Assessing the Shallow Water Habitat Mapping Extracted from High-Resolution Satellite Image with Multi Classification Algorithms

Abstract

Remote sensing technology is reliable in identifying the distribution of seabed cover yet there are still challenges in retrieving the data collection of shallow water habitats than with other objects on land. Classification algorithms based on remote sensing technology have been developed for application to map benthic habitats, such as Maximum Likelihood, Minimum Distance, and Support Vector Machine. This study focuses on examining those three classification algorithms to retrieve information on the benthic habitat in Pari Island, Jakarta using visual interpretation data for classification, and data field measurements for accuracy testing. This study used five classes of benthic objects, namely sand, sand-seagrass, rubble, seagrass, and coral. The results show how the proposed approach in this study provides an overall good classification of marine habitat with an accuracy produced 63.89–81.95%. The Support Vector Machine algorithm produced the highest accuracy rate of about 81.95%. The Support Vector Machine algorithm at a very high spatial resolution is considered to be capable of identifying, monitoring, and performing the rapid assessment of benthic habitat objects.

Spatial distribution of benthic habitats in Kapota Atoll (Wakatobi National Park, Indonesia) using remote sensing imagery

“…The machine learning algorithms (SVM and RF) used in this study have demonstrated excellent performance in mapping benthic habitats in Kapota Atoll. The SVM algorithm is commonly used for mapping benthic habitats and exhibits higher accuracy even with small amounts of data (Wahidin et al 2015;Nandika et al 2023). However, in this study, the RF algorithm applied to the pixel-based classification method produced more accurate benthic habitat maps.…”

Monitoring of Water Bodies and Reclaimed Lands Affected by Warfare Using Satellite Data

“…In global practice, based on the difference between spectral reflectance of water, soil, and vegetation covers, spectral indices for water, soil, and vegetation are calculated as the indicators of the natural state of mentioned surfaces [6,12]. Spectral indices are used to classify a certain type of land cover [13,14]. The Normalized Difference Water Index (NDWI) is used to identify water bodies on a background of soil and vegetation covers [15,16], the Normalized Difference Vegetation Index (NDVI) is used for vegetation cover, etc.…”