Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jinhua Liu, Jianli Ding, Xiangyu Ge, Jingzhe Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	total nitrogen discrete wavelet transform fractional order discretization machine learning hyperspectral emergent plants Science Q
Acceso en línea:	https://doaj.org/article/1514047ccd67480ea049eabbd72351f0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:1514047ccd67480ea049eabbd72351f0
record_format	dspace
spelling	oai:doaj.org-article:1514047ccd67480ea049eabbd72351f02021-11-25T18:55:04ZEvaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis10.3390/rs132246432072-4292https://doaj.org/article/1514047ccd67480ea049eabbd72351f02021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4643https://doaj.org/toc/2072-4292Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms of emergent plants, we coupled discrete wavelet transform (DWT) and fractional order discretization (FOD) techniques with three machine learning models (random forest (RF), bagging algorithm (bagging), and eXtreme Gradient Boosting (XGBoost)) to mine this potential spectral information. A total of 567 models were developed, and airborne hyperspectral data processed with various DWT scales and FOD techniques were compared. The effective information in the hyperspectral reflectance data were better emphasized after DWT processing. After DWT processing the original spectrum (OR), its sensitivity to TN in water was maximally improved by 0.22, and the correlation between FOD and TN in water was optimally increased by 0.57. The transformed spectral information enhanced the TN model accuracy, especially for FOD after DWT. For RF, 82% of the model R<sup>2</sup> values improved by 0.02~0.72 compared to the model using FOD spectra; 78.8% of the bagging values improved by 0.01~0.53 and 65.0% of the XGBoost values improved by 0.01~0.64. The XGBoost model with DWT coupled with grey relation analysis (GRA) yielded the best estimation accuracy, with the highest precision of R<sup>2</sup> = 0.91 for L6. In conclusion, appropriately scaled DWT analysis can substantially improve the accuracy of extracting TN from UAV hyperspectral images. These outcomes may facilitate the further development of accurate water quality monitoring in sophisticated global waters from drone or satellite hyperspectral data.Jinhua LiuJianli DingXiangyu GeJingzhe WangMDPI AGarticletotal nitrogendiscrete wavelet transformfractional order discretizationmachine learninghyperspectralemergent plantsScienceQENRemote Sensing, Vol 13, Iss 4643, p 4643 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	total nitrogen discrete wavelet transform fractional order discretization machine learning hyperspectral emergent plants Science Q
spellingShingle	total nitrogen discrete wavelet transform fractional order discretization machine learning hyperspectral emergent plants Science Q Jinhua Liu Jianli Ding Xiangyu Ge Jingzhe Wang Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis
description	Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms of emergent plants, we coupled discrete wavelet transform (DWT) and fractional order discretization (FOD) techniques with three machine learning models (random forest (RF), bagging algorithm (bagging), and eXtreme Gradient Boosting (XGBoost)) to mine this potential spectral information. A total of 567 models were developed, and airborne hyperspectral data processed with various DWT scales and FOD techniques were compared. The effective information in the hyperspectral reflectance data were better emphasized after DWT processing. After DWT processing the original spectrum (OR), its sensitivity to TN in water was maximally improved by 0.22, and the correlation between FOD and TN in water was optimally increased by 0.57. The transformed spectral information enhanced the TN model accuracy, especially for FOD after DWT. For RF, 82% of the model R<sup>2</sup> values improved by 0.02~0.72 compared to the model using FOD spectra; 78.8% of the bagging values improved by 0.01~0.53 and 65.0% of the XGBoost values improved by 0.01~0.64. The XGBoost model with DWT coupled with grey relation analysis (GRA) yielded the best estimation accuracy, with the highest precision of R<sup>2</sup> = 0.91 for L6. In conclusion, appropriately scaled DWT analysis can substantially improve the accuracy of extracting TN from UAV hyperspectral images. These outcomes may facilitate the further development of accurate water quality monitoring in sophisticated global waters from drone or satellite hyperspectral data.
format	article
author	Jinhua Liu Jianli Ding Xiangyu Ge Jingzhe Wang
author_facet	Jinhua Liu Jianli Ding Xiangyu Ge Jingzhe Wang
author_sort	Jinhua Liu
title	Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis
title_short	Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis
title_full	Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis
title_fullStr	Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis
title_full_unstemmed	Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis
title_sort	evaluation of total nitrogen in water via airborne hyperspectral data: potential of fractional order discretization algorithm and discrete wavelet transform analysis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1514047ccd67480ea049eabbd72351f0
work_keys_str_mv	AT jinhualiu evaluationoftotalnitrogeninwaterviaairbornehyperspectraldatapotentialoffractionalorderdiscretizationalgorithmanddiscretewavelettransformanalysis AT jianliding evaluationoftotalnitrogeninwaterviaairbornehyperspectraldatapotentialoffractionalorderdiscretizationalgorithmanddiscretewavelettransformanalysis AT xiangyuge evaluationoftotalnitrogeninwaterviaairbornehyperspectraldatapotentialoffractionalorderdiscretizationalgorithmanddiscretewavelettransformanalysis AT jingzhewang evaluationoftotalnitrogeninwaterviaairbornehyperspectraldatapotentialoffractionalorderdiscretizationalgorithmanddiscretewavelettransformanalysis
_version_	1718410507732385792

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Ejemplares similares