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

D. Algorithm Development

Satellite imageries were processed for carrying out multiple regression technique and reaching best algoritm model for DO retrival in Lake Edku. Analysis have been performed with the spatially distributed field measurements of Dissolved Oxygen as related to the spectral reflectance values derived from Landsat images at corresponding dates in year 2016. The available field data were divided into two groups for building algorithm models. Then performance has been tested with the reserved second group of data for validation process.

Based on the previously mentioned grounds of Streeter and Phelps DO modeling, as well as regional conditions defining water quality in coastal lakes of Egypt, the trophic and sediment-related properties were considered key factors in selection. Primary, the parameters included for developing DO derivative algorithms were Turbidity, Total Suspended Sediments (TSS), and Chlorophyll-a. Also, Temperature was added in the DO retrieval process as an important driver affecting Oxygen level in water, especially with the thermal anthropogenic releases and flow dynamic irregularity within Lake Edku. Cutomarily, DO concentration in water is inversely related to the temperature. Therefore, zonation of thermal property is expceted to have direct reflection on DO retrieved distribution. In process, alternate combinations of the considered parameters have been investigated for optimal model results.

Turbidity and TSS levels were deduced using findings of the recent research study of Abayazid and El-Gamal (2017). Authors concluded regional algorithm models for remotely sensed Turbidity and TSS in the Nile delta coastal zone, in terms of Landsat 8’s reflectance from spectral bands; Band2ref, Band4ref and Band5ref, as presented in Equations 3 and 4, respectively.

While literature applications showed various retrieval algorithms for Chlorophyll-a (e.g. Dona et al., 2014; Akbar et al., 2010; Brivio et al., 2001), best agreeable results for quantifying Chlorophyll-a in Lake Edku were found with the ratio between reflectance from spectral bands 2 and 4 of landsat 8 (Eq. 5).

Thermal spectral data have been converted to Temperature “T”, using the conversion formula presented in Equation 6 (USGS, 2015)

Where Lλ is the spectral radiance, and K1 and K2 are the thermal conversion constants found in Landsat imagery metadata files. Surface water temperature levels are calculated using averaged values of Thermal Infrared Bands (10) and (11).

Sensitivity analysis was performed to select best combination of the considered parameters, with different seasonal conditions experinced in the lake. Accordingly, the optimal DO retrieval algorithm model with best fitting predictions, as well as least data requirement and calculation efforts, has been selected for result demonstration.

III. RESULTS

Many factors control the DO concentration within a waterbody; both sources and sinks (e.g. consumption by flora and aerobic organisms, oxidation of carbonaceous and nitrogenous material, decomposition of organic material, photosynthetic activity, degrading inorganic chemicals, re-aeration possibility as well as temperature, and dynamics in flow. Drainage water inflowing into Lake Edku from the interconnected stream networks reflect the expanding urban activities and industrial facilities, beside the intensive agricultural processes. In addition, the aquacultural practices add an extra polluting source.

A. Remotely Sensed Input Parameters

The four parameters initially considered as inputs for DO modeling have been spatially derived for spring, summer, fall and winter seasons of year 2016, with corresponding Landsat imageries. Example illustrations are found in figures 4, 5, 6 and 7 for Temperature, Turbidity, TSS and Chlorophyll-a levels within Lake Edku, respectively.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

C. Developed Algorithm Model for DO Retrieval

Optimal derivative capacity for DO levels in Lake Edku, with least input parameter requirements, hence less processing works and costs, was found by using only Turbidity and natural logarithm of Temperature. The developed algorithm model, stated in Equation 7, proved reasonable fitness with regression coefficient of 0.79 (Fig. 8).

The proposed algorithm was then applied to the second group of data reserved for testing, and satellite-based derived DO concentrations were compared with the corresponding field measurements. Validation results proved acceptable predictive capacity of the developed algorithm model, with R2 of 0.66 (Fig. 9). Descriptive statistics for observed versus modeled yearly average DO levels within Lake Edku are presented in table (4). The developed model shows highly agreeable predictions with field measurements. However, the model failed to represent the very low DO concentration occurrence in the lake during summer season.

Figure 8.

Figure 9.

Once validated, the developed model has been applied to a set of landsat imageris to obtain retrieved DO spatial distribution in Lake Edku in time steps. Figure 10 illustrates comparison for observed versus derived DO concentrations during four seasons in Lake Edku. For the purpose of demonstration, figure (11) shows an example mapping of DO concentrations throughout the lake zones in winter time.

IV. DISCUSSION

Main feature characterizes the Edku lake system is the patchy pattern of water quality. Dissolved Oxygen level ranges from less than 2 mg/L, in extremely poor water conditions, to concentrations over 15 mg/L. However, the exceedingly high DO level, with regional temperatures range of 15–30 °C in coastal lakes, is considered 150-250% supersaturation and potentially signifies an unhealthy eutrophication condition (EPA, 1999). In investigating DO concentrations with reference to location within the lake, this case is clearly demonstrated in zone D of Lake Edku.

It has been noted that there are very high DO levels, detected both in field measurements as well as derived DO concentrations, in zone D which comprise entrapped waters with rare interaction. In reviewing this condition, it was found that the area experience intensive unwarranted aquatic flora growth, and accordingly high rate of the photosynthetic oxygen production, that coincides with less active hydrodynamics and water exchange.

On the other hand, Zone A, that is the nearest to major drainage inputs into the lake, has healthier DO concentrations. This zone, while loaded with excessive pollutants, experiences inflowing water velocity along with shallow water depth that allows partial compensation with higher re-aeration rate. Moderate range of DO concentrations exists mostly in zone B, and zone C, with combined effect of low temperature and suspended sediment concentrations as well as slower flow dynamics in zone B and localized tidal effect in zone C.

V. CONCLUSIONS

Developments and consequent concerns about water resources beneficial capacity require continuous monitoring. Field-based assessment of quality state is usually faced by limitations in spatial coverage, frequency of sampling as well as possible economic and accessibility obstcles. Meanwhile, applications with remote sensing techniques have proved successful retrival of water quality parameters, yet for optically active ones that have directly-detectable spectral signals.

This research study presents an approach for deriving a non-optically active property of water quality, Dissolved Oxygen (DO), with reference to other space-based retrievable parameters that affect and be affected by DO concentrations. Derivation methodology is based on the grounds of DO modeling, as well as regional conditions that define water quality in coastal lakes of Egypt. The selected ground truth data is distributed throughout the lake zones. Furthermore, the data covers the four seasons so that a wide range of water quality levels experienced in the lake is reflected in the developed model.

The study also presents results of sensitivity analysis for alternate input combinations. Consequently, optimal DO derivative algorithm model, with best predictive capacity and least data requirement, was found. The developed optimal model comprises two satellite-based inputs, namely Turbidity and Temperature, for the Edku coastal lake. With the acceptable predictive capacity achieved, the validated model facilitates regular assessment, with more frequent DO mapping, and possible following of historical changes.

Spatial distribution of DO concentrations reflects the patchy pattern within Lake Edku, with regard to interactions in boundary as well as irregular flow dynamics within. The detected zonation nature calls for specific remedial measures that vary for each section.

Finally, remote sensing techniques proved having the potential to play more roles in monitoring processes, and offering valuable information for sustainability management. The approach illustrated in this study sheds the light at the opportunity to expand applications with space-based earth observation products. The achieved promising results open the field for exploring more non-detectable water quality parameters that are interlinked with optically active properties in water. However further applications with finer imageries and intensive ground truth data are recommended for relationship with higher accuracy.