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Remote sensing models using Landsat satellite data to monitor algal blooms in Lake Champlain

机译:使用Landsat卫星数据监测尚普兰湖中藻华的遥感模型

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Lake Champlain is significantly impaired by excess phosphorus loading, requiring frequent lake-wide monitoring for eutrophic conditions and algal blooms. Satellite remote sensing provides regular, synoptic coverage of algal production over large areas with better spatial and temporal resolution compared with in situ monitoring. This study developed two algal production models using Landsat Enhanced Thematic Mapper Plus (ETM+) satellite imagery: a single band model and a band ratio model. The models predicted chlorophyll a concentrations to estimate algal cell densities throughout Lake Champlain. Each model was calibrated with in situ data compiled from summer 2006 (July 24 to September 10), and then validated with data for individual days in August 2007 and 2008. Validation results for the final single band and band ratio models produced Nash-Sutcliffe efficiency (NSE) coefficients of 0.65 and 0.66, respectively, confirming satisfactory model performance for both models. Because these models have been validated over multiple days and years, they can be applied for continuous monitoring of the lake.
机译:尚普兰湖因过多的磷负荷而受到严重损害,因此需要在全湖范围内频繁监测富营养化条件和藻华。与原位监测相比,卫星遥感技术可以对大面积的藻类生产进行常规的天气覆盖,并具有更好的时空分辨率。这项研究使用Landsat Enhanced Thematic Mapper Plus(ETM +)卫星图像开发了两种藻类生产模型:单波段模型和波段比率模型。该模型预测叶绿素a的浓度,以估计整个尚普兰湖的藻类细胞密度。每个模型均使用2006年夏季(7月24日至9月10日)收集的现场数据进行校准,然后使用2007年8月和2008年单日的数据进行验证。最终单波段和波段比率模型的验证结果产生了Nash-Sutcliffe效率(NSE)系数分别为0.65和0.66,证实了两个模型均具有令人满意的模型性能。由于这些模型已经经过数天和数年的验证,因此可以将其用于湖泊的连续监测。

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