Volume XLII-4/W19
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 233–240, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-233-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 233–240, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-233-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Dec 2019

23 Dec 2019

ASSESSMENT OF SEAGRASS PERCENT COVER AND WATER QUALITY USING UAV IMAGES AND FIELD MEASUREMENTS IN BOLINAO, PANGASINAN

M. K. M. R. Guerrero, J. A. M. Vivar, R. V. Ramos, and A. M. Tamondong M. K. M. R. Guerrero et al.
  • Department of Geodetic Engineering, University of the Philippines- Diliman, Quezon City, Philippines

Keywords: Chlorophyll-a, Turbidity, Seagrass Cover, Support Vector Machine, UAV images, Geostatistical techniques, Morans I, Ordinary Least Squares

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.