Integrating Hands-On Undergraduate Research in an Applied Spatial Science Senior Level Capstone Course

A senior within a spatial science Ecological Planning capstone course designed an undergraduate research project to increase his spatial science expertise and to assess the hands-on instruction methodology employed within the Bachelor of Science in Spatial Science program at Stephen F Austin State University. The height of 30 building features estimated remotely with LiDAR data, within the Pictometry remotely sensed web-based interface, and in situ with a laser rangefinder were compared to actual building feature height measurements. A comparison of estimated height with actual height indicated that all three estimation techniques tested were unbiased estimators of height. An ANOVA, conducted on the absolute height errors resulting in a p-value of 0.035, concluded the three height estimating techniques were statistically different at the 95% confidence interval. A Tukey pair-wise test found the remotely sensed Pictometry web-based interface was statistically more accurate than LiDAR data, while the laser range finder was not different from the others. The results indicate that height estimates within the Pictometry web-based interface could be used in lieu of time consuming and costly in situ height measurements. The findings also validate the interactive hands-on instruction methodology employed by Geographic Information Systems faculty within the Arthur Temple College of Forestry and Agriculture in producing spatial science graduates capable of utilizing spatial science technology to accurately quantify, qualify, map, and monitor natural resources

Towards 3D Matching of Point Clouds Derived from Oblique and Nadir Airborne Imagery

Because of the low-expense high-efficient image collection process and the rich 3D and texture information presented in the images, a combined use of 2D airborne nadir and oblique images to reconstruct 3D geometric scene has a promising market for future commercial usage like urban planning or first responders. The methodology introduced in this thesis provides a feasible way towards fully automated 3D city modeling from oblique and nadir airborne imagery. In this thesis, the difficulty of matching 2D images with large disparity is avoided by grouping the images first and applying the 3D registration afterward. The procedure starts with the extraction of point clouds using a modified version of the RIT 3D Extraction Workflow. Then the point clouds are refined by noise removal and surface smoothing processes. Since the point clouds extracted from different image groups use independent coordinate systems, there are translation, rotation and scale differences existing. To figure out these differences, 3D keypoints and their features are extracted. For each pair of point clouds, an initial alignment and a more accurate registration are applied in succession. The final transform matrix presents the parameters describing the translation, rotation and scale requirements. The methodology presented in the thesis has been shown to behave well for test data. The robustness of this method is discussed by adding artificial noise to the test data. For Pictometry oblique aerial imagery, the initial alignment provides a rough alignment result, which contains a larger offset compared to that of test data because of the low quality of the point clouds themselves, but it can be further refined through the final optimization. The accuracy of the final registration result is evaluated by comparing it to the result obtained from manual selection of matched points. Using the method introduced, point clouds extracted from different image groups could be combined with each other to build a more complete point cloud, or be used as a complement to existing point clouds extracted from other sources. This research will both improve the state of the art of 3D city modeling and inspire new ideas in related fields

Dense Point Cloud Extraction From Oblique Imagery

With the increasing availability of low-cost digital cameras with small or medium sized sensors, more and more airborne images are available with high resolution, which enhances the possibility in establishing three dimensional models for urban areas. The high accuracy of representation of buildings in urban areas is required for asset valuation or disaster recovery. Many automatic methods for modeling and reconstruction are applied to aerial images together with Light Detection and Ranging (LiDAR) data. If LiDAR data are not provided, manual steps must be applied, which results in semi-automated technique. The automated extraction of 3D urban models can be aided by the automatic extraction of dense point clouds. The more dense the point clouds, the easier the modeling and the higher the accuracy. Also oblique aerial imagery provides more facade information than nadir images, such as building height and texture. So a method for automatic dense point cloud extraction from oblique images is desired. In this thesis, a modified workflow for the automated extraction of dense point clouds from oblique images is proposed and tested. The result reveals that this modified workflow works well and a very dense point cloud can be extracted from only two oblique images with slightly higher accuracy in flat areas than the one extracted by the original workflow. The original workflow was established by previous research at the Rochester Institute of Technology (RIT) for point cloud extraction from nadir images. For oblique images, a first modification is proposed in the feature detection part by replacing the Scale-Invariant Feature Transform (SIFT) algorithm with the Affine Scale-Invariant Feature Transform (ASIFT) algorithm. After that, in order to realize a very dense point cloud, the Semi-Global Matching (SGM) algorithm is implemented in the second modification to compute the disparity map from a stereo image pair, which can then be used to reproject pixels back to a point cloud. A noise removal step is added in the third modification. The point cloud from the modified workflow is much denser compared to the result from the original workflow. An accuracy assessment is made in the end to evaluate the point cloud extracted from the modified workflow. From the two flat areas, subsets of points are selected from both original and modified workflow, and then planes are fitted to them, respectively. The Mean Squared Error (MSE) of the points to the fitted plane is compared. The point subsets from the modified workflow have slightly lower MSEs than the ones from the original workflow, respectively. This suggests a much more dense and more accurate point cloud can lead to clear roof borders for roof extraction and improve the possibility of 3D feature detection for 3D point cloud registration

Climate Change in the Hudson River Estuary: Promoting Adaptation and Resilience through Stakeholder Engagement in Design and Visualization

In response to the growing risk to communities from climate change impacts, Professor Cerra at the Cornell University School of Landscape Architecture developed the Climate Adaptive Design (CAD) program. CAD is being implemented as a partnership between the Hudson River Estuary Program (Estuary Program) and Cornell that utilizes participatory design and visualization to engage communities about planning for future climate impacts. The goal of CAD is to build climate resilience, galvanize community participation and education, and build links to external sources of support including local institutions of higher education. This thesis outlines background for the development of the program and reviews the literature with a focus on community resilience and adaptive, participatory planning and design. Based on this review, I developed a survey and metric for evaluating the resilience of communities participating in CAD or similar programs. Next, I describe case studies of communities that have already participated in the CAD program. Finally, I make policy recommendations based on these cases for further work by the Estuary Program through CAD or similar partnerships with higher educational institutions. By engaging municipalities in collaborative design, CAD aims to shift necessary conversations about climate adaptation to be a resilience-building and empowering process, and this thesis seeks to strengthen those efforts

Investigating methodologies for evaluating the effectiveness of Integrated Spatial Information System (ISIS) implementation in the valuation department of the City of Cape Town

Includes bibliographical references.The increasing need to develop fully integrated spatial information systems that help improve planning and decision making have led the countries to create partnerships as to facilitate the improved sharing of spatial data and to realise the full potential of spatial data infrastructure. In this process researchers and practitioners use appropriate methods, tools and frameworks to examine, analyse and evaluate the new implemented systems after its implementation. The attempt to find suitable methodologies for evaluating the effectiveness of the system has led to extensive research to develop, identify and test suitable methods and frameworks and to apply these to case studies. This research investigates the methodologies for evaluating the effectiveness of Integrated Spatial Information Systems (ISIS) implemented in the Valuation Department of the City of Cape Town. The spatial information systems of Valuation Department and the effectiveness of ISIS implementation in this Department are investigated

Calamities, Catastrophes, and Cataclysms: Current Trends in International Disaster Risk Management Practices for Cultural Heritage Sites

Several initiatives, conferences, and collaborative agreements in recent years have focused on cultural heritage protection in response to climate change and natural disasters. Despite an established network of institutions united in preserving the world’s cultural heritage, risk management planning for heritage properties remains in its infancy. This thesis asks what types of risk management for cultural heritage properties are currently being implemented and which organizations are doing this work. A review of disaster risk management activities of international heritage conservation groups reveals that organizations tend to focus their efforts on one of the three disaster phases: advance planning, emergency response, or post-disaster recovery. The reasons for this are directly related to the types of resources the agency or organization can commit to these activities: professional expertise, technical support, funding, local networks, or some combination of these. Recent examples show that collaboration between organizations with different resources but common goals can be successful, as in the case of the Haitian Gingerbread House project undertaken by the World Monuments Fund together with the Prince Claus Fund. Similar partnerships can be initiated before disaster strikes; to facilitate this, a centralized agency recognized by other international relief agencies that is capable of collecting data and coordinating response teams is needed. The most effective form of risk mitigation at any heritage site, however, is the inclusion of risk management procedures into general site management operations; regular maintenance and monitoring alone can substantially minimize damage and loss in unavoidable natural disasters

Urban forest ecosystem analysis using fused airborne hyperspectral and lidar data

Urban trees are strategically important in a city's effort to mitigate their carbon footprint, heat island effects, air pollution, and stormwater runoff. Currently, the most common method for quantifying urban forest structure and ecosystem function is through field plot sampling. However, taking intensive structural measurements on private properties throughout a city is difficult, and the outputs from sample inventories are not spatially explicit. The overarching goal of this dissertation is to develop methods for mapping urban forest structure and function using fused hyperspectral imagery and waveform lidar data at the individual tree crown scale. Urban forest ecosystem services estimated using the USDA Forest Service’s i-Tree Eco (formerly UFORE) model are based largely on tree species and leaf area index (LAI). Accordingly, tree species were mapped in my Santa Barbara, California study area for 29 species comprising >80% of canopy. Crown-scale discriminant analysis methods were introduced for fusing Airborne Visible Infrared Imaging Spectrometry (AVIRIS) data with a suite of lidar structural metrics (e.g., tree height, crown porosity) to maximize classification accuracy in a complex environment. AVIRIS imagery was critical to achieving an overall species-level accuracy of 83.4% while lidar data was most useful for improving the discrimination of small and morphologically unique species. LAI was estimated at both the field-plot scale using laser penetration metrics and at the crown scale using allometry. Agreement of the former with photographic estimates of gap fraction and the latter with allometric estimates based on field measurements was examined. Results indicate that lidar may be used reasonably to measure LAI in an urban environment lacking in continuous canopy and characterized by high species diversity. Finally, urban ecosystem services such as carbon storage and building energy-use modification were analyzed through combination of aforementioned methods and the i-Tree Eco modeling framework. The remote sensing methods developed in this dissertation will allow researchers to more precisely constrain urban ecosystem spatial analyses and equip cities to better manage their urban forest resource