Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China

Monitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1–2 km2 were first selected in this study based on the...

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Autores principales:	Jiang Qun'ou, Xu Lidan, Sun Siyang, Wang Meilin, Xiao Huijie
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	UAV hyper spectral remote sensing data Machine learning algorithm Retrieval model Water quality parameter Total nitrogen concentration Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/a366d2c3f29b467da2d24764f6afe33f
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spelling	oai:doaj.org-article:a366d2c3f29b467da2d24764f6afe33f2021-12-01T04:43:52ZRetrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China1470-160X10.1016/j.ecolind.2021.107356https://doaj.org/article/a366d2c3f29b467da2d24764f6afe33f2021-05-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21000212https://doaj.org/toc/1470-160XMonitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1–2 km2 were first selected in this study based on the spatial distribution of total nitrogen (TN) concentration changes estimated by the Landsat remote sensing data. And then twelve machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Ridge Regression (BRR), Lasso Regression (Lasso), Elastic Net (EN), Linear Regression (LR), Decision Tree Regression (DTR), K Neighbors Regression (KNR), Random Forest Regression (RFR), Extra Trees Regression (ETR), AdaBoost Regression (ABR) and Gradient Boosting Regression (GBR) were compared to construct a more accurate retrieval model by using the UAV hyper spectral remote sensing and ground monitoring data. And then the TN concentration was estimated after the process of dimensionality reduction and compressed sensing denoing. Finally, spatial heterogeneity of the TN concentration was analyzed in four sensitive areas of the Miyun reservoir. The results demonstrated that among the tested algorithms the Extra Trees Regression was best suitable for the construction of a TN concentration retrieval model on the basis of UAV hyper spectral data, and its absolute squared error was 0.000065. The spatial distribution of the TN showed that the concentration was highest within the water area of the Bulaotun village and the Houbajiazhuang village, while it was relatively low for the Chao river dam and Bai river dam. Additionally, no significant differences regarding the concentrations were shown in the single UAV flight area except the Houbajia village, which indicated that the water quality in Miyun reservoir was relatively stable and changed in a small interval. These conclusions can provide scientific references for water quality monitoring and management in the water reservoir.Jiang Qun'ouXu LidanSun SiyangWang MeilinXiao HuijieElsevierarticleUAV hyper spectral remote sensing dataMachine learning algorithmRetrieval modelWater quality parameterTotal nitrogen concentrationEcologyQH540-549.5ENEcological Indicators, Vol 124, Iss , Pp 107356- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	UAV hyper spectral remote sensing data Machine learning algorithm Retrieval model Water quality parameter Total nitrogen concentration Ecology QH540-549.5
spellingShingle	UAV hyper spectral remote sensing data Machine learning algorithm Retrieval model Water quality parameter Total nitrogen concentration Ecology QH540-549.5 Jiang Qun'ou Xu Lidan Sun Siyang Wang Meilin Xiao Huijie Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China
description	Monitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1–2 km2 were first selected in this study based on the spatial distribution of total nitrogen (TN) concentration changes estimated by the Landsat remote sensing data. And then twelve machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Ridge Regression (BRR), Lasso Regression (Lasso), Elastic Net (EN), Linear Regression (LR), Decision Tree Regression (DTR), K Neighbors Regression (KNR), Random Forest Regression (RFR), Extra Trees Regression (ETR), AdaBoost Regression (ABR) and Gradient Boosting Regression (GBR) were compared to construct a more accurate retrieval model by using the UAV hyper spectral remote sensing and ground monitoring data. And then the TN concentration was estimated after the process of dimensionality reduction and compressed sensing denoing. Finally, spatial heterogeneity of the TN concentration was analyzed in four sensitive areas of the Miyun reservoir. The results demonstrated that among the tested algorithms the Extra Trees Regression was best suitable for the construction of a TN concentration retrieval model on the basis of UAV hyper spectral data, and its absolute squared error was 0.000065. The spatial distribution of the TN showed that the concentration was highest within the water area of the Bulaotun village and the Houbajiazhuang village, while it was relatively low for the Chao river dam and Bai river dam. Additionally, no significant differences regarding the concentrations were shown in the single UAV flight area except the Houbajia village, which indicated that the water quality in Miyun reservoir was relatively stable and changed in a small interval. These conclusions can provide scientific references for water quality monitoring and management in the water reservoir.
format	article
author	Jiang Qun'ou Xu Lidan Sun Siyang Wang Meilin Xiao Huijie
author_facet	Jiang Qun'ou Xu Lidan Sun Siyang Wang Meilin Xiao Huijie
author_sort	Jiang Qun'ou
title	Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China
title_short	Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China
title_full	Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China
title_fullStr	Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China
title_full_unstemmed	Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China
title_sort	retrieval model for total nitrogen concentration based on uav hyper spectral remote sensing data and machine learning algorithms – a case study in the miyun reservoir, china
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/a366d2c3f29b467da2d24764f6afe33f
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Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China

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