Evaluation method for lake eutrophication using aerial hyperspectral image

Yan HUANG; Yanqi FANG; Mingzuan XU; Jianlong SHI; Kui YANG; Chao QI; Sen LIANG; Yan JI

doi:10.3788/IRLA20240475

Infrared and Laser Engineering, Volume. 54, Issue 5, 20240475(2025)

Evaluation method for lake eutrophication using aerial hyperspectral image

Yan HUANG^1,2,3, Yanqi FANG^1,2、*, Mingzuan XU^1,2, Jianlong SHI^1,2, Kui YANG^1,2, Chao QI^1,2, Sen LIANG^1,2, and Yan JI¹

Author Affiliations

¹Geological Exploration Technology Institute of Jiangsu Province, Nanjing 210049, China

²Jiangsu Province Engineering Research Center of Airborne Detecting and Intelligent Perceptive Technology, Nanjing 210049, China

³School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

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Figures & Tables(8)

Fig. 1. Schematic diagram of sampling location

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Fig. 2. Technology roadmap for evaluation of lake eutrophication using aerial hyperspectral data

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Fig. 3. Correlation between water quality parameters and reflectance, derivative. (a) Chla; (b) TP; (c) TN; (d) SSD; (e) CODmn

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Fig. 4. Comparison between inversion value and measured values. (a) Chla; (b) TP; (c) TN; (d) SSD; (e) CODmn; (f) TLI

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Fig. 5. TLI distribution map of the research area. (a) Measured data; (b) Aerial hyperspectral image

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Table 1. Grading for evaluation of lake eutrophication

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Table 1. Grading for evaluation of lake eutrophication

Nutrient status	Value
Poor nutrition	$ TLI\left(\mathrm{\Sigma }\right) < 30 $
Intermediate nutrition	$ 30\leqslant TLI\left(\mathrm{\Sigma }\right)\leqslant 50 $
Mild eutrophication	$ 50 < TLI\left(\mathrm{\Sigma }\right)\leqslant 60 $
Moderate eutrophication	$ 60 < TLI\left(\mathrm{\Sigma }\right)\leqslant 70 $
Severe eutrophication	$ TLI\left(\mathrm{\Sigma }\right) > 70 $

Table 2. Statistical table of feature band

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Table 2. Statistical table of feature band

Parameters	Feature band/nm
Chla	400.00,460.48,472.58,484.67,489.51,504.03,511.29,535.48,569.35,583.87,639.51,663.70,675.80,729.03,775.00,825.80,893.54
TP	400.00,402.41,407.25,416.93,489.51,501.61,506.45,542.74,557.25,564.51,581.45,726.61,767.74,779.83,782.25,787.09,796.77,806.45,820.96,893.54
TN	421.77,424.19,441.12,487.09,557.25,574.19,579.03,608.06,661.29,673.38,678.22,721.77,729.03,750.80,767.74,794.35,825.80,859.67,893.54
SSD	416.93,421.77,431.45,453.22,462.90,465.32,470.16,475.00,491.93,557.25,595.96,612.90,733.87,750.80,767.74,772.58,782.25,791.93,804.03,837.90,893.54
COD_mn	400.00,402.41,412.09,441.12,455.64,477.41,484.67,525.80,535.48,595.96,608.06,612.90,762.90,767.74,852.41,864.51,869.35,871.77,883.87,898.38

Table 3. Statistical table of accuracy between inversion values and measured values

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Table 3. Statistical table of accuracy between inversion values and measured values

Parameters	Model	Train		Test
Parameters	Model	R²	RMSE	R²	RMSE
Chla	PLSR	0.8896	8.2073	0.8259	9.2630
	LSSVR	0.9398	5.4246	0.8517	7.7950
	QPSO-LSSVR	0.9880	1.3562	0.9653	3.2125
TP	PLSR	0.8745	0.0069	0.7865	0.0058
	LSSVR	0.9620	0.0030	0.8021	0.0038
	QPSO-LSSVR	0.9895	0.0004	0.8707	0.0036
TN	PLSR	0.8701	0.1093	0.6093	0.2094
	LSSVR	0.8865	0.0837	0.6565	0.1412
	QPSO-LSSVR	0.9812	0.0099	0.8342	0.0980
SSD	PLSR	0.7853	0.1467	0.4170	0.1227
	LSSVR	0.8631	0.0425	0.5403	0.0839
	QPSO-LSSVR	0.9883	0.0010	0.8626	0.0298
COD_mn	PLSR	0.8297	0.2230	0.8200	0.2774
	LSSVR	0.8836	0.1358	0.8255	0.1538
	QPSO-LSSVR	0.9872	0.0086	0.8668	0.1478

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Yan HUANG, Yanqi FANG, Mingzuan XU, Jianlong SHI, Kui YANG, Chao QI, Sen LIANG, Yan JI. Evaluation method for lake eutrophication using aerial hyperspectral image[J]. Infrared and Laser Engineering, 2025, 54(5): 20240475

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Paper Information

Category: Special issue—Hyperspectral technology and applications

Received: Oct. 17, 2024

Accepted: --

Published Online: May. 26, 2025

The Author Email: Yanqi FANG (478178005@qq.com)

DOI:10.3788/IRLA20240475

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology