Assessing the Capability of Sentinel-2A Data for Mapping Seagrass Percent Cover in Jerowaru, East Lombok

Abstract

Remote sensing technology has been widely used in various applications related to natural resources and environment monitoring. In this paper, we evaluated the capability of new Sentinel-2A image to map the distribution and percent cover of seagrass in optically shallow water of Jerowaru coastal area, East Lombok. Seagrass distribution map was produced from radiometrically and geometrically corrected Sentinel-2A image with overall accuracy of 61.9%. Using empirical model, seagrass percent cover was predicted with maximum coefficient of determination (R2)

Multispectral Resampling of Seagrass Species Spectra: WorldView-2, Quickbird, Sentinel-2A, ASTER VNIR, and Landsat 8 OLI

“…Based on Karimunjawa Islands National Park report, there are nine seagrass species found in Karimunjawa Islands [10]. This study used five multispectral images that have been frequently used for remote sensing-based seagrass mapping [1][2] [5][11] [12]. These images are WorldView-2, Quickbird, Sentinel-2A, ASTER VNIR, and Landsat 8 OLI.…”

Assessment of Sentinel‐2A multispectral image for benthic habitat composition mapping

“…Benthic habitat map consists of three or four classes can be obtained with very good accuracy (>90%). Despite the classification scheme, the good accuracy of Sentinel‐2A to map benthic habitat is also reported in other researches [2, 4, 5]. It appears that accurate mapping of benthic habitat at less than five classes using Sentinel‐2A is not an issue.…”

“…However, Fauzan et al . [5] reported the performance of Sentinel‐2A image to map seagrass percent cover in tropics with error reaching 19% and had difficulties in mapping individual seagrass species. Since the results between the published report varies significantly, it is difficult, at present, to conclude the capability of Sentinel‐2A for benthic habitats mapping, hence requires more tests, especially for mapping that involves empirical modelling.…”

Branching Algorithm to Identify Bottom Habitat in the Optically Complex Coastal Waters of Atlantic Canada Using Sentinel-2 Satellite Imagery

“…Therefore, three separate data sources were used in model building/evaluation to maximize the amount of information available to the classification algorithm. The number of data points used in the current study lies within the number of points used for other coastal Sentinel-2 studies ranging from <150 (e.g., Fauzan et al, 2017;Poursanidis et al, 2019) to >1,000 (e.g., Traganos et al, 2018;Yucel-Gier et al, 2020).…”