Automatic Extraction of Open Water Using Imagery of Landsat Series

Abstract

<jats:p>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.</jats:p>

Automatic Detection of Algal Blooms Using Sentinel-2 MSI and Landsat OLI Images

“…They do not differ significantly in the SWIR2 band (Figure 10). Thus, SWIR2 has little potential to extract algal blooms from water, consistent with previous research [69]. Both moderate and severe algal blooms have significantly higher reflectance than turbid or clear water in NIR and part of the red-edge wavelength regions (bands 6, 7, 8a and 8 for Sentinel-2 MSI images and NIR for Landsat OLI images; Figure 10).…”

“…Another image quality issue that might affect extraction accuracy using spatial analysis is "salt and pepper" effects [69]. However, this was not found in our study because Landsat OLI and Sentinel-2 MSI images with geomatics and atmospheric correction have high image quality that overcome the issue.…”

“…First, turbid water has a strong influence on mapping algal blooms using spectral bands within the visible range (Figure 3; spectra downloaded from the USGS spectra library). Second, SWIR2 band was not selected as it has been shown previously in Lake Taihu, it cannot distinguish algal blooms and open water [69]. Finally, algal blooms have higher reflectance in red-edge, NIR and SWIR1 bands than turbid and open water (Figure 3).…”

“…The effects of other land cover types with high reflectance values were minimized with accurate surface water extraction that masked those features, retaining only open water regions. We have previously shown that the second shortwave infrared (SWIR2) band performs well to map open surface water with high turbidity or algal bloom coverage, but with islands, bridges and aquatic vegetation excluded [69].…”

“…Previous studies on Lake Taihu indicate that visible bands are highly influenced by turbidity [69]. This can make them unsuitable for water mapping as water-leaving radiance is dominated by particulate scattering properties instead of absorption in highly turbid water [73].…”