Monitoring spatiotemporal variability of water quality parameters Using Landsat imagery in Choghakhor International Wetland during the last 32 years

2020 ◽  
Vol 56 ◽  
pp. 6
Author(s):  
Ahmad Reza Pirali Zefrehei ◽  
Aliakbar Hedayati ◽  
Saeid Pourmanafi ◽  
Omid Beyraghdar Kashkooli ◽  
Rasoul Ghorbani
Keyword(s):  
Water Quality ◽  
Landsat Imagery ◽  
Quality Parameters ◽  
Freshwater Ecosystems ◽  
Spatiotemporal Variability ◽  
Chl A ◽  
Secchi Disk Transparency ◽  
Small Water ◽  
Long Term Changes

Use of Landsat is of importance in monitoring and assessment of long-term changes of water quality in freshwater ecosystems, especially in small water bodies. In this study, over a 32-year period (1985–2017), the changes in water surface temperature (WST), secchi disk transparency (SDT), and chlorophyll-a (Chl-a) concentration were estimated at the Choghakhor wetland using Landsat imagery. Based on WST three detectable temperature zones can be observed within the wetland aquatic environment where the highest amount was observed in thermal strips. The results showed Chl-a concentration volatility in different periods in the wetland as well as its long-term increasing trend. The western part of the wetland, as compared to other areas, was affected by these changes, which could be due to the human activity concentrated in this area. In contrast (SDT) showed a decreasing trend during this period that was consistent with the observed changes in Chl-a concentration. This could be due to an increase in organic matter load and suspended solids in the water body of wetland during this time. Comparison of the extracted satellite data with the field data showed the least RMSE and high R2. Also, ANOVA results showed significant spatio-temporal differences between the studied parameters in Choghakhor wetland (p < 0.05). The present study can help to detect long-term changes in Choghakhor wetland and help toward moving to optimal management and protection of this wetland.

scholarly journals Assessing the potential of integrating Landsat sensors for estimating chlorophyll-a concentration in a reservoir

2017 ◽  
Vol 49 (5) ◽  
pp. 1608-1617 ◽  
Author(s):  
Matias Bonansea ◽  
Claudia Rodriguez ◽  
Lucio Pinotti
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Landsat Imagery ◽  
Quality Parameters ◽  
Landsat 8 ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Sensing Applications ◽  
Wide Range ◽  
Quality Assessments

Abstract Landsat satellites, 5 and 7, have significant potential for estimating several water quality parameters, but to our knowledge, there are few investigations which integrate these earlier sensors with the newest and improved mission of Landsat 8 satellite. Thus, the comparability of water quality assessing across different Landsat sensors needs to be evaluated. The main objective of this study was to assess the feasibility of integrating Landsat sensors to estimate chlorophyll-a concentration (Chl-a) in Río Tercero reservoir (Argentina). A general model to retrieve Chl-a was developed (R2 = 0.88). Using observed versus predicted Chl-a values the model was validated (R2 = 0.89) and applied to Landsat imagery obtaining spatial representations of Chl-a in the reservoir. Results showed that Landsat 8 can be combined with Landsat 5 and 7 to construct an empirical model to estimate water quality characteristics, such as Chl-a in a reservoir. As the number of available and upcoming sensors with open access will increase with time, we expect that this trend will certainly further promote remote sensing applications and serve as a valuable basis for a wide range of water quality assessments.

scholarly journals Land–Lake Linkage and Remote Sensing Application in Water Quality Monitoring in Lake Okeechobee, Florida, USA

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 147
Author(s):  
Mohammad Hajigholizadeh ◽  
Angelica Moncada ◽  
Samuel Kent ◽  
Assefa M. Melesse
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Spatiotemporal Variability ◽  
Coefficient Of Determination ◽  
Lake Okeechobee ◽  
Wet Season ◽  
Chl A

The state of water quality of lakes is highly related to watershed processes which will be responsible for the delivery of sediment, nutrients, and other pollutants to receiving water bodies. The spatiotemporal variability of water quality parameters along with the seasonal changes were studied for Lake Okeechobee, South Florida. The dynamics of selected four water quality parameters: total phosphate (TP), total Kjeldahl nitrogen (TKN), total suspended solid (TSS), and chlorophyll-a (chl-a) were analyzed using data from satellites and water quality monitoring stations. Statistical approaches were used to establish correlation between reflectance and observed water quality records. Landsat Thematic Mapper (TM) data (2000 and 2007) and Landsat Operational Land Imager (OLI) in 2015 in dry and wet seasons were used in the analysis of water quality variability in Lake Okeechobee. Water quality parameters were collected from twenty-six (26) monitoring stations for model development and validation. In the regression model developed, individual bands, band ratios and various combination of bands were used to establish correlation, and hence generate the models. A stepwise multiple linear regression (MLR) approach was employed and the results showed that for the dry season, higher coefficient of determination (R2) were found (R2 = 0.84 for chl-a and R2 = 0.67 for TSS) between observed water quality data and the reflectance data from the remotely-sensed data. For the wet season, the R2 values were moderate (R2 = 0.48 for chl-a and R2 = 0.60 for TSS). It was also found that strong correlation was found for TP and TKN with chl-a, TSS, and selected band ratios. Total phosphate and TKN were estimated using best-fit multiple linear regression models as a function of reflectance data from Landsat TM and OLI, and ground data. This analysis showed a high coefficient of determination in dry season (R2 = 0.92 for TP and R2 = 0.94 for TKN) and in wet season (R2 = 0.89 for TP and R2 = 0.93 for TKN). Based on the findings, the Multiple linear regression (MLR) model can be a useful tool for monitoring large lakes like Lake Okeechobee and also predict the spatiotemporal variability of both optically active (Chl-a and TSS) and inactive water (nutrients) quality parameters.

Long Term Monitoring of the Macrobenthos of the Upper Clyde Estuary

1984 ◽  
Vol 16 (5-7) ◽  
pp. 359-373 ◽  
Author(s):  
Anne R Henderson
Keyword(s):  
Water Quality ◽  
Seasonal Variation ◽  
Organic Pollution ◽  
Long Term Changes ◽  
Pollution Loading ◽  
Faunal Density ◽  
History Of ◽  
Long Term Monitoring ◽  
Term Monitoring

The sublittoral macrobenthic invertebrate populations of the Upper Clyde Estuary are described. The estuary has a long history of organic pollution. The long term changes in species composition, faunal density and dominance patterns between 1974 and 1980 are presented. The fauna is dominated by brackish, pollution tolerant oligochaetes and polychaetes. Fluctuations in populations can be related to both seasonal variation in environmental conditions and long term improvements in water quality through a reduction in pollution loading to the estuary.

scholarly journals The consequences for stream water quality of long-term changes in landscape patterns: Implications for land use management and policies

2021 ◽  
Vol 109 ◽  
pp. 105679
Author(s):  
António Carlos Pinheiro Fernandes ◽  
Lisa Maria de Oliveira Martins ◽  
Fernando António Leal Pacheco ◽  
Luís Filipe Sanches Fernandes
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Stream Water ◽  
Landscape Patterns ◽  
Land Use Management ◽  
Stream Water Quality ◽  
Long Term Changes

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.

Long-term changes in water quality and ecological status of lakes in the Wielkopolska National Park and its buffer zone with special reference to effectiveness of the lakes’ protection

2016 ◽  
Vol 45 (4) ◽  
Author(s):  
Agnieszka E. Ławniczak
Keyword(s):  
Water Quality ◽  
Catchment Area ◽  
National Park ◽  
Ecological Status ◽  
Buffer Zone ◽  
Field Studies ◽  
Long Term Changes ◽  
Protection Strategies

AbstractThis paper evaluates water quality and ecological status of lakes located in the Wielkopolska National Park and its buffer zone. Changes in water quality were analyzed from 1974 to 2012 in order to assess the effectiveness of the protection strategies implemented on the studied lakes since 1957, i.e. the date when the park was established. The ecological status of the lakes was assessed with the use of macrophytes as well as hydromorphological and physicochemical analyses performed in 2012. Changes in water quality of the studied lakes within the last 40 years were analyzed based on available published and unpublished data, as well as field studies. All water bodies are characterized by advanced eutrophication. However, evaluation of the ecological status showed good status of the charophyte-dominated lakes, i.e. Lake Wielkowiejskie and Lake Budzyńskie. Lack of significant differences in physicochemical water qualities between the park and its buffer zone indicated that measures implemented to protect the water, particularly in the park, are ineffective. This study shows that more radical conservation measures are necessary to protect and improve the water quality, not only in WPN and its buffer zone but also in the whole catchment area.

Long-term Changes of Physicochemical Water Quality in Lake Youngrang, Korea

2017 ◽  
Vol 50 (1) ◽  
pp. 169-185 ◽  
Author(s):  
Bal Dev Bhattrai ◽  
Sungjin Kwak ◽  
Kwansoon Choi ◽  
Woomyung Heo
Keyword(s):  
Water Quality ◽  
Long Term Changes

scholarly journals Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987–2018) of Donghu Lake in Wuhan Using Landsat Images

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2192
Author(s):  
Xujie Yang ◽  
Yan Jiang ◽  
Xuwei Deng ◽  
Ying Zheng ◽  
Zhiying Yue
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll A ◽  
Dynamic Monitoring ◽  
Water Quality Parameter ◽  
Landsat Images ◽  
Chl A ◽  
Donghu Lake ◽  
Cubic Model

Chlorophyll a (Chl-a) concentration, which reflects the biomass and primary productivity of phytoplankton in water, is an important water quality parameter to assess the eutrophication status of water. The band combinations shown in the images of Donghu Lake (Wuhan City, China) captured by Landsat satellites from 1987 to 2018 were analyzed. The (B4 − B3)/(B4 + B3) [(Green − Red)/(Green + Red)] band combination was employed to construct linear, power, exponential, logarithmic and cubic polynomial models based on Chl-a values in Donghu Lake in April 2016. The correlation coefficient (R2), the relative error (RE) and the root mean square error (RMSE) of the cubic model were 0.859, 9.175% and 11.194 μg/L, respectively and those of the validation model were 0.831, 6.509% and 19.846μg/L, respectively. Remote sensing images from 1987 to 2018 were applied to the model and the spatial distribution of Chl-a concentrations in spring and autumn of these years was obtained. At the same time, the eutrophication status of Donghu Lake was monitored and evaluated based on the comprehensive trophic level index (TLI). The results showed that the TLI (∑) of Donghu Lake in April 2016 was 63.49 and the historical data on Chl-a concentration showed that Donghu Lake had been eutrophic. The distribution of Chl-a concentration in Donghu Lake was affected by factors such as construction of bridges and dams, commercial activities and enclosure culture in the lake. The overall distribution of Chl-a concentration in each sub-lake was higher than that in the main lake region and Chl-a concentration was highest in summer, followed by spring, autumn and winter. Based on the data of three long-term (2005–2018) monitoring points in Donghu Lake, the matching patterns between meteorological data and Chl-a concentration were analyzed. It revealed that the Chl-a concentration was relatively high in warmer years or rainy years. The long-term measured data also verified the accuracy of the cubic model for Chl-a concentration. The R2, RE and RMSE of the validation model were 0.641, 2.518% and 22.606 μg/L, respectively, which indicated that it was feasible to use Landsat images to retrieve long-term Chl-a concentrations. Based on longitudinal remote sensing data from 1987 to 2018, long-term and large-scale dynamic monitoring of Chl-a concentrations in Donghu Lake was carried out in this study, providing reference and guidance for lake water quality management in the future.

scholarly journals Interannual and long-term changes in the trophic state of a multibasin lake: effects of morphology, climate, winter aeration, and beaver activity

2016 ◽  
Vol 73 (3) ◽  
pp. 445-460 ◽  
Author(s):  
Dale M. Robertson ◽  
William J. Rose ◽  
Paul C. Reneau
Keyword(s):  
Water Quality ◽  
Trophic State ◽  
Secchi Depth ◽  
Climatic Changes ◽  
Main Flow ◽  
Temperature And Precipitation ◽  
Air Temperatures ◽  
Long Term Changes ◽  
P Sources

Little St. Germain Lake (LSG), a relatively pristine multibasin lake in Wisconsin, USA, was examined to determine how morphologic (internal), climatic (external), anthropogenic (winter aeration), and natural (beaver activity) factors affect the trophic state (phosphorus, P; chlorophyll, CHL; and Secchi depth, SD) of each of its basins. Basins intercepting the main flow and external P sources had highest P and CHL and shallowest SD. Internal loading in shallow, polymictic basins caused P and CHL to increase and SD to decrease as summer progressed. Winter aeration used to eliminate winterkill increased summer internal P loading and decreased water quality, while reductions in upstream beaver impoundments had little effect on water quality. Variations in air temperature and precipitation affected each basin differently. Warmer air temperatures increased productivity throughout the lake and decreased clarity in less eutrophic basins. Increased precipitation increased P in the basins intercepting the main flow but had little effect on the isolated deep West Bay. These relations are used to project effects of future climatic changes on LSG and other temperate lakes.

Sign in / Sign up

Export Citation Format

Share Document