Stacy A. C. Nelson

Per-pixel Classification of High Spatial Resolution Satellite Imagery for Urban Land-cover Mapping

Commercial high spatial resolution satellite data now provide a synoptic and consistent source of digital imagery with detail comparable to that of aerial photography. In the work described here, per-pixel classification, image fusion, and GIS-based map refinement techniques were tailored to pan-sharpened 0.61 m QuickBird imagery to develop a six-category urban land-cover map with 89.3 percent overall accuracy ( �� 0.87). The study area was a rapidly developing 71.5 km 2 part of suburban Raleigh, North Carolina, U.S.A., within the Neuse River basin. “Edge pixels” were a source of classification error as was spectral overlap between bare soil and impervious surfaces and among vegetated cover types. Shadows were not a significant source of classification error. These findings demonstrate that conventional spectral-based classification methods can be used to generate highly accurate maps of urban landscapes using high spatial resolution imagery.

Continental U.S. streamflow trends from 1940 to 2009 and their relationships with watershed spatial characteristics

Changes in streamflow are an important area of ongoing research in the hydrologic sciences. To better understand spatial patterns in past changes in streamflow, we examined relationships between watershed-scale spatial characteristics and trends in streamflow. Trends in streamflow were identified by analyzing mean daily flow observations between 1940 and 2009 from 967 U.S. Geological Survey stream gages. Results indicated that streamflow across the continental U.S., as a whole, increased while becoming less extreme between 1940 and 2009. However, substantial departures from the continental U.S. (CONUS) scale pattern occurred at the regional scale, including increased annual maxima, decreased annual minima, overall drying trends, and changes in streamflow variability. A subset of watersheds belonging to a reference data set exhibited significantly smaller trend magnitudes than those observed in nonreference watersheds. Boosted regression tree models were applied to examine the influence of watershed characteristics on streamflow trend magnitudes at both the CONUS and regional scale. Geographic location was found to be of particular importance at the CONUS scale while local variability in hydroclimate and topography tended to have a strong influence on regional-scale patterns in streamflow trends. This methodology facilitates detailed, data-driven analyses of how the characteristics of individual watersheds interact with large-scale hydroclimate forces to influence how changes in streamflow manifest.

An Object Extraction Approach for Impervious Surface Classification with Very-High-Resolution Imagery

Detailed land cover maps provide important information for research and decision-making but are often expensive to develop and can become outdated quickly. Widespread availability of aerial photography provides increased accessibility of high-resolution imagery and the potential to produce high-accuracy land cover classifications. However, these classifications often require expert knowledge and are time consuming. Our goal was to develop an efficient, accurate technique for classifying impervious surface in urbanizing Wake County, North Carolina. Using an iterative training technique, we classified 111 nonmosaicked, very-high-resolution images using the Feature Analyst software developed by Visual Learning Systems. Feature Analyst provides object extraction classifications by analyzing spatial context in relation to spectral data to classify high-resolution imagery. Our image classification results were 95 percent accurate in impervious surface extraction, with an overall total accuracy of 92 percent. Using this method, users with relatively limited geographic information system (GIS) training and modest budgets can produce highly accurate object-extracted classifications of impervious and pervious surface that are easily manipulated in a GIS.

High-resolution land cover change detection based on fuzzy uncertainty analysis and change reasoning

Land cover change detection is an important research and application area for analysts of remote sensing data. The primary objective of the research described here was to develop a change detection method capable of accommodating spatial and classification uncertainty in generating an accurate map of land cover change using high resolution satellite imagery. As a secondary objective, this method was designed to facilitate the mapping of particular types and locations of change based on specific study goals. Urban land cover change pertinent to surface water quality in Raleigh, North Carolina, was assessed using land cover classifications derived from pan-sharpened, 0.61 m QuickBird images from 2002 and 2005. Post-classification map errors were evaluated using a fuzzy logic approach. First, a ‘change index’ representing a quantitative gradient along which land cover change is characterized by both certainty and relevance, was created. The result was a continuous representation of change, a product type that retains more information and flexibility than discrete maps of change. Finally, fuzzy logic and change reasoning results were integrated into a binary change/no change map that quantified the most certain, likely, and relevant change regions within the study area. A ‘from-to’ change map was developed from this binary map inserting the type of change identified in the raw post-classification map. A from-to change map had an overall accuracy of 78.9% (κ = 0.747) and effectively mapped land cover changes posing a threat to water quality, including increases in impervious surface. This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and practical change analysis.

Principles of Applied Remote Sensing

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Key metabolites in tissue extracts of Elliptio complanata identified using 1 H nuclear magnetic resonance spectroscopy

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Application of GIS Techniques for Developing a Fish Index of Biotic Integrity for an Ecoregion with Low Species Richness

We used 1H-NMR to describe the freshwater mussel metabolome. Muscle, digestive gland, mantle and gill tissues yielded profiles with possible biomarkers of physiologic function. These preliminary studies provide evidence for potential use of digestive gland and mantle tissue for studying physiological impacts of location, sex and reproductive condition on Elliptio complanata.

Tissue Extraction Methods for Metabolic Profiling of a Freshwater Bivalve, Elliptio complanata (Lightfoot, 1786)

Abstract We describe a process for developing an index of biotic integrity (IBI) for resident fish communities in an ecoregion that exhibits low natural species richness. From 1990 to 2006, fish community samples were collected by the North Carolina Division of Water Quality (NCDWQ) at 36 sample sites in the Cape Fear, Lumber, and Yadkin river basins within the Sandhills region of North Carolina. The NCDWQ does not currently have an IBI capable of distinguishing significant differences between reference and non-reference streams. To develop a more robust method of measuring responses to anthropogenic disturbance, we delineated contributing watersheds for each of the 36 sample sites using a geographic information system, hydrologic modeling, and 20-foot-resolution digital elevation models derived from light-detection and ranging data. The 2001 National Land Cover Database (NLCD) and in situ habitat data were used to determine various land-use/land-cover and hydrologic variables within each watershed. These variables were then used to select the sites with absolute minimal anthropogenic impacts. We used the Kruskal-Wallis test to identify 11 fish-community metrics, 2 chemical metrics, and 9 individual species that were significantly different between reference and non-reference sites. Of the final 15 metrics, only 3 exhibited higher values in reference streams. Our results demonstrate that the abundance and richness of the Sandhills fish fauna are greater in areas more highly impacted by anthropogenic activities. By automating the process by which reference sites are chosen, we were able to produce a multi-metric IBI that reflects the varying levels of anthropogenic impacts on wadeable streams in the Sandhills.

Organizational Structures and Data Use in Volunteer Monitoring Organizations (VMOs)

Abstract: Much is still unknown about why freshwater mussels (Unionidae) are particularly sensitive to environmental change. A better understanding of freshwater mussel metabolism is needed, and the field of environmental metabolomics holds the promise to inform these questions. A number of protocols exist for the extraction of metabolites for identification from animal tissues. As a first step in the application of environmental metabolomics to the study of freshwater mussels, we compared extraction protocols using an inorganic oxidizing acid (perchloric acid), an organic nitrile (acetonitrile), and a salt/water solution (Ringers solution) to establish an uncomplicated, robust, repeatable and inexpensive tissue extraction protocol for freshwater mussel tissue. Perchloric acid resulted in notable extraction of energy-related nucleotides (AMP/ADP/ATP), yet had the lowest peak count of the three extraction methods and showed poor repeatability. Acetonitrile and Ringers solution yielded metabolite extraction results similar to each other with Ringers solution having the greatest number of peaks particularly in the 3.0–4.5 ppm sugar/amino acid range. Ringers solution is simple to use, safe and consistent and bears consideration when selecting an extraction protocol for 1H nuclear magnetic resonance experiments.

Comparison of Visual Survey and Mark–Recapture Population Estimates of a Benthic Fish in Hawaii

Complex environmental problems call for unique solutions to monitoring efforts alongside developing a more environmentally literate citizenry. Community-based monitoring (CBM) through the use of volunteer monitoring organizations helps to provide a part of the solution, particularly when CBM groups work with research scientists or government managers. This study of volunteer monitoring organizations (VMOs) active in 2009 in the United States was conducted via survey in order to better understand the organizational structure, data collection procedures and data use of water-quality monitoring by volunteers, focusing on North Carolina. Organizational structures and origins of monitoring groups are discussed and reveal a wide variety of types and history of programs. Data collection procedures including required training and quality assurance were explored and discussed through the survey. Many groups require training of a varied type, but fewer complete quality assurance plans. Multiple types of volunteer monitoring data uses were indicated, including management and research. This study suggests a lack of structure at the state level may hinder the usefulness of data collected for purposes other than local information and environmental education. Cooperation between research scientists and VMOs may aid organizations in publishing more of their data and developing a quality assurance plan.