This study evaluates the potential for mineral prospectivity mapping (MPM) within the Kerkesha area, southwestern Eritrea using remote sensing and geochemical data analysis. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data was used for mapping zones of hydrothermal alteration, while assessment of geologic structures is based on automated extraction of lineaments from a digital elevation model. Integration of these alteration and structural dataset with surface geochemical data were used in identifying pathfinder elements associated with Au-Cu-Zn mineralization as well as evaluating and delineating anomalous mineralization regions in this relatively underexplored region of Arabia Nubia Shield (ANS). Specifically, the modeling approach for the extraction and the interpretation of mineralization-related spectral footprints uses selective principal component analysis (SPCA), while the lineament features, which were extracted from different digital terrain models, were integrated with the soil geochemical data and modeled by principal component analysis (PCA). The results reveal a northeast-southwest trend of lineaments, delineate zones of hydrothermal alteration which indicate presence of multi-deposit type mineralization, and identify pathfinder elements. In addition, Au-Cu-Zn anomalous zones are extracted by one class support vector machine (OCSVM) and performances of such classification is validated by Kruskal-Wallis and Pearson’s Chi-square tests. The results show significance in differences between the anomalous and non-anomalous zones and existence of a relationship between known mineral deposits and predicted anomalies. The proposed MPM shows promising results for robust automated delineation and understanding of mineralization processes.

The goal of this study is to explore land use and land cover (LULC trends in Greater Accra Metropolitan Area (GAMA, a highly urbanized coastal region in Ghana by analyzing historical change rates and forecasting future scenarios. As industrialization and basic anthropological necessities increase in the region, natural land resources, specifically wetlands, are undergoing adverse changes. With the help of Modules for Land Use Change Evaluation (MOLUSCE which is a plugin in QGIS, the study identifies land-use changes in GAMA for 2002, 2013, and 2020 as well as forecasts and establishes potential land-use changes in 2030 and 2040. The presented approach incorporates well-known algorithms such as artificial neural networks (ANN for computing transition potential maps coupled with cellular automata (CA simulation. To analyze their impact on LULC between 2002 and 2013, five criteria were used in the CA-ANN framework, including elevation, slope, distance from roads, distance from towns, and distance from rivers. The validation of simulated LULC maps for 2020 indicates a good level of accuracy, with a kappa value of 0.70 and a correctness percentage of 78.50%. The future scenarios between 2020 and 2040 indicate that urban development and sprawl are expected to increase annually at a dynamic degree rate of 0.86% at the expense of natural land covers such as wetlands and vegetation. The major road network in GAMA spearheads the growth of developed areas, while slope and elevation act as constraints. A high ratio of impervious to pervious surfaces confirms the rapid urbanization of the area. Urbanization is likely to have a more detrimental effect on natural habitats in the near future less so in the distant future. This study emphasizes the importance of establishing appropriate urban planning policies and management methods for sustainable environmental conservation.

The morphology of vegetation-stabilized parabolic dunes has been seen as a source of climate information from their periods of stabilization. Several dune fields in the upper Muskegon River Valley in central Michigan, including fields not previously described in the literature, were mapped using Geographic Information System (GIS) based techniques. The mapping used terrain analyses, derived from a digital elevation model (DEM) and a Light Detection and Ranging (LiDAR) based terrain model in tandem with semi-automated based extraction followed by manual refinement of delineation and visualization techniques (VT). Terrain parameters and VT also guided digitization of dune crestlines. The morphology of the mapped dunes was characterized in terms of slope and aspect relationships, and comparisons were evaluated. Directional statistics were used to describe crestline orientations and circular variance. The morphology of these dune fields likely reflects a climate having an unequal bimodal wind regime, the dominant regime being northwesterly and the secondary regime being southerly or southwesterly. Based on visual interpretations, the location of downwind, compound parabolic forms are linked to locations likely to have had shallow groundwater, or to possibly suitable locations for parkland that could have induced precipitation ridges marking downwind dune field margins. Elongated dune forms are linked to rising terrain. Exploratory data analysis (EDA), primarily based on slope and aspect correlations, revealed a strongly asymmetric distribution in slopes of the fields, indicating the potential that a bimodal wind regime was present when the dunes stabilized. Cross-cutting relationships between dunes and terraces of the Muskegon River were observed which place formation of the dunes before the formation of paleo-meanders by a forerunner to that stream. The planform and distribution of the dunes in the dune fields also appear closely tied to the changing groundwater availability.

Early detection and locating of influenza outbreaks is one of the key priorities on a national level for preparedness and planning. This study presents the design and implementation of a web-based prototype software framework (Fluwitter) for pseudo real-time detection of influenza outbreaks from Twitter in space and time. Harnessing social media to track real-time influenza outbreaks can provide different perspectives in battling the spread of infectious diseases and lowering the cost of existing assessment methods. Specifically, Fluwitter follows a three-tier architecture system with a thin web client and a resourceful server environment. The server side system is composed of a PostGIS spatial database, a GeoServer instance, a web application for visualizing influenza maps and daemon applications for tweet streaming, pre-processing of data, semantic information extraction based on DBpediaSpotlight and WS4J, and geo-processing. The collected geo-tagged tweets are processed by semantic NLP techniques for detecting and extracting influenza related tweets. The synsets from the extracted influenza related tweets are tagged and ontology based semantic similarity scores produced by WUP and RES algorithms were derived for subsequent information extraction. To ensure better detection, the information extraction was calibrated by different rules produced by the semantic similarity scores. The optimized rule produced a final F-measure value of 0.72 and accuracy (ACC) value of 94.4%. The Twitter generated influenza cases were validated by weekly influenza related hospitalization records issued by ODH. The validation that was based on Pearson’s correlations suggested existence of moderate correlations for the Southeast region (r = 0.52), the Northwestern region (r = 0.38), and the Central region (r = 0.33). Although, additional work is needed, the potential strengths and benefits of the prototype are shown through a case study in Ohio that enables spatio-temporal assessment and visualization of influenza spread across the state.

Planning of shale gas infrastructure and drilling sites for hydraulic fracturing has important spatial implications. The evaluation of conflicting and competing objectives requires an explicit consideration of multiple criteria as they have important environmental and economic implications. This study presents a web-based multi-criteria spatial decision support system (SDSS) prototype with a flexible and user-friendly interface that could provide educational or decision-making capabilities with respect to hydraulic fracturing site-selection in eastern Ohio. One of the main features of this spatial decision support system is to emphasize potential trade-offs between important factors of environmental and economic implications from hydraulic fracturing activities using a weighted linear combination (WLC) method. WLC is a simple approach that integrates users' preferences into an overall assessment and offers a rationale for trade-offs between decision criteria and objectives. In the prototype, the GIS-enabled analytical components allow spontaneous visualization of available alternatives on maps which provide value-added features for decision support processes and derivation of final decision maps. The SDSS prototype exhibits a straightforward decision-making procedure with easy-to-use web interface and facilitates non-expert participation capabilities. It comprises of a mapping module, decision-making tool, group decision data statistics, and social media sharing tools. The system architecture combines a variety of closely related components using Silverlight, ArcGIS API for Silverlight, ArcGIS Server, and ArcSDE for SQL Server software. During the decision-making process, users are guided through a logical flow of successively presented forms and standardized criteria maps to generate visualization of trade-off scenarios and alternative solutions tailored to their personal preferences. Finally, the results and the preferences from all users are graphed for visualization and subsequent decision-making making.

Leaf area index (LAI) is an important indicator of ecosystem conditions and an important key biophysical variable to many ecosystem models. The LAI in this study was measured by Leica ScanStation C 10 Terrestrial Laser Scanner (TLS) and a hand-held Li-Cor LAI-2200 Plant Canopy Analyzer for understanding differences derived from the two sensors. A total of six different LAI estimates were generated using different methods for the comparisons. The results suggested that there was a reasonable agreement (i.e., the correlations r > 0.50) considering a total of 30 plots and limited land cover types sampled. The predicted LAI from spectral vegetation indices including WDVI, DVI, NDVI, SAVI, and PVI3 which were derived from Landsat TM imagery were used to identify statistical relationships and for the development of the Bayesian inference model. The Bayesian Linear Regression (BLR) approach was used to scale up LAI estimates and to produce continuous field surfaces for the Oak Openings Region in NW Ohio. The results from the BLR provided details about the parameter uncertainties but also insight about the potential that different LAIs can be used to predict foliage that has been adjusted by removing the wooden biomass with reasonable accuracy. For instance, the modeled residuals associated with the LAI estimates from TLS orthographic projection that consider only foliage had the lowest overall model uncertainty with lowest error and residual dispersion range among the six spatial LAI estimates. The deviation from the mean LAI prediction map derived from the six estimates hinted that sparse and open areas that relate to vegetation structure were associated with the highest error. However, although in many studies TLS has been shown to hold a great potential for quantifying vegetation structure, in this study the quantified relationship between LAI and the vegetation indices did not yield any statistical relationship that needs to be further explore.

This study presents the design and implementation of a web-based Participatory Geographic Information System (PGIS) framework intended for offshore wind suitability analysis. The PGIS prototype presented here integrates GIS and decision-making tools that are intended to involve different stakeholders and the public for solving complex planning problems and building consensus. Public involvement from the early planning stage of projects with a spatial nature is very important for future legitimacy and acceptance of these projects. Therefore, developing and executing a system that facilitates effective public involvement for resolving contentious issues can help fostering long-lasting agreements. The prototype here is a distributed and asynchronous PGIS that combines a discussion forum, mapping tool and decision tool. The PGIS is implemented following a thin-client server environment with three-tier architecture and the potential strengths and benefits of this PGIS are demonstrated in a hypothetical case study in Lake Erie, northern Ohio. In the hypothetical case study, participants evaluate the importance of three decision alternatives using different evaluation criteria for expressing their individual preferences. The individual preferences are aggregated by the Borda Count (BC) method for generating the group solution, which is used for synthesizing the different evaluation aspects such as the importance of criteria, ranking of the decision alternatives and planning issues related to environmental and socio-economic concerns from the participants.

Valles caldera, in north-central New Mexico, is considered one of the largest rhyolitic volcanoes in the United States due to the great amount of volcanic activity over the last 1.61 Ma. Although Valles caldera is currently dormant, there is potential for future volcanic activity, and therefore it is prudent to assess the risk to the surrounding area well before a disaster strikes. The primary objective of this study is to develop one of the first volcanic risk assessments of the Valles caldera region through the evaluation of the spatial extent of different volcanic hazards and the assessment of social and economic vulnerability of the area at risk.

In this study, hazard maps are generated with a GIS-based volcanic hazards tool designed to simulate ash fallout, pyroclastic density currents (PDCs), and lava flows based on the Late Quaternary (~55 ka) eruptions from within Valles caldera. Simulated ash fall deposits originating from El Cajete crater are calibrated to isopach and lithic isopleth maps of the Lower and Upper El Cajete ash fall deposits as constructed by Wolff et al. (2011) with modern environmental conditions. Additionally, the calibration of PDCs is conducted based on the distribution and runout of the Battleship Rock Ignimbrite. Once calibrated, hazards are simulated at two other vent locations determined from probability distributions of structural features, in order to generate the final hazard maps.

In assessing communities' hazard preparedness, social vulnerability is evaluated for all census-designated places within the study site through a principal component analysis of twenty-four variables shown to increase or decrease social vulnerability. Also, to assess the expected loss from hazards, economic vulnerability is evaluated through a multi-criteria evaluation (MCE) of population, land use, infrastructure, and economic production, where each factor is categorized and assigned a value representing relative vulnerability based on cost and importance.

Ultimately, the hazard maps and vulnerability assessments are aggregated through weighted linear combination and pairwise comparison matrices, creating a total of five risk maps. Although the actual maps provide greater detail, overall, the risk maps show that ash fall has the greatest impact, effecting areas up to 50 km S/SE of the caldera, including highly vulnerable cities, such as Los Alamos, White Rock, and Santa Fe. The PDCs and lava flow hazards, however, impact significantly smaller areas, primarily disturbing low vulnerability forest. The methodology presented in this paper allows for a robust analysis of the risk the Valles caldera area is faced with in the event of volcanic hazards, which is especially useful in focusing mitigation strategies to reduce the loss from such hazard events.

Predicting and mapping invasive wetland plant species is an important process for future management decisions and strategies. Controlling and mapping such plant species requires robust methods that are applicable at different ecological scales to map and monitor their spread. In particular, this study tested the feasibility of classification tree analysis (CTA) by using a high resolution Applanix 439 Digital Sensor System (DSS) aerial imagery (< 20 cm) and linear spectral unmixing (LSU) analysis by using Landsat Thematic Mapper (TM) data to produce different distribution maps of invasive flowering rush (Butomus umbellatus L.) potential in the Ottawa National Wildlife Refuge (ONWR) wetlands, in Northwest Ohio. The classification accuracy from CTA maps derived from different splitting rules was evaluated by kappa statistics. The overall accuracy within the different runs varied between 35 to 56 % while the "Gini" splitting rule had the best performance. The endmembers from the best CTA performing map were utilized by the LSU method for estimating sub-pixel endmember fractions at a broader geographical scale. The results derived from the aerial imagery were slightly better than those from the Landsat imagery, as the goodness of fit between the flowering rush fraction map and the data measured in the field was lower. This study was intended to demonstrate the potential for flowering rush mapping over larger area using knowledge developed from smaller geographical scale using high resolution imagery. Results indicate that both methods show promising results for the prediction of flowering rush, but additional research that encompass different field data collection techniques, datasets of imagery and modeling methods need to be explored.

The purpose of this study was to detect shallow landslides using hillshade maps derived from Light Detection and Ranging (LiDAR)-based Digital Elevation Model (DEM) and validated by field inventory. The landslide susceptibility mapping used an Artificial Neural Network (ANN) approach and back propagation method that was tested in the northern portion of the Cuyahoga Valley National Park CVNP) located in Northeast Ohio. The relationship between landslides and different predictor attributes extracted from the LiDAR-based-DEM such as slope, profile and plan curvatures, upslope drainage area, annual solar radiation, and wetness index was evaluated using a Geographic Information System (GIS) based investigation. The approach presented in this thesis required a training study area for the development of the susceptibility model and a validation study area to test the model. The results from the validation showed that within the very high susceptibility class, a total of 42 % of known landslides that were associated with 1.6% of total area were correctly predicted. On the other hand, the very low susceptibility class that represented 82 % of the total area was associated with 1 % of correctly predicted landslides. The results suggest that the majority of the known landslides occur within a small portion of the study area, which is consistent with field investigation and other studies. Sample probabilistic maps of landslide susceptibility potential and other products from this approach are summarized and presented for visualization which is intended to help park officials in effective management and planning.

This paper presents a spatial decision support system (SDSS) framework for evaluating the suitability for wind farm siting in Northwest Ohio. It is intended for regional planning but also for promoting group decision making that considers different participants in the development of decision alternatives. The framework integrates environmental and economic criteria and builds a hierarchy for wind farm siting using weighted linear combination (WLC) techniques and GIS functionality. The SDSS allows multiple participants to develop an understanding of the spatial data and to assign importance values to each factor. The WLC technique is used to combine the assigned values with map layers, which are standardized using fuzzy set theory, to produce individual suitability maps. The maps created by personal preferences from the participants are aggregated for producing a group solution using the Borda method. Sensitivity analysis is performed on the group solution to examine how small changes in the factor weights affect the calculated suitability scores. The results from the sensitivity analysis suggest that the economic objective is more sensitive than the environmental objective while population density and land use were the most sensitive factors.

Abstract to be added.