ESTIMATING AND MAPPING CHLOROPHYLL-A CONCENTRATION OF PHEWA LAKE OF KASKI DISTRICT USING LANDSAT IMAGERY

Abstract. Water is a major component in the living ecosystem. As water quality is degrading due to human intervention, continuous monitoring is necessary. One of the indicators is Chlorophyll-a (Chl-a) which indicates algal blooms which are often driven by eutrophication phenomena in freshwater. Lakes should be monitored for Chl-a because Chla-a is related to eutrophication phenomena which are an enrichment of water by nutrients salt. When the environment becomes enriched with nutrients the excessive growth can lead to the death of fish. In this study, the Remote Sensing (RS) and Geographic Information System (GIS) techniques were utilized to determine Chl-a concentration of Phewa Lake of Kaski district. We used Landsat 8 satellite imagery for estimation and mapping of the Chl-a concentration. In-situ measurements from different sample points were taken and used to form a regression model for Chl-a and its concentration over the water body was calculated. The preceding year’s (2016) in situ measurement data of Chl-a concentration at a specific location were assessed with the one evaluated from the regression model thus produced for the succeeding year (2017) using Root Mean Square Error (RMSE) technique. As a result, we concluded that the estimation and mapping of Chl-a of a lake in Nepal can be done with the help of RS and GIS techniques.

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

Hydrology Research ◽

10.2166/nh.2017.116 ◽

2017 ◽

Vol 49 (5) ◽

pp. 1608-1617 ◽

Cited By ~ 4

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.

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An Optimal Two Bands Ratio Model to Monitor Chlorophyll-a in Urban Lake Using Landsat 8 Data

E3S Web of Conferences ◽

10.1051/e3sconf/202014302003 ◽

2020 ◽

Vol 143 ◽

pp. 02003

Author(s):

Qi Chen ◽

Mutao Huang ◽

Kaiyuan Bai ◽

Xiaojuan Li

Keyword(s):

Chlorophyll A ◽

Environmental Issue ◽

Inland Waters ◽

Landsat 8 ◽

Ratio Model ◽

Landsat 8 Oli ◽

Band Ratio ◽

Chl A ◽

Quality Indexes

Chlorophyll-a (Chl-a) estimation in inland waters is an essential environmental issue. This study aimed to identify a band ratio model for Chl-a simulation using Landsat 8 OLI data and in situ Chl-a measuring in Lake Donghu. The band B1and B2, respectively at the wavelength of 443 nm and 483 nm, in the band ratio model [B1/B2] performed best in Chl-a estimation with the R2 of 0.6215. K-means cluster analysis based on water quality indexes (Chl-a, pH, DO, TN, TP, COD, Turbidity) was conducted to further improve the accuracy of inversion model. The MAPE of the optimal [B1/B2] algorithm has decreased by 4.81% and 39.87% respectively for 17 December 2017 (R2=0.7669, N=42) and 26 March 2018 (R2=0.9156, N=45).

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The Use of Landsat 8 and Sentinel-2 Time Series Data for Monitoring Drought, Chlorophyll-a and Turbidity in Lake Chad

10.5194/egusphere-egu2020-1886 ◽

2020 ◽

Author(s):

Sang Il Lee ◽

Willibroad Gabila Buma

Keyword(s):

Remote Sensing ◽

Chlorophyll A ◽

Time Series Data ◽

Series Data ◽

Landsat 8 ◽

Monitoring Networks ◽

Lake Chad ◽

Chl A ◽

Drought Conditions

A decline in Lake Chad&#8217;s water level has been observed for over two decades. With millions of people relying on the lake, and considering its dynamic behavior, methods for the continuous and spatially distributed retrieval of water quantity and quality parameters are vital for proper monitoring and management initiatives. Here, we propose an integrated approach for drought, chlorophyll-a (Chl-a) and turbidity monitoring in Lake Chad using satellite datasets.First, we used remote sensing information to constrain drought patterns over the immediate lake environment. Vegetation conditions within and around the lake was used to assess drought conditions in this area. Using Landsat multispectral images obtained between 1999 and 2018, Vegetation Temperature Condition Index (VTCI) was derived and used as an indicator for drought monitoring. Vegetation proportion from WorldView-03 images was used to evaluate the accuracy of methods used to derive VTCI. Obtained results showed that most areas experienced mild drought conditions.Secondly, we assessed the performance of band algorithms in estimating Chl-a concentrations and turbidity levels from Landsat-8 and Sentinel-2A and 2B images. A two-band semi-analytical Chl-a and turbidity retrieval model was used for estimating the Chl-a concentrations and turbidity levels between 2015 and 2019. Due to the absence of in-situ data, estimates from the extraction models were statistically compared with datasets obtained from WorldView-03. Further inter-comparison of Chl-a and turbidity retrieved from the two sensors was carried out.This study shows how satellite observations can be used to complement sparse and declining in situ drought, Chl-a and turbidity monitoring networks in this area. Solidifying the importance of remote sensing in areas that are difficult to access or with poor availability of conventional data sources.

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Evaluation of Sentinel-2 and Landsat 8 Images for Estimating Chlorophyll-a Concentrations in Lake Chad, Africa

Remote Sensing ◽

10.3390/rs12152437 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2437 ◽

Cited By ~ 3

Author(s):

Willibroad Gabila Buma ◽

Sang-Il Lee

Keyword(s):

Chlorophyll A ◽

Satellite Images ◽

Landsat 8 ◽

Lake Chad ◽

Chl A ◽

Estimation Algorithms ◽

In Situ Data ◽

Fluorescence Line ◽

Sentinel 2

Much effort has been applied in estimating the concentrations of chlorophyll-a (Chl a) in lakes. The optical complexity and lack of in situ data complicate estimating Chl a in such water bodies. We compared four established satellite reflectance algorithms—the two-band and three-band algorithms (2BDA, 3BDA), fluorescence line height (FLH), and normalized difference chlorophyll index (NDCI)—to estimate Chl a concentration in Lake Chad. We evaluated the performance and applicability of Landsat-8 (L8) and Sentinel-2 (S2) images with the four Chl a estimation algorithms. For accuracy, we compared the concentration levels from the four algorithms to those from Worldview-3 (WV3) images. We identified two promising algorithms that could be used alongside L8 and S2 satellite images to monitor Chl a concentrations in Lake Chad. With an averaged R2 of 0.8, the 3BDA and NDCI Chl a algorithms performed accurately with S2 and L8 images. For the S2 and L8 images, 3BDA had the highest performance when compared to the WV3 estimates. We demonstrate the usefulness of sensor images in improving water quality information for areas that are difficult to access or when conventional data are limited.

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Performance of a Handheld Chlorophyll-a Fluorometer: Potential Use for Rapid Algae Monitoring

Water ◽

10.3390/w13101409 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1409

Author(s):

Hamdhani Hamdhani ◽

Drew E. Eppehimer ◽

David Walker ◽

Michael T. Bogan

Keyword(s):

Chlorophyll A ◽

Surface Waters ◽

Harmful Algal Blooms ◽

Algal Blooms ◽

Water Quality Monitoring ◽

Ambient Light ◽

Calibration Equation ◽

Field Samples ◽

Potential Use

Chlorophyll-a measurements are an important factor in the water quality monitoring of surface waters, especially for determining the trophic status and ecosystem management. However, a collection of field samples for extractive analysis in a laboratory may not fully represent the field conditions. Handheld fluorometers that can measure chlorophyll-a in situ are available, but their performance in waters with a variety of potential light-interfering substances has not yet been tested. We tested a handheld fluorometer for sensitivity to ambient light and turbidity and compared these findings with EPA Method 445.0 using water samples obtained from two urban lakes in Tucson, Arizona, USA. Our results suggested that the probe was not sensitive to ambient light and performed well at low chlorophyll-a concentrations (<25 µg/L) across a range of turbidity levels (50–70 NTU). However, the performance was lower when the chlorophyll-a concentrations were >25 µg/L and turbidity levels were <50 NTU. To account for this discrepancy, we developed a calibration equation to use for this handheld fluorometer when field monitoring for potential harmful algal blooms in water bodies.

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Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model

Water ◽

10.3390/w13050664 ◽

2021 ◽

Vol 13 (5) ◽

pp. 664

Author(s):

Yun Xue ◽

Lei Zhu ◽

Bin Zou ◽

Yi-min Wen ◽

Yue-hong Long ◽

...

Keyword(s):

Neural Network ◽

Regression Model ◽

Convolutional Neural Network ◽

Chlorophyll A ◽

Language Processing ◽

Water Bodies ◽

Inversion Effect ◽

Least Squares Regression ◽

Chlorophyll A Concentration ◽

Chl A

For Case-II water bodies with relatively complex water qualities, it is challenging to establish a chlorophyll-a concentration (Chl-a concentration) inversion model with strong applicability and high accuracy. Convolutional Neural Network (CNN) shows excellent performance in image target recognition and natural language processing. However, there little research exists on the inversion of Chl-a concentration in water using convolutional neural networks. Taking China’s Dongting Lake as an example, 90 water samples and their spectra were collected in this study. Using eight combinations as independent variables and Chl-a concentration as the dependent variable, a CNN model was constructed to invert Chl-a concentration. The results showed that: (1) The CNN model of the original spectrum has a worse inversion effect than the CNN model of the preprocessed spectrum. The determination coefficient (RP2) of the predicted sample is increased from 0.79 to 0.88, and the root mean square error (RMSEP) of the predicted sample is reduced from 0.61 to 0.49, indicating that preprocessing can significantly improve the inversion effect of the model.; (2) among the combined models, the CNN model with Baseline1_SC (strong correlation factor of 500–750 nm baseline) has the best effect, with RP2 reaching 0.90 and RMSEP only 0.45. The average inversion effect of the eight CNN models is better. The average RP2 reaches 0.86 and the RMSEP is only 0.52, indicating the feasibility of applying CNN to Chl-a concentration inversion modeling; (3) the performance of the CNN model (Baseline1_SC (RP2 = 0.90, RMSEP = 0.45)) was far better than the traditional model of the same combination, i.e., the linear regression model (RP2 = 0.61, RMSEP = 0.72) and partial least squares regression model (Baseline1_SC (RP2 = 0.58. RMSEP = 0.95)), indicating the superiority of the convolutional neural network inversion modeling of water body Chl-a concentration.

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

Sensors ◽

10.3390/s18082699 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2699 ◽

Cited By ~ 2

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.

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Earth observation for monitoring and mapping of cyanobacteria blooms. Case studies on five Italian lakes

Journal of Limnology ◽

10.4081/jlimnol.2016.1565 ◽

2016 ◽

Vol 76 (s1) ◽

Cited By ~ 13

Author(s):

Mariano Bresciani ◽

Claudia Giardino ◽

Rosaria Lauceri ◽

Erica Matta ◽

Ilaria Cazzaniga ◽

...

Keyword(s):

Remote Sensing ◽

Algal Blooms ◽

Remote Sensing Data ◽

Late Summer ◽

Eutrophic Lake ◽

Cyanobacterial Blooms ◽

Landsat 8 ◽

Negative Effects ◽

Eutrophic Lakes ◽

Chl A

Cyanobacterial blooms occur in many parts of the world as a result of entirely natural causes or human activity. Due to their negative effects on water resources, efforts are made to monitor cyanobacteria dynamics. This study discusses the contribution of remote sensing methods for mapping cyanobacterial blooms in lakes in northern Italy. Semi-empirical approaches were used to flag scum and cyanobacteria and spectral inversion of bio-optical models was adopted to retrieve chlorophyll-a (Chl-a) concentrations. Landsat-8 OLI data provided us both the spatial distribution of Chl-a concentrations in a small eutrophic lake and the patchy distribution of scum in Lake Como. ENVISAT MERIS time series collected from 2003 to 2011 enabled the identification of dates when cyanobacterial blooms affected water quality in three small meso-eutrophic lakes in the same region. On average, algal blooms occurred in the three lakes for about 5 days a year, typically in late summer and early autumn. A suite of hyperspectral sensors on air- and space-borne platforms was used to map Chl-a concentrations in the productive waters of the Mantua lakes, finding values in the range of 20 to 100 mgm-3. The present findings were obtained by applying state of the art of methods applied to remote sensing data. Further research will focus on improving the accuracy of cyanobacteria mapping and adapting the algorithms to the new-generation of satellite sensors.

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SPATIAL-TEMPORAL DISTRIBUTION OF CHLOROPHYLL-A IN SOUTHERN PART OF THE MAKASSAR STRAIT

Jurnal Ilmu Kelautan SPERMONDE ◽

10.20956/jiks.v4i1.3804 ◽

2018 ◽

Vol 4 (1) ◽

Author(s):

Wasir Samad Daming ◽

Muhammad Anshar Amran ◽

Amir Hamzah Muhiddin ◽

Rahmadi Tambaru

Keyword(s):

Chlorophyll A ◽

Satellite Data ◽

Chlorophyll Concentration ◽

Temporal Distribution ◽

Fishing Ground ◽

Landsat 8 ◽

Trade Winds ◽

Chl A ◽

June July ◽

Potential Area

Surface chlorophyll-a (Chl-a) distribution have been analyzed with seasonal variation during southeast monsoon in southern part of Makassar Strait and Flores Sea. Satellite data of Landsat-8 is applied to this study to formulate the distribution of chlorophyll concentration during monsoonal wind period. The distribution of chlorophyll concentration was normally peaked condition in August during southeast monsoon. Satellite data showed that a slowdown in the rise of the distribution of chlorophyll in September with a lower concentration than normal is likely due to a weakening the strength of southeast trade winds during June – July – August 2016. Further analysis shows that the southern part of the Makassar strait is likely occurrence of upwelling characterized by increase in surface chlorophyll concentrations were identified as the potential area of fishing ground.

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OPTMIZATION OF BIO-OPTICAL MODEL PARAMETERS FOR TURBID LAKE WATER QUALITY ESTIMATION USING LANDSAT 8 AND WASI-2D

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w11-67-2020 ◽

2020 ◽

Vol XLII-3/W11 ◽

pp. 67-72

Author(s):

A. Manuel ◽

A. C. Blanco ◽

A. M. Tamondong ◽

R. Jalbuena ◽

O. Cabrera ◽

...

Keyword(s):

Water Quality ◽

Chlorophyll A ◽

The Philippines ◽

Quality Data ◽

Model Parameters ◽

Landsat 8 ◽

Water Quality Data ◽

Chlorophyll A Concentration ◽

In Situ Data

Abstract. Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

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