Application of habitat evaluation procedure for impact assessment studies in Laguna de Bay, Philippines

Bongco, Ireneo G.; Santos-Borja, Adelina C.; Nauta, T.

doi:10.1023/b:hydr.0000008599.71810.b8

Cited by 1 publication

(1 citation statement)

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“…The lake is formed by three distinct bays, that is, East Bay, West Bay, and Central Bay. Although Laguna Lake serves as a vital water supply, the water resource has been affected by population growth and rapid industrialization since the last decade [22].…”

Section: A Study Areamentioning

confidence: 99%

Multi-task Convolution Neural Network for Season-Insensitive Chlorophyll-A Estimation in Inland Water

Nguyen

Lin

Syariz

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Chlorophyll-a (Chl-a) concentration, a crucial indicator of phytoplankton biomass, is sensitive to seasonality. The variations in trophic states regarding seasonality and the changes of spectral properties of water bodies pose uncertainties to the accuracy of remote sensing semi-empirical models. In particular, lakes in subtropical regions generally experience different trophic states in dry and wet seasons. In this study, a season-insensitive Chl-a retrieval model using multi-task convolution neural network with multiple output layers (MCNN) is proposed. A layersharing network combined with data augmentation is adopted to alleviate the issue of insufficient quantity of in situ samples. In addition, a hyperparameter optimization is performed to automatically refine the MCNN architecture. To evaluate the accuracy of proposed method, Laguna Lake, one of the largest lakes in Southeast Asia, is selected as the validation target. The lake is characterized by oligotrophic and mesotrophic states in wet season, whereas the states change to mesotrophic and lowlevel eutrophic states in dry season. A collection of Sentinel-3 Ocean and Land Colour Instrument Level-2 images and 409 in situ samples with the Chl-a concentration range 1.24-22.30 mg m −3 were used for model calibration and evaluation. Experimental results showed that MCNN with the performance of average R 2 =0.74, RMSE=2.06 mg m −3 , Pearson's r=0.86 outperforms related semi-empirical models including normalized difference chlorophyll index, two-band and three-band models, and WaterNet. The Chl-a prediction accuracy was improved by 7.19-14.6%, in terms of RMSE, compared with WaterNet.

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Section: A Study Areamentioning

confidence: 99%