure and impacts of marine pollution from different sources on sensitive seagrass and coral reef systems. With high-resolution bathymetry data and a hydrodynamics model, we predicted the dispersal of pollutants from mainland hotel areas in the marine waters of the west coast of Zanzibar. We showed the gradient to exposure to pollutants was increasing with proximity to sources but could affect distant sites. We suggested the introduction of integrated pollution control from the hotels to reduce impacts on the critical seagrass and coral reef systems in the protected areas.
Original languageEnglish
PublisherAarhus University
Number of pages198
Publication statusPublished - Mar 2023


  • Marine ecosystem health, tropical waters, seagrasses, remote sensing, drones, seascape ecology, habitat suitability modeling, ecological indicators, Western Indian Ocean, Zanzibar.


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