Abstract
Abstract. The sensitivity to changes in water quality inherent to seagrass communities makes them vital for determining the overall health of the coastal ecosystem. Numerous efforts including community-based coastal resource management, conservation and rehabilitation plans are currently undertaken to protect these marine species. In this study, the relationship of water quality parameters, specifically chlorophyll-a (chl-a) and turbidity, with seagrass percent cover is assessed quantitatively. Support Vector Machine, a pixel-based image classification method, is applied to determine seagrass and non-seagrass areas from the orthomosaic which yielded a 91.0369% accuracy. In-situ measurements of chl-a and turbidity are acquired using an infinity-CLW water quality sensor. Geostatistical techniques are utilized in this study to determine accurate surfaces for chl-a and turbidity. In two hundred interpolation tests for both chl-a and turbidity, Simple Kriging (Gaussian-model type and Smooth- neighborhood type) performs best with Mean Prediction equal to −0.1371 FTU and 0.0061 μg/L, Root Mean Square Standardized error equal to −0.0688 FTU and −0.0048 μg/L, RMS error of 8.7699 FTU and 1.8006 μg/L and Average Standard Error equal to 10.8360 FTU and 1.6726 μg/L. Zones are determined using fishnet tool and Moran’s I to calculate for the seagrass percent cover. Ordinary Least Squares (OLS) is used as a regression analysis to quantify the relationship of seagrass percent cover and water quality parameters. The regression analysis result indicates that turbidity has an inverse relationship while chlorophyll-a has a direct relationship with seagrass percent cover.
Highlights
1.1 Background of the StudyThe Philippines is one of the countries that has an abundance in seagrasses
The primary objective of this study is to determine the relationship of seagrass percent cover and specific water quality parameters (Chl-a and turbidity) in Bolinao, Pangasinan using Aerial Photogrammetry, Remote Sensing techniques and Geographic Information Systems (GIS) techniques
The following are the specific objectives of the study: (1) to extract seagrass percent cover from images acquired by an Unmanned Aerial Vehicle (UAV) using pixel-based classification and remote sensing (RS) techniques; (2) to estimate water quality parameters, Chl-a and turbidity, using field measurements and geostatistical methods; and (3) to provide a quantitative assessment using regression analysis in determining the relationship of seagrass cover and water quality parametersChl-a and turbidity
Summary
The Philippines is one of the countries that has an abundance in seagrasses. Seagrasses play ecological and economic roles in Bolinao in a way that it contributes to the uplifting of the residents (Montano, 2005). There are various studies in Bolinao, Pangasinan that investigate the extent and cover of seagrasses as well as the water quality conditions in the coastal areas. In 2017, seagrass percent cover is determined in the same location based on aerial image interpretation and analysis (Dalagan, Manasan, 2017). The gap between these two studies gave way to studying the correlation between the seagrass percent cover and water quality
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