Using Landsat to extend the historical record of lacustrine phytoplankton blooms: A Lake Erie case study
Using Landsat to extend the historical record of lacustrine phytoplankton blooms: A Lake Erie case study
复制标题
DOI：
10.1016/j.rse.2016.12.013
复制
发表时间：
2017-03-15
影响因子：
13.5
通讯作者：
Michalak, Anna M.
中科院分区：
文献类型：
--
作者：
Ho, Jeff C.;Stumpf, Richard P.;Michalak, Anna M.
淡水湖泊中浮游植物水华的长期记录对于了解流域流域规模的变化以及为评估驱动水华的过程提供关键约束都是必要的。然而，由于现场采样的固有限制，以及现代星载传感器覆盖的时间周期较短，长期记录很少。来自陆地卫星等传感器的历史数据为创造过去水华的新记录提供了强大的潜力。在这里，我们使用一种新的基于多个度量的评估程序来评估使用Landsat 5图像生成长期水华记录的算法的适用性和稳健性。评估指标基于水华的存在、空间分布、规模和时间，使用2002-2011年MERIS的原位微囊藻生物量和遥感蓝藻指数(CI)数据。将这一过程应用于以伊利湖西部盆地为重点的测试案例，基于近红外阈值的算法性能最佳，该算法通过减去短波红外波段进行简单的大气校正，并结合基于色调阈值的额外的“绿色”过滤器。对1984-2001年实施该算法，揭示了MERIS和MODIS记录(2002-2015年)开始之前峰值水华强度的长期趋势，并使可用于了解该系统水华发生和生长的记录周期增加了一倍以上。更广泛地说，我们证明了陆地卫星观测可以用来识别水华的宏观尺度特征。对于伊利湖，最终的陆地卫星算法的性能与MERIS CI算法相当，尽管陆地卫星的光谱范围很宽，重访时间很长。(C)2017年作者。由爱思唯尔公司出版。
Long-term records of phytoplankton blooms in freshwater lakes are necessary both for understanding basin scale changes to watersheds and for providing a key constraint for assessing processes driving blooms. However, due to the inherent constraints of in situ sampling and the short time period covered by modem space borne sensors, few long-term records exist. Historical data from sensors such as Landsat offer strong potential for creating new records of past blooms. Here, we use a novel evaluation procedure based on multiple metrics to assess algorithm suitability and robustness for generating long-term bloom records using Landsat 5 imagery. Evaluation metrics are based on bloom presence, spatial distribution, magnitude and timing, using both in situ Microcystis biovolume and remotely-sensed Cyanobacterial Index (CI) data from MERIS for 2002-2011. Applying this procedure for a test case focusing on Lake Erie's western basin, an algorithm based on a near infrared threshold with simple atmospheric correction through subtraction of the shortwave infrared band, combined with an additional "greenness" filter based on a hue threshold, performs best. Implementing this algorithm for 1984-2001 reveals the long-term trends in peak bloom magnitude prior to the start of the MERIS and MODIS record (2002-2015), and more than doubles the period of record that can be used to understand bloom occurrence and growth for this system. More broadly, we demonstrate that Landsat observations can be used to identify macro-scale features of blooms. For Lake Erie, the performance of the final Landsat algorithm is comparable to that of the MERIS CI algorithm, despite Landsat's broad spectral bands and long revisit time. (C) 2017 The Authors. Published by Elsevier Inc.