Solang, Maxi Willem and Kepel, Rene Charles and Roeroe, Kakaskasen A. and Wicaksono, Pramaditya and Mingkid, Winda Mercedes and Kaparang, Franky Erens and Mantiri, Desy Maria Helena and Kemer, Kurniati and Hafizt, Muhammad and Salsabila, Huwaida N. (2024) Assessment of seagrass percent cover and aboveground carbon stock using linear and random forest regression based on Worldview-2 satellite imagery in Manado City waters, North Sulawesi Province, Indonesia. AACL Bioflux, 17 (2). 723 – 743. ISSN 18448143
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The seagrass ecosystem is renowned for making substantial contributions to preserving the balance of marine ecosystems, it offers significant ecological and financial benefits. The rising carbon emissions from several human activities may increase global warming. One method for reducing carbon emissions is to use blue carbon or coastal flora such as seagrass. The seagrass ecosystem has the potential to significantly absorb and store carbon permanently. This study aimed to quantify the density of seagrass Percent Cover (PCv) and aboveground carbon stocks (AGC) in the waters surrounding Manado City, North Sulawesi Province. The survey technique and location were selected with the help of purposeful sampling, while the seagrass data were collected using the line transect quadrant method with an area of 100 x 100 cm2 to the LIPI seagrass monitoring method. A total of 6 sites was determined for 193 sample plots, consisting of 72 for accuracy calculations and 121 for the regression model. The seagrass carbon stocks were determined using information on the cover percentage. The results showed that 6 types of seagrass were identified, namely Enhalus acoroides, Thalassia hemprichii, Syiringodium esoetifolium, Cymodecea rotundata, Cymodecea serrulata, Halodule pinifolia, and Halophila ovalis. T. hemprichii and E. acoroides accounted for 20.33 of the total cover, each. Station 4 had the highest concentration of seagrass AGC stock, measured by a field sample, at 11.180 g C m-2, while Station 1 had the lowest concentration, measured by a field sample at 6.748 g C m-2. The water area in Manado City fell into the medium category with an average percentage seagrass cover of 50.40. The seagrass AGC, or total ecosystem carbon store, determined based on the linear regression model, was of approximately 10.773 tons of carbon. In PCv and AGC accuracy analysis, seagrass PCv in B5 had an R2 value of 0.32, with a linear regression RMSE=25.61, a Pearson correlation r=0.59, and a Squared Pearson correlation R2=0.35. The linear regression mapping results showed that PCv was mostly found in the dense class (50–75) and close to coastal waters. This study suggested that linear regression was a better method than RFR for the seagrass AGC analysis. © 2024, BIOFLUX SRL. All rights reserved.
Item Type: | Article |
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Additional Information: | Cited by: 0 |
Uncontrolled Keywords: | aboveground carbon stock; mapping; random forest regression; remote sensing |
Subjects: | G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography |
Depositing User: | Sri Purwaningsih Purwaningsih |
Date Deposited: | 17 Jun 2025 00:54 |
Last Modified: | 17 Jun 2025 00:54 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/18909 |