Abstract
Open surface freshwater is an important resource for terrestrial ecosystems. However, climate change, seasonal precipitation cycling, and anthropogenic activities add high variability to its availability. Thus, timely and accurate mapping of open surface water is necessary. In this study, a methodology based on the concept of spatial autocorrelation was developed for automatic water extraction from Landsat series images using Taihu Lake in south-eastern China as an example. The results show that this method has great potential to extract continuous open surface water automatically, even when the water surface is covered by floating vegetation or algal blooms. The results also indicate that the second shortwave-infrared band (SWIR2) band performs best for water extraction when water is turbid or covered by surficial vegetation. Near-infrared band (NIR), first shortwave-infrared band (SWIR1), and SWIR2 have consistent extraction success when the water surface is not covered by vegetation. Low filter image processing greatly overestimated extracted water bodies, and cloud and image salt and pepper issues have a large impact on water extraction using the methods developed in this study.
Highlights
Open surface freshwater bodies, including lakes, reservoirs, rivers, streams, and ponds, are a significant sink and source of CO2 for aquatic and terrestrial ecosystems, important resources for agricultural, aquacultural, industrial, and residential use, and are integral to social economics, infrastructure stability, and emergency preparedness [1,2,3]
Among seven bands in the Landsat series, open water extraction from SWIR2 and SWIR1 is more stable than Near-infrared band (NIR), visible bands, and the coastal band from OLI imagery (Figure 3)
NIR, SWIR1, and SWIR2 bands perform better for open surface water extraction (Figure 4, more example extractions in Supplementary A in the supplement materials), especially when the water was shallow and turbid
Summary
Open surface freshwater bodies, including lakes, reservoirs, rivers, streams, and ponds, are a significant sink and source of CO2 for aquatic and terrestrial ecosystems, important resources for agricultural, aquacultural, industrial, and residential use, and are integral to social economics, infrastructure stability, and emergency preparedness [1,2,3]. Seasonal precipitation and anthropogenic activities lead to various changes (including predictable seasonal cycles or episodic variability) in open surface water that can substantially influence environmental security, ecological processes, and related ecosystem services [1,4,5]. Timely, frequent, and precise information on the spatial distribution and temporal change of open water is the foundation of sustainable water resource management, emergency response (flood or drought events), water-related disease control (e.g., malaria), economic development, and environment protection [4,5,6,7,8]. Remote sensing images, recording long-term spatial information of the earth’s surface, have proven their potential for tracking land cover change and ecological processes [9]. Among all the remote sensing platforms, Light Detection and Ranging (LiDAR) provides the most accurate open water maps at regional scales [3], but are limited in their ability to map surface water bodies at global
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