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Title: Sinkhole detection and quantification using LiDAR data.
Creator: Rajabi, Amirarsalan, Nam, Boo Hyun, Wang, Dingbao, Singh, Arvind, University of Central Florida
Abstract / Description: The state of Florida is highly prone to sinkhole incident and formation, mainly because of the soluble carbonate bedrock which is susceptible to dissolution and groundwater recharge that causes internal soil erosions. Numerous sinkholes, particularly in Central Florida, have occurred. Florida Subsidence Incident Report (FSIR) database contains verified sinkholes with Global Positioning System (GPS) information. In addition to existing detection methods such as subsurface exploration and...
Show moreThe state of Florida is highly prone to sinkhole incident and formation, mainly because of the soluble carbonate bedrock which is susceptible to dissolution and groundwater recharge that causes internal soil erosions. Numerous sinkholes, particularly in Central Florida, have occurred. Florida Subsidence Incident Report (FSIR) database contains verified sinkholes with Global Positioning System (GPS) information. In addition to existing detection methods such as subsurface exploration and geophysical methods, a remote sensing method can be an alternative and efficient means to detect and characterize sinkholes with a wide coverage. the first part of this study is aimed at developing a method to detect sinkholes in Missouri by using Light Detection and Ranging (LiDAR) data. Morphometrical parameters such as TPI (Topographic Position Index), CI (Convergence Index), SI (Slope Index), and DEM (Digital Elevation Model) have a high potential to help detect sinkholes, based on local ground conditions and study area. The GLM (General Linear Model) built in R software is used to obtain morphometrical indices of the study terrain to be trained and build a logistic regression model to detect sinkholes. In the second part of the study, a semi-automated model in ArcMap is then developed to detect sinkholes and also to estimate geometric characteristics of sinkholes (e.g. depth, length, circularity, area, and volume). This remote sensing technique has a potential to detect unreported sinkholes in rural and/or inaccessible areas.
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Date Issued: 2018
Identifier: CFE0007084, ucf:51992
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007084

Title: Flying under the LiDAR: relating forest structure to bat community diversity.
Creator: Butterfield, Anna, Weishampel, John, Noss, Reed, King, Joshua, University of Central Florida
Abstract / Description: Bats are important to many ecological processes such as pollination, insect (and by proxy, disease) control, and seed dispersal and can be used to monitor ecosystem health. However, they are facing unprecedented extinction risks from habitat degradation as well as pressures from pathogens (e.g., white-nose syndrome) and wind turbines. LiDAR allows ecologists to measure structural variables of forested landscapes with increased precision and accuracy at broader spatial scales than previously...
Show moreBats are important to many ecological processes such as pollination, insect (and by proxy, disease) control, and seed dispersal and can be used to monitor ecosystem health. However, they are facing unprecedented extinction risks from habitat degradation as well as pressures from pathogens (e.g., white-nose syndrome) and wind turbines. LiDAR allows ecologists to measure structural variables of forested landscapes with increased precision and accuracy at broader spatial scales than previously possible. This study used airborne LiDAR to classify forest habitat/canopy structure at the Ordway-Swisher Biological Station (OSBS) in north central Florida. LiDAR data were acquired by the National Ecological Observatory Network (NEON) airborne observation platform in summer 2014. OSBS consists of open-canopy pine savannas, closed-canopy hardwood hammocks, and seasonally inundated basin marshes. Multiple forest structural parameters (e.g., mean, maximum, and standard deviation of canopy height) were derived from LiDAR point clouds using the USDA software program FUSION. K-means clustering was used to segregate each 5x5 m raster across the ~3765 ha OSBS area into six different clusters based on the derived canopy metrics. Cluster averages for maximum, mean, and standard deviation of return heights ranged from 0 to 19.4 m, 0 to 15.3 m, and 0 to 3.0 m, respectively. To determine the relationships among these landscape-canopy features and bat species diversity and abundances, AnaBat II bat detectors were deployed from May to September in 2015 stratified by these distinct clusters. A statistical regression model selection approach was performed in order to evaluate how forest structural attributes such as understory clutter, vertical canopy structure, open and closed canopy, etc. and landscape metrics influence bat communities. The most informative models showed that a combination of site-specific (e.g., midstory clutter and entropy) and landscape level attributes (e.g., area of water and service road length) contributed to bat community patterns. This knowledge provides a deeper understanding of habitat-species interactions to better manage survival of these species and provides insight into new tools for landscape management as they apply to specific species.
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Date Issued: 2016
Identifier: CFE0006177, ucf:51151
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006177

Title: Development of an Automated Method for Identification of Wet and Dry Channel Segments Using LiDAR Data and Fuzzy Logic Cluster Analysis.
Creator: Rowney, Chris, Wang, Dingbao, Medeiros, Stephen, Kibler, Kelly, University of Central Florida
Abstract / Description: Research into the use of LiDAR data for purposes other than simple topographic elevation determination, such as urban land cover classification and the identification of forest biomass, has become prominent in recent years. In many cases, alternative analysis methodologies conducted using airborne LiDAR data are possible because the raw data collected during a survey can include information other than the classically used elevation and coordinate points, the X, Y, and Z of the plane. In...
Show moreResearch into the use of LiDAR data for purposes other than simple topographic elevation determination, such as urban land cover classification and the identification of forest biomass, has become prominent in recent years. In many cases, alternative analysis methodologies conducted using airborne LiDAR data are possible because the raw data collected during a survey can include information other than the classically used elevation and coordinate points, the X, Y, and Z of the plane. In particular, intensity return values for each point in a LiDAR grid have been found to provide a useful data set for wet and dry channel classification. LiDAR intensity return data are, in essence, a numeric representation of the characteristic light reflectivity of the object being scanned; the more reflective the object is, the higher the intensity return will be. Intensity data points are collected along the course of the channel network and within the perceived banks of the channel. Intensity data do not crisply reflect a perfectly wet or dry condition, but instead vary over a range such that each location can be viewed as partially wet and partially dry. It is advantageous to assess problems of this type using the methods of fuzzy logic. Specifically, the variance in LiDAR intensity return data is such that the use of fuzzy logic to identify intensity cluster centers, and thereby assign wet and dry condition identifiers based on fuzzy memberships, is a possibility. Membership within a fuzzy data set is characterized by a value representing the degree of membership. Typically, membership values range from 0 (representing non-membership) through 1 (representing full membership), with many observations found to be not at either extreme but instead at some intermediate value representing partial membership. The ultimate goal of this research was to design and develop an automated algorithm to identify wet and dry channel sections, given a previously identified channel network based on topographic elevation, using a combination of intensity return values from LiDAR data and fuzzy logic clustering methods, and to implement that algorithm in such a way as to produce reliable multi-class channel segments in ArcGIS. To enable control of calculations, limiting parameters were defined, specifically including the maximum allowable bank slope, and a filtering percentage to more accurately accommodate the study area.Alteration of the maximum allowable bank slope has been shown to affect the comparative quantity of high and low intensity centroids, but only in extreme bank slope conditions are the centroids changed enough to hamper results. However, interference from thick vegetation has been shown to lower intensity values in dry channel sections into the range of a wet channel. The addition of a filtering algorithm alleviates some of the interference, but not all. Overall results of the tool show an effective methodology where basic channel conditions are identified, but refinement of the tool could produce more accurate results.
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Date Issued: 2015
Identifier: CFE0006053, ucf:50975
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006053

Title: HEURISTIC 3D RECONSTRUCTION OF IRREGULAR SPACED LIDAR.
Creator: Shorter, Nicholas, Kasparis, Takis, University of Central Florida
Abstract / Description: As more data sources have become abundantly available, an increased interest in 3D reconstruction has emerged in the image processing academic community. Applications for 3D reconstruction of urban and residential buildings consist of urban planning, network planning for mobile communication, tourism information systems, spatial analysis of air pollution and noise nuisance, microclimate investigations, and Geographical Information Systems (GISs). Previous, classical, 3D reconstruction...
Show moreAs more data sources have become abundantly available, an increased interest in 3D reconstruction has emerged in the image processing academic community. Applications for 3D reconstruction of urban and residential buildings consist of urban planning, network planning for mobile communication, tourism information systems, spatial analysis of air pollution and noise nuisance, microclimate investigations, and Geographical Information Systems (GISs). Previous, classical, 3D reconstruction algorithms solely utilized aerial photography. With the advent of LIDAR systems, current algorithms explore using captured LIDAR data as an additional feasible source of information for 3D reconstruction. Preprocessing techniques are proposed for the development of an autonomous 3D Reconstruction algorithm. The algorithm is designed for autonomously deriving three dimensional models of urban and residential buildings from raw LIDAR data. First, a greedy insertion triangulation algorithm, modified with a proposed noise filtering technique, triangulates the raw LIDAR data. The normal vectors of those triangles are then passed to an unsupervised clustering algorithm – Fuzzy Simplified Adaptive Resonance Theory (Fuzzy SART). Fuzzy SART returns a rough grouping of coplanar triangles. A proposed multiple regression algorithm then further refines the coplanar grouping by further removing outliers and deriving an improved planar segmentation of the raw LIDAR data. Finally, further refinement is achieved by calculating the intersection of the best fit roof planes and moving nearby points close to that intersection to exist at the intersection, resulting in straight roof ridges. The end result of the aforementioned techniques culminates in a well defined model approximating the considered building depicted by the LIDAR data.
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Date Issued: 2006
Identifier: CFE0001315, ucf:47017
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001315

Title: LIDAR IN COASTAL STORM SURGE MODELING: MODELING LINEAR RAISED FEATURES.
Creator: Coggin, David, Hagen, Scott, University of Central Florida
Abstract / Description: A method for extracting linear raised features from laser scanned altimetry (LiDAR) datasets is presented. The objective is to automate the method so that elements in a coastal storm surge simulation finite element mesh might have their edges aligned along vertical terrain features. Terrain features of interest are those that are high and long enough to form a hydrodynamic impediment while being narrow enough that the features might be straddled and not modeled if element edges are not...
Show moreA method for extracting linear raised features from laser scanned altimetry (LiDAR) datasets is presented. The objective is to automate the method so that elements in a coastal storm surge simulation finite element mesh might have their edges aligned along vertical terrain features. Terrain features of interest are those that are high and long enough to form a hydrodynamic impediment while being narrow enough that the features might be straddled and not modeled if element edges are not purposely aligned. These features are commonly raised roadbeds but may occur due to other manmade alterations to the terrain or natural terrain. The implementation uses the TauDEM watershed delineation software included in the MapWindow open source Geographic Information System to initially extract watershed boundaries. The watershed boundaries are then examined computationally to determine which sections warrant inclusion in the storm surge mesh. Introductory work towards applying image analysis techniques as an alternate means of vertical feature extraction is presented as well. Vertical feature lines extracted from a LiDAR dataset for Manatee County, Florida are included in a limited storm surge finite element mesh for the county and Tampa Bay. Storm surge simulations using the ADCIRC-2DDI model with two meshes, one which includes linear raised features as element edges and one which does not, verify the usefulness of the method.
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Date Issued: 2008
Identifier: CFE0002350, ucf:47782
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002350

Title: APPLICATIONS OF AIRBORNE AND PORTABLE LIDAR IN THE STRUCTURAL DETERMINATION, MANAGEMENT, AND CONSERVATION OF SOUTHEASTERN U.S. PINE FORESTS.
Creator: Listopad, Claudia, Weishampel, John, University of Central Florida
Abstract / Description: Active remote sensing techniques, such as Light Detection and Ranging (LiDAR), have transformed the field of forestry and natural resource management in the last decade. Intensive assessments of forest resources and detailed structural assessments can now be accomplished faster and at multiple landscape scales. The ecological applications of having this valuable information at-hand are still only being developed. This work explores the use of two active remote sensing techniques, airborne and...
Show moreActive remote sensing techniques, such as Light Detection and Ranging (LiDAR), have transformed the field of forestry and natural resource management in the last decade. Intensive assessments of forest resources and detailed structural assessments can now be accomplished faster and at multiple landscape scales. The ecological applications of having this valuable information at-hand are still only being developed. This work explores the use of two active remote sensing techniques, airborne and portable LiDAR for forestry applications in a rapidly changing landscape, Southeastern Coastal Pine woodlands. Understanding the strengths and weaknesses of airborne and portable LiDAR, the tools used to extract structural information, and how to apply these to managing fire regimes are key to conserving unique upland pine ecosystems. Measuring habitat structure remotely and predicting habitat suitability through modeling will allow for the management of specific species of interest, such as threatened and endangered species. Chapter one focuses on the estimation of canopy cover and height measures across a variety of conditions of secondary upland pine and hardwood forests at Tall Timbers Research Station, FL. This study is unique since it uses two independent high resolution small-footprint LiDAR datasets (years 2002 and 2008) and extensive field plot and transect sampling for validation. Chapter One explores different tools available for metric derivation and tree extraction from discrete return airborne LiDAR data, highlighting strengths and weaknesses of each. Field and LiDAR datasets yielded better correlations for stand level comparisons, especially in canopy cover and mean height data extracted. Individual tree crown extraction from airborne LiDAR data significantly under-reported the total number of trees reported in the field datasets using either Fusion/LVD and LiDAR Analyst (Overwatch). Chapter two evaluates stand structure at the site of one of the longest running fire ecology studies in the US, located at Tall Timbers Research Station (TTRS) in the southeastern U.S. Small footprint high resolution discrete return LiDAR was used to provide an understanding of the impact of multiple disturbance regimes on forest structure, especially on the 3-dimensional spatial arrangement of multiple structural elements and structural diversity indices. LiDAR data provided sensitive detection of structural metrics, diversity, and fine-scale vertical changes in the understory and mid-canopy structure. Canopy cover and diversity indices were shown to be statistically higher in fire suppressed and less frequently burned plots than in 1- and 2-year fire interval treated plots, which is in general agreement with the increase from 2- to 3-year fire return interval being considered an "ecological threshold" for these systems (Masters et al. 2005). The results from this study highlight the value of the use of LiDAR in evaluating disturbance impacts on the three-dimensional structure of pine forest systems, particularly over large landscapes. Chapter three uses an affordable portable LiDAR system, first presented by Parker et al. (2004) and further modified for extra portability, to provide an understanding of structural differences between old-growth and secondary-growth forests in the Red Hills area of southwestern Georgia and North Florida. It also provides insight into the strengths and weaknesses in structural determination of ground-based portable systems in contrast to airborne LiDAR systems. Structural plot metrics obtained from airborne and portable LiDAR systems presented some similarities (i.e. canopy cover), but distinct differences appeared when measuring canopy heights (maximum and mean heights) using these different methods. Both the airborne and portable systems were able to provide gap detection and canopy cover estimation at the plot level. The portable system, when compared to the airborne LiDAR sensor, provides an underestimation of canopy cover in open forest systems (<50% canopy cover), but is more sensitive in detection of cover in hardwood woodland plots (>60% canopy cover). The strength of the portable LiDAR system lies in the detection of 3-dimensional fine structural changes (i.e. recruitment, encroachment) and with higher sensitivity in detecting lower canopy levels, often missed by airborne systems. Chapter four addresses a very promising application for fine-scale airborne LiDAR data, the creation of habitat suitability models for species of management and conservation concerns. This Chapter uses fine scale LiDAR metrics, such as canopy cover at various height strata, canopy height information, and a measure of horizontal vegetation distribution (clumped versus dispersed) to model the preferences of 10 songbirds of interest in southeast US woodlands. The results from this study highlight the rapidly changing nature of habitat conditions and how these impact songbird occurrence. Furthermore, Chapter four provides insight into the use of airborne LiDAR to provide specific management guidance to enhance the suitable habitat for 10 songbird species. The collection of studies presented here provides applied tools for the use of airborne and portable LiDAR for rapid assessment and responsive management in southeastern pine woodlands. The advantages of detecting small changes in three-dimensional vegetation structure and how these can impact habitat functionality and suitability for species of interest are explored throughout the next four chapters. The research presented here provides an original and important contribution in the application of airborne and portable LiDAR datasets in forest management and ecological studies.
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Date Issued: 2011
Identifier: CFE0003697, ucf:48831
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003697

Title: Biomass density based adjustment of LiDAR-derived digital elevation models: a machine learning approach.
Creator: Abdelwahab, Khalid, Medeiros, Stephen, Mayo, Talea, Wahl, Thomas, University of Central Florida
Abstract / Description: Salt marshes are valued for providing protective and non-protective ecosystem services. Accurate digital elevation models (DEMs) in salt marshes are crucial for modeling storm surges and determining the initial DEM elevations for modelling marsh evolution. Due to high biomass density, lidar DEMs in coastal wetlands are seldom reliable. In an aim to reduce lidar-derived DEM error, several multilinear regression and random forest models were developed and tested to estimate biomass density in...
Show moreSalt marshes are valued for providing protective and non-protective ecosystem services. Accurate digital elevation models (DEMs) in salt marshes are crucial for modeling storm surges and determining the initial DEM elevations for modelling marsh evolution. Due to high biomass density, lidar DEMs in coastal wetlands are seldom reliable. In an aim to reduce lidar-derived DEM error, several multilinear regression and random forest models were developed and tested to estimate biomass density in the salt marshes near Saint Marks Lighthouse in Crawfordville, Florida. Between summer of 2017 and spring of 2018, two field trips were conducted to acquire true elevation and biomass density measures. Lidar point cloud data were combined with vegetation monitoring imagery acquired from Sentinel-2 and Landsat Thematic Mapper (LTM) satellites, and 64 field biomass density samples were used as target variables for developing the models. Biomass density classes were assigned to each biomass sample using a quartile approach. Moreover, 346 in-situ elevation measures were used to calculate the lidar DEM errors. The best model was then used to estimate biomass densities at all 346 locations. Finally, an adjusted DEM was produced by deducting the quartile-based adjustment values from the original lidar DEM. A random forest regression model achieved the highest pseudo R2 value of 0.94 for predicting biomass density in g/m2. The adjusted DEM based on the estimated biomass densities reduced the root mean squared error of the original DEM from 0.38 m to 0.18 m while decreasing the raw mean error from 0.33 m to 0.14 m, improving both measures by 54% and 58%, respectively.
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Date Issued: 2019
Identifier: CFE0007594, ucf:52535
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007594

Title: Relating ancient Maya land use legacies to the contemporary forest of Caracol, Belize.
Creator: Hightower, Jessica, Weishampel, John, Quintana-Ascencio, Pedro, VonHolle, Mary, Chase, Arlen, University of Central Florida
Abstract / Description: Human land use legacies have significant and long lasting impacts across landscapes. However, investigating the impacts of ancient land use legacies ((>)400 years) remains problematic due to the difficulty in detecting ancient land uses, especially those beneath dense canopies. The city of Caracol, one of the most important Maya archaeological sites in Belize, was abandoned after the collapse of the Maya civilization (ca. A.D. 900), leaving behind numerous structures, causeways, and...
Show moreHuman land use legacies have significant and long lasting impacts across landscapes. However, investigating the impacts of ancient land use legacies ((>)400 years) remains problematic due to the difficulty in detecting ancient land uses, especially those beneath dense canopies. The city of Caracol, one of the most important Maya archaeological sites in Belize, was abandoned after the collapse of the Maya civilization (ca. A.D. 900), leaving behind numerous structures, causeways, and agricultural terraces that persist beneath the dense tropical forest of western Belize. LiDAR (Light Detection and Ranging) technology enables detection of below canopy Maya archaeological features, providing an ideal opportunity to study the effects of ancient land use legacies on contemporary tropical forest composition. LiDAR also provided us with a detailed record of the 3-dimensional forest structure over the 200 km2 study area. This allowed the investigation how ancient land uses continue to impact both forest composition, in terms of tree species, and forest structure. I recorded tree species over four land use categories: 1) structures, 2) causeways, 3) terraced, and 4) non-terraced land. Using non-metric multidimensional scaling (NMS) and multi-response permutation procedures (MRPP) to test for differences between the classes, I found significantly distinct tree communities associated with the presence of terraces and the underlying topography. Terraced slopes appear to function as micro-valleys on the side of a hill, creating an environmental "bridge" between slope and valley tree communities. Tree species composition over causeways and structures was also found to be significantly different from terraced and non-terraced plots. Forest structure was assessed by extracting LiDAR points for terraced (n=150) and non-terraced (n=150) 0.25 ha plots. I calculated average canopy height, canopy closure, and vertical diversity from the height bins of the LiDAR points, using slope, elevation, and aspect as covariates. Using PerMANOVA I determined that forest structure over terraces was significantly different from non-terraced land. Terraces appear to mediate the effect of slope, resulting in less structural variation between slope and non-sloped land. These results led to the conclusion that human land uses abandoned (>)1000 years ago continue to impact the contemporary forests.
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Date Issued: 2012
Identifier: CFE0004250, ucf:49497
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004250

Title: AN ASSESSMENT OF SEA TURTLE NESTING BEHAVIOR IN RELATION TO HURRICANE- AND RESTORATION-INDUCED BEACH MORPHODYNAMICS.
Creator: Long, Tonya, Weishampel, John, University of Central Florida
Abstract / Description: Coastal habitats are highly dynamic and vulnerable to landscape-level disturbances such as storms and restoration projects. Along the east coast of Florida these areas are particularly valuable as they provide significant nesting habitat for two sea turtle species, the threatened loggerhead (Caretta caretta) and the endangered green turtle (Chelonia mydas). This coast was heavily impacted by three major hurricanes in 2004 and in some areas by large restoration projects in 2005. Recent remote...
Show moreCoastal habitats are highly dynamic and vulnerable to landscape-level disturbances such as storms and restoration projects. Along the east coast of Florida these areas are particularly valuable as they provide significant nesting habitat for two sea turtle species, the threatened loggerhead (Caretta caretta) and the endangered green turtle (Chelonia mydas). This coast was heavily impacted by three major hurricanes in 2004 and in some areas by large restoration projects in 2005. Recent remote sensing methods allow for broad evaluation of the shoreline and thus the ability to assess sea turtle nesting habitat at a landscape scale. I collected nesting data for southern Brevard County, Florida from 1989 Ã‚Â– 2005 and for Canaveral National Seashore, Florida from 1995 Ã‚Â– 2005. I used LiDAR (Light Detection and Ranging) and IfSAR (Interferometric Synthetic Aperture Radar) remote sensing to map sea turtle nesting habitat in both areas following the 2004 hurricanes and any subsequent restoration. Canaveral National Seashore underwent no restoration while southern Brevard County received extensive restoration. Topographic variables (e.g., total sand volume, width, and slope) derived from the remote sensing data were compared across three time periods (pre-hurricane, post-hurricane, and recovery period) and I compared nesting success data from 2004 to 2005. I built regression models for 2004 and 2005 to determine which topographic features influenced loggerhead and green turtle nesting the most. Green turtle nesting success declined from 2004 to 2005 only in highly restored areas while loggerhead nesting sucess declined throughout. Hurricanes caused a reduction in most of the topographic variables and restoration predominantly impacted aspects of the beach profile (e.g. slope and width). Loggerheads responded to profile characteristics (e.g. upper and lower beach slopes) though green turtles showed no consistent response to topography. The results indicate that both loggerheads and green turtles are sensitive to beach restoration, although loggerhead nesting is more influenced by beach morphology and green turtle nesting may be influenced more by other dune features such as vegetation cover.
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Date Issued: 2010
Identifier: CFE0003051, ucf:48344
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003051

Title: CHARACTERIZING THE VERTICAL STRUCTURE AND STRUCTURAL DIVERSITY OF FLORIDA OAK SCRUB VEGETATION USING DISCRETE-RETURN LIDAR.
Creator: Angelo, James, Weishampel, John, University of Central Florida
Abstract / Description: Vertical structure, the top-to-bottom arrangement of aboveground vegetation, is an important component of forest and shrubland ecosystems. For many decades, ecologists have used foliage height profiles and other measures of vertical structure to identify discrete stages in post-disturbance succession and to quantify the heterogeneity of vegetation. Such studies have, however, required resource-intensive field surveys and have been limited to relatively small spatial extents (e.g.,
Show moreVertical structure, the top-to-bottom arrangement of aboveground vegetation, is an important component of forest and shrubland ecosystems. For many decades, ecologists have used foliage height profiles and other measures of vertical structure to identify discrete stages in post-disturbance succession and to quantify the heterogeneity of vegetation. Such studies have, however, required resource-intensive field surveys and have been limited to relatively small spatial extents (e.g., <15 ha). Light detection and ranging (lidar) is an active remote sensing technology with enormous potential to characterize the three-dimensional structure of vegetation over broad spatial scales. In this study, discrete-return lidar data were used to create vertical profiles for over 500 vegetation patches on approximately 1000 ha of an oak scrub landscape in the Kennedy Space Center/Merritt Island National Wildlife Refuge area on the east-central coast of Florida. Nonparametric multivariate analysis of variance (NPMANOVA) tests detected significant differences among the profiles belonging to the four predominant land use/land cover (LULC) types in the study area. For the dominant LULC category (Herbaceous upland non-forested), pairwise NPMANOVA comparisons indicated that there were significant differences between vertical profiles for some of the distinct time since fire (TSF) values. Measures of vertical structural diversity (VSD) were calculated from the vertical profiles and then null, linear, and quadratic models relating VSD to TSF were compared via an Akaike information criterion (AIC) model selection procedure. As predicted by the Intermediate Disturbance Hypothesis, the quadratic model was the best model for the Herbaceous upland non-forested LULC category, but it explained less than 3% of the total variation in VSD. When fire frequency was considered in conjunction with TSF for this LULC category, however, the model that was quadratic in both predictor variables was the best model among the candidates and explained over 6% of the total variation in VSD. These results support the Extended Keystone Hypothesis, which predicts that disturbance generates discrete structural patterns across landscapes, and the Intermediate Disturbance Hypothesis, since the VSD of the predominant LULC category was maximized at intermediate levels of fire disturbance (i.e., intermediate values of TSF and/or fire frequency). In addition to demonstrating the ability of discrete-return lidar to characterize the vertical structure of vegetation at the landscape scale, this research has potential management implications. Using the techniques developed in this study, practitioners can compare the vertical structure of managed ecosystems to reference natural systems to evaluate the efficacy of managed disturbance regimes.
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Date Issued: 2010
Identifier: CFE0003254, ucf:48520
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003254

Title: Hydrologic controls on the natural drainage networks extracted from high-resolution topographic data.
Creator: Hooshyar, Milad, Wang, Dingbao, Medeiros, Stephen, Singh, Arvind, Kibler, Kelly, Weishampel, John, University of Central Florida
Abstract / Description: Drainage networks are important geomorphologic and hydrologic features which significantly control runoff generation. Drainage networks are composed of unchannelized valleys and channels. At valley heads, flow changes from unconfined sheet flow on the hillslope to confined flow in valley. Localized confined flow dominates in valleys as a result of convergent topography with positive curvature. Channels initiate at some distance down from the valley head, and the transition from unchannelized...
Show moreDrainage networks are important geomorphologic and hydrologic features which significantly control runoff generation. Drainage networks are composed of unchannelized valleys and channels. At valley heads, flow changes from unconfined sheet flow on the hillslope to confined flow in valley. Localized confined flow dominates in valleys as a result of convergent topography with positive curvature. Channels initiate at some distance down from the valley head, and the transition from unchannelized valley to channel is referred to as the channel head. Channel heads occur at a point where fluvial transport dominates over diffusive transport.From the hydrologic perspective, channels are categorized as perennial, intermittent, and ephemeral streams based on the flow durations. Perennial streams flow for the most of the time during normal years and are maintained by groundwater discharge. Intermittent (i.e. seasonal) streams flow during certain times of the year receiving water from surface sources such as melting snow or from groundwater. Lastly, ephemeral streams flow only in direct response to precipitation without continuous surface flow. In this dissertation, the hydrologic controls on the drainage networks extracted from high resolution Digital Elevation Models (DEMs) based on Light Detection and Ranging (LiDAR) are investigated. A method for automatic extraction of valley and channel networks from high-resolution DEMs is presented. This method utilizes both positive (i.e., convergent topography) and negative (i.e., divergent topography) curvature to delineate the valley network. The valley and ridge skeletons are extracted using the pixels' curvature and the local terrain conditions. The valley network is generated by checking the terrain for the existence of at least one ridge between two intersecting valleys. The transition from unchannelized to channelized sections (i.e., channel head) in each 1st-order valley tributary is identified independently by categorizing the corresponding contours using an unsupervised approach based on K-means clustering. The method does not require a spatially constant channel initiation threshold (e.g., curvature or contributing area). Moreover, instead of a point attribute (e.g., curvature), the proposed clustering method utilizes the shape of contours, which reflects the entire cross-sectional profile including possible banks. The method was applied to three catchments: Indian Creek and Mid Bailey Run in Ohio, and Feather River in California. The accuracy of channel head extraction from the proposed method is comparable to state-of-the-art channel extraction methods. Valleys extracted from DEMs may be wet (flowing) or dry at any given time depending on the hydrologic conditions. The temporal dynamics of flowing streams are vitally important for understanding hydrologic processes including surface water and groundwater interaction and hydrograph recession. However, observations of wet channel networks are limited, especially in headwater catchments. Near infrared LiDAR data provide an opportunity to map wet channel networks owing to the fine spatial resolution and strong absorption of light energy by water surfaces. A systematic method is developed to map wet channel networks by integrating elevation and signal intensity of ground returns. The signal intensity thresholds for identifying wet pixels are extracted from frequency distributions of intensity return within the convergent topography extent using a Gaussian mixture model. Moreover, the concept of edge in digital image processing, defined based on the intensity gradient, is utilized to enhance detection of small wet channels. The developed method was applied to the Lake Tahoe area based on eight LiDAR acquisitions during recession periods in five watersheds. A power-law relationship between streamflow and wetted channel length during recession periods was derived, and the scaling exponent (L?Q^0.38) is within the range of reported values from fieldwork in other regions.Several studies in the past focused on the relationship between drainage density (i.e., drainage length divided by drainage area) and long-term climate and reported a U-shape pattern. In this dissertation, this relationship was re-visited and the effect of drainage area on drainage density was investigated. Long-term climate was quantified by climate aridity indices which is the ratio between long-term potential evaporation and precipitation. 120 study sites across the United States with minimal human disturbance and a wide range of climate aridity index were selected based on the availability of LiDAR data. The drainage networks were delineated from LiDAR-based 1 m DEMs using the proposed curvature-based method. Despite the U-shaped relationship in the literature, our result shows a significant decreasing trend in the drainage density versus climate aridity index in arid regions; whereas no trend is observed in humid watersheds. This observation and its discrepancy with the reported pattern in the literature are justified considering the dynamics of the runoff erosive force and the resistance of vegetation and the climate controls on them. Our findings suggest that natural drainage networks in arid regions are more sensitive to the change in long-term climate conditions compared with drainage networks in humid climate. It was also found that drainage density has a decreasing trend with drainage area in arid regions; however, no trend was observed in humid regions. In a broader sense, the findings influence our understanding of the formation of drainage networks and the response of hydrologic systems to climate change. The formation and growth of river channels and their network evolution are governed by the erosional and depositional processes operating on the landscape due to movement of water. The branching angles, i.e., the angle between two adjoining channels, in drainage networks are important features related to the network topology and contain valuable information about the forming mechanisms of the landscape. Based on channel networks extracted from 1 m Digital Elevation Models of 120 catchments with minimal human impacts across the United States, we showed that the junction angles have two distinct modes with ?1 ? 49.5(&)deg; and ?2 ? 75.0(&)deg;. The observed angles are physically explained as the optimal angles that result in minimum energy dissipation and are linked to the exponent characterizing slope-area curve. Our findings suggest that the flow regimes, debris-flow dominated or fluvial, have distinct characteristic angles which are functions of the scaling exponent of the slope-area curve. These findings enable us to understand the geomorphologic signature of hydrologic processes on drainage networks and develop more refined landscape evolution models.
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Date Issued: 2017
Identifier: CFE0006604, ucf:51278
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006604

Title: Evaluation of the three-dimensional patterns and ecological impacts of the invasive Old World climbing fern (Lygodium microphyllum).
Creator: Maldonado, Alexis, Weishampel, John, VonHolle, Mary, Hinkle, Charles, University of Central Florida
Abstract / Description: Invasion by non-native species has had significant ecological and economic impacts on a global scale. In the state of Florida, Old World climbing fern (Lygodium microphyllum) is an invasive plant listed by FLEPPC as a category one invader with significant ecological impacts that threaten native plant diversity. This species relies on existing vegetative structures for support to climb into the forest canopy and forms dense mats that cover tree crowns. This subsequently affects the resources...
Show moreInvasion by non-native species has had significant ecological and economic impacts on a global scale. In the state of Florida, Old World climbing fern (Lygodium microphyllum) is an invasive plant listed by FLEPPC as a category one invader with significant ecological impacts that threaten native plant diversity. This species relies on existing vegetative structures for support to climb into the forest canopy and forms dense mats that cover tree crowns. This subsequently affects the resources available to other species present. Quantifying the structural changes due to the presence of this species has proved logistically difficult, especially on a large spatial scale.Airborne LiDAR (Light Detection And Ranging) technology is a form of remote sensing that measures the elevation of surfaces over a site. In this study I utilized LiDAR to calculate various forest structure metrics at Jonathan Dickinson State Park (JDSP) in Hobe Sound, Florida across various management frequencies and densities of Old World climbing fern. These data were used to quantify the degree to which this invasive species alters forest structure across these two gradients. I also recorded species composition in the field to relate how Old World climbing fern impacts native plant diversity. Structural measurements including average canopy height, height of median energy (HOME), rugosity, canopy openness, and vertical structural diversity (LHDI) were calculated for a total of three hundred 0.25ha sites stratified by invasion density and management frequency.Using a combination of univariate and multivariate statistical analyses I found that the presence of Old World Climbing fern altered the physical structure of the forest communities itivinvades. Higher percent cover of Old World climbing fern decreased structural diversity while increased management effort was found to mitigate those impacts. The management for Old World Climbing fern was also found to impact both species richness and diversity at JDSP. I also demonstrated that there were several species that were not found and others that were more common in the presence of Old World climbing fern and that there was a relationship between management and what species were present. The results show that both Old World climbing fern and the management practices used to control it have had significant ecological impacts on the natural communities in South Florida.
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Date Issued: 2014
Identifier: CFE0005206, ucf:50651
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005206

Title: Influence of Topographic Elevation Error On Modeled Storm Surge.
Creator: Bilskie, Matthew, Hagen, Scott, Wang, Dingbao, Chopra, Manoj, University of Central Florida
Abstract / Description: The following presents a method for determining topographic elevation error for overland unstructured finite element meshes derived from bare earth LiDAR for use in a shallow water equations model. This thesis investigates the development of an optimal interpolation method to produce minimal error for a given element size. In hydrodynamic studies, it is vital to represent the floodplain as accurately as possible since terrain is a critical factor that influences water flow. An essential step...
Show moreThe following presents a method for determining topographic elevation error for overland unstructured finite element meshes derived from bare earth LiDAR for use in a shallow water equations model. This thesis investigates the development of an optimal interpolation method to produce minimal error for a given element size. In hydrodynamic studies, it is vital to represent the floodplain as accurately as possible since terrain is a critical factor that influences water flow. An essential step in the development of a coastal inundation model is processing and resampling dense bare earth LiDAR to a DEM and ultimately to the mesh nodes; however, it is crucial that the correct DEM grid size and interpolation method be employed for an accurate representation of the terrain. The following research serves two purposes: 1) to assess the resolution and interpolation scheme of bare earth LiDAR data points in terms of its ability to describe the bare earth topography and its subsequent performance during relevant tide and storm surge simulations.
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Date Issued: 2012
Identifier: CFE0004520, ucf:49265
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004520

Title: A laser radar employing linearly chirped pulses from a mode-locked laser for long range, unambiguous, sub-millimeter resolution ranging and velocimetry.
Creator: Piracha, Mohammad Umar, ,, University of Central Florida
Abstract / Description: Light detection and ranging (lidar) is used for various applications such as remote sensing, altimetry and imaging. In this talk, a linearly chirped pulse source is introduced that generates wavelength-swept pulses exhibiting ~6 nm optical bandwidth with (>) 20 km coherence length. The chirped pulses are used in an interferometric lidar setup to perform distance measurements with sub-millimeter resolution (using pulses that are a few meters long), at target distances (>) 10 km, with at least...
Show moreLight detection and ranging (lidar) is used for various applications such as remote sensing, altimetry and imaging. In this talk, a linearly chirped pulse source is introduced that generates wavelength-swept pulses exhibiting ~6 nm optical bandwidth with (>) 20 km coherence length. The chirped pulses are used in an interferometric lidar setup to perform distance measurements with sub-millimeter resolution (using pulses that are a few meters long), at target distances (>) 10 km, with at least 25 dB signal-to-noise ratio at the receiver. A pulse repetition rate of 20 MHz provides fast update rates, while chirped pulse amplification allows easy amplification of optical signals to high power levels that are required for long range operation. A pulse tagging scheme based on phase modulation is used to demonstrate unambiguous, long range measurements. In addition to this, simultaneous measurement of target range and Doppler velocity is performed using a target moving at a speed of over 330 km/h (205 mph) inside the laboratory. In addition to this, spectral phase modulation of the chirped pulses is demonstrated to compensate for the undesirable ripple in the group delay of the chirped pulses. Moreover, spectral amplitude modulation is used to generate pulses with Gaussian temporal intensity profiles and a two-fold increase in the lidar range resolution (284 um) is observed.
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Date Issued: 2012
Identifier: CFE0004423, ucf:49409
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004423

Title: UNSUPERVISED BUILDING DETECTION FROM IRREGULARLY SPACED LIDAR AND AERIAL IMAGERY.
Creator: Shorter, Nicholas, Kasparis, Takis, University of Central Florida
Abstract / Description: As more data sources containing 3-D information are becoming available, an increased interest in 3-D imaging has emerged. Among these is the 3-D reconstruction of buildings and other man-made structures. A necessary preprocessing step is the detection and isolation of individual buildings that subsequently can be reconstructed in 3-D using various methodologies. Applications for both building detection and reconstruction have commercial use for urban planning, network planning for mobile...
Show moreAs more data sources containing 3-D information are becoming available, an increased interest in 3-D imaging has emerged. Among these is the 3-D reconstruction of buildings and other man-made structures. A necessary preprocessing step is the detection and isolation of individual buildings that subsequently can be reconstructed in 3-D using various methodologies. Applications for both building detection and reconstruction have commercial use for urban planning, network planning for mobile communication (cell phone tower placement), spatial analysis of air pollution and noise nuisances, microclimate investigations, geographical information systems, security services and change detection from areas affected by natural disasters. Building detection and reconstruction are also used in the military for automatic target recognition and in entertainment for virtual tourism. Previously proposed building detection and reconstruction algorithms solely utilized aerial imagery. With the advent of Light Detection and Ranging (LiDAR) systems providing elevation data, current algorithms explore using captured LiDAR data as an additional feasible source of information. Additional sources of information can lead to automating techniques (alleviating their need for manual user intervention) as well as increasing their capabilities and accuracy. Several building detection approaches surveyed in the open literature have fundamental weaknesses that hinder their use; such as requiring multiple data sets from different sensors, mandating certain operations to be carried out manually, and limited functionality to only being able to detect certain types of buildings. In this work, a building detection system is proposed and implemented which strives to overcome the limitations seen in existing techniques. The developed framework is flexible in that it can perform building detection from just LiDAR data (first or last return), or just nadir, color aerial imagery. If data from both LiDAR and aerial imagery are available, then the algorithm will use them both for improved accuracy. Additionally, the proposed approach does not employ severely limiting assumptions thus enabling the end user to apply the approach to a wider variety of different building types. The proposed approach is extensively tested using real data sets and it is also compared with other existing techniques. Experimental results are presented.
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Date Issued: 2009
Identifier: CFE0002783, ucf:48125
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002783

Title: Understanding Hydroclimatic Controls on Stream Network Dynamics using LiDAR Data.
Creator: Kim, Seoyoung, Wang, Dingbao, Medeiros, Stephen, Nam, Boo Hyun, Singh, Arvind, Sumner, David, University of Central Florida
Abstract / Description: This dissertation investigates the hydroclimatic controls on drainage network dynamics and characterizes the variation of drainage density in various climate regions. The methods were developed to extract the valley and wet channel networks based on Light Detection and Ranging (LiDAR) data including the elevation and intensity of laser returns. The study watersheds were selected based on the availability of streamflow observations and LiDAR data. Climate aridity index was used as a...
Show moreThis dissertation investigates the hydroclimatic controls on drainage network dynamics and characterizes the variation of drainage density in various climate regions. The methods were developed to extract the valley and wet channel networks based on Light Detection and Ranging (LiDAR) data including the elevation and intensity of laser returns. The study watersheds were selected based on the availability of streamflow observations and LiDAR data. Climate aridity index was used as a quantitative indicator for climate. The climate controls on drainage density were re-visited using watersheds with minimal anthropogenic interferences and compared with the U-shape relationship reported in the previous studies. A curvature-based method was developed to extract a valley network from 1-m LiDAR-based Digital Elevation Models. The relationship between drainage density and climate aridity index showed a monotonic increasing trend and the discrepancy was explained by human interventions and underestimated drainage density due to the coarse spatial resolution (30-meter) of the topographic maps used in previous research. Observations of wet channel networks are limited, especially in headwater catchments in comparison with the importance of stream network expansion and contraction. A systematic method was developed to extract wet channel networks based on the signal intensities of LiDAR ground returns, which are lower on water surfaces than on dry surfaces. The frequency distributions of intensities associated with wet surface and dry surface returns were constructed. With the aid of LiDAR-based ground elevations, signal intensity thresholds were identified for extracting wet channels. The developed method was applied to Lake Tahoe area during recession periods in five watersheds. A power-law relationship between streamflow and wet channel length was obtained and the scaling exponent was consistent with the reported findings from field work in other regions.Perennial streams flow for the most of the time during normal years and are usually defined based on a flow duration threshold. The streamflow characteristics of perennial streams in this research were assessed using the relationship between streamflow exceedance probability and wet channel ratio based on wet channel networks extracted from LiDAR data. Non-dimensional analysis based on the relationship between streamflow exceedance probability and wet channel ratio showed that results were consistent with previous research about perennial stream definition, and provided the possibility to use wet channel ratio to define perennial streams. Wetlands are important natural resources and need to be monitored regularly in order to understand their inundation dynamics, function and health. Wetland mapping is a key part of monitoring programs. A framework for detecting wetland was developed based on LiDAR elevation and intensity information. After masking out densely vegetated areas, wet areas were identified based on signal intensity of ground returns for barrier islands in East-Central Florida. The intensity threshold of wet surface was identified by decomposing composite probability distribution functions using a Gamma mixture model and the Expectation-Maximization algorithm. This method showed good potential for wetland mapping.The methodology developed in this dissertation demonstrated that incorporating LiDAR data into the drainage networks, stream network dynamics and wetlands results in enhanced understanding of hydroclimatic controls on stream network dynamics. LiDAR data provide a rich information source including elevation and intensity, and are of great benefit to hydrologic research community.
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Date Issued: 2016
Identifier: CFE0006532, ucf:51372
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006532

Title: Species and habitat interactions of the gopher tortoise: A keystone species?.
Creator: Catano, Christopher, Hinkle, Charles, Stout, I, Jenkins, David, University of Central Florida
Abstract / Description: Species-species and species-habitat interactions have been demonstrated to be important in influencing diversity across a variety of ecosystems. Despite generalities in the importance of these interactions, appropriate mechanisms to explain them are absent in many systems. In sandhill systems of the southeast U.S., gopher tortoises have been hypothesized to be a crucial species in the maintenance of diversity and function. However, the mechanisms and magnitude in which they influence their...
Show moreSpecies-species and species-habitat interactions have been demonstrated to be important in influencing diversity across a variety of ecosystems. Despite generalities in the importance of these interactions, appropriate mechanisms to explain them are absent in many systems. In sandhill systems of the southeast U.S., gopher tortoises have been hypothesized to be a crucial species in the maintenance of diversity and function. However, the mechanisms and magnitude in which they influence their communities and habitats have rarely been empirically quantified. I examined how habitat structure influences tortoise abandonment of burrows and how tortoise densities influence non-volant vertebrate community diversity. Tortoise burrow abandonment is directly influenced by canopy closure, with each percent increase in canopy cover relating to a ~2% increase in the probability of burrow abandonment. In addition, tortoise burrow density was positively correlated with diversity and evenness, but not species richness. This influence was directly proportional to burrow density, supporting a dominance role for this species and rejecting the commonly asserted keystone species mechanism. I also quantified the influence of tortoises in influencing diversity relative to other environmental and habitat variables. Through this research, I have demonstrated that disturbance and habitat structure are important, but diversity responds most to density of burrows in the habitat. These findings demonstrate the intricate relationships interacting to maintaining diversity in sandhill systems. In particular, habitat change leading to declines of gopher tortoises may have drastic negative impacts on vertebrate species diversity.
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Date Issued: 2012
Identifier: CFE0004526, ucf:49246
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004526

Title: Incorporating Remotely Sensed Data into Coastal Hydrodynamic Models: Parameterization of Surface Roughness and Spatio-Temporal Validation of Inundation Area.
Creator: Medeiros, Stephen, Hagen, Scott, Weishampel, John, Wang, Dingbao, Yeh, Gour-Tsyh, University of Central Florida
Abstract / Description: This dissertation investigates the use of remotely sensed data in coastal tide and inundation models, specifically how these data could be more effectively integrated into model construction and performance assessment techniques. It includes a review of numerical wetting and drying algorithms, a method for constructing a seamless digital terrain model including the handling of tidal datums, an investigation into the accuracy of land use / land cover (LULC) based surface roughness...
Show moreThis dissertation investigates the use of remotely sensed data in coastal tide and inundation models, specifically how these data could be more effectively integrated into model construction and performance assessment techniques. It includes a review of numerical wetting and drying algorithms, a method for constructing a seamless digital terrain model including the handling of tidal datums, an investigation into the accuracy of land use / land cover (LULC) based surface roughness parameterization schemes, an application of a cutting edge remotely sensed inundation detection method to assess the performance of a tidal model, and a preliminary investigation into using 3-dimensional airborne laser scanning data to parameterize surface roughness.A thorough academic review of wetting and drying algorithms employed by contemporary numerical tidal models was conducted. Since nearly all population centers and valuable property are located in the overland regions of the model domain, the coastal models must adequately describe the inundation physics here. This is accomplished by techniques that generally fall into four categories: Thin film, Element removal, Depth extrapolation, and Negative depth. While nearly all wetting and drying algorithms can be classified as one of the four types, each model is distinct and unique in its actual implementation.The use of spatial elevation data is essential to accurate coastal modeling. Remotely sensed LiDAR is the standard data source for constructing topographic digital terrain models (DTM). Hydrographic soundings provide bathymetric elevation information. These data are combined to form a seamless topobathy surface that is the foundation for distributed coastal models. A three-point inverse distance weighting method was developed in order to account for the spatial variability of bathymetry data referenced to tidal datums. This method was applied to the Tampa Bay region of Florida in order to produce a seamless topobathy DTM.Remotely sensed data also contribute to the parameterization of surface roughness. It is used to develop land use / land cover (LULC) data that is in turn used to specify spatially distributed bottom friction and aerodynamic roughness parameters across the model domain. However, these parameters are continuous variables that are a function of the size, shape and density of the terrain and above-ground obstacles. By using LULC data, much of the variation specific to local areas is generalized due to the categorical nature of the data. This was tested by comparing surface roughness parameters computed based on field measurements to those assigned by LULC data at 24 sites across Florida. Using a t-test to quantify the comparison, it was proven that the parameterizations are significantly different. Taking the field measured parameters as ground truth, it is evident that parameterizing surface roughness based on LULC data is deficient.In addition to providing input parameters, remotely sensed data can also be used to assess the performance of coastal models. Traditional methods of model performance testing include harmonic resynthesis of tidal constituents, water level time series analysis, and comparison to measured high water marks. A new performance assessment that measures a model's ability to predict the extent of inundation was applied to a northern Gulf of Mexico tidal model. The new method, termed the synergetic method, is based on detecting inundation area at specific points in time using satellite imagery. This detected inundation area is compared to that predicted by a time-synchronized tidal model to assess the performance of model in this respect. It was shown that the synergetic method produces performance metrics that corroborate the results of traditional methods and is useful in assessing the performance of tidal and storm surge models. It was also shown that the subject tidal model is capable of correctly classifying pixels as wet or dry on over 85% of the sample areas.Lastly, since it has been shown that parameterizing surface roughness using LULC data is deficient, progress toward a new parameterization scheme based on 3-dimensional LiDAR point cloud data is presented. By computing statistics for the entire point cloud along with the implementation of moving window and polynomial fit approaches, empirical relationships were determined that allow the point cloud to estimate surface roughness parameters. A multi-variate regression approach was chosen to investigate the relationship(s) between the predictor variables (LiDAR statistics) and the response variables (surface roughness parameters). It was shown that the empirical fit is weak when comparing the surface roughness parameters to the LiDAR data. The fit was improved by comparing the LiDAR to the more directly measured source terms of the equations used to compute the surface roughness parameters. Future work will involve using these empirical relationships to parameterize a model in the northern Gulf of Mexico and comparing the hydrodynamic results to those of the same model parameterized using contemporary methods. In conclusion, through the work presented herein, it was demonstrated that incorporating remotely sensed data into coastal models provides many benefits including more accurate topobathy descriptions, the potential to provide more accurate surface roughness parameterizations, and more insightful performance assessments. All of these conclusions were achieved using data that is readily available to the scientific community and, with the exception of the Synthetic Aperture Radar (SAR) from the Radarsat-1 project used in the inundation detection method, are available free of charge. Airborne LiDAR data are extremely rich sources of information about the terrain that can be exploited in the context of coastal modeling. The data can be used to construct digital terrain models (DTMs), assist in the analysis of satellite remote sensing data, and describe the roughness of the landscape thereby maximizing the cost effectiveness of the data acquisition.
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Date Issued: 2012
Identifier: CFE0004271, ucf:49506
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004271

LiDAR (10)	+ -
remote sensing (4)	+ -
lidar (3)	+ -
DEM (2)	+ -
Florida (2)	+ -

LADAR (2)	+ -
LIDAR (2)	+ -
Lidar (2)	+ -
storm surge (2)	+ -
3-D structure (1)	+ -
3D Reconstruction (1)	+ -
ADCIRC (1)	+ -
Ancient Agriculture (1)	+ -
Beach Erosion (1)	+ -
Beach Restoration (1)	+ -
Biodiversity (1)	+ -
Biomass Density (1)	+ -
Building Detection (1)	+ -
Building Reconstruction (1)	+ -
Channel Network (1)	+ -
Channel network (1)	+ -
Chirp (1)	+ -
Climate (1)	+ -
Coastal Wetlands (1)	+ -
Curvature (1)	+ -
Digital Elevation Model (1)	+ -
Distance Measurement (1)	+ -
Drainage density (1)	+ -
Ecosystem Engineer (1)	+ -
Elevation Accuracy (1)	+ -

University of Central Florida (18)	+ -
Weishampel, John (8)	+ -
Wang, Dingbao (6)	+ -
Medeiros, Stephen (5)	+ -
Hagen, Scott (3)	+ -

Singh, Arvind (3)	+ -
Hinkle, Charles (2)	+ -
Kasparis, Takis (2)	+ -
Kibler, Kelly (2)	+ -
Nam, Boo Hyun (2)	+ -
Shorter, Nicholas (2)	+ -
VonHolle, Mary (2)	+ -
, (1)	+ -
Abdelwahab, Khalid (1)	+ -
Angelo, James (1)	+ -
Bilskie, Matthew (1)	+ -
Butterfield, Anna (1)	+ -
Catano, Christopher (1)	+ -
Chase, Arlen (1)	+ -
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Coggin, David (1)	+ -
Hightower, Jessica (1)	+ -
Hooshyar, Milad (1)	+ -
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