基于无人机多光谱数据的海口长钦湖水质参数遥感反演及其与景观格局的相互关系

doi:10.16258/j.cnki.1674-5906.2025.12.010

生态环境学报 ›› 2025, Vol. 34 ›› Issue (12): 1930-1943.DOI: 10.16258/j.cnki.1674-5906.2025.12.010

• 研究论文【环境科学】 • 上一篇下一篇

基于无人机多光谱数据的海口长钦湖水质参数遥感反演及其与景观格局的相互关系

李清云¹(), 肖亦敏¹, 林卫²^,⁴, 雷金睿²^,⁵^,^*(), 王韫镭³, 孔玫蔓¹, 杨云芳¹

1.浙江广厦建设职业技术大学城乡建设学院，浙江东阳 322100
2.海南省林业科学研究院（海南省红树林研究院），海南海口 571100
3.中国热带农业科学院香料饮料研究所，海南万宁 571533
4.海南大学热带农林学院，海南海口 570228
5.海口市湿地保护工程技术研究开发中心，海南海口 571100

收稿日期:2025-07-08 出版日期:2025-12-18 发布日期:2025-12-10
通讯作者: *E-mail：raykingre@163.com
作者简介:李清云（1987年生），女，博士研究生，研究方向为湿地生态与水环境科学。E-mail: lqy181268@126.com
基金资助:
海南省自然科学基金项目(723QN275);人才引进科研启动项目(2024ZX076);海南省财政科技计划项目(jscx202024)

Remote Sensing Inversion of Water Quality Parameters and Their Correlation with Landscape Patterns in Haikou Changqin Lake Based on UAV Multispectral Data

LI Qingyun¹(), XIAO Yimin¹, LIN Wei²^,⁴, LEI Jinrui²^,⁵^,^*(), WANG Yunlei³, KONG Meiman¹, YANG Yunfang¹

1. Zhejiang Guangsha Vocational and Technical University of construction, College of Urban and Rural Construction, Dongyang 322100, P. R. China
2. Hainan Academy of Forestry Sciences (Hainan Mangrove Research Institute), Haikou 571100, P. R. China
3. Chinese Academy of Tropical Agricultural Sciences, Spiced Beverage Research Institute, Wanning 571533, P. R. China
4. College of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, P. R. China
5. Haikou Wetland Protection Engineering Technology Research and Development Center, Haikou 571100, P. R. China

Received:2025-07-08 Online:2025-12-18 Published:2025-12-10

摘要/Abstract

摘要： 土地利用景观是影响城市重要水域水质分布的关键因素，精确、高效监测水域水质对解释水质与景观格局之间的相关性具有重要意义，同时也是水质监管与治理的重要基础。选取海口市长钦湖为研究区，通过同步采集水质样品与无人机多光谱遥感数据，建立旱、雨季湿地水体总磷（TP）、总氮（TN）、浊度（TUB）和叶绿素a（Chl-a）浓度的最优反演模型，生成水体水质参数的空间分布，并分析其空间变化与景观格局的相互关系。结果表明，1）长钦湖湿地旱、雨季TP、TN、Chl-a反演模型均以多项式模型呈现最优精度，在TN反演模型中旱季以线性回归模型精度最优，在TUB反演模型中雨季以幂函数回归模型精度最高，反演结果显示长钦湖水质污染程度轻、状况较好。2）长钦湖水质参数存在一定时空变异特性，TP、TN、Chl-a平均及最大浓度雨季大于旱季，TUB平均及最大浓度旱季显著大于雨季。3）缓冲区尺度120 m为长钦湖景观变化重要节点，随缓冲区扩大，AI、LPI值递增，CONTAG、SHDI、SHEI值递减，DIVISION、PD、LSI先减后增，120 m后景观异质性与破碎化程度随半径增大而升高。4）旱季在特定尺度内水质参数与建设用地、旱地、LPI具有相关性；雨季TP、TUB浓度与各景观类型及格局指数无相关性，TN浓度与道路、PD、SHDI显著正相关，Chl-a浓度与建设用地呈正相关。研究成果可为城市重点湿地水质的高效监测及保护工作提供科学层面的支撑。

关键词: 水质参数, 无人机, 多光谱, 遥感反演, 景观格局, 相关性

Abstract:

Land use and landscape patterns are key factors influencing the distribution of water quality in critical urban water bodies. Accurate and efficient water quality monitoring is essential for elucidating the correlation between water quality and landscape patterns, as well as for providing a scientific basis for water quality regulation and management. In this study, Changqin Lake in Haikou City was selected as the study area. By synchronously collecting water samples and UAV-based multispectral remote sensing data, optimal inversion models for total phosphorus (TP), total nitrogen (TN), turbidity (TUB), and chlorophyll-a (Chl-a) concentrations in wetland water during the dry and wet seasons were developed. Spatial distributions of these water quality parameters were generated, and their relationships with the landscape patterns were analyzed. The results showed that 1) For TP, TN, and Chl-a, the best was for TN in the dry season, and power function regression was optimal for TUB in the wet season. The inversion results suggested that Changqin Lake exhibited relatively low pollution levels and had good water quality. 2) Spatiotemporal variations were observed in water quality parameters. The average and maximum concentrations of TP, TN, and Chl-a were higher in the wet season than in the dry season, whereas TUB showed significantly higher values in the dry season. 3) A 120-meter buffer zone was identified as a critical threshold for landscape pattern changes in Changqin Lake. As the buffer zone expanded, the aggregation index (AI) and largest patch index (LPI) increased, whereas the contagion index (CONTAG), Shannon’s diversity index (SHDI), and Shannon’s evenness index (SHEI) decreased. The division index (DIVISION), patch density (PD), and landscape shape index (LSI) initially decreased before increasing. Beyond 120 m, landscape heterogeneity and fragmentation intensified with increasing radius. 4) During the dry season, water quality parameters were correlated with construction land, dry farmland, and LPI at specific scales. In the wet season, TP and TUB were not correlated with landscape types or pattern indices, whereas TN was significantly positively correlated with roads, PD, and SHDI, and Chl-a was positively correlated with constructed land. These findings provide scientific support for the efficient monitoring and conservation of the water quality in key urban wetlands.

Key words: water quality parameters, UAV, multispectral, remote sensing inversion, landscape pattern, correlation

中图分类号:

X824
X832

李清云, 肖亦敏, 林卫, 雷金睿, 王韫镭, 孔玫蔓, 杨云芳. 基于无人机多光谱数据的海口长钦湖水质参数遥感反演及其与景观格局的相互关系[J]. 生态环境学报, 2025, 34(12): 1930-1943.

LI Qingyun, XIAO Yimin, LIN Wei, LEI Jinrui, WANG Yunlei, KONG Meiman, YANG Yunfang. Remote Sensing Inversion of Water Quality Parameters and Their Correlation with Landscape Patterns in Haikou Changqin Lake Based on UAV Multispectral Data[J]. Ecology and Environmental Sciences, 2025, 34(12): 1930-1943.

图/表 14

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光谱参数	计算公式	光谱参数	计算公式
V1	R₁	V9	(R₂+R₃)/R₅
V2	R₂	V10	(R₂+R₃)R₅
V3	R₃	V11	R₁/(R₅+R₂)
V4	R₄	V12	R₂+R₃
V5	R₅	V13	R₂+R₃+R₅
V6	R₃/R₂	V14	R₃/R₅
V7	R₂+R₅	V15	R₂/R₁
V8	(R₃+R₅)/R₂	V16	R₅/R₁

光谱参数	计算公式	光谱参数	计算公式
V1	R₁	V9	(R₂+R₃)/R₅
V2	R₂	V10	(R₂+R₃)R₅
V3	R₃	V11	R₁/(R₅+R₂)
V4	R₄	V12	R₂+R₃
V5	R₅	V13	R₂+R₃+R₅
V6	R₃/R₂	V14	R₃/R₅
V7	R₂+R₅	V15	R₂/R₁
V8	(R₃+R₅)/R₂	V16	R₅/R₁

水质要素	旱季模型	TP模型表达式	r²	RMSE	水质要素	雨季模型	模型表达式	r²	RMSE
TP	U_TP1	y=0.001x−0.296	0.0793	0.258	TP	U_TP15	y=1.166x−1.950	0.4767	0.080
	E_TP1	y=0.067e^0.001^x	0.0578	0.690		E_TP15	y=0.002e^2.652^x	0.4202	0.203
	P_TP1	y=0.153×10⁻³x^1.091	0.0330	0.699		P_TP15	y =0.009x^5.366	0.4152	0.204
	PL_TP1	y=9.204×10⁻⁶x²−0.019x+9.799	0.6686	0.156		PL_TP15	y=5.429x²−20.958x+20.568	0.5364	0.076
TN	U_TN8	y= −18.983x+27.059	0.6313	0.656	TN	U_TN4	y= −0.008x+10.210	0.4578	0.748
	E_TN8	y=1.515×10⁵e^−8.607^x	0.5964	0.320		E_TN4	y=63.535e^−0.004^x	0.4623	0.328
	P_TN8	y=40.683x^−11.327	0.6008	0.319		P_TN4	y=1.311×10⁹x^−2.952	0.4309	0.337
	PL_TN8	y=133.490x²−369.451x+256.818	0.7158	0.582		PL_TN4	y= −4.206×10⁻⁵x²+0.064x−20.474	0.5806	0.665
TUB	U_TUB14	y= −8.585x+24.670	0.1661	2.198	TUB	U_TUB5	y=0.048x−49.652	0.8828	1.824
	E_TUB14	y=1.162×10²e^−1.444^x	0.2347	0.298		E_TUB5	y=0.160e^0.003^x	0.8184	0.163
	P_TUB14	y=35.051x^−2.385	0.2066	0.303		P_TUB5	y=5.709×10⁻¹⁴x^4.601	0.8396	0.154
	PL_TUB14	y= −101.361x²+349.731x−290.720	0.6497	1.440		PL_TUB5	y= −2.591×10⁻⁵x²+0.119x−98.470	0.8885	1.798
Chl-a	U_CHL14	y=22.879x−37.235	0.4177	2.787	Chl-a	U_CHL9	y= −5.989x +36.235	0.3067	4.193
	E_CHL14	y=1.804×10⁻⁶e^7.790^x	0.3154	1.184		E_CHL9	y=148.488e^−0.680^x	0.1614	0.722
	P_CHL14	y=0.001x^13.567	0.3068	1.192		P_CHL9	y=1506.542x^−3.566	0.1819	0.713
	PL_CHL14	y=213.491x²−732.793x+629.278	0.7241	1.939		PL_CHL9	y=14.054x²−145.658x+380.245	0.6299	3.096

水质要素	旱季模型	TP模型表达式	r²	RMSE	水质要素	雨季模型	模型表达式	r²	RMSE
TP	U_TP1	y=0.001x−0.296	0.0793	0.258	TP	U_TP15	y=1.166x−1.950	0.4767	0.080
	E_TP1	y=0.067e^0.001^x	0.0578	0.690		E_TP15	y=0.002e^2.652^x	0.4202	0.203
	P_TP1	y=0.153×10⁻³x^1.091	0.0330	0.699		P_TP15	y =0.009x^5.366	0.4152	0.204
	PL_TP1	y=9.204×10⁻⁶x²−0.019x+9.799	0.6686	0.156		PL_TP15	y=5.429x²−20.958x+20.568	0.5364	0.076
TN	U_TN8	y= −18.983x+27.059	0.6313	0.656	TN	U_TN4	y= −0.008x+10.210	0.4578	0.748
	E_TN8	y=1.515×10⁵e^−8.607^x	0.5964	0.320		E_TN4	y=63.535e^−0.004^x	0.4623	0.328
	P_TN8	y=40.683x^−11.327	0.6008	0.319		P_TN4	y=1.311×10⁹x^−2.952	0.4309	0.337
	PL_TN8	y=133.490x²−369.451x+256.818	0.7158	0.582		PL_TN4	y= −4.206×10⁻⁵x²+0.064x−20.474	0.5806	0.665
TUB	U_TUB14	y= −8.585x+24.670	0.1661	2.198	TUB	U_TUB5	y=0.048x−49.652	0.8828	1.824
	E_TUB14	y=1.162×10²e^−1.444^x	0.2347	0.298		E_TUB5	y=0.160e^0.003^x	0.8184	0.163
	P_TUB14	y=35.051x^−2.385	0.2066	0.303		P_TUB5	y=5.709×10⁻¹⁴x^4.601	0.8396	0.154
	PL_TUB14	y= −101.361x²+349.731x−290.720	0.6497	1.440		PL_TUB5	y= −2.591×10⁻⁵x²+0.119x−98.470	0.8885	1.798
Chl-a	U_CHL14	y=22.879x−37.235	0.4177	2.787	Chl-a	U_CHL9	y= −5.989x +36.235	0.3067	4.193
	E_CHL14	y=1.804×10⁻⁶e^7.790^x	0.3154	1.184		E_CHL9	y=148.488e^−0.680^x	0.1614	0.722
	P_CHL14	y=0.001x^13.567	0.3068	1.192		P_CHL9	y=1506.542x^−3.566	0.1819	0.713
	PL_CHL14	y=213.491x²−732.793x+629.278	0.7241	1.939		PL_CHL9	y=14.054x²−145.658x+380.245	0.6299	3.096

水质要素	旱季模型	TP回归方程	r²	回归方程斜率	水质要素	雨季模型	TP回归方程	r²	回归方程斜率
TP	UTP1	y=0.072x+0.839	0.0374	0.0720	TP	UTP15	y=0.354x+0.267	0.5377	0.3539
	ETP1	y=0.018x+0.208	0.0610	0.0183		ETP15	y=0.398x+0.277	0.6183	0.3979
	PTP1	y=0.023x+0.330	0.0391	0.0232		PTP15	y=0.356x+0.259	0.6023	0.3557
	PLTP1	y=0.442x+0.215	0.5356	0.4421		PLTP15	y=0.472x+0.230	0.7627	0.4715
TN	UTN8	y=0.667x+0.577	0.7745	0.6677	TN	UTN4	y=0.533x+1.511	0.5917	0.5327
	ETN8	y=0.530x+0.744	0.7403	0.5298		ETN4	y=0.440x+0.756	0.5063	0.4403
	PTN8	y=0.529x+0.745	0.7345	0.5288		PTN4	y=0.525x+1.358	0.4906	0.5247
	PLTN8	y=0.545x+0.796	0.6691	0.5452		PLTN4	y=0.909x−0.284	0.6802	0.9085
TUB	UTUB14	y=0.262x+7.042	0.2844	0.2616	TUB	UTUB5	y=0.838x−0.888	0.9642	0.8376
	ETUB14	y=0.363x+5.800	0.2311	0.3634		ETUB5	y=0.729x−2.597	0.9809	0.7294
	PTUB14	y=0.330x+6.113	0.2135	0.3300		PTUB5	y=1.183x−5.147	0.9821	1.1825
	PLTUB14	y=0.671x+2.792	0.7670	0.6713		PLTUB5	y=0.759x−0.620	0.9414	0.7592
Chl-a	UCHL14	y=0.465x+2.120	0.6844	0.4654	Chl-a	UCHL9	y=0.467x+3.825	0.2836	0.4674
	ECHL14	y=0.493x+1.039	0.8304	0.4932		ECHL9	y=0.278x+3.650	0.3460	0.2784
	PCHL14	y=0.588x+1.432	0.8163	0.5877		PCHL9	y=0.294x+3.516	0.3669	0.2944
	PLCHL14	y=0.940x+0.461	0.8416	0.9395		PLCHL9	y=0.546x+2.479	0.6691	0.5456

基于无人机多光谱数据的海口长钦湖水质参数遥感反演及其与景观格局的相互关系

Remote Sensing Inversion of Water Quality Parameters and Their Correlation with Landscape Patterns in Haikou Changqin Lake Based on UAV Multispectral Data

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水质参数	季节	最大值	最小值	平均值	标准差
ρ_(TP)/ (mg·L⁻¹)	旱季	1.20	0.01	0.28	0.22
ρ_(TP)/ (mg·L⁻¹)	雨季	0.62	0.34	0.48	0.07
ρ_(TN)/ (mg·L⁻¹)	旱季	5.72	1.25	2.57	0.75
ρ_(TN)/ (mg·L⁻¹)	雨季	3.87	1.66	3.47	0.43
ρ_(TUB)/ (mg·L⁻¹)	旱季	10.95	1.85	9.07	1.55
ρ_(TUB)/ (mg·L⁻¹)	雨季	3.47	0.84	1.57	0.54
ρ_(Chl-a)/ (μg·L⁻¹)	旱季	20.79	0.46	4.83	3.48
ρ_(Chl-a)/ (μg·L⁻¹)	雨季	13.67	2.85	6.14	2.02

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