The Ability of Lyzenga's Algorithm for Seagrass Mapping using Sentinel-2A Imagery on Small Island, Spermonde Archipelago, Indonesia

Abstract

The existence of the value of depth variation in the study area is also a requirement in the application of this algorithm. The purpose of this study is to determine the ability of the use of algorithms in the mapping of seagrass by comparing the results of classification between imagery using the Lyzenga's algorithm with imagery without using the Lyzenga's algorithm. The result of imagery classification applying the Lyzenga's algorithm shows the visibility of baseline water objects that are more easily recognizable using Depth Invariant Index (DII) value format as evidenced by the increase of 2 classes of seagrass cover. In this study there are five seagrass cover classes of shallow water in Pajenekang Island, Spermonde-Indonesia Islands using Lyzenga's algorithm, namely class are 1) Very good: > 75%; 2) Good: 50,575,4%; 3) Rather Good: 25,5-50,4%; 4) Bad: 5,5-25,4%; and 5) Very Bad: <5.5%.

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