scholarly journals Evaluation of Unoccupied Aircraft System (UAS) Remote Sensing Reflectance Retrievals for Water Quality Monitoring in Coastal Waters

2021 ◽  
Vol 9 ◽  
Author(s):  
Anna E. Windle ◽  
Greg M. Silsbe
Keyword(s):  
Water Quality ◽  
Coastal Waters ◽  
Total Suspended Solid ◽  
Water Quality Monitoring ◽  
Suspended Solid ◽  
Quality Monitoring ◽  
Future Research ◽  
Quality Properties ◽  
Water Constituents

Unoccupied aircraft systems (UAS, or drones) equipped with off-the-shelf multispectral sensors originally designed for terrestrial applications can also be used to derive water quality properties in coastal waters. The at-sensor total radiance a UAS measured constitutes the sum of water-leaving radiance (LW) and incident radiance reflected off the sea surface into the detector’s field of view (LSR). LW is radiance that emanates from the water and contains a spectral shape and magnitude governed by optically active water constituents interacting with downwelling irradiance while LSR is independent of water constituents and is instead governed by a given sea-state surface reflecting light; a familiar example is sun glint. Failure to accurately account for LSR can significantly influence Rrs, resulting in inaccurate water quality estimates once algorithms are applied. The objective of this paper is to evaluate the efficacy of methods that remove LSR from total UAS radiance measurements in order to derive more accurate remotely sensed retrievals of scientifically valuable in-water constituents. UAS derived radiometric measurements are evaluated against in situ hyperspectral Rrs measurements to determine the best performing method of estimating and removing surface reflected light and derived water quality estimates. It is recommended to use a pixel-based approach that exploits the high absorption of water at NIR wavelengths to estimate and remove LSR. Multiple linear regressions applied to UAS derived Rrs measurements and in situ chlorophyll a and total suspended solid concentrations resulted in 37 and 9% relative error, respectively, which is comparable to coastal water quality algorithms found in the literature. Future research could account for the high resolution and multi-angular aspect of LSR by using a combination of photogrammetry and radiometry techniques. Management implications from this research include improved water quality monitoring of coastal and inland water bodies in order to effectively track trends, identify and mitigate pollution sources, and discern potential human health risks.

Monitoring and modelling mining impacts on water quality in Chindwin River Basin, Myanmar

2020 ◽  
Author(s):  
Thanapon Piman ◽  
Chayanis Krittasudthacheew ◽  
Shakthi K. Gunawardanaa ◽  
Sangam Shresthaa
Keyword(s):  
Water Quality ◽  
Heavy Metal ◽  
Drinking Water ◽  
Water Treatment ◽  
River Basin ◽  
Total Suspended Solid ◽  
Water Quality Monitoring ◽  
Suspended Solid ◽  
Quality Monitoring ◽  
Mining Activities

<p>The Chindwin River, a major tributary of the Ayeyarwady River in Myanmar, is approximately 850 km long with a watershed area of 115,300 km<sup>2</sup>. The Chindwin River is essential for local livelihoods, drinking water, ecosystems, navigation, agriculture, and industries such as logging and mining. Over the past two decades, Myanmar’s rapid economic development has resulted in drastic changes to socio-economic and ecological conditions in the basin. Water users in the basin reported that there is a rapid extension of gold and jade mining and they observed a noticeable decline in water quality along with increased sedimentation and turbidity. So far, however, Myanmar has not undertaken a comprehensive scientific study in the Chindwin River Basin to assess water quality and sources of water pollution and to effectively address issues of river basin degradation and concerns for public health and safety. This study aims to assess the status of water quality in the Chindwin River and the potential impact of mining activities on the water quality and loading through monitoring program and modeling approach. 17 locations in the upper, middle and lower parts of the Chindwin River Basin were selected for water quality monitoring. These sites are located near Homalin, Kalewa, Kani and Monywa townships where human activities and interventions could affect water quality. Water quality sampling and testing in the Chindwin River was conducted two times per year: in the dry season (May-June) and in the wet season (September-October) during 2015-2017. We monitored 21 parameters including heavy metals such as Lead (Pb), Mercury (Hg), Copper (Cu) and Iron (Fe). The observed values of Mercury in Uru River in the upper Chindwin River Basin which located nearby gold mining sites shown higher than the WHO drinking standard. This area also has high values of turbidity and Total Suspended Solid. The SHETRAN hydrological model, PHREEQC geochemical model and LOADEST model were used to quantify the heavy metal loads in the Uru River. Results from scenario analysis indicate an increase in Arsenic and Mercury load under increment of concentration due to expansions in mining areas. In both baseline and future climate conditions, the Uru downstream area shows the highest load effluent in both Arsenic and Mercury. These heavy metal loads will intensify the declining water quality condition in Chindwin River and can impact negatively on human health who use water for drinking. Therefore, we recommend that water quality monitoring should continue to provide scientific-evidence for decision-makers to manage water quality and mining activities properly.  Water treatment systems for drinking water are required to remove turbidity, Total Suspended Solid, and Mercury from raw water sources. Raising awareness of relevant stakeholders (local people, farmers, private sectors, etc.) is necessary as many people living in the Chindwin River Basin are using water directly from the river and other waterways without proper water treatment.</p>

scholarly journals Fundamentals of in Situ Digital Camera Methodology for Water Quality Monitoring of Coast and Ocean

Sensors ◽  
2009 ◽  
Vol 9 (7) ◽  
pp. 5825-5843 ◽  
Author(s):  
Lonneke Goddijn-Murphy ◽  
Damien Dailloux ◽  
Martin White ◽  
Dave Bowers
Keyword(s):  
Water Quality ◽  
Digital Camera ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals Monitoring surface water quality using social media in the context of citizen science

2017 ◽  
Vol 21 (2) ◽  
pp. 949-961 ◽  
Author(s):  
Hang Zheng ◽  
Yang Hong ◽  
Di Long ◽  
Hua Jing
Keyword(s):  
Water Quality ◽  
Social Media ◽  
Surface Water ◽  
Surface Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Future Research ◽  
Water Quality Data ◽  
Essential Information

Abstract. Surface water quality monitoring (SWQM) provides essential information for water environmental protection. However, SWQM is costly and limited in terms of equipment and sites. The global popularity of social media and intelligent mobile devices with GPS and photography functions allows citizens to monitor surface water quality. This study aims to propose a method for SWQM using social media platforms. Specifically, a WeChat-based application platform is built to collect water quality reports from volunteers, which have been proven valuable for water quality monitoring. The methods for data screening and volunteer recruitment are discussed based on the collected reports. The proposed methods provide a framework for collecting water quality data from citizens and offer a primary foundation for big data analysis in future research.

scholarly journals Field Spectroscopy as a Tool for Enhancing Water Quality Monitoring in the ACE Basin, SC

2017 ◽  
pp. 41-55 ◽  
Author(s):  
Caitlyn C. Mayer ◽  
Khalid A. Ali
Keyword(s):  
Water Quality ◽  
South Carolina ◽  
Chlorophyll A ◽  
Algal Species ◽  
Water Quality Monitoring ◽  
Water Systems ◽  
Quality Monitoring ◽  
Least Squares Regression ◽  
Monitoring Methods

The Ashepoo, Combahee, Edisto (ACE) Basin in South Carolina is one of the largest undeveloped estuaries in the Southeastern United States. This system is monitored and protected by several government agencies to ensure its health and preservation. However, as populations in surrounding cities rapidly expand and land is urbanized, the surrounding water systems may decline from an influx of contaminants, leading to hypoxia, fish kills, and eutrophication. Conventional in situ water quality monitoring methods are timely and costly. Satellite remote sensing methods are used globally to monitor water systems and can produce an instantaneous synopsis of color-producing agents (CPAs), including chlorophyll-a, suspended matter (TSM), and colored-dissolved organic matter by applying bio-optical models. In this study, field, laboratory, and historical land use land cover (LULC) data were collected during the summers of 2002, 2011, 2015, and 2016. The results indicated higher levels of chlorophyll, ranging from 2.94 to 12.19 μg/L, and TSM values were from 60.4 to 155.2 mg/L between field seasons, with values increasing with time. A model was developed using multivariate, partial least squares regression (PLSR) to identify wavelengths that are more sensitive to chlorophyll-a (R2 = 0.49; RMSE = 1.8 μg/L) and TSM (R2 = 0.40; RMSE = 12.9 mg/L). The imbrication of absorption and reflectance features characterizing sediments and algal species in ACE Basin waters make it difficult for remote sensors to distinguish variations among in situ concentrations. The results from this study provide a strong foundation for the future of water quality monitoring and for the protection of biodiversity in the ACE basin.

scholarly journals Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

2019 ◽  
Vol 11 (14) ◽  
pp. 1674 ◽  
Author(s):  
Fangling Pu ◽  
Chujiang Ding ◽  
Zeyi Chao ◽  
Yue Yu ◽  
Xin Xu
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Inland Lakes ◽  
Quality Classification ◽  
Water Quality Classification

Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality levels in China are difficult to estimate because of their weak optical characteristics and lack of explicit correlation between remote-sensing images and parameters. To address the problems, a convolutional neural network (CNN) with hierarchical structure was designed to represent the relationship between Landsat8 images and in situ water-quality levels. A transfer-learning strategy in the CNN model was introduced to deal with the lack of in situ measurement data. After the CNN model was trained by spatially and temporally matched Landsat8 images and in situ water-quality data that were collected from official websites, the surface quality of the whole water body could be classified. We tested the CNN model at the Erhai and Chaohu lakes in China, respectively. The experiment results demonstrate that the CNN model outperformed widely used machine-learning methods. The trained model at Erhai Lake can be used for the water-quality classification of Chaohu Lake. The introduced CNN model and the water-quality classification method could cover the whole lake with low costs. The proposed method has potential in inland-lake monitoring.

scholarly journals KUALITAS AIR BAGI KEHIDUPAN ORGANISME BAGIAN TENGAH DAN HILIR SUNGAI MUSI BERDASARKAN PADA SUMBER POLUTAN

2017 ◽  
Vol 14 (3) ◽  
pp. 253
Author(s):  
Siswanta Kaban ◽  
Husnah Husnah ◽  
Siti Nurul Aida
Keyword(s):  
Water Quality ◽  
Oxygen Demand ◽  
Total Suspended Solid ◽  
High Water ◽  
Suspended Solid ◽  
Pollution Source ◽  
Source Distribution ◽  
Musi River

Penelitian ini dilakukan untuk mengetahui kualitas air Sungai Musi tahun 2007 sampai dengan 2008 di bagian tengah dan hilir berdasarkan pada sumber polutan. Empat belas stasiun pengambilan contoh ditetapkan sebagai sumber polutan seperti industri maupun pemukiman penduduk, dan referensi yang jauh dari industri maupun pemukiman yang digunakan sebagai pembanding. Pada setiap stasiun, pengambilan contoh dilakukan 3 kali waktu pengambilan, yaitu bulan April, Juni, dan Januari yang dapat mewakili 3 musim yang berbeda pada tahun tersebut. Beberapa parameter diukur in situ sementara beberapa lain dianalisis di laboratorium dengan standar methods (AWWAWEF, 2005). Dari hasil penelitian didapatkan bahwa industri yang bergerak di bidang pengolahan kelapa sawit dan karet cenderung menurunkan kualitas perairan di Sungai Musi. Kandungan logam berat dalam sedimen di Sungai Musi relatif rendah dengan kandungan Cr+6 dan Pb yang tertinggi masing-masing 13,481 dan 1,747 μg per g. Curah hujan cenderung menurunkan beberapa parameter fisika dan kimia kualitas perairan. Potensi pencemaran cenderung ditemukan di bagian hilir Sungai Musi, karena sebaran industri dan intensitas pemanfaatan perairan cukup tinggi di bagian sungai tersebut. Study in order to know distribution of pollution source and its effect on water quality of the middle and down stream of Musi River was conducted in April and June 2007 and January 2008. Fourteen sampling sites were selected based on the pollution source and the minimal degradation site (reference sites). Parameters observed were pollution source distribution and water and sediment parameters such as physical and chemical parameters. Water sample was collected at 0.5 m from water surface by using Kemmerer water sampler while sediment samples were taken by using Ekman grab. Some of the parameters were analyzed in situ while the rest were analyzed in laboratory. Results indicated that oil palm and rubber industries were mostly the pollution source in Musi River. Potential pollution source was mostly found in the middle and down stream of Musi River since most of pollution source and high water utilization found in this area. Water quality parameters except total suspended solid and biochemical oxygen demand, were still in the range that can be tolerated by the aquatic organisms. Rain fall tends to decrease water quality of the river. Concentration of heavy metal such as Chrom (Cr+6) and plumbum in the sediment were in still in low concentration with the highest concentration reaching 13.481 and 1.747 μg per g respectively.

scholarly journals Near real time water quality monitoring of Chivero and Manyame lakes of Zimbabwe

2018 ◽  
Vol 378 ◽  
pp. 85-92 ◽  
Author(s):  
Ronald Muchini ◽  
Webster Gumindoga ◽  
Sydney Togarepi ◽  
Tarirai Pinias Masarira ◽  
Timothy Dube
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Remotely Sensed ◽  
Quality Parameters ◽  
Point Sources ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Quality Information ◽  
Web Portal

Abstract. Zimbabwe's water resources are under pressure from both point and non-point sources of pollution hence the need for regular and synoptic assessment. In-situ and laboratory based methods of water quality monitoring are point based and do not provide a synoptic coverage of the lakes. This paper presents novel methods for retrieving water quality parameters in Chivero and Manyame lakes, Zimbabwe, from remotely sensed imagery. Remotely sensed derived water quality parameters are further validated using in-situ data. It also presents an application for automated retrieval of those parameters developed in VB6, as well as a web portal for disseminating the water quality information to relevant stakeholders. The web portal is developed, using Geoserver, open layers and HTML. Results show the spatial variation of water quality and an automated remote sensing and GIS system with a web front end to disseminate water quality information.

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