Efficient Wetland Surface Water Detection and Monitoring via Landsat: Comparison with in situ Data from the Everglades Depth Estimation Network

Abstract

The U.S. Geological Survey is developing new Landsat science products. One, named Dynamic Surface Water Extent (DSWE), is focused on the representation of ground surface inundation as detected in cloud-/shadow-/snow-free pixels for scenes collected over the U.S. and its territories. Characterization of DSWE uncertainty to facilitate its appropriate use in science and resource management is a primary objective. A unique evaluation dataset developed from data made publicly available through the Everglades Depth Estimation Network (EDEN) was used to evaluate one candidate DSWE algorithm that is relatively simple, requires no scene-based calibration data, and is intended to detect inundation in the presence of marshland vegetation. A conceptual model of expected algorithm performance in vegetated wetland environments was postulated, tested and revised. Agreement scores were calculated at the level of scenes and vegetation communities, vegetation index classes, water depths, and individual EDEN gage sites for a variety of temporal aggregations. Landsat Archive cloud cover attribution errors were documented. Cloud cover had some effect on model performance. Error rates increased with vegetation cover. Relatively low error rates for locations of little/no vegetation were unexpectedly dominated by omission errors due to variable substrates and mixed pixel effects. Examined discrepancies between satellite and in situ modeled inundation demonstrated the utility of such comparisons for EDEN database improvement. Importantly, there seems no trend or bias in candidate algorithm performance as a function of time or general hydrologic conditions, an important finding for long-term monitoring. The developed database and knowledge gained from this analysis will be used for improved evaluation of candidate DSWE algorithms as well as other measurements made on Everglades surface inundation, surface water heights and vegetation using radar, lidar and hyperspectral instruments. Although no other sites have such an OPEN ACCESSRemote Sens. 2015, 7 12504 extensive in situ network or long-term records, the broader applicability of this and other candidate DSWE algorithms is being evaluated in other wetlands using this work as a guide. Continued interaction among DSWE producers and potential users will help determine whether the measured accuracies are adequate for practical utility in resource management.

Multi-Decadal Surface Water Dynamics in North American Tundra

“…For the current study, the DSWE data product [42] was selected as input for the annual water maps instead of the custom classification algorithm in the decadal water maps. The DSWE algorithm The study area is located primarily in the Southern Arctic ecoregion and is characterized by low topography and numerous small to moderate sized water bodies.…”

“…For the current study, the DSWE data product [42] was selected as input for the annual water maps instead of the custom classification algorithm in the decadal water maps. The DSWE algorithm uses a hierarchical series of spectral tests on the visible and short wave infrared bands of Landsat to determine the presence of water.…”

“…The Dynamic Surface Water Extent (DSWE) algorithm [42] uses the Landsat surface reflectance data to produce maps of surface water extent for each Landsat scene that is available in the archive. This results in a standardized mapping of water across the Landsat suite of instruments including Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+).…”

Quantifying Sub-Pixel Surface Water Coverage in Urban Environments Using Low-Albedo Fraction from Landsat Imagery

“…The automatic or intelligent scheme should be further investigated to satisfy the demands from its complicated image scenes in massive Landsat datasets. The last one is that most recently proposed methods including the enhanced water index (EWI) [39] and dynamic surface water extent (DSWE) [40] have not been considered in comparisons with the LAF. Further performance contrast with modifications of MNDWI and newly-proposed methods on more Landsat images is essential to promote the LAF in real-word applications.…”

“…The second is that we did not carefully investigate the water extraction problem in the presence of cloud and SLC-gaps. Many algorithms including the multi-temporal linear regression algorithm [39] and the GNSPI algorithm [40] have been proposed to detect the thick clouds and fill gap pixels in SLC-OFF Landsat imagery. The combination of the above algorithms with our LAF would be a promising direction to extend the LAF into urban water extraction of any archived Landsat images.…”