scholarly journals Effect of Reduced Anthropogenic Activities on Water Quality in Lake Vembanad, India

2021 ◽  
Vol 13 (9) ◽  
pp. 1631
Author(s):  
Gemma Kulk ◽  
Grinson George ◽  
Anas Abdulaziz ◽  
Nandini Menon ◽  
Varunan Theenathayalan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Sustainable Development ◽  
Sustainable Management ◽  
Marine Pollution ◽  
Anthropogenic Activities ◽  
Landsat 8 ◽  
Anthropogenic Pressures ◽  
Socioeconomic Impacts ◽  
Agriculture Industry

The United Nation’s Sustainable Development Goal Life Below Water (SDG-14) aims to “conserve and sustainably use the oceans, seas, and marine resources for sustainable development”. Within SDG-14, targets 14.1 and 14.2 deal with marine pollution and the adverse impacts of human activities on aquatic systems. Here, we present a remote-sensing-based analysis of short-term changes in the Vembanad-Kol wetland system in the southwest of India. The region has experienced high levels of anthropogenic pressures, including from agriculture, industry, and tourism, leading to adverse ecological and socioeconomic impacts with consequences not only for achieving the targets set out in SDG-14, but also those related to water quality (SDG-6) and health (SDG-3). To move towards the sustainable management of coastal and aquatic ecosystems such as Lake Vembanad, it is important to understand how both natural and anthropogenic processes affect water quality. In 2020, a unique opportunity arose to study water quality in Lake Vembanad during a period when anthropogenic pressures were reduced due to a nationwide lockdown in response to the global pandemic caused by SARS-CoV-2 (25 March–31 May 2020). Using Sentinel-2 and Landsat-8 multi-spectral remote sensing and in situ observations to analyse changes in five different water quality indicators, we show that water quality improved in large areas of Lake Vembanad during the lockdown in 2020, especially in the more central and southern regions, as evidenced by a decrease in total suspended matter, turbidity, and the absorption by coloured dissolved organic matter, all leading to clearer waters as indicated by the Forel-Ule classification of water colour. Further analysis of longer term trends (2013–2020) showed that water quality has been improving over time in the more northern regions of Lake Vembanad independent of the lockdown. The improvement in water quality during the lockdown in April–May 2020 illustrates the importance of addressing anthropogenic activities for the sustainable management of coastal ecosystems and water resources.

scholarly journals Analysis and Classification of Stormwater and Wastewater Runoff From the Tijuana River Using Remote Sensing Imagery

2020 ◽  
Vol 8 ◽  
Author(s):  
Mariam Ayad ◽  
Jingjing Li ◽  
Benjamin Holt ◽  
Christine Lee
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Southern California ◽  
Marine Pollution ◽  
The United States ◽  
Open Ocean ◽  
Landsat 8 ◽  
Spectral Features ◽  
Dominant Type

Urban runoff represents the primary cause of marine pollution in the Southern California coastal oceans. This study focuses on water quality issues originating from the Tijuana River watershed, which spans the southwest border of the United States and Mexico. Frequent discharge events into the coastal ocean at this boundary include stormwater and wastewater. This study focuses on differences in spectral features, as assessed by RapidEye, Sentinel-2 A/B, and Landsat-8 satellite data, along with physical and biological in situ data, to characterize and classify plumes into four key categories: stormwater, wastewater, open ocean/no plume, and mixed (when both types of plumes are present). Key spectral differences in the visible to NIR bands showed that stormwater had elevated reflectance (0.02 to 0.09), followed by mixed (0 to 0.08), wastewater (0 to 0.05), and open ocean/no plume (0 to 0.03) events. We also examined biophysical parameters and found that stormwater events had the highest values in remote sensing based estimates of colored dissolved organic matter (CDOM) (0.98 to 2.1 m–1) and turbidity (12.4 to 45.7 FNU) and also had a large range for in situ variables of enterococcus bacteria and flow rates. This study also finds that the use of spectral features in a hierarchical cluster analysis can correctly classify stormwater from wastewater plumes when there is a dominant type. These results of this study will enable improved determination of the transport of both types of plumes and transboundary monitoring of coastal water quality across the Southern California/Baja California region.

Influence of Anthropogenic Pressures on Groundwater Quality from a Rural Area

2017 ◽  
Vol 68 (8) ◽  
pp. 1744-1748
Author(s):  
Catalina Stoica ◽  
Gabriela Geanina Vasile ◽  
Alina Banciu ◽  
Daniela Niculescu ◽  
Irina Lucaciu ◽  
...  
Keyword(s):  
Water Quality ◽  
Anthropogenic Activities ◽  
Groundwater Resources ◽  
Bacterial Load ◽  
Animal Husbandry ◽  
Anthropogenic Factors ◽  
Contamination Level ◽  
Anthropogenic Pressures ◽  
Limit Values ◽  
Physico Chemical

During the past few decades, the anthropogenic activities induced worldwide changes in the ecological systems, including the aquatic systems. This work analysed the contamination level of groundwater resources from a rural agglomeration (Central-Western part of Prahova County) by biological and physico-chemical approaches. The study was performed during the autumn of 2016 on several sampling sites (four drilling wells, depth higher than 100 m supplying three villages; two wells lower than 10 m depth and one spring). The water quality was evaluated by comparison with the limit values of the drinking water quality legislation (Law no.458/2002) and the Order 621/2014 (applicable to all groundwater bodies of Romania). The results showed that phenols and metals (iron and manganese) exceeded the threshold values in all sampling sites. Moreover, the anthropogenic factors including agriculture, use of fertilizers, manures, animal husbandry led to an increase of the bacterial load, particularly at wells sites.

scholarly journals Detecting spatiotemporal variability in the physicochemical properties of water in the Lower Mearim using remote sensing data

2020 ◽  
Vol 42 ◽  
pp. e32
Author(s):  
George Colares Silva Filho ◽  
Juliana Martins dos Santos ◽  
Paulo Cesar Mendes Villis ◽  
Ingrid Santos Gonçalves ◽  
Isael Coelho Correia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Physicochemical Properties ◽  
Monitoring Program ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Spatiotemporal Variability ◽  
Landsat 8 ◽  
Solids Content

Natural or anthropogenic chemical compounds of different origins often accumulate in estuarine regions. These compounds may alter the water quality. Therefore, It is important to constantly monitor the quality of estuarine regions. A combination of remote sensing and traditional sampling can lead to a better monitoring program for water quality parameters. The objective of this work is to assess the spatiotemporal variability of the physicochemical properties of water in the lower region of the Mearim River and estimate water quality parameters via remote sensing. Samples were collected at 16 points, from Baixo Arari to the mouth of the watershed, using a multiparameter meter and Landsat 8 satellite images. The physicochemical parameters of the water had high salinity levels, between 2.30 and 20.10 parts per trillion; a high total dissolved solids content, between 2.77 and 19.70 g/L; and minimum dissolved oxygen values. Estimating the physicochemical properties of the water via remote sensing proved feasible, particularly in the dry season when there is less cloud cover.

scholarly journals Changes in vegetation cover on Stara planina: Towards sustainable management of ski resorts in sensitive areas

2015 ◽  
Vol 95 (2) ◽  
pp. 25-40 ◽  
Author(s):  
Ivan Potic ◽  
Marko Joksimovic ◽  
Rajko Golic
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Sustainable Management ◽  
Sustainable Forest Management ◽  
High Mountain ◽  
Landsat 8 ◽  
Mountain Areas ◽  
Ski Resorts ◽  
Landsat 7 ◽  
Multispectral Analysis

Tourism is an indicator and the consequence of the development of many countries. Among the priority areas of the tourism strategy are high mountain areas with complex ecosystems. Mountain tourism in Serbia, as well as continental country is one of the leading forms of tourism through various projects stimulated by the state. In the last ten years, build up and expand the ski slopes of Stara planina in eastern Serbia, leading to various, mostly negative changes in the environment. This paper analyzes the changes in the forest areas of the site Babin Zub in years 2000 and 2013, using satellite imagery (Landsat 7 and Landsat 8) and remote sensing software. We used unsupervised multispectral analysis resolution 30 m and obtained data on forest areas. The aim is to draw attention to the change of vegetation cover and degradation of forest areas. Following to the experiences of the world's ski resorts, the paper presents the opportunities and examples of restoration of ski runs, and sustainable forest management in the studied highland area.

scholarly journals Spatial patterns of water quality in Xingu River Basin (Amazonia) prior to the Belo Monte dam impoundment

2015 ◽  
Vol 75 (3 suppl 1) ◽  
pp. 34-46 ◽  
Author(s):  
JL. Rodrigues-Filho ◽  
DS. Abe ◽  
P. Gatti-Junior ◽  
GR. Medeiros ◽  
RM. Degani ◽  
...  
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Spatial Patterns ◽  
Amazon Basin ◽  
Environmental Changes ◽  
Anthropogenic Activities ◽  
Data Sampling ◽  
Anthropogenic Pressures ◽  
Xingu River ◽  
Belo Monte

Abstract The Xingu River, one of the most important of the Amazon Basin, is characterized by clear and transparent waters that drain a 509.685 km2 watershed with distinct hydrological and ecological conditions and anthropogenic pressures along its course. As in other basins of the Amazon system, studies in the Xingu are scarce. Furthermore, the eminent construction of the Belo Monte for hydropower production, which will alter the environmental conditions in the basin in its lower middle portion, denotes high importance of studies that generate relevant information that may subsidize a more balanced and equitable development in the Amazon region. Thus, the aim of this study was to analyze the water quality in the Xingu River and its tributaries focusing on spatial patterns by the use of multivariate statistical techniques, identifying which water quality parameters were more important for the environmental changes in the watershed. Data sampling were carried out during two complete hydrological cycles in twenty-five sampling stations. The data of twenty seven variables were analyzed by Spearman's correlation coefficients, cluster analysis (CA), and principal component analysis (PCA). The results showed a high auto-correlation between variables (> 0.7). These variables were removed from multivariate analyzes because they provided redundant information about the environment. The CA resulted in the formation of six clusters, which were clearly observed in the PCA and were characterized by different water quality. The statistical results allowed to identify a high spatial variation in the water quality, which were related to specific features of the environment, different uses, influences of anthropogenic activities and geochemical characteristics of the drained basins. It was also demonstrated that most of the sampling stations in the Xingu River basin showed good water quality, due to the absence of local impacts and high power of depuration of the river itself.

Sustainable management of water environment in anthropopressure area for municipal purposes, water quality improvement and utilization of renewable energy sources

2021 ◽  
Author(s):  
Artur Wilczynski ◽  
Barbara Namyslowska-Wilczynska ◽  
Pawel Maslankiewicz
Keyword(s):  
Water Quality ◽  
Sustainable Development ◽  
Sustainable Management ◽  
Renewable Energy Sources ◽  
Local Population ◽  
Water Environment ◽  
Energy Sources ◽  
Industrial Areas ◽  
Post Industrial

<p>The Authors deal with the problems of management of post-industrial areas affected by high unemployment leading to prevailing frustration, increasing migration of the population and other socially and economically adverse phenomena. This is accompanied by the considerable degradation of the natural environment and the deterioration in the quality of life of the local population. The aim of the studies is showing the possibilities to create a model to manage the resources in order to achieve sustainable development, particularly to manage water and energy in the post-industrial areas. The studies have been carried out for a region heavily industrialized in the recent past with present closed down mines. In particular, the work relates to examine the possibility of: improving the quality of the surface and underground waters, using the water as a source of low-temperature heat, storing energy and producing energy in local renewable sources, ensuring proper water retention, developing post-industrial tourism through managing the post-industrial facilities and making them available. Innovation of work is to create an integrated model of resources and the environment management, in particular water resources and non-conventional energy.</p><p>The proposals constitute a contribution to the implementation of the principles of sustainable development by focusing on three of its dimensions - economic, environmental and social. At the same time it would be a way for the sustainable management of the water environment in the areas of anthropopressure, in industrial and post-industrial areas, for the purpose of collecting water, improving water quality and the use of local energy sources. The possibility of energy storage, which is extremely important in the area of an intensive development of unconventional sources of energy (wind farms, solar sources, hydroelectric, etc.), the characteristics of which are irregular, referred to simply as chimeric sources, also exists.</p>

scholarly journals THE EVALUATION OF WATER POLLUTION WITH THE HELP OF REMOTE SENSING TOOLS

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 403-408
Author(s):  
P. Šádek ◽  
J. Struhár
Keyword(s):  
Water Pollution ◽  
Remote Sensing ◽  
Water Quality ◽  
Vegetation Index ◽  
Aquatic Vegetation ◽  
Pollution Assessment ◽  
Vegetation Period ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Landsat 8

<p><strong>Abstract.</strong> With the growing population, there is a growing demand for quality drinking water. Especially in developing parts of the world, this is a serious problem. The aim of this work is to test remote sensing methods for water quality monitoring. The presented part of the project is focused on introducing the process of water pollution assessment using vegetation indices, which are derived only using RGB images. Water quality monitoring is based on satellite imagery Landsat 8 and UAV images Phantom 3. As reference data was used in-site measurements in profiles points. In-site measurements were repeated every month in the vegetation period from April to September. Based on regression analysis, the equation for the calculation of the amount of chlorophyll and the statistical evaluation of the quality of these equations is derived for each vegetation index. The best results were achieved using the ratio aquatic vegetation index (RAVI) and ExG (Excess green) indices of 97% and 96.8% respectively.</p>

scholarly journals Does Social Distancing Have an Effect on Water Quality? : An Evidence from Chlorophyll-a Level in the Water of Populated Southeast Asian Coasts

2020 ◽  
Author(s):  
Andrio Adwibowo
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Anthropogenic Activities ◽  
Remote Sensing Data ◽  
Sea Surface ◽  
Nutrient Contents ◽  
Social Distancing

The COVID 19 related social distancing is hypothesized can affect the environmental quality including the air and water quality. Correspondingly, this study aims to study how the reduction of activities of people living near the rivers and the coastal areas due to social distancing may decrease the discharges of materials and nutrients to the water body. The chlorophyll-a was used as bio indicators of nutrient contents related to the anthropogenic activities in the coast. The study was conducted in the Jakarta coast considering that this coast was surrounded by populated cities with total population equal to 16 million people. The chlorophyll-a was measured in mg/m3 and monitored using remote sensing data from January to April 2020 representing the period before and after the implementation of social distancing. The determinant environmental factor measured was sea surface temperature (0C). The study considered that there were reductions of levels and areas of chlorophyll-a in the coast. The chlorophyll-a levels were reduced from January to April (p&lt;0.05). The chlorophyll-a levels for January, February, March, and April were 7.36 mg/m3 (95%CI: 6.34-8.37), 7.90 mg/m3 (95%CI: 7.32-8.47), 6.52 mg/m3 (95%CI: 5.37-7.66), and 4.21 mg/m3 (95%CI: 3.34-5.07) respectively. However, the differences of chlorophyll-a were not influenced by the sea surface temperature factor (p&gt;0.05). Based on remote sensing data in January and February, the sizes of coastal areas with chlorophyll-a levels &gt;7.00 mg/m3 were larger than areas observed in March and April. Contrarily, the coastal area sizes with low chlorophyll-a levels &lt;5.00 mg/m3 were increasing in April. To conclude the dynamic of anthropogenic activities in coastal setting is responsible and associated with the water quality and nutrient contents as indicated by chlorophyll-a levels.

scholarly journals Land Use Land Cover Analysis using Geospatial Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2561-2566
Author(s):  
Babita Singh
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Anthropogenic Activities ◽  
Google Earth ◽  
Landscape Configuration ◽  
Landsat 8 ◽  
Visual Interpretation ◽  
Open Forest

Abstract: Remote sensing and Geographic information system (GIS) techniques can be used for the changing pattern of landscape. The study was conducted in Dehradun, Haridwar and Pauri Garhwal Districts of Uttarakhand State, India. In order to understand dynamics of landscape and to examine changes in the land use/cover due to anthropogenic activities, two satellite images (Landsat 5 and Landsat 8) for 1998 and 2020 were used. Google Earth Engine was used to perform supervised classification. Spectral indices (NDVI, MNDWI, SAVI, NDBI) were calculated in order to identify land cover classes. Both 1998 and 2020 satellite images were classified broadly into six classes namely agriculture, built-up, dense forest, open forest, scrub and waterbody. Using high resolution google earth satellite images and visual interpretation, overall accuracy assessment was performed. For land cover/use change analysis, these images were imported to GIS platform. Landscape configuration was observed by calculating various landscape metrices Images. It was observed that scrub land area had increased from 11 % to 14 % but a decrease in agriculture by 4.65 %. The increased value of NP, PD, PLAND, LPI and decrease in AI landscape indices shows that land fragmentation had increased since 1998. The most fragmented classes were scrub (PD - 3.32 to 5.18) and open forest (PD - 3.57 to 5.07). Decrease in AI for open forest, agriculture, built-up indicated that more fragmented patches of these classes were present. The result confirmed increase in the fragmentation of landscape from 1998 onwards. Keywords: GIS, LULC, landscape metrics, Remote Sensing

scholarly journals A Remote Sensing and Machine Learning Based Framework For The Assessment of Spatiotemporal Water Quality Along The Middle Ganga Basin

2021 ◽  
Author(s):  
Ashwitha Krishnaraj ◽  
Ramesh H
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Satellite Data ◽  
Water Resource Management ◽  
Geographical Area ◽  
Gradient Boosting ◽  
Landsat 8 ◽  
Physico Chemical ◽  
Extreme Gradient Boosting

Abstract Exploring qualitative measures of any waterbody is as vital as quantitative analysis for the sustainability of our water resources. Thus, examining the dynamics of spatiotemporal behaviour of dominant Water Quality Parameters (WQPs) along any waterbody is indeed critical for proposing the appropriate water resource management. This study aims to create a Machine learning model for mapping the dominant optical and non-optical WQPs such as Electrical Conductivity (EC), pH, Temperature (Temp), Total Dissolved Solids (TDS), Silicon Dioxide (SiO2) and Dissolved Oxygen (DO) using satellite data. However, the association between WQPs and satellite data is strenuous to model precisely using simple regression theory. In this context, we developed remote sensing-based Extreme Gradient Boosting (XGBoost) and Multilayer Perceptron (MLP) regressor with optimized Hyperparameters to understand the spatiotemporal variations of WQPs using Landsat-8 imageries. We evaluated six years of satellite data for the geographical area covering from Ankinghat to Chopan (20 sampling stations under Central Water Commission (CWC), Middle Ganga Division (MGD) II) for characterizing the trends of dominant Physico-chemical WQPs across the four clusters identified in our previous study. Through the developed XGBoost and MLP regression models between measured WQPs and the remote sensing reflectance for the pixels corresponding to the sampling stations, a significant coefficient of determination (R2) in the range of 0.88- 0.98 for XGBoost and 0.72-0.97 for MLP have generated with bands B1-B4 and their ratios more consistent. Indeed, our findings recommend fewer in-situ measurements to generate reliable Landsat-8 based ML models to estimate Spatio-temporal variations of Physico-chemical and biological WQPs to facilitate better management of our waterbodies.

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