| Abstrak/Abstract |
Coral reefs and seagrass are critical coastal resources due to their role in the
ecosystem benefits for the coastal environment in terms of biodiversity, coastal protection,
fisheries, and tourism. It is therefore important to preserve and protect these species. Coral
and seagrass percent cover mapping is a simple approach to assess coral and seagrass
condition. The application of remote sensing of coral and seagrass percent cover mapping is
very challenging with respect to performance and accuracy. This research aims to utilize
remote sensing data for coral and seagrass percent cover mapping. Linear and machine
learning regressions (RF and SVM) were used to develop a coral and seagrass percent cover
model from a Sentinel-2 MSI images. The Sentinel-2 MSI images were transformed into
deglint, water column (DII), principal component, and mean texture analysis as input bands
for the model. The results showed that coral percent cover mapping accuracy is relatively
low (RMSE = ±17%) due to various problems, limitations, and an inaccurate model, whereas
the results of the seagrass percent cover map had higher accuracy, with RMSE ±11%. The
results obtained indicate that the seagrass percent cover map is suitable for use as basic
information to support coastal management. However, the coral percent cover map is not an
optimal information source due to its low accuracy. |
