Everglades Depth Estimation Network Research Articles

Extraction of Absolute Water Level Using TanDEM-X Bistatic Observations With a Large Perpendicular Baseline

The application of Wetland synthetic aperture radar interferometry (InSAR) has often been restricted in practical hydrological monitoring because it is based on relative estimates of water level changes between two synthetic aperture radar acquisitions, as opposed to absolute water levels obtained by ground measurements. TanDEM-X bistatic observations can provide absolute water level estimates using simultaneous phase measurements by a two-satellite constellation with TerraSAR-X. We evaluated two datasets of TanDEM-X bistatic observations acquired during an experimental science phase on August 26 and 31, 2015, with a very large baseline configuration to extract absolute water levels of Everglades wetland in southern Florida, USA. The perpendicular baselines are 1.43 and 1.36 km, and the ambiguities of height were calculated as 3.61 and 3.90 m in each interferometric pair, respectively. Hourly water level measurements provided by the Everglades depth estimation network (EDEN) were used to verify the estimated absolute water levels. Several stage stations located in densely vegetated areas that showed incoherence were excluded from the verification as outliers. The verification results show an excellent agreement (degree of determination > 0.95) between the InSAR derived absolute water levels and the stage station measurements. The root mean square error (RMSE) between the TanDEM-X results and stage records was 0.77 and 0.66 m, respectively. Severe volume decorrelations over the vegetated area, owing to the large perpendicular baselines, were detected, despite near zero temporal baseline of the bistatic observations. The absolute water levels can be used as excellent constraints for wetland surface flow models.

A novel approach to monitoring wetland dynamics using CYGNSS: Everglades case study

Global Navigation Satellite System-Reflectometry (GNSS-R) is a remote sensing technique that uses scattered navigation signals opportunistically for science applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first science-driven GNSS-R satellite mission. Although the CYGNSS mission is motivated and designed to better observe weather processes, the data are also useful for other applications, including surface hydrology. This paper aims to demonstrate that CYGNSS has a capability for frequent, high-resolution observations of the wetland dynamics across a wide range of timescales in the tropics. Using matched estimates of water depth from the Everglades Depth Estimation Network (EDEN), we develop methods for mapping inundation over the South Florida Everglades wetland region using CYGNSS data. CYGNSS enables inundation mapping at rapid timescales throughout the diverse Everglades ecosystem, and the data could be complementary to those of well-established observational platforms. The strengths of GNSS-R could be leveraged with the strengths of other techniques to enable studies of wetlands dynamically at short timescales.

Improved Automated Detection of Subpixel-Scale Inundation—Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests

In order to produce useful hydrologic and aquatic habitat data from the Landsat system, the U.S. Geological Survey has developed the “Dynamic Surface Water Extent” (DSWE) Landsat Science Product. DSWE will provide long-term, high-temporal resolution data on variations in inundation extent. The model used to generate DSWE is composed of five decision-rule based tests that do not require scene-based training. To allow its general application, required inputs are limited to the Landsat at-surface reflectance product and a digital elevation model. Unlike other Landsat-based water products, DSWE includes pixels that are only partially covered by water to increase inundation dynamics information content. Previously published DSWE model development included one wetland-focused test developed through visual inspection of field-collected Everglades spectra. A comparison of that test’s output against Everglades Depth Estimation Network (EDEN) in situ data confirmed the expectation that omission errors were a prime source of inaccuracy in vegetated environments. Further evaluation exposed a tendency toward commission error in coniferous forests. Improvements to the subpixel level “partial surface water” (PSW) component of DSWE was the focus of this research. Spectral mixture models were created from a variety of laboratory and image-derived endmembers. Based on the mixture modeling, a more “aggressive” PSW rule improved accuracy in herbaceous wetlands and reduced errors of commission elsewhere, while a second “conservative” test provides an alternative when commission errors must be minimized. Replication of the EDEN-based experiments using the revised PSW tests yielded a statistically significant increase in mean overall agreement (4%, p = 0.01, n = 50) and a statistically significant decrease (11%, p = 0.009, n = 50) in mean errors of omission. Because the developed spectral mixture models included image-derived vegetation endmembers and laboratory spectra for soil groups found across the US, simulations suggest where the revised DSWE PSW tests perform as they do in the Everglades and where they may prove problematic. Visual comparison of DSWE outputs with an unusual variety of coincidently collected images for locations spread throughout the US support conclusions drawn from Everglades quantitative analyses and highlight DSWE PSW component strengths and weaknesses.

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

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 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.

Morphometric and hydrologic characteristics of alligator holes in Everglades National Park, Florida from 1994 to 2007

ABSTRACTAlligator (Alligator mississippiensis) holes are a key feature in the Everglades landscape providing aquatic refuge for alligators and other aquatic organisms. The morphology of the hole as well as its location in the landscape may influence its function as an aquatic refugium. Morphologic and hydrologic characteristics and dynamics of 50 alligator holes in Everglades National Park, Florida were examined based on 1‐m resolution digital images and field measurements from 1994 to 2007. Major morphometric parameters of alligator holes calculated for the study area included surface area, diameter, major axis orientation, basin depth, and circularity index. We used basin depth along with surface water modelling from the Everglades Depth Estimation Network to describe alligator hole hydroperiod and examine relationships among morphologic features, habitat, and hydrology. Alligator holes in this study were similar morphologically (pond surface area, basin depth, and sediment depth) among locations and habitats, and for the most part over time with the exception of holes in the Rocky glades. Alligator holes with greater surface area were not necessarily deeper holes. Hole hydroperiod was not correlated with surface area, and larger holes did not dry out less frequently than smaller holes. Although marsh hydroperiods varied by location and habitat, alligator hole hydroperiod did not, illustrating that across the landscape alligator holes provide aquatic refugia under a range of hydrologic conditions. Published in 2012. This article is a US Government work and is in the public domain in the USA.

Landscape unit based digital elevation model development for the freshwater wetlands within the Arthur C. Marshall Loxahatchee National Wildlife Refuge, Southeastern Florida

The hydrologic regime is a critical limiting factor in the delicate ecosystem of the greater Everglades freshwater wetlands in south Florida that has been severely altered by management activities in the past several decades. “Getting the water right” is regarded as the key to successful restoration of this unique wetland ecosystem. An essential component to represent and model its hydrologic regime, specifically water depth, is an accurate ground Digital Elevation Model (DEM). The Everglades Depth Estimation Network (EDEN) supplies important hydrologic data, and its products (including a ground DEM) have been well received by scientists and resource managers involved in Everglades restoration. This study improves the EDEN DEMs of the Loxahatchee National Wildlife Refuge, also known as Water Conservation Area 1 (WCA1), by adopting a landscape unit (LU) based interpolation approach. The study first filtered the input elevation data based on newly available vegetation data, and then created a separate geostatistical model (universal kriging) for each LU. The resultant DEMs have encouraging cross-validation and validation results, especially since the validation is based on an independent elevation dataset (derived by subtracting water depth measurements from EDEN water surface elevations). The DEM product of this study will directly benefit hydrologic and ecological studies as well as restoration efforts. The study will also be valuable for a broad range of wetland studies.

Validation and ecosystem applications of the EDEN water‐surface model for the Florida Everglades

AbstractThe Everglades Depth Estimation Network (EDEN) is an integrated network of real‐time water‐level monitoring, ground‐elevation modelling, and water‐surface modelling that provides scientists and water managers with current (2000‐present), on‐line water‐level and water‐depth information for the freshwater Everglades. Continuous daily spatial interpolations of surface water‐level gage data from the EDEN water‐surface model are presented on grid with 400‐m spacing. The direct model output is continuous daily surface‐water level, and other hydrologic data such as water depth and hydroperiod can be derived together with ground digital elevation models.This paper validated the spatially continuous EDEN water‐surface model for the Everglades, Florida by using an independent field‐measured dataset. Three model applications were also demonstrated: to estimate site‐specific ground elevation, to create water‐depth time series for tree islands, and to generate contiguous water coverage areas. We found that there were no statistically significant differences between model‐predicted and field‐observed water‐level data in central Everglades (p = 0·51). Over 95% of the predicted‐water levels matched observed‐water levels within the range of ± 5 cm. Overall, the model is reliable by a root mean square error (RMSE) of 3·3 cm.The accurate, high‐resolution hydrological data, generated over broad spatial and temporal scales by the EDEN water‐surface model, provides a previously missing key to understanding the habitat requirements and linkages among native and invasive populations, including fish, wildlife, wading birds, and plants. The EDEN model is a powerful tool that could be adapted for other ecosystem‐scale restoration and management programs worldwide. Copyright © 2009 John Wiley & Sons, Ltd.

Validation of daily surface water depth model of the Greater Everglades based on real-time stage monitoring and aerial ground elevation survey

As part of the Everglades Depth Estimation Network (EDEN) project, this paper describes validation of raster-based daily surface water depth models of the Greater Everglades in Florida developed using real-time stage data and elevation data obtained from a survey with an aerial height finder. Daily median stage data obtained at over 200 locations were interpolated using the multiquadric radial basis function. Surface water depth was obtained by subtracting a digital elevation model from the interpolated stage raster. The model was validated with 751 independent field measurements of surface water depth between 1999 and 2004. Correlations between prediction error and both density of the monitoring gages and distance from a major linear geographic feature, such as a canal, were weak, suggesting that the error does not depend on these features. South Florida has distinct dry and wet seasons and the study area is dominated by sawgrass and wet prairie. Seasonality and ground vegetation type significantly affect prediction error. Correlation between observed and predicted water depth was high for all combination of season and vegetation type (0.83–0.96). Model validation using an equivalence test provided evidence of equivalence between predicted and observed water depths in dry season prairie-dominated and wet season sawgrass-dominated areas with the strict test and in dry season sawgrass-dominated areas with the liberal test, but not in wet season prairie-dominated areas. Equivalence between observed and predicted water depth for both dry season sawgrass- and wet season prairie-dominated areas were confirmed with the strict test after further model calibrations using linear regressions.

Estimation of water surface elevations for the Everglades, Florida

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level monitoring gages and modeling methods that provides scientists and managers with current (2000–present) online water surface and water depth information for the freshwater domain of the Greater Everglades. This integrated system presents data on a 400-m square grid to assist in (1) large-scale field operations; (2) integration of hydrologic and ecologic responses; (3) supporting biological and ecological assessment of the implementation of the Comprehensive Everglades Restoration Plan (CERP); and (4) assessing trophic-level responses to hydrodynamic changes in the Everglades. This paper investigates the radial basis function multiquadric method of interpolation to obtain a continuous freshwater surface across the entire Everglades using radio-transmitted data from a network of water-level gages managed by the US Geological Survey (USGS), the South Florida Water Management District (SFWMD), and the Everglades National Park (ENP). Since the hydrological connection is interrupted by canals and levees across the study area, boundary conditions were simulated by linearly interpolating along those features and integrating the results together with the data from marsh stations to obtain a continuous water surface through multiquadric interpolation. The absolute cross-validation errors greater than 5 cm correlate well with the local outliers and the minimum distance between the closest stations within 2000-m radius, but seem to be independent of vegetation or season.

Spatially Continuous Interpolation of Water Stage and Water Depths Using the Everglades Depth Estimation Network (EDEN)

Circular 1521, a 34-page report by Leonard Pearlstine, Aaron Higer, Monica Palaseanu, Ikuko Fujisaki, and Frank Mazzotti, documents the procedures used in generation of EDEN water stage and water depth interpolated surfaces. Includes references. Published by the UF Department of Wildlife Ecology and Conservation, July 2007. Minor revision September 2007. CIR1521/UW278: Spatially Continuous Interpolation of Water Stage and Water Depths Using the Everglades Depth Estimation Network (EDEN) (ufl.edu)