A review of research on application of remote sensing in lake water quality monitoring

2021 ◽  
Author(s):  
Wenrui Han
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Lake Water ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Lake Water Quality ◽  
Research On Application

scholarly journals Analysis of recurring patchiness in satellite-derived chlorophyll a to aid the selection of representative sites for lake water quality monitoring

2021 ◽  
Vol 104 ◽  
pp. 102547
Author(s):  
Moritz K Lehmann ◽  
Eike M Schütt ◽  
Martin Hieronymi ◽  
James Dare ◽  
Hajo Krasemann
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Lake Water ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Lake Water Quality ◽  
Selection Of

Spatial and Temporal Assessment of Titiwangsa Lake Water Quality Using Chemometrics Analysis

2018 ◽  
Vol 7 (3.14) ◽  
pp. 20
Author(s):  
Hafizan Juahir ◽  
Adiana Ghazali ◽  
Azimah Ismail ◽  
Mahadzirah Mohamad ◽  
Firdaus Mohamad Hamzah ◽  
...  
Keyword(s):  
Water Quality ◽  
Lake Water ◽  
Monitoring Program ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Current Status ◽  
Small Scale ◽  
Lake Water Quality ◽  
Night Time ◽  
Time Period

Titiwangsa Lake is a renowned recreational lake in Kuala Lumpur, Malaysia. The present study was purposely to define the current status of Titiwangsa Lake water quality and propose a water quality monitoring program to conserve and sustain the health of this lake. Samples were collected in January 2017 during the day and night-time periods. Spatial classification using hierarchical agglomerative cluster analysis (HACA) has clustered the sampling stations into low, medium and high contaminated areas. Temporal classification of discriminant analysis (DA) forward stepwise mode has highlighted DO, chlorophyll-a and E-coli are the significant variables. They showed a lower range of data during the day-time period compared to night-time period. DA backward stepwise model showed  significant variables of total suspended solid (TSS) and total phosphate (TP) were higher in concentration during the day-time period as compared to night-time period. The significant of varimax factors (VFs) in the principal component analysis (PCA) might contribute by the landscaping, small-scale domestic wastewater, urban stormwater and land erosion. In a nutshell, based on HACA classification, samples can be collected at only three stations represent each cluster during the next water quality monitoring activities as this could reduce the time and cost of sampling and sample analysis. 

scholarly journals Suitability assessment of lake water quality monitoring on waterbody images acquired by HJ-1A hyperspectral imager:a case study of Lake Chaohu

2011 ◽  
Vol 23 (5) ◽  
pp. 789-795 ◽  
Author(s):  
WANG Yanfei ◽  
◽  
LI Yunmei ◽  
LV Heng ◽  
WU Chuanqing ◽  
...  
Keyword(s):  
Water Quality ◽  
Lake Water ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Lake Water Quality ◽  
Lake Chaohu ◽  
Suitability Assessment

Lake water quality monitoring in New Zealand

SciVee ◽  
2011 ◽  
Author(s):  
Chris Mcbride
Keyword(s):  
Water Quality ◽  
New Zealand ◽  
Lake Water ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Lake Water Quality

TABACARIE LAKE WATER QUALITY MONITORING

2005 ◽  
Vol 4 (2) ◽  
pp. 169-176 ◽  
Author(s):  
Elisabeta Chirila ◽  
Simona Dobrinas ◽  
Ionela Carazeanu ◽  
Camelia Draghici
Keyword(s):  
Water Quality ◽  
Lake Water ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Lake Water Quality

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

scholarly journals Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

2019 ◽  
Vol 11 (14) ◽  
pp. 1674 ◽  
Author(s):  
Fangling Pu ◽  
Chujiang Ding ◽  
Zeyi Chao ◽  
Yue Yu ◽  
Xin Xu
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Inland Lakes ◽  
Quality Classification ◽  
Water Quality Classification

Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality levels in China are difficult to estimate because of their weak optical characteristics and lack of explicit correlation between remote-sensing images and parameters. To address the problems, a convolutional neural network (CNN) with hierarchical structure was designed to represent the relationship between Landsat8 images and in situ water-quality levels. A transfer-learning strategy in the CNN model was introduced to deal with the lack of in situ measurement data. After the CNN model was trained by spatially and temporally matched Landsat8 images and in situ water-quality data that were collected from official websites, the surface quality of the whole water body could be classified. We tested the CNN model at the Erhai and Chaohu lakes in China, respectively. The experiment results demonstrate that the CNN model outperformed widely used machine-learning methods. The trained model at Erhai Lake can be used for the water-quality classification of Chaohu Lake. The introduced CNN model and the water-quality classification method could cover the whole lake with low costs. The proposed method has potential in inland-lake monitoring.

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