Abstract
Remote sensing provides a synoptic view of the earth surface that can provide spatial and temporal trends necessary for comprehensive water quality (WQ) monitoring and assessment. This study explores the applicability of Landsat 8 and regression analysis in developing models for estimating WQ parameters such as pH, dissolved oxygen (DO), total dissolved solids (TDS), total suspended solids (TSS), biological oxygen demand (BOD), turbidity, and conductivity. The input image was radiometrically-calibrated using fast line-of-sight atmospheric analysis (FLAASH) and then atmospherically corrected to obtain surface reflectance (SR) bands using FLAASH and dark object subtraction (DOS) for comparison. SR bands derived using FLAASH and DOS, water indices, band ratio, and principal component analysis (PCA) images were utilized as input data. Feature vectors were then collected from the input bands and subsequently regressed together with the WQ data. Forward regression results yielded significant high R2 values for all WQ parameters except TSS and conductivity which had only 60.1% and 67.7% respectively. Results also showed that the regression models of pH, BOD, TSS, TDS, DO, and conductivity are highly significant to SR bands derived using DOS. Furthermore, the results of this study showed the promising potential of using RS-based WQ models in performing periodic WQ monitoring and assessment.
Highlights
Water is among the most important resources
Only 7 physical water quality (WQ) parameters were considered during this study: pH, dissolved oxygen (DO), biological oxygen demand (BOD), total suspended solids (TSS), total dissolved solids (TDS), conductivity and turbidity
A total of 7 raw bands, 7 pre-processed bands using FLAASH, 7 pre-processed bands using dark object subtraction (DOS), 18 principal component (PC) bands, 3 water indexes, and 1 Band ratio (BR) were used in this study
Summary
Water is among the most important resources. water bodies are under acute seasonal scarcity due to the increased rate of human intervention and human-induced modification of natural processes [1]. Governmentsponsored WQ monitoring programs usually employ field measurements and collection of water samples for subsequent laboratory analysis in a traditional way. While such conventional approach to WQ monitoring data is accurate at a specific location and time, in most cases, it cannot provide enough information on overall WQ. Water monitoring data and reports in the Philippines usually only have point-specific datasets which lack spatiotemporal trends that are rather vital in the monitoring, assessment, and identification of water management strategies Though this in-situ measurement offers high accuracy, it is not feasible to provide a simultaneous WQ database on a regional scale [3]. Satellite images have been used successfully in water management protocols such as to conduct inventory and water balance assessment [10,11,12], to assess flood areas [13], and for WQ change detection and monitoring [14, 15]
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