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谢杰, 王心源, 张洁, 等. 基于TM/ETM+影响分析巢湖叶绿素a浓度变化趋势[J]. 中国环境科学, 2010, 30(5): 677-682. XIE Jie, WANG Xinyuan, ZHANG Jie, et al. Analysing developing trend of chlorophyll-a concentration in Chaohu Lake based on TM/ETM+ image. China Environmental Science, 2010, 30(5): 677-682. (in Chinese)

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  • 标题: 基于Landsat数据的洱海蓝藻动态变化监测The Dynamic Monitoring of Erhai Cyanobacteria Based on Landsat Data

    作者: 李泠潞, 李嘉璇, 成功

    关键字: 蓝藻水华, 叶绿素a, 动态监测, LandsatCyanobacteria Bloom, Chlorophyll-a, Dynamic Monitoring, Landsat

    期刊名称: 《Journal of Water Resources Research》, Vol.5 No.2, 2016-04-27

    摘要: 近年来,位于云南省境内的洱海湖区蓝藻水华现象频发引起了人们的关注。叶绿素a是水体初级生产力的重要指标,可以有效反映浮游植物生物量状况及其变化趋势。本文选取了蓝藻水华易爆发期(2009年5月~11月)洱海湖区Landsat TM/ETM+数据,在对图像进行了辐射定标、大气校正、条带修复等预处理的基础上,运用41.57 × (TM3 + TM4) − 0.697波段组合模型对其进行叶绿素a浓度反演,反演结果表明,洱海湖区叶绿素a含量逐渐升高,在10月份达到了峰值,11月开始下降。各时相叶绿素a浓度反演结果与实际采样分析结果基本一致,验证了利用Landsat TM/ETM+数据进行洱海湖区叶绿素a浓度动态监测的有效性。该方法具有周期短、信息丰富、现势性强、成本低等优势,值得进一步推广。 In recent years, algal bloom occurring in Erhai frequently attracts wide attention. The chlorophyll-a concentration, which can effectively reflect the status of phytoplankton biomass and its change trend, is an important parameter for evaluating primary productivity of the water. Using the Landsat TM/ETM+ remote sensing images when cyanobacteria bloom erupted (from May to November in 2009), after pre- processing like radiometric calibration, atmospheric correction and destriping on the images, the chlo-rophyll-a concentration was quantitatively inversed by the band combination of 41.57*(TM3 + TM4) − 0.697. The results showed that chlorophyll-a concentration in Erhai Lake increased gradually and reached the highest value in October, then decreased in November. Chlorophyll-a concentrations in each phase inversion result are consistent with the actual results of sampling and analysis that demonstrates the effectiveness of the use of Landsat TM/ETM+ data on dynamic monitoring chlorophyll-a concentration of Erhai Lake. This method is worthy of further promotion with short period, rich information, high actuality and low cost.

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