Evaluating remote sensing approaches for mapping the bathymetry of Lake Manzala, Egypt

• Published in: Euro-Mediterranean journal for environmental integration 2021-12, Vol.6 (3), p.77, Article 77
• Main Authors: Elshazly, Rana E., Armanuos, Asaad M., Zeidan, Bakenaz A., Elshemy, Mohamed
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Aquatic Pollution; Artificial neural networks; Atmospheric conditions; Atmospheric correction; Atmospheric models; Atmospheric Protection/Air Quality Control/Air Pollution; Bathymeters; Bathymetry; Coasts; Decision trees; Earth and Environmental Science; Earth Sciences; Environmental Chemistry; Environmental impact; Environmental Management; Environmental Science and Engineering; Generalized linear models; Global positioning systems; GPS; Hydrologic models; Lagoons; Lakes; Landsat; Landsat satellites; Logarithms; Neural networks; Performance evaluation; Principal components analysis; Remote sensing; Root-mean-square errors; Satellite imagery; Statistical models; Support vector machines; Topical Collection; Waste Management/Waste Technology; Waste Water Technology; Water Management; Water Pollution Control
• ISSN: 2365-6433; 2365-7448
• DOI: 10.1007/s41207-021-00285-0

Lake Manzala is considered the largest lagoon in Egypt. The lake has significant economic and environmental impacts that should be managed based on management models. The bathymetry of this lake, which is time consuming and laborious to obtain in the field, is one of the key input files for developing hydrological models of the lake. Remote sensing technology is used to determine satellite bathymetric maps with reasonable accuracy. The objective of the present work was to utilize Landsat 8 satellite imagery to determine the bathymetry of Lake Manzala. Generalized linear model (GLM), artificial neural network (ANN), decision tree, bagging (BAG, an ensemble regression algorithm), least-squares boosting fitting ensemble (LSB), and support vector machine (SVM) approaches were used in this study to process the images and manage the database of each image. The Landsat images were corrected for atmospheric conditions and the sunglint effect. Then values from the logarithms of corrected reflectance bands (coastal, blue, green, and red) and their ratio logarithms at locations corresponding to GPS surveys were extracted. Two assessing metrics, root mean square error (RMSE) and correlation ( R ), were used to calibrate the derived logarithm values for the model using observed data for the lake. The results show that the BAG and decision tree approaches perform well for Lake Manzala. Such methodologies should be applied for bathymetry determination, especially for shallow lakes, to save monitoring effort and costs. This approach facilitates the development of management models for lakes.