卫星像元尺度的湖泊浮叶/挺水植被覆盖度估算模型及应用
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1:南京信息工程大学,南京 210044 ;2:中国科学院南京地理与湖泊研究所,湖泊与流域水安全全国重点实验室,南京 211135 ;3:中国科学院大学南京学院,南京 211135 ;4:云南省昆明市滇池高原湖泊研究院,昆明 650228

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国家自然科学基金项目(42271377)和云南省省市一体化专项项目(202202AH210006)联合资助


Estimation model and application of satellite pixel-scale floating/emergent aquatic vegetation coverage in lakes
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1:Nanjing University of Information Science & Technology, Nanjing 210044 , P.R.China ;2: State Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135 , P.R.China ;3:University of Chinese Academy of Sciences, Nanjing, Nanjing 211135 , P.R.China ;4:Kunming Dianchi and Plateau Lakes Institute, Yunnan Province,Kunming 650228 , P.R.China

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    摘要:

    浮叶/挺水植被是重要的湖泊水生植被类群,其面积/覆盖度是湖泊生态健康评估及固碳潜力核算的重要参数,大面积、精确获取湖泊中浮叶/挺水植被面积/覆盖度及其变化信息,对湖泊生态修复及碳汇核算至关重要。卫星遥感是获取湖泊浮叶/挺水植被面积/覆盖度最有效的手段。然而,传统的卫星监测方法只能判别卫星像元内是否存在水生植被,无法定量估算像元内植被的覆盖度,进而无法定量、精准地获取湖泊中浮叶/挺水植被的面积/覆盖度。围绕该问题,本研究利用无人机、Sentinel-2 MSI和Landsat 8 OLI遥感数据,基于XGBoost建模方法,采用逐步升尺度的思路,分别构建了基于Sentinel-2 MSI和Landsat 8 OLI像元尺度的浮叶/挺水植被覆盖度定量估算模型,并成功地应用于四大淡水湖泊。结果表明:基于Sentinel和Landsat的估算模型测试集R2分别为0.95和0.97,均方根误差分别为7.85%和4.80%,平均绝对误差分别为5.35%和3.35%。1990-2022年,鄱阳湖和洞庭湖浮叶/挺水植被面积呈显著的增加趋势,太湖呈先增后减的趋势,洪泽湖增加趋势不显著。本研究利用Sentinel和Landsat影像构建的估算模型在四大淡水湖中实现了覆盖度定量化、长时序监测,展现出较好的稳健性和应用潜力,有望为湖泊生态系统的碳汇核算和固碳潜力评估提供方法和数据支撑。

    Abstract:

    Floating/emergent aquatic vegetation (FEAV) is an important group of aquatic vegetation in lakes, and its area or coverage is a significant parameter for assessing lake ecological health and estimating carbon sequestration potential. Accurately and extensively obtaining information on the area/coverage and changes of FEAV in lakes is crucial for lake ecological restoration and carbon sink accounting. Satellite remote sensing is the most effective means to obtain the area or coverage of FEAV in lakes. However, traditional satellite monitoring methods can only determine the presence or absence of aquatic vegetation within satellite pixels, and cannot quantitatively estimate the coverage of aquatic vegetation in the pixels. Consequently, it is impossible to obtain precise quantitative data on the area/coverage of FEAV in lakes. To address this issue, we utilized UAV, Sentinel-2 MSI, and Landsat 8 OLI data, employing the XGBoost model and a stepwise upscaling approach to develop quantitative estimation models for FEAV coverage at pixel scales based on Sentinel-2 MSI and Landsat 8 OLI, successfully applying these models to the four major freshwater lakes. The models were successfully applied to Chinas four largest freshwater lakes. The results showed that the test sets of the two estimation models based on Sentinel and Landsat images hadR2 of 0.95 and 0.97, root mean square error of 7.85% and 4.80%, and mean absolute error of 5.35% and 3.35%, respectively. From 1990 to 2022, FEAV area in Lake Poyang and Lake Dongting showed highly significant increasing trends, while Lake Taihu showed an increasing and then decreasing trend, while Lake Hongze had a non-significant increasing trend. The estimation models constructed using Sentinel and Landsat images have achieved quantification and long-term monitoring of coverage in the four major freshwater lakes, demonstrating good robustness and application potential. These are expected to provide methodological and data support for carbon sink calculations and carbon sequestration potential assessments in lake ecosystems.

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秦海涛,罗菊花,徐颖,张春雨,徐亚田,孟迪,何锋,鲁露.卫星像元尺度的湖泊浮叶/挺水植被覆盖度估算模型及应用.湖泊科学,2025,37(6):2189-2201. DOI:10.18307/2025.0633

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  • 收稿日期:2024-09-18
  • 最后修改日期:2025-01-15
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  • 在线发布日期: 2025-11-03
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