A Study on Selecting Segmentation Object of Object-Oriented Inversion Based on Hyperspectral Chlorophyll-A in Chaohu Lake

2014 ◽  
Vol 675-677 ◽  
pp. 1153-1157
Author(s):  
Xue Jiao Hou ◽  
Jing Liu ◽  
Wei Cui
Keyword(s):  
Chlorophyll A ◽  
Object Oriented ◽  
Hyperspectral Data ◽  
Single Band ◽  
Quality Data ◽  
Chaohu Lake ◽  
Band Model ◽  
Water Quality Data ◽  
Combination Model ◽  
Selection Of

Beased on in Situ Water Quality Data and Hyperspectral Data from HJ-1A satellite in Chaohu Lake, through Contrasting the Object-Oriented Chlorophyll-a Inversion Precision Of single Band with Two-Band Model, the Results Show: (1) In Hyperspectral Object-Oriented Remote Sensing inversion, the Inversion Effect of Choosing Combination Model to Segment Is superior to that of Choosing the Single Band Directly, and Using Combination model to Segment can Certain Degree Solve the Problem that Commercial Softwares cannot Segment all Hyperspectral Data at the same Time.(2)When Inversing Chlorophyll-a Concentration with Hyperspectral Data, the Single Bands Constituting the Optimal Model Are not Always in the Traditional Characteristics Band Range of Chlorophyll-a. so All bands should be Comprehensively Analyzed to take Full Advantages Of hyperspectral Data when Inversing. these Conclusions Will provide Basis for the Future Segmentation Object Selection of Object-Orientedon Hyperspectral Lakes Chlorophyll-a Inversion and Certain Reference for Band Selection of Hyperspectral Inversion Model.

scholarly journals SPECTRAL FEATURE ANALYSIS FOR QUANTITATIVE ESTIMATION OF CYANOBACTERIA CHLOROPHYLL-A

2016 ◽  
Vol XLI-B7 ◽  
pp. 91-98
Author(s):  
Yi Lin ◽  
Zhanglin Ye ◽  
Yugan Zhang ◽  
Jie Yu
Keyword(s):  
Chlorophyll A ◽  
Vegetation Index ◽  
Quantitative Estimation ◽  
Vegetation Indices ◽  
Spectral Feature ◽  
Hyperspectral Data ◽  
Single Band ◽  
Band Width ◽  
Band Model ◽  
Cyanobacteria Bloom

In recent years, lake eutrophication caused a large of Cyanobacteria bloom which not only brought serious ecological disaster but also restricted the sustainable development of regional economy in our country. <i>Chlorophyll-a</i> is a very important environmental factor to monitor water quality, especially for lake eutrophication. Remote sensed technique has been widely utilized in estimating the concentration of <i>chlorophyll-a</i> by different kind of vegetation indices and monitoring its distribution in lakes, rivers or along coastline. For each vegetation index, its quantitative estimation accuracy for different satellite data might change since there might be a discrepancy of spectral resolution and channel center between different satellites. The purpose this paper is to analyze the spectral feature of <i>chlorophyll-a</i> with hyperspectral data (totally 651 bands) and use the result to choose the optimal band combination for different satellites. The analysis method developed here in this study could be useful to recognize and monitor cyanobacteria bloom automatically and accrately. <br><br> In our experiment, the reflectance (from 350nm to 1000nm) of wild cyanobacteria in different consistency (from 0 to 1362.11ug/L) and the corresponding <i>chlorophyll-a</i> concentration were measured simultaneously. Two kinds of hyperspectral vegetation indices were applied in this study: simple ratio (SR) and narrow band normalized difference vegetation index (NDVI), both of which consists of any two bands in the entire 651 narrow bands. Then multivariate statistical analysis was used to construct the linear, power and exponential models. After analyzing the correlation between <i>chlorophyll-a</i> and single band reflectance, SR, NDVI respetively, the optimal spectral index for quantitative estimation of cyanobacteria <i>chlorophyll-a</i>, as well corresponding central wavelength and band width were extracted. Results show that: Under the condition of water disturbance, SR and NDVI are both suitable for quantitative estimation of <i>chlorophyll-a</i>, and more effective than the traditional single band model; the best regression models for SR, NDVI with <i>chlorophyll-a</i> are linear and power, respectively. Under the condition without water disturbance, the single band model works the best. For the SR index, there are two optimal band combinations, which is comprised of infrared (700nm-900nm) and blue-green range (450nm-550nm), infrared and red range (600nm-650nm) respectively, with band width between 45nm to 125nm. For NDVI, the optimal band combination includes the range from 750nm to 900nm and 700nm to 750nm, with band width less than 30nm. For single band model, band center located between 733nm-935nm, and its width mustn’t exceed the interval where band center located in. <br><br> This study proved , as for SR or NDVI, the centers and widths are crucial factors for quantitative estimating <i>chlorophyll-a</i>. As for remote sensor, proper spectrum channel could not only improve the accuracy of recognizing cyanobacteria bloom, but reduce the redundancy of hyperspectral data. Those results will provide better reference for designing the suitable spectrum channel of customized sensors for cyanobacteria bloom monitoring at a low altitude. In other words, this study is also the basic research for developing the real-time remote sensing monitoring system with high time and high spatial resolution.

scholarly journals SPECTRAL FEATURE ANALYSIS FOR QUANTITATIVE ESTIMATION OF CYANOBACTERIA CHLOROPHYLL-A

2016 ◽  
Vol XLI-B7 ◽  
pp. 91-98
Author(s):  
Yi Lin ◽  
Zhanglin Ye ◽  
Yugan Zhang ◽  
Jie Yu
Keyword(s):  
Chlorophyll A ◽  
Vegetation Index ◽  
Quantitative Estimation ◽  
Vegetation Indices ◽  
Spectral Feature ◽  
Hyperspectral Data ◽  
Single Band ◽  
Band Width ◽  
Band Model ◽  
Cyanobacteria Bloom

In recent years, lake eutrophication caused a large of Cyanobacteria bloom which not only brought serious ecological disaster but also restricted the sustainable development of regional economy in our country. &lt;i&gt;Chlorophyll-a&lt;/i&gt; is a very important environmental factor to monitor water quality, especially for lake eutrophication. Remote sensed technique has been widely utilized in estimating the concentration of &lt;i&gt;chlorophyll-a&lt;/i&gt; by different kind of vegetation indices and monitoring its distribution in lakes, rivers or along coastline. For each vegetation index, its quantitative estimation accuracy for different satellite data might change since there might be a discrepancy of spectral resolution and channel center between different satellites. The purpose this paper is to analyze the spectral feature of &lt;i&gt;chlorophyll-a&lt;/i&gt; with hyperspectral data (totally 651 bands) and use the result to choose the optimal band combination for different satellites. The analysis method developed here in this study could be useful to recognize and monitor cyanobacteria bloom automatically and accrately. &lt;br&gt;&lt;br&gt; In our experiment, the reflectance (from 350nm to 1000nm) of wild cyanobacteria in different consistency (from 0 to 1362.11ug/L) and the corresponding &lt;i&gt;chlorophyll-a&lt;/i&gt; concentration were measured simultaneously. Two kinds of hyperspectral vegetation indices were applied in this study: simple ratio (SR) and narrow band normalized difference vegetation index (NDVI), both of which consists of any two bands in the entire 651 narrow bands. Then multivariate statistical analysis was used to construct the linear, power and exponential models. After analyzing the correlation between &lt;i&gt;chlorophyll-a&lt;/i&gt; and single band reflectance, SR, NDVI respetively, the optimal spectral index for quantitative estimation of cyanobacteria &lt;i&gt;chlorophyll-a&lt;/i&gt;, as well corresponding central wavelength and band width were extracted. Results show that: Under the condition of water disturbance, SR and NDVI are both suitable for quantitative estimation of &lt;i&gt;chlorophyll-a&lt;/i&gt;, and more effective than the traditional single band model; the best regression models for SR, NDVI with &lt;i&gt;chlorophyll-a&lt;/i&gt; are linear and power, respectively. Under the condition without water disturbance, the single band model works the best. For the SR index, there are two optimal band combinations, which is comprised of infrared (700nm-900nm) and blue-green range (450nm-550nm), infrared and red range (600nm-650nm) respectively, with band width between 45nm to 125nm. For NDVI, the optimal band combination includes the range from 750nm to 900nm and 700nm to 750nm, with band width less than 30nm. For single band model, band center located between 733nm-935nm, and its width mustn’t exceed the interval where band center located in. &lt;br&gt;&lt;br&gt; This study proved , as for SR or NDVI, the centers and widths are crucial factors for quantitative estimating &lt;i&gt;chlorophyll-a&lt;/i&gt;. As for remote sensor, proper spectrum channel could not only improve the accuracy of recognizing cyanobacteria bloom, but reduce the redundancy of hyperspectral data. Those results will provide better reference for designing the suitable spectrum channel of customized sensors for cyanobacteria bloom monitoring at a low altitude. In other words, this study is also the basic research for developing the real-time remote sensing monitoring system with high time and high spatial resolution.

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

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.

scholarly journals Spatial Distribution Of Chlorofil-a In Laguna Kabori Manokwari Regency

2020 ◽  
pp. 77-85
Author(s):  
Suci Widya Warnetti ◽  
Thomas Frans Pattiasina ◽  
Fitriyah Irmawati Elyas Saleh ◽  
Alianto Alianto ◽  
Selfanie Talakua ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Chlorophyll A ◽  
Spatial Data ◽  
Water Productivity ◽  
Photosynthetic Pigment ◽  
Quality Data ◽  
Sampling Point ◽  
Water Quality Data ◽  
Depth Measurement ◽  
Chlorophyll A Content

Kabori Lagoon is a lagoon located in Manokwari Regency, West Papua, where the water quality data is still very limited. Chlorophyll-a is the most important photosynthetic pigment for aquatic plants such as phytoplankton. The purpose of this study was to determine the horizontal distribution of chlorophyll-a based on the spatial data approach in the waters of the Kabori Lagoon. The study was conducted from April to May 2019. The sampling point was determined by purposive sampling at eight points for sampling of chlorophyll-a and water parameters including physics and chemistry (temperature, DO, salinity, brightness and depth). Measurement of physical and chemical parameters is carried out in situ. Chlorophyll-a analysis was carried out at the Laboratory of Water Productivity and Quality, Faculty of Marine and Fisheries Sciences, Hasanuddin University. Spatial data processing used ArcGIS 10.4 and Surfer 16 to create chlorophyll-a distribution maps and bathymetry. The results showed that the chlorophyll-a content in Observation I was in the range of 0.12-2.40 mg/m3 and in Observation II it was in the range of 1.43-10.84 mg/m3. Based on the chlorophyll-a content, the Kabori Lagoon is included in the mesotrophic category. Spatially, the distribution of chlorophyll-a content varies, but tends to be higher in the southern part of the lagoon than in the northern part of the lagoon.  Spatial distribution, Chlorophyll-A, Kabori Lagoon, Mesotrophic

scholarly journals ESTIMATION OF CHLOROPHYLL-A CONCENTRATION IN SAMPALOC LAKE USING UAS MULTISPECTRAL REMOTE SENSING AND REGRESSION ANALYSIS

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 297-303
Author(s):  
R. M. G. Maravilla ◽  
J. P. Quinalayo ◽  
A. C. Blanco ◽  
C. G. Candido ◽  
E. V. Gubatanga ◽  
...  
Keyword(s):  
Water Quality ◽  
Regression Analysis ◽  
Chlorophyll A ◽  
Linear Regression Analysis ◽  
Quality Data ◽  
Water Quality Data ◽  
Microalgal Biomass ◽  
Chlorophyll A Concentration ◽  
Chl A

Abstract. Sampaloc Lake is providing livelihood for the residents through aquaculture. An increase in the quantity of fish pens inside the lake threatens its water quality condition. One parameter being monitored is microalgal biomass by measuring Chlorophyll-a concentration. This study aims to generate a chlorophyll-a concentration model for easier monitoring of the lake. In-situ water quality data were collected using chl-a data logger and water quality meter at 357 and 12 locations, respectively. Using Parrot Sequoia+ Multispectral Camera, 1496 of 2148 images were acquired and calibrated, producing 18x18cm resolution Green (G), Red(R), Red Edge (RE) and Near Infrared (NIR) reflectance images. NIR was used to mask out non-water features, and to correct sun glint. The in-situ data and the pixel values extracted were used for Simple Linear Regression Analysis. A model with 5 variables – R/NIR, RE2, NIR2, R/NIR2, and NIR/RE2, was generated, yielding an R2 of 0.586 and RMSE of 0.958 μg/l. A chlorophyll-a concentration map was produced, showing that chl-a is higher where fish pens are located and lowers as it moves away from the pens. Although there are apparent fish pens on certain areas of the lake, it still yields low chlorophyll-a because of little amount of residential area or establishments adjacent to it. Also, not all fish pens have the same concentration of Chlorophyll-a due to inconsistent population per fish pen. The center of the lake has low chlorophyll-a as it is far from human activities. The only outlet, Sabang Creek, also indicates high concentration of Chlorophyll-a.

scholarly journals A comparative analysis on water quality in an urban micro watershed

2017 ◽  
Vol 28 (4) ◽  
pp. 566-578 ◽  
Author(s):  
Karen Tavares Zambrano ◽  
Cristiano Poleto ◽  
Jefferson Nascimento Oliveira
Keyword(s):  
Water Quality ◽  
Comparative Analysis ◽  
Quality Parameters ◽  
Quality Data ◽  
Water Quality Data ◽  
Content Type ◽  
Quality Water ◽  
Practical Implications ◽  
Selection Of

Purpose This study presents a comparative analysis of water quality data in an urban micro watershed to study the magnitude of impacts on the water quality parameters over the last decade. The purpose of this paper is to evaluate the degree of deterioration using the water quality index. Design/methodology/approach Rapid urban growth without proper land use and occupation planning results in the overload of urban water resources. Therefore, a literature review was conducted on the research subject published in the dissertation databases of the Engineering Faculty of Ilha Solteira, which resulted in the selection of two dissertations on water quality in the Ipê Stream, Ilha Solteira – SP, Brazil. The results will be evaluated according to the Brazilian laws and regulations in force. Findings This study shows that pollution and degradation in the stream intensified during the study period, with the most impacted areas within the urban perimeter. Practical implications The increasing impacts underscore the need for efficient measures such as implementation of retention reservoirs, elimination of clandestine sewage connections and restoration of riparian forests. Originality/value This study highlights the need to monitor the water quality of streams in order to establish preventive and mitigating measures to avert the growing environmental impacts and to ensure quality water for future generations.

scholarly journals A GIS-based spatiotemporal study of the variability of water quality in the Dubai Creek, UAE

2015 ◽  
Vol 51 (3) ◽  
pp. 219-232 ◽  
Author(s):  
Tarig A. Ali ◽  
Maruf Mortula ◽  
Serter Atabay ◽  
Ehsan Navadeh
Keyword(s):  
Water Quality ◽  
Regression Analysis ◽  
Chlorophyll A ◽  
Multiple Logistic Regression Analysis ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Ordinary Least Squares ◽  
Spatiotemporal Variability ◽  
Quality Data ◽  
Water Quality Data

This paper presents the outcomes of a study on the water quality of Dubai Creek which aimed to assess its eutrophication status. Field water quality data from stations along the creek collected in 2012 and 2013 were used. Ordinary least squares (OLS) and spatial autocorrelation analyses were used as part of geographic information system (GIS)-based exploratory regression analysis to study the relationship between chlorophyll-a and nutrients, specifically total nitrogen and phosphate. Multiple logistic regression analysis was used to study the vulnerability of the creek to eutrophication. Results showed unique trends of spatiotemporal variability of chlorophyll-a and nutrients. OLS modeling showed high correlation between field and modeled chlorophyll-a values between Al Garhoud Bridge and Sanctuary stations, located about 2 km upstream and downstream of the Sewage Treatment Plant (STP) Outfall station. Furthermore, results showed the lower half of the creek was more vulnerable to eutrophication than the upper, which was believed to be due to the location of the STP station, poor flushing, shallow water depth, and irregular circulation patterns in the creek. Accordingly, this study recommends development of a mitigation plan in order to control the levels of nutrients in the creek.

scholarly journals Artificial Neural Network Model to Prediction of Eutrophication and Microcystis Aeruginosa Bloom

2020 ◽  
Vol 4 (2) ◽  
pp. 129-135 ◽  
Author(s):  
Pawalee Srisuksomwong ◽  
Jeeraporn Pekkoh
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Chlorophyll A ◽  
Microcystis Aeruginosa ◽  
Quality Data ◽  
Water Quality Data ◽  
Severe Problem ◽  
Temperature Conductivity ◽  
Artificial Neural

Maekuang reservoir is one of the water resources which provides water supply, livestock, and recreational in Chiangmai city, Thailand. The water quality and Microcystis aeruginosa are a severe problem in many reservoirs. M. aeruginosa is the most widespread toxic cyanobacteria in Thailand. Difficulty prediction for planning protects Maekuang reservoirs, the artificial Neural Network (ANN) model is a powerful tool that can be used to machine learning and prediction by observation data. ANN is able to learn from previous data and has been used to predict the value in the future. ANN consists of three layers as input, hidden, and output layer. Water quality data is collected biweekly at Maekuang reservoir (1999-2000). Input data for training, including nutrients (ammonium, nitrate, and phosphorus), Secchi depth, BOD, temperature, conductivity, pH, and output data for testing as Chlorophyll a and M. aeruginosa cells. The model was evaluated using four performances, namely; mean squared error (MSE), root mean square error (RMSE), sum of square error (SSE), and percentage error. It was found that the model prediction agreed with experimental data. C01-C08 scenarios focused on M. aeruginosa bloom prediction, and ANN tested for prediction of Chlorophyll a bloom shown on M01-M09 scenarios. The findings showed, this model has been validated for prediction of Chlorophyll a and shows strong agreement for nitrate, Log cell, and Chlorophyll a. Results indicate that the ANN can be predicted eutrophication indicators during the summer season, and ANN has efficient for providing the new data set and predict the behavior of M. aeruginosa bloom process.

Thirty years of hourly dissolved oxygen in a large shallow river illustrates discrepancy between a primary producer tipping point and river metabolism

2020 ◽  
Author(s):  
Jacob Diamond ◽  
Florentina Moatar ◽  
Matthew Cohen ◽  
Alain Poirel ◽  
Cécile Martinet ◽  
...  
Keyword(s):  
Dissolved Oxygen ◽  
Primary Production ◽  
Chlorophyll A ◽  
High Frequency ◽  
Tipping Point ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set ◽  
Primary Producer

&lt;p&gt;Large-scale efforts to reduce cultural eutrophication of freshwater systems have had varied success because internal feedbacks can stabilize the high nutrient, high productivity, and turbid conditions associated with eutrophic systems. We examined these feedbacks using a unique 40-year water quality data set from the middle Loire River, France, where phosphorus and phytoplankton concentrations have decreased by an order of magnitude from 1980&amp;#8211;2018. We focused on ecosystem metabolism as an integrative measure to elucidate cause-effect relationships of both bottom-up (e.g., nutrient concentrations) and top-down (e.g., consumer populations) effects on river trophic state.&lt;/p&gt;&lt;p&gt;The dataset combined both long-term (30 years), high-frequency (hourly) measurements of dissolved oxygen (DO) and long-term (40 years), low-frequency (monthly) measures of nutrients, plus several supporting biological surveys of primary producer and consumer densities. Using hourly measurements of DO, we estimated gross primary production (GPP), ecosystem respiration (ER), and net ecosystem production (NEP = GPP &amp;#8211; ER), and from the resulting long time series of metabolic fluxes, we tested the hypothesis that GPP and ER responded to changes in water column concentrations of algal pigments (chlorophyll a) and phosphorus. We further tested the hypothesis that change points in the patterns of ecological behavior were contemporaneous with notable changes in river management.&lt;/p&gt;&lt;p&gt;Despite well-established links between phosphorus, chlorophyll-a and primary production, GPP was resilient to the drastic reductions in both P concentrations and phytoplankton. Indeed, GPP has only recently decreased (~25%), despite chlorophyll-a concentrations reaching a new minima 10 years earlier in response to colonization of the invasive Corbicula sp. clam in the year 2000. Declines in ER are only half (~12%) the decline in GPP, shifting the river from an autotrophic state (i.e., positive NEP) to a heterotrophic state (i.e., negative NEP). Moreover, Granger causality analysis suggested that daily primary production and respiration have decoupled over this period. With earlier phytoplankton dominance, daily ER was strongly linked to recent autochthonous GPP, but more recently daily GPP has far less influence on subsequent ER. We interpret this partially as a reduction in carbon and nutrient turnover rates resulting from the community shift from algae to macrophytes, and attendant changes in nutrient sources (now primarily from sediment) and carbon stocks (now principally in the sediment). This study illustrates the benefit of long-term high-frequency data collection for understanding pattern and process in aquatic ecosystems, and illustrates a compelling example of process resilience contrasted with an ecosystem tipping point in the context of global change.&lt;/p&gt;

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