Interannual and Seasonal Variations of Hydrological Connectivity in a Large Shallow Wetland of North China Estimated from Landsat 8 Images
Hydrological connectivity is an important characteristic of wetlands that maintains the stability and functions of an ecosystem. This study investigates the temporal variations of hydrological connectivity and their driving mechanism in Baiyangdian Lake, a large shallow wetland in North China, using...
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doaj-81ad95378f96427082808a0bebdece292021-03-24T00:04:21ZengMDPI AGRemote Sensing2072-42922021-03-01131214121410.3390/rs13061214Interannual and Seasonal Variations of Hydrological Connectivity in a Large Shallow Wetland of North China Estimated from Landsat 8 ImagesZiqi Li0Wenchao Sun1Haiyang Chen2Baolin Xue3Jingshan Yu4Zaifeng Tian5Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, ChinaBeijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, ChinaBeijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, ChinaBeijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, ChinaBeijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, ChinaHebei Provincial Laboratory of Water Environmental Science, Hebei Provincial Academy of Environmental Science, Yaqing Street 30, Shijiazhuang 050051, ChinaHydrological connectivity is an important characteristic of wetlands that maintains the stability and functions of an ecosystem. This study investigates the temporal variations of hydrological connectivity and their driving mechanism in Baiyangdian Lake, a large shallow wetland in North China, using a time series of open water surface area data derived from 36 Landsat 8 multispectral images from 2013–2019 and in situ measured water level data. Water area classification was implemented using the Google Earth Engine. Six commonly used indexes for extracting water surface data from satellite images were compared and the best performing index was selected for the water classification. A composite hydrological connectivity index computed from open water area data derived from Landsat 8 images was developed based on several landscape pattern indices and applied to Baiyangdian Lake. The results show that, reflectance in the near-infrared band is the most accurate index for water classification with >98% overall accuracy because of its sensitivity to different land cover types. The slopes of the best-fit linear relationships between the computed hydrological connectivity and observed water level show high variability between years. In most years, hydrological connectivity generally increases when water levels increase, with an average R<sup>2</sup> of 0.88. The spatial distribution of emergent plants also varies year to year owing to interannual variations of the climate and hydrological regime. This presents a possible explanation for the variations in the annual relationship between hydrological connectivity and water level. For a given water level, the hydrological connectivity is generally higher in spring than summer and autumn. This can be explained by the fact that the drag force exerted by emergent plants, which reduces water flow, is smaller than that for summer and autumn owing to seasonal variations in the phenological characteristics of emergent plants. Our study reveals that both interannual and seasonal variations in the hydrological connectivity of Baiyangdian Lake are related to the growth of emergent plants, which occupy a large portion of the lake area. Proper vegetation management may therefore improve hydrological connectivity in this wetland.https://www.mdpi.com/2072-4292/13/6/1214Baiyangdian Lakehydrological connectivitywater indexLandsat 8temporal variation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ziqi Li Wenchao Sun Haiyang Chen Baolin Xue Jingshan Yu Zaifeng Tian |
spellingShingle |
Ziqi Li Wenchao Sun Haiyang Chen Baolin Xue Jingshan Yu Zaifeng Tian Interannual and Seasonal Variations of Hydrological Connectivity in a Large Shallow Wetland of North China Estimated from Landsat 8 Images Remote Sensing Baiyangdian Lake hydrological connectivity water index Landsat 8 temporal variation |
author_facet |
Ziqi Li Wenchao Sun Haiyang Chen Baolin Xue Jingshan Yu Zaifeng Tian |
author_sort |
Ziqi Li |
title |
Interannual and Seasonal Variations of Hydrological Connectivity in a Large Shallow Wetland of North China Estimated from Landsat 8 Images |
title_short |
Interannual and Seasonal Variations of Hydrological Connectivity in a Large Shallow Wetland of North China Estimated from Landsat 8 Images |
title_full |
Interannual and Seasonal Variations of Hydrological Connectivity in a Large Shallow Wetland of North China Estimated from Landsat 8 Images |
title_fullStr |
Interannual and Seasonal Variations of Hydrological Connectivity in a Large Shallow Wetland of North China Estimated from Landsat 8 Images |
title_full_unstemmed |
Interannual and Seasonal Variations of Hydrological Connectivity in a Large Shallow Wetland of North China Estimated from Landsat 8 Images |
title_sort |
interannual and seasonal variations of hydrological connectivity in a large shallow wetland of north china estimated from landsat 8 images |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-03-01 |
description |
Hydrological connectivity is an important characteristic of wetlands that maintains the stability and functions of an ecosystem. This study investigates the temporal variations of hydrological connectivity and their driving mechanism in Baiyangdian Lake, a large shallow wetland in North China, using a time series of open water surface area data derived from 36 Landsat 8 multispectral images from 2013–2019 and in situ measured water level data. Water area classification was implemented using the Google Earth Engine. Six commonly used indexes for extracting water surface data from satellite images were compared and the best performing index was selected for the water classification. A composite hydrological connectivity index computed from open water area data derived from Landsat 8 images was developed based on several landscape pattern indices and applied to Baiyangdian Lake. The results show that, reflectance in the near-infrared band is the most accurate index for water classification with >98% overall accuracy because of its sensitivity to different land cover types. The slopes of the best-fit linear relationships between the computed hydrological connectivity and observed water level show high variability between years. In most years, hydrological connectivity generally increases when water levels increase, with an average R<sup>2</sup> of 0.88. The spatial distribution of emergent plants also varies year to year owing to interannual variations of the climate and hydrological regime. This presents a possible explanation for the variations in the annual relationship between hydrological connectivity and water level. For a given water level, the hydrological connectivity is generally higher in spring than summer and autumn. This can be explained by the fact that the drag force exerted by emergent plants, which reduces water flow, is smaller than that for summer and autumn owing to seasonal variations in the phenological characteristics of emergent plants. Our study reveals that both interannual and seasonal variations in the hydrological connectivity of Baiyangdian Lake are related to the growth of emergent plants, which occupy a large portion of the lake area. Proper vegetation management may therefore improve hydrological connectivity in this wetland. |
topic |
Baiyangdian Lake hydrological connectivity water index Landsat 8 temporal variation |
url |
https://www.mdpi.com/2072-4292/13/6/1214 |
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