scholarly journals Estimating concentrations of chlorophyll-a and suspended sediments in a continental aquatic ecosystem using remote sensing

2020 ◽  
Vol 14 ◽  
Author(s):  
Nicole Sarah Carvalho Ponte ◽  
Valéria Severo De Noronha ◽  
Fernanda Helena Oliveira da Silva ◽  
Julyanne Braga Cruz Amaral ◽  
Fernando Bezerra Lopes
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Aquatic Ecosystem ◽  
Suspended Solids ◽  
Remote Sensing Data ◽  
Total Suspended Solids ◽  
Suspended Sediments ◽  
Reservoir Water ◽  
Chlorophyll A Concentration ◽  
Local Rainfall

Water is an important resource for life and as such, its monitoring is essential. The aim of this study therefore was to estimate concentrations of chlorophyll-a and total suspended solids (TSS) using orbital remote sensing data. The study area was the General Sampaio reservoir, located in the Curu River Basin, in the state of Ceará. The concentration of chlorophyll-a and sediments were mapped based on orbital images captured by the Operational Land Imager (OLI) sensor. The images were acquired on 14 September 2017 and 13 June 2018. The chlorophyll-a concentration was estimated using a model employing a ratio of the blue (450-510 nm) and green (530-590 nm) bands, and the TSS concentration was estimated from the red band (640-690 nm) of the OLI sensor. The chlorophyll-a concentration in September 2017 varied from 37.03 to 43.0 mg L-1, and in June 2018 it ranged from 42.9 to 62.2 mg L-1. The TSS concentration in September 2017 varied from 1.8 to 4.2 mg L-1, while in June 2018, a period that corresponded to the highest local rainfall, the variation was greater, between 2.1 and 17.73 mg L-1. The highest concentrations of suspended sediments were seen in the tributaries of the Curu River and the Salvação Stream. It was possible to estimate the concentrations of chlorophyll-a and TSS using orbital images. The reservoir water was classified as eutrophic throughout the study period.

Band Selection of Hyperspectral Chlorophyll-A Concentration Inversion Based on Parallel Ant Colony Algorithm

2014 ◽  
Vol 675-677 ◽  
pp. 1158-1162 ◽  
Author(s):  
Cong Tang ◽  
Yang Wu ◽  
Jing Huang
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Ant Colony Algorithm ◽  
Measurement Data ◽  
Remote Sensing Data ◽  
Ant Colony ◽  
Band Selection ◽  
Band Model ◽  
Concentration Data ◽  
Chlorophyll A Concentration

In chlorophyll-A concentration inversion which based on hyperspectral remote sensing image, must use ground synchronous spectrum measurement data to find the best three band combination by iterative-method, we proposed the problem to analysis the existing band selection methods of chlorophyll-A concentration inversion and put forward a band selection method of the hyperspectral chlorophyll-A concentration inversion three-band model which based on parallel ant colony algorithm. The method is based on analysis of the optical properties and three band model theory, determines the interval of the sensitive band from hundreds of bands，then takes band reflectance of the sampling points and Correlation coefficient of sample points of chlorophyll-A concentration as the initial pheromone list, and updates distance parameter list constantly to let the parallel work ants find the best band combinations. Experimental results show that the method can select the optimal three-band fast and efficient by the remote sensing data and samples of chlorophyll-A concentration data, and the steps of chlorophyll-A concentration inversion can be simplified greatly.

scholarly journals Spatiotemporal Evolution of Chlorophyll-a Concentration From MODIS Data Inversion in the Middle and Lower Reaches of the Hanjiang River, China

2021 ◽  
Author(s):  
Zhuo Chen ◽  
Ming Dou ◽  
Rui Xia ◽  
Guiqiu Li ◽  
Lisha Shen
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Remote Sensing Data ◽  
Observation Data ◽  
Model Errors ◽  
Spatiotemporal Evolution ◽  
Chlorophyll A Concentration ◽  
Sensing Data ◽  
Hanjiang River ◽  
Water Blooms

Abstract The global construction of water projects has led to a clear trend of river and lake reservoir formation, spurring increasingly serious ecological environmental deterioration, especially that caused by the frequent occurrence of water blooms. Because of monitoring technology limitations, monitoring the algae content index in water has lagged behind the conventional water quality index, which makes sample monitoring too sparse in many rivers and the monitoring data incoherent, so it cannot truly reflect the evolution of water eutrophication. With moderate resolution imaging spectroradiometer (MODIS) remote sensing data monitoring, continuous chlorophyll-a observation data can be collected effectively. This method has important guiding significance for the early warning and control of water blooms. This study considers the middle and lower reaches of the Hanjiang River in China, based on the current remote sensing communication technology, MODIS remote sensing data, and statistical methods and measured chlorophyll-a concentration correlation analysis. Through the use of the trial and error method to establish the band ratio model and BP neural network model, two types of model errors were compared to determine the optimal algorithm settings for the middle and lower reaches of the Hanjiang River chlorophyll-a inversion. Subsequently, the algorithm model for 2000 to 2011 in the middle and lower reaches of the Hanjiang River chlorophyll-a concentration inversion and the results of the inversion analysis of spatiotemporal evolution characteristics we used to determine the influence of various environmental factors on the chlorophyll-a concentration change.

scholarly journals Spatiotemporal evolution of chlorophyll-a concentration from MODIS data inversion in the middle and lower reaches of the Hanjiang River, China

2021 ◽  
Author(s):  
zhuo Chen ◽  
ming Dou ◽  
Rui Xia ◽  
Guiqiu Li ◽  
Lisha Shen
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Remote Sensing Data ◽  
Observation Data ◽  
Model Errors ◽  
Spatiotemporal Evolution ◽  
Chlorophyll A Concentration ◽  
Sensing Data ◽  
Hanjiang River ◽  
Water Blooms

The global construction of water projects has led to a clear trend of river and lake reservoir formation, spurring increasingly serious ecological environmental deterioration, especially that caused by the frequent occurrence of water blooms. Because of monitoring technology limitations, monitoring the algae content index in water has lagged behind the conventional water quality index, which makes sample monitoring too sparse in many rivers and the monitoring data incoherent, so it cannot truly reflect the evolution of water eutrophication. With moderate resolution imaging spectroradiometer (MODIS) remote sensing data monitoring, continuous chlorophyll-a observation data can be collected effectively. This method has important guiding significance for the early warning and control of water blooms. This study considers the middle and lower reaches of the Hanjiang River in China, based on the current remote sensing communication technology, MODIS remote sensing data, and statistical methods and measured chlorophyll-a concentration correlation analysis. Through the use of the trial and error method to establish the band ratio model and BP neural network model, two types of model errors were compared to determine the optimal algorithm settings for the middle and lower reaches of the Hanjiang River chlorophyll-a inversion. Subsequently, the algorithm model for 2000 to 2011 in the middle and lower reaches of the Hanjiang River chlorophyll-a concentration inversion and the results of the inversion analysis of spatiotemporal evolution characteristics we used to determine the influence of various environmental factors on the chlorophyll-a concentration change.

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