Seagrass Habitat Suitability Map at Merambong Shoal, Johor: A Preliminary Study Using Multibeam Echosounder and Maxent Modelling

Abstract

Abstract. In recent years, there has been an increasing interest to use high-resolution multibeam dataset and Species Distribution Modelling (SDM) for seagrass habitat suitability model. This requires a specific variable derived from multibeam data and in-situ seagrass occurrence samples. The purpose of this study was (1) to derive variables from multibeam bathymetry data to be used in seagrass habitat suitability model, (2) to produce seagrass habitat suitability model using Maximum Entropy (MaxEnt), and (3) to quantify the contribution of each variable for predicting seagrass habitat suitability map. The study area was located at Merambong Shoal, covering an area of 0.04 km2, situated along Johor Strait. First, twelve (12) variables were derived from bathymetry data collected from multibeam echosounder using Benthic Terrain Modeller (BTM) tool. Secondly, all variables and seagrass occurrence samples were integrated in MaxEnt to produce seagrass habitat suitability map. The results showed that the Area Under Curve (AUC) values based on training and test data were 0.88 and 0.65, respectively. The northwest region of survey area indicated higher habitat suitability of seagrass, while the southeast region of survey area indicated lower suitability. Bathymetry mean found to be the most contributed variables among others. The spatial distribution of seagrass from modelling technique agreed with the previous studies and they are found to be distributed at depths ranging from 2.2 to 3.4 meters whilst less suitable with increasing of water depth. This study concludes that seagrass habitat suitability map with high-resolution pixel size (0.5 meter) can be produced at Merambong Shoal using acoustic data from multibeam echosounder coupled with MaxEnt and underwater video observations.

A bathymetric mapping and SLAM dataset with high-precision ground truth for marine robotics

“…In recent years, multibeam sonars have greatly improved in resolution while also becoming available at price points suitable for low-cost surface and underwater vehicles. This has lead to their expanded use (Clague et al, 2017;Janowski et al, 2018;Kapetanovic et al, 2020;Lim et al, 2020;Muhamad and Che Hasan, 2019) and a need for accompanying navigation instrumentation and processing solutions. Sensor-aided navigation methods such as simultaneous localization and mapping (SLAM) have the ability to improve mapping data products and compensate for many of the shortcomings of inexpensive navigation sensors (Paull et al, 2013).…”