While many studies have completed accuracy assessments on the National Land Cover Dataset (NLCD), little research has utilized a detailed digitized land cover dataset, like that available for the Town of Blacksburg, for this comparison. This study aims to evaluate the information available from a detailed land cover dataset and compare it with the National Land Cover Dataset (NLCD) at a localized scale. More specifically, it utilizes the detailed land cover dataset for the Town of Blacksburg to analyze the land cover distribution for varying land uses including single-family residential, multi-family residential, and non-residential. In addition, an application scenario assigns an area-weighted curve number to watersheds based on each land cover dataset. This study exhibits the importance of obtaining detailed land cover datasets for cities and towns. Furthermore, it shows the comprehensive information and subsequent quantifications that can be surmised from a detailed land cover dataset. / Master of Science

Northwest Montana has seen a significant increase in its population in the past twenty years. The increase in population, and associated development, is thought to be associated with "amenity migration"; people moving to an area to exploit the recreational opportunities that are unique to that area. Impervious surfaces can serve as a suitable proxy for tracking the spread of various anthropogenic influences on an ecosystem; it impacts groundwater recharge, increases overall surface runoff as well as pollution and sediment load, and fragments landscapes. In this study, an Artificial Neural Network model was developed to update NLCD impervious surface product (2011) in Flathead County, Montana. Four Landsat 8 images from 2015 and 2016 were used to characterize imperviousness. This multi-temporal analytical method was designed to reduce the spectral confusion between impervious surface and soil/agricultural lands. We compared the neural network-predicted impervious surface maps with 2011 NLCD. When all four neural network prediction images agreed with a change of 50% or more from the 2011 NLCD map, the average of those four images replaced that pixel from the 2011 imperviousness map. Compared to the ground truth, the method used showed significant promise, with an R2 of 0.73 and RMSE of 0.123. A comparison of the artificial neural network model results and the 2011 NLCD data showed a continuation of urbanization trends; the urban cores of towns in the study remain static while the majority of impervious surface development takes place along the perimeter of urban areas. / Master of Science / Remotely sensed Landsat data can be used to rapidly detect and estimate changes in impervious surface cover. This study used artificial neural networks in conjunction with the National Landcover Database’s 2011 Percent Developed Imperviousness layer and Landsat 8 data from four dates between the summer of 2015 and fall of 2016 to predict impervious surface cover in 2016, by deriving spectral relationships between Landsat data and impervious surfaces. We found that by requiring agreement between the four dates’ neural networks outputs, we eliminated many of the false positives that arose from exposed soil. Using this method, we achieved an R2 of 0.73 and RMSE of .123, sampling only the areas along a rural-urban gradient, in an area with significant seasonal spectral variability.

The forests of the eastern United States reflect complex and highly dynamic patterns of change. This thesis seeks to explore the highly variable nature of these changes and to develop techniques that will enable researchers to examine their temporal and spatial patterns. The objectives of this research are to: 1) determine whether the forest change dynamics in the eastern US differ across levels of the urban hierarchy; 2) identify and explore key micropolitan areas that deviate from anticipated trends in forest change; and 3) develop and apply techniques for Big Data exploration of Landsat satellite images for forest cover analysis over large regions. Results demonstrate that forest change at the micropolitan level of urbanization differs from rural and metropolitan forest dynamics. The work highlights the dynamic nature of forest change within the Piedmont Atlantic megaregion, largely attributed to the forestry industry. This is by far the most dominant change phenomenon in the region but is not necessarily indicative of permanent forest change. A longer temporal analysis may be required to separate the contribution of the forest industry from permanent forest conversion in the region. Techniques utilized in this work suggest that emerging tools that provide supercomputing/parallel processing capabilities for the analysis of big satellite data open the door for researchers to better address different landscape signals and to investigate large regions at a high temporal and spatial resolution. The opportunity now exists to conduct initial assessments regarding spatio-temporal land cover trends in the southeast in a manner previously not possible. / Master of Science

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No description available.

The National Wetlands Inventory (NWI) is the definitive source for wetland resources in the United States. The NWI production unit in Hadley, MA has begun to upgrade their digital map database, integrating descriptors for assessment of wetland functions. Updating is conducted manually and some automation is needed to increase production and efficiency. This study assigned landscape position descriptor codes to NWI wetland polygons and correlated polygon environmental properties with public domain terrain, soils, hydrology, and vegetation data within the Coastal Plain of Virginia. Environmental properties were applied to a non-metric multidimensional scaling technique to identify similarities within individual landscape positions based on wetland plant indicators, primary and secondary hydrology indicators, and field indicators of hydric soils. Individual NWI landscape position classes were linked to field-validated environmental properties. Measures provided by this analysis indicated that wetland plant occurrence and wetland plant status obtained a stress value of 0.136 (Kruskal's stress measure = poor), which is a poor indicator when determining correlation among wetland environmental properties. This is due principally to the highly-variable plant distribution and wetland plant status found among the field-validated sites. Primary and secondary hydrology indicators obtained a stress rating of 0.097 (Kruskal's stress measure = good) for correlation. The hydrology indicators measured in this analysis had a high level of correlation with all NWI landscape position classes due the common occurrence of at least one primary hydrology indicator in all field validated wetlands. The secondary indicators had an increased accuracy in landscape position discrimination over the primary indicators because they were less ubiquitous. Hydric soil characteristics listed in the 1987 Manual and NTCHS field indicators of hydric soils proved to be a relatively poor indicator, based on Kruskal's stress measure of 0.117, for contrasting landscape position classes because the same values occurred across all classes. The six NWI field–validated landscape position classes used in this study were then further applied in a public domain digital data analysis. Mean pixel attribute values extracted from the 180 field-validated wetlands were analyzed using cluster analysis. The percent hydric soil component displayed the greatest variance when compared to elevation and slope curvature, streamflow and waterbody, Cowardin classification, and wetland vegetation type. Limitations of the soil survey data included: variable date of acquisition, small scale compared to wetland size, and variable quality. Flow had limitations related to its linear attributes, therefore is often found insignificant when evaluating pixel values that are mean of selected pixels across of wetland landscape position polygons. NLCD data limitations included poor quality resolution (large pixel size) and variable classification of cover types. The three sources of information that would improve wetland mapping and modeling the subtle changes in elevation and slope curvature that characterize wetland landscapes are: recent high resolution leaf-off aerial photography, high-quality soil survey data, and high-resolution elevation data. Due to the data limitations and the choice of variables used in this study, development of models and rules that clearly separate the six different landscape positions was not possible, and thus automation of coding could not be attempted. / Master of Science