Empirical Estimation of Total Nitrogen and Total Phosphorus Concentration of Urban Water Bodies in China Using High Resolution IKONOS Multispectral Imagery

Liu, Jiaming; Zhang, Yan Jun; Yuan, Di; Xing-yuan, Song

doi:10.3390/w7116551

Cited by 48 publications

(34 citation statements)

References 46 publications

(63 reference statements)

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“…These findings are in accordance with Gholizabeh et al [52]; however, it was determined by this study that these ratios not only have the capability of estimating optical parameters, but also non-optical values. Lui et al [56] determined that with the use of high-resolution imagery, linear (multiple linear regression) and non-linear (artificial neural network) models with strong predictive capabilities could be developed for TN and TP. The basis of these relationships is explained by the high spectral correlation that TN and TP have with SDD, TSS, and Chl-a [57].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Optical and Non-Optical Water Quality Parameters in Oligotrophic and Eutrophic Aquatic Systems Using a Small Unmanned Aerial System

Arango

Nairn

2019

Drones

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The purpose of this study was to create different statistically reliable predictive algorithms for trophic state or water quality for optical (total suspended solids (TSS), Secchi disk depth (SDD), and chlorophyll-a (Chl-a)) and non-optical (total phosphorus (TP) and total nitrogen (TN)) water quality variables or indicators in an oligotrophic system (Grand River Dam Authority (GRDA) Duck Creek Nursery Ponds) and a eutrophic system (City of Commerce, Oklahoma, Wastewater Lagoons) using remote sensing images from a small unmanned aerial system (sUAS) equipped with a multispectral imaging sensor. To develop these algorithms, two sets of data were acquired: (1) In-situ water quality measurements and (2) the spectral reflectance values from sUAS imagery. Reflectance values for each band were extracted under three scenarios: (1) Value to point extraction, (2) average value extraction around the stations, and (3) point extraction using kriged surfaces. Results indicate that multiple variable linear regression models in the visible portion of the electromagnetic spectrum best describe the relationship between TSS (R2 = 0.99, p-value = <0.01), SDD (R2 = 0.88, p-value = <0.01), Chl-a (R2 = 0.85, p-value = <0.01), TP (R2 = 0.98, p-value = <0.01) and TN (R2 = 0.98, p-value = <0.01). In addition, this study concluded that ordinary kriging does not improve the fit between the different water quality parameters and reflectance values.

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Section: Discussionmentioning

confidence: 99%

Prediction of Optical and Non-Optical Water Quality Parameters in Oligotrophic and Eutrophic Aquatic Systems Using a Small Unmanned Aerial System

Arango

Nairn

2019

Drones

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“…The Support Vector Regression (SVR) Model is the main algorithm used for machine learning. We used the Support Vector Regression Model to estimate the WQI for the arid area 50 – 52 .…”

Section: Methodsmentioning

confidence: 99%

Evaluation of water quality based on a machine learning algorithm and water quality index for the Ebinur Lake Watershed, China

Wang

Fei

Ding

2017

Sci Rep

115

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The water quality index (WQI) has been used to identify threats to water quality and to support better water resource management. This study combines a machine learning algorithm, WQI, and remote sensing spectral indices (difference index, DI; ratio index, RI; and normalized difference index, NDI) through fractional derivatives methods and in turn establishes a model for estimating and assessing the WQI. The results show that the calculated WQI values range between 56.61 and 2,886.51. We also explore the relationship between reflectance data and the WQI. The number of bands with correlation coefficients passing a significance test at 0.01 first increases and then decreases with a peak appearing after 1.6 orders. WQI and DI as well as RI and NDI correlation coefficients between optimal band combinations of the peak also appear after 1.6 orders with R2 values of 0.92, 0.58 and 0.92. Finally, 22 WQI estimation models were established by POS-SVR to compare the predictive effects of these models. The models based on a spectral index of 1.6 were found to perform much better than the others, with an R2 of 0.92, an RMSE of 58.4, and an RPD of 2.81 and a slope of curve fitting of 0.97.

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“…The fast line-of-sight atmospheric analysis of the spectral hypercube (FLAASH) atmospheric correction method incorporates the MODTRAN 4 radiation transfer code. FLAASH can eliminate the effect caused by the atmosphere and convert spectral radiance to water-surface reflectance [25][26][27]. Radiometric calibration and FLAASH atmospheric correction were processed by ENVI image-processing software.…”

Section: Remote-sensing Image Preprocessingmentioning

confidence: 99%

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

Ding

Chao

et al. 2019

Remote Sensing

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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.water-quality measurement, the cost is high, limiting the usage of in situ sampling and measurement. Optimization of a monitoring network is considered a trade-off between water-monitoring precision and cost [2]. A comprehensive and low-cost water-quality monitoring method was our goal.Remote-sensing monitoring of water quality and its parameters is considered as a promising and cost-effective monitoring method. There are many free-of-charge remote-sensing data, such as Landsat and Sentinel open-access data. Remote sensing provides a synoptic, repetitive, and consistent view of a water body. Water-quality-related variables, such as surface temperature, chlorophyll-a (Chl-a), turbidity and suspended solids (TSS) can be estimated from remote-sensing images according to their reflectance from the water surface [3]. The accuracy and stability of the remote-sensing inversion of surface-reflectance variables (Chl-a and SS) have improved after several years [4].Nonoptically active variables related to water pollution, such as total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and dissolved oxygen (DO), cannot be directly sensed from water-surface reflectance because of their weak optical characteristics and low signal-to-noise ratio [3]. These nonoptically active variables are closely related to optically active parameters such as Chl-a, TSS, and colored dissolved organic matter (CDOM) [3,5]. Different kinds of regression models have been introduced to establish the relation between nonoptically active variables and optical active variabl...

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Empirical Estimation of Total Nitrogen and Total Phosphorus Concentration of Urban Water Bodies in China Using High Resolution IKONOS Multispectral Imagery

Cited by 48 publications

References 46 publications

Prediction of Optical and Non-Optical Water Quality Parameters in Oligotrophic and Eutrophic Aquatic Systems Using a Small Unmanned Aerial System

Prediction of Optical and Non-Optical Water Quality Parameters in Oligotrophic and Eutrophic Aquatic Systems Using a Small Unmanned Aerial System

Evaluation of water quality based on a machine learning algorithm and water quality index for the Ebinur Lake Watershed, China

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

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