| 引用本文: | 徐良泉,苏涛,雷波,王仁义,刘欣蓓,孟成,邸俊楠.基于FAI-L方法的巢湖水域藻华提取方法研究.湖泊科学,2023,35(4):1222-1233. DOI:10.18307/2023.0416 |
| Xu Liangquan,Su Tao,Lei Bo,Wang Renyi,Liu Xinpei,Meng Cheng,Di Junnan.The method of algal bloom extraction in Lake Chaohu waters based on FAI-L method. J. Lake Sci.2023,35(4):1222-1233. DOI:10.18307/2023.0416 |
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| 基于FAI-L方法的巢湖水域藻华提取方法研究 |
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徐良泉1,2, 苏涛1,2, 雷波3, 王仁义1, 刘欣蓓1, 孟成1, 邸俊楠1
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1.安徽理工大学, 空间信息与测绘工程学院, 淮南 232001;2.安徽理工大学矿山采动灾害空天地协同监测与预警安徽普通高校重点实验室, 淮南 232001;3.中国水利水电科学研究院水利研究所, 北京 100048
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| 摘要: |
| 近年来随着人类的活动日益加剧,水体富营养化问题已经严重威胁到湖泊生态安全。为了快速并准确地获取藻华爆发的范围,本文提出浮游藻类指数线性拟合模型 (FAI linear fitting model,FAI-L)。在以往的研究中,NDVI(normalized difference vegetation index)已经广泛应用于藻华的识别中,且采用坡度计算获取NDVI阈值的方法也得到验证,相对于NDVI,FAI对环境条件的改变敏感度较低,且由于FAI增加了短红外波段,能够有效地降低部分大气和薄云的影响,对藻华的识别有较高的精度,但是FAI识别藻华的阈值如何确定的问题没有有效的解决办法。本文通过建立NDVI与FAI的线性拟合方程,利用NDVI阈值确定FAI阈值,能够有效地解决FAI阈值确定问题。通过Landsat8和Sentinel-2的提取结果显示:(1)FAI-L相对于NDVI提取结果在精度上有较大提升。采用该方法对于Landsat8影像的藻华提取精度为97.16%,相对于NDVI的提取精度(91.72%)提高了5.44%。(2)以Sentinel-2数据为基础探究FAI-L的适用性情况,结果显示对于高分辨率的遥感影像,FAI-L的提取精度达到97.10%,对高分辨率影像具有良好的适用性。(3)FAI-L可以很好地排除云层对藻华提取的干扰,且对藻华的边缘区域和较小的零星区域有更好的提取效果。通过FAI-L能够较好地获取巢湖水域藻华的空间分布情况,可以有效降低由于云层造成的误判现象,具有较高的精度和适用性,可为藻华提取光谱敏感指数的阈值确定提供新方法。 |
| 关键词: 巢湖 NDVI FAI-L 藻华 线性回归方程 |
| DOI：10.18307/2023.0416 |
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| 基金项目:安徽理工大学引进人才科研启动项目(ZY030)和国家重点研发计划项目(2018YFC0407703)联合资助。 |
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| The method of algal bloom extraction in Lake Chaohu waters based on FAI-L method |
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Xu Liangquan1,2, Su Tao1,2, Lei Bo3, Wang Renyi1, Liu Xinpei1, Meng Cheng1, Di Junnan1
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1.School of Spatial Informatics and Geomatics Engineering, Anhui University of Science and Technology, Huainan 232001, P. R. China;2.Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes, Anhui University of Science and Technology, KLAHEI (KLAHEI18015), Huainan 232001, P. R. China;3.Department of Irrigation and Drainage, China Institute of Water Resources and Hydropower Research, Beijing 100048, P. R. China
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| Abstract: |
| In recent years, with increasing human activities, water eutrophication has seriously threatened the ecological security of lakes. In order to obtain the range of algal bloom quickly and accurately, FAI linear fitting model of planktonic algae index (FAI-L) is proposed in this paper. In previous studies, NDVI (normalized difference vegetation index) has been widely used in algal bloom identification, and the method of obtaining NDVI threshold by slope calculation has also been verified. Compared with NDVI, FAI is less sensitive to changes in environmental conditions and has higher accuracy in the identification of algal blooms due to the addition of short infrared bands to effectively reduce the impacts of atmosphere and thin clouds. However, there is no effective solution to the problem of how to determine the threshold for identifying algal blooms by FAI. This paper established the linear fitting equation between NDVI and FAI and used NDVI threshold to determine FAI threshold, which can effectively solve the problem of FAI threshold determination. The extraction results of Landsat8 and Sentinel-2 showed that: (1) Compared with NDVI, the accuracy of FAI-L was greatly improved. The extraction accuracy of algal blooms from Landsat8 images was 97.16%, which was 5.44% higher than that of NDVI (91.72%). (2) Based on Sentinel-2 data, the applicability of FAI-L was explored. The results showed that for high-resolution remote sensing images, the extraction accuracy of FAI-L was 97.10%, and had good applicability. (3) FAI-L could eliminate the interference of clouds on the extraction of algal blooms and had a better extraction effect on the edge areas and small sporadic areas of algal blooms. FAI-L can better obtain the spatial distribution of algal blooms in Lake Chaohu waters, effectively reduce the misjudgment caused by clouds, and has high accuracy and applicability. It can provide a new method for determining the threshold of the spectral sensitivity index of algal bloom extraction. |
| Key words: Lake Chaohu NDVI FAI-L algal blooms Linear regression equation |
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