Monitoring benthic habitats using Lyzenga model features from Landsat multi-temporal images in Google Earth Engine

Abstract

Benthic habitats have conventionally been monitored through physical site visits by diving. This requires the use of expensive resources and time to achieve the desired results. This study adopts remotely sensed Landsat multi-temporal images to monitor benthic habitats within marine protected areas in Kenya. We adopt the Lyzenga approach to compute depth invariant index features on Google Earth Engine (GEE) API to aid benthic classification. Random Forest (RF) and Support Vector Machines (SVM) machine learning algorithms were used for the supervised classification of benthic habitats based on training classes selected under the guidance of an initial K-means unsupervised classification. RF had a higher overall accuracy of 83.48% compared to SVM at 52.69% as per 2015 reference data. We, therefore, used RF to map benthic habitats between 2003 and 2018. Findings indicate that there are high conversions between corals, seagrass and sand benthic classes. Since environmental and anthropogenic pressures act synergistically in causing changes, an in-depth research would be necessary to assess the vulnerability of benthic habitats to these factors. However, the use of multi-spectral imagery and powerful GEE cloud computing platform can aid monitoring and acquisition of marine data.