School of Spatial Information and Geomatics Engineering, Anhui University of Science and Technology, Huainan, 232001, China.
Department of Irrigation and Drainage, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China.
Environ Monit Assess. 2024 Sep 9;196(10):909. doi: 10.1007/s10661-024-13082-3.
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.
目前，全球越来越多的湖泊正经历着蓝藻水华的爆发，高精度、快速监测水体中藻类的空间分布是一项重要任务。遥感技术是水体藻类监测的有效手段之一。研究表明，浮游藻类指数（FAI）在监测蓝藻水华方面优于标准化差异植被指数（NDVI）和增强型植被指数（EVI）等方法。然而，与 NDVI 方法相比，FAI 方法在确定阈值方面存在困难，如何选择具有最高分类精度的阈值具有挑战性。在本研究中，选择 FAI 线性拟合模型（FAI-L）来解决 FAI 阈值难以确定的问题。创新性地结合 FAI 指数和 NDVI 指数，利用 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，本文选择多时相 Landsat8、HJ-1B 和 Sentinel-2 遥感图像作为数据源，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 方法提取蓝藻水华的精度普遍高于 NDVI 和 FAI。在不同的数据源和不同的研究区域下，FAI-L 方法提取蓝藻水华的平均精度为 95.13%，分别比 NDVI 和 FAI 高 6.98%和 18.43%。（2）FAI-L 方法提取蓝藻水华的平均精度在 84.09%~99.03%之间，标准差为 4.04，稳定性高，适用性强。（3）对于同时多源图像数据，FAI-L 方法提取蓝藻水华的平均精度最高，为 95.93%，分别比 NDVI 和 FAI 方法高 6.77%和 13.26%。本文发现，FAI-L 方法在提取蓝藻水华方面具有较高的精度和稳定性，能够很好地提取蓝藻水华的空间分布，可为蓝藻水华监测提供一种新方法。
