SPATIO-TEMPORAL WATER QUALITY MAPPING USING SATELLITE DATA AROUND A MANGROVE PLANTATION IN CAGSAO, CALABANGA, CAMARINES SUR

Abstract

The Cagsao mangrove is a thriving young forest along the San Miguel Bay (SMB), Camarines Sur. To establish the Spatio-temporal Water Quality mapping, data from the Chesapeake Bay, an estuary in the United States of America (USA), was sourced as the train set for this study. Spatio-temporal maps of chlorophyll and dissolved oxygen were generated using Linear Regression (LR) models which were derived from the train set and satellite images of the SMB. GNU (GNU’s not UNIX) Octave was used for the image processing, computing, and analysis. There were three phases in the image processing conducted in this study, 1) extraction of image data of the corresponding measure points from the train area, 2) conversion of the satellite study area to a two-color raster image, and 3) generation of the spatio-temporal maps from the analysis. The study found that the SMB is in the range of Mesotrophic to Moderate Eutrophic classification. The decay from two other point sources (Manga River and Libmanan River) was compared to that of Tigman River, an adjacent river to the Cagsao mangrove forest to determine variations and impact of the mangrove forest in the water quality of the SMB. The presence of Cagsao mangrove forest was found to affect the gap of increasing chlorophyll levels from shore toward the bay center in the adjacent Tigman River unlike Manga River and Libmanan River, which have both no adjacent mangrove forest in the river mouth area. The corresponding satellite images for the dataset taken during and near the date of the train area measurements were also extracted.