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

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

Date: May 2013
Creator: Liu, Haijian
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.
Contributing Partner: UNT Libraries
Spatial Analysis of North Central Texas Traffic Fatalities 2001-2006

Spatial Analysis of North Central Texas Traffic Fatalities 2001-2006

Date: December 2010
Creator: Rafferty, Paula S.
Description: A traditional two dimensional (planar) statistical analysis was used to identify the clustering types of North Central Texas traffic fatalities occurring in 2001-2006. Over 3,700 crash locations clustered in ways that were unlike other researched regions. A two dimensional (x and y coordinates) space was manipulated to mimic a one dimensional network to identify the tightest clustering of fatalities in the nearly 400,000 crashes reported from state agencies from 2003-2006. The roadway design was found to significantly affect crash location. A one dimensional (linear) network analysis was then used to measure the statistically significant clustering of flow variables of after dark crashes and daylight crashes. Flow variables were determined to significantly affect crash location after dark. The linear and planar results were compared and the one dimensional, linear analysis was found to be more accurate because it did not over detect the clustering of events on a network.
Contributing Partner: UNT Libraries
GIS application in emergency management of terrorism events on the University of North Texas campus.

GIS application in emergency management of terrorism events on the University of North Texas campus.

Date: August 2008
Creator: Tsang, Yuenting
Description: This thesis presents a Web-based geographic information system (GIS) application for campus emergency management that allows users to visualize, integrate, and analyze student population, facilities, and hazard data for efficient emergency management of University of North Texas before, during, and after a terrorism event. End-users can locate and search the source area of an event on a digital map from the ArcIMS-based Website. The website displays corresponding population information and attributes of impacted facilities in real time. School officials and first responders including police, firefighters and medical personnel can promptly plan the appropriate rescue and response procedures according to the displayed results. Finally, the thesis outlines the limitations of Web-based GIS in the arena of campus emergency management.
Contributing Partner: UNT Libraries
Developing a wildlife tracking extension for ArcGIS

Developing a wildlife tracking extension for ArcGIS

Date: May 2009
Creator: Chen, Cai
Description: Wildlife tracking is an essential task to gain better understanding of the migration pattern and use of space of the wildlife. Advances in computer technology and global positioning systems (GPS) have lowered costs, reduced processing time, and improved accuracy for tracking wild animals. In this thesis, a wildlife tracking extension is developed for ArcGIS 9.x, which allows biologists and ecologists to effectively track, visualize and analyze the movement patterns of wild animals. The extension has four major components: (1) data import; (2) tracking; (3) spatial and temporal analysis; and (4) data export. Compared with existing software tools for wildlife tracking, the major features of the extension include: (1) wildlife tracking capabilities using a dynamic data layer supported by a file geodatabase with 1 TB storage limit; (2) spatial clustering of wildlife locations; (3) lacunarity analysis of one-dimensional individual animal trajectories and two-dimensional animal locations for better understanding of animal movement patterns; and (4) herds evolvement modeling and graphic representation. The application of the extension is demonstrated using simulated data, test data collected by a GPS collar, and a real dataset collected by ARGOS satellite telemetry for albatrosses in the Pacific Ocean.
Contributing Partner: UNT Libraries
Gis, Modeling And Human Civilization: The Birth Of Geo-social Engineering

Gis, Modeling And Human Civilization: The Birth Of Geo-social Engineering

Date: December 2011
Creator: Morris, E. Scott
Description: Computer-based, mathematical models have significant value in describing the processes behind urban development and its inhabitants. The following research describes the theories and concepts behind modeling and offers insight into the potential future of the field. First, the research covers a brief history of applicable modeling strategies. This is followed by a summary of current popular approaches. The numerical background of geo-social engineering is developed through mathematical techniques. Geo-social engineering is the integration of modeling into the basic design human civilization. The mathematical models will be incorporated into a design of a computer program. From this, a possible geo-social model structure is presented and its architecture is described.
Contributing Partner: UNT Libraries
Selecting Optimal Residential Locations Using Fuzzy GIS Modeling

Selecting Optimal Residential Locations Using Fuzzy GIS Modeling

Date: December 2006
Creator: Tang, Zongpei
Description: Integrating decision analytical techniques in geographic information systems (GIS) can help remove the two primary obstacles in spatial decision making: inaccessibility to required geographic data and difficulties in synthesizing various criteria. I developed a GIS model to assist people seeking optimal residential locations. Fuzzy set theory was used to codify criteria for each factor used in evaluating residential locations, and weighted linear combination (WLC) was employed to simulate users' preferences in decision making. Three examples were used to demonstrate the applications in the study area. The results from the examples were analyzed. The model and the ArcGIS Extension can be used in other geographic areas for residential location selection, or in other applications of spatial decision making.
Contributing Partner: UNT Libraries
Using Geographic Information Systems for the Functional Assessment of Texas Coastal Prairie Freshwater Wetlands Around Galveston Bay

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

Date: May 2010
Creator: Enwright, Nicholas
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 ...
Contributing Partner: UNT Libraries
Finding Terroir in Southwest Iowa

Finding Terroir in Southwest Iowa

Date: August 2013
Creator: Deines, Dory
Description: Terroir combines the physical landscape of the vineyard with the grapevines and the methods and techniques used to produce wine from the grapes. This study used a GIS to identify the characteristics of the physical landscape in Pottawattamie, Mills, Montgomery, Fremont, and Page counties in southwestern Iowa. The components were combined in the GIS using a weighted linear index to identify areas suitable for vineyard development and to identify the general characteristics of the area. Vineyard owners were interviewed to help determine the weighting system to use in the GIS and to determine their perceptions of how the physical landscape impacts their vineyards, as well as to determine what grape varieties they plant in their vineyards and their decisions on making wine from these grapes. This information was collected to identify whether the vineyard owners had developed a sense of place for their vineyards and how this sense might aid them in the development of a terroir for their wines. The resulting perceptions about the individual wineries were then considered in conjunction with the results from the GIS modeling to understand how the physical landscape influences the concepts of sense of place and terroir in southwest Iowa. The physical landscape ...
Contributing Partner: UNT Libraries
Comparison of IKONOS Derived Vegetation Index and LiDAR Derived Canopy Height Model for Grassland Management.

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

Date: December 2009
Creator: Parker, Gary
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 ...
Contributing Partner: UNT Libraries
High Resolution Satellite Images and LiDAR Data for Small-Area Building Extraction and Population Estimation

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

Date: December 2009
Creator: Ramesh, Sathya
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.
Contributing Partner: UNT Libraries
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