目前,世界各地越来越多的湖泊正在经历蓝藻水华的爆发,而高精度快速监测水体中藻类的空间分布是一项重要任务。遥感技术是监测水体藻类的有效手段之一。研究表明,浮藻指数(FAI)在监测蓝藻水华方面优于标准化差异植被指数(NDVI)和增强植被指数(EVI)等方法。然而,与NDVI方法相比,FAI方法难以确定阈值,如何选择分类准确率最高的阈值具有挑战性。在这项研究中,选择FAI线性拟合模型(FAI-L)来解决FAI阈值难以确定的问题。创新结合FAI指数和NDVI指数,并使用NDVI指数找到FAI指数的阈值。为了分析FAI-L提取蓝藻水华的适用性,本文选择了多时相Landsat8,HJ-1B,和Sentinel-2遥感图像作为数据源,并以中国的巢湖和太湖为研究区,提取蓝藻水华。结果表明:(1)FAI-L法提取蓝藻水华的准确度普遍高于NDVI和FAI法。在不同的数据来源和不同的研究领域下,FAI-L法提取蓝藻水华的平均准确率为95.13%,分别比NDVI和FAI高出6.98%和18.43%。(2)FAI-L法提取蓝藻水华的平均准确率在84.09~99.03%之间,标准偏差为4.04,具有高度的稳定性和适用性。(3)对于同步的多源图像数据,FAI-L法提取蓝藻水华的平均准确度最高,95.93%,比NDVI和FAI方法高6.77%和13.26%,分别。在本文中,发现FAI-L法提取蓝藻水华具有较高的准确性和稳定性,它可以很好地提取蓝藻水华的空间分布,为蓝藻水华监测提供了一种新的方法。
Currently, more and more lakes around the world are experiencing outbreaks of cyanobacterial blooms, and high-precision and rapid monitoring of the spatial distribution of algae in water bodies is an important task. Remote sensing technology is one of the effective means for monitoring algae in water bodies. Studies have shown that the Floating Algae Index (FAI) is superior to methods such as the Standardized Differential Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in monitoring cyanobacterial blooms. However, compared to the NDVI method, the FAI method has difficulty in determining the threshold, and how to choose the threshold with the highest classification accuracy is challenging. In this study, FAI linear fitting model (FAI-L) is selected to solve the problem that FAI threshold is difficult to determine. Innovatively combine FAI index and NDVI index, and use NDVI index to find the threshold of FAI index. In order to analyze the applicability of FAI-L to extract cyanobacterial blooms, this paper selected multi-temporal Landsat8, HJ-1B, and Sentinel-2 remote sensing images as data sources, and took Chaohu Lake and Taihu Lake in
China as research areas to extract cyanobacterial blooms. The results show that (1) the accuracy of extracting cyanobacterial bloom by FAI-L method is generally higher than that by NDVI and FAI. Under different data sources and different research areas, the average accuracy of extracting cyanobacterial blooms by FAI-L method is 95.13%, which is 6.98% and 18.43% higher than that by NDVI and FAI respectively. (2) The average accuracy of FAI-L method for extracting cyanobacterial blooms varies from 84.09 to 99.03%, with a standard deviation of 4.04, which is highly stable and applicable. (3) For simultaneous multi-source image data, the FAI-L method has the highest average accuracy in extracting cyanobacterial blooms, at 95.93%, which is 6.77% and 13.26% higher than NDVI and FAI methods, respectively. In this paper, it is found that FAI-L method shows high accuracy and stability in extracting cyanobacterial blooms, and it can extract the spatial distribution of cyanobacterial blooms well, which can provide a new method for monitoring cyanobacterial blooms.