基于Landsat卫星影像的草海水质遥感反演及综合营养状态评价
Remote Sensing of Caohai Lake Water Quality Using Landsat Satellite Images
投稿时间:2018-11-29  修订日期:2020-06-07
中文关键词:草海  遥感  水质  营养评价  时空分布
英文关键词:Caohai Lake: remote sensing  water quality  nutrient enrichment  temporal-spatial distribution.
基金项目:广西高校无人机重点实验室主任基金/开放项目:基于无人机遥感影像的SIFT匹配算法研究(WRJ2015KF03)
作者单位E-mail
陈艳 贵州师范大学地理与环境科学学院山地资源与环境遥感重点实验室贵州 贵阳 550025 1587356209@qq.com 
刘绥华 贵州师范大学地理与环境科学学院山地资源与环境遥感重点实验室贵州 贵阳 550025  
王堃 贵州师范大学地理与环境科学学院山地资源与环境遥感重点实验室贵州 贵阳 550025  
宋善海 贵州师范大学地理与环境科学学院山地资源与环境遥感重点实验室贵州 贵阳 550025  
梁萍萍 贵州师范大学地理与环境科学学院山地资源与环境遥感重点实验室贵州 贵阳 550025  
陈芳 贵州师范大学地理与环境科学学院山地资源与环境遥感重点实验室贵州 贵阳 550025  
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中文摘要:
      为更高效、大范围的获取草海水质状况,分析草海水体营养状态,本文利用实测水质数据与遥感影像的关系建立反演模型,反演草海2000-2015年水质指标,包括Chl-a、TN、TP、CODMn、SD,并用综合营养状态指数法(TLI)对草海水质情况进行评价。结果表明:(1)通过波段组合与实测数据建立水质参数反演模型能高效、大面积的获得草海水质分布情况,评价得知草海从2000 - 2005年、2010 - 2015年,整体水质均为中营养、轻度富营养状态,TLI表现为先升高,后降低;(2)草海水质季节变化明显,四个季节的综合营养状态指数为春季>夏季>冬季>秋季,主要原因是草海春、夏季节农耕及旅游活动等较频繁;(3)空间上看,草海入水口及湖周围综合营养指数比湖中心及出水口高,其原因是因为入水口靠近城市,城市污水排入湖中,湖四周农业污染源大量存在所致。
英文摘要:
      Water quality assessment is basic to water environment research and conservation. Water quality monitoring based on remote sensing is widely applied because it is efficient, economical and can be used to monitor large areas. In this study, we developed a regression model based on the relationship between measured water quality data and spectral data from Landsat satellite images. After verification, the model was used to estimate water quality parameters (WQPs) from the different spectral bands of satellite imagery. WQPs included chlorophyll a (chl-a), total nitrogen (TN), total phosphorous (TP), chemical oxygen demand (CODMn) and transparency (SD). The water quality data for developing the model were measured at 33 sampling sites in August of 2017 and Landsat satellite imagery, providing spectral data for the model, were obtained in October of 2017. The regression model was then used to estimate WQPs from Landsat satellite images from the same months in 2000, 2005, 2010 and 2015. The estimated WQPs were then used to calculate Caohai Lake water quality indices for 2000, 2005, 2010 and 2015. The nutritional status of Caohai Lake was evaluated using the Trophic Level Index (TLI). The model successfully met the study objective of providing information on current water quality conditions in Caohai wetlands, derived from Landsat images, that is valuable for conserving Caohai wetland and controlling pollution. Results show that: (1) Water quality parameters estimated from the regression model, based on spectral bands from satellite imagery, can show the large-scale distribution of water in Caohai Lake. Overall water quality of Caohai Lake in 2000, 2005 was mesotrophic, and was slightly eutrophic in 2010 and 2015.The TLI increased and then decreased; (2) There is clearly seasonal variation of water quality in Caohai Lake. The TLI across four seasons was in the order: spring > summer > winter > autumn and attributed to intensive farming and tourism around Caohai Lake in spring and summer. (3) Spatially, the TLI of the inlet and surrounding area of Caohai Lake was higher than that at the lake center and outlet. The inlet is near Weining County, and the discharge of municipal sewage and nonpoint source agricultural pollution are serious.
陈艳,刘绥华,王堃,宋善海,梁萍萍,陈芳.2020.基于Landsat卫星影像的草海水质遥感反演及综合营养状态评价[J].水生态学杂志,41(3):24-31.
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