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A rich Internet application for automated detection of road blockage in post-disaster scenarios

Description: This paper presents the development of a rich Internet application for automated detection of road blockage in post-disaster scenarios using volunteered geographic information from OpenStreetMap street centerlines and airborne light detection and ranging (LiDAR) data.
Date: February 25, 2014
Creator: Liu, W.; Dong, Pinliang; Liu, S. & Liu, J.
Partner: UNT College of Arts and Sciences

LiDAR Data for Characterizing Linear and Planar Geomorphic Markers in Tectonic Geomorphology

Description: This paper provides a brief review of airborne light detection and ranging (LiDAR) data for characterizing linear and planar geomorphic markers in tectonic geomorphology, including traces of active faults and surface deformation caused by earthquakes. Challenges and opportunities of LiDAR for the study of tectonic geomorphology and coseismic deformation are also discussed.
Date: November 28, 2014
Creator: Dong, Pinliang
Partner: UNT College of Arts and Sciences

Parcel-Based Change Detection Using Multi-Temporal LiDAR Data in the City of Surrey, British Columbia, Canada

Description: Change detection is amongst the most effective critical examination methods used in remote sensing technology. In this research, new methods are proposed for building and vegetation change detection using only LiDAR data without using any other remotely sensed data. Two LiDAR datasets from 2009 and 2013 will be used in this research. These datasets are provided by the City of Surrey. A Parcel map which shows parcels in the study area will be also used in this research because the objective of this research is detecting changes based on parcels. Different methods are applied to detect changes in buildings and vegetation respectively. Three attributes of object –slope, building volume, and building height are derived and used in this study. Changes in buildings are not only detected but also categorized based on their attributes. In addition, vegetation change detection is performed based on parcels. The output shows parcels with a change of vegetation. Accuracy assessment is done by using measures of completeness, correctness, and quality of extracted regions. Accuracy assessments suggest that building change detection is performed with better results.
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Date: December 2016
Creator: Yigit, Aykut
Partner: UNT Libraries

Archaeological Site Vulnerability Modeling for Cultural Resources Management Based on Historic Aerial Photogrammetry and LiDAR

Description: GIS has been utilized in cultural resources management for decades, yet its application has been largely isolated to predicting the occurrence of archaeological sites. Federal and State agencies are required to protect archaeological sites that are discovered on their lands, but their resources and personnel are very limited. A new methodology is evaluated that uses modern light detection and ranging (LiDAR) and historic aerial photogrammetry to create digital terrain models (DTMs) capable of identifying sites that are most at risk of damage from changes in terrain. Results revealed that photogrammetric modeling of historic aerial imagery, with limitations, can be a useful decision making tool for cultural resources managers to prioritize conservation and monitoring efforts. An attempt to identify key environmental factors that would be indicative of future topographic changes did not reveal conclusive results. However, the methodology proposed has the potential to add an affordable temporal dimension to future digital terrain modeling and land management. Furthermore, the methods have global applicability because they can be utilized in any region with an arid environment.
Date: August 2015
Creator: Helton, Erin King
Partner: UNT Libraries

Investigation on Segmentation, Recognition and 3D Reconstruction of Objects Based on LiDAR Data Or MRI

Description: Segmentation, recognition and 3D reconstruction of objects have been cutting-edge research topics, which have many applications ranging from environmental and medical to geographical applications as well as intelligent transportation. In this dissertation, I focus on the study of segmentation, recognition and 3D reconstruction of objects using LiDAR data/MRI. Three main works are that (I). Feature extraction algorithm based on sparse LiDAR data. A novel method has been proposed for feature extraction from sparse LiDAR data. The algorithm and the related principles have been described. Also, I have tested and discussed the choices and roles of parameters. By using correlation of neighboring points directly, statistic distribution of normal vectors at each point has been effectively used to determine the category of the selected point. (II). Segmentation and 3D reconstruction of objects based on LiDAR/MRI. The proposed method includes that the 3D LiDAR data are layered, that different categories are segmented, and that 3D canopy surfaces of individual tree crowns and clusters of trees are reconstructed from LiDAR point data based on a region active contour model. The proposed method allows for delineations of 3D forest canopy naturally from the contours of raw LiDAR point clouds. The proposed model is suitable not only for a series of ideal cone shapes, but also for other kinds of 3D shapes as well as other kinds dataset such as MRI. (III). Novel algorithms for recognition of objects based on LiDAR/MRI. Aimed to the sparse LiDAR data, the feature extraction algorithm has been proposed and applied to classify the building and trees. More importantly, the novel algorithms based on level set methods have been provided and employed to recognize not only the buildings and trees, the different trees (e.g. Oak trees and Douglas firs), but also the subthalamus nuclei (STNs). By using the novel algorithms based ...
Date: May 2015
Creator: Tang, Shijun
Partner: UNT Libraries

Using Geographic Information Systems for the Functional Assessment of Texas Coastal Prairie Freshwater Wetlands Around Galveston Bay

Description: The objective of this study was to deploy a conceptual framework developed by M. Forbes using a geographic information system (GIS) approach to assess the functionality of wetlands in the Galveston Bay Area of Texas. This study utilized geospatial datasets which included National Wetland Inventory maps (NWI), LiDAR data, National Agriculture Imagery Program (NAIP) imagery and USGS National Land Cover data to assess the capacity of wetlands to store surface water and remove pollutants, including nitrogen, phosphorus, heavy metals, and organic compounds. The use of LiDAR to characterize the hydrogeomorphic characteristics of wetlands is a key contribution of this study to the science of wetland functional assessment. LiDAR data was used to estimate volumes for the 7,370 wetlands and delineate catchments for over 4,000 wetlands, located outside the 100-yr floodplain, within a 2,075 square mile area around Galveston Bay. Results from this study suggest that coastal prairie freshwater wetlands typically have a moderate capacity to store surface water from precipitation events, remove ammonium, and retain phosphorus and heavy metals and tend to have a high capacity for removing nitrate and retainremove organic compounds. The results serve as a valuable survey instrument for increasing the understanding of coastal prairie freshwater wetlands and support a cumulative estimate of the water quality and water storage functions on a regional scale.
Date: May 2010
Creator: Enwright, Nicholas
Partner: UNT Libraries

Comparison of IKONOS Derived Vegetation Index and LiDAR Derived Canopy Height Model for Grassland Management.

Description: Forest encroachment is understood to be the main reason for prairie grassland decline across the United States. In Texas and Oklahoma, juniper has been highlighted as particularly opportunistic. This study assesses the usefulness of three remote sensing techniques to aid in locating the areas of juniper encroachment for the LBJ Grasslands in Decatur, Texas. An object based classification was performed in eCognition and final accuracy assessments placed the overall accuracy at 94%, a significant improvement over traditional pixel based methods. Image biomass was estimated using normalized difference vegetation index (NDVI) for 1 meter resolution IKONOS winter images. A high correlation between the sum of NDVI for tree objects and field tree biomass was determined where R = 0.72, suggesting NDVI sum of a tree area is plausible. However, issues with NDVI saturation and regression produced unrealistically high biomass estimates for large NDVI. Canopy height model (CHM) derived from 3-5m LiDAR data did not perform as well. LiDAR typically used for digital elevation model (DEM) production was acquired for the CHM and produced correlations of R = 0.26. This suggests an inability for this particular dataset to identify juniper trees. When points that registered a tree height where correlated with field values, an R = 0.5 was found, suggesting denser point spacing would be necessary for this type of LiDAR data. Further refining of the methods used in this study could yield such information as the amount of juniper tree for a given location, fuel loads for prescribed burns and better information for the best approach to remove the juniper and ultimately management juniper encroachment into grasslands.
Date: December 2009
Creator: Parker, Gary
Partner: UNT Libraries

High Resolution Satellite Images and LiDAR Data for Small-Area Building Extraction and Population Estimation

Description: Population estimation in inter-censual years has many important applications. In this research, high-resolution pan-sharpened IKONOS image, LiDAR data, and parcel data are used to estimate small-area population in the eastern part of the city of Denton, Texas. Residential buildings are extracted through object-based classification techniques supported by shape indices and spectral signatures. Three population indicators -building count, building volume and building area at block level are derived using spatial joining and zonal statistics in GIS. Linear regression and geographically weighted regression (GWR) models generated using the three variables and the census data are used to estimate population at the census block level. The maximum total estimation accuracy that can be attained by the models is 94.21%. Accuracy assessments suggest that the GWR models outperformed linear regression models due to their better handling of spatial heterogeneity. Models generated from building volume and area gave better results. The models have lower accuracy in both densely populated census blocks and sparsely populated census blocks, which could be partly attributed to the lower accuracy of the LiDAR data used.
Date: December 2009
Creator: Ramesh, Sathya
Partner: UNT Libraries

Urban surface characterization using LiDAR and aerial imagery.

Description: Many calamities in history like hurricanes, tornado and flooding are proof to the large scale impact they cause to the life and economy. Computer simulation and GIS helps in modeling a real world scenario, which assists in evacuation planning, damage assessment, assistance and reconstruction. For achieving computer simulation and modeling there is a need for accurate classification of ground objects. One of the most significant aspects of this research is that it achieves improved classification for regions within which light detection and ranging (LiDAR) has low spatial resolution. This thesis describes a method for accurate classification of bare ground, water body, roads, vegetation, and structures using LiDAR data and aerial Infrared imagery. The most basic step for any terrain modeling application is filtering which is classification of ground and non-ground points. We present an integrated systematic method that makes classification of terrain and non-terrain points effective. Our filtering method uses the geometric feature of the triangle meshes created from LiDAR samples and calculate the confidence for every point. Geometric homogenous blocks and confidence are derived from TIN model and gridded LiDAR samples. The results from two representations are used in a classifier to determine if the block belongs ground or otherwise. Another important step is detection of water body, which is based on the LiDAR sample density of the region. Objects like tress and bare ground are characterized by the geometric features present in the LiDAR and the color features in the infrared imagery. These features are fed into a SVM classifier which detects bare-ground in the given region. Similarly trees are extracted using another trained SVM classifier. Once we obtain bare-grounds and trees, roads are extracted by removing the bare grounds. Structures are identified by the properties of non-ground segments. Experiments were conducted using LiDAR samples and Infrared imagery ...
Date: December 2009
Creator: Sarma, Vaibhav
Partner: UNT Libraries

Quantifying Forest Vertical Structure to Determine Bird Habitat Quality in the Greenbelt Corridor, Denton, Tx

Description: This study presents the integration of light detection and range (LiDAR) and hyperspectral remote sensing to create a three-dimensional bird habitat map in the Greenbelt Corridor of the Elm Fork of the Trinity River. This map permits to examine the relationship between forest stand structure, landscape heterogeneity, and bird community composition. A biannual bird census was conducted at this site during the breeding seasons of 2009 and 2010. Census data combined with the three-dimensional map suggest that local breeding bird abundance, community structure, and spatial distribution patterns are highly influenced by vertical heterogeneity of vegetation surface. For local breeding birds, vertical heterogeneity of canopy surface within stands, connectivity to adjacent forest patches, largest forest patch index, and habitat (vegetation) types proved to be the most influential factors to determine bird community assemblages. Results also highlight the critical role of secondary forests to increase functional connectivity of forest patches. Overall, three-dimensional habitat descriptions derived from integrated LiDAR and hyperspectral data serve as a powerful bird conservation tool that shows how the distribution of bird species relates to forest composition and structure at various scales.
Date: August 2013
Creator: Matsubayashi, Shiho
Partner: UNT Libraries

Automated Treetop Detection and Tree Crown Identification Using Discrete-return Lidar Data

Description: Accurate estimates of tree and forest biomass are essential for a wide range of applications. Automated treetop detection and tree crown discrimination using LiDAR data can greatly facilitate forest biomass estimation. Previous work has focused on homogenous or single-species forests, while few studies have focused on mixed forests. In this study, a new method for treetop detection is proposed in which the treetop is the cluster center of selected points rather than the highest point. Based on treetop detection, tree crowns are discriminated through comparison of three-dimensional shape signatures. The methods are first tested using simulated LiDAR point clouds for trees, and then applied to real LiDAR data from the Soquel Demonstration State Forest, California, USA. Results from both simulated and real LiDAR data show that the proposed method has great potential for effective detection of treetops and discrimination of tree crowns.
Date: May 2013
Creator: Liu, Haijian
Partner: UNT Libraries