Distinguishing and mapping of aquatic vegetations and yellow algae bloom with Landsat satellite data in a complex shallow Lake, China during 1986–2018
Distinguishing and mapping of aquatic vegetations and yellow algae bloom with Landsat satellite data in a complex shallow Lake, China during 1986–2018
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DOI：
10.1016/j.ecolind.2020.106073
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发表时间：
2020-05
影响因子：
6.9
通讯作者：
Song Qing;A. Runa;Buri Shun;Wenjing Zhao;Y. Bao;Yanling Hao
中科院分区：
文献类型：
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作者：
Song Qing;A. Runa;Buri Shun;Wenjing Zhao;Y. Bao;Yanling Hao
近几十年来，全球湖泊遭受了藻类大量繁殖和水生植被的损失或扩张。遥感被认为是监测水生植被和藻华的有效方法。但是由于光谱特征的相似性，单个的光谱指数无法将它们区分开来。本文通过光谱特征分析，寻找适合于乌兰苏海复杂水环境中突发性植被、沉水植被和漂浮黄藻的光谱指标。发现近红外波段适合提取开阔水域，短波红外波段适合提取突水植被，而绿色和红色波段是区分水下水生植被和黄藻的特征光谱。因此，我们首先使用归一化植被指数（NDVI）提取开阔水域，然后使用紧急植被光谱指数（EVSI）提取紧急植被。然后，建立了一种新的大型藻类指数（MAI），用于区分水下水生植被和黄藻。针对现场测量数据和Landsat-8业务陆地成像仪（OLI）数据测试了光谱指数的适用性。结果表明，这些光谱指标的组合是分离水生植被和黄藻的有效方法。将该方法应用于时间序列Landsat图像，研究了乌兰素海水生植被和黄藻的季节和年际动态及其对气温的响应。结果表明：从5月到7月，突发性植被面积逐渐增加；沉水植被覆盖面积从5月开始逐渐增加，8月达到覆盖最大值，从9月下旬开始逐渐减少。黄藻在5月下旬出现，6、7月达到最大值，10月消失。长期变化分析表明，1986 ~ 2014年，突发性植被面积呈增加趋势，2014年以后呈减少趋势。潜水植被面积1986-2008年呈上升趋势，2009 - 2013年呈急剧下降趋势，2014年呈显著上升趋势。黄藻爆发主要发生在1998 ~ 2010年。黄藻区对短期平均温度较为敏感，而新兴植被区对较长时间尺度的温度较为敏感。潜水植被面积与气温的相关性不显著。此外，我们的研究还表明，MAI概念可以扩展并适用于其他高分辨率卫星传感器（如GF-2 PMS）和其他不同藻华地区（如黄海）。
Global lakes have suffered from algae blooms and loss or expansion of aquatic vegetations in recent decades. Remote sensing is considered as an effective approach to monitor aquatic vegetations and algae blooms. However, individual spectral index is unable to separate them due to the similarity in spectral features. In this paper, spectral characteristics analyses were conducted to find suitable spectral indices for distinguishing emergent vegetation, submerged aquatic vegetation and floating yellow algae in a complex aquatic environment, Ulansuhai Lake, China. It was found that near infrared band was appropriate to extract open water, and short-wave infrared band was suitable for extracting emergent vegetation, whereas the green and red bands were the characteristic spectra for distinguishing submerged aquatic vegetation and yellow algae. Hence, we firstly used the normalized difference vegetation index (NDVI) to extract open water, and the emergent vegetation spectral index (EVSI) to extract emergent vegetation. Then, a new developed macroalgae index (MAI) was used for distinguishing submerged aquatic vegetation and yellow algae. The applicability of the spectral indices was tested against both in situ measurements and Landsat-8 Operational Land Imager (OLI) data. The results indicated that the combination of these spectral indices was an effective method to separate aquatic vegetations and yellow algae. The proposed method was then applied to time-series Landsat images for investigating the seasonal and inter annual dynamics of aquatic vegetations and yellow algae and their responses to air temperature in the Ulansuhai Lake. The results show that emergent vegetation area increased from May to its maximum in July. The submerged aquatic vegetation area gradually increased from May to its maximum coverage in August, and decreased from late September. The yellow algae appeared in late May, and reached its maximum area in June or July, and disappeared in October. The long-term variation analyses showed that emergent vegetation area increased from 1986 to 2014, and was decreasing from 2014. The area of submerged aquatic vegetation increased during 1986–2008, and sharply decreased from 2009 to 2013, followed by a significant increasing from 2014. The yellow algae bloom mainly outbroke during the period of 1998 to 2010. We also found that the yellow algae area was more sensitive to short term mean temperature, while the area of emergent vegetation was sensitive to longer timescale of temperature. The submerged aquatic vegetation area had no significant correlation with air temperature. Besides, our study also indicated that the MAI concept can be extendable and applicable to other high-resolution satellite sensors (e.g., GF-2 PMS) and other regions with different algae blooms (e.g., Yellow Sea).