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This study entails generation of empirical ordinary least squares regression models to estimate water parameters. It uses remote sensing for environmental monitoring of Pasig River located in the Philippines. This uses measurements of primary water quality (WQ) parameters defined on Department of Environment and Natural Resources Administrative Order 2016-08 recorded on the Pasig River Unified Monitoring Stations (PRUMS) report from January to June of 2019. Sentinel-2 images are utilized to estimate biological oxygen demand (BOD), Chloride, Color, Dissolved Oxygen (DO), Fecal Coliform, Nitrate, pH, Phosphate, Temperature, and Total suspended solids (TSS). Feature generation involved calculation of different band reflectances from the satellite image. Exhaustive feature selection through application of a Pearson Correlation threshold was applied to limit number of independent variables. The box-cox transformations of water quality parameters (except for Temperature) were used as dependent variables and the selected features are used as dependent variables for the ordinary least squares regression model. The root mean square error (RMSE) values for the models which are computed using the k-fold cross validation technique showed outliers, especially for the TSS model (>547000 mg/L), which made its average negative RMSE so large. Tests for multicollinearity, autocorrelation, and homoscedasticity indicated problems in models created. However, normality of residuals indicates that models allow us to roughly estimate water quality for the river as a whole with the advantages of remote sensing, enabling a better perspective for its spatial distribution. [ABSTRACT FROM AUTHOR]

Mangroves are considered one of the most undervalued ecosystems in the world. It provides shelter to a wide range of species and protection from natural hazards to coastal communities. The Philippines, being a country with long coastlines, benefits greatly from mangroves. Historically, it had 400,000–500,000 hectares of mangroves forest in 1920, which declined to 120,000 hectares in 1994 due to rapid industrialization, particularly the conversion of these forests into aquaculture such as fishponds Mangrove forest in the Philippines saw a rapid decline between 1920 and 1994 due to aquaculture conversion and land reclamation Mangrove Vegetation Index (MVI), an established mangrove detection algorithm, was applied on Landsat satellite images of Manila Bay to map the extent of the mangrove forest from 1990 to 2020. Thirteen time-series maps were produced. Area computation showed that the coastline of Bulacan had the most mangroves, while the coastline of Metro Manila had the least throughout the years. [ABSTRACT FROM AUTHOR]

This study entails generation of empirical ordinary least squares regression models to estimate water parameters. It uses remote sensing for environmental monitoring of Pasig River located in the Philippines. This uses measurements of primary water quality (WQ) parameters defined on Department of Environment and Natural Resources Administrative Order 2016-08 recorded on the Pasig River Unified Monitoring Stations (PRUMS) report from January to June of 2019. Sentinel-2 images are utilized to estimate biological oxygen demand (BOD), Chloride, Color, Dissolved Oxygen (DO), Fecal Coliform, Nitrate, pH, Phosphate, Temperature, and Total suspended solids (TSS). Feature generation involved calculation of different band reflectances from the satellite image. Exhaustive feature selection through application of a Pearson Correlation threshold was applied to limit number of independent variables. The box-cox transformations of water quality parameters (except for Temperature) were used as dependent variables and the selected features are used as dependent variables for the ordinary least squares regression model. The root mean square error (RMSE) values for the models which are computed using the k-fold cross validation technique showed outliers, especially for the TSS model (>547000 mg/L), which made its average negative RMSE so large. Tests for multicollinearity, autocorrelation, and homoscedasticity indicated problems in models created. However, normality of residuals indicates that models allow us to roughly estimate water quality for the river as a whole with the advantages of remote sensing, enabling a better perspective for its spatial distribution. [ABSTRACT FROM AUTHOR]

Mangroves are considered one of the most undervalued ecosystems in the world. It provides shelter to a wide range of species and protection from natural hazards to coastal communities. The Philippines, being a country with long coastlines, benefits greatly from mangroves. Historically, it had 400,000–500,000 hectares of mangroves forest in 1920, which declined to 120,000 hectares in 1994 due to rapid industrialization, particularly the conversion of these forests into aquaculture such as fishponds Mangrove forest in the Philippines saw a rapid decline between 1920 and 1994 due to aquaculture conversion and land reclamation Mangrove Vegetation Index (MVI), an established mangrove detection algorithm, was applied on Landsat satellite images of Manila Bay to map the extent of the mangrove forest from 1990 to 2020. Thirteen time-series maps were produced. Area computation showed that the coastline of Bulacan had the most mangroves, while the coastline of Metro Manila had the least throughout the years. [ABSTRACT FROM AUTHOR]

The article focuses on the study conducted in the Philippines regarding the toxicity of pesticide contamination from the rice water paddy overflow to fish and frogs. The authors discuss the toxicity of chlorpyrifos, fenubucarb, monocrotophos and methyl parathion residues as well as the proper water and pesticide management in lowland irrigated rice production. Moreover, they found out that separate application of methyl parathion and monocrotophos as well as the spraying of rice at 45 days after planting did not result in fish mortality but the combination of chlorpyrifos and fenubucarb caused 100% fish mortality.

The article presents a study on the environmental effect of molluscicides metaldehyde and niclosamide in a rice paddy ecosystem. The study analyzed the fate of the two molluscicides sprayed in the rice paddy ecosystem at Central Experiment Station in the University of the Philippines, Los Baños, Philippines through hydrolysis and Tracor Inc.'s 560 gas chromatograph with electron capture detector (ECD). Results showed that a decline in niclosamide while metaldehyde residues were higher.