Browsing by Subject "LiDAR"
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Item Duluth 1-Meter Land Cover Classification (Impervious Surface Focused)(2016-08-01) Host, Trevor K; Rampi, Lian P; Knight, Joe F; jknight@umn.edu; Knight, Joe FA high-resolution (1-meter) land cover classification raster dataset was completed for three different geographic areas in Minnesota: Duluth, Rochester, and the seven-county Twin Cities Metropolitan area. This classification was created using high-resolution multispectral National Agriculture Imagery Program (NAIP) leaf-on imagery (2015), spring leaf-off imagery (2011- 2014), Multispectral derived indices, LiDAR data, LiDAR derived products, and other thematic ancillary data including the updated National Wetlands Inventory, LiDAR building footprints, airport, OpenStreetMap roads and railroads centerlines. These data sets were integrated using an Object-Based Image Analysis (OBIA) approach to classify 12 land cover classes: Deciduous Tree Canopy, Coniferous Tree Canopy, Buildings, Bare Soil, other Paved surface, Extraction, Row Crop, Grass/Shrub, Lakes, Rivers, Emergent Wetland, Forest and Shrub Wetland. We mapped the 12 classes by using an OBIA approach through the creation of customized rule sets for each area. We used the Cognition Network Language (CNL) within the software eCognition Developer to develop the customized rule sets. The eCognition Server was used to execute a batch and parallel processing which greatly reduced the amount of time to produce the classification. The classification results were evaluated for each area using independent stratified randomly generated points. Accuracy assessment estimators included overall accuracies, producers accuracy, users accuracy, and kappa coefficient. The combination of spectral data and LiDAR through an OBIA method helped to improve the overall accuracy results providing more aesthetically pleasing maps of land cover classes with highly accurate results.Item Evaluating American marten habitat quality using airborne light detection and ranging (LiDAR) data(2018-09) Joyce, MichaelUnderstanding the factors that influence animal distribution and density is a central theme in animal ecology. For imperiled species, understanding the resources and conditions that allow animals to occupy the landscape is critical for development of effective conservation strategies. Not surprisingly, habitat selection is a common focus of wildlife research. This dissertation project focused on addressing two main challenges that limit the application of a fitness-based approach to understanding habitat selection: 1) data on fine-scale habitat resources and conditions required for the development and testing of resource- and fitness-based definitions of habitat are generally not available across entire study areas, and 2) indirect measurements of fitness (e.g., survival or reproductive success) are often not considered when assessing habitat selection patterns, in part, because of the difficulty of measuring fitness correlates for free-ranging animals with long life-spans and large home ranges. My first two chapters address the first challenge by using airborne light detection and ranging (LiDAR) data to measure fine-scale characteristics known to be selected by my focal species, American martens (Martes americana). In Chapter 1, I demonstrate that LiDAR data can be used to detect individual pieces of coarse woody debris, an important habitat component that provides martens with foraging habitat and access to the subnivean layer. In Chapter 2, I created statistical models to estimate 5 response variables relating to tree size and density and evaluated how well models will perform when imputed across the entire study area. I found that the models I created performed well when applied to new data, but that performance depended on the response variable being modeled. My last two chapters address the second challenge by evaluating how landscape and forest structure influence mortality risk for martens. In Chapter 3, I evaluated factors influencing harvest mortality risk. I found that martens living close to roads have higher harvest risk because trappers use roads to set and check traps efficiently. Consequently, distribution of roads can have a profound impact on habitat quality, which has important implications for gene flow and population structure. In Chapter 4, I used LiDAR data and classified satellite imagery to examine the role of forest structure in mediating interactions between martens and predators. I found that sites where martens were killed by predators were associated with non-forested areas including wetlands, shrublands, and young and regenerating forests. Although martens generally avoid non-forested areas that are associated with higher predation risk, martens must move near or through risky areas while moving across heterogeneous, managed landscapes.Item High-resolution Mapping of Urban Land Use Intensity in Watersheds of the St. Louis River Estuary(University of Minnesota Duluth, 2015-07) Host, George E; Meysembourg, Paul; Johnson, Lucinda BAgriculture and development are the source of a multitude of environmental stressors influencing coastal ecosystems, including sediment and nutrient runoff, alterations to hydrologic and thermal regimes, delivery of pollutants and loss of habitat. Many studies have addressed the effects of land use on aquatic ecosystem, but fundamental issues of scale remain unresolved. Land use data are common inputs to environmental indicator development, hydrologic models such as SWMM or HSPF, and decision support models such as the EPA National Stormwater Calculator. The difference in areal estimates of urban land cover between NLCD and higher resolution land classification can result in significant differences in predicted amounts of runoff and infiltration. Using these data to develop remediation strategies using green or gray infrastructure could potentially result in costly errors through under or over-engineering retention structures. For this reason, we initiated this project to expand the Stueve et al. (2014) methodology, which focused on a single watershed, to multiple urban watersheds entering the St. Louis River Estuary (SLRE). We then used these data to develop indices of urban land use intensity, focusing on impervious surface, building footprints, building heights and height diversity within municipal parcels. Finally, we assessed the relationship of these urban land use intensity indices to water quality data collected in nine tributary watersheds of the St. Louis River.Item Identifying erosional hotspots in streams along the North Shore of Lake Superior, Minnesota using high-resolution elevation and soils data(2013-09) Wick, Molly JaneMany streams on the North Shore of Lake Superior, Minnesota, USA, are impaired for turbidity driven by excess fine sediment loading. The goal of this project was to develop a GIS-based model using new, openly-available, high-resolution remote datasets to predict erosional hotspots at a reach scale, based on three study watersheds: Amity Creek, the Talmadge River, and the French River. The ability to identify erosional hotspots, or locations that are highly susceptible to erosion, using remote data would be helpful for watershed managers in implementing practices to reduce turbidity in these streams. Erosion in streams is a balance between driving forces, largely controlled by topography; and resisting forces, controlled by the materials that make up a channel's bed and banks. New high-resolution topography and soils datasets for the North Shore provide the opportunity to extract these driving and resisting forces from remote datasets and possibly predict erosion potential and identify erosional hotspots. We used 3-meter LiDAR-derived DEMs to calculate a stream power-based erosion index, to identify stream reaches with high radius of curvature, and to identify stream reaches proximal to high bluffs. We used the Soil Survey Geographic (SSURGO) Database to investigate changes in erodibility along the channel. Because bedrock exposure significantly limits erodibility, we investigated bedrock exposure using bedrock outcrop maps made available by the Minnesota Geological Survey (MGS, Hobbs, 2002; Hobbs, 2009), and by using a feature extraction tool to remotely map bedrock exposure using high-resolution air photos and LiDAR data. Predictions based on remote data were compared with two datasets. Bank Erosion Hazard Index surveys, which are surveys designed to evaluate erosion susceptibility of banks, were collected along the three streams. In addition, a 500-year flood event during our field season gave us the opportunity to collect erosion data after a major event and validate our erosion hotspot predictions. Regressions between predictors and field datasets indicate that the most significant variables are bedrock exposure, the stream power-based erosion index, and bluff proximity. A logistic model developed using the three successful predictors for Amity Creek watershed was largely unsuccessful. A threshold-based model including the three successful predictors (stream power-based erosion index, bluff proximity, and bedrock exposure) was 70% accurate for predicting erosion hotspots along Amity Creek. The limited predictive power of the models stemmed in part from differences in locations of erosion hotspots in a single large-scale flood event and long-term erosion hotspots. The inability to predict site-specific characteristics like large woody debris or vegetation patterns makes predicting erosion hotspots in a given event very difficult. A field dataset including long-term erosion data may improve the model significantly. This model also requires high resolution bedrock exposure data which may limit its application to other North Shore streams.Item Identifying Erosional Hotspots in Streams along the North Shore of Lake Superior, Minnesota using High-Resolution Elevation and Soils Data(2013) Wick, Molly JaneThis is a University of Minnesota Water Resources Science master’s thesis describing original research to determine fluvial erosion in three coastal streams (Amity, Talmadge and French) of Minnesota’s Lake Superior shoreline. All three streams have elevated levels of turbidity, with potential for damage to fisheries. The goal of this project was to develop a GIS-based model using new, openly-available, high-resolution LiDAR datasets to predict erosional hotspots at a reach scale. The abstract summarizing the study’s key findings is extracted and reproduced below. Abstract: “Many streams on the North Shore of Lake Superior, Minnesota, USA, are impaired for turbidity driven by excess fine sediment loading. The goal of this project was to develop a GIS-based model using new, openly-available, high-resolution remote datasets to predict erosional hotspots at a reach scale, based on three study watersheds: Amity Creek, the Talmadge River, and the French River. The ability to identify erosional hotspots, or locations that are highly susceptible to erosion, using remote data would be helpful for watershed managers in implementing practices to reduce turbidity in these streams. “Erosion in streams is a balance between driving forces, largely controlled by topography; and resisting forces, controlled by the materials that make up a channel’s bed and banks. New high-resolution topography and soils datasets for the North Shore provide the opportunity to extract these driving and resisting forces from remote datasets and possibly predict erosion potential and identify erosional hotspots. We used 3-meter LiDAR-derived DEMs to calculate a stream power-based erosion index, to identify stream reaches with high radius of curvature, and to identify stream reaches proximal to high bluffs. We used the Soil Survey Geographic (SSURGO) Database to investigate changes in erodibility along the channel. Because bedrock exposure significantly limits erodibility, we investigated bedrock exposure using bedrock outcrop maps made available by the Minnesota Geological Survey (MGS, Hobbs, 2002; Hobbs, 2009), and by using a feature extraction tool to remotely map bedrock exposure using high-resolution air photos and LiDAR data. “Predictions based on remote data were compared with two datasets. Bank Erosion Hazard Index surveys, which are surveys designed to evaluate erosion susceptibility of banks, were collected along the three streams. In addition, a 500-year flood event during our field season gave us the opportunity to collect erosion data after a major event and validate our erosion hotspot predictions. Regressions between predictors and field datasets indicate that the most significant variables are bedrock exposure, the stream power-based erosion index, and bluff proximity. A logistic model developed using the three successful predictors for Amity Creek watershed was largely unsuccessful. A threshold-based model including the three successful predictors (stream power-based erosion index, bluff proximity, and bedrock exposure) was 70% accurate for predicting erosion hotspots along Amity Creek. The limited predictive power of the models stemmed in part from differences in locations of erosion hotspots in a single large-scale flood event and long-term erosion hotspots. The inability to predict site-specific characteristics like large woody debris or vegetation patterns makes predicting erosion hotspots in a given event very difficult. A field dataset including long-term erosion data may improve the model significantly. This model also requires high resolution bedrock exposure data which may limit its application to other North Shore streams.”Item Multi-scale structural and kinematic analysis of a Neoarchean shear zone in northeastern Minnesota: Implications for assembly of the southern Superior Province(2014-08) Dyess, JonathanThis dissertation is a multi-scale structural and kinematic analysis of the Shagawa Lake shear zone in northeastern Minnesota (USA). The Neoarchean Shagawa Lake shear zone is an ~70 km long ~7 km wide subvertical package of L-S tectonites located within the Wawa Subprovince of the Archean Superior Province. In this dissertation, I (1) discuss a new method for mapping regional tectonic fabrics using high-resolution LiDAR altimetry data; (2) examine the geometric relationships between metamorphic foliation, elongation lineation, vorticity, and non-coaxial shear direction within individual L-S tectonites; and (3) incorporate LiDAR, field, and microstructural data sets into a comprehensive structural and kinematic analysis of the Western Shagawa Lake shear zone. Lastly, I discuss implications for assembly of the southern Superior Province. In Chapter one I examine an Archean granite-greenstone terrane in NE Minnesota to illustrate the application of high-resolution LiDAR altimetry to mapping regional tectonic fabrics in forested, glaciated areas. I describe the recognition of lineaments and distinguishing between tectonic and glacial lineament fabrics. I use a 1-m posted LiDAR derived bare-earth digital elevation model (DEM) to construct multiple shaded-relief images for lineament mapping with sun elevation of 45˚ and varying sun azimuth in 45˚ intervals. Two suites of lineaments are apparent. Suite A has a unimodal orientation, mean trend of 035, and consists of short (> 2 km long) lineaments within sediment deposits and bedrock. Suite B lineaments, which are longer (1-30 km) than those of suite A, have a quasi-bimodal orientation distribution, with maximum trends of 065 and 090. Only one lineament suite is visible in areas where suites A and B are parallel. I interpret suite A as a surficial geomorphologic fabric related to recent glaciation, and suite B as a proxy for the regional tectonic fabric. Field measurements of regional tectonic foliation trajectories are largely consistent with suite B lineaments across the study area. Although not all suite B lineaments correlate to mapped structures, our analysis demonstrates that high-resolution LiDAR altimetry can be useful in mapping regional tectonic fabrics in glaciated terrane. In Chapter two I present a detailed kinematic study of seven Neoarchean L-S tectonite samples in order to determine vorticity and non-coaxial shear direction relative to foliation and elongation lineation. Samples are from L-S tectonites of the Wawa Subprovince, more specifically the Vermilion District of NE Minnesota, a NE-trending belt of greenschist grade supracrustal rocks and granitoid bodies. Supracrustal rocks host multiple L-S tectonite packages with a well-developed sub-vertical metamorphic foliation and elongation lineation; elongation lineation generally plunges steeply to obliquely although rare zones of shallow plunge occur locally. The Wawa Subprovince is widely interpreted as a transpressional margin with shear zones recording unidirectional dextral strike-slip, an interpretation held up as fundamental evidence for Archean plate-tectonic processes. However vorticity and shear direction within the Vermilion District L-S tectonites remain unconstrained. I compare data from thin-sections, x-ray computed tomography, and quartz crystallographic fabric analysis to monoclinic shear models to determine vorticity and better understand geometric relationships between vorticity, non-coaxial shear direction, foliation, and elongation lineation. Kinematic indicators in thin-section and image slices from X-ray computed tomography consistently record asymmetric microstructural fabrics in foliation-normal lineation-parallel planes, whereas planes normal to foliation and elongation lineation display dominantly symmetric microstructural fabrics. Mantled porphyroclast 3D-shapes and star-volume distribution analyses indicate that porphyroclast short-axes are normal to foliation and long-axes parallel elongation lineation. Quartz crystallographic preferred orientation data show a-axes maxima sub-parallel to foliation-normal lineation-parallel planes. Kinematic data are consistent with a vorticity axis within the foliation plane and normal to elongation lineation; thus non-coaxial shear direction is sub-parallel to elongation lineation. Data are inconsistent with shear models in which non-coaxial shear direction is normal to lineation, or vorticity axis is normal to foliation. Data indicate that tectonites record non-coaxial shear broadly parallel to elongation lineation regardless of lineation geographic orientation. In Chapter three I present a detailed structural and kinematic study of the eastern Shagawa Lake shear zone. A subvertical metamorphic foliation strikes NE; elongation lineation forms a splayed orientation distribution; however 70% of elongation lineations pitch ≥ 60° to the NE or SW. Strike-parallel elongation lineations occur within localized zones. Non-coaxial shear direction is sub-parallel to elongation lineation. Kinematic indicators record N-side-up and S-side-up shear, and local strike-slip shear--with both right-lateral and left-lateral shear sense recorded. Elongation lineation and kinematic indicators appear consistent within individual outcrops, but can vary significantly between outcrops. I recognize no strain partitioning, crosscutting relationships between multiple shear events, or metamorphic overprinting within the study area. L-S tectonites record roughly isobaric/isothermal greenschist facies metamorphic conditions across the Shagawa Lake shear zone. The Shagawa Lake shear zone records a broad deformation event characterized by dominantly dip-slip shear in varying directions with multiple shear-senses. Structural and kinematic data indicate that the Shagawa Lake shear zone records deformation within a rheologically weak crust. These data are inconsistent with existing sagduction-diapirism/crustal overturn models and with plate-tectonic/terrane accretion scenarios for assembly of the southern Superior Province. Channel flow induced collapse and exhumation of high-grade crustal material during regional shortening provides a plausible mechanism for assembly of the southern Superior Province and is consistent with the rheological implications of this study.Item The North Shore Data Consortium: Acquiring and Distributing High-Resolution Geospatial Information(University of Minnesota Duluth, 2012) Host, George E; Sjerven, GeraldThis project came about because of strong local and regional interests in the acquisition and distribution of high-resolution spatial data needed for land use planning, natural resource management, and environmental assessment. Included among these data was LiDAR – light detection and ranging imagery which provides very high resolution (0.5 to 1 m) elevation data, which in turn can be used to generate other spatial data, such as hierarchically structured watersheds, topographic moisture indices, and refined wetland classifications. In spite of the strong interests in these data sources, acquisition was hampered because no single agency had the mandate for developing interagency strategies to coordinate funding and planning for largescale data acquisition projects. To address this, we proposed to create the North Shore Data Consortium (NSDC), with the purpose of developing specific funding strategies and timelines for collecting LiDAR and contemporary high-resolution aerial photography for the region. The Consortium created partnerships among local, state, and federal agencies along with interested NGOs to develop data standards, provide for data sharing and leveraging of funds for contracting LiDAR acquisition. The NSDC worked closely with the Ditigal Elevation Committee of the Governor's Council on Geographic Information, who coordinates statewide data efforts. A secondary goal of the proposal was to facilitate data distribution and training.Item Rochester 1-Meter Land Cover Classification (Impervious Surface Focused)(2016-08-01) Host, Trevor K; Rampi, Lian P; Knight, Joe F; jknight@umn.edu; Knight, Joe F; Remote Sensing and Geospatial Analysis LaboratoryA high-resolution (1-meter) land cover classification raster dataset was completed for three different geographic areas in Minnesota: Duluth, Rochester, and the seven-county Twin Cities Metropolitan area. This classification was created using high-resolution multispectral National Agriculture Imagery Program (NAIP) leaf-on imagery (2015), spring leaf-off imagery (2011- 2014), Multispectral derived indices, LiDAR data, LiDAR derived products, and other thematic ancillary data including the updated National Wetlands Inventory, LiDAR building footprints, airport, OpenStreetMap roads and railroads centerlines. These data sets were integrated using an Object-Based Image Analysis (OBIA) approach to classify 12 land cover classes: Deciduous Tree Canopy, Coniferous Tree Canopy, Buildings, Bare Soil, other Paved surface, Extraction, Row Crop, Grass/Shrub, Lakes, Rivers, Emergent Wetland, Forest and Shrub Wetland. We mapped the 12 classes by using an OBIA approach through the creation of customized rule sets for each area. We used the Cognition Network Language (CNL) within the software eCognition Developer to develop the customized rule sets. The eCognition Server was used to execute a batch and parallel processing which greatly reduced the amount of time to produce the classification. The classification results were evaluated for each area using independent stratified randomly generated points. Accuracy assessment estimators included overall accuracies, producers accuracy, users accuracy, and kappa coefficient. The combination of spectral data and LiDAR through an OBIA method helped to improve the overall accuracy results providing more aesthetically pleasing maps of land cover classes with highly accurate results.Item Twin Cities Metropolitan Area 1-Meter Land Cover Classification (Impervious Surface Focused)(2016-08-01) Host, Trevor K; Rampi, Lian P; Knight, Joe F; jknight@umn.edu; Knight, Joe F; Remote Sensing and Geospatial Analysis LaboratoryA high-resolution (1-meter) land cover classification raster dataset was completed for three different geographic areas in Minnesota: Duluth, Rochester, and the seven-county Twin Cities Metropolitan area. This classification was created using high-resolution multispectral National Agriculture Imagery Program (NAIP) leaf-on imagery (2015), spring leaf-off imagery (2011- 2014), Multispectral derived indices, LiDAR data, LiDAR derived products, and other thematic ancillary data including the updated National Wetlands Inventory, LiDAR building footprints, airport, OpenStreetMap roads and railroads centerlines. These data sets were integrated using an Object-Based Image Analysis (OBIA) approach to classify 12 land cover classes: Deciduous Tree Canopy, Coniferous Tree Canopy, Buildings, Bare Soil, other Paved surface, Extraction, Row Crop, Grass/Shrub, Lakes, Rivers, Emergent Wetland, Forest and Shrub Wetland. We mapped the 12 classes by using an OBIA approach through the creation of customized rule sets for each area. We used the Cognition Network Language (CNL) within the software eCognition Developer to develop the customized rule sets. The eCognition Server was used to execute a batch and parallel processing which greatly reduced the amount of time to produce the classification. The classification results were evaluated for each area using independent stratified randomly generated points. Accuracy assessment estimators included overall accuracies, producers accuracy, users accuracy, and kappa coefficient. The combination of spectral data and LiDAR through an OBIA method helped to improve the overall accuracy results providing more aesthetically pleasing maps of land cover classes with highly accurate results.