Study on Spatiotemporal Variability of Water Quality Parameters in Florida Bay Using Remote Sensing

Gholizadeh, Mohammad Haji; Melesse, Assefa M.

doi:10.4172/2469-4134.1000207

Cited by 21 publications

(13 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies using Landsat TM, ETM+, and OLI show that the blue, green, red, and NIR bands are useful for the determination of TSS [8,19,[68][69][70]. Literature also shows that TM, ETM+, OLI, and MODIS are the most frequently used sensors for developing algorithms to study seasonal TSS variability in coastal and estuarine Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies DOI: http://dx.doi.org /10.5772/intechopen.81657 areas, due to the large amount of archived remote sensing data [24,71,72]. The recently launched MSI sensor onboard Sentinel-2A and Sentinel-2B provide high spatial resolution of 10-20 m with a high temporal resolution of 5 days.…”

Section: Turbidity Total Suspended Sediments and Stormwater Runoff mentioning

confidence: 99%

Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies

Hafeez¹,

Wong²,

Abbas³

et al. 2019

Monitoring of Marine Pollution

View full text Add to dashboard Cite

Recently, the marine habitat has been under pollution threat, which impacts many human activities as well as human life. Increasing concerns about pollution levels in the oceans and coastal regions have led to multiple approaches for measuring and mitigating marine pollution, in order to achieve sustainable marine water quality. Satellite remote sensing, covering large and remote areas, is considered useful for detecting and monitoring marine pollution. Recent developments in sensor technologies have transformed remote sensing into an effective means of monitoring marine areas. Different remote sensing platforms and sensors have their own capabilities for mapping and monitoring water pollution of different types, characteristics, and concentrations. This chapter will discuss and elaborate the merits and limitations of these remote sensing techniques for mapping oil pollutants, suspended solid concentrations, algal blooms, and floating plastic waste in marine waters.

show abstract

Section: Turbidity Total Suspended Sediments and Stormwater Runoff mentioning

confidence: 99%

Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies

Hafeez¹,

Wong²,

Abbas³

et al. 2019

Monitoring of Marine Pollution

View full text Add to dashboard Cite

show abstract

“…Taking R 2 as the comparison standard of retrieval accuracy, this research surveyed most existing publications regarding the retrieval of TP, TN and COD from 2000 to present and compared the performance of the proposed method in this research with previous research works, Figure 12 shows the results of comparison. Since the average R 2 of TP in the existing publications [21,22,[24][25][26][27]29] was 0.653, the performance of TP retrieval was improved by about 44% over the average of the past research works. For TN, the average R 2 in the existing publications [21, [23][24][25][26][27] was 0.613, and the performance of TN retrieval in this research was improved by 43% over the average of the past research works.…”

Section: Comparison Of Retrieval Accuracymentioning

confidence: 99%

“…Since the average R 2 of TP in the existing publications [21,22,[24][25][26][27]29] was 0.653, the performance of TP retrieval was improved by about 44% over the average of the past research works. For TN, the average R 2 in the existing publications [21, [23][24][25][26][27] was 0.613, and the performance of TN retrieval in this research was improved by 43% over the average of the past research works. For COD, the average R 2 in the past research works [17][18][19][20] was 0.722, and the performance of retrieval COD in this research was slightly higher than the upper quartile of the past research works.…”

Section: Comparison Of Retrieval Accuracymentioning

confidence: 99%

“…Over the past several decades, scholars have carried out extensive research on water quality monitoring by remote sensing, and have achieved good results in estimating optically active parameters, such as Chlorophyll-a (Chl-a), suspended solids (SS), colored dissolved organic matter (CDOM), turbidity and transparency [11][12][13][14][15][16]. There are however few studies on non-optically active parameters, such as chemical oxygen demand (COD), total phosphorus (TP), and total nitrogen (TN) [17,18,[27][28][29][19][20][21][22][23][24][25][26]. Estimating non-optically active parameters directly from spectral characteristics is difficult.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

Huang¹,

Guo²,

Chen³

et al. 2020

Preprint

View full text Add to dashboard Cite

Water quality retrieval for small urban waterbodies by remote sensing get used to be difficult due to coarse spatial resolution of the remote sensing imagery. The recently launched Sentinel-2 produces imagery with a spatial resolution of 10 m. It provides an opportunity to solve the problem of retrieving water quality for small waterbodies. Additionally, many water management issues also require fine resolution of imagery, e.g. illegal discharge to an urban waterbody. Since illegal discharges are an important issue for urban water management, chemical oxygen demand (COD), total phosphorous (TP), and total nitrogen (TN) were chosen as the target parameters for water quality retrieval in this study. COD, TP and TN, however, are non-optically active parameters. There were limited studies in the past to retrieve these parameters in comparison with optically active parameters, e.g. Chlorophyll-A etc. This study compared three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Neural Networks (NN), to investigate the opportunity to retrieve the above non-optically active parameters. Results showed that R 2 of TP, TN, and COD by NN, RF and SVR were 0.94, 0.88, and 0.86, respectively. The performances of water quality retrieval for these non-optically active parameters were significantly improved by the optimized machine learning models. These models hence solved the problem to use remote sensing data to retrieve these non-optically active water quality parameters and provided a new monitoring strategy for small waterbodies. Water quality mapping obtained by Sentinel-2 imagery provided a full spatial coverage of the water quality characterization for the entire water surface. Compared with water samples collecting and testing, it greatly reduced labor cost, reagents cost, and waste treatment cost. It also may help identify illegal discharges to urban waterbodies. The method developed in this research provides a new practical and efficient water quality monitoring strategy in managing water with consideration of environmental sustainability.

show abstract

“…& Burce, 2019), water temperature (WT) (Giardino et al, 2001), total phosphorus (TP) (Albert Moses et al, 2014), dissolved oxygen (DO) (Albert Moses et al, 2014), biochemical oxygen demand (BOD) (Japitana & Burce, 2019) and chemical oxygen demand (COD) (Gholizadeh & Melesse, 2017;Gholizadeh et al, 2016). Among these water quality parameters, the Chl-a concentration serves as an indicator to evaluate the trophic state of a water body, since it is a direct indicator the phytoplankton concentration.…”

mentioning

confidence: 99%

Mapping the chlorophyll‐a concentrations in hypereutrophic Krishnagiri Reservoir (India) using Landsat 8 Operational Land Imager

Arunbabu

Murali

2020

Lakes & Reservoirs

View full text Add to dashboard Cite

Krishnagiri Reservoir exhibits a hypereutrophic status and continuously receives external sediment and nutrient loads, in addition to its internal phosphorus loading, both affecting the reservoir water quality. Increased nutrient loading attributable to changing anthropogenic activities in the catchment area will further exacerbate the deteriorating trophic status. Temporal Satellite imageries can play a crucial role in the rapid assessment of the trophic status of the reservoir over a large spatial extent. The eutrophication status of freshwater systems is directly related to the chlorophyll‐a (Chl‐a) concentration, which represents a major trophic state indicator by reflecting green and absorbing violet‐blue and orange‐red light of the solar energy spectrum. The present study was undertaken to map seasonal Chl‐a concentration variations using Landsat 8 Operational Land Imager (OLI) images. Multiple regression equations developed using reflectance in the Green, Near Infrared, Shortwave Infrared 1 and 2 and Coastal bands (R2 = .635) were found to be the best fit of the model in mapping the Chl‐a concentration variations in Krishnagiri Reservoir. The derived regression model also can be used to determine the trophic state of the reservoir and can facilitate a more rapid assessment for developing management strategies for sustainable reservoir water quality management.

show abstract

Study on Spatiotemporal Variability of Water Quality Parameters in Florida Bay Using Remote Sensing

Cited by 21 publications

References 28 publications

Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies

Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

Mapping the chlorophyll‐a concentrations in hypereutrophic Krishnagiri Reservoir (India) using Landsat 8 Operational Land Imager

Contact Info

Product

Resources

About