| 引用本文: | 赵方睿,王强,穆春生,刘阁,温志丹,陶慧,宋开山.基于遥感的1984—2019年查干湖及周边湖泊透明度变化.湖泊科学,2025,37(1):328-338. DOI:10.18307/2025.0154 |
| Zhao Fangrui,Wang Qiang,Mu Chunsheng,Liu Ge,Wen Zhidan,Tao Hui,Song Kaishan.Changes in transparency of Lake Chagan and its surrounding lakes from 1984 to 2019 based on remote sensing. J. Lake Sci.2025,37(1):328-338. DOI:10.18307/2025.0154 |
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| 摘要: |
| 湖泊透明度是衡量水质变化和湖泊水生生态系统健康的重要指标。湖泊透明度一般通过塞氏盘现场观测获得，其变化主要由水体中非藻类颗粒物、藻类和黄色物质（CDOM）浓度共同决定，因此可以通过光学遥感进行反演。本研究基于2004—2009年8次查干湖实地采样获得的132条透明度数据，通过分析同步Landsat天顶反射率产品数据(TOA)不同波段组合与透明度之间的相关性，发现以红波段和蓝波段比值和红绿蓝波段均值AV(B3，B1)组合建立的透明度反演模型精度最高，误差最小，进而基于此模型反演了1984—2019年查干湖以及周边湖泊透明度。结果表明：查干湖透明度范围在1.0~63.0 cm之间，年均值为17.6 cm。查干湖透明度年均值变化分为两个阶段：1984—2001年间透明度波动范围较大，没有明显趋势；2002—2019年期间呈上升趋势（2.3 cm/10 a）。查干湖透明度与风速、降水量、湖泊面积和归一化植被指数具有一定的相关性。通过长时间尺度上遥感监测查干湖水体透明度的时空格局变化，分析其变化特征与规律，可为查干湖流域生态治理提供科学依据。 |
| 关键词: 查干湖 透明度 Landsat 遥感 |
| DOI：10.18307/2025.0154 |
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| 基金项目:吉林省科技发展计划项目 (20220203024SF)；国家自然科学基金项目（42171374，42371390）联合资助 |
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| Changes in transparency of Lake Chagan and its surrounding lakes from 1984 to 2019 based on remote sensing |
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Zhao Fangrui1，2，Wang Qiang2，Mu Chunsheng2，Liu Ge2，Wen Zhidan2，Tao Hui2，Song Kaishan2
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1.College of Life Sciences, Northeast Normal University, Changchun 130024 , P.R.China ；2.Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102 , P.R.China
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| Abstract: |
| Water clarity is an important indicator for assessing eutrophication in lakes or reservoirs. Traditionally, water clarity is determined in the field using Secchi disk depth (SDD), which is time consuming, labour intensive and not suitable for large scale water clarity assessment. Water clarity is mainly determined by non-algal particulate matter, algal abundance and colored dissolved organic matter (CDOM) in the water column, which can be monitored by optical remote sensing. In this study, eight cruises were conducted over Lake Chagan in 2004-2009 and in situ measured SDD were determined. Correlation analyses between in situ measured SDD and Landsat calibrated top-of-atmosphere (TOA) reflectance were performed to determine the best band or band combinations. Based on the in situ measured SDD and TOA pairs, we developed an algorithm based on the ratio of red and blue bands and the average of red, green and blue, e.g. AV(B3, B1), to estimate the SDD in Lake Chagan. High model calibration accuracy was achieved, with lower RMSE and MAPE for model validation. The algorithm was applied to Landsat images to derive SDD distribution maps from 1984 to 2019. The Landsat-derived results indicate that the SDD in Lake Chagan ranges from 1.0 cm to 63.0 cm with an annual mean of 17.6 cm, with a significant increasing trend during 2002-2019 with an increasing trend of 2.3 cm/10 a. The annual mean values of the SDD variation dynamics in Lake Chagan went through two phases. From 1984 to 2001, the SDD showed no obvious trend, and then it showed a significant increasing trend. Our analysis showed that the SDD in Lake Chagan had some relationship with wind speed, precipitation and the normalized difference vegetation index (NDVI). The changes in water transparency in Lake Chagan were monitored by remote sensing on a long time scale, and the characteristics and rules of the changes were analyzed to provide a scientific basis for ecological management in the Lake Chagan Basin. |
| Key words: Lake Chagan transparency Landsat remote sensing |
