Improving Sinkhole Mapping Using LiDAR Data and Locating Sinkhole Hotspots in Johnson City, TN

Predicting infrastructure damage and economic impact of sinkholes requires high accuracy mapping distribution and development. The study mapped sinkholes and sinkhole hotpsots in Johnson City, TN using LiDAR-derived Digital Elevation Model (DEM) and a database of known sinkholes which were matched to LiDAR-derived depressions. For all matched depressions (n = 404), three metrics were calculated: circularity index, ratio of length to width of the Minimum Bounding Rectangle (MBR) and percent coverage of the MBR by the depression, and 3,634 new sinkholes were identified. Newly developed hotspots were identified in north Johnson City and other areas in the south near the Johnson City Medical Center. The methodology developed can be applied to identify hotspots in other small metropolitan cities and the hotspot map produced can be employed in hazard mitigation planning, resource allocation, and made available publicly to property owners and insurance companies

r.survey: a tool for calculating visibility of variable- size objects based on orientation

Identification of terrain surface features can be done using approaches such as visual observation or remote sensing image processing. Accurate detection of survey targets at the ground level primarily depends on human visual acuity or sensor resolution, and then on acquisition geometry (i.e. the relative position and orientation between the surveyor and the terrain). Further, the delimitation of the observer's viewshed boundary or of the sensor's ground footprint is sometimes insufficient to ensure that all enclosed targets can be correctly detected. Size and orientation can hamper ground target visibility. In this paper we describe a new release of r.survey, an open-source spatial analysis tool for terrain survey assessment. This tool offers the necessary information to assess how terrain morphology is perceived by observers and/or sensors by means of three basic visibility metrics: 3D distance, view angle, and solid angle. It is also fully customizable, allowing single or multiple observation points, ground or aerial point of view, and size setting of the observed target, making it useful for many different purposes.This work was supported by the postdoctoral fellowship program of the Basque Government obtained by one of the authors [grant numbers POS_2019_1_0020] in collaboration with the Geological Survey of Canada; the research group IT1029-16 of the University of the Basque Country (UPV/EHU); and the geomorphology group of CNR IRPI

r.survey: a tool for calculating visibility of variable-size objects based on orientation

Identification of terrain surface features can be done using approaches such as visual observation or remote sensing image processing. Accurate detection of survey targets at the ground level primarily depends on human visual acuity or sensor resolution, and then on acquisition geometry (i.e. the relative position and orientation between the surveyor and the terrain). Further, the delimitation of the observer's viewshed boundary or of the sensor's ground footprint is sometimes insufficient to ensure that all enclosed targets can be correctly detected. Size and orientation can hamper ground target visibility. In this paper we describe a new release of r.survey, an open-source spatial analysis tool for terrain survey assessment. This tool offers the necessary information to assess how terrain morphology is perceived by observers and/or sensors by means of three basic visibility metrics: 3D distance, view angle, and solid angle. It is also fully customizable, allowing single or multiple observation points, ground or aerial point of view, and size setting of the observed target, making it useful for many different purposes. This work was supported by the postdoctoral fellowship program of the Basque Government obtained by one of the authors [grant numbers POS_2019_1_0020] in collaboration with the Geological Survey of Canada; the research group IT1029-16 of the University of the Basque Country (UPV/EHU); and the geomorphology group of CNR IRPI

Design and implementation of Web-based GIS for forest fragmentation analysis

The advantages and limitations of current web GIS software for forest fragmentation information and analysis functionality were investigated using Landsat Thematic Mapper data of 1987 to 1999 for a test site in northern West Virginia. ESRI\u27s ArcIMS technology was used to build a Web-based forest fragmentation analysis system to query, represent, and analyze the status of forest fragmentation using landscape metrics. Both ArcIMS HTML and Java fragmentation analysis tools were constructed. The web GIS was evaluated with respect to accessibility, navigation, interactive cartographic functionality, and spatial analysis functionality. The current ArcIMS approach was found to offer only limited support for the spatial analysis functions required for fragmentation analysis. A variety of enhancements to the current web GIS software are recommended, including support for polygon-based spatial query, interactive representation and operation for raster data, and the integration of user-side and server-side data for spatial analysis

The Urban Health Index: A Handbook for its Calculation and Use

The Urban Health Index (UHI) is a single metric that can be used to measure and map the disparities in health determinants and outcomes in urban areas. This Handbook is primarily intended for those who want to calculate the UHI for a particular geographic area of interest

Master of Science

thesisHuman-environment interaction has long been a primary theme of geographic thought. Public lands policies, and particularly wilderness designations, significantly shape the natural environment in western states such as Utah. Geographic information science and the Internet are now important parts of the policy-making toolkit, replacing paper maps and potentially leading to more democratization of wilderness and other important, long-term land use decisions. Geographical concepts such as regions are often employed in public land debates. Nongeographers have driven many of these developments. The goal of this research is to demonstrate a simple, low-cost, and accurate geographic information system (GIS) using an open-source approach and freely distributable datasets. The online Utah Wilderness Atlas will provide spatial and descriptive wildlands resource information to a general audience. It is now easier than ever to produce and exchange geospatial data; however, such data can still be difficult to use. Datasets vary in accuracy, source scale, and spatial extent and may be poorly documented. Casual users may not know where to look for the most appropriate or reliable data, and they may not have the skills or the computer software to convert specialized file formats into meaningful maps. The Utah Wilderness Atlas provides maps that can be read with a standard Web browser

Ecological Security Analysis of Land Use Changes in Lavasanat Basin Using Landscape Metrics

Continuous urbanization over the past decades has caused a large concentration of human population in these areas. Due to the rapid growth of the population and the rapid development of urban disorder in Iran, changes in land use and land cover are occurring rapidly and the sustainability of cities is decreasing day by day. Therefore, understanding the effects of urban growth on the ecosystem and determining the relationship between urban dynamics and ecological security are vital for effective urban planning and environmental protection, to support and support sustainable development.The purpose of this study was to monitor and predict land use changes over a 4 year period (2040-2000) with the Markov Chain Model (CA-Markov) in the Lavasanat Basin of Tehran Province and to evaluate the ecological security of this area over time periods. Landsat satellite imagery was used to investigate land use changes. According to the existing land use in the area, five land uses were considered, barren land, pasture land, irrigated land and agricultural and agricultural land. To quantify the landscape patterns in class metrics of NP, LSI, IJI, CA, PLAND and LPI. And NP, LSI, IJI, ED, PD and SPILT metrics were calculated on the landscape surface.Forecasting results for 2040 shows that at each floor level, the number of spots other than the Bayer floor will decrease with the current trend

MAPPING AND DECOMPOSING SCALE-DEPENDENT SOIL MOISTURE VARIABILITY WITHIN AN INNER BLUEGRASS LANDSCAPE

There is a shared desire among public and private sectors to make more reliable predictions, accurate mapping, and appropriate scaling of soil moisture and associated parameters across landscapes. A discrepancy often exists between the scale at which soil hydrologic properties are measured and the scale at which they are modeled for management purposes. Moreover, little is known about the relative importance of hydrologic modeling parameters as soil moisture fluctuates with time. More research is needed to establish which observation scales in space and time are optimal for managing soil moisture variation over large spatial extents and how these scales are affected by fluctuations in soil moisture content with time. This research fuses high resolution geoelectric and light detection and ranging (LiDAR) as auxiliary measures to support sparse direct soil sampling over a 40 hectare inner BluegrassKentucky (USA) landscape. A Veris 3100 was used to measure shallow and deep apparent electrical conductivity (aEC) in tandem with soil moisture sampling on three separate dates with ascending soil moisture contents ranging from plant wilting point to near field capacity. Terrain attributes were produced from 2010 LiDAR ground returns collected at ≤1 m nominal pulse spacing. Exploratory statistics revealed several variables best associate with soil moisture, including terrain features (slope, profile curvature, and elevation), soil physical and chemical properties (calcium, cation exchange capacity, organic matter, clay and sand) and aEC for each date. Multivariate geostatistics, time stability analyses, and spatial regression were performed to characterize scale-dependent soil moisture patterns in space with time to determine which soil-terrain parameters influence soil moisture distribution. Results showed that soil moisture variation was time stable across the landscape and primarily associated with long-range (~250 m) soil physicochemical properties. When the soils approached field capacity, however, there was a shift in relative importance from long-range soil physicochemical properties to short-range (~70 m) terrain attributes, albeit this shift did not cause time instability. Results obtained suggest soil moisture’s interaction with soil-terrain parameters is time dependent and this dependence influences which observation scale is optimal to sample and manage soil moisture variation