A significant increase in campus-based emergencies warrants the investigation into emergency management information systems that serve a novice crisis decision-maker. Institutions of higher education that are not large enough to have dedicated emergency management offices generally press novice decision-makers into emergency management roles. An investigation was conducted to assess the impact of an emergency management geospatial information system on the decision performance of novice crisis managers through the use of a scenario-based simulation. A mixed method sequential explanatory method was used to collect quasi-experimental data on decision time, decision accuracy and situational awareness. Qualitative analysis was conducted through interviews with participants. Statistical results indicate the decision accuracy is positively affected by the use of an emergency management geospatial information system. Data Envelopment Analysis (DEA) is non-parametric linear programming method used to identify decision-making units in a data set that are optimal in their use of single or a set of resources (inputs) in delivering a set of expected results (outputs). DEA indicated that efficiency ratios from the geospatial information system group outperform the traditional group. Geospatial information systems hold much promise in providing systems that are easy to use, promote heightened levels of situational awareness and decision support.

This research assesses shifts in tornado occurrence pattens in space and time employing continental United States tornado records with an Enhanced Fujita (EF) rating equal or greater than 1. In similar research, most researchers discard tornado records prior to 1980 due to factors including: magnitude anomalies related to development of the Fujita Scale, unpredictability in tornado reporting (escalating populace, storm spotters, and technologic improvements), and better data records from the Census Bureau. We therefore constructed two datasets using tornados recorded in the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database: 1950-2019 (dataset 1) and 1980-2019 (dataset 2). The goals for this study were to 1) determine whether spatiotemporal patterns of recorded tornado activity have shifted over time, and 2) determine whether inclusion of pre-1980 tornado data changes the findings from 1). This study employed Space-Time Pattern Mining (STPM) to construct four spacetime cubes (STC) in ArcGIS Pro. Emerging Hot Spot Analysis (EHS) was employed to identify the changes in tornado occurrence (number of incidents in a STC cell) and magnitude (sum of tornado EF ratings for all incidents in a STC cell). EHS displayed increased tornado activity in the Southeast and decreased activity for areas in the Great Plains for both occurrence and magnitude in both datasets. This is interpreted as significant intensifying hot spots in the Southeast region and diminishing hot spots in the Great Plains indicating an east-south-east shift for both datasets. Similar findings for both datasets indicate that inclusion of the less reliable pre-1980’s tornado data does not change the results and we recommend that the practice of discarding pre-1980’s tornado data in tornado occurrence research be reconsidered.

<div> <div> <p>Due to the large volumes of spatial data, data is stored on clusters of machines that inter-communicate to achieve a task. In such distributed environment; communicating intermediate results among computing nodes dominates execution time. Communication overhead is even more dominant if processing is in memory. Moreover, the way spatial data is partitioned affects overall processing cost. Various partitioning strategies influence the size of the intermediate results. Spatial data poses the following additional challenges: 1)Storage load balancing because of the skewed distribution of spatial data over the underlying space, 2)Query load imbalance due to skewed query workload and query hotspots over both time and space, and 3)Lack of effective utilization of the computing resources. We introduce a new kNN query evaluation technique, termed BCDB, for evaluating nearest-neighbor queries (NN-queries, for short). In contrast to clustered partitioning of spatial data, BCDB explores the use of declustered partitioning of data to address data and query skew. BCDB uses summaries of the underling data and a coarse-grained index to localize processing of the NN-query on each local node as much as possible. The coarse-grained index is locally traversed using a new uncertain version of classical distance browsing resulting in minimal O( √k) elements to be communicated across all processing nodes.</p> </div> </div>

Due to the advances in location-acquisition techniques, citizen geospatial data has emerged with opportunity for research, development, innovation, and business. A variety of research has been developed to study society and citizens through exploring patterns from geospatial data. In this thesis, we investigate patterns of population and human sentiments using GPS trajectory data and geo-tagged tweets. Kernel density estimation and emerging hot spot analysis are first used to demonstrate population distribution across space and time. Then a flow extraction model is proposed based on density difference for human movement detection and visualization. Case studies with volleyball game in West Lafayette and traffics in Puerto Rico verify the effectiveness of this method. Flow maps are capable of tracking clustering behaviors and direction maps drawn upon the orientation of vectors can precisely identify location of events. This thesis also analyzes patterns of human sentiments. Polarity of tweets is represented by a numeric value based on linguistics rules. Sentiments of four US college cities are analyzed according to its distribution on citizen, time, and space. The research result suggests that social media can be used to understand patterns of public sentiment and well-being.

When comparing numerous educational advantages of Geographic Information Systems (GIS) with the slow integration of GIS practice within education globally, results are confounding. This paradoxical development is also found within South Africa. In fact, GIS has been included in the Further Education and Training (FET) phase by the Department of Basic Education (DoBE) since 2006. However, following the same global trend, curriculum development in South Africa has outpaced educational GIS software research. In addition, the e-learning White paper of SA also urges software development. Barriers hindering GIS practice include the lack of suitable curriculum-aligned GIS software within the South African digital divide context. A need therefore exists for further research regarding educational GIS practice applications within South Africa. Bearing this in mind, a case study was done investigating the viability of an educationally orientated Interactive-GIS-Tutor (I-GIS-T) application within FET phase in Geography. The study was conducted with the grade 11 Geography learners of a secondary school in a rural area of KwaZulu-Natal, as well as with their Geography teacher and two other Geography teachers of the same school. These three teachers have different ICT/GIS abilities and years of teaching experience. Furthermore, the study aimed to identify the main GIS educational barriers, globally and locally, as well as to investigate the viability of the I-GIS-T in relation to these identified barriers. The strategy followed was a case study evaluation, with a qualitative approach to data collection and analysis, supported by quantitative data, since this was most suited to the research questions and context. Pragmatism was therefore the underpinning philosophy within this case study. One-on-one semi-structured teacher interviews were conducted to identify the main barriers of GIS education within the FET phases. Data collection by means of questionnaires, individual interviews, focus group interviews, video recordings and field notes provided a thick description regarding the viability of the I-GIS-T within the natural class setting. ATLAS.tiTM and SPSS software were utilised with analysis of qualitative and supportive quantitative data. Attitudinal tests provided supportive quantitative data. Findings indicated that main GIS practice barriers, globally as well as in the school of study, were the lack of preparation time, a full curriculum, lack of GIS support, complex educational GIS software and the teacher‟s lack of ICT skills. The grade 11 Geography teacher and most of the learners evaluated the I-GIS-T as workable. The I-GIS-T also surmounted the main GIS practice barriers. Furthermore, GIS attitudinal tests revealed an overall positive shift on all the attitudinal questions. The combination of lack of basic computer skills and language (where English is not the mother tongue) were the main reasons why some learners suggested that they struggled with the software. Future I-GIS-T development recommended incorporation of a multi-language choice component, as well as exploratory activities. Within this case study, learners who have mastered basic computer skills found the I-GIS-T effective and workable and therefore a viable GIS software application option within the FET phase Geography. In order to be able to generalise statistically, further quantitative research is suggested. In fact, future quantitative research, employing SEM (Structural Equation Modeling) within the Technology Acceptance Model (TAM) might prove the I-GIS-T to be a viable option within FET schools throughout SA, as well as in other developing countries. / Thesis (MEd (Curriculum Development))--North-West University, Potchefstroom Campus, 2013

When comparing numerous educational advantages of Geographic Information Systems (GIS) with the slow integration of GIS practice within education globally, results are confounding. This paradoxical development is also found within South Africa. In fact, GIS has been included in the Further Education and Training (FET) phase by the Department of Basic Education (DoBE) since 2006. However, following the same global trend, curriculum development in South Africa has outpaced educational GIS software research. In addition, the e-learning White paper of SA also urges software development. Barriers hindering GIS practice include the lack of suitable curriculum-aligned GIS software within the South African digital divide context. A need therefore exists for further research regarding educational GIS practice applications within South Africa. Bearing this in mind, a case study was done investigating the viability of an educationally orientated Interactive-GIS-Tutor (I-GIS-T) application within FET phase in Geography. The study was conducted with the grade 11 Geography learners of a secondary school in a rural area of KwaZulu-Natal, as well as with their Geography teacher and two other Geography teachers of the same school. These three teachers have different ICT/GIS abilities and years of teaching experience. Furthermore, the study aimed to identify the main GIS educational barriers, globally and locally, as well as to investigate the viability of the I-GIS-T in relation to these identified barriers. The strategy followed was a case study evaluation, with a qualitative approach to data collection and analysis, supported by quantitative data, since this was most suited to the research questions and context. Pragmatism was therefore the underpinning philosophy within this case study. One-on-one semi-structured teacher interviews were conducted to identify the main barriers of GIS education within the FET phases. Data collection by means of questionnaires, individual interviews, focus group interviews, video recordings and field notes provided a thick description regarding the viability of the I-GIS-T within the natural class setting. ATLAS.tiTM and SPSS software were utilised with analysis of qualitative and supportive quantitative data. Attitudinal tests provided supportive quantitative data. Findings indicated that main GIS practice barriers, globally as well as in the school of study, were the lack of preparation time, a full curriculum, lack of GIS support, complex educational GIS software and the teacher‟s lack of ICT skills. The grade 11 Geography teacher and most of the learners evaluated the I-GIS-T as workable. The I-GIS-T also surmounted the main GIS practice barriers. Furthermore, GIS attitudinal tests revealed an overall positive shift on all the attitudinal questions. The combination of lack of basic computer skills and language (where English is not the mother tongue) were the main reasons why some learners suggested that they struggled with the software. Future I-GIS-T development recommended incorporation of a multi-language choice component, as well as exploratory activities. Within this case study, learners who have mastered basic computer skills found the I-GIS-T effective and workable and therefore a viable GIS software application option within the FET phase Geography. In order to be able to generalise statistically, further quantitative research is suggested. In fact, future quantitative research, employing SEM (Structural Equation Modeling) within the Technology Acceptance Model (TAM) might prove the I-GIS-T to be a viable option within FET schools throughout SA, as well as in other developing countries. / Thesis (MEd (Curriculum Development))--North-West University, Potchefstroom Campus, 2013

<p>Accurate 3D landscape models of cities or mountains have wide applications in mission</p> <p>planning, navigation, geological studies, etc. Lidar scanning using drones can provide high</p> <p>accuracy 3D landscape models, but the data is more expensive to collect as the area of</p> <p>each scan is limited. Thanks to recent maturation of Very-High-Resolution (VHR) optical</p> <p>imaging on satellites, people nowadays have access to stereo images that are collected on a</p> <p>much larger area than Lidar scanning. My research addresses unique challenges in satellite</p> <p>stereo, including stereo rectification with pushbroom sensors, dense stereo matching using</p> <p>image pairs with varied appearance, e.g. sun angles and surface plantation, and rasterized</p> <p>digital surface model (DSM) generation. The key contributions include the Continuous 3D-</p> <p>Label Semi-Global Matching (CoSGM) and a large scale dataset for satellite stereo processing</p> <p>and DSM evaluation.</p>

<p dir="ltr">Canopy height is a fundamental metric for extracting valuable information about forested areas. Over the past decade, Lidar technology has provided a straightforward approach to measuring canopy height using various platforms such as terrestrial, unmanned aerial vehicle (UAV), airborne, and satellite sensors. However, satellite Lidar data, even with its global coverage, has a sparse sampling pattern that doesn’t provide continuous coverage over the globe. In contrast, satellites like LANDSAT offer seamless and widespread coverage of the Earth's surface through spectral data. Can we exploit the abundant spectral information from satellites like LANDSAT and ECOSTRESS to infer structural information obtained from Lidar satellites like Global Ecosystem Dynamic Investigation (GEDI)? This study aims to develop a deep learning model that can infer canopy height derived from sparsely observed Lidar waveforms using multi-sensor spectral data from spaceborne platforms. Specifically designed for localized site, the model focuses on county-level canopy height estimation, taking advantage of the relationship between canopy height and spectral reflectance that can be established in a local setting – something which might not exist universally. The study hopes to achieve a framework that can be easily replicable as height is a dynamic metric which changes with time and thus requires repeated computation for different time periods.</p><p dir="ltr">The thesis presents a series of experiments designed to comprehensively understand the influence of different spectral datasets on the model’s performance and its effectiveness in different types of test sites. Experiment 1 and 2 utilize Landsat spectral band values to extrapolate canopy height, while Experiment 3 and 4 incorporate ECOSTRESS land surface temperature and emissivity band values in addition to Landsat data. Tippecanoe County, predominantly composed of cropland, serves as the test site for Experiment 1 and 3, while Monroe County, primarily covered by forests, serves as the test site for Experiment 2 and 4. When compared to the Airborne Lidar dataset from the United States Geological Survey (USGS) – 3D Elevation Program (3DEP), the model achieves a Root Mean Square Error (RMSE) of 4.604m for Tippecanoe County using Landsat features while 5.479m for Monroe County. After integrating Landsat and ECOSTRESS features, the RMSE improves to 4.582m for Tippecanoe County but deteriorates to 5.860m for Monroe County. Overall, the study demonstrates comparable results to previous research without requiring feature engineering or extensive pre-processing. Furthermore, it successfully introduces a novel methodology for integrating multiple sources of satellite data to address this problem.</p>

<p dir="ltr">We explore the efficacy of sparsity and ensemble model in the classification of hyperspectral images, a pivotal task in remote sensing applications. While Convolutional Neural Networks (CNNs) and Transformer models have shown promise in this domain, each exhibits distinct limitations; CNNs excel in capturing the spatial/local features but falter to capture spectral features, whereas Transformers captures the spectral features at the expense of spatial features. Furthermore, the computational cost associated with training several independent CNN and Transformer networks becomes expensive. To address these limitations, we propose a novel ensemble framework comprising pruned CNNs and Transformers, optimizing both spatial and spectral feature utilization while curbing computational costs. By integrating sparsity through model pruning, our approach effectively reduces redundancy and computational complexity without compromising accuracy. Through extensive experimentation, we find that our method achieves comparable accuracy to its non-sparse counterparts while decreasing the computational cost. Our contribution enhances remote sensing analytics by demonstrating the potential of sparse and ensemble models in improving the precision and computational efficiency of hyperspectral image classification.</p>

<p>Integrating unoccupied aerial systems (UAS) into agricultural remote sensing has revolutionized several domains, including crop yield estimation. This research arises from the need to combat citrus greening disease, a major threat to citrus production. Accurately estimating crop yields is crucial for evaluating the effectiveness of treatments and controls for this disease. In response, our study examined the efficacy of phenotypic data extracted from multi-temporal RGB and multispectral UAS images in estimating individual citrus tree yields before harvest and then using this as an indicator to analyze the effectiveness of the treatments and control choice.</p> <p>This study presents machine learning-based regression models for estimating individual citrus tree yields, utilizing the diverse features extracted to provide comprehensive insights into the citrus trees under investigation. Four machine learning algorithms, random forest regression, extreme gradient boosting regression, adaptive boosting, and support vector regression, were employed to build the yield estimation models. The experiment was designed in two phases: single-temporal and multi-temporal modeling.</p>