Mangrove ecosystems are one of the blue carbon parameters with large carbon storage capabilities. The carbon sequestration is crucial in addressing the greenhouse effect that causes the rise of carbon emissions in the atmosphere. One of the measurements that can be made is the estimation of mangrove above-ground biomass (AGB). This is due to the binding of carbon stored in the form of mangrove biomass. The aim of this research is to estimate and map the spatial distribution of mangrove above-ground biomass (AGB) is conducted using WorldView-3 (WV-3) image, a high spatial resolution remote sensing data. We used Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) to estimate mangrove above-ground biomass (AGB). Field data obtained from measuring mangrove tree diameter at breast height (DBH) which is then calculated using the allometric equation. We conducted regression analysis between field data and vegetation indices (NDVI and SAVI) to determine the most accurate vegetation index for estimating mangrove above-ground biomass values varied across the mangrove forest. This results of this research shows that the NDVI vegetation index provided higher accuracy for mangrove AGB estimation than SAVI index with R2 of 0,603 and resulting the AGB value between 750 to 1.300 kg/m2.
Seagrass, a marine angiosperm, plays a crucial role in providing significant ecosystem services. Due to its highly dynamic nature, seagrass cover can exhibit monthly or seasonal fluctuations. This research aims to investigate the dynamics of seagrass cover changes on Gili Lawang Island, East Lombok Regency throughout the period of 2022-2023, utilizing timeseries PlanetScope images. To develop a model for estimating seagrass cover percentage, we employed a stepwise regression approach that integrated sunglint-corrected Planetscope level 3B bands with field seagrass data. Training and accuracy assessment samples were collected using the photo-quadrate method, spatially distributed across various coastal characteristics of Gili Lawang Island. The obtained time-series seagrass percent cover maps were further analyzed in conjunction with climatic data to discern the underlying patterns governing seagrass cover dynamics. The novelty of this study lies in its potential to serve as a foundation for future research endeavors, such as the analysis of carbon stock dynamics in seagrass fields, and as a basis for establishing seagrass conservation zones in Gili Lawang.
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