基于多时相Landsat8影像赣南地区水体污染识别研究

王瑞; 帅宁; 黄帅帅; 肖莹; 张艳; 黄诗乔; 刁鑫鹏

doi:10.13568/j.cnki.651094.651316.2023.11.29.0002

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基于多时相Landsat8影像赣南地区水体污染识别研究

更新时间：2026-01-21

- 基于多时相Landsat8影像赣南地区水体污染识别研究
- Journal of Xinjiang University (Natural Science Edition in Chinese and English) Vol. 41, Issue 3, Pages: 344-353(2024)
- 作者机构：
  
  1. 赣南科技学院资源与建筑工程学院
  2. 赣州市资源与环境遥感重点实验室
  3. 赣南科技学院文法学院
  4. 中国矿业大学环境与测绘学院
- 作者简介：
- 基金信息：
- DOI：10.13568/j.cnki.651094.651316.2023.11.29.0002
  CLC： X52
- Published：2024
- 稿件说明：
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[1]王瑞,帅宁,黄帅帅,等.基于多时相Landsat8影像赣南地区水体污染识别研究[J].新疆大学学报(自然科学版)(中英文),2024,41(03):344-353.
[1]王瑞,帅宁,黄帅帅,等.基于多时相Landsat8影像赣南地区水体污染识别研究[J].新疆大学学报(自然科学版)(中英文),2024,41(03):344-353. DOI： 10.13568/j.cnki.651094.651316.2023.11.29.0002.

DOI：10.13568/j.cnki.651094.651316.2023.11.29.0002.

摘要

赣南地区水资源丰富，受人类活动、农业、资源开采等的影响，存在一定水质污染问题．传统多采用单点化验分析水污染，此方法只代表局部和典型情况，而基于多光谱遥感反演水质参数能区域高精度定量反演．以赣南区域部分河段为研究区，利用Landsat8 OLI卫星影像，采用支持向量机方法对水体识别．结合赣南地区实际情况，选取水质参数氨氮、总磷、叶绿素a为研究对象，采用统计回归方法建立遥感波普信息与实测数据间的响应关系模型，反演结果氨氮总体浓度在0.2～0.4 mg/L、总磷0.06～0.18 mg/L、叶绿素a 5.01～6.81μg/L．同时采用决定系数（R2）和平均绝对误差（MAE）进行模型可行性及反演精度验证，反演结果R2分别为0.840、0.745、0.745

MAE分别为0.054、0.049、0.069，反演模型方法可靠，能够准确识别排污口，为保护水资源及防治水体污染提供可靠的技术支撑．

Abstract

The water resources in southern Jiangxi Province are abundant. Under the influence of human activities

agriculture and resource exploitation

there are some problems of water pollution in this area. The traditional single point analysis method is used to analyze water pollution

which only represents local and typical conditions

while the water quality parameter inversion based on multi-temporal remote sensing can be used for regional high-precision quantitative inversion. Based on Landsat8 OLI satellite images

this paper adopts support vector machine(SVM) method to identify water bodies. Based on the actual situation in southern Jiangxi Province

water quality parameters such as ammonia nitrogen

total phosphorus and chlorophyll a were selected as the research objects

and a response relationship model between remote sensing pop information and measured data was established by statistical regression method. The inversion results showed that the total concentration of ammonia nitrogen was 0.2～0.4 mg/L

total phosphorus was 0.06～0.18 mg/L

and chlorophyll a was 5.01～6.81μg/L. Determination coefficient(R2) and mean absolute error(MAE) were used to verify the model feasibility and inversion accuracy. The inversion results R2 were 0.840

0.745 and 0.745

respectively. MAE were 0.054

0.049 and 0.069

respectively. The method of inversion model is reliable and can accurately identify sewage outlets

providing reliable technical support for protecting water resources and preventing water pollution.

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references

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