John W. Jones

Hydrologic Effects of Urbanization and Climate Change on the Flint River Basin, Georgia

AbstractThe potential effects of long-term urbanization and climate change on the freshwater resources of the Flint River basin were examined by using the Precipitation-Runoff Modeling System (PRMS). PRMS is a deterministic, distributed-parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land cover on streamflow and multiple intermediate hydrologic states. Precipitation and temperature output from five general circulation models (GCMs) using one current and three future climate-change scenarios were statistically downscaled for input into PRMS. Projections of urbanization through 2050 derived for the Flint River basin by the Forecasting Scenarios of Future Land-Cover (FORE-SCE) land-cover change model were also used as input to PRMS. Comparison of the central tendency of streamflow simulated based on the three climate-change scenarios showed a slight decrease in overall streamflow relative to simulations under current conditions, mostly ca...

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.

Remote sensing of vegetation pattern and condition to monitor changes in everglades biogeochemistry

Ground-based studies of biogeochemistry and vegetation patterning yield process understanding, but the amount of information gained by ground-based studies can be greatly enhanced by efficient, synoptic, and temporally resolute monitoring afforded by remote sensing. The variety of presently available Everglades vegetation maps reflects both the wide range of application requirements and the need to balance cost and capability. More effort needs to be applied to documenting and understanding vegetation distribution and condition as indicators of biogeochemistry and contamination. Ground-based and remote sensing studies should be modified to maximize their synergy and utility for adaptive management.

Small Reservoir Distribution, Rate of Construction, and Uses in the Upper and Middle Chattahoochee Basins of the Georgia Piedmont, USA, 1950-2010

Construction of small reservoirs affects ecosystem processes in numerous ways including fragmenting stream habitat, altering hydrology, and modifying water chemistry. While the upper and middle Chattahoochee River basins within the Southeastern United States Piedmont contain few natural lakes, they have a high density of small reservoirs (more than 7500 small reservoirs in the nearly 12,000 km2 basin). Policymakers and water managers in the region have little information about small reservoir distribution, uses, or the cumulative inundation of land cover caused by small reservoir construction. Examination of aerial photography reveals the spatiotemporal patterns and extent of small reservoir construction from 1950 to 2010. Over that 60 year timeframe, the area inundated by water increased nearly six fold (from 19 reservoirs covering 0.16% of the study area in 1950 to 329 reservoirs covering 0.95% of the study area in 2010). While agricultural practices were associated with reservoir creation from 1950 to 1970, the highest rates of reservoir construction occurred during subsequent suburban development between 1980 and 1990. Land cover adjacent to individual reservoirs transitioned over time through agricultural abandonment, land reforestation, and conversion to development during suburban expansion. The prolific rate of ongoing small reservoir creation, particularly in newly urbanizing regions and developing counties, necessitates additional attention from watershed managers and continued scientific research into cumulative environmental impacts at the watershed scale.

The use of local indicators of spatial association to improve LiDAR-derived predictions of potential amphibian breeding ponds

We examined whether spatially explicit information improved models that use LiDAR return signal intensity to discriminate in-pond habitat from terrestrial habitat at 24 amphibian breeding ponds. The addition of Local Indicators of Spatial Association (LISA) to LiDAR return intensity data significantly improved predictive models at all ponds, reduced residual error by as much as 74%, and appeared to improve models by reducing classification errors associated with types of in-pond vegetation. We conclude that LISA statistics can help maximize the information content that can be extracted from time resolved LiDAR return data in models that predict the occurrence of small, seasonal ponds.

Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery

We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications.

An approach to regional wetland digital elevation model development using a differential global positioning system and a custom-built helicopter-based surveying system

Accurate topographic data are critical to restoration science and planning for the Everglades region of South Florida, USA. They are needed to monitor and simulate water level, water depth and hydroperiod and are used in scientific research on hydrologic and biologic processes. Because large wetland environments and data acquisition challenge conventional ground-based and remotely sensed data collection methods, the United States Geological Survey (USGS) adapted a classical data collection instrument to global positioning system (GPS) and geographic information system (GIS) technologies. Data acquired with this instrument were processed using geostatistics to yield sub-water level elevation values with centimetre accuracy (±15 cm). The developed database framework, modelling philosophy and metadata protocol allow for continued, collaborative model revision and expansion, given additional elevation or other ancillary data.

Automated Extraction of Surface Water Extent from Sentinel-1 Data

Accurately quantifying surface water extent in wetlands is critical to understanding their role in ecosystem processes. However, current regional- to global-scale surface water products lack the spatial or temporal resolution necessary to characterize heterogeneous or variable wetlands. Here, we proposed a fully automatic classification tree approach to classify surface water extent using Sentinel-1 synthetic aperture radar (SAR) data and training datasets derived from prior class masks. Prior classes of water and non-water were generated from the Shuttle Radar Topography Mission (SRTM) water body dataset (SWBD) or composited dynamic surface water extent (cDSWE) class probabilities. Classification maps of water and non-water were derived over two distinct wetlandscapes: the Delmarva Peninsula and the Prairie Pothole Region. Overall classification accuracy ranged from 79% to 93% when compared to high-resolution images in the Prairie Pothole Region site. Using cDSWE class probabilities reduced omission errors among water bodies by 10% and commission errors among non-water class by 4% when compared with results generated by using the SWBD water mask. These findings indicate that including prior water masks that reflect the dynamics in surface water extent (i.e., cDSWE) is important for the accurate mapping of water bodies using SAR data.

High resolution water body mapping for SWAT evaporative modelling in the Upper Oconee watershed of Georgia, USA

Assistant Professor, Institute for Environmental & Spatial Analysis, University of North Georgia, 3820 Mundy Mill Road, Oakwood, GA 30566, USA Research Geographer, U.S. Geological Survey Eastern Geographic Science Center, 12201 Sunrise Valley Drive, Reston, VA 20192, USA Correspondence Amber R. Ignatius, ORISE Participant, Ecosystems Research Division, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 960 College Station Rd, Athens, GA 30605, USA. Email: arignatius@gmail.com Funding information U.S. Geological Survey Land Remote Sensing Program; National Science Foundation Doctoral Dissertation Research Improvement Grant, Grant/Award Number: BCS‐1103102

Automated extraction of inland surface water extent from Sentinel-1 data

Two automated approaches, including Bayesian probability thresholding and regression tree based methods were utilized to detect the surface water extent with training dataset from prior class probabilities of water and non-water from two datasets. First, prior water and non-water masks were classified using SRTM Water Body Dataset (SWBD) and long-term summarized Dynamic Surface Water Extent (DSWE) class probabilities. Then, fully automatic algorithms were developed to derive water probability and classify surface water extent using Sentinel-1 data. Results over three representative study regions, including the Delmarva Peninsula, Florida Everglades and Prairie Pothole regions, indicate that the automated algorithm is efficient in monitoring open water inundation extent, and detection of partial water extent is possible using Sentienl-1 SAR data.