Automatic Detection of Algal Blooms Using Sentinel-2 MSI and Landsat OLI Images
Algal bloom is a serious global issue for inland waters, posing poses a serious threat to aquatic ecosystems. The timely and accurate detection of algal blooms is critical for their control, management and forecasting. Optical satellite imagery with short revisit times has been widely used to monito...
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| Published in | IEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; pp. 8497 - 8511 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
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IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | Algal bloom is a serious global issue for inland waters, posing poses a serious threat to aquatic ecosystems. The timely and accurate detection of algal blooms is critical for their control, management and forecasting. Optical satellite imagery with short revisit times has been widely used to monitor algal blooms in marine and large inland waters. However, such images typically are of coarse resolution, limiting their utility to map algal blooms in small inland waters. We developed a new method to map the spatial extent of algal blooms using sentinel-2 multispectral instrument (MSI) and Landsat operational land imager (OLI) images with higher spatial resolution but lower temporal resolution based on the concept of local indicator of spatial association. The mapping results was applied to measure the duration and frequency of algal blooms in Lake Taihu from 2017 to 2020. Our results show that the developed methodology is able to extract the spatial distribution of moderate algal blooms using near-infrared and red-edge bands (bands 6, 7, 8, and 8a of sentinel-2 MSI images or band 5 of Landsat OLI images) by comparison with MODIS FAI data ( R 2 = 0.888 for sentinel-2 MSI and R 2 = 0.85 for Landsat OLI, P < 0.05). However, the temporal resolution of combined Landsat OLI and sentinel-2 MSI images (i.e., up to 2-3 days) is insufficient to monitor algal blooms during the summer time in Lake Taihu due to cloud effects and rapid algal change. Our research has benefits for the management of small inland waters with complex water conditions. |
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| AbstractList | Algal bloom is a serious global issue for inland waters, posing poses a serious threat to aquatic ecosystems. The timely and accurate detection of algal blooms is critical for their control, management and forecasting. Optical satellite imagery with short revisit times has been widely used to monitor algal blooms in marine and large inland waters. However, such images typically are of coarse resolution, limiting their utility to map algal blooms in small inland waters. We developed a new method to map the spatial extent of algal blooms using sentinel-2 multispectral instrument (MSI) and Landsat operational land imager (OLI) images with higher spatial resolution but lower temporal resolution based on the concept of local indicator of spatial association. The mapping results was applied to measure the duration and frequency of algal blooms in Lake Taihu from 2017 to 2020. Our results show that the developed methodology is able to extract the spatial distribution of moderate algal blooms using near-infrared and red-edge bands (bands 6, 7, 8, and 8a of sentinel-2 MSI images or band 5 of Landsat OLI images) by comparison with MODIS FAI data ( R 2 = 0.888 for sentinel-2 MSI and R 2 = 0.85 for Landsat OLI, P < 0.05). However, the temporal resolution of combined Landsat OLI and sentinel-2 MSI images (i.e., up to 2–3 days) is insufficient to monitor algal blooms during the summer time in Lake Taihu due to cloud effects and rapid algal change. Our research has benefits for the management of small inland waters with complex water conditions. Algal bloom is a serious global issue for inland waters, posing poses a serious threat to aquatic ecosystems. The timely and accurate detection of algal blooms is critical for their control, management and forecasting. Optical satellite imagery with short revisit times has been widely used to monitor algal blooms in marine and large inland waters. However, such images typically are of coarse resolution, limiting their utility to map algal blooms in small inland waters. We developed a new method to map the spatial extent of algal blooms using sentinel-2 multispectral instrument (MSI) and Landsat operational land imager (OLI) images with higher spatial resolution but lower temporal resolution based on the concept of local indicator of spatial association. The mapping results was applied to measure the duration and frequency of algal blooms in Lake Taihu from 2017 to 2020. Our results show that the developed methodology is able to extract the spatial distribution of moderate algal blooms using near-infrared and red-edge bands (bands 6, 7, 8, and 8a of sentinel-2 MSI images or band 5 of Landsat OLI images) by comparison with MODIS FAI data (R2 = 0.888 for sentinel-2 MSI and R2 = 0.85 for Landsat OLI, P < 0.05). However, the temporal resolution of combined Landsat OLI and sentinel-2 MSI images (i.e., up to 2-3 days) is insufficient to monitor algal blooms during the summer time in Lake Taihu due to cloud effects and rapid algal change. Our research has benefits for the management of small inland waters with complex water conditions. |
| Author | Zhu, Mengyuan Luan, Zhaoqing Pu, Yihan Xu, Dandan Shi, Kun |
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| SubjectTerms | Algae Algal bloom Algal blooms Aquatic ecosystems Artificial satellites cyanobacteria Detection Eutrophication Imagery Inland waters Lakes Landsat Landsat operational land imager (OLI) Landsat satellites Microorganisms Monitoring Remote sensing Resolution Satellite imagery sentinel-2 multispectral instrument (MSI) Spaceborne remote sensing spatial autocorrelation Spatial discrimination Spatial distribution Spatial resolution Temporal resolution |
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| Title | Automatic Detection of Algal Blooms Using Sentinel-2 MSI and Landsat OLI Images |
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