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Mapping Underwater Pemba Via Satellite

An update on our exciting developments in Bathymetry and Habitat Mapping around the coasts of Pemba

In an effort to better understand the marine environment we are working within, the Kwanini Foundation recently completed seafloor and habitat mapping of offshore areas adjacent to the Manta Resort, as well as further south, near Njao. Seafloor mapping measures the water depth of a body of water, known as the bathymetry. Habitat mapping, as the name suggests, maps the different types of habitats present on the seafloor (known as the benthic habitats) and would include categories such as coral, sand and rock for example.

In order to achieve this, we worked with a UK based company, GeoSmart Decisions (GSD), who were able to source freely available satellite imagery with a 10m spatial resolution, and more detailed imagery with a 2m resolution from WorldView3 and DigitalGlobe satellites. These images were entered into a computational model developed by GSD to create a Satellite Derived Bathymetry (SDB) map and a benthic habitat map. The Kwanini Foundation also provided ground truthing data on depths and habitat types within the sites to further refine the model and produce more accurate results.
Bathymetry Mapping

The free imagery was used to first create a bathymetry map for the Management Zone encompassing the Makangale and Tondooni shehias (see Figure 1). The higher resolution imagery was then used to map the seafloor within the Kwanini MPA and a potential new MPA near Njao. We also used the imagery to create a 3D impression of the seafloor in which you can clearly see how sheer the reef slope is. The results from the Kwanini MPA are shown in Figure 2 and 3.

The method for the satellite derived bathymetry is based on how light behaves within the water column; this relationship can be affected by water quality, seabed reflectance and atmospheric effects. Different wavelengths of light are absorbed more rapidly as depth increases, and the relationship between depth and the attenuation of light through the water column therefore becomes weaker. This weakening of the relationship begins around 8m. Therefore, at depths beyond 15 meters there is little or no empirical relationship (Gao, 2009). This method is therefore more useful for mapping the shallower water areas of our sites.
Habitat Mapping

As for the habitat mapping, we provided site-based data from our numerous underwater surveys on key habitat types and their approximate locations. This data was fed into the model with the satellite imagery to produce the output shown in Figure 4. We also produced a habitat map for a potential new MPA near Njao. Unfortunately, because we were unable to finish our site surveys (due to COVID-19) the results were not as accurate as we would have liked.

We encountered a few challenges with this output due to effects of wave crests from the image on the data. To remove these effects the resultant model values were ‘smoothed’ for improved visual representation. Additionally, the steep reef slope that we encounter within the Kwanini MPA and Njao was difficult to map as depths change significantly within the 2m pixel area of the satellite imagery, therefore, the habitats along the reef slope did not map out as well as we would have liked, and further site surveys are needed. Our inability to undertake additional ground truthing surveys due to the current COVID 19 Pandemic meant that a lot of the data that was fed into the model was based on approximations. Once we are able to get back onto site, we can collect additional data to obtain more accurate results for the habitat mapping and refine the maps.
We have used these maps to inform our Marine Protected Area Management Plan, which is currently under preparation. The maps can help us to record changes in the areas over time and provide a good visual aid to present to our wider Kwanini committee when discussing why it is so important for us to protect these marine environments.

Ref: Gao, J. (2009). Bathymetric mapping by means of remote sensing: methods, accuracy and limitations. Progress in Physical Geography, 33(1), 103-116.