Geology topics

E. Terrence Slonecker

Publications and source records attributed to E. Terrence Slonecker.

31 records · Page 2Linked to original sources

USGS2013

Landscape indicators and land cover change in the Mid-Atlantic Region of the United States, 1973-2001

Landscape indicators, derived from land use and land cover data as well as other data, were used to calculate the ecological consequences of land cover change in terms of nitrate loading and physical bird habitat. Both were modeled from 1973, 1992, and 2001 land cover data in the Mid-Atlantic region of the United States. Land cover statistics and trends are calculated for three time periods. In general, forest gain and agricultural loss was found in areas of improving landscape indicators and forest loss and agricultural gain was found to occur in areas of declining indicators, which was confirmed by high-resolution aerial photographic analysis.

Delaware, Maryland, New Jersey, New York, North Ca↗

USGS2013

Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission

Precise monitoring of agricultural crop biomass and yield quantities is critical for crop production management and prediction. The goal of this study was to compare hyperspectral narrowband (HNB) versus multispectral broadband (MBB) reflectance data in studying irrigated cropland characteristics of five leading world crops (cotton, wheat, maize, rice, and alfalfa) with the objectives of: 1. Modeling crop productivity, and 2. Discriminating crop types. HNB data were obtained from Hyperion hyperspectral imager and field ASD spectroradiometer, and MBB data were obtained from five broadband sensors: Landsat-7 Enhanced Thematic Mapper Plus (ETM +), Advanced Land Imager (ALI), Indian Remote Sensing (IRS), IKONOS, and QuickBird. A large collection of field spectral and biophysical variables were gathered for the 5 crops in Central Asia throughout the growing seasons of 2006 and 2007. Overall, the HNB and hyperspectral vegetation index (HVI) crop biophysical models explained about 25% greater variability when compared with corresponding MBB models. Typically, 3 to 7 HNBs, in multiple linear regression models of a given crop variable, explained more than 93% of variability in crop models. The evaluation of λ 1 (400–2500 nm) versus λ 2 (400–2500 nm) plots of various crop biophysical variables showed that the best two-band normalized difference HVIs involved HNBs centered at: (i) 742 nm and 1175 nm (HVI742-1175), (ii) 1296 nm and 1054 nm (HVI1296-1054), (iii) 1225 nm and 697 nm (HVI1225-697), and (iv) 702 nm and 1104 nm (HVI702-1104). Among the most frequently occurring HNBs in various crop biophysical models, 74% were located in the 1051–2331 nm spectral range, followed by 10% in the moisture sensitive 970 nm, 6% in the red and red-edge (630–752 nm), and the remaining 10% distributed between blue (400–500 nm), green (501–600 nm), and NIR (760–900 nm). Discriminant models, used for discriminating 3 or 4 or 5 crop types, showed significantly higher accuracies when using HNBs (> 90%) over MBBs data (varied between 45 and 84%). Finally, the study highlighted 29 HNBs of Hyperion that are optimal in the study of agricultural crops and potentially significant to the upcoming NASA HyspIRI mission. Determining optimal and redundant bands for a given application will help overcoming the Hughes' phenomenon (or curse of high dimensionality of data).

Syr Darya River Basin↗

USGS2013

Advanced and applied remote sensing of environmental conditions

"Remote sensing” is a general term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotely sensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth’s surface. Remotely sensed data are fundamental to geographic science. The U.S. Geological Survey’s (USGS) Eastern Geographic Science Center (EGSC) is currently conducting and promoting the research and development of several different aspects of remote sensing science in both the laboratory and from overhead instruments. Spectroscopy is the science of recording interactions of energy and matter and is the bench science for all remote sensing. Visible and infrared analysis in the laboratory with special instruments called spectrometers enables the transfer of this research from the laboratory to multispectral (5–15 broad bands) and hyperspectral (50–300 narrow contiguous bands) analyses from aircraft and satellite sensors. In addition, mid-wave (3–5 micrometers, µm) and long-wave (8–14 µm) infrared data analysis, such as attenuated total reflectance (ATR) spectral analysis, are also conducted. ATR is a special form of vibrational infrared spectroscopy that has many applications in chemistry and biology but has recently been shown to be especially diagnostic for vegetation analysis.

Fact Sheet↗

USGS2013

Consequences of land use and land cover change

The U.S. Geological Survey (USGS) Climate and Land Use Change Mission Area is one of seven USGS mission areas that focuses on making substantial scientific "...contributions to understanding how Earth systems interact, respond to, and cause global change". Using satellite and other remotely sensed data, USGS scientists monitor patterns of land cover change over space and time at regional, national, and global scales. These data are analyzed to understand the causes and consequences of changing land cover, such as economic impacts, effects on water quality and availability, the spread of invasive species, habitats and biodiversity, carbon fluctuations, and climate variability. USGS scientists are among the leaders in the study of land cover, which is a term that generally refers to the vegetation and artificial structures that cover the land surface. Examples of land cover include forests, grasslands, wetlands, water, crops, and buildings. Land use involves human activities that take place on the land. For example, "grass" is a land cover, whereas pasture and recreational parks are land uses that produce a cover of grass.

Fact Sheet↗

USGS2011

The use of historical imagery in the remediation of an urban hazardous waste site

The information derived from the interpretation of historical aerial photographs is perhaps the most basic multitemporal application of remote-sensing data. Aerial photographs dating back to the early 20th century can be extremely valuable sources of historical landscape activity. In this application, imagery from 1918 to 1927 provided a wealth of information about chemical weapons testing, storage, handling, and disposal of these hazardous materials. When analyzed by a trained photo-analyst, the 1918 aerial photographs resulted in 42 features of potential interest. When compared with current remedial activities and known areas of contamination, 33 of 42 or 78.5% of the features were spatially correlated with areas of known contamination or other remedial hazardous waste cleanup activity.

IEEE Journal of Selected Topics in Applied Earth O↗

USGS2011

An evaluation of traditional and emerging remote sensing technologies for the detection of fugitive contamination at selected Superfund hazardous waste sites

This report represents a remote sensing research effort conducted by the U.S. Geological Survey in cooperation with the U.S. Environmental Protection Agency (EPA) for the EPA Office of Inspector General. The objective of this investigation was to explore the efficacy of remote sensing as a technology for postclosure monitoring of hazardous waste sites as defined under the Comprehensive Environmental Response Compensation and Liability Act of 1980 (Public Law 96-510, 42 U.S.C. §9601 et seq.), also known as \"Superfund.\" Five delisted Superfund sites in Maryland and Virginia were imaged with a hyperspectral sensor and visited for collection of soil, water, and spectral samples and inspection of general site conditions. This report evaluates traditional and hyperspectral imagery and field spectroscopic measurement techniques in the characterization and analysis of fugitive (anthropogenic, uncontrolled) contamination at previously remediated hazardous waste disposal sites.

Open-File Report↗

USGS2011

Graphic products used in the evaluation of traditional and emerging remote sensing technologies for the detection of fugitive contamination at selected superfund hazardous waste sites

This report presents the overhead imagery and field sampling results used to prepare U.S. Geological Survey Open-File Report 2011-1050, 'Evaluation of Traditional and Emerging Remote Sensing Technologies for the Detection of Fugitive Contamination at Selected Superfund Hazardous Waste Sites'. These graphic products were used in the evaluation of remote sensing technology in postclosure monitoring of hazardous waste sites and represent an ongoing research effort. Soil sampling results presented here were accomplished with field portable x-ray fluoresence (XRF) technology and are used as screening tools only representing the current conditions of metals and other contaminants at selected Superfund hazardous waste sites.

Open-File Report↗

USGS2009

Spectroscopic analysis of arsenic uptake in Pteris Ferns

Two arsenic-accumulating Pteris ferns ( Pteris cretica mayii and Pteris multifida ), along with a non-accumulating control fern ( Nephrolepis exaltata ) were grown in greenhouse conditions in clean sand spiked with 0, 20, 50, 100 and 200 ppm sodium arsenate. Spectral data were collected for each of five replicates prior to harvest at 4-week intervals. Fern samples were analyzed for total metals content and Partial Least Squares and Stepwise Linear Regression techniques were used to develop models from the spectral data. Results showed that Pteris cretica mayii and Pteris multifida are confirmed hyperaccumulators of inorganic arsenic and that reasonably accurate predictive models of arsenic concentration can be developed from the first derivative of spectral reflectance of the hyperaccumulating Pteris ferns. Both the arsenic uptake and spectral results indicate that there is some species-specific variability but the results compare favorably with previously published data and additional research is recommended.

Remote Sensing↗

USGS2009

An automated imagery orthorectification pilot

Automated orthorectification of raw image products is now possible based on the comprehensive metadata collected by Global Positioning Systems and Inertial Measurement Unit technology aboard aircraft and satellite digital imaging systems, and based on emerging pattern-matching and automated image-to-image and control point selection capabilities in many advanced image processing systems. Automated orthorectification of standard aerial photography is also possible if a camera calibration report and sufficient metadata is available. Orthorectification of historical imagery, for which only limited metadata was available, was also attempted and found to require some user input, creating a semi-automated process that still has significant potential to reduce processing time and expense for the conversion of archival historical imagery into geospatially enabled, digital formats, facilitating preservation and utilization of a vast archive of historical imagery. Over 90 percent of the frames of historical aerial photos used in this experiment were successfully orthorectified to the accuracy of the USGS 100K base map series utilized for the geospatial reference of the archive. The accuracy standard for the 100K series maps is approximately 167 feet (51 meters). The main problems associated with orthorectification failure were cloud cover, shadow and historical landscape change which confused automated image-to-image matching processes. Further research is recommended to optimize automated orthorectification methods and enable broad operational use, especially as related to historical imagery archives.

Journal of Remote Sensing↗

USGS2009

Research Implementation and Quality Assurance Project Plan: An Evaluation of Hyperspectral Remote Sensing Technologies for the Detection of Fugitive Contamination at Selected Superfund Hazardous Waste Sites

This project is a research collaboration between the U.S. Environmental Protection Agency (EPA) Office of Inspector General (OIG) and the U.S. Geological Survey (USGS) Eastern Geographic Science Center (EGSC), for the purpose of evaluating the utility of hyperspectral remote sensing technology for post-closure monitoring of residual contamination at delisted and closed hazardous waste sites as defined under the Comprehensive Environmental Response Compensation and Liability Act [CERCLA (also known as 'Superfund')] of 1980 and the Superfund Amendments and Reauthorization Act (SARA) of 1986.

Open-File Report↗

USGS2009

A landscape indicator approach to the identification and articulation of the consequences of land-cover change in the Mid-Atlantic Region, 1973-2001

Landscape indicators, derived from land-use and land-cover data, hydrology, nitrate deposition, and elevation data, were used by Jones and others (2001a) to calculate the ecological consequences of land-cover change. Nitrate loading and physical bird habitat were modeled from 1973 and 1992 land-cover and other spatial data for the Mid-Atlantic region. Utilizing the same methods, this study extends the analysis another decade with the use of the 2001 National Land Cover Dataset. Land-cover statistics and trends are calculated for three time periods: 1973-1992, 1992-2001 and 1973-2001. In addition, high-resolution aerial photographs (1 meter or better ground-sample distance) were acquired and analyzed for thirteen pairs of adjacent USGS 7.5 minute quadrangle maps in areas where distinct positive or negative changes to nitrogen loading and bird habitat were previously calculated. During the entire 30 year period, the data show that there was extensive loss of agriculture and forest area and a major increase in urban land-cover classes. However, the majority of the conversion of other classes to urban occurred during the 1992-2001 period. During the 1973-1992 period, there was only moderate increase in urban area, while there was an inverse relationship between agricultural change and forest change. In general, forest gain and agricultural loss was found in areas of improving landscape indicators, and forest loss and agricultural gain was found to occur in areas of declining indicators related to habitat and nitrogen loadings, which was generally confirmed by the aerial photographic analysis. In terms of the specific model results, bird habitat, which is mainly related to the extent of forest cover, declined overall with forest extent, but was also affected more in the decline of habitat quality. Nitrate loading, which is mainly related to agricultural land cover actually improved from 1992-2001, and in the overall study, mainly due to the conversion of agriculture to forests and urban. The high-resolution imagery analysis was significant in that it confirmed, at a very local level, the specific land-cover changes that were driving the landscape metrics and model results that were calculated from moderate resolution land-cover data and models. These were generally subtle changes in patch size of agriculture, forest, and urban areas, but had substantial effects on bird habitat and nitrogen loadings. This analysis of high-resolution imagery demonstrates and confirms the important ability of moderate-resolution land-cover data to capture significant landscape-level activity that is directly related to specific metrics of ecological significance. It also demonstrates consistent landscape-scale relationships between data derived from high-resolution, moderate-resolution and landscape-model sources. Finally, many of the areas of improvement and decline in bird habitat and nitrogen loadings appear to be potentially regional in nature and likely reflect some local trend in landscape activity. Although the use of ecoregions as sampling units has been criticized in recent years, these results show that basic changes in Level 1 land-cover categories, such as forest and agriculture, may still reflect ecoregional patterns and considerations at some scale of mapping and analysis. This is a potentially important area for future landscape-indicator research. This and other follow-on research opportunities are discussed.

Open-File Report↗

USGS2008

Detecting changes in riparian habitat conditions based on patterns of greenness change: A case study from the Upper San Pedro River Basin, USA

Healthy riparian ecosystems in arid and semi-arid regions exhibit shifting patterns of vegetation in response to periodic flooding. Their conditions also depend upon the amount of grazing and other human uses. Taking advantage of these system properties, we developed and tested an approach that utilizes historical Landsat data to track changes in the patterns of greenness (Normalized Difference Vegetation Index) within riparian zones. We tested the approach in the Upper San Pedro River of southeastern Arizona of the US, an unimpounded river system that flows north into the US from northern Mexico. We evaluated changes in the pattern of greenness in the San Pedro River National Conservation Area (SPRNCA), an area protected from grazing and development since 1988, and in a relatively unprotected area north of the SPRNCA (NA). The SPRNCA exhibited greater positive changes in greenness than did the NA. The SPRNCA also exhibited larger, more continuous patches of positive change than did the NA. These pattern differences may reflect greater pressures from grazing and urban sprawl in the NA than in the SPRNCA, as well as differences in floodplain width, depth to ground water, and base geology. The SPRNCA has greater amounts of ground and surface water available to support a riparian gallery forest than does the NA, and this may have influenced changes during the study period. Estimates of the direction of greenness change (positive or negative) from satellite imagery were similar to estimates derived from aerial photography , except in areas where changes were from one type of shrub community to another, and in areas with agriculture. Change estimates in these areas may be more difficult because of relatively low greenness values, and because of differences in soil moisture, sun-angle, and crop rotations among the dates of data collection. The potential for applying a satellite-based, greenness change approach to evaluate riparian ecosystem condition over broad geographic areas is also discussed.

Arizona↗

USGS2006

Quantifying the Components of Impervious Surfaces

This study's objectives were to (1) determine the relative contribution of impervious surface individual components by collecting digital information from high-resolution imagery, 1-meter or better; and to (2) determine which of the more advanced techniques, such as spectral unmixing or the application of coefficients to land use or land cover data, was the most suitable method that could be used by State and local governments as well as Federal agencies to efficiently measure the imperviousness in any given watershed or area of interest. The components of impervious surfaces, combined from all the watersheds and time periods from objective one were the following: buildings 29.2-percent, roads 28.3-percent, parking lots 24.6-percent; with the remaining three totaling 14-percent - driveways, sidewalks, and other, where other were any other features that were not contained within the first five. Results from objective two were spectral unmixing techniques will ultimately be the most efficient method of determining imperviousness, but are not yet accurate enough as it is critical to achieve accuracy better than 10-percent of the truth, of which the method is not consistently accomplishing as observed in this study. Of the three techniques in coefficient application tested, land use coefficient application was not practical, while if the last two methods, coefficients applied to land cover data, were merged, their end results could be to within 5-percent or better, of the truth. Until the spectral unmixing technique has been further refined, land cover coefficients should be used, which offer quick results, but not current as they were developed for the 1992 National Land Characteristics Data.

Open-File Report↗