Integrating sentinel-2 spectral-imagery and field data of seagrass coverage with species identification in the coastal of Riau Islands, Indonesia

Abstract

Seagrass plays an important role in marine ecosystem. Plans for sustainable management of marine ecosystem should give due attention to this marine critical habitat. One effort to monitoring the long-term management of seagrass is to use spatial data using remote sensing techniques. Satellite imagery offers an efficient and cost-effective means of estimating water conditions in shallow environments. This study aims to map the coverage of the seagrass meadows using satellite images spatially, determine the species composition of the seagrass meadows, and examine the accuracy level from the results obtained by the Sentinel-2 images. Investigations reported here were conducted in the Riau Islands (4 Stations at Lingga Island and 2 Stations at Singkep Island) in Indonesia from 3 - 7 October 2020. The satellite image data used Sentinel-2 at the acquisition year of 2019 based on the method of Depth Invariant Index (DII) with Support Vector Machine (SVM) classification. The in situ observations were made from 6 October 2020, same with the validation date, to verify the data obtained through satellite imagery. This was needed for calculating the accuracy of results based on seagrass images and identifying the species using the Seagrass-Watch (Transect Quadrant). The result showed that the seagrass in the Riau Islands can be detected with the DII method with seagrass coverage area of 175 km2 from the satellite in Lingga and Singkep Island. Percentage of Seagrass coverage for all transects was estimated to be about 78%. The seagrasses species identified were: Halophila ovalis, Halophila minor, Thalassia hemprichii, and the dominant species was Enhalus acoroides.

Monitoring the Spatial and Interannual Dynamic of Zostera noltei

“…Sentinel-2 data have been widely used to map different habitats and several studies have demonstrated their reliability in mapping and monitoring changes in marine biocenoses such as coral (Hedley et al 2012 ), mangrove (Pham et al 2019b , 2020 ) and seagrass beds Topouzelis et al 2016 ; Traganos and Reinartz 2018b ; Ha et al 2020 , 2021 ; Wicaksono et al 2021 ; Nur et al 2021 ; Ivajnšič et al 2022 ; Hartoni et al 2022 ). Many authors have used vegetation indices as the input for RF classification to map plant communities in terrestrial wetlands (Fletcher 2016 ) and coastal wetlands (Zoffoli et al 2020 ; Martínez Prentice et al 2021 ; Benmokhtar et al 2021 ), and compared to many machine learning algorithms and support vector machine techniques, the RF algorithm has produced promising results in terms of classifying seagrass (Zhang et al 2013 ; Traganos and Reinartz 2018b ; Ha et al 2020 ).…”

“…High-resolution sensors like SPOT-7 (1.5 m pixels) and Pléiades 1 A and Pléiades 1B satellites, which deliver 0.5 m imagery products with a 20 km swath, or WorldView with almost 0.3-0.4 m pixels, can counteract 2018, 08.20.2019, 09.28.2020 the limitations of Sentinel-2 (10 m pixels) for detecting smaller patches of seagrass and limiting the confusion of mixed pixels, but this requires funding. Sentinel-2 data have been widely used to map different habitats and several studies have demonstrated their reliability in mapping and monitoring changes in marine biocenoses such as coral (Hedley et al 2012), mangrove (Pham et al 2019b(Pham et al , 2020 and seagrass beds Topouzelis et al 2016;Traganos and Reinartz 2018b;Ha et al 2020Ha et al , 2021Wicaksono et al 2021;Nur et al 2021;Ivajnšič et al 2022;Hartoni et al 2022). Many authors have used vegetation indices as the input for RF classification to map plant communities in terrestrial wetlands (Fletcher 2016) and coastal wetlands (Zoffoli et al 2020;Martínez Prentice et al 2021;Benmokhtar et al 2021), and compared to many machine learning algorithms and support vector machine techniques, the RF algorithm has produced promising results in terms of classifying seagrass (Zhang et al 2013;Traganos and Reinartz 2018b;Ha et al 2020).…”