Assessment of Spatial Distribution of Algal Biomass in a Subtropical Lake in China by Modeling of Landsat ETM+ Data Coupled with Field Data

2014 ◽  
Vol 522-524 ◽  
pp. 12-20
Author(s):  
Wan Min Ni ◽  
Jiang Ying Zhang ◽  
Teng Da Ding ◽  
Jia Guo Qi
Keyword(s):  
Water Quality ◽  
Regression Models ◽  
Trophic State ◽  
Vegetation Index ◽  
Chl A ◽  
Subtropical Lake ◽  
Multiple Regression Models ◽  
Remote Sensing Techniques

The objective of this research is to construct an efficient way of monitoring water quality and assessing trophic state using remote sensing techniques in Qinshan Lake of Hangzhou, China. Two Landsat ETM+ images were acquired and simultaneous in situ measurements, sampling and analysis were conducted. Results of the study indicated that the ratio of ETM+1/ETM+3 was the most effective single band in estimating chlorophyll-a (Chl-a), followed by normalized ratio vegetation index (NRVI). Two multiple regression models with determination coefficients were further constructed between logarithmically transformed Chl-a and the combination of ETM+1/ETM+3, ETM+2/ETM+3, and ETM+3/ETM+4 of ten sample sites. The resulting models, Log (Chl-a)=1.65 + 0.87*(ETM+1 / ETM+3) 3.39*(ETM+2 / ETM+3) + 0.89*(ETM+3 / ETM+4), and Log (Chl-a)=2.94 1.37*(ETM+1 / ETM+3) + 0.40*(ETM+2 / ETM+3) 0.20*(ETM+3 / ETM+4), both showed strong ability to evaluate the distribution of Chl-a, with R2 of 0.72 and 0.92, respectively. Then two trophic state maps generated for Qinshan Lake using this model could identify zones with a higher potential for eutrophication, which turned out to be an appropriate method for synoptic monitoring of water quality in lakes. Similar modeling can be made for any given subtropical lake, to provide rapid and long term assessment of water quality and also useful information for decision making.

scholarly journals Long-Term Water Quality Patterns in an Estuarine Reservoir and the Functional Changes in Relations of Trophic State Variables Depending on the Construction of Serial Weirs in Upstream Reaches

2021 ◽  
Vol 18 (23) ◽  
pp. 12568
Author(s):  
Namsrai Jargal ◽  
Ho-Seong Lee ◽  
Kwang-Guk An
Keyword(s):  
Water Quality ◽  
Organic Matter ◽  
Algal Biomass ◽  
Trophic State ◽  
Suspended Solids ◽  
State Variables ◽  
Chl A ◽  
Functional Changes ◽  
Estuarine Reservoir

Water quality degradation is one of the major problems with artificial lakes in estuaries. Long-term spatiotemporal patterns of water quality in a South Korean estuarine reservoir were analyzed using seasonal datasets from 2002 to 2020, and some functional changes in relations of trophic state variables due to the construction of serial weirs in the upper river were also investigated. A total of 19 water quality parameters were used for the study, including indicators of organic matter, nutrients, suspended solids, water clarity, and fecal pollution. In addition, chlorophyll-a (CHL-a) was used to assess algal biomass. An empirical regression model, trophic state index deviation (TSID), and principal component analysis (PCA) were applied. Longitudinal fluctuations in nutrients, organic matter, sestonic CHL-a, and suspended solids were found along the axis of the riverine (Rz), transition (Tz), and lacustrine zones (Lz). The degradation of water quality was seasonally caused by resuspension of sediments, monsoon input due to rainfall inflow, and intensity of Asian monsoon, and was also related to intensive anthropic activities within the catchment. The empirical model and PCA showed that light availability was directly controlled by non-algal turbidity, which was a more important regulator of CHL-a than total nitrogen (TN) and total phosphorus (TP). The TSID supported our hypothesis on the non-algal turbidity. We also found that the construction of serial upper weirs influenced nutrient regime, TSS, CHL-a level, and trophic state in the estuarine reservoir, resulting in lower TP and TN but high CHL-a and high TN/TP ratios. The proportions of both dissolved color clay particles and blue-green algae in the TSID additionally increased. Overall, the long-term patterns of nutrients, suspended solids, and algal biomass changed due to seasonal runoff, turnover time, and reservoir zones along with anthropic impacts of the upper weir constructions, resulting in changes in trophic state variables and their mutual relations in the estuarine reservoir.

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

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.

scholarly journals Assessing the Trend of the Trophic State of Lake Ladoga Based on Multi-Year (1997–2019) CMEMS GlobColour-Merged CHL-OC5 Satellite Observations

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6881
Author(s):  
Augustine-Moses Gaavwase Gbagir ◽  
Alfred Colpaert
Keyword(s):  
Water Quality ◽  
Trophic State ◽  
Current Knowledge ◽  
Management Strategies ◽  
Paper Mill ◽  
Warming Trend ◽  
Satellite Observations ◽  
Lake Ladoga ◽  
General Reference ◽  
Chl A

The trophic state of Lake Ladoga was studied during the period 1997–2019, using the Copernicus Marine Environmental Monitoring Service (CMEMS) GlobColour-merged chlorophyll-a OC5 algorithm (GlobColour CHL-OC5) satellite observations. Lake Ladoga, in general, is mesotrophic but certain parts of the lake have been eutrophic since the 1960s due to the discharge of wastewater from industrial, urban, and agricultural sources. Since then, many ecological assessments of the Lake’s state have been made. These studies have indicated that various changes are taking place in the lake and continuous monitoring of the lake is essential to update the current knowledge of its state. The aim of this study was to assess the long-term trend in chl-a in Lake Ladoga. The results showed a gradual reduction in chl-a concentration, indicating a moderate improvement. Chl-a concentrations (minimum-maximum values) varied spatially. The shallow southern shores did not show any improvement while the situation in the north is much better. The shore areas around the functioning paper mill at Pitkäranta and city of Sortavala still show high chl-a values. These findings provide a general reference on the current trophic state of Lake Ladoga that could contribute to improve policy and management strategies. It is assumed that the present warming trend of surface water may result in phytoplankton growth increase, thus partly offsetting a decrease in nutrient load. Precipitation is thought to be increasing, but the influence on water quality is less clear. Future studies could assess the current chemical composition to determine the state of water quality of Lake Ladoga.

scholarly journals Periods of Extreme Shallow Depth Hinder but Do Not Stop Long-Term Improvements of Water Quality in Lake Apopka, Florida (USA)

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 538 ◽  
Author(s):  
Gaohua Ji ◽  
Karl Havens
Keyword(s):  
Water Quality ◽  
Eutrophic Lake ◽  
Water Levels ◽  
Shallow Depth ◽  
Benthivorous Fish ◽  
Chl A ◽  
Lake Apopka ◽  
Toxic Cyanobacteria ◽  
Long Term Trends

We recently documented that during times of extreme shallow depth, there are severe effects on the water quality of one of the largest shallow lakes in the southeastern USA—Lake Apopka. During those times, total phosphorus (TP), total nitrogen (TN), chlorophyll-a (Chl-a) and toxic cyanobacteria blooms increase, and Secchi transparency (SD) declines. The lake recovers when water levels rise in subsequent years. In this paper, we determined whether extreme shallow depth events, particularly when they re-occur frequently, can stop the long-term recovery of a shallow eutrophic lake undergoing nutrient reduction programs. Apopka is an ideal location for this case study because the State of Florida has spent over 200 million USD in order to reduce the inputs of P to the lake, to build large filter marshes to treat the water, and to remove large quantities of benthivorous fish that contribute to internal P loading. We obtained data from 1985 to 2018, a period that had relatively stable water levels for nearly 15 years, and then three successive periods of extreme shallow depth, and we examined the long-term trends in TP, TN, Chl-a, and SD. There were significant decreasing trends in all of these water quality variables, and even though water quality deteriorated during periods of extreme shallow depth, and reduced the slope of the long-term trends, it did not stop the recovery. However, in the future, if climate change leads to more frequent shallow depth events, which in lakes such as Apopka, result in the concentration of water and nutrients, it is unclear whether the resilience we document here will continue, vs. the lake not responding to further nutrient input reductions.

SentiLake - Sentinel-2 satellite data-based service for monitoring of Latvian lakes

2020 ◽  
Author(s):  
Dainis Jakovels ◽  
Agris Brauns ◽  
Jevgenijs Filipovs ◽  
Tuuli Soomets
Keyword(s):  
Water Quality ◽  
Satellite Data ◽  
Ecological Status ◽  
Quality Parameters ◽  
Water Bodies ◽  
Water Quality Parameters ◽  
Chl A ◽  
Regular Monitoring ◽  
Sentinel 2

<p>Lakes and water reservoirs are important ecosystems providing such services as drinking water, recreation, support for biodiversity as well as regulation of carbon cycling and climate. There are about 117 million lakes worldwide and a high need for regular monitoring of their water quality. European Union Water Framework Directive (WFD) stipulates that member states shall establish a programme for monitoring the ecological status of all water bodies larger than 50 ha, in order to ensure future quality and quantity of inland waters. But only a fraction of lakes is included in in-situ monitoring networks due to limited resources. In Latvia, there are 2256 lakes larger than 1 ha covering 1.5% of Latvian territory, and approximately 300 lakes are larger than 50 ha, but only 180 are included in Inland water monitoring program, in addition, most of them are monitored once in three to six years. Besides, local municipalities are responsible for the management of lakes, and they are also interested in the assessment of ecological status and regular monitoring of these valuable assets. </p><p>Satellite data is a feasible way to monitor lakes over a large region with reasonable frequency and support the WFD status assessment process. There are several satellite-based sensors (eg. MERIS, MODIS, OLCI) available specially designed for monitoring of water quality parameters, however, they are limited only to use for large water bodies due to a coarse spatial resolution (250...1000 m/pix). Sentinel-2 MSI is a space-borne instrument providing 10...20 m/pix multispectral data on a regular basis (every 5 days at the equator and 2..3 days in Latvia), thus making it attractive for monitoring of inland water bodies, especially the small ones (<1 km<sup>2</sup>). </p><p>Development of Sentinel-2 satellite data-based service (SentiLake) for monitoring of Latvian lakes is being implemented within the ESA PECS for Latvia program. The pilot territory covers two regions in Latvia and includes more than 100 lakes larger than 50 ha. Automated workflow for selecting and processing of available Sentinel-2 data scenes for extracting of water quality parameters (chlorophyll-a and TSM concentrations) for each target water body has been developed. Latvia is a northern country with a frequently cloudy sky, therefore, optical remote sensing is challenging in or region. However, our results show that 1...4 low cloud cover Sentinel-2 data acquisitions per month could be expected due to high revisit frequency of Sentinel-2 satellites. Combination of C2X and C2RCC processors was chosen for the assessment of chl-a concentration showing the satisfactory performance - R<sup>2</sup> = 0,82 and RMSE = 21,2 µg/l. Chl-a assessment result is further converted and presented as a lake quality class. It is expected that SentiLake will provide supplementary data to limited in situ data for filling gaps and retrospective studies, as well as a visual tool for communication with the target audience.</p>

scholarly journals Accomplishments of the MUSICA project to provide accurate, long-term, global and high-resolution observations of tropospheric {H<sub>2</sub>O,<i>δ</i>D} pairs – a review

2016 ◽  
Vol 9 (7) ◽  
pp. 2845-2875 ◽  
Author(s):  
Matthias Schneider ◽  
Andreas Wiegele ◽  
Sabine Barthlott ◽  
Yenny González ◽  
Emanuel Christner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Measurement Techniques ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Horizontal Resolution ◽  
Atmospheric Composition ◽  
Remote Sensing Techniques ◽  
Middle Infrared

Abstract. In the lower/middle troposphere, {H2O,δD} pairs are good proxies for moisture pathways; however, their observation, in particular when using remote sensing techniques, is challenging. The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) addresses this challenge by integrating the remote sensing with in situ measurement techniques. The aim is to retrieve calibrated tropospheric {H2O,δD} pairs from the middle infrared spectra measured from ground by FTIR (Fourier transform infrared) spectrometers of the NDACC (Network for the Detection of Atmospheric Composition Change) and the thermal nadir spectra measured by IASI (Infrared Atmospheric Sounding Interferometer) aboard the MetOp satellites. In this paper, we present the final MUSICA products, and discuss the characteristics and potential of the NDACC/FTIR and MetOp/IASI {H2O,δD} data pairs. First, we briefly resume the particularities of an {H2O,δD} pair retrieval. Second, we show that the remote sensing data of the final product version are absolutely calibrated with respect to H2O and δD in situ profile references measured in the subtropics, between 0 and 7 km. Third, we reveal that the {H2O,δD} pair distributions obtained from the different remote sensors are consistent and allow distinct lower/middle tropospheric moisture pathways to be identified in agreement with multi-year in situ references. Fourth, we document the possibilities of the NDACC/FTIR instruments for climatological studies (due to long-term monitoring) and of the MetOp/IASI sensors for observing diurnal signals on a quasi-global scale and with high horizontal resolution. Fifth, we discuss the risk of misinterpreting {H2O,δD} pair distributions due to incomplete processing of the remote sensing products.

scholarly journals Chlorophyll and Suspended Solids Estimation in Portuguese Reservoirs (Aguieira and Alqueva) from Sentinel-2 Imagery

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2479
Author(s):  
Vítor Hugo Neves ◽  
Giorgio Pace ◽  
Jesús Delegido ◽  
Sara C. Antunes
Keyword(s):  
Water Quality ◽  
Satellite Data ◽  
Suspended Solids ◽  
Low Cost ◽  
Water Quality Assessment ◽  
Fine Tuning ◽  
Future Research ◽  
Chl A ◽  
Sentinel 2

Reservoirs have been subject to anthropogenic stressors, becoming increasingly degraded. The evaluation of ecological potential in reservoirs is remarkably challenging, and consistent and regular monitoring using the traditional in situ methods defined in the WFD is often time- and money-consuming. Alternatively, remote sensing offers a low-cost, high frequency, and practical complement to these methods. This paper proposes a novel approach, using a C2RCC processor to analyze Sentinel-2 imagery data to retrieve information on water quality in two reservoirs of Portugal, Aguieira and Alqueva. We evaluate the temporal and spatial evolution of Chl a and total suspended solids (TSS), between 2018 and 2020, comparing in situ and satellite data. Generally, Alqueva reservoir allowed lower relative (NRMSE = 8.9% for Chl a and NRMSE = 21.9% for TSS) and systematic (NMBE = 1.7% for Chl a and NMBE = 2.0% for TSS) errors than Aguieira, where some fine-tuning would be required. Our paper shows how satellite data can be fundamental for water-quality assessment to support the effective and sustainable management of inland waters. In addition, it proposes solutions for future research in order to improve upon the methods used and solve the challenges faced in this study.

scholarly journals Modelling lake trophic state: A random forest approach

2015 ◽  
Author(s):  
Jeffrey W Hollister ◽  
W. Bryan Milstead ◽  
Betty J. Kreakie
Keyword(s):  
Water Quality ◽  
Random Forests ◽  
Trophic State ◽  
Mean Squared Error ◽  
Quality Data ◽  
Full Model ◽  
Water Quality Data ◽  
Squared Error ◽  
The Mean

Productivity of lentic ecosystems is well studied and it is widely accepted that as nutrient inputs increase, productivity increases and lakes transition from lower trophic state (e.g. oligotrophic) to higher trophic states (e.g. eutrophic). These broad trophic state classifications are good predictors of ecosystem condition, services, and disservices (e.g. recreation, aesthetics, and harmful algal blooms). While the relationship between nutrients and trophic state provides reliable predictions, it requires in situ water quality data in order to parameterize the model. This limits the application of these models to lakes with existing and, more importantly, available water quality data. To address this, we take advantage of the availability of a large national lakes water quality database (i.e. the National Lakes Assessment), land use/land cover data, lake morphometry data, other universally available data, and apply data mining approaches to predict trophic state. Using this data and random forests, we first model chlorophyll a, then classify the resultant predictions into trophic states. The full model estimates chlorophyll a with both in situ and universally available data. The mean squared error and adjusted R2 of this model was 0.09 and 0.8, respectively. The second model uses universally available GIS data only. The mean squared error was 0.22 and the adjusted R2 was 0.48. The accuracy of the trophic state classifications derived from the chlorophyll a predictions were 69% for the full model and 49% for the “GIS only” model. Random forests extend the usefulness of the class predictions by providing prediction probabilities for each lake. This allows us to make trophic state predictions and also indicate the level of uncertainity around those predictions. For the full model, these predicted class probabilites ranged from 0.42 to 1. For the GIS only model, they ranged from 0.33 to 0.96. It is our conclusion that in situ data are required for better predictions, yet GIS and universally available data provide trophic state predictions, with estimated uncertainty, that still have the potential for a broad array of applications. The source code and data for this manuscript are available from https://github.com/USEPA/LakeTrophicModelling.

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