The Chesapeake Bay Watershed covers 165,800 square kilometers and is supplied with water and sediment from five major physiographic provinces: Appalachian Plateau, Blue Ridge, Coastal Plain, Piedmont, and the Valley and Ridge. Suspended-sediment loads measured in the Chesapeake Bay Watershed showed that the Piedmont Physiographic Province has the highest rates of modern (20th Century) sediment yields, measured at U.S. Geological Survey streamflow-gaging stations, and the lowest rates of background or geologic rates of erosion (~10,000 years) measured with in situ beryllium-10. In the agricultural and urbanizing Little Conestoga Creek Watershed, a Piedmont watershed, sources of sediment using the 'sediment-fingerprinting' approach showed that streambanks were the most important source (63 percent), followed by cropland (37 percent). Cesium-137 inventories, which quantify erosion rates over a 40-year period, showed average cropland erosion of 19.39 megagrams per hectare per year in the Little Conestoga Creek Watershed. If this erosion rate is extrapolated to the 13 percent of the watershed that is in cropland, then cropland could contribute almost four times the measured suspended-sediment load transported out of the watershed (27,600 megagrams per hectare per year), indicating that much of the eroded sediment is being deposited in channel and upland storage. The Piedmont has had centuries of land-use change, from forest to agriculture, to suburban and urban areas, and in some areas, back to forest. These land-use changes mobilized a large percentage of sediment that was deposited in upland and channel storage, and behind thousands of mill dams. The effects of these land-use changes on erosion and sediment transport are still being observed today as stored sediment in streambanks is a source of sediment. Cropland is also an important source of sediment. The Coastal Plain Physiographic Province has had the lowest sediment yields in the 20th Century and with sandy soils, contributes little fine-grained sediment. In the agricultural Pocomoke River Watershed, a Coastal Plain watershed, cesium-137 mass-balance results indicate that erosion and deposition are both occurring on cropland fields. Sources of sediment using the sediment-fingerprinting approach for the Pocomoke River were distributed as follows: cropland (46 percent), ditch beds (34 percent), ditch banks and streambanks (7 percent), and forest (13 percent). Cropland was a source of sediment for the two largest peak flow events, which occurred during harvesting when the ground may have been bare. The Pocomoke River Watershed is heavily ditched and channelized, conditions that are favorable for ditch bed and bank erosion. In the mixed land use (forested, agricultural, and urbanizing) Mattawoman Creek Watershed, a Coastal Plain watershed, sources of sediment using the sediment-fingerprinting approach were distributed as follows: streambanks (30 percent), forest (29 percent), construction (25 percent), and cropland (17 percent). Mattawoman Creek Watershed drains a rapidly developing region with 182 hectares (approximately 1.26 percent of the watershed) under construction. Sediment from construction sites was also determined as a source of sediment in the Mattawoman Creek Watershed. The sediment-fingerprinting source results for the three watersheds analyzed, show that in all watersheds, both the stream corridor and agriculture were significant sources of sediment. Forest as a source of sediment in the Mattawoman Creek Watershed may indicate that these forests are being disturbed and forest soils are eroding. Bare ground can be an important sediment source. Spatial analysis of bare ground in the Little Conestoga Creek Watershed using satellite imagery between 2000 and 2005 showed that the majority of bare ground was classified as pasture. Bare ground was correlated to the growing season with the highest percentages occurring in the early spring (April, 34 percent) and a

We apply linear deconvolution methods to derive mineral and glass proportions for eight field sample training sites at seven dune fields: (1) Algodones, California; (2) Big Dune, Nevada; (3) Bruneau, Idaho; (4) Great Kobuk Sand Dunes, Alaska; (5) Great Sand Dunes National Park and Preserve, Colorado; (6) Sunset Crater, Arizona; and (7) White Sands National Monument, New Mexico. These dune fields were chosen because they represent a wide range of mineral grain mixtures and allow us to gauge a better understanding of both compositional and sorting effects within terrestrial and extraterrestrial dune systems. We also use actual ASTER TIR emissivity imagery to map the spatial distribution of these minerals throughout the seven dune fields and evaluate the effects of degraded spectral resolution on the accuracy of mineral abundances retrieved. Our results show that hyper-spectral data convolutions of our laboratory emissivity spectra outperformed multispectral data convolutions of the same data with respect to the mineral, glass and lithic abundances derived. Both the number and wavelength position of spectral bands greatly impacts the accuracy of linear deconvolution retrieval of feldspar proportions (e.g. K-feldspar vs. plagioclase) especially, as well as the detection of certain mafic and carbonate minerals. In particular, ASTER mapping results show that several of the dune sites display patterns such that less dense minerals typically have higher abundances near the center of the active and most evolved dunes in the field, while more dense minerals and glasses appear to be more abundant along the margins of the active dune fields.

Worldview-3 (WV3) 16-band multispectral data were used to map exposed bedrock and mine waste piles associated with legacy open-pit mining of sandstone-hosted roll-front uranium deposits along the South Texas Coastal Plain. We used the “spectral hourglass” approach to extract spectral endmembers representative of these features from the image. This approach first requires calibrating the imagery to reflectance, then masking for vegetation, followed by spatial and spectral data reduction using a principal component analysis-based procedure that reduces noise and identifies homogeneous targets which are “pure” enough to be considered spectral endmembers. In this case, we used a single WV3 image which covered an ~11.5 km by ~19.5 km area of Karnes, Atascosa and Live Oak Counties, underlain by mined rocks from the Jackson Group and Catahoula Formation. Up to 58 spectral endmembers were identified using a further multi-dimensional class segregation method and were used as inputs for spectral angle mapper (SAM) classification. SAM classification resulted in the identification of at least 117 mine- and mine waste-related features, most of which were previously unknown. Class similarity was further evaluated, and the dominant minerals in each class were identified by comparison to spectral libraries and measured samples of actual Jackson Group uranium host rocks. Redundant classes were eliminated, and SAM was run a second time using a reduced set of 23 endmembers, which were found to map these same features as effectively as using the full 58 set of endmembers, but with significantly reduced noise and spectral outliers. Our classification results were validated by evaluating detailed scale mapping of three known mine sites (Esse-Spoonamore, Wright-McCrady and Garbysch-Thane) with published ground truth information about the vegetation cover, extent of erosion and exposure of waste pile materials and/or geologic information about host lithology and mineralization. Despite successful demonstration of the utility of WV3 data for inventorying mine features, additional landscape features such as bare agricultural fields and oil and gas drill pads were also identified. The elimination of such features will require combining the spectral classification maps presented in this study with high-quality topographic data. Also, the spectral endmembers identified during the course of this study could be useful for larger-scale mapping efforts using additional well-calibrated WV3 images beyond the coverage of our initial study area.