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Assessment of a Non-Optical Water Quality Property Using Space-based Imagery in Egyptian Coastal Lake Cover

Assessment of a Non-Optical Water Quality Property Using Space-based Imagery in Egyptian Coastal Lake

International Journal of Advanced Network, Monitoring and Controls
Volume 4 (2019): Issue 3 (January 2019)
By:  and    
Open Access
|Nov 2019
Figures & tables

Figures & Tables

Figure 1.

Edku Lake

Figure 2.

Field measurement locations in Lake Edku zones A, B, C, and D

Figure 3.

Observed DO data during four seasons in Lake Edku zones

TABLE I.

USED LANDSAT 8 (OLI) SCENES AND DATES OF ACQUISITION

Scene ID (path177/row38)Date Acquired
“LC81770382016071LGN01”11-Mar-2016
“LC81770382016151LGN01”30-May-2016
“LC81770382016231LGN01”18-Aug-2016
“LC81770382016343LGN01”8-Dec-2016
TABLE II.

LANDSAT 8 (OLI) SPECTRAL BANDS CONSIDERED IN THIS STUDY

Landsat 8 (OLI) bandsSpectral range (μm)
Band 2 (Visible)0.450 - 0.51
Band 3 (Visible)0.53 - 0.59
Band 4 (Visible)0.64 - 0.67
Band 5 (Near-Infrared)0.85 - 0.88
Thermal Infrared Sensor (TIRS)
Thermal Infrared (Band 10)10.6 - 11.19
Thermal Infrared (Band 11)11.5 - 12.51
Figure 4.

Spatial distribution of derived Temperature (ºC) within Lake Edku in spring, 2016

Figure 5.

Spatial distribution of derived Turbidity (NTU) within Lake Edku in spring, 2016

Figure 6.

Spatial distribution of derived TSS (mg/L) within Lake Edku in spring, 2016

Figure 7.

Spatial distribution of derived Chlorophyll-a (mg/m3) within Lake Edku in spring, 2016

TABLE III.

SENSITIVITY ANALYSIS FOR OPTIMAL DO RETRIEVAL ALGORITHM MODEL

SeasonsInput ParametersRegression Coefficient (R2)
Spring & Fall & WinterTurb, TSS, Chl, Ln-temp0.618
Summer & Fall & WinterTurb, TSS, Chl, Ln-temp0.630
Spring & Summer & FallTurb, TSS, Chl, Ln-temp0.657
Summer & FallTurb, TSS, Chl, Ln-temp0.781
Spring & FallTurb, TSS, Chl, Ln-temp0.751
Spring & WinterTurb, TSS, Chl, Ln-temp0.651
Spring & Summer & FallTurb, TSS, Ln-temp0.613
Summer & Fall & WinterTurb, TSS, Ln-temp0.601
Spring & Fall & WinterTurb, TSS, Ln-temp0.584
Spring & FallTurb, TSS, Chl0.644
Spring & FallTSS, Chl, Ln-temp0.676
Spring & WinterTSS, Chl, Ln-temp0.554
Summer & FallTurb, TSS, Ln-temp0.766
Summer & FallTurb, Chl, Ln-temp0.798
Summer & FallTSS, Ln-temp0.756
Summer & FallTurb, Ln-temp0.792
Figure 8.

Predictive capacity of developed algorithm model for DO Satellite-based retrieval

Figure 9.

Validation of the developed model for DO retrieval

TABLE IV.

DESCRIPTIVE STATISTICS FOR OBSERVED VERSUS DERIVED YEARLY DO LEVELS WITHIN LAKE EDKU

Descriptive StatisticsDo (mg/L)
ObservedModeled
Mean10.4409.064
Minimum1.5604.552
Maximum18.10720.271
Standard Deviation4.0423.841
Figure 10.

Observed versus derived DO concentrations during four seasons in Lake Edku

Figure 11.

Retrieved DO concentrations in Lake Edku during winter season

DOI: https://doi.org/10.21307/ijanmc-2019-059 | Journal eISSN: 2470-8038
Journal RSS Feed
Language: English
Page range: 53 - 64
Published on: Nov 11, 2019
Published by: Xi’an Technological University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Remote Sensing,
Algorithm Model,
Coastal lake,
Dissolved Oxygen
Related subjects:
Computer sciences,
Computer sciences, other

© 2019 Hala O. Abayazid, Ahmed El-Adawy, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 4 (2019): Issue 3 (January 2019)
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