Biodiversity in environmental assessment-current practice and tools for prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gontier, Mikael; Balfors, Berit; Moertberg, Ulla

Habitat loss and fragmentation are major threats to biodiversity. Environmental impact assessment and strategic environmental assessment are essential instruments used in physical planning to address such problems. Yet there are no well-developed methods for quantifying and predicting impacts of fragmentation on biodiversity. In this study, a literature review was conducted on GIS-based ecological models that have potential as prediction tools for biodiversity assessment. Further, a review of environmental impact statements for road and railway projects from four European countries was performed, to study how impact prediction concerning biodiversity issues was addressed. The results of the study showed the existing gapmore » between research in GIS-based ecological modelling and current practice in biodiversity assessment within environmental assessment.« less

Use of the AGNPS model to assess impacts of development and best management practices in an urban watershed

NASA Astrophysics Data System (ADS)

Cross, J. A.

2006-12-01

A Geographical Information System (GIS) is an invaluable tool in the estimation of land use changes and spatial variability in urban areas. (Non-Point Source (NPS) models provide hypothetical opportunities to assess impacts which storm water management strategies and land use changes have on watersheds by predicting loadings on a watershed scale. This study establishes a methodology for analyzing land use changes and management associated with them by utilizing a GIS analysis of impervious surfaces and AGricultural Non- Point Source (AGNPS) modeling. The GIS analysis of Total Impervious Area (TIA) was used to quantify increases in development and provided land use data for use in AGNPS modeling in a small artificially- delineated urban watershed. AGNPS modeling was executed in several different scenarios to predict changes in NPS loadings associated with increases in TIA and its subsequent management in a small artificially- delineated urban watershed. Data editing, creation and extracting was completed using ArcView (3.2) GeoMedia (6) GIS systems. The GIS analysis quantified the increase in urbanization via TIA within the Bluebonnet Swamp Watershed (BSW) in East Baton Rouge Parish (EBRP), Louisiana. The BSW had significant increases in urbanization in the 8 year time span of 1996 2004 causing and increase in quantity and decrease in quality of subsequent runoff. Datasets made available from the GIS analysis included TIA and the change in percentage from 1996 to 2004. This information is fundamental for the AGNPS model because it was used to calculate TIA percentages within each AGNPS cell. A 30 year daily climate file was used to execute AGNPS in different land use and storm water management scenarios within the 1100 acre BSW. Runoff qualities and quantities were then compared for different periods of 1996 and 2004. Predictions of sediment, erosion and runoff were compared according by scenario year. Management practices were also simulated by changing the Runoff Curve Number (RCN) within AGNPS and their results were also compared. This study provides an aid to planners and managers in estimating increases in urbanization by artificially- delineated watershed. It also in illustrates how to use AGNPS to predict NPS pollution and the influence that change in TIA, land use and storm water management strategies have on sediment loadings, erosion and runoff in a watershed.

Analysis of errors introduced by geographic coordinate systems on weather numeric prediction modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao, Yanni; Cervone, Guido; Barkley, Zachary

Most atmospheric models, including the Weather Research and Forecasting (WRF) model, use a spherical geographic coordinate system to internally represent input data and perform computations. However, most geographic information system (GIS) input data used by the models are based on a spheroid datum because it better represents the actual geometry of the earth. WRF and other atmospheric models use these GIS input layers as if they were in a spherical coordinate system without accounting for the difference in datum. When GIS layers are not properly reprojected, latitudinal errors of up to 21 km in the midlatitudes are introduced. Recent studiesmore » have suggested that for very high-resolution applications, the difference in datum in the GIS input data (e.g., terrain land use, orography) should be taken into account. However, the magnitude of errors introduced by the difference in coordinate systems remains unclear. This research quantifies the effect of using a spherical vs. a spheroid datum for the input GIS layers used by WRF to study greenhouse gas transport and dispersion in northeast Pennsylvania.« less

Analysis of errors introduced by geographic coordinate systems on weather numeric prediction modeling

DOE PAGES

Cao, Yanni; Cervone, Guido; Barkley, Zachary; ...

2017-09-19

Most atmospheric models, including the Weather Research and Forecasting (WRF) model, use a spherical geographic coordinate system to internally represent input data and perform computations. However, most geographic information system (GIS) input data used by the models are based on a spheroid datum because it better represents the actual geometry of the earth. WRF and other atmospheric models use these GIS input layers as if they were in a spherical coordinate system without accounting for the difference in datum. When GIS layers are not properly reprojected, latitudinal errors of up to 21 km in the midlatitudes are introduced. Recent studiesmore » have suggested that for very high-resolution applications, the difference in datum in the GIS input data (e.g., terrain land use, orography) should be taken into account. However, the magnitude of errors introduced by the difference in coordinate systems remains unclear. This research quantifies the effect of using a spherical vs. a spheroid datum for the input GIS layers used by WRF to study greenhouse gas transport and dispersion in northeast Pennsylvania.« less

Analysis of errors introduced by geographic coordinate systems on weather numeric prediction modeling

NASA Astrophysics Data System (ADS)

Cao, Yanni; Cervone, Guido; Barkley, Zachary; Lauvaux, Thomas; Deng, Aijun; Taylor, Alan

2017-09-01

Most atmospheric models, including the Weather Research and Forecasting (WRF) model, use a spherical geographic coordinate system to internally represent input data and perform computations. However, most geographic information system (GIS) input data used by the models are based on a spheroid datum because it better represents the actual geometry of the earth. WRF and other atmospheric models use these GIS input layers as if they were in a spherical coordinate system without accounting for the difference in datum. When GIS layers are not properly reprojected, latitudinal errors of up to 21 km in the midlatitudes are introduced. Recent studies have suggested that for very high-resolution applications, the difference in datum in the GIS input data (e.g., terrain land use, orography) should be taken into account. However, the magnitude of errors introduced by the difference in coordinate systems remains unclear. This research quantifies the effect of using a spherical vs. a spheroid datum for the input GIS layers used by WRF to study greenhouse gas transport and dispersion in northeast Pennsylvania.

EuGI: a novel resource for studying genomic islands to facilitate horizontal gene transfer detection in eukaryotes.

PubMed

Clasen, Frederick Johannes; Pierneef, Rian Ewald; Slippers, Bernard; Reva, Oleg

2018-05-03

Genomic islands (GIs) are inserts of foreign DNA that have potentially arisen through horizontal gene transfer (HGT). There are evidences that GIs can contribute significantly to the evolution of prokaryotes. The acquisition of GIs through HGT in eukaryotes has, however, been largely unexplored. In this study, the previously developed GI prediction tool, SeqWord Gene Island Sniffer (SWGIS), is modified to predict GIs in eukaryotic chromosomes. Artificial simulations are used to estimate ratios of predicting false positive and false negative GIs by inserting GIs into different test chromosomes and performing the SWGIS v2.0 algorithm. Using SWGIS v2.0, GIs are then identified in 36 fungal, 22 protozoan and 8 invertebrate genomes. SWGIS v2.0 predicts GIs in large eukaryotic chromosomes based on the atypical nucleotide composition of these regions. Averages for predicting false negative and false positive GIs were 20.1% and 11.01% respectively. A total of 10,550 GIs were identified in 66 eukaryotic species with 5299 of these GIs coding for at least one functional protein. The EuGI web-resource, freely accessible at http://eugi.bi.up.ac.za , was developed that allows browsing the database created from identified GIs and genes within GIs through an interactive and visual interface. SWGIS v2.0 along with the EuGI database, which houses GIs identified in 66 different eukaryotic species, and the EuGI web-resource, provide the first comprehensive resource for studying HGT in eukaryotes.

Management of a water distribution network by coupling GIS and hydraulic modeling: a case study of Chetouane in Algeria

NASA Astrophysics Data System (ADS)

Abdelbaki, Chérifa; Benchaib, Mohamed Mouâd; Benziada, Salim; Mahmoudi, Hacène; Goosen, Mattheus

2017-06-01

For more effective management of water distribution network in an arid region, Mapinfo GIS (8.0) software was coupled with a hydraulic model (EPANET 2.0) and applied to a case study region, Chetouane, situated in the north-west of Algeria. The area is characterized not only by water scarcity but also by poor water management practices. The results showed that a combination of GIS and modeling permits network operators to better analyze malfunctions with a resulting more rapid response as well as facilitating in an improved understanding of the work performed on the network. The grouping of GIS and modeling as an operating tool allows managers to diagnosis a network, to study solutions of problems and to predict future situations. The later can assist them in making informed decisions to ensure an acceptable performance level for optimal network operation.

Using a coupled eco-hydrodynamic model to predict habitat for target species following dam removal

USGS Publications Warehouse

Tomsic, C.A.; Granata, T.C.; Murphy, R.P.; Livchak, C.J.

2007-01-01

A habitat suitability index (HSI) model was developed for a water quality sensitive fish (Greater Redhorse) and macroinvertebrate (Plecoptera) species to determine the restoration success of the St. John Dam removal for the Sandusky River (Ohio). An ArcGIS?? model was created for pre- and post-dam removal scenarios. Inputs to the HSI model consist of substrate distributions from river surveys, and water level and velocity time series, outputs from a hydrodynamic model. The ArcGIS?? model predicted habitat suitability indices at 45 river cross-sections in the hydrodynamic model. The model was programmed to produce polygon layers, using graphical user interfaces that were displayed in the ArcGIS?? environment. The results of the model clearly show an increase of habitat suitability from pre- to post-dam removal periods and in the former reservoir. The change in suitability of the model is attributed mostly to the change in depth in the river following the dam removal for both the fish and invertebrate species. The results of the invertebrate model followed the same positive trend as species enumerations from the river basin. ?? 2007 Elsevier B.V. All rights reserved.

Topography and vegetation as predictors of snow water equivalent across the alpine treeline ecotone at Lee Ridge, Glacier National Park, Montana, U.S.A.

USGS Publications Warehouse

Geddes, C.A.; Brown, D.G.; Fagre, D.B.

2005-01-01

We derived and implemented two spatial models of May snow water equivalent (SWE) at Lee Ridge in Glacier National Park, Montana. We used the models to test the hypothesis that vegetation structure is a control on snow redistribution at the alpine treeline ecotone (ATE). The statistical models were derived using stepwise and "best" subsets regression techniques. The first model was derived from field measurements of SWE, topography, and vegetation taken at 27 sample points. The second model was derived using GIS-based measures of topography and vegetation. Both the field- (R² = 0.93) and GIS-based models (R² = 0.69) of May SWE included the following variables: site type (based on vegetation), elevation, maximum slope, and general slope aspect. Site type was identified as the most important predictor of SWE in both models, accounting for 74.0% and 29.5% of the variation, respectively. The GIS-based model was applied to create a predictive map of SWE across Lee Ridge, predicting little snow accumulation on the top of the ridge where vegetation is scarce. The GIS model failed in large depressions, including ephemeral stream channels. The models supported the hypothesis that upright vegetation has a positive effect on accumulation of SWE above and beyond the effects of topography. Vegetation, therefore, creates a positive feedback in which it modifies its, environment and could affect the ability of additional vegetation to become established.

USE OF GIS AND ANCILLARY VARIABLES TO PREDICT VOLATILE ORGANIC COMPOUND AND NITROGEN DIOXIDE LEVELS AT UNMONITORED LOCATIONS

EPA Science Inventory

This paper presents a GIS-based regression spatial method, known as land-use regression (LUR) modeling, to estimate ambient air pollution exposures used in the EPA El Paso Children's Health Study. Passive measurements of select volatile organic compounds (VOC) and nitrogen dioxi...

Computational Approaches to Predict Indices of ...

EPA Pesticide Factsheets

As nutrient inputs increase, productivity increases and lakes transition from low trophic state (e.g., oligotrophic) to higher trophic states (e.g., eutrophic). These broad trophic state classifications are good predictors of ecosystem health and the potential for ecosystem services (e.g., recreation, aesthetics, and fisheries). Additionally, some ecosystem disservices, such as cyanobacteria blooms, are also associated with increased nutrient inputs. Thus, trophic state can be used as a proxy for cyanobacteria bloom risk. To explore this idea, we construct two random forest models of trophic state (as determined by chlorophyll a concentration). First we define an “All Variable” model that estimates trophic state with both in situ and universally available data, and then we reduce this to a “GIS Only” model that uses only the universally available data. The “All Variables” model had a root mean square error (RMSE) of 0.09 and R2 of 0.8; whereas, the “GIS Only” model was 0.22 and 0.48 for RMSE and R2, respectively. Examining the “GIS Only” model (i.e., the model that has broadest applicability) we see that in spite of lower overall accuracy, it still has better than even odds (i.e., prediction probability is > 50%) of being correct in more than 1091 of the 1138 lakes included in this model. The “GIS Only” model has tremendous potential for exploring spatial trends at the national level since the datasets required to parameterize the

Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties.

PubMed

Xiaopeng, Q I; Liang, Wei; Barker, Laurie; Lekiachvili, Akaki; Xingyou, Zhang

Temperature changes are known to have significant impacts on human health. Accurate estimates of population-weighted average monthly air temperature for US counties are needed to evaluate temperature's association with health behaviours and disease, which are sampled or reported at the county level and measured on a monthly-or 30-day-basis. Most reported temperature estimates were calculated using ArcGIS, relatively few used SAS. We compared the performance of geostatistical models to estimate population-weighted average temperature in each month for counties in 48 states using ArcGIS v9.3 and SAS v 9.2 on a CITGO platform. Monthly average temperature for Jan-Dec 2007 and elevation from 5435 weather stations were used to estimate the temperature at county population centroids. County estimates were produced with elevation as a covariate. Performance of models was assessed by comparing adjusted R 2 , mean squared error, root mean squared error, and processing time. Prediction accuracy for split validation was above 90% for 11 months in ArcGIS and all 12 months in SAS. Cokriging in SAS achieved higher prediction accuracy and lower estimation bias as compared to cokriging in ArcGIS. County-level estimates produced by both packages were positively correlated (adjusted R 2 range=0.95 to 0.99); accuracy and precision improved with elevation as a covariate. Both methods from ArcGIS and SAS are reliable for U.S. county-level temperature estimates; However, ArcGIS's merits in spatial data pre-processing and processing time may be important considerations for software selection, especially for multi-year or multi-state projects.

Tile prediction schemes for wide area motion imagery maps in GIS

NASA Astrophysics Data System (ADS)

Michael, Chris J.; Lin, Bruce Y.

2017-11-01

Wide-area surveillance, traffic monitoring, and emergency management are just several of many applications benefiting from the incorporation of Wide-Area Motion Imagery (WAMI) maps into geographic information systems. Though the use of motion imagery as a GIS base map via the Web Map Service (WMS) standard is not a new concept, effectively streaming imagery is particularly challenging due to its large scale and the multidimensionally interactive nature of clients that use WMS. Ineffective streaming from a server to one or more clients can unnecessarily overwhelm network bandwidth and cause frustratingly large amounts of latency in visualization to the user. Seamlessly streaming WAMI through GIS requires good prediction to accurately guess the tiles of the video that will be traversed in the near future. In this study, we present an experimental framework for such prediction schemes by presenting a stochastic interaction model that represents a human user's interaction with a GIS video map. We then propose several algorithms by which the tiles of the stream may be predicted. Results collected both within the experimental framework and using human analyst trajectories show that, though each algorithm thrives under certain constraints, the novel Markovian algorithm yields the best results overall. Furthermore, we make the argument that the proposed experimental framework is sufficient for the study of these prediction schemes.

A data base approach for prediction of deforestation-induced mass wasting events

NASA Technical Reports Server (NTRS)

Logan, T. L.

1981-01-01

A major topic of concern in timber management is determining the impact of clear-cutting on slope stability. Deforestation treatments on steep mountain slopes have often resulted in a high frequency of major mass wasting events. The Geographic Information System (GIS) is a potentially useful tool for predicting the location of mass wasting sites. With a raster-based GIS, digitally encoded maps of slide hazard parameters can be overlayed and modeled to produce new maps depicting high probability slide areas. The present investigation has the objective to examine the raster-based information system as a tool for predicting the location of the clear-cut mountain slopes which are most likely to experience shallow soil debris avalanches. A literature overview is conducted, taking into account vegetation, roads, precipitation, soil type, slope-angle and aspect, and models predicting mass soil movements. Attention is given to a data base approach and aspects of slide prediction.

Informing geobiology through GIS site suitability analysis: locating springs in mantle units of ophiolites

NASA Astrophysics Data System (ADS)

Bowman, A.; Cardace, D.; August, P.

2012-12-01

Springs sourced in the mantle units of ophiolites serve as windows to the deep biosphere, and thus hold promise in elucidating survival strategies of extremophiles, and may also inform discourse on the origin of life on Earth. Understanding how organisms can survive in extreme environments provides clues to how microbial life responds to gradients in pH, temperature, and oxidation-reduction potential. Spring locations associated with serpentinites have traditionally been located using a variety of field techniques. The aqueous alteration of ultramafic rocks to serpentinites is accompanied by the production of very unusual formation fluids, accessed by drilling into subsurface flow regimes or by sampling at related surface springs. The chemical properties of these springs are unique to water associated with actively serpentinizing rocks; they reflect a reducing subsurface environment reacting at low temperatures producing high pH, Ca-rich formation fluids with high dissolved hydrogen and methane. This study applies GIS site suitability analysis to locate high pH springs upwelling from Coast Range Ophiolite serpentinites in Northern California. We used available geospatial data (e.g., geologic maps, topography, fault locations, known spring locations, etc.) and ArcGIS software to predict new spring localities. Important variables in the suitability model were: (a) bedrock geology (i.e., unit boundaries and contacts for peridotite, serpentinite, possibly pyroxenite, or chromite), (b) fault locations, (c) regional data for groundwater characteristics such as pH, Ca2+, and Mg2+, and (d) slope-aspect ratio. The GIS model derived from these geological and environmental data sets predicts the latitude/longitude points for novel and known high pH springs sourced in serpentinite outcrops in California. Field work confirms the success of the model, and map output can be merged with published environmental microbiology data (e.g., occurrence of hydrogen-oxidizers) to showcase patterns in microbial community structure. Discrepancies between predicted and actual spring locations are then used to tune GIS suitability analysis, re-running the model with corrected geo-referenced data. This presentation highlights a powerful GIS-based technique for accelerating field exploration in this area of ongoing research.

Prediction of sedimentation using integration of RS, RUSLE model and GIS in Cameron Highlands, Pahang, Malaysia

NASA Astrophysics Data System (ADS)

Ghani, A. H. A.; Lihan, T.; Rahim, S. A.; Musthapha, M. A.; Idris, W. M. R.; Rahman, Z. A.

2013-11-01

Soil erosion and sediment yield are strongly affected by land use change. Spatially distributed erosion models are of great interest to predict soil erosion loss and sediment yield. Hence, the objective of this study was to determine sediment yield using Revised Universal Soil Loss Equation (RUSLE) model in Geographical Information System (GIS) environment at Cameron Highlands, Pahang, Malaysia. Sediment yield at the study area was determined using RUSLE model in GIS environment The RUSLE factors were computed by utilizing information on rainfall erosivity (R) using interpolation of rainfall data, soil erodibility (K) using soil map and field measurement, vegetation cover (C) using satellite images, length and steepness (LS) using contour map and conservation practices using satellite images based on land use/land cover. Field observations were also done to verify the predicted sediment yield. The results indicated that the rate of sediment yield in the study area ranged from very low to extremely high. The higher SY value can be found at middle and lower catchments of Cameron Highland. Meanwhile, the lower SY value can be found at the north part of the study area. Sediment yield value turned out to be higher close to the river due to the topographic characteristic, vegetation type and density, climate and land use within the drainage basin.

Modeling critical habitat for Flammulated Owls (Otus flammeolus)

Treesearch

David A. Christie; Astrid M. van Woudenberg

1997-01-01

Multiple logistic regression analysis was used to produce a prediction model for Flammulated Owl (Otus flammeolus) breeding habitat within the Kamloops Forest Region in south-central British Columbia. Using the model equation, a pilot habitat prediction map was created within a Geographic Information System (GIS) environment that had a 75.7 percent...

Application of Geographic Information System (GIS) to Model the Hydrocarbon Migration: Case Study from North-East Malay Basin, Malaysia

NASA Astrophysics Data System (ADS)

Rudini; Nasir Matori, Abd; Talib, Jasmi Ab; Balogun, Abdul-Lateef

2018-03-01

The purpose of this study is to model the migration of hydrocarbon using Geographic Information System (GIS). Understanding hydrocarbon migration is important since it can mean the difference between success and failure in oil and gas exploration project. The hydrocarbon migration modeling using geophysical method is still not accurate due to the limitations of available data. In recent years, GIS has emerged as a powerful tool for subsurface mapping and analysis. Recent studies have been carried out about the abilities of GIS to model hydrocarbon migration. Recent advances in GIS support the establishment and monitoring of prediction hydrocarbon migration. The concept, model, and calculation are based on the current geological situation. The spatial data of hydrocarbon reservoirs is determined by its geometry of lithology and geophysical attributes. Top of Group E horizon of north-east Malay basin was selected as the study area due to the occurrence of hydrocarbon migration. Spatial data and attributes data such as seismic data, wells log data and lithology were acquired and processed. Digital Elevation Model (DEM) was constructed from the selected horizon as a result of seismic interpretation using the Petrel software. Furthermore, DEM was processed in ArcGIS as a base map to shown hydrocarbon migration in north-east Malay Basin. Finally, all the data layers were overlaid to produce a map of hydrocarbon migration. A good data was imported to verify the model is correct.

Modeling of geoelectric parameters for assessing groundwater potentiality in a multifaceted geologic terrain, Ipinsa Southwest, Nigeria - A GIS-based GODT approach

NASA Astrophysics Data System (ADS)

Mogaji, Kehinde Anthony; Omobude, Osayande Bright

2017-12-01

Modeling of groundwater potentiality zones is a vital scheme for effective management of groundwater resources. This study developed a new multi-criteria decision making algorithm for groundwater potentiality modeling through modifying the standard GOD model. The developed model christened as GODT model was applied to assess groundwater potential in a multi-faceted crystalline geologic terrain, southwestern, Nigeria using the derived four unify groundwater potential conditioning factors namely: Groundwater hydraulic confinement (G), aquifer Overlying strata resistivity (O), Depth to water table (D) and Thickness of aquifer (T) from the interpreted geophysical data acquired in the area. With the developed model algorithm, the GIS-based produced G, O, D and T maps were synthesized to estimate groundwater potential index (GWPI) values for the area. The estimated GWPI values were processed in GIS environment to produce groundwater potential prediction index (GPPI) map which demarcate the area into four potential zones. The produced GODT model-based GPPI map was validated through application of both correlation technique and spatial attribute comparative scheme (SACS). The performance of the GODT model was compared with that of the standard analytic hierarchy process (AHP) model. The correlation technique results established 89% regression coefficients for the GODT modeling algorithm compared with 84% for the AHP model. On the other hand, the SACS validation results for the GODT and AHP models are 72.5% and 65%, respectively. The overall results indicate that both models have good capability for predicting groundwater potential zones with the GIS-based GODT model as a good alternative. The GPPI maps produced in this study can form part of decision making model for environmental planning and groundwater management in the area.

Geographical information system (GIS) application for flood prediction at Sungai Sembrong

NASA Astrophysics Data System (ADS)

Kamin, Masiri; Ahmad, Nor Farah Atiqah; Razali, Siti Nooraiin Mohd; Hilaham, Mashuda Mohamad; Rahman, Mohamad Abdul; Ngadiman, Norhayati; Sahat, Suhaila

2017-10-01

The occurrence of flood is one of natural disaster that often beset Malaysia. The latest incident that happened in 2007 was the worst occurrence of floods ever be set in Johor. Reporting floods mainly focused on rising water rising levels, so about once a focus on the area of flood delineation. A study focused on the effectiveness of using Geographic Information System (GIS) to predict the flood by taking Sg. Sembrong, Batu Pahat, Johor as study area. This study combined hydrological model and water balance model in the display to show the expected flood area for future reference. The minimum, maximum and average rainfall data for January 2007 at Sg Sembrong were used in this study. The data shows that flood does not occurs at the minimum and average rainfall of 17.2mm and 2mm respectively. At maximum rainfall, 203mm, shows the flood area was 9983 hectares with the highest level of the water depth was 2m. The result showed that the combination of hydrological models and water balance model in GIS is very suitable to be used as a tool to obtain preliminary information on flood immediately. Besides that, GIS system is a very powerful tool used in hydrology engineering to help the engineer and planner to imagine the real situation of flood events, doing flood analysis, problem solving and provide a rational, accurate and efficient decision making.

Latin hypercube approach to estimate uncertainty in ground water vulnerability

USGS Publications Warehouse

Gurdak, J.J.; McCray, J.E.; Thyne, G.; Qi, S.L.

2007-01-01

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability. ?? 2007 National Ground Water Association.

A global network for the control of snail-borne disease using satellite surveillance and geographic information systems.

PubMed

Malone, J B; Bergquist, N R; Huh, O K; Bavia, M E; Bernardi, M; El Bahy, M M; Fuentes, M V; Kristensen, T K; McCarroll, J C; Yilma, J M; Zhou, X N

2001-04-27

At a team residency sponsored by the Rockefeller Foundation in Bellagio, Italy, 10-14 April 2000 an organizational plan was conceived to create a global network of collaborating health workers and earth scientists dedicated to the development of computer-based models that can be used for improved control programs for schistosomiasis and other snail-borne diseases of medical and veterinary importance. The models will be assembled using GIS methods, global climate model data, sensor data from earth observing satellites, disease prevalence data, the distribution and abundance of snail hosts, and digital maps of key environmental factors that affect development and propagation of snail-borne disease agents. A work plan was developed for research collaboration and data sharing, recruitment of new contributing researchers, and means of access of other medical scientists and national control program managers to GIS models that may be used for more effective control of snail-borne disease. Agreement was reached on the use of compatible GIS formats, software, methods and data resources, including the definition of a 'minimum medical database' to enable seamless incorporation of results from each regional GIS project into a global model. The collaboration plan calls for linking a 'central resource group' at the World Health Organization, the Food and Agriculture Organization, Louisiana State University and the Danish Bilharziasis Laboratory with regional GIS networks to be initiated in Eastern Africa, Southern Africa, West Africa, Latin America and Southern Asia. An Internet site, www.gnosisGIS.org, (GIS Network On Snail-borne Infections with special reference to Schistosomiasis), has been initiated to allow interaction of team members as a 'virtual research group'. When completed, the site will point users to a toolbox of common resources resident on computers at member organizations, provide assistance on routine use of GIS health maps in selected national disease control programs and provide a forum for development of GIS models to predict the health impacts of water development projects and climate variation.

Predicting habitat suitability for wildlife in southeastern Arizona using Geographic Information Systems: scaled quail, a case study

Treesearch

Kirby D. Bristow; Susan R. Boe; Richard A. Ockenfels

2005-01-01

Studies have used Geographic Information Systems (GIS) to evaluate habitat suitability for wildlife on a landscape scale, yet few have established the accuracy of these models. Based on documented habitat selection patterns of scaled quail (Callipepla squamata pallida), we produced GIS covers for several habitat parameters to create a map of...

Genetic interaction networks: better understand to better predict

PubMed Central

Boucher, Benjamin; Jenna, Sarah

2013-01-01

A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances. PMID:24381582

Using remote sensing, ecological niche modeling, and Geographic Information Systems for Rift Valley fever risk assessment in the United States

NASA Astrophysics Data System (ADS)

Tedrow, Christine Atkins

The primary goal in this study was to explore remote sensing, ecological niche modeling, and Geographic Information Systems (GIS) as aids in predicting candidate Rift Valley fever (RVF) competent vector abundance and distribution in Virginia, and as means of estimating where risk of establishment in mosquitoes and risk of transmission to human populations would be greatest in Virginia. A second goal in this study was to determine whether the remotely-sensed Normalized Difference Vegetation Index (NDVI) can be used as a proxy variable of local conditions for the development of mosquitoes to predict mosquito species distribution and abundance in Virginia. As part of this study, a mosquito surveillance database was compiled to archive the historical patterns of mosquito species abundance in Virginia. In addition, linkages between mosquito density and local environmental and climatic patterns were spatially and temporally examined. The present study affirms the potential role of remote sensing imagery for species distribution prediction, and it demonstrates that ecological niche modeling is a valuable predictive tool to analyze the distributions of populations. The MaxEnt ecological niche modeling program was used to model predicted ranges for potential RVF competent vectors in Virginia. The MaxEnt model was shown to be robust, and the candidate RVF competent vector predicted distribution map is presented. The Normalized Difference Vegetation Index (NDVI) was found to be the most useful environmental-climatic variable to predict mosquito species distribution and abundance in Virginia. However, these results indicate that a more robust prediction is obtained by including other environmental-climatic factors correlated to mosquito densities (e.g., temperature, precipitation, elevation) with NDVI. The present study demonstrates that remote sensing and GIS can be used with ecological niche and risk modeling methods to estimate risk of virus establishment in mosquitoes and transmission to humans. Maps delineating the geographic areas in Virginia with highest risk for RVF establishment in mosquito populations and RVF disease transmission to human populations were generated in a GIS using human, domestic animal, and white-tailed deer population estimates and the MaxEnt potential RVF competent vector species distribution prediction. The candidate RVF competent vector predicted distribution and RVF risk maps presented in this study can help vector control agencies and public health officials focus Rift Valley fever surveillance efforts in geographic areas with large co-located populations of potential RVF competent vectors and human, domestic animal, and wildlife hosts. Keywords. Rift Valley fever, risk assessment, Ecological Niche Modeling, MaxEnt, Geographic Information System, remote sensing, Pearson's Product-Moment Correlation Coefficient, vectors, mosquito distribution, mosquito density, mosquito surveillance, United States, Virginia, domestic animals, white-tailed deer, ArcGIS

GIS Based Distributed Runoff Predictions in Variable Source Area Watersheds Employing the SCS-Curve Number

NASA Astrophysics Data System (ADS)

Steenhuis, T. S.; Mendoza, G.; Lyon, S. W.; Gerard Marchant, P.; Walter, M. T.; Schneiderman, E.

2003-04-01

Because the traditional Soil Conservation Service Curve Number (SCS-CN) approach continues to be ubiquitously used in GIS-BASED water quality models, new application methods are needed that are consistent with variable source area (VSA) hydrological processes in the landscape. We developed within an integrated GIS modeling environment a distributed approach for applying the traditional SCS-CN equation to watersheds where VSA hydrology is a dominant process. Spatial representation of hydrologic processes is important for watershed planning because restricting potentially polluting activities from runoff source areas is fundamental to controlling non-point source pollution. The methodology presented here uses the traditional SCS-CN method to predict runoff volume and spatial extent of saturated areas and uses a topographic index to distribute runoff source areas through watersheds. The resulting distributed CN-VSA method was incorporated in an existing GWLF water quality model and applied to sub-watersheds of the Delaware basin in the Catskill Mountains region of New York State. We found that the distributed CN-VSA approach provided a physically-based method that gives realistic results for watersheds with VSA hydrology.

Modeling the distribution of white spruce (Picea glauca) for Alaska with high accuracy: an open access role-model for predicting tree species in last remaining wilderness areas

Treesearch

Bettina Ohse; Falk Huettmann; Stefanie M. Ickert-Bond; Glenn P. Juday

2009-01-01

Most wilderness areas still lack accurate distribution information on tree species. We met this need with a predictive GIS modeling approach, using freely available digital data and computer programs to efficiently obtain high-quality species distribution maps. Here we present a digital map with the predicted distribution of white spruce (Picea glauca...

Application of GIS to modified models of vehicle emission dispersion

NASA Astrophysics Data System (ADS)

Jin, Taosheng; Fu, Lixin

This paper reports on a preliminary study of the forecast and evaluation of transport-related air pollution dispersion in urban areas. Some modifications of the traditional Gauss dispersion models are provided, and especially a crossroad model is built, which considers the great variation of vehicle emission attributed to different driving patterns at the crossroad. The above models are combined with a self-developed geographic information system (GIS) platform, and a simulative system with graphical interfaces is built. The system aims at visually describing the influences on the urban environment by urban traffic characteristics and therefore gives a reference to the improvement of urban air quality. Due to the introduction of a self-developed GIS platform and a creative crossroad model, the system is more effective, flexible and accurate. Finally, a comparison of the simulated (predicted) and observed hourly concentration is given, which indicates a good simulation.

Modelling the spatial distribution of Fasciola hepatica in bovines using decision tree, logistic regression and GIS query approaches for Brazil.

PubMed

Bennema, S C; Molento, M B; Scholte, R G; Carvalho, O S; Pritsch, I

2017-11-01

Fascioliasis is a condition caused by the trematode Fasciola hepatica. In this paper, the spatial distribution of F. hepatica in bovines in Brazil was modelled using a decision tree approach and a logistic regression, combined with a geographic information system (GIS) query. In the decision tree and the logistic model, isothermality had the strongest influence on disease prevalence. Also, the 50-year average precipitation in the warmest quarter of the year was included as a risk factor, having a negative influence on the parasite prevalence. The risk maps developed using both techniques, showed a predicted higher prevalence mainly in the South of Brazil. The prediction performance seemed to be high, but both techniques failed to reach a high accuracy in predicting the medium and high prevalence classes to the entire country. The GIS query map, based on the range of isothermality, minimum temperature of coldest month, precipitation of warmest quarter of the year, altitude and the average dailyland surface temperature, showed a possibility of presence of F. hepatica in a very large area. The risk maps produced using these methods can be used to focus activities of animal and public health programmes, even on non-evaluated F. hepatica areas.

Implementing GIS in real estate price prediction and mass valuation: the case study of Nicosia District

NASA Astrophysics Data System (ADS)

Yiorkas, Charalambos; Dimopoulos, Thomas

2017-09-01

When the European Commission, International Monetary Fund and European Central Bank arrived in Cyprus to assist for a sustainable solution on the crisis on the banking sector, one of the first things they ordered was a New General Valuation (a mass appraisal that would revalue all properties in Cyprus as on 1st of January 2013), that it would be used for taxation purposes. The above indicates the importance of property mass appraising tools. This task was successfully conducted by the Department of Lands and Surveys. Authors aim to move a step further and implement the use of GIS and GWR techniques to improve the results of the New General Valuation. On a sample of comparative evidences for flats in Nicosia District, GIS was used to measure the impact of spatial attributes on real estate prices and to construct a prediction model in terms of spatially estimating apartment values. In addition to the structural property characteristics, some spatial attributes (landmarks) were also analysed to assess their contribution on the prices of the apartments, including the Central Business District (CBD), schools and universities, as well as the major city roads and the restricted zone that divides the country into two parts; the occupied by Turkish area and the Greek area. The values of the spatial attributes, or locational characteristics, were determined by employing GIS, considering an established model of multicriteria analysis. The price prediction model was analysed using the OLS method and calibrated based on the GWR method. The results of the statistic process indicate an accuracy of 81.34%, showing better performance than the mass valuation system applied by the Department of Land and Surveys in Cyprus with accuracy of 66.76%. This approach suggests that GIS systems are fundamentally important in mass valuation procedures in order to identify the spatial pattern of the attributes, provided that the database is comprised by a sufficient number of comparable information and it is continuously updated.

[Ecology suitability study of Ephedra intermedia].

PubMed

Ma, Xiao-Hui; Lu, You-Yuan; Huang, De-Dong; Zhu, Tian-Tian; Lv, Pei-Lin; Jin, Ling

2017-06-01

The study aims at predicting ecological suitability of Ephedra intermedia in China by using maximum entropy Maxent model combined with GIS, and finding the main ecological factors affecting the distribution of E. intermedia suitability in appropriate growth area. Thirty-eight collected samples of E. intermedia and E. intermedia and 116 distribution information from CVH information using ArcGIS technology were analyzed. MaxEnt model was applied to forecast the E. intermedia in our country's ecology. E. intermedia MaxEnt ROC curve model training data and testing data sets the AUC value was 0.986 and 0.958, respectively, which were greater than 0.9, tending to be 1.The calculated E. intermedia habitat suitability by the model showed a high accuracy and credibility, which indicated that MaxEnt model could well predict the potential distribution area of E. intermedia in China. Copyright© by the Chinese Pharmaceutical Association.

A multiscaled model of southwestern willow flycatcher breeding habitat

USGS Publications Warehouse

Hatten, J.R.; Paradzick, C.E.

2003-01-01

The southwestern willow flycatcher (SWFL; Empidonax traillii extimus) is an endangered songbird whose habitat has declined dramatically over the last century. Understanding habitat selection patterns and the ability to identify potential breeding areas for the SWFL is crucial to the management and conservation of this species. We developed a multiscaled model of SWTL breeding habitat with a Geographic Information System (GIS), survey data, GIS variables, and multiple logistic regressions. We obtained presence and absence survey data from a riverine ecosystem and a reservoir delta in south-central Arizona, USA, in 1999. We extracted the GIS variables from satellite imagery and digital elevation models to characterize vegetation and floodplain within the project area. We used multiple logistic regressions within a cell-based (30 X 30 m) modeling environment to (1) determine associations between GIS variables and breeding-site occurrence at different spatial scales (0.09-72 ha), and (2) construct a predictive model. Our best model explained 54% of the variability in breeding-site occurrence with the following variables: vegetation density at the site (0.09 ha), proportion of dense vegetation and variability in vegetation density within a 4.5-ha neighborhood, and amount of floodplain or flat terrain within a 41-ha neighborhood. The density of breeding sites was highest in areas that the model predicted to be most suitable within the project area and at an external test site 200 km away. Conservation efforts must focus on protecting not only occupied patches, but also surrounding riparian forests and floodplain to ensure long-term viability of SWTL. We will use the multiscaled model to map SWTL breeding habitat in Arizona, prioritize future survey effort, and examine changes in habitat abundance and quality over time.

The use of GIS and multi-criteria evaluation (MCE) to identify agricultural land management practices which cause surface water pollution in drinking water supply catchments.

PubMed

Grayson, Richard; Kay, Paul; Foulger, Miles

2008-01-01

Diffuse pollution poses a threat to water quality and results in the need for treatment for potable water supplies which can prove costly. Within the Yorkshire region, UK, nitrates, pesticides and water colour present particular treatment problems. Catchment management techniques offer an alternative to 'end of pipe' solutions and allow resources to be targeted to the most polluting areas. This project has attempted to identify such areas using GIS based modelling approaches in catchments where water quality data were available. As no model exists to predict water colour a model was created using an MCE method which is capable of predicting colour concentrations at the catchment scale. CatchIS was used to predict pesticide and nitrate N concentrations and was found to be generally capable of reliably predicting nitrate N loads at the catchment scale. The pesticides results did not match the historic data possibly due to problems with the historic pesticide data and temporal and spatially variability in pesticide usage. The use of these models can be extended to predict water quality problems in catchments where water quality data are unavailable and highlight areas of concern. IWA Publishing 2008.

A geographic information system to predict soil erosion potential in rural transportation construction project

DOT National Transportation Integrated Search

1995-06-30

Topographic surface modeling using a Geographic Information System (GIS) can be useful for the prediction of soil erosion resulting from highway construction projects. The assumption is that terrain, along with other parameters, will influence the po...

Globally-Applicable Predictive Wildfire Model   a Temporal-Spatial GIS Based Risk Analysis Using Data Driven Fuzzy Logic Functions

NASA Astrophysics Data System (ADS)

van den Dool, G.

2017-11-01

This study (van den Dool, 2017) is a proof of concept for a global predictive wildfire model, in which the temporal-spatial characteristics of wildfires are placed in a Geographical Information System (GIS), and the risk analysis is based on data-driven fuzzy logic functions. The data sources used in this model are available as global datasets, but subdivided into three pilot areas: North America (California/Nevada), Europe (Spain), and Asia (Mongolia), and are downscaled to the highest resolution (3-arc second). The GIS is constructed around three themes: topography, fuel availability and climate. From the topographical data, six derived sub-themes are created and converted to a fuzzy membership based on the catchment area statistics. The fuel availability score is a composite of four data layers: land cover, wood loads, biomass, biovolumes. As input for the climatological sub-model reanalysed daily averaged, weather-related data is used, which is accumulated to a global weekly time-window (to account for the uncertainty within the climatological model) and forms the temporal component of the model. The final product is a wildfire risk score (from 0 to 1) by week, representing the average wildfire risk in an area. To compute the potential wildfire risk the sub-models are combined usinga Multi-Criteria Approach, and the model results are validated against the area under the Receiver Operating Characteristic curve.

Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties

PubMed Central

Xiaopeng, QI; Liang, WEI; BARKER, Laurie; LEKIACHVILI, Akaki; Xingyou, ZHANG

2015-01-01

Temperature changes are known to have significant impacts on human health. Accurate estimates of population-weighted average monthly air temperature for US counties are needed to evaluate temperature’s association with health behaviours and disease, which are sampled or reported at the county level and measured on a monthly—or 30-day—basis. Most reported temperature estimates were calculated using ArcGIS, relatively few used SAS. We compared the performance of geostatistical models to estimate population-weighted average temperature in each month for counties in 48 states using ArcGIS v9.3 and SAS v 9.2 on a CITGO platform. Monthly average temperature for Jan-Dec 2007 and elevation from 5435 weather stations were used to estimate the temperature at county population centroids. County estimates were produced with elevation as a covariate. Performance of models was assessed by comparing adjusted R2, mean squared error, root mean squared error, and processing time. Prediction accuracy for split validation was above 90% for 11 months in ArcGIS and all 12 months in SAS. Cokriging in SAS achieved higher prediction accuracy and lower estimation bias as compared to cokriging in ArcGIS. County-level estimates produced by both packages were positively correlated (adjusted R2 range=0.95 to 0.99); accuracy and precision improved with elevation as a covariate. Both methods from ArcGIS and SAS are reliable for U.S. county-level temperature estimates; However, ArcGIS’s merits in spatial data pre-processing and processing time may be important considerations for software selection, especially for multi-year or multi-state projects. PMID:26167169

A genetic network that suppresses genome rearrangements in Saccharomyces cerevisiae and contains defects in cancers

PubMed Central

Putnam, Christopher D.; Srivatsan, Anjana; Nene, Rahul V.; Martinez, Sandra L.; Clotfelter, Sarah P.; Bell, Sara N.; Somach, Steven B.; E.S. de Souza, Jorge; Fonseca, André F.; de Souza, Sandro J.; Kolodner, Richard D.

2016-01-01

Gross chromosomal rearrangements (GCRs) play an important role in human diseases, including cancer. The identity of all Genome Instability Suppressing (GIS) genes is not currently known. Here multiple Saccharomyces cerevisiae GCR assays and query mutations were crossed into arrays of mutants to identify progeny with increased GCR rates. One hundred eighty two GIS genes were identified that suppressed GCR formation. Another 438 cooperatively acting GIS genes were identified that were not GIS genes, but suppressed the increased genome instability caused by individual query mutations. Analysis of TCGA data using the human genes predicted to act in GIS pathways revealed that a minimum of 93% of ovarian and 66% of colorectal cancer cases had defects affecting one or more predicted GIS gene. These defects included loss-of-function mutations, copy-number changes associated with reduced expression, and silencing. In contrast, acute myeloid leukaemia cases did not appear to have defects affecting the predicted GIS genes. PMID:27071721

Comparing GIS-based habitat models for applications in EIA and SEA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gontier, Mikael, E-mail: gontier@kth.s; Moertberg, Ulla, E-mail: mortberg@kth.s; Balfors, Berit, E-mail: balfors@kth.s

Land use changes, urbanisation and infrastructure developments in particular, cause fragmentation of natural habitats and threaten biodiversity. Tools and measures must be adapted to assess and remedy the potential effects on biodiversity caused by human activities and developments. Within physical planning, environmental impact assessment (EIA) and strategic environmental assessment (SEA) play important roles in the prediction and assessment of biodiversity-related impacts from planned developments. However, adapted prediction tools to forecast and quantify potential impacts on biodiversity components are lacking. This study tested and compared four different GIS-based habitat models and assessed their relevance for applications in environmental assessment. The modelsmore » were implemented in the Stockholm region in central Sweden and applied to data on the crested tit (Parus cristatus), a sedentary bird species of coniferous forest. All four models performed well and allowed the distribution of suitable habitats for the crested tit in the Stockholm region to be predicted. The models were also used to predict and quantify habitat loss for two regional development scenarios. The study highlighted the importance of model selection in impact prediction. Criteria that are relevant for the choice of model for predicting impacts on biodiversity were identified and discussed. Finally, the importance of environmental assessment for the preservation of biodiversity within the general frame of biodiversity conservation is emphasised.« less

Determining Historical Pesticide Deposition on Cape Cod through Sediment Core Analysis:A Validation of GIS as An Exposure Assessment Tool

NASA Astrophysics Data System (ADS)

Feingold, B. J.; Benoit, G.; Rudel, R.

2006-12-01

Geographic Information Systems (GIS) has emerged as a powerful tool to assess current and historical exposure to environmental pollutants. GIS aids in the visualization and understanding of associations between exposure to contaminants and disease. This study is an example of the bridge between environmental science and public health and of how new technology such as GIS can be incorporated into these fields to strengthen both the research and the communication of scientific results. It attempts to validate a GIS-based aerial drift model which predicts the residential exposure to and boundaries of historical organochlorine pesticide (OCP) drift from applications on cranberry bogs, tree pest sprayings and others by analytically quantifying the historical pesticide deposition in a transect of lakes radiating from a distinct spray source. This model was previously used to assess historical residential exposure to OCPs in an environmental epidemiological case-control study of breast cancer incidence on Cape Cod, MA, where the incidence rate of the disease is significantly higher than in the rest of the state. The model's validation in this current study is essential to establishing its predictive ability and thus, its further use. Ground truthing of the model was done through the collection and analysis of sediment cores along a transect of five hydrologically independent kettle ponds radiating from a distinct OCP tree-pest spray area. Measurements of OCP concentrations, total carbon and total organic carbon were determined, and dating of the sediments was completed using 210Pb and verified using 137Cs. Each 50-cm core was sliced into 25 2- cm sections for the analyses, creating a fine-scale depositional history in each pond. Information gathered from each core allows for the determination of the extent and degree of dissipation of individual spray events of a known source area and determine how well the model fits the actual data.

Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment

PubMed Central

Hashim, Mazlan

2015-01-01

This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, and distance to road were extracted from SAR data, SPOT 5 and WorldView-1 images. The relationships between the detected landslide locations and these ten related factors were identified by using GIS-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models. The landslide inventory map which has a total of 92 landslide locations was created based on numerous resources such as digital aerial photographs, AIRSAR data, WorldView-1 images, and field surveys. Then, 80% of the landslide inventory was used for training the statistical models and the remaining 20% was used for validation purpose. The validation results using the Relative landslide density index (R-index) and Receiver operating characteristic (ROC) demonstrated that the SMCE model (accuracy is 96%) is better in prediction than AHP (accuracy is 91%) and WLC (accuracy is 89%) models. These landslide susceptibility maps would be useful for hazard mitigation purpose and regional planning. PMID:25898919

Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment.

PubMed

Shahabi, Himan; Hashim, Mazlan

2015-04-22

This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, and distance to road were extracted from SAR data, SPOT 5 and WorldView-1 images. The relationships between the detected landslide locations and these ten related factors were identified by using GIS-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models. The landslide inventory map which has a total of 92 landslide locations was created based on numerous resources such as digital aerial photographs, AIRSAR data, WorldView-1 images, and field surveys. Then, 80% of the landslide inventory was used for training the statistical models and the remaining 20% was used for validation purpose. The validation results using the Relative landslide density index (R-index) and Receiver operating characteristic (ROC) demonstrated that the SMCE model (accuracy is 96%) is better in prediction than AHP (accuracy is 91%) and WLC (accuracy is 89%) models. These landslide susceptibility maps would be useful for hazard mitigation purpose and regional planning.

The impact of geographic information systems on emergency management decision making at the U.S. Department of Homeland Security

NASA Astrophysics Data System (ADS)

King, Steven Gray

Geographic information systems (GIS) reveal relationships and patterns from large quantities of diverse data in the form of maps and reports. The United States spends billions of dollars to use GIS to improve decisions made during responses to natural disasters and terrorist attacks, but precisely how GIS improves or impairs decision making is not known. This research examined how GIS affect decision making during natural disasters, and how GIS can be more effectively used to improve decision making for emergency management. Using a qualitative case study methodology, this research examined decision making at the U.S. Department of Homeland Security (DHS) during a large full-scale disaster exercise. This study indicates that GIS provided decision makers at DHS with an outstanding context for information that would otherwise be challenging to understand, especially through the integration of multiple data sources and dynamic three-dimensional interactive maps. Decision making was hampered by outdated information, a reliance on predictive models based on hypothetical data rather than actual event data, and a lack of understanding of the capabilities of GIS beyond cartography. Geospatial analysts, emergency managers, and other decision makers who use GIS should take specific steps to improve decision making based on GIS for disaster response and emergency management.

Spatio-temporal modeling of chronic PM 10 exposure for the Nurses' Health Study

NASA Astrophysics Data System (ADS)

Yanosky, Jeff D.; Paciorek, Christopher J.; Schwartz, Joel; Laden, Francine; Puett, Robin; Suh, Helen H.

2008-06-01

Chronic epidemiological studies of airborne particulate matter (PM) have typically characterized the chronic PM exposures of their study populations using city- or county-wide ambient concentrations, which limit the studies to areas where nearby monitoring data are available and which ignore within-city spatial gradients in ambient PM concentrations. To provide more spatially refined and precise chronic exposure measures, we used a Geographic Information System (GIS)-based spatial smoothing model to predict monthly outdoor PM10 concentrations in the northeastern and midwestern United States. This model included monthly smooth spatial terms and smooth regression terms of GIS-derived and meteorological predictors. Using cross-validation and other pre-specified selection criteria, terms for distance to road by road class, urban land use, block group and county population density, point- and area-source PM10 emissions, elevation, wind speed, and precipitation were found to be important determinants of PM10 concentrations and were included in the final model. Final model performance was strong (cross-validation R2=0.62), with little bias (-0.4 μg m-3) and high precision (6.4 μg m-3). The final model (with monthly spatial terms) performed better than a model with seasonal spatial terms (cross-validation R2=0.54). The addition of GIS-derived and meteorological predictors improved predictive performance over spatial smoothing (cross-validation R2=0.51) or inverse distance weighted interpolation (cross-validation R2=0.29) methods alone and increased the spatial resolution of predictions. The model performed well in both rural and urban areas, across seasons, and across the entire time period. The strong model performance demonstrates its suitability as a means to estimate individual-specific chronic PM10 exposures for large populations.

Development of efficient and cost-effective distributed hydrological modeling tool MWEasyDHM based on open-source MapWindow GIS

NASA Astrophysics Data System (ADS)

Lei, Xiaohui; Wang, Yuhui; Liao, Weihong; Jiang, Yunzhong; Tian, Yu; Wang, Hao

2011-09-01

Many regions are still threatened with frequent floods and water resource shortage problems in China. Consequently, the task of reproducing and predicting the hydrological process in watersheds is hard and unavoidable for reducing the risks of damage and loss. Thus, it is necessary to develop an efficient and cost-effective hydrological tool in China as many areas should be modeled. Currently, developed hydrological tools such as Mike SHE and ArcSWAT (soil and water assessment tool based on ArcGIS) show significant power in improving the precision of hydrological modeling in China by considering spatial variability both in land cover and in soil type. However, adopting developed commercial tools in such a large developing country comes at a high cost. Commercial modeling tools usually contain large numbers of formulas, complicated data formats, and many preprocessing or postprocessing steps that may make it difficult for the user to carry out simulation, thus lowering the efficiency of the modeling process. Besides, commercial hydrological models usually cannot be modified or improved to be suitable for some special hydrological conditions in China. Some other hydrological models are open source, but integrated into commercial GIS systems. Therefore, by integrating hydrological simulation code EasyDHM, a hydrological simulation tool named MWEasyDHM was developed based on open-source MapWindow GIS, the purpose of which is to establish the first open-source GIS-based distributed hydrological model tool in China by integrating modules of preprocessing, model computation, parameter estimation, result display, and analysis. MWEasyDHM provides users with a friendly manipulating MapWindow GIS interface, selectable multifunctional hydrological processing modules, and, more importantly, an efficient and cost-effective hydrological simulation tool. The general construction of MWEasyDHM consists of four major parts: (1) a general GIS module for hydrological analysis, (2) a preprocessing module for modeling inputs, (3) a model calibration module, and (4) a postprocessing module. The general GIS module for hydrological analysis is developed on the basis of totally open-source GIS software, MapWindow, which contains basic GIS functions. The preprocessing module is made up of three submodules including a DEM-based submodule for hydrological analysis, a submodule for default parameter calculation, and a submodule for the spatial interpolation of meteorological data. The calibration module contains parallel computation, real-time computation, and visualization. The postprocessing module includes model calibration and model results spatial visualization using tabular form and spatial grids. MWEasyDHM makes it possible for efficient modeling and calibration of EasyDHM, and promises further development of cost-effective applications in various watersheds.

Mapping and monitoring Mt. Graham Red Squirrel habitat with GIS and thematic mapper imagery

USGS Publications Warehouse

Hatten, James R.; Koprowski, John L.; Sanderson, H. Reed; Koprowski, John L.

2009-01-01

To estimate the Mt. Graham red squirrel (MGRS) population, personnel visit a proportion of middens each year to determine their occupancy (Snow in this vol.). The method results in very tight confidence intervals (high precision), but the accuracy of the population estimate is dependent upon knowing where all the middens are located. I hypothesized that there might be areas outside the survey boundary that contained Mt. Graham red squirrel middens, but the ruggedness of the Pinaleno Mountains made mountain-wide surveys difficult. Therefore, I started exploring development of a spatially explicit (geographic information system [GIS]-based) habitat model in 1998 that could identify MGRS habitat remotely with satellite imagery and a GIS. A GIS-based model would also allow us to assess changes in MGRS habitat between two time periods because Landsat passes over the same location every 16 days, imaging the earth in 185 km swaths (Aronoff 1989). Specifically, the objectives of this analysis were to (1) develop a pattern recognition model for MGRS habitat, (2) map potential (predicted/modeled) MGRS habitat, (3) identify changes in potential MGRS habitat between 1993 and 2003, and (4) evaluate the current location of the MGRS survey boundary.

A GIS model predicting potential distributions of a lineage: a test case on hermit spiders (Nephilidae: Nephilengys).

PubMed

Năpăruş, Magdalena; Kuntner, Matjaž

2012-01-01

Although numerous studies model species distributions, these models are almost exclusively on single species, while studies of evolutionary lineages are preferred as they by definition study closely related species with shared history and ecology. Hermit spiders, genus Nephilengys, represent an ecologically important but relatively species-poor lineage with a globally allopatric distribution. Here, we model Nephilengys global habitat suitability based on known localities and four ecological parameters. We geo-referenced 751 localities for the four most studied Nephilengys species: N. cruentata (Africa, New World), N. livida (Madagascar), N. malabarensis (S-SE Asia), and N. papuana (Australasia). For each locality we overlaid four ecological parameters: elevation, annual mean temperature, annual mean precipitation, and land cover. We used linear backward regression within ArcGIS to select two best fit parameters per species model, and ModelBuilder to map areas of high, moderate and low habitat suitability for each species within its directional distribution. For Nephilengys cruentata suitable habitats are mid elevation tropics within Africa (natural range), a large part of Brazil and the Guianas (area of synanthropic spread), and even North Africa, Mediterranean, and Arabia. Nephilengys livida is confined to its known range with suitable habitats being mid-elevation natural and cultivated lands. Nephilengys malabarensis, however, ranges across the Equator throughout Asia where the model predicts many areas of high ecological suitability in the wet tropics. Its directional distribution suggests the species may potentially spread eastwards to New Guinea where the suitable areas of N. malabarensis largely surpass those of the native N. papuana, a species that prefers dry forests of Australian (sub)tropics. Our model is a customizable GIS tool intended to predict current and future potential distributions of globally distributed terrestrial lineages. Its predictive potential may be tested in foreseeing species distribution shifts due to habitat destruction and global climate change.

A GIS Model Predicting Potential Distributions of a Lineage: A Test Case on Hermit Spiders (Nephilidae: Nephilengys)

PubMed Central

Năpăruş, Magdalena; Kuntner, Matjaž

2012-01-01

Background Although numerous studies model species distributions, these models are almost exclusively on single species, while studies of evolutionary lineages are preferred as they by definition study closely related species with shared history and ecology. Hermit spiders, genus Nephilengys, represent an ecologically important but relatively species-poor lineage with a globally allopatric distribution. Here, we model Nephilengys global habitat suitability based on known localities and four ecological parameters. Methodology/Principal Findings We geo-referenced 751 localities for the four most studied Nephilengys species: N. cruentata (Africa, New World), N. livida (Madagascar), N. malabarensis (S-SE Asia), and N. papuana (Australasia). For each locality we overlaid four ecological parameters: elevation, annual mean temperature, annual mean precipitation, and land cover. We used linear backward regression within ArcGIS to select two best fit parameters per species model, and ModelBuilder to map areas of high, moderate and low habitat suitability for each species within its directional distribution. For Nephilengys cruentata suitable habitats are mid elevation tropics within Africa (natural range), a large part of Brazil and the Guianas (area of synanthropic spread), and even North Africa, Mediterranean, and Arabia. Nephilengys livida is confined to its known range with suitable habitats being mid-elevation natural and cultivated lands. Nephilengys malabarensis, however, ranges across the Equator throughout Asia where the model predicts many areas of high ecological suitability in the wet tropics. Its directional distribution suggests the species may potentially spread eastwards to New Guinea where the suitable areas of N. malabarensis largely surpass those of the native N. papuana, a species that prefers dry forests of Australian (sub)tropics. Conclusions Our model is a customizable GIS tool intended to predict current and future potential distributions of globally distributed terrestrial lineages. Its predictive potential may be tested in foreseeing species distribution shifts due to habitat destruction and global climate change. PMID:22238692

Replacement of SSE (Release 6) with NASA's Prediction of Worldwide Energy Resource (POWER) Project GIS-enabled Web Data Portal:

Atmospheric Science Data Center

2018-03-15

... effort has been developed under the Prediction Of Worldwide Energy Resource (POWER) Project funded largely by NASA Earth Applied Sciences ... to NASA's satellite and modeling analysis for Renewable Energy, Sustainable Buildings and Agroclimatology applications.  A new POWER ...

Exploratory modeling of forest disturbance scenarios in central Oregon using computational experiments in GIS

Treesearch

Deana D. Pennington

2007-01-01

Exploratory modeling is an approach used when process and/or parameter uncertainties are such that modeling attempts at realistic prediction are not appropriate. Exploratory modeling makes use of computational experimentation to test how varying model scenarios drive model outcome. The goal of exploratory modeling is to better understand the system of interest through...

Sustainable Army Training Lands/Carrying Capacity: Training Use Distribution Model (TUDM)

DTIC Science & Technology

2002-05-01

management (Senft, Rittenhouse, and Woodmansee 1983; Van Manen and Pelton 1997). The underlying principle behind this type of modeling application...Texas Rangeland. Technical Manuscript EN-95/02 (USACERL February 1995). tr o Van Manen , F.T. and M.R. Pelton. 1997. “A GIS Model to Predict Black

Estimating Prediction Uncertainty from Geographical Information System Raster Processing: A User's Manual for the Raster Error Propagation Tool (REPTool)

USGS Publications Warehouse

Gurdak, Jason J.; Qi, Sharon L.; Geisler, Michael L.

2009-01-01

The U.S. Geological Survey Raster Error Propagation Tool (REPTool) is a custom tool for use with the Environmental System Research Institute (ESRI) ArcGIS Desktop application to estimate error propagation and prediction uncertainty in raster processing operations and geospatial modeling. REPTool is designed to introduce concepts of error and uncertainty in geospatial data and modeling and provide users of ArcGIS Desktop a geoprocessing tool and methodology to consider how error affects geospatial model output. Similar to other geoprocessing tools available in ArcGIS Desktop, REPTool can be run from a dialog window, from the ArcMap command line, or from a Python script. REPTool consists of public-domain, Python-based packages that implement Latin Hypercube Sampling within a probabilistic framework to track error propagation in geospatial models and quantitatively estimate the uncertainty of the model output. Users may specify error for each input raster or model coefficient represented in the geospatial model. The error for the input rasters may be specified as either spatially invariant or spatially variable across the spatial domain. Users may specify model output as a distribution of uncertainty for each raster cell. REPTool uses the Relative Variance Contribution method to quantify the relative error contribution from the two primary components in the geospatial model - errors in the model input data and coefficients of the model variables. REPTool is appropriate for many types of geospatial processing operations, modeling applications, and related research questions, including applications that consider spatially invariant or spatially variable error in geospatial data.

GIS, geostatistics, metadata banking, and tree-based models for data analysis and mapping in environmental monitoring and epidemiology.

PubMed

Schröder, Winfried

2006-05-01

By the example of environmental monitoring, some applications of geographic information systems (GIS), geostatistics, metadata banking, and Classification and Regression Trees (CART) are presented. These tools are recommended for mapping statistically estimated hot spots of vectors and pathogens. GIS were introduced as tools for spatially modelling the real world. The modelling can be done by mapping objects according to the spatial information content of data. Additionally, this can be supported by geostatistical and multivariate statistical modelling. This is demonstrated by the example of modelling marine habitats of benthic communities and of terrestrial ecoregions. Such ecoregionalisations may be used to predict phenomena based on the statistical relation between measurements of an interesting phenomenon such as, e.g., the incidence of medically relevant species and correlated characteristics of the ecoregions. The combination of meteorological data and data on plant phenology can enhance the spatial resolution of the information on climate change. To this end, meteorological and phenological data have to be correlated. To enable this, both data sets which are from disparate monitoring networks have to be spatially connected by means of geostatistical estimation. This is demonstrated by the example of transformation of site-specific data on plant phenology into surface data. The analysis allows for spatial comparison of the phenology during the two periods 1961-1990 and 1991-2002 covering whole Germany. The changes in both plant phenology and air temperature were proved to be statistically significant. Thus, they can be combined by GIS overlay technique to enhance the spatial resolution of the information on the climate change and use them for the prediction of vector incidences at the regional scale. The localisation of such risk hot spots can be done by geometrically merging surface data on promoting factors. This is demonstrated by the example of the transfer of heavy metals through soils. The predicted hot spots of heavy metal transfer can be validated empirically by measurement data which can be inquired by a metadata base linked with a geographic information system. A corresponding strategy for the detection of vector hot spots in medical epidemiology is recommended. Data on incidences and habitats of the Anophelinae in the marsh regions of Lower Saxony (Germany) were used to calculate a habitat model by CART, which together with climate data and data on ecoregions can be further used for the prediction of habitats of medically relevant vector species. In the future, this approach should be supported by an internet-based information system consisting of three components: metadata questionnaire, metadata base, and GIS to link metadata, surface data, and measurement data on incidences and habitats of medically relevant species and related data on climate, phenology, and ecoregional characteristic conditions.

Spatial predictive mapping using artificial neural networks

NASA Astrophysics Data System (ADS)

Noack, S.; Knobloch, A.; Etzold, S. H.; Barth, A.; Kallmeier, E.

2014-11-01

The modelling or prediction of complex geospatial phenomena (like formation of geo-hazards) is one of the most important tasks for geoscientists. But in practice it faces various difficulties, caused mainly by the complexity of relationships between the phenomena itself and the controlling parameters, as well by limitations of our knowledge about the nature of physical/ mathematical relationships and by restrictions regarding accuracy and availability of data. In this situation methods of artificial intelligence, like artificial neural networks (ANN) offer a meaningful alternative modelling approach compared to the exact mathematical modelling. In the past, the application of ANN technologies in geosciences was primarily limited due to difficulties to integrate it into geo-data processing algorithms. In consideration of this background, the software advangeo® was developed to provide a normal GIS user with a powerful tool to use ANNs for prediction mapping and data preparation within his standard ESRI ArcGIS environment. In many case studies, such as land use planning, geo-hazards analysis and prevention, mineral potential mapping, agriculture & forestry advangeo® has shown its capabilities and strengths. The approach is able to add considerable value to existing data.

Use of a GIS-based hybrid artificial neural network to prioritize the order of pipe replacement in a water distribution network.

PubMed

Ho, Cheng-I; Lin, Min-Der; Lo, Shang-Lien

2010-07-01

A methodology based on the integration of a seismic-based artificial neural network (ANN) model and a geographic information system (GIS) to assess water leakage and to prioritize pipeline replacement is developed in this work. Qualified pipeline break-event data derived from the Taiwan Water Corporation Pipeline Leakage Repair Management System were analyzed. "Pipe diameter," "pipe material," and "the number of magnitude-3( + ) earthquakes" were employed as the input factors of ANN, while "the number of monthly breaks" was used for the prediction output. This study is the first attempt to manipulate earthquake data in the break-event ANN prediction model. Spatial distribution of the pipeline break-event data was analyzed and visualized by GIS. Through this, the users can swiftly figure out the hotspots of the leakage areas. A northeastern township in Taiwan, frequently affected by earthquakes, is chosen as the case study. Compared to the traditional processes for determining the priorities of pipeline replacement, the methodology developed is more effective and efficient. Likewise, the methodology can overcome the difficulty of prioritizing pipeline replacement even in situations where the break-event records are unavailable.

Road landslide information management and forecasting system base on GIS.

PubMed

Wang, Wei Dong; Du, Xiang Gang; Xie, Cui Ming

2009-09-01

Take account of the characters of road geological hazard and its supervision, it is very important to develop the Road Landslides Information Management and Forecasting System based on Geographic Information System (GIS). The paper presents the system objective, function, component modules and key techniques in the procedure of system development. The system, based on the spatial information and attribute information of road geological hazard, was developed and applied in Guizhou, a province of China where there are numerous and typical landslides. The manager of communication, using the system, can visually inquire all road landslides information based on regional road network or on the monitoring network of individual landslide. Furthermore, the system, integrated with mathematical prediction models and the GIS's strongpoint on spatial analyzing, can assess and predict landslide developing procedure according to the field monitoring data. Thus, it can efficiently assists the road construction or management units in making decision to control the landslides and to reduce human vulnerability.

Advanced GIS Exercise: Predicting Rainfall Erosivity Index Using Regression Analysis

ERIC Educational Resources Information Center

Post, Christopher J.; Goddard, Megan A.; Mikhailova, Elena A.; Hall, Steven T.

2006-01-01

Graduate students from a variety of agricultural and natural resource fields are incorporating geographic information systems (GIS) analysis into their graduate research, creating a need for teaching methodologies that help students understand advanced GIS topics for use in their own research. Graduate-level GIS exercises help students understand…

The influence of hazard models on GIS-based regional risk assessments and mitigation policies

USGS Publications Warehouse

Bernknopf, R.L.; Rabinovici, S.J.M.; Wood, N.J.; Dinitz, L.B.

2006-01-01

Geographic information systems (GIS) are important tools for understanding and communicating the spatial distribution of risks associated with natural hazards in regional economies. We present a GIS-based decision support system (DSS) for assessing community vulnerability to natural hazards and evaluating potential mitigation policy outcomes. The Land Use Portfolio Modeler (LUPM) integrates earth science and socioeconomic information to predict the economic impacts of loss-reduction strategies. However, the potential use of such systems in decision making may be limited when multiple but conflicting interpretations of the hazard are available. To explore this problem, we conduct a policy comparison using the LUPM to test the sensitivity of three available assessments of earthquake-induced lateral-spread ground failure susceptibility in a coastal California community. We find that the uncertainty regarding the interpretation of the science inputs can influence the development and implementation of natural hazard management policies. Copyright ?? 2006 Inderscience Enterprises Ltd.

Use of USLE/GIS methodology for predicting soil loss in a semiarid agricultural watershed.

PubMed

Erdogan, Emrah H; Erpul, Günay; Bayramin, Ilhami

2007-08-01

The Universal Soil Loss Equation (USLE) is an erosion model to estimate average soil loss that would generally result from splash, sheet, and rill erosion from agricultural plots. Recently, use of USLE has been extended as a useful tool predicting soil losses and planning control practices in agricultural watersheds by the effective integration of the GIS-based procedures to estimate the factor values in a grid cell basis. This study was performed in the Kazan Watershed located in the central Anatolia, Turkey, to predict soil erosion risk by the USLE/GIS methodology for planning conservation measures in the site. Rain erosivity (R), soil erodibility (K), and cover management factor (C) values of the model were calculated from erosivity map, soil map, and land use map of Turkey, respectively. R values were site-specifically corrected using DEM and climatic data. The topographical and hydrological effects on the soil loss were characterized by LS factor evaluated by the flow accumulation tool using DEM and watershed delineation techniques. From resulting soil loss map of the watershed, the magnitude of the soil erosion was estimated in terms of the different soil units and land uses and the most erosion-prone areas where irreversible soil losses occurred were reasonably located in the Kazan watershed. This could be very useful for deciding restoration practices to control the soil erosion of the sites to be severely influenced.

Comparison of Drainmod Based Watershed Scale Models

Treesearch

Glenn P. Fernandez; George M. Chescheir; R. Wayne Skaggs; Devendra M. Amatya

2004-01-01

Watershed scale hydrology and water quality models (DRAINMOD-DUFLOW, DRAINMOD-W, DRAINMOD-GIS and WATGIS) that describe the nitrogen loadings at the outlet of poorly drained watersheds were examined with respect to their accuracy and uncertainty in model predictions. Latin Hypercube Sampling (LHS) was applied to determine the impact of uncertainty in estimating field...

GIS-based analysis and modelling with empirical and remotely-sensed data on coastline advance and retreat

NASA Astrophysics Data System (ADS)

Ahmad, Sajid Rashid

With the understanding that far more research remains to be done on the development and use of innovative and functional geospatial techniques and procedures to investigate coastline changes this thesis focussed on the integration of remote sensing, geographical information systems (GIS) and modelling techniques to provide meaningful insights on the spatial and temporal dynamics of coastline changes. One of the unique strengths of this research was the parameterization of the GIS with long-term empirical and remote sensing data. Annual empirical data from 1941--2007 were analyzed by the GIS, and then modelled with statistical techniques. Data were also extracted from Landsat TM and ETM+ images. The band ratio method was used to extract the coastlines. Topographic maps were also used to extract digital map data. All data incorporated into ArcGIS 9.2 were analyzed with various modules, including Spatial Analyst, 3D Analyst, and Triangulated Irregular Networks. The Digital Shoreline Analysis System was used to analyze and predict rates of coastline change. GIS results showed the spatial locations along the coast that will either advance or retreat over time. The linear regression results highlighted temporal changes which are likely to occur along the coastline. Box-Jenkins modelling procedures were utilized to determine statistical models which best described the time series (1941--2007) of coastline change data. After several iterations and goodness-of-fit tests, second-order spatial cyclic autoregressive models, first-order autoregressive models and autoregressive moving average models were identified as being appropriate for describing the deterministic and random processes operating in Guyana's coastal system. The models highlighted not only cyclical patterns in advance and retreat of the coastline, but also the existence of short and long-term memory processes. Long-term memory processes could be associated with mudshoal propagation and stabilization while short-term memory processes were indicative of transitory hydrodynamic and other processes. An innovative framework for a spatio-temporal information-based system (STIBS) was developed. STIBS incorporated diverse datasets within a GIS, dynamic computer-based simulation models, and a spatial information query and graphical subsystem. Tests of the STIBS proved that it could be used to simulate and visualize temporal variability in shifting morphological states of the coastline.

Multimodeling Framework for Predicting Water Quality in Fragmented Agriculture-Forest Ecosystems

NASA Astrophysics Data System (ADS)

Rose, J. B.; Guber, A.; Porter, W. F.; Williams, D.; Tamrakar, S.; Dechen Quinn, A.

2012-12-01

Both livestock and wildlife are major contributors of nonpoint pollution of surface water bodies. The interactions among them can substantially increase the chance of contamination especially in fragmented agriculture-forest landscapes, where wildlife (e.g. white tailed deer) can transmit diseases between remote farms. Unfortunately, models currently available for predicting fate and transport of microorganisms in these ecosystems do not account for such interactions. The objectives of this study are to develop and test a multimodeling framework that assesses the risk of microbial contamination of surface water caused by wildlife-livestock interactions in fragmented agriculture-forest ecosystems. The framework consists of a modified Soil Water Assessment Tool (SWAT), KINematic Runoff and EROSion model (KINEROS2) with the add-on module STWIR (Microorganism Transport with Infiltration and Runoff), RAMAS GIS, SIR compartmental model and Quantitative Microbial Risk Assessment model (QMRA). The watershed-scale model SWAT simulates plant biomass growth, wash-off of microorganisms from foliage and soil, overland and in-stream microbial transport, microbial growth, and die-off in foliage and soil. RAMAS GIS model predicts the most probable habitat and subsequent population of white-tailed deer based on land use and crop biomass. KINEROS-STWIR simulates overland transport of microorganisms released from soil, surface applied manure, and fecal deposits during runoff events at high temporal and special resolutions. KINEROS-STWIR and RAMAS GIS provide input for an SIR compartmental model which simulates disease transmission within and between deer groups. This information is used in SWAT model to account for transmission and deposition of pathogens by white tailed deer in stream water, foliage and soil. The QMRA approach extends to microorganisms inactivated in forage and water consumed by deer. Probabilities of deer infections and numbers of infected animals are computed based on a dose-response approach, including Beta Poisson and Maximum Risk models, which take into account pathogen variation in infectivity. An example of the Multimodeling framework performance for a fragmented agriculture-forest ecosystem will be shown in the presentation.

Study of Earthquake Disaster Prediction System of Langfang city Based on GIS

NASA Astrophysics Data System (ADS)

Huang, Meng; Zhang, Dian; Li, Pan; Zhang, YunHui; Zhang, RuoFei

2017-07-01

In this paper, according to the status of China’s need to improve the ability of earthquake disaster prevention, this paper puts forward the implementation plan of earthquake disaster prediction system of Langfang city based on GIS. Based on the GIS spatial database, coordinate transformation technology, GIS spatial analysis technology and PHP development technology, the seismic damage factor algorithm is used to predict the damage of the city under different intensity earthquake disaster conditions. The earthquake disaster prediction system of Langfang city is based on the B / S system architecture. Degree and spatial distribution and two-dimensional visualization display, comprehensive query analysis and efficient auxiliary decision-making function to determine the weak earthquake in the city and rapid warning. The system has realized the transformation of the city’s earthquake disaster reduction work from static planning to dynamic management, and improved the city’s earthquake and disaster prevention capability.

Integrated permanent plot and aerial monitoring for the spruce budworm decision support system

Treesearch

David A. MacLean

2000-01-01

Spruce budworm (Choristoneura fumiferana Clem.) outbreaks cause severe mortality and growth loss of spruce and fir forest over ranch of eastern North America. The Spruce Budworm Decision Support System (DSS) links prediction and interpretation models to the ARC/1NFO GIS, under an ArcView graphical user interface. It helps forest managers predict...

Coupling groundwater and riparian vegetation models to assess effects of reservoir releases

USGS Publications Warehouse

Springer, Abraham E.; Wright, Julie M.; Shafroth, Patrick B.; Stromberg, Juliet C.; Patten, Duncan T.

1999-01-01

Although riparian areas in the arid southwestern United States are critical for maintaining species diversity, their extent and health have been declining since Euro‐American settlement. The purpose of this study was to develop a methodology to evaluate the potential for riparian vegetation restoration and groundwater recharge. A numerical groundwater flow model was coupled with a conceptual riparian vegetation model to predict hydrologic conditions favorable to maintaining riparian vegetation downstream of a reservoir. A Geographic Information System (GIS) was used for this one‐way coupling. Constant and seasonally varying releases from the dam were simulated using volumes anticipated to be permitted by a regional water supplier. Simulations indicated that seasonally variable releases would produce surface flow 5.4–8.5 km below the dam in a previously dry reach. Using depth to groundwater simulations from the numerical flow model with conceptual models of depths to water necessary for maintenance of riparian vegetation, the GIS analysis predicted a 5‐ to 6.5‐fold increase in the area capable of sustaining riparian vegetation.

Real-time micro-modelling of city evacuations

NASA Astrophysics Data System (ADS)

Löhner, Rainald; Haug, Eberhard; Zinggerling, Claudio; Oñate, Eugenio

2018-01-01

A methodology to integrate geographical information system (GIS) data with large-scale pedestrian simulations has been developed. Advances in automatic data acquisition and archiving from GIS databases, automatic input for pedestrian simulations, as well as scalable pedestrian simulation tools have made it possible to simulate pedestrians at the individual level for complete cities in real time. An example that simulates the evacuation of the city of Barcelona demonstrates that this is now possible. This is the first step towards a fully integrated crowd prediction and management tool that takes into account not only data gathered in real time from cameras, cell phones or other sensors, but also merges these with advanced simulation tools to predict the future state of the crowd.

Calculating meal glycemic index by using measured and published food values compared with directly measured meal glycemic index.

PubMed

Dodd, Hayley; Williams, Sheila; Brown, Rachel; Venn, Bernard

2011-10-01

Glycemic index (GI) testing is normally based on individual foods, whereas GIs for meals or diets are based on a formula using a weighted sum of the constituents. The accuracy with which the formula can predict a meal or diet GI is questionable. Our objective was to compare the GI of meals, obtained by using the formula and by using both measured food GI and published values, with directly measured meal GIs. The GIs of 7 foods were tested in 30 healthy people. The foods were combined into 3 meals, each of which provided 50 g available carbohydrate, including a staple (potato, rice, or spaghetti), vegetables, sauce, and pan-fried chicken. The mean (95% CI) meal GIs determined from individual food GI values and by direct measurement were as follows: potato meal [predicted, 63 (56, 70); measured, 53 (46, 62)], rice meal [predicted, 51 (45, 56); measured, 38 (33, 45)], and spaghetti meal [predicted, 54 (49, 60); measured, 38 (33, 44)]. The predicted meal GIs were all higher than the measured GIs (P < 0.001). The extent of the overestimation depended on the particular food, ie, 12, 15, and 19 GI units (or 22%, 40%, and 50%) for the potato, rice, and spaghetti meals, respectively. The formula overestimated the GI of the meals by between 22% and 50%. The use of published food values also overestimated the measured meal GIs. Investigators using the formula to calculate a meal or diet GI should be aware of limitations in the method. This trial is registered with the Australian and New Zealand Clinical Trials Registry as ACTRN12611000210976.

An information model for managing multi-dimensional gridded data in a GIS

NASA Astrophysics Data System (ADS)

Xu, H.; Abdul-Kadar, F.; Gao, P.

2016-04-01

Earth observation agencies like NASA and NOAA produce huge volumes of historical, near real-time, and forecasting data representing terrestrial, atmospheric, and oceanic phenomena. The data drives climatological and meteorological studies, and underpins operations ranging from weather pattern prediction and forest fire monitoring to global vegetation analysis. These gridded data sets are distributed mostly as files in HDF, GRIB, or netCDF format and quantify variables like precipitation, soil moisture, or sea surface temperature, along one or more dimensions like time and depth. Although the data cube is a well-studied model for storing and analyzing multi-dimensional data, the GIS community remains in need of a solution that simplifies interactions with the data, and elegantly fits with existing database schemas and dissemination protocols. This paper presents an information model that enables Geographic Information Systems (GIS) to efficiently catalog very large heterogeneous collections of geospatially-referenced multi-dimensional rasters—towards providing unified access to the resulting multivariate hypercubes. We show how the implementation of the model encapsulates format-specific variations and provides unified access to data along any dimension. We discuss how this framework lends itself to familiar GIS concepts like image mosaics, vector field visualization, layer animation, distributed data access via web services, and scientific computing. Global data sources like MODIS from USGS and HYCOM from NOAA illustrate how one would employ this framework for cataloging, querying, and intuitively visualizing such hypercubes. ArcGIS—an established platform for processing, analyzing, and visualizing geospatial data—serves to demonstrate how this integration brings the full power of GIS to the scientific community.

Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

PubMed Central

Weller, Daniel; Shiwakoti, Suvash; Bergholz, Peter; Grohn, Yrjo; Wiedmann, Martin

2015-01-01

Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n = 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce. PMID:26590280

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis.

PubMed

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis☆

PubMed Central

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-01-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster–Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty–sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights. PMID:25843987

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis

NASA Astrophysics Data System (ADS)

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

Integrating models to predict regional haze from wildland fire.

Treesearch

D. McKenzie; S.M. O' Neill; N. Larkin; R.A. Norheim

2006-01-01

Visibility impairment from regional haze is a significant problem throughout the continental United States. A substantial portion of regional haze is produced by smoke from prescribed and wildland fires. Here we describe the integration of four simulation models, an array of GIS raster layers, and a set of algorithms for fire-danger calculations into a modeling...

ABM and GIS-based multi-scenarios volcanic evacuation modelling of Merapi

NASA Astrophysics Data System (ADS)

Jumadi, Carver, Steve; Quincey, Duncan

2016-05-01

Conducting effective evacuation is one of the successful keys to deal with such crisis. Therefore, a plan that considers the probability of the spatial extent of the hazard occurrences is needed. Likewise, the evacuation plan in Merapi is already prepared before the eruption on 2010. However, the plan could not be performed because the eruption magnitude was bigger than it was predicted. In this condition, the extent of the hazardous area was increased larger than the prepared hazard model. Managing such unpredicted situation need adequate information that flexible and adaptable to the current situation. Therefore, we applied an Agent-based Model (ABM) and Geographic Information System (GIS) using multi-scenarios hazard model to support the evacuation management. The methodology and the case study in Merapi is provided.

Statistical modeling of landslide hazard using GIS

Treesearch

Peter V. Gorsevski; Randy B. Foltz; Paul E. Gessler; Terrance W. Cundy

2001-01-01

A model for spatial prediction of landslide hazard was applied to a watershed affected by landslide events that occurred during the winter of 1995-96, following heavy rains, and snowmelt. Digital elevation data with 22.86 m x 22.86 m resolution was used for deriving topographic attributes used for modeling. The model is based on the combination of logistic regression...

Environmental exposure modeling and monitoring of human pharmaceutical concentrations in the environment

USGS Publications Warehouse

Versteeg, D.J.; Alder, A. C.; Cunningham, V. L.; Kolpin, D.W.; Murray-Smith, R.; Ternes, T.

2005-01-01

Human pharmaceuticals are receiving increased attention as environmental contaminants. This is due to their biological activity and the number of monitoring programs focusing on analysis of these compounds in various environmental media and compartments. Risk assessments are needed to understand the implications of reported concentrations; a fundamental part of the risk assessment is an assessment of environmental exposures. The purpose of this chapter is to provide guidance on the use of predictive tools (e.g., models) and monitoring data in exposure assessments for pharmaceuticals in the environment. Methods to predict environmental concentrations from equations based on first principles are presented. These equations form the basis of existing GIS (geographic information systems)-based systems for understanding the spatial distribution of pharmaceuticals in the environment. The pharmaceutical assessment and transport (PhATE), georeferenced regional exposure assessment tool for European rivers (GREAT-ER), and geographical information system (GIS)-ROUT models are reviewed and recommendations are provided concerning the design and execution of monitoring studies. Model predictions and monitoring data are compared to evaluate the relative utility of each approach in environmental exposure assessments. In summary, both models and monitoring data can be used to define representative exposure concentrations of pharmaceuticals in the environment in support of environmental risk assessments.

Evaluating agricultural nonpoint-source pollution using integrated geographic information systems and hydrologic/water quality model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tim, U.S.; Jolly, R.

1994-01-01

Considerable progress has been made in developing physically based, distributed parameter, hydrologic/water quality (HIWQ) models for planning and control of nonpoint-source pollution. The widespread use of these models is often constrained by the excessive and time-consuming input data demands and the lack of computing efficiencies necessary for iterative simulation of alternative management strategies. Recent developments in geographic information systems (GIS) provide techniques for handling large amounts of spatial data for modeling nonpoint-source pollution problems. Because a GIS can be used to combine information from several sources to form an array of model input data and to examine any combinations ofmore » spatial input/output data, it represents a highly effective tool for HiWQ modeling. This paper describes the integration of a distributed-parameter model (AGNPS) with a GIS (ARC/INFO) to examine nonpoint sources of pollution in an agricultural watershed. The ARC/INFO GIS provided the tools to generate and spatially organize the disparate data to support modeling, while the AGNPS model was used to predict several water quality variables including soil erosion and sedimentation within a watershed. The integrated system was used to evaluate the effectiveness of several alternative management strategies in reducing sediment pollution in a 417-ha watershed located in southern Iowa. The implementation of vegetative filter strips and contour buffer (grass) strips resulted in a 41 and 47% reduction in sediment yield at the watershed outlet, respectively. In addition, when the integrated system was used, the combination of the above management strategies resulted in a 71% reduction in sediment yield. In general, the study demonstrated the utility of integrating a simulation model with GIS for nonpoini-source pollution control and planning. Such techniques can help characterize the diffuse sources of pollution at the landscape level. 52 refs., 6 figs., 1 tab.« less

The role of remote sensing and GIS for spatial prediction of vector-borne diseases transmission: a systematic review.

PubMed

Palaniyandi, M

2012-12-01

There have been several attempts made to the appreciation of remote sensing and GIS for the study of vectors, biodiversity, vector presence, vector abundance and the vector-borne diseases with respect to space and time. This study was made for reviewing and appraising the potential use of remote sensing and GIS applications for spatial prediction of vector-borne diseases transmission. The nature of the presence and the abundance of vectors and vector-borne diseases, disease infection and the disease transmission are not ubiquitous and are confined with geographical, environmental and climatic factors, and are localized. The presence of vectors and vector-borne diseases is most complex in nature, however, it is confined and fueled by the geographical, climatic and environmental factors including man-made factors. The usefulness of the present day availability of the information derived from the satellite data including vegetation indices of canopy cover and its density, soil types, soil moisture, soil texture, soil depth, etc. is integrating the information in the expert GIS engine for the spatial analysis of other geoclimatic and geoenvironmental variables. The present study gives the detailed information on the classical studies of the past and present, and the future role of remote sensing and GIS for the vector-borne diseases control. The ecological modeling directly gives us the relevant information to understand the spatial variation of the vector biodiversity, vector presence, vector abundance and the vector-borne diseases in association with geoclimatic and the environmental variables. The probability map of the geographical distribution and seasonal variations of horizontal and vertical distribution of vector abundance and its association with vector -borne diseases can be obtained with low cost remote sensing and GIS tool with reliable data and speed.

Significance of Thermal Fluvial Incision and Bedrock Transfer due to Ice Advection on Greenland Ice Sheet Topography

NASA Astrophysics Data System (ADS)

Crozier, J. A.; Karlstrom, L.; Yang, K.

2017-12-01

Ice sheet surface topography reflects a complicated combination of processes that act directly upon the surface and that are products of ice advection. Using recently-available high resolution ice velocity, imagery, ice surface elevation, and bedrock elevation data sets, we seek to determine the domain of significance of two important processes - thermal fluvial incision and transfer of bedrock topography through the ice sheet - on controlling surface topography in the ablation zone. Evaluating such controls is important for understanding how melting of the GIS surface during the melt season may be directly imprinted in topography through supraglacial drainage networks, and indirectly imprinted through its contribution to basal sliding that affects bedrock transfer. We use methods developed by (Karlstrom and Yang, 2016) to identify supraglacial stream networks on the GIS, and use high resolution surface digital elevation models as well as gridded ice velocity and melt rate models to quantify surface processes. We implement a numerically efficient Fourier domain bedrock transfer function (Gudmundsson, 2003) to predict surface topography due to ice advection over bedrock topography obtained from radar. Despite a number of simplifying assumptions, the bedrock transfer function predicts the observed ice sheet surface in most regions of the GIS with ˜90% accuracy, regardless of the presence or absence of supraglacial drainage networks. This supports the hypothesis that bedrock is the most significant driver of ice surface topography on wavelengths similar to ice thickness. Ice surface topographic asymmetry on the GIS is common, with slopes in the direction of ice flow steeper than those faced opposite to ice flow, consistent with bedrock transfer theory. At smaller wavelengths, topography consistent with fluvial erosion by surface hydrologic features is evident. We quantify the effect of ice advection versus fluvial thermal erosion on supraglacial longitudinal stream profiles, as a function of location on the GIS (hence ice thickness and background melt rate) using spectral techniques to quantify longitudinal stream profiles. This work should provide a predictive guide for which processes are responsible for ice sheet topography scales from several m (DEM resolution) up to several ice thicknesses.

Tools for beach health data management, data processing, and predictive model implementation

USGS Publications Warehouse

,

2013-01-01

This fact sheet describes utilities created for management of recreational waters to provide efficient data management, data aggregation, and predictive modeling as well as a prototype geographic information system (GIS)-based tool for data visualization and summary. All of these utilities were developed to assist beach managers in making decisions to protect public health. The Environmental Data Discovery and Transformation (EnDDaT) Web service identifies, compiles, and sorts environmental data from a variety of sources that help to define climatic, hydrologic, and hydrodynamic characteristics including multiple data sources within the U.S. Geological Survey and the National Oceanic and Atmospheric Administration. The Great Lakes Beach Health Database (GLBH-DB) and Web application was designed to provide a flexible input, export, and storage platform for beach water quality and sanitary survey monitoring data to compliment beach monitoring programs within the Great Lakes. A real-time predictive modeling strategy was implemented by combining the capabilities of EnDDaT and the GLBH-DB for timely, automated prediction of beach water quality. The GIS-based tool was developed to map beaches based on their physical and biological characteristics, which was shared with multiple partners to provide concepts and information for future Web-accessible beach data outlets.

Sea Level Affecting Marshes Model (SLAMM) ‐ New functionality for predicting changes in distribution of submerged aquatic vegetation in response to sea level rise

USGS Publications Warehouse

Lee II, Henry; Reusser, Deborah A.; Frazier, Melanie R; McCoy, Lee M; Clinton, Patrick J.; Clough, Jonathan S.

2014-01-01

The “Sea‐Level Affecting Marshes Model” (SLAMM) is a moderate resolution model used to predict the effects of sea level rise on marsh habitats (Craft et al. 2009). SLAMM has been used extensively on both the west coast (e.g., Glick et al., 2007) and east coast (e.g., Geselbracht et al., 2011) of the United States to evaluate potential changes in the distribution and extent of tidal marsh habitats. However, a limitation of the current version of SLAMM, (Version 6.2) is that it lacks the ability to model distribution changes in seagrass habitat resulting from sea level rise. Because of the ecological importance of SAV habitats, U.S. EPA, USGS, and USDA partnered with Warren Pinnacle Consulting to enhance the SLAMM modeling software to include new functionality in order to predict changes in Zostera marina distribution within Pacific Northwest estuaries in response to sea level rise. Specifically, the objective was to develop a SAV model that used generally available GIS data and parameters that were predictive and that could be customized for other estuaries that have GIS layers of existing SAV distribution. This report describes the procedure used to develop the SAV model for the Yaquina Bay Estuary, Oregon, appends a statistical script based on the open source R software to generate a similar SAV model for other estuaries that have data layers of existing SAV, and describes how to incorporate the model coefficients from the site‐specific SAV model into SLAMM to predict the effects of sea level rise on Zostera marina distributions. To demonstrate the applicability of the R tools, we utilize them to develop model coefficients for Willapa Bay, Washington using site‐specific SAV data.

A review of geographic information system and remote sensing with applications to the epidemiology and control of schistosomiasis in China.

PubMed

Yang, Guo-Jing; Vounatsou, Penelope; Zhou, Xiao-Nong; Utzinger, Jürg; Tanner, Marcel

2005-01-01

Geographic information system (GIS) and remote sensing (RS) technologies offer new opportunities for rapid assessment of endemic areas, provision of reliable estimates of populations at risk, prediction of disease distributions in areas that lack baseline data and are difficult to access, and guidance of intervention strategies, so that scarce resources can be allocated in a cost-effective manner. Here, we focus on the epidemiology and control of schistosomiasis in China and review GIS and RS applications to date. These include mapping prevalence and intensity data of Schistosoma japonicum at a large scale, and identifying and predicting suitable habitats for Oncomelania hupensis, the intermediate host snail of S. japonicum, at a small scale. Other prominent applications have been the prediction of infection risk due to ecological transformations, particularly those induced by floods and water resource developments, and the potential impact of climate change. We also discuss the limitations of the previous work, and outline potential new applications of GIS and RS techniques, namely quantitative GIS, WebGIS, and utilization of emerging satellite information, as they hold promise to further enhance infection risk mapping and disease prediction. Finally, we stress current research needs to overcome some of the remaining challenges of GIS and RS applications for schistosomiasis, so that further and sustained progress can be made to control this disease in China and elsewhere.

Towards generalised reference condition models for environmental assessment: a case study on rivers in Atlantic Canada.

PubMed

Armanini, D G; Monk, W A; Carter, L; Cote, D; Baird, D J

2013-08-01

Evaluation of the ecological status of river sites in Canada is supported by building models using the reference condition approach. However, geography, data scarcity and inter-operability constraints have frustrated attempts to monitor national-scale status and trends. This issue is particularly true in Atlantic Canada, where no ecological assessment system is currently available. Here, we present a reference condition model based on the River Invertebrate Prediction and Classification System approach with regional-scale applicability. To achieve this, we used biological monitoring data collected from wadeable streams across Atlantic Canada together with freely available, nationally consistent geographic information system (GIS) environmental data layers. For the first time, we demonstrated that it is possible to use data generated from different studies, even when collected using different sampling methods, to generate a robust predictive model. This model was successfully generated and tested using GIS-based rather than local habitat variables and showed improved performance when compared to a null model. In addition, ecological quality ratio data derived from the model responded to observed stressors in a test dataset. Implications for future large-scale implementation of river biomonitoring using a standardised approach with global application are presented.

Geographic patterns of cigarette butt waste in the urban environment.

PubMed

Marah, Maacah; Novotny, Thomas E

2011-05-01

This reports the initial phase of a study to quantify the spatial pattern of cigarette butt waste in an urban environment. Geographic Information Systems (GIS) was used to create a weighted overlay analysis model which was then applied to the locations of businesses where cigarettes are sold or are likely to be consumed and venues where higher concentrations of butts may be deposited. The model's utility was tested using a small-scale litter audit in three zip codes of San Diego, California. We found that cigarette butt waste is highly concentrated around businesses where cigarettes are sold or consumed. The mean number of butts for predicted high waste sites was 38.1 (SD 18.87), for predicted low waste sites mean 4.8 (SD 5.9), p<0.001. Cigarette butt waste is not uniformly distributed in the urban environment, its distribution is linked to locations and patterns of sales and consumption. A GIS and weighted overlay model may be a useful tool in predicting urban locations of greater and lesser amounts of cigarette butt waste. These data can in turn be used to develop economic cost studies and plan mitigation strategies in urban communities.

Geographic patterns of cigarette butt waste in the urban environment

PubMed Central

Novotny, Thomas E

2011-01-01

Background This reports the initial phase of a study to quantify the spatial pattern of cigarette butt waste in an urban environment. Methods Geographic Information Systems (GIS) was used to create a weighted overlay analysis model which was then applied to the locations of businesses where cigarettes are sold or are likely to be consumed and venues where higher concentrations of butts may be deposited. The model's utility was tested using a small-scale litter audit in three zip codes of San Diego, California. Results We found that cigarette butt waste is highly concentrated around businesses where cigarettes are sold or consumed. The mean number of butts for predicted high waste sites was 38.1 (SD 18.87), for predicted low waste sites mean 4.8 (SD 5.9), p<0.001. Conclusions Cigarette butt waste is not uniformly distributed in the urban environment, its distribution is linked to locations and patterns of sales and consumption. A GIS and weighted overlay model may be a useful tool in predicting urban locations of greater and lesser amounts of cigarette butt waste. These data can in turn be used to develop economic cost studies and plan mitigation strategies in urban communities. PMID:21504924

Spatial discretization of large watersheds and its influence on the estimation of hillslope sediment yield

USDA-ARS?s Scientific Manuscript database

The combined use of water erosion models and geographic information systems (GIS) has facilitated soil loss estimation at the watershed scale. Tools such as the Geo-spatial interface for the Water Erosion Prediction Project (GeoWEPP) model provide a convenient spatially distributed soil loss estimat...

Coupling of Bayesian Networks with GIS for wildfire risk assessment on natural and agricultural areas of the Mediterranean

NASA Astrophysics Data System (ADS)

Scherb, Anke; Papakosta, Panagiota; Straub, Daniel

2014-05-01

Wildfires cause severe damages to ecosystems, socio-economic assets, and human lives in the Mediterranean. To facilitate coping with wildfire risks, an understanding of the factors influencing wildfire occurrence and behavior (e.g. human activity, weather conditions, topography, fuel loads) and their interaction is of importance, as is the implementation of this knowledge in improved wildfire hazard and risk prediction systems. In this project, a probabilistic wildfire risk prediction model is developed, with integrated fire occurrence and fire propagation probability and potential impact prediction on natural and cultivated areas. Bayesian Networks (BNs) are used to facilitate the probabilistic modeling. The final BN model is a spatial-temporal prediction system at the meso scale (1 km2 spatial and 1 day temporal resolution). The modeled consequences account for potential restoration costs and production losses referred to forests, agriculture, and (semi-) natural areas. BNs and a geographic information system (GIS) are coupled within this project to support a semi-automated BN model parameter learning and the spatial-temporal risk prediction. The coupling also enables the visualization of prediction results by means of daily maps. The BN parameters are learnt for Cyprus with data from 2006-2009. Data from 2010 is used as validation data set. A special focus is put on the performance evaluation of the BN for fire occurrence, which is modeled as binary classifier and thus, could be validated by means of Receiver Operator Characteristic (ROC) curves. With the final best models, AUC values of more than 70% for validation could be achieved, which indicates potential for reliable prediction performance via BN. Maps of selected days in 2010 are shown to illustrate final prediction results. The resulting system can be easily expanded to predict additional expected damages in the mesoscale (e.g. building and infrastructure damages). The system can support planning of preventive measures (e.g. state resources allocation for wildfire prevention and preparedness) and assist recuperation plans of damaged areas.

Analysis of Giga-size Earth Observation Data in Open Source GRASS GIS 7 - from Desktop to On-line Solutions.

NASA Astrophysics Data System (ADS)

Stepinski, T. F.; Mitasova, H.; Jasiewicz, J.; Neteler, M.; Gebbert, S.

2014-12-01

GRASS GIS is a leading open source GIS for geospatial analysis and modeling. In addition to being utilized as a desktop GIS it also serves as a processing engine for high performance geospatial computing for applications in diverse disciplines. The newly released GRASS GIS 7 supports big data analysis including temporal framework, image segmentation, watershed analysis, synchronized 2D/3D animations and many others. This presentation will focus on new GRASS GIS 7-powered tools for geoprocessing giga-size earth observation (EO) data using spatial pattern analysis. Pattern-based analysis connects to human visual perception of space as well as makes geoprocessing of giga-size EO data possible in an efficient and robust manner. GeoPAT is a collection of GRASS GIS 7 modules that fully integrates procedures for pattern representation of EO data and patterns similarity calculations with standard GIS tasks of mapping, maps overlay, segmentation, classification(Fig 1a), change detections etc. GeoPAT works very well on a desktop but it also underpins several GeoWeb applications (http://sil.uc.edu/ ) which allow users to do analysis on selected EO datasets without the need to download them. The GRASS GIS 7 temporal framework and high resolution visualizations will be illustrated using time series of giga-size, lidar-based digital elevation models representing the dynamics of North Carolina barrier islands over the past 15 years. The temporal framework supports efficient raster and vector data series analysis and simplifies data input for visual analysis of dynamic landscapes (Fig. 1b) allowing users to rapidly identify vulnerable locations, changes in built environment and eroding coastlines. Numerous improvements in GRASS GIS 7 were implemented to support terabyte size data processing for reconstruction of MODIS land surface temperature (LST) at 250m resolution using multiple regressions and PCA (Fig. 1c) . The new MODIS LST series (http://gis.cri.fmach.it/eurolst/) includes 4 maps per day since year 2000, provide improved data for the epidemiological predictions, viticulture, assessment of urban heat islands and numerous other applications. The presentation will conclude with outline of future development for big data interfaces to further enhance the web-based GRASS GIS data analysis.

Utilization of GIS/GPS-Based Information Technology in Commercial Crop Decision Making in California, Washington, Oregon, Idaho, and Arizona

PubMed Central

Thomas, C. S.; Skinner, P. W.; Fox, A. D.; Greer, C. A.; Gubler, W. D.

2002-01-01

Ground-based weather, plant-stage measurements, and remote imagery were geo-referenced in geographic information system (GIS) software using an integrated approach to determine insect and disease risk and crop cultural requirements. Weather forecasts and disease weather forecasts for agricultural areas were constructed with elevation, weather, and satellite data. Models for 6 insect pests and 12 diseases of various crops were calculated and presented daily in georeferenced maps for agricultural areas in northern California and Washington. Grape harvest dates and yields also were predicted with high accuracy. The data generated from the GIS global positioning system (GPS) analyses were used to make management decisions over a large number of acres in California, Washington, Oregon, Idaho, and Arizona. Information was distributed daily over the Internet as regional weather, insect, and disease risk maps as industry-sponsored or subscription-based products. Use of GIS/GPS technology for semi-automated data analysis is discussed. PMID:19265934

An Approach to Integrate a Space-Time GIS Data Model with High Performance Computers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Dali; Zhao, Ziliang; Shaw, Shih-Lung

2011-01-01

In this paper, we describe an approach to integrate a Space-Time GIS data model on a high performance computing platform. The Space-Time GIS data model has been developed on a desktop computing environment. We use the Space-Time GIS data model to generate GIS module, which organizes a series of remote sensing data. We are in the process of porting the GIS module into an HPC environment, in which the GIS modules handle large dataset directly via parallel file system. Although it is an ongoing project, authors hope this effort can inspire further discussions on the integration of GIS on highmore » performance computing platforms.« less

Using a remote sensing/GIS model to predict southwestern Willow Flycatcher breeding habitat along the Rio Grande, New Mexico

USGS Publications Warehouse

Hatten, James R.; Sogge, Mark K.

2007-01-01

Introduction The Southwestern Willow Flycatcher (Empidonax traillii extimus; hereafter SWFL) is a federally endangered bird (USFWS 1995) that breeds in riparian areas in portions of New Mexico, Arizona, southwestern Colorado, extreme southern Utah and Nevada, and southern California (USFWS 2002). Across this range, it uses a variety of plant species as nesting/breeding habitat, but in all cases prefers sites with dense vegetation, high canopy, and proximity to surface water or saturated soils (Sogge and Marshall 2000). As of 2005, the known rangewide breeding population of SWFLs was roughly 1,214 territories, with approximately 393 territories distributed among 36 sites in New Mexico (Durst et al. 2006), primarily along the Rio Grande. One of the key challenges facing the management and conservation of the Southwestern Willow Flycatcher is that riparian areas are dynamic, with individual habitat patches subject to cycles of creation, growth, and loss due to drought, flooding, fire, and other disturbances. Former breeding patches can lose suitability, and new habitat can develop within a matter of only a few years, especially in reservoir drawdown zones. Therefore, measuring and predicting flycatcher habitat - either to discover areas that might support SWFLs, or to identify areas that may develop into appropriate habitat - requires knowledge of recent/current habitat conditions and an understanding of the factors that determine flycatcher use of riparian breeding sites. In the past, much of the determination of whether a riparian site is likely to support breeding flycatchers has been based on qualitative criteria (for example, 'dense vegetation' or 'large patches'). These determinations often require on-the-ground field evaluations by local or regional SWFL experts. While this has proven valuable in locating many of the currently known breeding sites, it is difficult or impossible to apply this approach effectively over large geographic areas (for example, the middle Rio Grande). The SWFL Recovery Plan (USFWS 2002) recognizes the importance of developing new approaches to habitat identification, and recommends the development of drainage-scale, quantitative habitat models. In particular, the plan suggests using models based on remote sensing and Geographic Information System (GIS) technology that can capture the relatively dynamic habitat changes that occur in southwestern riparian systems. In 1999, Arizona Game and Fish Department (AGFD) developed a GIS-based model (Hatten and Paradzick 2003) to identify SWFL breeding habitat from Landsat Thematic Mapper imagery and 30-m resolution digital elevation models (DEMs). The model was developed with presence/absence survey data acquired along the San Pedro and Gila rivers, and from the Salt River and Tonto Creek inlets to Roosevelt Lake in southern Arizona (collectively called the project area). The GIS-based model used a logistic regression equation to divide riparian vegetation into 5 probability classes based upon characteristics of riparian vegetation and floodplain size. This model was tested by predicting SWFL breeding habitat at Alamo Lake, Arizona, located 200 km from the project area (Hatten and Paradzick 2003). The GIS-based model performed as expected by identifying riparian areas with the highest SWFL nest densities, located in the higher probability classes. In 2002, AGFD applied the GIS-based model throughout Arizona, for riparian areas below 1,524 m (5,000 ft) elevation and within 1.6 km of perennial or intermittent waters (Dockens et al. 2004). Overall model accuracy (using probability classes 1-5, with class 5 having the greatest probability of nesting activity) for predicting the location of 2001 nest sites was 96.5 percent; accuracy decreased when fewer probability classes were defined as suitable. Map accuracy, determined from errors of commission, increased in higher probability classes in a fashion similar to errors of omission. Map accuracy, li

DRAINMOD-GIS: a lumped parameter watershed scale drainage and water quality model

Treesearch

G.P. Fernandez; G.M. Chescheir; R.W. Skaggs; D.M. Amatya

2006-01-01

A watershed scale lumped parameter hydrology and water quality model that includes an uncertainty analysis component was developed and tested on a lower coastal plain watershed in North Carolina. Uncertainty analysis was used to determine the impacts of uncertainty in field and network parameters of the model on the predicted outflows and nitrate-nitrogen loads at the...

A simple rapid approach using coupled multivariate statistical methods, GIS and trajectory models to delineate areas of common oil spill risk

NASA Astrophysics Data System (ADS)

Guillen, George; Rainey, Gail; Morin, Michelle

2004-04-01

Currently, the Minerals Management Service uses the Oil Spill Risk Analysis model (OSRAM) to predict the movement of potential oil spills greater than 1000 bbl originating from offshore oil and gas facilities. OSRAM generates oil spill trajectories using meteorological and hydrological data input from either actual physical measurements or estimates generated from other hydrological models. OSRAM and many other models produce output matrices of average, maximum and minimum contact probabilities to specific landfall or target segments (columns) from oil spills at specific points (rows). Analysts and managers are often interested in identifying geographic areas or groups of facilities that pose similar risks to specific targets or groups of targets if a spill occurred. Unfortunately, due to the potentially large matrix generated by many spill models, this question is difficult to answer without the use of data reduction and visualization methods. In our study we utilized a multivariate statistical method called cluster analysis to group areas of similar risk based on potential distribution of landfall target trajectory probabilities. We also utilized ArcView™ GIS to display spill launch point groupings. The combination of GIS and multivariate statistical techniques in the post-processing of trajectory model output is a powerful tool for identifying and delineating areas of similar risk from multiple spill sources. We strongly encourage modelers, statistical and GIS software programmers to closely collaborate to produce a more seamless integration of these technologies and approaches to analyzing data. They are complimentary methods that strengthen the overall assessment of spill risks.

Prediction of groundwater flowing well zone at An-Najif Province, central Iraq using evidential belief functions model and GIS.

PubMed

Al-Abadi, Alaa M; Pradhan, Biswajeet; Shahid, Shamsuddin

2015-10-01

The objective of this study is to delineate groundwater flowing well zone potential in An-Najif Province of Iraq in a data-driven evidential belief function model developed in a geographical information system (GIS) environment. An inventory map of 68 groundwater flowing wells was prepared through field survey. Seventy percent or 43 wells were used for training the evidential belief functions model and the reset 30 % or 19 wells were used for validation of the model. Seven groundwater conditioning factors mostly derived from RS were used, namely elevation, slope angle, curvature, topographic wetness index, stream power index, lithological units, and distance to the Euphrates River in this study. The relationship between training flowing well locations and the conditioning factors were investigated using evidential belief functions technique in a GIS environment. The integrated belief values were classified into five categories using natural break classification scheme to predict spatial zoning of groundwater flowing well, namely very low (0.17-0.34), low (0.34-0.46), moderate (0.46-0.58), high (0.58-0.80), and very high (0.80-0.99). The results show that very low and low zones cover 72 % (19,282 km(2)) of the study area mostly clustered in the central part, the moderate zone concentrated in the west part covers 13 % (3481 km(2)), and the high and very high zones extended over the northern part cover 15 % (3977 km(2)) of the study area. The vast spatial extension of very low and low zones indicates that groundwater flowing wells potential in the study area is low. The performance of the evidential belief functions spatial model was validated using the receiver operating characteristic curve. A success rate of 0.95 and a prediction rate of 0.94 were estimated from the area under relative operating characteristics curves, which indicate that the developed model has excellent capability to predict groundwater flowing well zones. The produced map of groundwater flowing well zones could be used to identify new wells and manage groundwater storage in a sustainable manner.

Spatially distributed storm runoff modeling using remote sensing and geographic information systems

NASA Astrophysics Data System (ADS)

Melesse, Assefa Mekonnen

Advances in scientific knowledge and new techniques of remote sensing permit a better understanding of the physical land features governing hydrologic processes, and make possible efficient, large-scale hydrologic modeling. The need for land-cover and hydrologic response change detection at a larger scale and at times of the year when hydrologic studies are critical makes satellite imagery the most cost effective, efficient and reliable source of data. The use of a Geographic Information System (GIS) to store, manipulate and visualize these data, and ultimately to estimate runoff from watersheds, has gained increasing attention in recent years. In this work, remotely-sensed data and GIS tools were used to estimate the changes in land-cover, and to estimate runoff response, for three watersheds (Etonia, Econlockhatchee, and S-65A sub-basins) in Florida. Land-use information from Digital Orthophoto Quarter Quadrangles (DOQQ), Landsat Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) were analyzed for the years 1973, 1984, 1990, 1995, and 2000. Spatial distribution of land-cover was assessed over time. The corresponding infiltration excess runoff response of the study areas due to these changes was estimated using the United States Department of Agriculture, Natural Resources Conservation Service Curve Number (USDA-NRCS-CN) method. A Digital Elevation Model (DEM)-GIS technique was developed to predict stream response to runoff events based on the travel time from each grid cell to the watershed outlet. The method was tested on a representative watershed (Simms Creek) in the Etonia sub-basin. Simulated and observed runoff volume and hydrographs were compared for 17 storm events. Isolated storms, with volumes of not less than 12.75 mm (0.5 inch) were selected. This is the minimum amount of rainfall volume recommended for the NRCS-CN method. Results show that the model predicts the runoff response of the study area with an average efficiency of 57%. Comparison of the runoff prediction to Snyder's synthetic Unit hydrograph method and TOPMODEL shows the spatially distributed infiltration excess travel time model performs better than both the Snyder's method and TOPMODEL. The model is applicable to ungaged watersheds and useful for predicting runoff hydrographs resulting from changes in the land-cover.

Applications and issues of GIS as tool for civil engineering modeling

USGS Publications Warehouse

Miles, S.B.; Ho, C.L.

1999-01-01

A tool that has proliferated within civil engineering in recent years is geographic information systems (GIS). The goal of a tool is to supplement ability and knowledge that already exists, not to serve as a replacement for that which is lacking. To secure the benefits and avoid misuse of a burgeoning tool, engineers must understand the limitations, alternatives, and context of the tool. The common benefits of using GIS as a supplement to engineering modeling are summarized. Several brief case studies of GIS modeling applications are taken from popular civil engineering literature to demonstrate the wide use and varied implementation of GIS across the discipline. Drawing from the case studies, limitations regarding traditional GIS data models find the implementation of civil engineering models within current GIS are identified and countered by discussing the direction of the next generation of GIS. The paper concludes by highlighting the potential for the misuse of GIS in the context of engineering modeling and suggests that this potential can be reduced through education and awareness. The goal of this paper is to promote awareness of the issues related to GIS-based modeling and to assist in the formulation of questions regarding the application of current GIS. The technology has experienced much publicity of late, with many engineers being perhaps too excited about the usefulness of current GIS. An undoubtedly beneficial side effect of this, however, is that engineers are becoming more aware of GIS and, hopefully, the associated subtleties. Civil engineers must stay informed of GIS issues and progress, but more importantly, civil engineers must inform the GIS community to direct the technology development optimally.

Anthropogenic Land-use Change and the Dynamics of Amazon Forest Biomass

NASA Technical Reports Server (NTRS)

Laurance, William F.

2004-01-01

This project was focused on assessing the effects of prevailing land uses, such as habitat fragmentation, selective logging, and fire, on biomass and carbon storage in Amazonian forests, and on the dynamics of carbon sequestration in regenerating forests. Ancillary goals included developing GIs models to help predict the future condition of Amazonian forests, and assessing the effects of anthropogenic climate change and ENS0 droughts on intact and fragmented forests. Ground-based studies using networks of permanent plots were linked with remote-sensing data (including Landsat TM and AVHRR) at regional scales, and higher-resolution techniques (IKONOS imagery, videography, LIDAR, aerial photographs) at landscape and local scales. The project s specific goals were quite eclectic and included: Determining the effects of habitat fragmentation on forest dynamics, floristic composition, and the various components of above- and below-ground biomass. Assessing historical and physical factors that affect trajectories of forest regeneration and carbon sequestration on abandoned lands. Extrapolating results from local studies of biomass dynamics in fragmented and regenerating forests to landscape and regional scales in Amazonia, using remote sensing and GIS. Testing the hypothesis that intact Amazonian forests are functioning as a significant carbon sink. Examining destructive synergisms between forest fragmentation and fire. Assessing the short-term impacts of selective logging on aboveground biomass. Developing GIS models that integrate current spatial data on forest cover, deforestation, logging, mining, highway and roads, navigable rivers, vulnerability to wild fires, protected areas, and existing and planned infrastructure projects, in an effort to predict the future condition of Brazilian Amazonian forests over the next 20-25 years. Devising predictive spatial models to assess the influence of varied biophysical and anthropogenic predictors on Amazonian deforestation.

In Vitro Dissolution of Fluconazole and Dipyridamole in Gastrointestinal Simulator (GIS), Predicting in Vivo Dissolution and Drug-Drug Interaction Caused by Acid-Reducing Agents.

PubMed

Matsui, Kazuki; Tsume, Yasuhiro; Amidon, Gregory E; Amidon, Gordon L

2015-07-06

Weakly basic drugs typically exhibit pH-dependent solubility in the physiological pH range, displaying supersaturation or precipitation along the gastrointestinal tract. Additionally, their oral bioavailabilities may be affected by coadministration of acid-reducing agents that elevate gastric pH. The purpose of this study was to assess the feasibility of a multicompartmental in vitro dissolution apparatus, Gastrointestinal Simulator (GIS), in predicting in vivo dissolution of certain oral medications. In vitro dissolution studies of fluconazole, a BCS class I, and dipyridamole, a BCS class II weak bases (class IIb), were performed in the GIS as well as United States Pharmacopeia (USP) apparatus II and compared with the results of clinical drug-drug interaction (DDI) studies. In both USP apparatus II and GIS, fluconazole completely dissolved within 60 min regardless of pH, reflecting no DDI between fluconazole and acid-reducing agents in a clinical study. On the other hand, seven-fold and 15-fold higher concentrations of dipyridamole than saturation solubility were observed in the intestinal compartments in GIS with gastric pH 2.0. Precipitation of dipyridamole was also observed in the GIS, and the percentage of dipyridamole in solution was 45.2 ± 7.0%. In GIS with gastric pH 6.0, mimicking the coadministration of acid-reducing agents, the concentration of dipyridamole was equal to its saturation solubility, and the percentage of drug in solution was 9.3 ± 2.7%. These results are consistent with the clinical DDI study of dipyridamole with famotidine, which significantly reduced the Cmax and area under the curve. An In situ mouse infusion study combined with GIS revealed that high concentration of dipyridamole in the GIS enhanced oral drug absorption, which confirmed the supersaturation of dipyridamole. In conclusion, GIS was shown to be a useful apparatus to predict in vivo dissolution for BCS class IIb drugs.

Design of AN Intelligent Individual Evacuation Model for High Rise Building Fires Based on Neural Network Within the Scope of 3d GIS

NASA Astrophysics Data System (ADS)

Atila, U.; Karas, I. R.; Turan, M. K.; Rahman, A. A.

2013-09-01

One of the most dangerous disaster threatening the high rise and complex buildings of today's world including thousands of occupants inside is fire with no doubt. When we consider high population and the complexity of such buildings it is clear to see that performing a rapid and safe evacuation seems hard and human being does not have good memories in case of such disasters like world trade center 9/11. Therefore, it is very important to design knowledge based realtime interactive evacuation methods instead of classical strategies which lack of flexibility. This paper presents a 3D-GIS implementation which simulates the behaviour of an intelligent indoor pedestrian navigation model proposed for a self -evacuation of a person in case of fire. The model is based on Multilayer Perceptron (MLP) which is one of the most preferred artificial neural network architecture in classification and prediction problems. A sample fire scenario following through predefined instructions has been performed on 3D model of the Corporation Complex in Putrajaya (Malaysia) and the intelligent evacuation process has been realized within a proposed 3D-GIS based simulation.

Establishing Esri ArcGIS Enterprise Platform Capabilities to Support Response Activities of the NASA Earth Science Disasters Program

NASA Astrophysics Data System (ADS)

Molthan, A.; Seepersad, J.; Shute, J.; Carriere, L.; Duffy, D.; Tisdale, B.; Kirschbaum, D.; Green, D. S.; Schwizer, L.

2017-12-01

NASA's Earth Science Disasters Program promotes the use of Earth observations to improve the prediction of, preparation for, response to, and recovery from natural and technological disasters. NASA Earth observations and those of domestic and international partners are combined with in situ observations and models by NASA scientists and partners to develop products supporting disaster mitigation, response, and recovery activities among several end-user partners. These products are accompanied by training to ensure proper integration and use of these materials in their organizations. Many products are integrated along with other observations available from other sources in GIS-capable formats to improve situational awareness and response efforts before, during and after a disaster. Large volumes of NASA observations support the generation of disaster response products by NASA field center scientists, partners in academia, and other institutions. For example, a prediction of high streamflows and inundation from a NASA-supported model may provide spatial detail of flood extent that can be combined with GIS information on population density, infrastructure, and land value to facilitate a prediction of who will be affected, and the economic impact. To facilitate the sharing of these outputs in a common framework that can be easily ingested by downstream partners, the NASA Earth Science Disasters Program partnered with Esri and the NASA Center for Climate Simulation (NCCS) to establish a suite of Esri/ArcGIS services to support the dissemination of routine and event-specific products to end users. This capability has been demonstrated to key partners including the Federal Emergency Management Agency using a case-study example of Hurricane Matthew, and will also help to support future domestic and international disaster events. The Earth Science Disasters Program has also established a longer-term vision to leverage scientists' expertise in the development and delivery of end-user training, increase public awareness of NASA's Disasters Program, and facilitate new partnerships with disaster response organizations. Future research and development will foster generation of products that leverage NASA's Earth observations for disaster prediction, preparation and mitigation, response, and recovery.

Possibilities of GIS for Plant Introduction

USDA-ARS?s Scientific Manuscript database

The Ecological Atlas of Russia and Neighboring Countries was used to predict the distribution of introduced species. Acer negundo was used as an example and an ecological model was developed using the natural distribution of the species in North America and some American ecological variables: namely...

Remote sensing and GIS-based prediction and assessment of copper-gold resources in Thailand

NASA Astrophysics Data System (ADS)

Yang, Shasha; Wang, Gongwen; Du, Wenhui; Huang, Luxiong

2014-03-01

Quantitative integration of geological information is a frontier and hotspot of prospecting decision research in the world. The forming process of large scale Cu-Au deposits is influenced by complicated geological events and restricted by various geological factors (stratum, structure and alteration). In this paper, using Thailand's copper-gold deposit district as a case study, geological anomaly theory is used along with the typical copper and gold metallogenic model, ETM+ remote sensing images, geological maps and mineral geology database in study area are combined with GIS technique. These techniques create ore-forming information such as geological information (strata, line-ring faults, intrusion), remote sensing information (hydroxyl alteration, iron alteration, linear-ring structure) and the Cu-Au prospect targets. These targets were identified using weights of evidence model. The research results show that the remote sensing and geological data can be combined to quickly predict and assess for exploration of mineral resources in a regional metallogenic belt.

Estimating Rooftop Suitability for PV: A Review of Methods, Patents, and Validation Techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Melius, J.; Margolis, R.; Ong, S.

2013-12-01

A number of methods have been developed using remote sensing data to estimate rooftop area suitable for the installation of photovoltaics (PV) at various geospatial resolutions. This report reviews the literature and patents on methods for estimating rooftop-area appropriate for PV, including constant-value methods, manual selection methods, and GIS-based methods. This report also presents NREL's proposed method for estimating suitable rooftop area for PV using Light Detection and Ranging (LiDAR) data in conjunction with a GIS model to predict areas with appropriate slope, orientation, and sunlight. NREL's method is validated against solar installation data from New Jersey, Colorado, and Californiamore » to compare modeled results to actual on-the-ground measurements.« less

Development of AHPDST Vulnerability Indexing Model for Groundwater Vulnerability Assessment Using Hydrogeophysical Derived Parameters and GIS Application

NASA Astrophysics Data System (ADS)

Mogaji, K. A.

2017-04-01

Producing a bias-free vulnerability assessment map model is significantly needed for planning a scheme of groundwater quality protection. This study developed a GIS-based AHPDST vulnerability index model for producing groundwater vulnerability model map in the hard rock terrain, Nigeria by exploiting the potentials of analytic hierarchy process (AHP) and Dempster-Shafer theory (DST) data mining models. The acquired borehole and geophysical data in the study area were processed to derive five groundwater vulnerability conditioning factors (GVCFs), namely recharge rate, aquifer transmissivity, hydraulic conductivity, transverse resistance and longitudinal conductance. The produced GVCFs' thematic maps were multi-criterially analyzed by employing the mechanisms of AHP and DST models to determine the normalized weight ( W) parameter for the GVCFs and mass function factors (MFFs) parameter for the GVCFs' thematic maps' class boundaries, respectively. Based on the application of the weighted linear average technique, the determined W and MFFs parameters were synthesized to develop groundwater vulnerability potential index (GVPI)-based AHPDST model algorithm. The developed model was applied to establish four GVPI mass/belief function indices. The estimates based on the applied GVPI belief function indices were processed in GIS environment to create prospective groundwater vulnerability potential index maps. The most representative of the resulting vulnerability maps (the GVPIBel map) was considered for producing the groundwater vulnerability potential zones (GVPZ) map for the area. The produced GVPZ map established 48 and 52% of the areal extent to be covered by the lows/moderate and highs vulnerable zones, respectively. The success and the prediction rates of the produced GVPZ map were determined using the relative operating characteristics technique to give 82.3 and 77.7%, respectively. The analyzed results reveal that the developed GVPI-based AHPDST model algorithm is capable of producing efficient groundwater vulnerability potential zones prediction map and characterizing the predicted zones uncertainty via the DST mechanism processes in the area. The produced GVPZ map in this study can be used by decision makers to formulate appropriate groundwater management strategies and the approach may be well opted in other hard rock regions of the world, especially in economically poor nations.

A female black bear denning habitat model using a geographic information system

USGS Publications Warehouse

Clark, J.D.; Hayes, S.G.; Pledger, J.M.

1998-01-01

We used the Mahalanobis distance statistic and a raster geographic information system (GIS) to model potential black bear (Ursus americanus) denning habitat in the Ouachita Mountains of Arkansas. The Mahalanobis distance statistic was used to represent the standard squared distance between sample variates in the GIS database (forest cover type, elevation, slope, aspect, distance to streams, distance to roads, and forest cover richness) and variates at known bear dens. Two models were developed: a generalized model for all den locations and another specific to dens in rock cavities. Differences between habitat at den sites and habitat across the study area were represented in 2 new GIS themes as Mahalanobis distance values. Cells similar to the mean vector derived from the known dens had low Mahalanobis distance values, and dissimilar cells had high values. The reliability of the predictive model was tested by overlaying den locations collected subsequent to original model development on the resultant den habitat themes. Although the generalized model demonstrated poor reliability, the model specific to rock dens had good reliability. Bears were more likely to choose rock den locations with low Mahalanobis distance values and less likely to choose those with high values. The model can be used to plan the timing and extent of management actions (e.g., road building, prescribed fire, timber harvest) most appropriate for those sites with high or low denning potential. 

Replacement of SSE with NASA's POWER Project GIS-enabled Web Data Portal

Atmospheric Science Data Center

2018-04-30

Replacement of SSE with NASA's POWER Project GIS-enabled Web Data Portal Friday, March ... 2018 Replacement of SSE (Release 6) with NASA's Prediction of Worldwide Energy Resource (POWER) Project GIS-enabled Web ... Worldwide Energy Resource (POWER) Project funded largely by NASA Earth Applied Sciences program.   The new POWER web portal ...

Water resources planning and modelling tools for the assessment of land use change in the Luvuvhu Catchment, South Africa

NASA Astrophysics Data System (ADS)

Jewitt, G. P. W.; Garratt, J. A.; Calder, I. R.; Fuller, L.

In arid and semi-arid areas, total evaporation is a major component of the hydrological cycle and seasonal water shortages and drought are common. In these areas, the role of land use and land use change is particularly important and it is imperative that land and water resources are well managed. To aid efficient water management, it is useful to demonstrate how changing land use affects water resources. A convenient framework to consider this is through the use of the ‘blue-water’ and ‘green-water’ classification of Falkenmark, where green-water represents water use by land and blue-water represents runoff. In this study the hydrological response of nine land-use scenarios were simulated for the upper reaches of the Mutale River, an important tributary of the Luvuvhu River in S. Africa. The ACRU and HYLUC land use sensitive hydrological models, were used to investigate the change in blue and green water under the various land-use scenarios. The GIS software ArcGIS(8.3) was used to analyse available spatial data to generate inputs required by the hydrological models. The scenarios investigated included the current land use in the catchment, an increase or decrease in forest cover, and an increase or decrease in the area irrigated. Both models predict that increasing either forestry or irrigation significantly reduces the proportion of blue water in the catchment. The predictions from the models were combined with maps of catchment land use, to illustrate the changes in distribution of green and blue water in a user-friendly manner. The use of GIS in this way is designed to enable policy-makers and managers to quickly assimilate the water resource implication of the land use change.

Study on the total amount control of atmospheric pollutant based on GIS.

PubMed

Wang, Jian-Ping; Guo, Xi-Kun

2005-08-01

To provide effective environmental management for total amount control of atmospheric pollutants. An atmospheric diffusion model of sulfur dioxide on the surface of the earth was established and tested in Shantou of Guangdong Province on the basis of an overall assessment of regional natural environment, social economic state of development, pollution sources and atmospheric environmental quality. Compared with actual monitoring results in a studied region, simulation values fell within the range of two times of error and were evenly distributed in the two sides of the monitored values. Predicted with the largest emission model method, the largest emission of sulfur dioxide would be 54,279.792 tons per year in 2010. The mathematical model established and revised on the basis of GIS is more rational and suitable for the regional characteristics of total amount control of air pollutants.

Prediction of wood Quality in Small-Diameter Douglas-Fir using site and Stand Characteristics

Treesearch

C.D. Morrow; T.M. Gorman; J.W. Evans; D.E. Kretschmann; C.A. Hatfield

2013-01-01

Standing stress wave measurements were taken on 274 small-diameter Douglas-fir trees in western Montana. Stand, site, and soil measurements collected in the field and remotely through geographical information system (GIS) data layers were used to model dynamic modulus of elasticity (DMOE) in those trees. The best fit linear model developed resulted in an adjusted

Smart caching based on mobile agent of power WebGIS platform.

PubMed

Wang, Xiaohui; Wu, Kehe; Chen, Fei

2013-01-01

Power information construction is developing towards intensive, platform, distributed direction with the expansion of power grid and improvement of information technology. In order to meet the trend, power WebGIS was designed and developed. In this paper, we first discuss the architecture and functionality of power WebGIS, and then we study caching technology in detail, which contains dynamic display cache model, caching structure based on mobile agent, and cache data model. We have designed experiments of different data capacity to contrast performance between WebGIS with the proposed caching model and traditional WebGIS. The experimental results showed that, with the same hardware environment, the response time of WebGIS with and without caching model increased as data capacity growing, while the larger the data was, the higher the performance of WebGIS with proposed caching model improved.

Using the information value method in a geographic information system and remote sensing for malaria mapping: a case study from India.

PubMed

Rai, Praveen Kumar; Nathawat, Mahendra Singh; Rai, Shalini

2013-01-01

This paper explores the scope of malaria-susceptibility modelling to predict malaria occurrence in an area. An attempt has been made in Varanasi district, India, to evaluate the status of malaria disease and to develop a model by which malaria-prone zones could be predicted using five classes of relative malaria susceptibility, i.e.very low, low, moderate, high and very high categories. The information value (Info Val) method was used to assess malaria occurrence and various time-were used as the independent variables. A geographical information system (GIS) is employed to investigate associations between such variables and distribution of different mosquitoes responsible for malaria transmission. Accurate prediction of risk depends on a number of variables, such as land use, NDVI, climatic factors, population, distance to health centres, ponds, streams and roads etc., all of which have an influence on malaria transmission or reporting. Climatic factors, particularly rainfall, temperature and relative humidity, are known to have a major influence on the biology of mosquitoes. To produce a malaria-susceptibility map using this method, weightings are calculated for various classes in each group. The groups are then superimposed to prepare a Malaria Susceptibility Index (MSI) map. We found that 3.87% of the malaria cases were found in areas with a low malaria-susceptibility level predicted from the model, whereas 39.86% and 26.29% of malaria cases were found in predicted high and very high susceptibility level areas, respectively. Malaria susceptibility modelled using a GIS may have a role in predicting the risks of malaria and enable public health interventions to be better targeted.

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2013-02-01

The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e.g., DT, SVM and ANFIS) is viable. As far as the performance of the models are concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative susceptibility.

Effect of Variable Spatial Scales on USLE-GIS Computations

NASA Astrophysics Data System (ADS)

Patil, R. J.; Sharma, S. K.

2017-12-01

Use of appropriate spatial scale is very important in Universal Soil Loss Equation (USLE) based spatially distributed soil erosion modelling. This study aimed at assessment of annual rates of soil erosion at different spatial scales/grid sizes and analysing how changes in spatial scales affect USLE-GIS computations using simulation and statistical variabilities. Efforts have been made in this study to recommend an optimum spatial scale for further USLE-GIS computations for management and planning in the study area. The present research study was conducted in Shakkar River watershed, situated in Narsinghpur and Chhindwara districts of Madhya Pradesh, India. Remote Sensing and GIS techniques were integrated with Universal Soil Loss Equation (USLE) to predict spatial distribution of soil erosion in the study area at four different spatial scales viz; 30 m, 50 m, 100 m, and 200 m. Rainfall data, soil map, digital elevation model (DEM) and an executable C++ program, and satellite image of the area were used for preparation of the thematic maps for various USLE factors. Annual rates of soil erosion were estimated for 15 years (1992 to 2006) at four different grid sizes. The statistical analysis of four estimated datasets showed that sediment loss dataset at 30 m spatial scale has a minimum standard deviation (2.16), variance (4.68), percent deviation from observed values (2.68 - 18.91 %), and highest coefficient of determination (R2 = 0.874) among all the four datasets. Thus, it is recommended to adopt this spatial scale for USLE-GIS computations in the study area due to its minimum statistical variability and better agreement with the observed sediment loss data. This study also indicates large scope for use of finer spatial scales in spatially distributed soil erosion modelling.

Prediction of Floor Water Inrush: The Application of GIS-Based AHP Vulnerable Index Method to Donghuantuo Coal Mine, China

NASA Astrophysics Data System (ADS)

Wu, Qiang; Liu, Yuanzhang; Liu, Donghai; Zhou, Wanfang

2011-09-01

Floor water inrush represents a geohazard that can pose significant threat to safe operations for instance in coal mines in China and elsewhere. Its occurrence is controlled by many factors, and the processes are often not amenable to mathematical expressions. To evaluate the water inrush risk, the paper proposes the vulnerability index approach by coupling the analytic hierarchy process (AHP) and geographic information system (GIS). The detailed procedures of using this innovative approach are shown in a case study in China (Donghuantuo Coal Mine). The powerful spatial data analysis functions of GIS was used to establish the thematic layer of each of the six factors that control the water inrush, and the contribution weights of each factor was determined with the AHP method. The established AHP evaluation model was used to determine the threshold value for each risk level with a histogram of the water inrush vulnerability index. As a result, the mine area was divided into five regions with different vulnerability levels which served as general guidelines for the mine operations. The prediction results were further corroborated with the actual mining data, and the evaluation result is satisfactory.

Information Technology Implementation and Sustainment Model: Data Collection Instrument

DTIC Science & Technology

2005-03-01

users (Wing and Bettinger , 2003). A GIS is a computerized system for spatial (geographically-referenced) data management (Davis and Schultz, 1990:3...AFIT/GEM/ENV/05M-15 Abstract The goal of this research was to develop a data collection instrument for an existing information technology...implementation and sustsinment model. In 2003, a unique system dynamics model was developed at the Air Force Institute of Technology to predict the

A GIS-BASED WETLAND ASESSMENT MODEL FOR PREDICTING AVIAN HABITAT SUITABILITY UNDER UNCERTAINTY

EPA Science Inventory

The Farmington Bay region of the Great Salt Lake is fringed with an array of wetland complexes providing critical avian habitat. Land use and hydrological changes resulting from projected urban population increases and combined with the low topographical relief of the lake basin...

Using a novel flood prediction model and GIS automation to measure the valley and channel morphology of large river networks

EPA Science Inventory

Traditional methods for measuring river valley and channel morphology require intensive ground-based surveys which are often expensive, time consuming, and logistically difficult to implement. The number of surveys required to assess the hydrogeomorphic structure of large river n...

Predicting thermal regimes of stream networks across the northeast United States: Natural and anthropogenic influences

EPA Science Inventory

We used STARS (Spatial Tools for the Analysis of River Systems), an ArcGIS geoprocessing toolbox, to create spatial stream networks. We then developed and assessed spatial statistical models for each of these metrics, incorporating spatial autocorrelation based on both distance...

Black Sea GIS developed in MHI

NASA Astrophysics Data System (ADS)

Zhuk, E.; Khaliulin, A.; Zodiatis, G.; Nikolaidis, A.; Isaeva, E.

2016-08-01

The work aims at creating the Black Sea geoinformation system (GIS) and complementing it with a model bank. The software for data access and visualization was developed using client server architecture. A map service based on MapServer and MySQL data management system were chosen for the Black Sea GIS. Php-modules and python-scripts are used to provide data access, processing, and exchange between the client application and the server. According to the basic data types, the module structure of GIS was developed. Each type of data is matched to a module which allows selection and visualization of the data. At present, a GIS complement with a model bank (the models build in to the GIS) and users' models (programs launched on users' PCs but receiving and displaying data via GIS) is developed.

Smart Caching Based on Mobile Agent of Power WebGIS Platform

PubMed Central

Wang, Xiaohui; Wu, Kehe; Chen, Fei

2013-01-01

Power information construction is developing towards intensive, platform, distributed direction with the expansion of power grid and improvement of information technology. In order to meet the trend, power WebGIS was designed and developed. In this paper, we first discuss the architecture and functionality of power WebGIS, and then we study caching technology in detail, which contains dynamic display cache model, caching structure based on mobile agent, and cache data model. We have designed experiments of different data capacity to contrast performance between WebGIS with the proposed caching model and traditional WebGIS. The experimental results showed that, with the same hardware environment, the response time of WebGIS with and without caching model increased as data capacity growing, while the larger the data was, the higher the performance of WebGIS with proposed caching model improved. PMID:24288504

Programming an Artificial Neural Network Tool for Spatial Interpolation in GIS - A Case Study for Indoor Radio Wave Propagation of WLAN.

PubMed

Sen, Alper; Gümüsay, M Umit; Kavas, Aktül; Bulucu, Umut

2008-09-25

Wireless communication networks offer subscribers the possibilities of free mobility and access to information anywhere at any time. Therefore, electromagnetic coverage calculations are important for wireless mobile communication systems, especially in Wireless Local Area Networks (WLANs). Before any propagation computation is performed, modeling of indoor radio wave propagation needs accurate geographical information in order to avoid the interruption of data transmissions. Geographic Information Systems (GIS) and spatial interpolation techniques are very efficient for performing indoor radio wave propagation modeling. This paper describes the spatial interpolation of electromagnetic field measurements using a feed-forward back-propagation neural network programmed as a tool in GIS. The accuracy of Artificial Neural Networks (ANN) and geostatistical Kriging were compared by adjusting procedures. The feedforward back-propagation ANN provides adequate accuracy for spatial interpolation, but the predictions of Kriging interpolation are more accurate than the selected ANN. The proposed GIS ensures indoor radio wave propagation model and electromagnetic coverage, the number, position and transmitter power of access points and electromagnetic radiation level. Pollution analysis in a given propagation environment was done and it was demonstrated that WLAN (2.4 GHz) electromagnetic coverage does not lead to any electromagnetic pollution due to the low power levels used. Example interpolated electromagnetic field values for WLAN system in a building of Yildiz Technical University, Turkey, were generated using the selected network architectures to illustrate the results with an ANN.

Programming an Artificial Neural Network Tool for Spatial Interpolation in GIS - A Case Study for Indoor Radio Wave Propagation of WLAN

PubMed Central

Şen, Alper; Gümüşay, M. Ümit; Kavas, Aktül; Bulucu, Umut

2008-01-01

Wireless communication networks offer subscribers the possibilities of free mobility and access to information anywhere at any time. Therefore, electromagnetic coverage calculations are important for wireless mobile communication systems, especially in Wireless Local Area Networks (WLANs). Before any propagation computation is performed, modeling of indoor radio wave propagation needs accurate geographical information in order to avoid the interruption of data transmissions. Geographic Information Systems (GIS) and spatial interpolation techniques are very efficient for performing indoor radio wave propagation modeling. This paper describes the spatial interpolation of electromagnetic field measurements using a feed-forward back-propagation neural network programmed as a tool in GIS. The accuracy of Artificial Neural Networks (ANN) and geostatistical Kriging were compared by adjusting procedures. The feedforward back-propagation ANN provides adequate accuracy for spatial interpolation, but the predictions of Kriging interpolation are more accurate than the selected ANN. The proposed GIS ensures indoor radio wave propagation model and electromagnetic coverage, the number, position and transmitter power of access points and electromagnetic radiation level. Pollution analysis in a given propagation environment was done and it was demonstrated that WLAN (2.4 GHz) electromagnetic coverage does not lead to any electromagnetic pollution due to the low power levels used. Example interpolated electromagnetic field values for WLAN system in a building of Yildiz Technical University, Turkey, were generated using the selected network architectures to illustrate the results with an ANN. PMID:27873854

Predicting runoff induced mass loads in urban watersheds: Linking land use and pyrethroid contamination.

PubMed

Chinen, Kazue; Lau, Sim-Lin; Nonezyan, Michael; McElroy, Elizabeth; Wolfe, Becky; Suffet, Irwin H; Stenstrom, Michael K

2016-10-01

Pyrethroid pesticide mass loadings in the Ballona Creek Watershed were calculated using the volume-concentration method with a Geographic Information Systems (GIS) to explore potential relationships between urban land use, impervious surfaces, and pyrethroid runoff flowing into an urban stream. A calibration of the GIS volume-concentration model was performed using 2013 and 2014 wet-weather sampling data. Permethrin and lambda-cyhalothrin were detected as the highest concentrations; deltamethrin, lambda-cyhalothrin, permethrin and cyfluthrin were the most frequently detected synthetic pyrethroids. Eight neighborhoods within the watershed were highlighted as target areas based on a Weighted Overlay Analysis (WOA) in GIS. Water phase concentration of synthetic pyrethroids (SPs) were calculated from the reported usage. The need for stricter BMP and consumer product controls was identified as a possible way of reducing the detections of pyrethroids in Ballona Creek. This model has significant implications for determining mass loadings due to land use influence, and offers a flexible method to extrapolate data for a limited amount of samplings for a larger watershed, particularly for chemicals that are not subject to environmental monitoring. Offered as a simple approach to watershed management, the GIS-volume concentration model has the potential to be applied to other target pesticides and is useful for simulating different watershed scenarios. Further research is needed to compare results against other similar urban watersheds situated in mediterranean climates. Copyright © 2016 Elsevier Ltd. All rights reserved.

ESB-based Sensor Web integration for the prediction of electric power supply system vulnerability.

PubMed

Stoimenov, Leonid; Bogdanovic, Milos; Bogdanovic-Dinic, Sanja

2013-08-15

Electric power supply companies increasingly rely on enterprise IT systems to provide them with a comprehensive view of the state of the distribution network. Within a utility-wide network, enterprise IT systems collect data from various metering devices. Such data can be effectively used for the prediction of power supply network vulnerability. The purpose of this paper is to present the Enterprise Service Bus (ESB)-based Sensor Web integration solution that we have developed with the purpose of enabling prediction of power supply network vulnerability, in terms of a prediction of defect probability for a particular network element. We will give an example of its usage and demonstrate our vulnerability prediction model on data collected from two different power supply companies. The proposed solution is an extension of the GinisSense Sensor Web-based architecture for collecting, processing, analyzing, decision making and alerting based on the data received from heterogeneous data sources. In this case, GinisSense has been upgraded to be capable of operating in an ESB environment and combine Sensor Web and GIS technologies to enable prediction of electric power supply system vulnerability. Aside from electrical values, the proposed solution gathers ambient values from additional sensors installed in the existing power supply network infrastructure. GinisSense aggregates gathered data according to an adapted Omnibus data fusion model and applies decision-making logic on the aggregated data. Detected vulnerabilities are visualized to end-users through means of a specialized Web GIS application.

ESB-Based Sensor Web Integration for the Prediction of Electric Power Supply System Vulnerability

PubMed Central

Stoimenov, Leonid; Bogdanovic, Milos; Bogdanovic-Dinic, Sanja

2013-01-01

Electric power supply companies increasingly rely on enterprise IT systems to provide them with a comprehensive view of the state of the distribution network. Within a utility-wide network, enterprise IT systems collect data from various metering devices. Such data can be effectively used for the prediction of power supply network vulnerability. The purpose of this paper is to present the Enterprise Service Bus (ESB)-based Sensor Web integration solution that we have developed with the purpose of enabling prediction of power supply network vulnerability, in terms of a prediction of defect probability for a particular network element. We will give an example of its usage and demonstrate our vulnerability prediction model on data collected from two different power supply companies. The proposed solution is an extension of the GinisSense Sensor Web-based architecture for collecting, processing, analyzing, decision making and alerting based on the data received from heterogeneous data sources. In this case, GinisSense has been upgraded to be capable of operating in an ESB environment and combine Sensor Web and GIS technologies to enable prediction of electric power supply system vulnerability. Aside from electrical values, the proposed solution gathers ambient values from additional sensors installed in the existing power supply network infrastructure. GinisSense aggregates gathered data according to an adapted Omnibus data fusion model and applies decision-making logic on the aggregated data. Detected vulnerabilities are visualized to end-users through means of a specialized Web GIS application. PMID:23955435

GRASS GIS: a peer-reviewed scientific platform and future research repository

NASA Astrophysics Data System (ADS)

Chemin, Yann; Petras, Vaclav; Petrasova, Anna; Landa, Martin; Gebbert, Sören; Zambelli, Pietro; Neteler, Markus; Löwe, Peter; Di Leo, Margherita

2015-04-01

Geographical Information System (GIS) is known for its capacity to spatially enhance the management of natural resources. While being often used as an analytical tool, it also represents a collaborative scientific platform to develop new algorithms. Thus, it is critical that GIS software as well as the algorithms are open and accessible to anybody [18]. We present how GRASS GIS, a free and open source GIS, is used by many scientists to implement and perform geoprocessing tasks. We will show how integrating scientific algorithms into GRASS GIS helps to preserve reproducibility of scientific results over time [15]. Moreover, subsequent improvements are tracked in the source code version control system and are immediately available to the public. GRASS GIS therefore acts as a repository of scientific peer-reviewed code, algorithm library, and knowledge hub for future generation of scientists. In the field of hydrology, with the various types of actual evapotranspiration (ET) models being developed in the last 20 years, it becomes necessary to inter-compare methods. Most of already published ETa models comparisons address few number of models, and small to medium areas [3, 6, 7, 22, 23]. With the large amount of remote sensing data covering the Earth, and the daily information available for the past ten years (i.e. Aqua/Terra-MODIS) for each pixel location, it becomes paramount to have a more complete comparison, in space and time. To address this new experimental requirement, a distributed computing framework was designed, and created [3, 4]. The design architecture was built from original satellite datasets to various levels of processing until reaching the requirement of various ETa models input dataset. Each input product is computed once and reused in all ETa models requiring such input. This permits standardization of inputs as much as possible to zero-in variations of models to the models internals/specificities. All of the ET models are available in the new GRASS GIS version 7 as imagery modules and replicability is complete for future research. A set of modules for multiscale analysis of landscape structure was added in 1992 by [1], who developed the r.le model similar to FRAGSTATS ([10]). The modules were gradually improved to become r.li in 2006. Further development continued, with a significant speed up [9] and new interactive user interface. The development of spatial interpolation module v.surf.rst started in 1988 [11] and continued by introduction of new interpolation methods and finally full integration into GRASS GIS version 4 [13]. Since then it was improved several times [8]. The module is an important part of GRASS GIS and is taught at geospatial modeling courses, for example at North Carolina State University [14]. GRASS GIS entails several modules that constitute the result of active research on natural hazard. The r.sim.water simulation model [12] for overland flow under rainfall excess conditions was integrated into the Emergency Routing Decision Planning system as a WPS [17]. It was also utilized by [16] and is now part of Tangible Landscape, a tangible GIS system, which also incorporated the r.damflood, a dam break inundation simulation [2]. The wildfire simulation toolset, originally developed by [24], implementing Rothermel's model [21], available through the GRASS GIS modules r.ros and r.spread, is object of active research. It has been extensively tested and recently adapted to European fuel types ([5, 19, 20]). References [1] Baker, W.L., Cai, Y., 1992. The r.le programs for multiscale analysis of landscape structure using the GRASS geographical information system. Landscape Ecology, 7(4):291-302. [2] Cannata M. and Marzocchi R., 2012. Two-dimensional dam break flooding simulation: a GIS embedded approach. - Natural Hazards 61(3):1143-1159. [3] Chemin, Y.H., 2012. A Distributed Benchmarking Framework for Actual ET Models. In Evapotranspiration - Remote Sensing and Modeling, Intech (Eds). [4] Chemin, Y. H. , 2014. Remote Sensing Raster Programming, 3rd Ed., Lulu (Eds). [5] Di Leo, M., de Rigo, D., Rodriguez-Aseretto, D., Bosco, C., Petroliagkis, T., Camia, A., San-Miguel-Ayanz, J., 2013. Dynamic data driven ensemble for wildfire behaviour assessment: A case study. IFIP Advances in Information and Communication Technology, vol. 413, pp. 11-22, 2013, ISSN:1868-4238. Special issue: "Environmental Software Systems. Fostering sharing information". [6] García, M., Villagarcía, L., Contreras, S., Domingo, F. & Puigdefábregas, J. (2007). Comparison of three operative models for estimating the surface water deficit using aster reflective and thermal data, Sensors 7(6): 860-883. [7] Gao, Y. & Long, D. ,2008. Intercomparison of remote sensing-based models for estimation of evapotranspiration and accuracy assessment based on swat, Hydrological Processes 22: 4850-4869. [8] GRASS GIS Trac, changelog for v.surf.rst, 2015. http://trac.osgeo.org/grass/ [9] GRASS GIS Trac, changelog for r.li, 2015. http://trac.osgeo.org/grass/ [10] McGarigal, K., and B. J. Marks. 1995. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. USDA For. Serv. Gen. Tech. Rep. PNW-351 [11] Mitas, L., and Mitasova H., 1988, General variational approach to the approximation problem, Computers and Mathematics with Applications, v.16, p. 983-992. [12] Mitas, L., and Mitasova, H., 1998, Distributed soil erosion simulation for effective erosion prevention. Water Resources Research, 34(3), 505-516. [13] Mitasova, H. and Mitas, L., 1993: Interpolation by Regularized Spline with Tension: I. Theory and Implementation, Mathematical Geology, 25, 641-655. [14] North Carolina State University, Geospatial Modeling Course, GIS/MEA582, 2015. http://courses.ncsu.edu/ [15] Petras, V., Gebbert, S., 2014. Testing framework for GRASS GIS: ensuring reproducibility of scientific geospatial computing. Poster presented at: AGU Fall Meeting, December 15-19, 2014, San Francisco, USA. [16] Petrasova, A., Harmon, B., Petras, V., Mitasova, H., 2014. GIS-based environmental modeling with tangible interaction and dynamic visualization. In: Ames, D.P., Quinn, N.W.T., Rizzoli, A.E. (Eds.), Proceedings of the 7th International Congress on Environmental Modelling and Software, June 15-19, San Diego, California, USA. ISBN: 978-88-9035-744-2 [17] Raghavan, v., Choosumrong, S., Yoshida, D., Vinayaraj, P., 2014. Deploying Dynamic Routing Service for Emergency Scenarios using pgRouting, GRASS and ZOO. In Proc. of FOSS4G Europe, Jacobs University, Bremen, Germany, July 15-17, 2014. [18] Rocchini, D., Neteler, M. ,2012. Let the four freedoms paradigm apply to ecology. Trends in Ecology & Evolution, 27: 310-311. [19] Rodriguez-Aseretto, D., de Rigo, D., Di Leo, M., Cortés, A., and San-Miguel-Ayanz, J., 2013. A data-driven model for large wildfire behaviour prediction in europe. Procedia Computer Science, vol. 18, pp. 1861-1870. [20] de Rigo, D., Rodriguez-Aseretto, D., Bosco, C., Di Leo, M., and San-Miguel-Ayanz, J., 2013. An architecture for adaptive robust modelling of wildfire behaviour under deep uncertainty. in Environmental Software Systems. Fostering Information Sharing, ser. IFIP Advances in Information and Communication Technology, J. Ȟrebíˇ cek, G. Schimak, M. Kubásek, and A. Rizzoli, Eds. Springer Berlin Heidelberg, 2013, vol. 413, pp. 367-380. [21] Rothermel, R. C., 1983. How to predict the spread and intensity of forest and range fires. US Forest Service, Gen. Tech. Rep. INT-143. Ogden, Utah. [22] Suleiman, A., Al-Bakri, J., Duqqah, M. & Crago, R. ,2008. Intercomparison ofevapotranspiration estimates at the different ecological zones in jordan, Journal of Hydrometeorology 9(5): 903-919. [23] Timmermans, W. J., Kustas, W. P., Anderson, M. C. & French, A. N. ,2007. An intercomparison of the surface energy balance algorithm for land (sebal) and the two-source energy balance (tseb) modeling schemes, Remote Sensing of Environment 108(4): 369 - 384. [24] Xu, Jianping, 1994. Simulating the spread of wildfires using a geographic information system and remote sensing. Ph. D. Dissertation, Rutgers University, New Brunswick, New Jersey.

Quasar X-Ray Spectra At z=1.5

NASA Technical Reports Server (NTRS)

Siemiginowska, Aneta

2001-01-01

The predicted counts for ASCA observation was much higher than actually observed counts in the quasar. However, there are three weak hard x-ray sources in the GIS field. We are adding them to the source counts in modeling of hard x-ray background. The work is in progress. We have published a paper in Ap.J. on the luminosity function and the quasar evolution. Based on the theory described in this paper we are predicting a number of sources and their contribution to the x-ray background at different redshifts. These model predictions will be compared to the observed data in the final paper.

The review of some chosen approaches to foresee the development of urban areas. (Polish Title: Przeglad wybranych podejsc w zakresie prognozowania rozwoju obszarow miast)

NASA Astrophysics Data System (ADS)

Radło-Kulisiewicz, M.

2015-12-01

The practical importance of Geographical Information Systems in urban planning and managing of urban areas is becoming much more explicit. Managing small cities usually needs simple GIS spatial analysis tools to support planners' decisions. Otherwise, the urban dynamic is bigger and factors affecting changes in city are combined. These analyses are not sufficient and then a need for more advanced and sophisticated solutions can appear. The aim of this article is to introduce popular techniques for urban modelling and underlying importance of GIS as an environment for creating simple models, which let t easy decisions in creating vision of a city be taken. The Article touches on the following issues related to the planning and management of urban space; from the applicable standards concerning materials planning in Poland, through the possibilities that give us network solutions useful at the municipal and country level, to existing techniques in modelling cities in the world. The background for these questions are the Geographical Information Systems (their role in this respect), that naturally fit into this theme. The ability to analyze multi-source data at different levels of detail, in different variants and ranges, predispose the GIS to environmental urban management. While also taking into account social - economic factors, integrated with GIS predictive modeling techniques, allows us to understand dependencies that navigate complex urban phenomena. City management in an integrated and thoughtful manner and will reduce the costs associated with the expansion of the urban fabric and avoid the chaos of urban development.

Modeling the Hydrologic Effects of Large-Scale Green Infrastructure Projects with GIS

NASA Astrophysics Data System (ADS)

Bado, R. A.; Fekete, B. M.; Khanbilvardi, R.

2015-12-01

Impervious surfaces in urban areas generate excess runoff, which in turn causes flooding, combined sewer overflows, and degradation of adjacent surface waters. Municipal environmental protection agencies have shown a growing interest in mitigating these effects with 'green' infrastructure practices that partially restore the perviousness and water holding capacity of urban centers. Assessment of the performance of current and future green infrastructure projects is hindered by the lack of adequate hydrological modeling tools; conventional techniques fail to account for the complex flow pathways of urban environments, and detailed analyses are difficult to prepare for the very large domains in which green infrastructure projects are implemented. Currently, no standard toolset exists that can rapidly and conveniently predict runoff, consequent inundations, and sewer overflows at a city-wide scale. We demonstrate how streamlined modeling techniques can be used with open-source GIS software to efficiently model runoff in large urban catchments. Hydraulic parameters and flow paths through city blocks, roadways, and sewer drains are automatically generated from GIS layers, and ultimately urban flow simulations can be executed for a variety of rainfall conditions. With this methodology, users can understand the implications of large-scale land use changes and green/gray storm water retention systems on hydraulic loading, peak flow rates, and runoff volumes.

Estimating erosion in a riverine watershed: Bayou Liberty-Tchefuncta River in Louisiana.

PubMed

Martin, August; Gunter, James T; Regens, James L

2003-01-01

GOAL, SCOPE, BACKGROUND: Sheet erosion from agricultural, forest and urban lands may increase stream sediment loads as well as transport other pollutants that adversely affect water quality, reduce agricultural and forest production, and increase infrastructure maintenance costs. This study uses spatial analysis techniques and a numerical modeling approach to predict areas with the greatest sheet erosion potential given different soils disturbance scenarios. A Geographic Information System (GIS) and the Universal Soil Loss Equation (USLE) were used to estimate sheet erosion from 0.64 ha parcels of land within the watershed. The Soil Survey of St. Tammany Parish, Louisiana was digitized, required soil attributes entered into the GIS database, and slope factors determined for each 80 x 80 meter parcel in the watershed. The GIS/USLE model used series-specific erosion K factors, a rainfall factor of 89, and a GIS database of scenario-driven cropping and erosion control practice factors to estimate potential soil loss due to sheet erosion. A general trend of increased potential sheet erosion occurred for all land use categories (urban, agriculture/grasslands, forests) as soil disturbance increases from cropping, logging and construction activities. Modeling indicated that rapidly growing urban areas have the greatest potential for sheet erosion. Evergreen and mixed forests (production forest) had lower sheet erosion potentials; with deciduous forests (mostly riparian) having the least sheet erosion potential. Erosion estimates from construction activities may be overestimated because of the value chosen for the erosion control practice factor. This study illustrates the ease with which GIS can be integrated with the Universal Soil Loss Equation to identify areas with high sheet erosion potential for large scale management and policy decision making. The GIS/USLE modeling approach used in this study offers a quick and inexpensive tool for estimating sheet erosion within watersheds using publicly available information. This method can quickly identify discrete locations with relatively precise spatial boundaries (approximately 80 meter resolution) that have a high sheet erosion potential as well as areas where management interventions might be appropriate to prevent or ameliorate erosion.

Advances in wind erosion modelling in Europe

NASA Astrophysics Data System (ADS)

Borrelli, Pasquale; Lugato, Emanuele; Alewell, Christine; Montanarella, Luca; Panagos, Panos

2017-04-01

Soil erosion by wind is a serious environmental problem often resulting in severe forms of soil degradation. Wind erosion is also a phenomenon relevant for Europe, although this land degradation process has been overlooked until very recently. The state-of-the-art literature presents wind erosion as a process that locally affects the semi-arid areas of the Mediterranean region as well as the temperate climate areas of the northern European countries. Actual observations, field measurements and modelling assessments, however, are all extremely limited and highly unequally distributed across Europe. As a result, we currently lack comprehensive understanding about where and when wind erosion occurs in Europe, and the intensity of erosion that poses a threat to agricultural productivity. Today's challenge is to integrate the insights of local experiments and field-scale models into a new generation of large-scale wind erosion models. While naturally being less accurate than field-scale models, these large-scale modelling approaches still provide essential knowledge about where and when wind erosion occurs and can disclose the level of risk for agricultural productivity in specific areas. Here, we present a geographic information system (GIS) version of the RWEQ (named GIS-RWEQ) to quantitatively assess soil loss by wind over large study areas (Land Degradation & Development, DOI: 10.1002/ldr.2588). The model designed to predict the daily soil loss potential at a ca. 1 km2 spatial resolution shows high consistency with local measurements reported in literature. The average soil loss predicted by GIS-RWEQ for the European arable land totals 62 million Mg yr-1, with an average area-specific soil loss of 0.53 Mg yr-1. The JRC model RUSLE2015, for the same area estimates 295 million Mg yr-1 of soil loss due to water erosion. Notably, soil loss by wind erosion in the European arable land could be as high as 20% of water erosion, even though the areas affected are mainly concentrated in hotspots.

Section 4. The GIS Weasel User's Manual

USGS Publications Warehouse

Viger, Roland J.; Leavesley, George H.

2007-01-01

INTRODUCTION The GIS Weasel was designed to aid in the preparation of spatial information for input to lumped and distributed parameter hydrologic or other environmental models. The GIS Weasel provides geographic information system (GIS) tools to help create maps of geographic features relevant to a user's model and to generate parameters from those maps. The operation of the GIS Weasel does not require the user to be a GIS expert, only that the user have an understanding of the spatial information requirements of the environmental simulation model being used. The GIS Weasel software system uses a GIS-based graphical user interface (GUI), the C programming language, and external scripting languages. The software will run on any computing platform where ArcInfo Workstation (version 8.0.2 or later) and the GRID extension are accessible. The user controls the processing of the GIS Weasel by interacting with menus, maps, and tables. The purpose of this document is to describe the operation of the software. This document is not intended to describe the usage of this software in support of any particular environmental simulation model. Such guides are published separately.

SWAT-based streamflow and embayment modeling of Karst-affected Chapel branch watershed, South Carolina

Treesearch

Devendra Amatya; M. Jha; A.E. Edwards; T.M. Williams; D.R. Hitchcock

2011-01-01

SWAT is a GIS-based basin-scale model widely used for the characterization of hydrology and water quality of large, complex watersheds; however, SWAT has not been fully tested in watersheds with karst geomorphology and downstream reservoir-like embayment. In this study, SWAT was applied to test its ability to predict monthly streamflow dynamics for a 1,555 ha karst...

Integration of GIS and Bim for Indoor Geovisual Analytics

NASA Astrophysics Data System (ADS)

Wu, B.; Zhang, S.

2016-06-01

This paper presents an endeavour of integration of GIS (Geographical Information System) and BIM (Building Information Modelling) for indoor geovisual analytics. The merits of two types of technologies, GIS and BIM are firstly analysed in the context of indoor environment. GIS has well-developed capabilities of spatial analysis such as network analysis, while BIM has the advantages for indoor 3D modelling and dynamic simulation. This paper firstly investigates the important aspects for integrating GIS and BIM. Different data standards and formats such as the IFC (Industry Foundation Classes) and GML (Geography Markup Language) are discussed. Their merits and limitations in data transformation between GIS and BIM are analysed in terms of semantic and geometric information. An optimized approach for data exchange between GIS and BIM datasets is then proposed. After that, a strategy of using BIM for 3D indoor modelling, GIS for spatial analysis, and BIM again for visualization and dynamic simulation of the analysis results is presented. Based on the developments, this paper selects a typical problem, optimized indoor emergency evacuation, to demonstrate the integration of GIS and BIM for indoor geovisual analytics. The block Z of the Hong Kong Polytechnic University is selected as a test site. Detailed indoor and outdoor 3D models of the block Z are created using a BIM software Revit. The 3D models are transferred to a GIS software ArcGIS to carry out spatial analysis. Optimized evacuation plans considering dynamic constraints are generated based on network analysis in ArcGIS assuming there is a fire accident inside the building. The analysis results are then transferred back to BIM software for visualization and dynamic simulation. The developed methods and results are of significance to facilitate future development of GIS and BIM integrated solutions in various applications.

δ2H and δ18O of human body water: a GIS model to distinguish residents from non-residents in the contiguous USA.

PubMed

Podlesak, David W; Bowen, Gabriel J; O'Grady, Shannon; Cerling, Thure E; Ehleringer, James R

2012-06-01

An understanding of the factors influencing the isotopic composition of body water is important to determine the isotopic composition of tissues that are used to reconstruct movement patterns of humans. The δ(2)H and δ(18)O values of body water (δ(2)H(bw) and δ(18)O(bw)) are related to the δ(2)H and δ(18)O values of drinking water (δ(2)H(dw) and δ(18)O(dw)), but clearly distinct because of other factors including the composition of food. Here, we develop a mechanistic geographical information system (GIS) model to produce spatial projections of δ(2)H(bw) and δ(18)O(bw) values for the USA. We investigate the influence of gender, food, and drinking water on the predicted values by comparing them with the published values. The strongest influence on the predicted values was related to the source of δ(2)H(dw) and δ(18)O(dw) values. We combine the model with equations that describe the rate of turnover to produce estimates for the time required for a non-resident to reach an isotopic equilibrium with a resident population.

A GIS model-based assessment of the environmental distribution of gamma-hexachlorocyclohexane in European soils and waters.

PubMed

Vizcaíno, P; Pistocchi, A

2010-10-01

The MAPPE GIS based multimedia model is used to produce a quantitative description of the behaviour of gamma-hexachlorocyclohexane (gamma-HCH) in Europe, with emphasis on continental surface waters. The model is found to reasonably reproduce gamma-HCH distributions and variations along the years in atmosphere and soil; for continental surface waters, concentrations were reasonably well predicted for year 1995, when lindane was still used in agriculture, while for 2005, assuming severe restrictions in use, yields to substantial underestimation. Much better results were yielded when same mode of release as in 1995 was considered, supporting the conjecture that for gamma-HCH, emission data rather that model structure and parameterization can be responsible for wrong estimation of concentrations. Future research should be directed to improve the quality of emission data. Joint interpretation of monitoring and modelling results, highlights that lindane emissions in Europe, despite the marked decreasing trend, persist beyond the provisions of existing legislation. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Wilderness recreation participation: Projections for the next half century

Treesearch

J. M. Bowker; D. Murphy; H. K. Cordell; D. B. K. English; J. C. Bergstrom; C. M. Starbuck; C. J. Betz; G. T. Green; P. Reed

2007-01-01

This paper explores the influence of demographic and spatial variables on individual participation in wildland area recreation. Data from the National Survey on Recreation and the Environment (NSRE) are combined with GIS-based distance measures to develop nonlinear regression models used to predict both participation and the number of days of participation in...

Defining and predicting urban-wildland interface zones using a GIS-based model

Treesearch

Lawrence R. Gering; Angel V. Chun; Steve Anderson

2000-01-01

Resource managers are beginning to experience a deluge of management conflicts as urban population centers expand into formerly wildland settings. Fire suppression, recreational, watershed management, and traditional forest management practices are activities that have become contentious in many locales. A better understanding of the interface zone between these two...

A GIS-derived integrated moisture index to predict forest composition and productivity of Ohio forests (U.S.A.)

Treesearch

Louis R. Iverson; Martin E. Dale; Charles T. Scott; Anantha Prasad; Anantha Prasad

1997-01-01

A geographic information system (GIS) approach was used in conjunction with forest-plot data to develop an integrated moisture index (IMI), which was then used to predict forest productivity (site index) and species composition for forests in Ohio. In this region, typical of eastern hardwoods across the Midwest and southern Appalachians, topographic aspect and position...

The impact of supersaturation level for oral absorption of BCS class IIb drugs, dipyridamole and ketoconazole, using in vivo predictive dissolution system: Gastrointestinal Simulator (GIS).

PubMed

Tsume, Yasuhiro; Matsui, Kazuki; Searls, Amanda L; Takeuchi, Susumu; Amidon, Gregory E; Sun, Duxin; Amidon, Gordon L

2017-05-01

The development of formulations and the assessment of oral drug absorption for Biopharmaceutical Classification System (BCS) class IIb drugs is often a difficult issue due to the potential for supersaturation and precipitation in the gastrointestinal (GI) tract. The physiological environment in the GI tract largely influences in vivo drug dissolution rates of those drugs. Thus, those physiological factors should be incorporated into the in vitro system to better assess in vivo performance of BCS class IIb drugs. In order to predict oral bioperformance, an in vitro dissolution system with multiple compartments incorporating physiologically relevant factors would be expected to more accurately predict in vivo phenomena than a one-compartment dissolution system like USP Apparatus 2 because, for example, the pH change occurring in the human GI tract can be better replicated in a multi-compartmental platform. The Gastrointestinal Simulator (GIS) consists of three compartments, the gastric, duodenal and jejunal chambers, and is a practical in vitro dissolution apparatus to predict in vivo dissolution for oral dosage forms. This system can demonstrate supersaturation and precipitation and, therefore, has the potential to predict in vivo bioperformance of oral dosage forms where this phenomenon may occur. In this report, in vitro studies were performed with dipyridamole and ketoconazole to evaluate the precipitation rates and the relationship between the supersaturation levels and oral absorption of BCS class II weak base drugs. To evaluate the impact of observed supersaturation levels on oral absorption, a study utilizing the GIS in combination with mouse intestinal infusion was conducted. Supersaturation levels observed in the GIS enhanced dipyridamole and ketoconazole absorption in mouse, and a good correlation between their supersaturation levels and their concentration in plasma was observed. The GIS, therefore, appears to represent in vivo dissolution phenomena and demonstrate supersaturation and precipitation of dipyridamole and ketoconazole. We therefore conclude that the GIS has been shown to be a good biopredictive tool to predict in vivo bioperformance of BCS class IIb drugs that can be used to optimize oral formulations. Copyright © 2017. Published by Elsevier B.V.

STEMS-Air: a simple GIS-based air pollution dispersion model for city-wide exposure assessment.

PubMed

Gulliver, John; Briggs, David

2011-05-15

Current methods of air pollution modelling do not readily meet the needs of air pollution mapping for short-term (i.e. daily) exposure studies. The main limiting factor is that for those few models that couple with a GIS there are insufficient tools for directly mapping air pollution both at high spatial resolution and over large areas (e.g. city wide). A simple GIS-based air pollution model (STEMS-Air) has been developed for PM(10) to meet these needs with the option to choose different exposure averaging periods (e.g. daily and annual). STEMS-Air uses the grid-based FOCALSUM function in ArcGIS in conjunction with a fine grid of emission sources and basic information on meteorology to implement a simple Gaussian plume model of air pollution dispersion. STEMS-Air was developed and validated in London, UK, using data on concentrations of PM(10) from routinely available monitoring data. Results from the validation study show that STEMS-Air performs well in predicting both daily (at four sites) and annual (at 30 sites) concentrations of PM(10). For daily modelling, STEMS-Air achieved r(2) values in the range 0.19-0.43 (p<0.001) based solely on traffic-related emissions and r(2) values in the range 0.41-0.63 (p<0.001) when adding information on 'background' levels of PM(10). For annual modelling of PM(10), the model returned r(2) in the range 0.67-0.77 (P<0.001) when compared with monitored concentrations. The model can thus be used for rapid production of daily or annual city-wide air pollution maps either as a screening process in urban air quality planning and management, or as the basis for health risk assessment and epidemiological studies. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

GIS-MODFLOW: Ein kleines OpenSource-Werkzeug zur Anbindung von GIS-Daten an MODFLOW

NASA Astrophysics Data System (ADS)

Gossel, Wolfgang

2013-06-01

The numerical model MODFLOW (Harbaugh 2005) is an efficient and up-to-date tool for groundwater flow modelling. On the other hand, Geo-Information-Systems (GIS) provide useful tools for data preparation and visualization that can also be incorporated in numerical groundwater modelling. An interface between both would therefore be useful for many hydrogeological investigations. To date, several integrated stand-alone tools have been developed that rely on MODFLOW, MODPATH and transport modelling tools. Simultaneously, several open source-GIS codes were developed to improve functionality and ease of use. These GIS tools can be used as pre- and post-processors of the numerical model MODFLOW via a suitable interface. Here we present GIS-MODFLOW as an open-source tool that provides a new universal interface by using the ESRI ASCII GRID data format that can be converted into MODFLOW input data. This tool can also treat MODFLOW results. Such a combination of MODFLOW and open-source GIS opens new possibilities to render groundwater flow modelling, and simulation results, available to larger circles of hydrogeologists.

Remote sensing and GIS analysis for demarcation of coastal hazard line along the highly eroding Krishna-Godavari delta front

NASA Astrophysics Data System (ADS)

Kallepalli, Akhil; Kakani, Nageswara Rao; James, David B.

2016-10-01

Coastal regions, especially river deltas are highly resourceful and hence densely populated; but these extremely low-lying lands are vulnerable to rising sea levels due to global warming threatening the life and property in these regions. Recent IPCC (2013) predictions of 26-82cm global sea level rise are now considered conservative as subsequent investigations such as by Met Office, UK indicated a vertical rise of about 190cm, which would displace 10% of the world's population living within 10 meters above the sea level. Therefore, predictive models showing the hazard line are necessary for efficient coastal zone management. Remote sensing and GIS technologies form the mainstay of such predictive models on coastal retreat and inundation to future sea-level rise. This study is an attempt to estimate the varying trends along the Krishna-Godavari (K-G) delta region. Detailed maps showing various coastal landforms in the K-G delta region were prepared using the IRS-P6 LISS 3 images. The rate of shoreline shift during a 31-year period along different sectors of the 330km long K-G delta coast was estimated using Landsat-2 and IRS-P6 LISS 3 images between 1977 and 2008. With reference to a selected baseline from along an inland position, End Point Rate (EPR), Shoreline Change Envelope (SCE) and Net Shoreline Movement (NSM) were calculated, using a GIS-based Digital Shoreline Analysis System (DSAS). The results showed that the shoreline migrated landward up to a maximum distance of 3.13km resulting in a net loss of about 42.10km2 area during this 31-year period. Further, considering the nature of landforms and EPR, the future hazard line is predicted for the area, which also indicated a net erosion of about 57.68km2 along the K-G delta coast by 2050 AD.

Perspectives on why digital ecologies matter: combining population genetics and ecologically informed agent-based models with GIS for managing dipteran livestock pests.

PubMed

Peck, Steven L

2014-10-01

It is becoming clear that handling the inherent complexity found in ecological systems is an essential task for finding ways to control insect pests of tropical livestock such as tsetse flies, and old and new world screwworms. In particular, challenging multivalent management programs, such as Area Wide Integrated Pest Management (AW-IPM), face daunting problems of complexity at multiple spatial scales, ranging from landscape level processes to those of smaller scales such as the parasite loads of individual animals. Daunting temporal challenges also await resolution, such as matching management time frames to those found on ecological and even evolutionary temporal scales. How does one deal with representing processes with models that involve multiple spatial and temporal scales? Agent-based models (ABM), combined with geographic information systems (GIS), may allow for understanding, predicting and managing pest control efforts in livestock pests. This paper argues that by incorporating digital ecologies in our management efforts clearer and more informed decisions can be made. I also point out the power of these models in making better predictions in order to anticipate the range of outcomes possible or likely. Copyright © 2014 International Atomic Energy Agency 2014. Published by Elsevier B.V. All rights reserved.

Net-infiltration map of the Navajo Sandstone outcrop area in western Washington County, Utah

USGS Publications Warehouse

Heilweil, Victor M.; McKinney, Tim S.

2007-01-01

As populations grow in the arid southwestern United States and desert bedrock aquifers are increasingly targeted for future development, understanding and quantifying the spatial variability of net infiltration and recharge becomes critically important for inventorying groundwater resources and mapping contamination vulnerability. A Geographic Information System (GIS)-based model utilizing readily available soils, topographic, precipitation, and outcrop data has been developed for predicting net infiltration to exposed and soil-covered areas of the Navajo Sandstone outcrop of southwestern Utah. The Navajo Sandstone is an important regional bedrock aquifer. The GIS model determines the net-infiltration percentage of precipitation by using an empirical equation. This relation is derived from least squares linear regression between three surficial parameters (soil coarseness, topographic slope, and downgradient distance from outcrop) and the percentage of estimated net infiltration based on environmental tracer data from excavations and boreholes at Sand Hollow Reservoir in the southeastern part of the study area.Processed GIS raster layers are applied as parameters in the empirical equation for determining net infiltration for soil-covered areas as a percentage of precipitation. This net-infiltration percentage is multiplied by average annual Parameter-elevation Regressions on Independent Slopes Model (PRISM) precipitation data to obtain an infiltration rate for each model cell. Additionally, net infiltration on exposed outcrop areas is set to 10 percent of precipitation on the basis of borehole net-infiltration estimates. Soils and outcrop net-infiltration rates are merged to form a final map.Areas of low, medium, and high potential for ground-water recharge have been identified, and estimates of net infiltration range from 0.1 to 66 millimeters per year (mm/yr). Estimated net-infiltration rates of less than 10 mm/yr are considered low, rates of 10 to 50 mm/yr are considered medium, and rates of more than 50 mm/yr are considered high. A comparison of estimated net-infiltration rates (determined from tritium data) to predicted rates (determined from GIS methods) at 12 sites in Sand Hollow and at Anderson Junction indicates an average difference of about 50 percent. Two of the predicted values were lower, five were higher, and five were within the estimated range. While such uncertainty is relatively small compared with the three order-of-magnitude range in predicted net-infiltration rates, the net-infiltration map is best suited for evaluating relative spatial distribution rather than for precise quantification of recharge to the Navajo aquifer at specific locations. An important potential use for this map is land-use zoning for protecting high net-infiltration parts of the aquifer from potential surface contamination.

Predicting the potential distribution of invasive exotic species using GIS and information-theoretic approaches: A case of ragweed (Ambrosia artemisiifolia L.) distribution in China

USGS Publications Warehouse

Hao, Chen; LiJun, Chen; Albright, Thomas P.

2007-01-01

Invasive exotic species pose a growing threat to the economy, public health, and ecological integrity of nations worldwide. Explaining and predicting the spatial distribution of invasive exotic species is of great importance to prevention and early warning efforts. We are investigating the potential distribution of invasive exotic species, the environmental factors that influence these distributions, and the ability to predict them using statistical and information-theoretic approaches. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, for most species, absence data are not available. Presented with the challenge of developing a model based on presence-only information, we developed an improved logistic regression approach using Information Theory and Frequency Statistics to produce a relative suitability map. This paper generated a variety of distributions of ragweed (Ambrosia artemisiifolia L.) from logistic regression models applied to herbarium specimen location data and a suite of GIS layers including climatic, topographic, and land cover information. Our logistic regression model was based on Akaike's Information Criterion (AIC) from a suite of ecologically reasonable predictor variables. Based on the results we provided a new Frequency Statistical method to compartmentalize habitat-suitability in the native range. Finally, we used the model and the compartmentalized criterion developed in native ranges to "project" a potential distribution onto the exotic ranges to build habitat-suitability maps. ?? Science in China Press 2007.

A comparison between index of entropy and catastrophe theory methods for mapping groundwater potential in an arid region.

PubMed

Al-Abadi, Alaa M; Shahid, Shamsuddin

2015-09-01

In this study, index of entropy and catastrophe theory methods were used for demarcating groundwater potential in an arid region using weighted linear combination techniques in geographical information system (GIS) environment. A case study from Badra area in the eastern part of central of Iraq was analyzed and discussed. Six factors believed to have influence on groundwater occurrence namely elevation, slope, aquifer transmissivity and storativity, soil, and distance to fault were prepared as raster thematic layers to facility integration into GIS environment. The factors were chosen based on the availability of data and local conditions of the study area. Both techniques were used for computing weights and assigning ranks vital for applying weighted linear combination approach. The results of application of both modes indicated that the most influential groundwater occurrence factors were slope and elevation. The other factors have relatively smaller values of weights implying that these factors have a minor role in groundwater occurrence conditions. The groundwater potential index (GPI) values for both models were classified using natural break classification scheme into five categories: very low, low, moderate, high, and very high. For validation of generated GPI, the relative operating characteristic (ROC) curves were used. According to the obtained area under the curve, the catastrophe model with 78 % prediction accuracy was found to perform better than entropy model with 77 % prediction accuracy. The overall results indicated that both models have good capability for predicting groundwater potential zones.

A simulation method for the stability analysis of landscape scenarios by using a NetLogo application in GIS environment

NASA Astrophysics Data System (ADS)

Gobattoni, Federica; Lauro, Giuliana; Leone, Antonio; Monaco, Roberto; Pelorosso, Raffaele

2010-05-01

Landscape continually evolves under the influence of a complex and broad range of natural processes, directly or indirectly determined by land use, but also under the impact of anthropic actions of planning and territorial management. While processes such as earthquakes, landslides, and so on, are manifestations of this evolutionary process, human decisions concerning land government (cropping, urbanization etc.) may affect dramatically the landscape evolution in a complex mechanism of cause-and-effect leading to accelerated erosion phenomena, hydro-geological instability and flood events. To better understand landscape evolution and change in time, several numerical and empirical models have been developed and implemented with the aim to explore and explain such complex processes; reproducing landscape evolution through models and schematic representation of reality could be a powerful and reliable tool for natural resources planning and decision making in land management. Even understanding interactions and relations between the involved variables, predicting how the system will react to external inputs such as political, social or economic constraints, could be strongly difficult. Decision support systems could help in choosing among possible alternatives by integrating different sources of information and "What if" scenarios could be developed as possible future states of the world that represent alternative plausible conditions under different assumptions (Mahmoud M. wt al., 2009). Modelling approaches can be successfully applied to describe and assess both the natural spatial environmental variability and the anthropic impacts at different temporal and spatial scales even if they usually takes into account each aspect of the environmental system separately and without looking directly at landscape as a unique system and without understanding its intrinsic evolution mechanisms (H. Siegrist, 2002, S. Demberel, 2003, A. Brenner, 2005). GIS-based models which could be able to predict the response of the landscape working as a unique system, are expected to advance through a development of sustainable planning strategies and to evaluate the equilibrium-non equilibrium status of landscape evolution and the availability of vital resources in space and time. In this context mathematical models adapted in GIS environment may really give an heavy contribution in such a complex problem- solving, providing a real and concrete Decision System Support. An integrated GIS (Geographic Information System)-based approach was developed (G. Lauro, R. Monaco, 2008) combining an ecological graph model for the analysis of the relationship between spatial pattern and ecological flows and a mathematical model, based on a system of two nonlinear differential equations, that studies meta-stability and bifurcation phenomena. These equations are mainly based on a balance law between a logistic growth of bio-energy and its reduction due to limiting factors coming from environmental constraints. The energy exchange among them will be more or less strong depending on the degree of permeability of the barriers which can obstruct the energy passage from each "landscape unit" to the other. Through NetLogo, a cross-platform multi-agent programmable modelling environment, a completely automatic GIS-based mathematical model, based on the ecological graph and on the cited two differential equations, is presented and discussed here. A study case in Central Italy is analysed to better underline the importance of such a user friendly model in GIS environment.

Natural and environmental vulnerability analysis through remote sensing and GIS techniques: a case study of Indigirka River basin, Eastern Siberia, Russia

NASA Astrophysics Data System (ADS)

Boori, Mukesh S.; Choudhary, Komal; Kupriyanov, Alexander; Sugimoto, Atsuko; Evers, Mariele

2016-10-01

The aim of this research work is to understand natural and environmental vulnerability situation and its cause such as intensity, distribution and socio-economic effect in the Indigirka River basin, Eastern Siberia, Russia. This paper identifies, assess and classify natural and environmental vulnerability using landscape pattern from multidisciplinary approach, based on remote sensing and Geographical Information System (GIS) techniques. A model was developed by following thematic layers: land use/cover, vegetation, wetland, geology, geomorphology and soil in ArcGIS 10.2 software. According to numerical results vulnerability classified into five levels: low, sensible, moderate, high and extreme vulnerability by mean of cluster principal. Results are shows that in natural vulnerability maximum area covered by moderate (29.84%) and sensible (38.61%) vulnerability and environmental vulnerability concentrated by moderate (49.30%) vulnerability. So study area has at medial level vulnerability. The results found that the methodology applied was effective enough in the understanding of the current conservation circumstances of the river basin in relation to their environment with the help of remote sensing and GIS. This study is helpful for decision making for eco-environmental recovering and rebuilding as well as predicting the future development.

[Establishment of comprehensive prediction model of acute gastrointestinal injury classification of critically ill patients].

PubMed

Wang, Yan; Wang, Jianrong; Liu, Weiwei; Zhang, Guangliang

2018-03-25

To develop the comprehensive prediction model of acute gastrointestinal injury (AGI) grades of critically ill patients. From April 2015 to November 2015, the binary channel gastrointestinal sounds (GIS) monitor system which has been developed and verified by the research group was used to gather and analyze the GIS of 60 consecutive critically ill patients who were admitted in Critical Care Medicine of Chinese PLA General Hospital. Also, the AGI grades (Grande I(-IIII(, the higher the level, the heavier the gastrointestinal dysfunction) were evaluated. Meanwhile, the clinical data and physiological and biochemical indexes of included patients were collected and recorded daily, including illness severity score (APACHE II( score, consisting of the acute physiology score, age grade and chronic health evaluation), sequential organ failure assessment (SOFA score, including respiration, coagulation, liver, cardioascular, central nervous system and kidney) and Glasgow coma scale (GCS); body mass index, blood lactate and glucose, and treatment details (including mechanical ventilation, sedatives, vasoactive drugs, enteral nutrition, etc.) Then principal component analysis was performed on the significantly correlated GIS (five indexes of gastrointestinal sounds were found to be negatively correlated with AGI grades, which included the number, percentage of time, mean power, maximum power and maximum time of GIS wave from the channel located at the stomach) and clinical factors after standardization. The top 5 post-normalized main components were selected for back-propagation (BP) neural network training, to establish comprehensive AGI grades models of critically ill patients based on the neural network model. The 60 patients aged 19 to 98 (mean 54.6) years and included 42 males (70.0%). There were 22 cases of multiple fractures, 15 cases of severe infection, 7 cases of cervical vertebral fracture, 7 cases of aortic repair, 5 cases of post-toxicosis and 4 cases of cerebral trauma. There were 33 emergency operation, 10 cases of elecoperectomy and 17 cases of drug treatment. There were 56 cases of diabetes(93.3%). Forty-five cases (75.0%) used vasoactive drugs, 37 cases (61.7%) used mechanical ventilation and 44 cases (73.3%) used enteral nutrition. APACHE II( score were 4.0 to 28.0(average 16.8) points. Four clinical factors were significantly positively related with AGI grades, including lactic acid level (r=0.215, P=0.000), SOFA score (r=0.383, P=0.000), the use of vascular active drugs (r=0.611, P=0.000) and mechanical ventilation (r=0.142, P=0.014). In addition to the five indexes of gastric bowel sounds which were found to be negatively correlated with AGI grades, the characteristics of 333 by 9 were composed of these nine indexes with high correlation of AGI grades. Five main components were selected after principal component analysis of these nine correlated indexes. A comprehensive AGI grades model of critically ill patients with a fitting degree of 0.967 3 and an accuracy rate of 82.61% was built by BP artificial neural network. The comprehensive model to classify AGI grades with the GIS is developed, which can help further predicting the classification of AGI grades of critically ill patients.

Soil erosion modeled with USLE, GIS, and remote sensing: a case study of Ikkour watershed in Middle Atlas (Morocco)

NASA Astrophysics Data System (ADS)

El Jazouli, Aafaf; Barakat, Ahmed; Ghafiri, Abdessamad; El Moutaki, Saida; Ettaqy, Abderrahim; Khellouk, Rida

2017-12-01

The Ikkour watershed located in the Middle Atlas Mountain (Morocco) has been a subject of serious soil erosion problems. This study aimed to assess the soil erosion susceptibility in this mountainous watershed using Universal Soil Loss Equation (USLE) and spectral indices integrated with Geographic Information System (GIS) environment. The USLE model required the integration of thematic factors' maps which are rainfall aggressiveness, length and steepness of the slope, vegetation cover, soil erodibility, and erosion control practices. These factors were calculated using remote sensing data and GIS. The USLE-based assessment showed that the estimated total annual potential soil loss was about 70.66 ton ha-1 year-1. This soil loss is favored by the steep slopes and degraded vegetation cover. The spectral index method, offering a qualitative evaluation of water erosion, showed different degrees of soil degradation in the study watershed according to FI, BI, CI, and NDVI. The results of this study displayed an agreement between the USLE model and spectral index approach, and indicated that the predicted soil erosion rate can be due to the most rugged land topography and an increase in agricultural areas. Indeed, these results can further assist the decision makers in implementation of suitable conservation program to reduce soil erosion.

[GIS and scenario analysis aid to water pollution control planning of river basin].

PubMed

Wang, Shao-ping; Cheng, Sheng-tong; Jia, Hai-feng; Ou, Zhi-dan; Tan, Bin

2004-07-01

The forward and backward algorithms for watershed water pollution control planning were summarized in this paper as well as their advantages and shortages. The spatial databases of water environmental function region, pollution sources, monitoring sections and sewer outlets were built with ARCGIS8.1 as the platform in the case study of Ganjiang valley, Jiangxi province. Based on the principles of the forward algorithm, four scenarios were designed for the watershed pollution control. Under these scenarios, ten sets of planning schemes were generated to implement cascade pollution source control. The investment costs of sewage treatment for these schemes were estimated by means of a series of cost-effective functions; with pollution source prediction, the water quality was modeled with CSTR model for each planning scheme. The modeled results of different planning schemes were visualized through GIS to aid decision-making. With the results of investment cost and water quality attainment as decision-making accords and based on the analysis of the economic endurable capacity for water pollution control in Ganjiang river basin, two optimized schemes were proposed. The research shows that GIS technology and scenario analysis can provide a good guidance to the synthesis, integrity and sustainability aspects for river basin water quality planning.

Bim-Gis Integrated Geospatial Information Model Using Semantic Web and Rdf Graphs

NASA Astrophysics Data System (ADS)

Hor, A.-H.; Jadidi, A.; Sohn, G.

2016-06-01

In recent years, 3D virtual indoor/outdoor urban modelling becomes a key spatial information framework for many civil and engineering applications such as evacuation planning, emergency and facility management. For accomplishing such sophisticate decision tasks, there is a large demands for building multi-scale and multi-sourced 3D urban models. Currently, Building Information Model (BIM) and Geographical Information Systems (GIS) are broadly used as the modelling sources. However, data sharing and exchanging information between two modelling domains is still a huge challenge; while the syntactic or semantic approaches do not fully provide exchanging of rich semantic and geometric information of BIM into GIS or vice-versa. This paper proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graphs. The novelty of the proposed solution comes from the benefits of integrating BIM and GIS technologies into one unified model, so-called Integrated Geospatial Information Model (IGIM). The proposed approach consists of three main modules: BIM-RDF and GIS-RDF graphs construction, integrating of two RDF graphs, and query of information through IGIM-RDF graph using SPARQL. The IGIM generates queries from both the BIM and GIS RDF graphs resulting a semantically integrated model with entities representing both BIM classes and GIS feature objects with respect to the target-client application. The linkage between BIM-RDF and GIS-RDF is achieved through SPARQL endpoints and defined by a query using set of datasets and entity classes with complementary properties, relationships and geometries. To validate the proposed approach and its performance, a case study was also tested using IGIM system design.

Mapping the risk of sudden oak death in Oregon: prioritizing locations for early detection and eradication

Treesearch

V& aacute; clavík Tom& aacute; & scaron; ; Alan Kanaskie; Ellen Goheen; Janet Ohmann; Everett Hansen; Ross Meentemeyer

2010-01-01

Phytophthora ramorum was first discovered in forests of southwestern Oregon in 2001. Despite intense eradication efforts, disease continues to spread from initially infested sites because of the late discovery of disease outbreaks and incomplete detection. Here we present two GIS predictive models of sudden oak death (SOD) establishment and spread...

Terrain and landform influence on Tsuga canadensis (L.) Carriere (Eastern Hemlock) distribution in the southern Appalachian Mountains

Treesearch

G. Narayanaraj; P.V. Bolstad; K.J. Elliott; J.M. Vose

2010-01-01

We examined the relationships between hemlock distribution and abundance and terrain attributes for the Coweeta Basin in the southern Appalachian Mountains. Field measurements were combined with GIS mapping methods to develop predictive models of abundance and distribution of Tsuga canadensis (L.) Carriere (eastern hemlock) and evaluate the co-...

An Overview of the GIS Weasel

USGS Publications Warehouse

Viger, Roland J.

2008-01-01

This fact sheet provides a high-level description of the GIS Weasel, a software system designed to aid users in preparing spatial information as input to lumped and distributed parameter environmental simulation models (ESMs). The GIS Weasel provides geographic information system (GIS) tools to help create maps of geographic features relevant to the application of a user?s ESM and to generate parameters from those maps. The operation of the GIS Weasel does not require a user to be a GIS expert, only that a user has an understanding of the spatial information requirements of the model. The GIS Weasel software system provides a GIS-based graphical user interface (GUI), C programming language executables, and general utility scripts. The software will run on any computing platform where ArcInfo Workstation (version 8.1 or later) and the GRID extension are accessible. The user controls the GIS Weasel by interacting with menus, maps, and tables.

Use of Remote Sensing/Geographical Information Systems (RS/GIS) to Identify the Distributional Limits of Soil-Transmitted Helminths (STHs) and Their Association to Prevalence of Intestinal Infection in School-Age Children in Four Rural Communities in Boaco, Nicaragua

NASA Technical Reports Server (NTRS)

Moreno, Max J.; Al-Hamdan, Mohammad Z.; Parajon, David G.; Rickman, Douglas L.; Luvall, Jeffrey; Parajon, Laura C.; Martinez, Roberto A.; Estes, Sue

2011-01-01

STHs can infect all members of a population but school-age children living in poverty are at greater risk. Infection can be controlled with drug treatment, health education and sanitation. Helminth control programs often lack resources and reliable information to identify areas of highest risk to guide interventions and to monitor progress. Objectives: To use RS/GIS to identify the environmental variables that correlate with the ecology of STHs and with the prevalence of STH infections. Methods: Geo-referenced in situ prevalence data will be overlaid over an ecological map derived from the RS environmental data using ESRI s ArcGIS 9.3. Prevalence data and RS environmental data matching at the same geographical location will be analyzed for correlation and those RS environmental variables that better correlate with prevalence data will be included in a multivariate regression model. Temperature, vegetation, and distance to bodies of water will be inferred using data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, and Thematic Mapper (TM) and Enhance Thematic Mapper Plus (ETM+) satellite sensors onboard Landsat 5 and Landsat 7 respectively. Elevation will be estimated with data from The Shuttle Radar Topography Mission (SRTM). Prevalence and intensity of infections will be determined by parasitological survey (Kato Katz) of children enrolled in rural schools in Boaco, Nicaragua, in the communities of El Roblar, Cumaica Norte, Malacatoya 1, and Malacatoya 2). Expected Results: Associations between RS environmental data and prevalence in situ data will be determined and their applications to public health will be discussed. Discussion/Conclusions: The use of RS/GIS data to predict the prevalence of STH infections could be useful for helminth control programs, providing improved geographical guidance of interventions while increasing cost-effectiveness. Learning Objectives: (1) To identify the RS environmental variables that can help predict the prevalence of STH infections. (2) To understand potential applications of RS/GIS to national helminth control programs. (3) To asses the applicability of RS/GIS to control STH infections.

A study on spatial decision support systems for HIV/AIDS prevention based on COM GIS technology

NASA Astrophysics Data System (ADS)

Yang, Kun; Luo, Huasong; Peng, Shungyun; Xu, Quanli

2007-06-01

Based on the deeply analysis of the current status and the existing problems of GIS technology applications in Epidemiology, this paper has proposed the method and process for establishing the spatial decision support systems of AIDS epidemic prevention by integrating the COM GIS, Spatial Database, GPS, Remote Sensing, and Communication technologies, as well as ASP and ActiveX software development technologies. One of the most important issues for constructing the spatial decision support systems of AIDS epidemic prevention is how to integrate the AIDS spreading models with GIS. The capabilities of GIS applications in the AIDS epidemic prevention have been described here in this paper firstly. Then some mature epidemic spreading models have also been discussed for extracting the computation parameters. Furthermore, a technical schema has been proposed for integrating the AIDS spreading models with GIS and relevant geospatial technologies, in which the GIS and model running platforms share a common spatial database and the computing results can be spatially visualized on Desktop or Web GIS clients. Finally, a complete solution for establishing the decision support systems of AIDS epidemic prevention has been offered in this paper based on the model integrating methods and ESRI COM GIS software packages. The general decision support systems are composed of data acquisition sub-systems, network communication sub-systems, model integrating sub-systems, AIDS epidemic information spatial database sub-systems, AIDS epidemic information querying and statistical analysis sub-systems, AIDS epidemic dynamic surveillance sub-systems, AIDS epidemic information spatial analysis and decision support sub-systems, as well as AIDS epidemic information publishing sub-systems based on Web GIS.

Monitoring Areal Snow Cover Using NASA Satellite Imagery

NASA Technical Reports Server (NTRS)

Harshburger, Brian J.; Blandford, Troy; Moore, Brandon

2011-01-01

The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data can be obtained from a digital elevation model (DEM) for the area of interest.

Propagule pressure and stream characteristics influence introgression: Cutthroat and rainbow trout in British Columbia

USGS Publications Warehouse

Bennett, S.N.; Olson, J.R.; Kershner, J.L.; Corbett, P.

2010-01-01

Hybridization and introgression between introduced and native salmonids threaten the continued persistence of many inland cutthroat trout species. Environmental models have been developed to predict the spread of introgression, but few studies have assessed the role of propagule pressure. We used an extensive set of fish stocking records and geographic information system (GIS) data to produce a spatially explicit index of potential propagule pressure exerted by introduced rainbow trout in the Upper Kootenay River, British Columbia, Canada. We then used logistic regression and the information-theoretic approach to test the ability of a set of environmental and spatial variables to predict the level of introgression between native westslope cutthroat trout and introduced rainbow trout. Introgression was assessed using between four and seven co-dominant, diagnostic nuclear markers at 45 sites in 31 different streams. The best model for predicting introgression included our GIS propagule pressure index and an environmental variable that accounted for the biogeoclimatic zone of the site (r2 = 0.62). This model was 1.4 times more likely to explain introgression than the next-best model, which consisted of only the propagule pressure index variable. We created a composite model based on the model-averaged results of the seven top models that included environmental, spatial, and propagule pressure variables. The propagule pressure index had the highest importance weight (0.995) of all variables tested and was negatively related to sites with no introgression. This study used an index of propagule pressure and demonstrated that propagule pressure had the greatest influence on the level of introgression between a native and introduced trout in a human-induced hybrid zone. ?? 2010 by the Ecological Society of America.

Novel GIS approaches to watershed science and management: Description, prediction, and integration

EPA Science Inventory

Spatial data and geographic information systems (GIS) are playing an increasingly important role in watershed science and management, particularly in the face of increasing climate uncertainty and demand for water resources. Concomitantly, scientists and managers are presented wi...

A Framework for Sharing and Integrating Remote Sensing and GIS Models Based on Web Service

PubMed Central

Chen, Zeqiang; Lin, Hui; Chen, Min; Liu, Deer; Bao, Ying; Ding, Yulin

2014-01-01

Sharing and integrating Remote Sensing (RS) and Geographic Information System/Science (GIS) models are critical for developing practical application systems. Facilitating model sharing and model integration is a problem for model publishers and model users, respectively. To address this problem, a framework based on a Web service for sharing and integrating RS and GIS models is proposed in this paper. The fundamental idea of the framework is to publish heterogeneous RS and GIS models into standard Web services for sharing and interoperation and then to integrate the RS and GIS models using Web services. For the former, a “black box” and a visual method are employed to facilitate the publishing of the models as Web services. For the latter, model integration based on the geospatial workflow and semantic supported marching method is introduced. Under this framework, model sharing and integration is applied for developing the Pearl River Delta water environment monitoring system. The results show that the framework can facilitate model sharing and model integration for model publishers and model users. PMID:24901016

A framework for sharing and integrating remote sensing and GIS models based on Web service.

PubMed

Chen, Zeqiang; Lin, Hui; Chen, Min; Liu, Deer; Bao, Ying; Ding, Yulin

2014-01-01

Sharing and integrating Remote Sensing (RS) and Geographic Information System/Science (GIS) models are critical for developing practical application systems. Facilitating model sharing and model integration is a problem for model publishers and model users, respectively. To address this problem, a framework based on a Web service for sharing and integrating RS and GIS models is proposed in this paper. The fundamental idea of the framework is to publish heterogeneous RS and GIS models into standard Web services for sharing and interoperation and then to integrate the RS and GIS models using Web services. For the former, a "black box" and a visual method are employed to facilitate the publishing of the models as Web services. For the latter, model integration based on the geospatial workflow and semantic supported marching method is introduced. Under this framework, model sharing and integration is applied for developing the Pearl River Delta water environment monitoring system. The results show that the framework can facilitate model sharing and model integration for model publishers and model users.

Spatiotemporal floodplain mapping and prediction using HEC-RAS - GIS tools: Case of the Mejerda river, Tunisia

NASA Astrophysics Data System (ADS)

Ben Khalfallah, C.; Saidi, S.

2018-06-01

The floods have become a scourge in recent years (Floods of, 2003, 2006, 2009, 2011, and 2012), increasingly frequent and devastating. Tunisia does not escape flooding problems, the flood management requires basically a better knowledge of the phenomenon (flood), and the use of predictive methods. In order to limit this risk, we became interested in hydrodynamics modeling of Medjerda basin. To reach this aim, rainfall distribution is studied and mapped using GIS tools. In addition, flood and return period estimation of rainfall are calculated using Hyfran. Also, Simulations of recent floods are calculated and mapped using HEC-RAS and HEC-GeoRAS for the most recent flood occurred in February-March 2015 in Medjerda basin. The analysis of the results shows a good correlation between simulated parameters and those measured. There is a flood of the river exceeding 240 m3/s (DGRE, 2015) and more flowing sections are observed in the future simulations; for return periods of 10yr, 20yr and 50yr.

Integrated model for predicting rice yield with climate change

NASA Astrophysics Data System (ADS)

Park, Jin-Ki; Das, Amrita; Park, Jong-Hwa

2018-04-01

Rice is the chief agricultural product and one of the primary food source. For this reason, it is of pivotal importance for worldwide economy and development. Therefore, in a decision-support-system both for the farmers and in the planning and management of the country's economy, forecasting yield is vital. However, crop yield, which is a dependent of the soil-bio-atmospheric system, is difficult to represent in statistical language. This paper describes a novel approach for predicting rice yield using artificial neural network, spatial interpolation, remote sensing and GIS methods. Herein, the variation in the yield is attributed to climatic parameters and crop health, and the normalized difference vegetation index from MODIS is used as an indicator of plant health and growth. Due importance was given to scaling up the input parameters using spatial interpolation and GIS and minimising the sources of error in every step of the modelling. The low percentage error (2.91) and high correlation (0.76) signifies the robust performance of the proposed model. This simple but effective approach is then used to estimate the influence of climate change on South Korean rice production. As proposed in the RCP8.5 scenario, an upswing in temperature may increase the rice yield throughout South Korea.

Landslide hazard assessment: recent trends and techniques.

PubMed

Pardeshi, Sudhakar D; Autade, Sumant E; Pardeshi, Suchitra S

2013-01-01

Landslide hazard assessment is an important step towards landslide hazard and risk management. There are several methods of Landslide Hazard Zonation (LHZ) viz. heuristic, semi quantitative, quantitative, probabilistic and multi-criteria decision making process. However, no one method is accepted universally for effective assessment of landslide hazards. In recent years, several attempts have been made to apply different methods of LHZ and to compare results in order to find the best suited model. This paper presents the review of researches on landslide hazard mapping published in recent years. The advanced multivariate techniques are proved to be effective in spatial prediction of landslides with high degree of accuracy. Physical process based models also perform well in LHZ mapping even in the areas with poor database. Multi-criteria decision making approach also play significant role in determining relative importance of landslide causative factors in slope instability process. Remote Sensing and Geographical Information System (GIS) are powerful tools to assess landslide hazards and are being used extensively in landslide researches since last decade. Aerial photographs and high resolution satellite data are useful in detection, mapping and monitoring landslide processes. GIS based LHZ models helps not only to map and monitor landslides but also to predict future slope failures. The advancements in Geo-spatial technologies have opened the doors for detailed and accurate assessment of landslide hazards.

Identification of potential wetlands in training areas on Ravenna Army Ammunition Plant, Ohio, and guidelines for their management

USGS Publications Warehouse

Schalk, C.W.; Tertuliani, J.S.; Darner, R.A.

1999-01-01

Potential wetlands in training areas on Ravenna Army Ammunition Plant, Ohio, were mapped by use of geographic information system (GIS) data layers and field inspection. The GIS data layers were compiled from existing sources and interpretation of aerial photography. Data layers used in the GIS analysis were wetland-plant communities, hydric soils, National Wetlands Inventory designated areas, and wet areas based on photogrammetry. According to review of these data layers, potential wetlands constitute almost one-third of the land in the training areas. A composite map of these four data layers was compiled for use during inspection of the training areas. Field inspection focused on the presence of hydrophytic vegetation and macroscopic evidences of wetland hydrology. Results of the field inspection were in general agreement with those predicted by the GIS analysis, except that some wet areas were more extensive than predicted because of high amounts of precipitation during critical periods of 1995 and 1996. Guidelines for managing wetlands in the training areas are presented.

Estuarine Sediment Deposition during Wetland Restoration: A GIS and Remote Sensing Modeling Approach

NASA Technical Reports Server (NTRS)

Newcomer, Michelle; Kuss, Amber; Kentron, Tyler; Remar, Alex; Choksi, Vivek; Skiles, J. W.

2011-01-01

Restoration of the industrial salt flats in the San Francisco Bay, California is an ongoing wetland rehabilitation project. Remote sensing maps of suspended sediment concentration, and other GIS predictor variables were used to model sediment deposition within these recently restored ponds. Suspended sediment concentrations were calibrated to reflectance values from Landsat TM 5 and ASTER using three statistical techniques -- linear regression, multivariate regression, and an Artificial Neural Network (ANN), to map suspended sediment concentrations. Multivariate and ANN regressions using ASTER proved to be the most accurate methods, yielding r2 values of 0.88 and 0.87, respectively. Predictor variables such as sediment grain size and tidal frequency were used in the Marsh Sedimentation (MARSED) model for predicting deposition rates for three years. MARSED results for a fully restored pond show a root mean square deviation (RMSD) of 66.8 mm (<1) between modeled and field observations. This model was further applied to a pond breached in November 2010 and indicated that the recently breached pond will reach equilibrium levels after 60 months of tidal inundation.

A habitat model for the Virginia northern flying squirrel (Glaucomys sabrinus fuscus) in the central Appalachian Mountains

Treesearch

J.M. Menzel; W.M. Ford; J.W. Edwards; L.J. Ceperley; L.J. Ceperley

2006-01-01

The Virginia northern flying squirrel (Glaucomys sabrinus fuscus) is an endangered sciurid that occurs in the Allegheny Mountains of Virginia and West Virginia. Despite its status, few of its ecological requirements have been synthesized for landscape-level predictive distributions to facilitate habitat delineation efforts. Using logistic regression, we developed a GIS...

Impact of trucking network flow on preferred biorefinery locations in the southern United States

Treesearch

Timothy M. Young; Lee D. Han; James H. Perdue; Stephanie R. Hargrove; Frank M. Guess; Xia Huang; Chung-Hao Chen

2017-01-01

The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated...

Predicting the distribution of bed material accumulation using river network sediment budgets

NASA Astrophysics Data System (ADS)

Wilkinson, Scott N.; Prosser, Ian P.; Hughes, Andrew O.

2006-10-01

Assessing the spatial distribution of bed material accumulation in river networks is important for determining the impacts of erosion on downstream channel form and habitat and for planning erosion and sediment management. A model that constructs spatially distributed budgets of bed material sediment is developed to predict the locations of accumulation following land use change. For each link in the river network, GIS algorithms are used to predict bed material supply from gullies, river banks, and upstream tributaries and to compare total supply with transport capacity. The model is tested in the 29,000 km2 Murrumbidgee River catchment in southeast Australia. It correctly predicts the presence or absence of accumulation in 71% of river links, which is significantly better performance than previous models, which do not account for spatial variability in sediment supply and transport capacity. Representing transient sediment storage is important for predicting smaller accumulations. Bed material accumulation is predicted in 25% of the river network, indicating its importance as an environmental problem in Australia.

Combining photorealistic immersive geovisualization and high-resolution geospatial data to enhance human-scale viewshed modelling

NASA Astrophysics Data System (ADS)

Tabrizian, P.; Petrasova, A.; Baran, P.; Petras, V.; Mitasova, H.; Meentemeyer, R. K.

2017-12-01

Viewshed modelling- a process of defining, parsing and analysis of landscape visual space's structure within GIS- has been commonly used in applications ranging from landscape planning and ecosystem services assessment to geography and archaeology. However, less effort has been made to understand whether and to what extent these objective analyses predict actual on-the-ground perception of human observer. Moreover, viewshed modelling at the human-scale level require incorporation of fine-grained landscape structure (eg., vegetation) and patterns (e.g, landcover) that are typically omitted from visibility calculations or unrealistically simulated leading to significant error in predicting visual attributes. This poster illustrates how photorealistic Immersive Virtual Environments and high-resolution geospatial data can be used to integrate objective and subjective assessments of visual characteristics at the human-scale level. We performed viewshed modelling for a systematically sampled set of viewpoints (N=340) across an urban park using open-source GIS (GRASS GIS). For each point a binary viewshed was computed on a 3D surface model derived from high-density leaf-off LIDAR (QL2) points. Viewshed map was combined with high-resolution landcover (.5m) derived through fusion of orthoimagery, lidar vegetation, and vector data. Geo-statistics and landscape structure analysis was performed to compute topological and compositional metrics for visual-scale (e.g., openness), complexity (pattern, shape and object diversity), and naturalness. Based on the viewshed model output, a sample of 24 viewpoints representing the variation of visual characteristics were selected and geolocated. For each location, 360o imagery were captured using a DSL camera mounted on a GIGA PAN robot. We programmed a virtual reality application through which human subjects (N=100) immersively experienced a random representation of selected environments via a head-mounted display (Oculus Rift CV1), and rated each location on perceived openness, naturalness and complexity. Regression models were performed to correlate model outputs with participants' responses. The results indicated strong, significant correlations for openness, and naturalness and moderate correlation for complexity estimations.

Quantitative modeling of soil genesis processes

NASA Technical Reports Server (NTRS)

Levine, E. R.; Knox, R. G.; Kerber, A. G.

1992-01-01

For fine spatial scale simulation, a model is being developed to predict changes in properties over short-, meso-, and long-term time scales within horizons of a given soil profile. Processes that control these changes can be grouped into five major process clusters: (1) abiotic chemical reactions; (2) activities of organisms; (3) energy balance and water phase transitions; (4) hydrologic flows; and (5) particle redistribution. Landscape modeling of soil development is possible using digitized soil maps associated with quantitative soil attribute data in a geographic information system (GIS) framework to which simulation models are applied.

3D Visualization Development of SIUE Campus

NASA Astrophysics Data System (ADS)

Nellutla, Shravya

Geographic Information Systems (GIS) has progressed from the traditional map-making to the modern technology where the information can be created, edited, managed and analyzed. Like any other models, maps are simplified representations of real world. Hence visualization plays an essential role in the applications of GIS. The use of sophisticated visualization tools and methods, especially three dimensional (3D) modeling, has been rising considerably due to the advancement of technology. There are currently many off-the-shelf technologies available in the market to build 3D GIS models. One of the objectives of this research was to examine the available ArcGIS and its extensions for 3D modeling and visualization and use them to depict a real world scenario. Furthermore, with the advent of the web, a platform for accessing and sharing spatial information on the Internet, it is possible to generate interactive online maps. Integrating Internet capacity with GIS functionality redefines the process of sharing and processing the spatial information. Enabling a 3D map online requires off-the-shelf GIS software, 3D model builders, web server, web applications and client server technologies. Such environments are either complicated or expensive because of the amount of hardware and software involved. Therefore, the second objective of this research was to investigate and develop simpler yet cost-effective 3D modeling approach that uses available ArcGIS suite products and the free 3D computer graphics software for designing 3D world scenes. Both ArcGIS Explorer and ArcGIS Online will be used to demonstrate the way of sharing and distributing 3D geographic information on the Internet. A case study of the development of 3D campus for the Southern Illinois University Edwardsville is demonstrated.

Spatial and temporal distribution of mass loss from the Greenland Ice Sheet since AD 1900.

PubMed

Kjeldsen, Kristian K; Korsgaard, Niels J; Bjørk, Anders A; Khan, Shfaqat A; Box, Jason E; Funder, Svend; Larsen, Nicolaj K; Bamber, Jonathan L; Colgan, William; van den Broeke, Michiel; Siggaard-Andersen, Marie-Louise; Nuth, Christopher; Schomacker, Anders; Andresen, Camilla S; Willerslev, Eske; Kjær, Kurt H

2015-12-17

The response of the Greenland Ice Sheet (GIS) to changes in temperature during the twentieth century remains contentious, largely owing to difficulties in estimating the spatial and temporal distribution of ice mass changes before 1992, when Greenland-wide observations first became available. The only previous estimates of change during the twentieth century are based on empirical modelling and energy balance modelling. Consequently, no observation-based estimates of the contribution from the GIS to the global-mean sea level budget before 1990 are included in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Here we calculate spatial ice mass loss around the entire GIS from 1900 to the present using aerial imagery from the 1980s. This allows accurate high-resolution mapping of geomorphic features related to the maximum extent of the GIS during the Little Ice Age at the end of the nineteenth century. We estimate the total ice mass loss and its spatial distribution for three periods: 1900-1983 (75.1 ± 29.4 gigatonnes per year), 1983-2003 (73.8 ± 40.5 gigatonnes per year), and 2003-2010 (186.4 ± 18.9 gigatonnes per year). Furthermore, using two surface mass balance models we partition the mass balance into a term for surface mass balance (that is, total precipitation minus total sublimation minus runoff) and a dynamic term. We find that many areas currently undergoing change are identical to those that experienced considerable thinning throughout the twentieth century. We also reveal that the surface mass balance term shows a considerable decrease since 2003, whereas the dynamic term is constant over the past 110 years. Overall, our observation-based findings show that during the twentieth century the GIS contributed at least 25.0 ± 9.4 millimetres of global-mean sea level rise. Our result will help to close the twentieth-century sea level budget, which remains crucial for evaluating the reliability of models used to predict global sea level rise.

Spatial and temporal distribution of mass loss from the Greenland Ice Sheet since AD 1900

NASA Astrophysics Data System (ADS)

Kjeldsen, Kristian K.; Korsgaard, Niels J.; Bjørk, Anders A.; Khan, Shfaqat A.; Box, Jason E.; Funder, Svend; Larsen, Nicolaj K.; Bamber, Jonathan L.; Colgan, William; van den Broeke, Michiel; Siggaard-Andersen, Marie-Louise; Nuth, Christopher; Schomacker, Anders; Andresen, Camilla S.; Willerslev, Eske; Kjær, Kurt H.

2015-12-01

The response of the Greenland Ice Sheet (GIS) to changes in temperature during the twentieth century remains contentious, largely owing to difficulties in estimating the spatial and temporal distribution of ice mass changes before 1992, when Greenland-wide observations first became available. The only previous estimates of change during the twentieth century are based on empirical modelling and energy balance modelling. Consequently, no observation-based estimates of the contribution from the GIS to the global-mean sea level budget before 1990 are included in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Here we calculate spatial ice mass loss around the entire GIS from 1900 to the present using aerial imagery from the 1980s. This allows accurate high-resolution mapping of geomorphic features related to the maximum extent of the GIS during the Little Ice Age at the end of the nineteenth century. We estimate the total ice mass loss and its spatial distribution for three periods: 1900-1983 (75.1 ± 29.4 gigatonnes per year), 1983-2003 (73.8 ± 40.5 gigatonnes per year), and 2003-2010 (186.4 ± 18.9 gigatonnes per year). Furthermore, using two surface mass balance models we partition the mass balance into a term for surface mass balance (that is, total precipitation minus total sublimation minus runoff) and a dynamic term. We find that many areas currently undergoing change are identical to those that experienced considerable thinning throughout the twentieth century. We also reveal that the surface mass balance term shows a considerable decrease since 2003, whereas the dynamic term is constant over the past 110 years. Overall, our observation-based findings show that during the twentieth century the GIS contributed at least 25.0 ± 9.4 millimetres of global-mean sea level rise. Our result will help to close the twentieth-century sea level budget, which remains crucial for evaluating the reliability of models used to predict global sea level rise.

Aurora Research: Earth/Space Data Fusion Powered by GIS and Python

NASA Astrophysics Data System (ADS)

Kalb, V. L.; Collado-Vega, Y. M.; MacDonald, E.; Kosar, B.

2017-12-01

The Aurora Borealis and Australis Borealis are visually spectacular, but are also an indicator of Sun-magnetosphere-ionosphere energy transfer during geomagnetic storms. The Saint Patrick's Day Storm of 2015 is a stellar example of this, and is the focus of our study that utilizes the Geographical Information Services of ArcGIS to bring together diverse and cross disciplinary data for analysis. This research leverages data from a polar-orbiting Earth science sensor band that is exquisitely sensitive to visible light, namely the Day/Night Band (DNB) of the VIIRS instrument onboard the Suomi NPP satellite. This Sun-synchronous data source can provide high temporal and spatial resolution observations of the aurorae, which is not possible with current space science instruments. This data can be compared with auroral model data, solar wind measurements, and citizen science data of aurora observations and tweets. While the proposed data sources are diverse in type and format, their common attribute is location. This is exploited by bringing all the data into ArcGIS for mapping and analysis. The Python programming language is used extensively to automate the data preprocessing, group the DNB and citizen science observations to temporal windows associated with an auroral model timestep, and print the data to a pdf mapbook for sharing with team members. There are several goals for this study: compare the auroral model predictions with DNB data, look for fine-grained structure of the aurora in the DNB data, compare citizen science data with DNB values, and correlate DNB intensity with solar wind data. This study demonstrates the benefits of using a GIS platform to bring together data that is diverse in type and format for scientific exploration, and shows how Python can be used to scale up to large datasets.

On Improving the Quality and Interpretation of Environmental Assessments using Statistical Analysis and Geographic Information Systems

NASA Astrophysics Data System (ADS)

Karuppiah, R.; Faldi, A.; Laurenzi, I.; Usadi, A.; Venkatesh, A.

2014-12-01

An increasing number of studies are focused on assessing the environmental footprint of different products and processes, especially using life cycle assessment (LCA). This work shows how combining statistical methods and Geographic Information Systems (GIS) with environmental analyses can help improve the quality of results and their interpretation. Most environmental assessments in literature yield single numbers that characterize the environmental impact of a process/product - typically global or country averages, often unchanging in time. In this work, we show how statistical analysis and GIS can help address these limitations. For example, we demonstrate a method to separately quantify uncertainty and variability in the result of LCA models using a power generation case study. This is important for rigorous comparisons between the impacts of different processes. Another challenge is lack of data that can affect the rigor of LCAs. We have developed an approach to estimate environmental impacts of incompletely characterized processes using predictive statistical models. This method is applied to estimate unreported coal power plant emissions in several world regions. There is also a general lack of spatio-temporal characterization of the results in environmental analyses. For instance, studies that focus on water usage do not put in context where and when water is withdrawn. Through the use of hydrological modeling combined with GIS, we quantify water stress on a regional and seasonal basis to understand water supply and demand risks for multiple users. Another example where it is important to consider regional dependency of impacts is when characterizing how agricultural land occupation affects biodiversity in a region. We developed a data-driven methodology used in conjuction with GIS to determine if there is a statistically significant difference between the impacts of growing different crops on different species in various biomes of the world.

Refining climate change projections for organisms with low dispersal abilities: a case study of the Caspian whip snake.

PubMed

Sahlean, Tiberiu C; Gherghel, Iulian; Papeş, Monica; Strugariu, Alexandru; Zamfirescu, Ştefan R

2014-01-01

Climate warming is one of the most important threats to biodiversity. Ectothermic organisms such as amphibians and reptiles are especially vulnerable as climatic conditions affect them directly. Ecological niche models (ENMs) are increasingly popular in ecological studies, but several drawbacks exist, including the limited ability to account for the dispersal potential of the species. In this study, we use ENMs to explore the impact of global climate change on the Caspian whip snake (Dolichophis caspius) as model for organisms with low dispersal abilities and to quantify dispersal to novel areas using GIS techniques. Models generated using Maxent 3.3.3 k and GARP for current distribution were projected on future climatic scenarios. A cost-distance analysis was run in ArcGIS 10 using geomorphological features, ecological conditions, and human footprint as "costs" to dispersal of the species to obtain a Maximum Dispersal Range (MDR) estimate. All models developed were statistically significant (P<0.05) and recovered the currently known distribution of D. caspius. Models projected on future climatic conditions using Maxent predicted a doubling of suitable climatic area, while GARP predicted a more conservative expansion. Both models agreed on an expansion of suitable area northwards, with minor decreases at the southern distribution limit. The MDR area calculated using the Maxent model represented a third of the total area of the projected model. The MDR based on GARP models recovered only about 20% of the total area of the projected model. Thus, incorporating measures of species' dispersal abilities greatly reduced estimated area of potential future distributions.

Application of MODFLOW and geographic information system to groundwater flow simulation in North China Plain, China

NASA Astrophysics Data System (ADS)

Wang, Shiqin; Shao, Jingli; Song, Xianfang; Zhang, Yongbo; Huo, Zhibin; Zhou, Xiaoyuan

2008-10-01

MODFLOW is a groundwater modeling program. It can be compiled and remedied according to the practical applications. Because of its structure and fixed data format, MODFLOW can be integrated with Geographic Information Systems (GIS) technology for water resource management. The North China Plain (NCP), which is the politic, economic and cultural center of China, is facing with water resources shortage and water pollution. Groundwater is the main water resource for industrial, agricultural and domestic usage. It is necessary to evaluate the groundwater resources of the NCP as an entire aquifer system. With the development of computer and internet information technology it is also necessary to integrate the groundwater model with the GIS technology. Because the geological and hydrogeological data in the NCP was mainly in MAPGIS format, the powerful function of GIS of disposing of and analyzing spatial data and computer languages such as Visual C and Visual Basic were used to define the relationship between the original data and model data. After analyzing the geological and hydrogeological conditions of the NCP, the groundwater flow numerical simulation modeling was constructed with MODFLOW. On the basis of GIS, a dynamic evaluation system for groundwater resources under the internet circumstance was completed. During the process of constructing the groundwater model, a water budget was analyzed, which showed a negative budget in the NCP. The simulation period was from 1 January 2002 to 31 December 2003. During this period, the total recharge of the groundwater system was 49,374 × 106 m3 and the total discharge was 56,530 × 106 m3 the budget deficit was -7,156 × 106 m3. In this integrated system, the original data including graphs and attribution data could be stored in the database. When the process of evaluating and predicting groundwater flow was started, these data were transformed into files that the core program of MODFLOW could read. The calculated water level and drawdown could be displayed and reviewed online.

Remote Sensing of Bioindicators for Forest Health Assessment

NASA Astrophysics Data System (ADS)

Kefauver, Shawn Carlisle

The impacts of tropospheric ozone on forest health in Mediterranean type climates in California, USA and Catalonia, Spain were investigated using a combination of remote sensing, Geographic Information System (GIS), and field studies focused on sensitive bioindicator conifer species and ambient ozone monitoring. For the field validation of impacts of tropospheric ozone on conifer health, the Ozone Injury Index (OII) was applied to the bioindicator species Pinus ponderosa, Pinus jeffreyi, and Pinus uncinata. Combining these three tools, it was possible to build meaningful ecological models covering large areas to enhance our understanding of the biotic and abiotic interactions which affect forest health. Regression models predicting ozone injury improved considerably when incorporating ozone exposure with GIS related to plant water status, including water availability and water usage, as a proxies for estimating the stomatal conductance and ozone uptake R2=0.35, p = 0.016 in Catalonia, R2=0.36, p < 0.001 in Yosemite and R2=0.33, p = 0.007 in Sequoia/Kings Canyon National Parks in California). Individual OII components in Catalonia were modeled with improved success compared to the original full OII, in particular visible chlorotic mottling (R2=0.60, p < 0.001). The visual chlorotic mottling component of the OII was the most strongly correlated to remote sensing indices, in particular the photochemical reflectance index (PRI; R2=0.28, p=0.0044 for OIIVI-amount and R 2=0.33 and p=0.0016 for OIIVI -severity). Regression models assessing ozone injury to conifers using imaging spectroscopy techniques also improved when incorporating the GIS proxies of stomatal conductance (R 2=0.59, p<0.0001 for OII in California and R2=0.68, p<0.0001 for OIIVI in Catalonia). Finally, taking advantage of a time series of ambient ozone monitoring in Catalonia, it was found that all models improved when incorporating the cumulative exposure to ozone over a period of three years (R2=0.56, p<0.0001 with imaging spectroscopy indices alone and R2=0.77, p<0.0001 with GIS added) and that it was possible to model the three year average ambient ozone using a modified version of the OII (P<0.0001, R2=0.53, RMSE=2.73 with only the OII subcomponents VI-Severity and FWHORL and P<0.0001, R2 = 0.90, RMSE = 1.35 with GIS).

GIS-modeled indicators of traffic-related air pollutants and adverse pulmonary health among children in El Paso, Texas.

PubMed

Svendsen, Erik R; Gonzales, Melissa; Mukerjee, Shaibal; Smith, Luther; Ross, Mary; Walsh, Debra; Rhoney, Scott; Andrews, Gina; Ozkaynak, Halûk; Neas, Lucas M

2012-10-01

Investigators examined 5,654 children enrolled in the El Paso, Texas, public school district by questionnaire in 2001. Exposure measurements were first collected in the late fall of 1999. School-level and residence-level exposures to traffic-related air pollutants were estimated using a land use regression model. For 1,529 children with spirometry, overall geographic information system (GIS)-modeled residential levels of traffic-related ambient air pollution (calibrated to a 10-ppb increment in nitrogen dioxide levels) were associated with a 2.4% decrement in forced vital capacity (95% confidence interval (CI): -4.0, -0.7) after adjustment for demographic, anthropomorphic, and socioeconomic factors and spirometer/technician effects. After adjustment for these potential covariates, overall GIS-modeled residential levels of traffic-related ambient air pollution (calibrated to a 10-ppb increment in nitrogen dioxide levels) were associated with pulmonary function levels below 85% of those predicted for both forced vital capacity (odds ratio (OR) = 3.10, 95% CI: 1.65, 5.78) and forced expiratory volume in 1 second (OR = 2.35, 95% CI: 1.38, 4.01). For children attending schools at elevations above 1,170 m, a 10-ppb increment in modeled nitrogen dioxide levels was associated with current asthma (OR = 1.56, 95% CI: 1.08, 2.50) after adjustment for demographic, socioeconomic, and parental factors and random school effects. These results are consistent with previous studies in Europe and California that found adverse health outcomes in children associated with modeled traffic-related air pollutants.

SiSeRHMap v1.0: a simulator for mapped seismic response using a hybrid model

NASA Astrophysics Data System (ADS)

Grelle, G.; Bonito, L.; Lampasi, A.; Revellino, P.; Guerriero, L.; Sappa, G.; Guadagno, F. M.

2015-06-01

SiSeRHMap is a computerized methodology capable of drawing up prediction maps of seismic response. It was realized on the basis of a hybrid model which combines different approaches and models in a new and non-conventional way. These approaches and models are organized in a code-architecture composed of five interdependent modules. A GIS (Geographic Information System) Cubic Model (GCM), which is a layered computational structure based on the concept of lithodynamic units and zones, aims at reproducing a parameterized layered subsoil model. A metamodeling process confers a hybrid nature to the methodology. In this process, the one-dimensional linear equivalent analysis produces acceleration response spectra of shear wave velocity-thickness profiles, defined as trainers, which are randomly selected in each zone. Subsequently, a numerical adaptive simulation model (Spectra) is optimized on the above trainer acceleration response spectra by means of a dedicated Evolutionary Algorithm (EA) and the Levenberg-Marquardt Algorithm (LMA) as the final optimizer. In the final step, the GCM Maps Executor module produces a serial map-set of a stratigraphic seismic response at different periods, grid-solving the calibrated Spectra model. In addition, the spectra topographic amplification is also computed by means of a numerical prediction model. This latter is built to match the results of the numerical simulations related to isolate reliefs using GIS topographic attributes. In this way, different sets of seismic response maps are developed, on which, also maps of seismic design response spectra are defined by means of an enveloping technique.

GEOGRAPHICAL INFORMATION SYSTEM, DECISION SUPPORT SYSTEMS, AND URBAN STORMWATER MANAGEMENT

EPA Science Inventory

The full report reviews the application of Geographic Inforamtion System (GIS) technology to the field of urban stormwater modeling. The GIS literature is reviewed in the context of its use as a spatial database for urban stormwater modeling, integration of GIS and hydroloic time...

International Research Workshop on Integrating GIS and Environmental Modeling: Problems, Prospects, and Research Needs

NASA Technical Reports Server (NTRS)

Parks, Bradley; Meeson, Blanche W. (Technical Monitor)

2001-01-01

The 4th International Conference on Integrating GIS and Environmental Modeling (GIS/EM4) was convened in Banff, Canada, September 2-8, 2000 at The Banff Centre for Conferences. The meeting's purpose, like it's predecessors was to reformulate, each three to four years, the collaborative research agenda for integrating spatio-temporal analysis with environmental simulation modeling.

Development of a GIS interface for WEPP Model application to Great Lakes forested watersheds

Treesearch

J. R. Frankenberger; S. Dun; D. C. Flanagan; J. Q. Wu; W. J. Elliot

2011-01-01

This presentation will highlight efforts on development of a new online WEPP GIS interface, targeted toward application in forested regions bordering the Great Lakes. The key components and algorithms of the online GIS system will be outlined. The general procedures used to provide input to the WEPP model and to display model output will be demonstrated.

Assisting Australian indigenous resource management and sustainable utilization of species through the use of GIS and environmental modeling techniques.

PubMed

Gorman, Julian; Pearson, Diane; Whitehead, Peter

2008-01-01

Information on distribution and relative abundance of species is integral to sustainable management, especially if they are to be harvested for subsistence or commerce. In northern Australia, natural landscapes are vast, centers of population few, access is difficult, and Aboriginal resource centers and communities have limited funds and infrastructure. Consequently defining distribution and relative abundance by comprehensive ground survey is difficult and expensive. This highlights the need for simple, cheap, automated methodologies to predict the distribution of species in use, or having potential for use, in commercial enterprise. The technique applied here uses a Geographic Information System (GIS) to make predictions of probability of occurrence using an inductive modeling technique based on Bayes' theorem. The study area is in the Maningrida region, central Arnhem Land, in the Northern Territory, Australia. The species examined, Cycas arnhemica and Brachychiton diversifolius, are currently being 'wild harvested' in commercial trials, involving sale of decorative plants and use as carving wood, respectively. This study involved limited and relatively simple ground surveys requiring approximately 7 days of effort for each species. The overall model performance was evaluated using Cohen's kappa statistics. The predictive ability of the model for C. arnhemica was classified as moderate and for B. diversifolius as fair. The difference in model performance can be attributed to the pattern of distribution of these species. C. arnhemica tends to occur in a clumped distribution due to relatively short distance dispersal of its large seeds and vegetative growth from long-lived rhizomes, while B. diversifolius seeds are smaller and more widely dispersed across the landscape. The output from analysis predicts trends in species distribution that are consistent with independent on-site sampling for each species and therefore should prove useful in gauging the extent of resource availability. However, some caution needs to be applied as the models tend to over predict presence which is a function of distribution patterns and of other variables operating in the landscape such as fire histories which were not included in the model due to limited availability of data.

Vertical Integration of Geographic Information Sciences: A Recruitment Model for GIS Education

ERIC Educational Resources Information Center

Yu, Jaehyung; Huynh, Niem Tu; McGehee, Thomas Lee

2011-01-01

An innovative vertical integration model for recruiting to GIS education was introduced and tested following four driving forces: curriculum development, GIS presentations, institutional collaboration, and faculty training. Curriculum development was a useful approach to recruitment, student credit hour generation, and retention-rate improvement.…

Optimal waste-to-energy strategy assisted by GIS For sustainable solid waste management

NASA Astrophysics Data System (ADS)

Tan, S. T.; Hashim, H.

2014-02-01

Municipal solid waste (MSW) management has become more complex and costly with the rapid socio-economic development and increased volume of waste. Planning a sustainable regional waste management strategy is a critical step for the decision maker. There is a great potential for MSW to be used for the generation of renewable energy through waste incineration or landfilling with gas capture system. However, due to high processing cost and cost of resource transportation and distribution throughout the waste collection station and power plant, MSW is mostly disposed in the landfill. This paper presents an optimization model incorporated with GIS data inputs for MSW management. The model can design the multi-period waste-to-energy (WTE) strategy to illustrate the economic potential and tradeoffs for MSW management under different scenarios. The model is capable of predicting the optimal generation, capacity, type of WTE conversion technology and location for the operation and construction of new WTE power plants to satisfy the increased energy demand by 2025 in the most profitable way. Iskandar Malaysia region was chosen as the model city for this study.

A Decision Analysis Tool for Climate Impacts, Adaptations, and Vulnerabilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Omitaomu, Olufemi A; Parish, Esther S; Nugent, Philip J

Climate change related extreme events (such as flooding, storms, and drought) are already impacting millions of people globally at a cost of billions of dollars annually. Hence, there are urgent needs for urban areas to develop adaptation strategies that will alleviate the impacts of these extreme events. However, lack of appropriate decision support tools that match local applications is limiting local planning efforts. In this paper, we present a quantitative analysis and optimization system with customized decision support modules built on geographic information system (GIS) platform to bridge this gap. This platform is called Urban Climate Adaptation Tool (Urban-CAT). Formore » all Urban-CAT models, we divide a city into a grid with tens of thousands of cells; then compute a list of metrics for each cell from the GIS data. These metrics are used as independent variables to predict climate impacts, compute vulnerability score, and evaluate adaptation options. Overall, the Urban-CAT system has three layers: data layer (that contains spatial data, socio-economic and environmental data, and analytic data), middle layer (that handles data processing, model management, and GIS operation), and application layer (that provides climate impacts forecast, adaptation optimization, and site evaluation). The Urban-CAT platform can guide city and county governments in identifying and planning for effective climate change adaptation strategies.« less

Use of remote sensing and a geographical information system in a national helminth control programme in Chad.

PubMed Central

Brooker, Simon; Beasley, Michael; Ndinaromtan, Montanan; Madjiouroum, Ester Mobele; Baboguel, Marie; Djenguinabe, Elie; Hay, Simon I.; Bundy, Don A. P.

2002-01-01

OBJECTIVE: To design and implement a rapid and valid epidemiological assessment of helminths among schoolchildren in Chad using ecological zones defined by remote sensing satellite sensor data and to investigate the environmental limits of helminth distribution. METHODS: Remote sensing proxy environmental data were used to define seven ecological zones in Chad. These were combined with population data in a geographical information system (GIS) in order to define a sampling protocol. On this basis, 20 schools were surveyed. Multilevel analysis, by means of generalized estimating equations to account for clustering at the school level, was used to investigate the relationship between infection patterns and key environmental variables. FINDINGS: In a sample of 1023 schoolchildren, 22.5% were infected with Schistosoma haematobium and 32.7% with hookworm. None were infected with Ascaris lumbricoides or Trichuris trichiura. The prevalence of S. haematobium and hookworm showed marked geographical heterogeneity and the observed patterns showed a close association with the defined ecological zones and significant relationships with environmental variables. These results contribute towards defining the thermal limits of geohelminth species. Predictions of infection prevalence were made for each school surveyed with the aid of models previously developed for Cameroon. These models correctly predicted that A. lumbricoides and T. trichiura would not occur in Chad but the predictions for S. haematobium were less reliable at the school level. CONCLUSION: GIS and remote sensing can play an important part in the rapid planning of helminth control programmes where little information on disease burden is available. Remote sensing prediction models can indicate patterns of geohelminth infection but can only identify potential areas of high risk for S. haematobium. PMID:12471398

The GIS weasel - An interface for the development of spatial information in modeling

USGS Publications Warehouse

Viger, R.J.; Markstrom, S.M.; Leavesley, G.H.; ,

2005-01-01

The GIS Weasel is a map and Graphical User Interface (GUI) driven tool that has been developed as an aid to modelers in the delineation, characterization of geographic features, and their parameterization for use in distributed or lumped parameter physical process models. The interface does not require user expertise in geographic information systems (GIS). The user does need knowledge of how the model will use the output from the GIS Weasel. The GIS Weasel uses Workstation ArcInfo and its the Grid extension. The GIS Weasel will run on all platforms that Workstation ArcInfo runs (i.e. numerous flavors of Unix and Microsoft Windows).The GIS Weasel requires an input ArcInfo grid of some topographical description of the Area of Interest (AOI). This is normally a digital elevation model, but can be the surface of a ground water table or any other data that flow direction can be resolved from. The user may define the AOI as a custom drainage area based on an interactively specified watershed outlet point, or use a previously created map. The user is then able to use any combination of the GIS Weasel's tool set to create one or more maps for depicting different kinds of geographic features. Once the spatial feature maps have been prepared, then the GIS Weasel s many parameterization routines can be used to create descriptions of each element in each of the user s created maps. Over 200 parameterization routines currently exist, generating information about shape, area, and topological association with other features of the same or different maps, as well many types of information based on ancillary data layers such as soil and vegetation properties. These tools easily integrate other similarly formatted data sets.

Application of GIS to predict malaria hotspots based on Anopheles arabiensis habitat suitability in Southern Africa

NASA Astrophysics Data System (ADS)

Gwitira, Isaiah; Murwira, Amon; Zengeya, Fadzai M.; Shekede, Munyaradzi Davis

2018-02-01

Malaria remains a major public health problem and a principal cause of morbidity and mortality in most developing countries. Although malaria still presents health problems, significant successes have been recorded in reducing deaths resulting from the disease. As malaria transmission continues to decline, control interventions will increasingly depend on the ability to define high-risk areas known as malaria hotspots. Therefore, there is urgent need to use geospatial tools such as geographic information system to detect spatial patterns of malaria and delineate disease hot spots for better planning and management. Thus, accurate mapping and prediction of seasonality of malaria hotspots is an important step towards developing strategies for effective malaria control. In this study, we modelled seasonal malaria hotspots as a function of habitat suitability of Anopheles arabiensis (A. Arabiensis) as a first step towards predicting likely seasonal malaria hotspots that could provide guidance in targeted malaria control. We used Geographical information system (GIS) and spatial statistic methods to identify seasonal hotspots of malaria cases at the country level. In order to achieve this, we first determined the spatial distribution of seasonal malaria hotspots using the Getis Ord Gi* statistic based on confirmed positive malaria cases recorded at health facilities in Zimbabwe over four years (1996-1999). We then used MAXENT technique to model habitat suitability of A. arabiensis from presence data collected from 1990 to 2002 based on bioclimatic variables and altitude. Finally, we used autologistic regression to test the extent to which malaria hotspots can be predicted using A. arabiensis habitat suitability. Our results show that A. arabiensis habitat suitability consistently and significantly (p < 0.05) predicts malaria hotspots from 1996 to 1999. Overall, our results show that malaria hotspots can be predicted using A. arabiensis habitat suitability, suggesting the possibility of developing models for malaria early warning based on vector habitat suitability.

Flood prediction, its risk and mitigation for the Babura River with GIS

NASA Astrophysics Data System (ADS)

Tarigan, A. P. M.; Hanie, M. Z.; Khair, H.; Iskandar, R.

2018-03-01

This paper describes the flood prediction along the Babura River, the catchment of which is within the comparatively larger watershed of the Deli River which crosses the centre part of Medan City. The flood plain and ensuing inundation area were simulated using HECRAS based on the available data of rainfall, catchment, and river cross-sections. The results were shown in a GIS format in which the city map of Medan and other infrastructure layers were stacked for spatial analysis. From the resulting GIS, it can be seen that 13 sub-districts were likely affected by the flood, and then the risk calculation of the flood damage could be estimated. In the spirit of flood mitigation thoughts, 6 locations of evacuation centres were identified and 15 evacuation routes were recommended to reach the centres. It is hoped that the flood prediction and its risk estimation in this study will inspire the preparedness of the stakeholders for the probable threat of flood disaster.

Numerical Model Metrics Tools in Support of Navy Operations

NASA Astrophysics Data System (ADS)

Dykes, J. D.; Fanguy, P.

2017-12-01

Increasing demands of accurate ocean forecasts that are relevant to the Navy mission decision makers demand tools that quickly provide relevant numerical model metrics to the forecasters. Increasing modelling capabilities with ever-higher resolution domains including coupled and ensemble systems as well as the increasing volume of observations and other data sources to which to compare the model output requires more tools for the forecaster to enable doing more with less. These data can be appropriately handled in a geographic information system (GIS) fused together to provide useful information and analyses, and ultimately a better understanding how the pertinent model performs based on ground truth.. Oceanographic measurements like surface elevation, profiles of temperature and salinity, and wave height can all be incorporated into a set of layers correlated to geographic information such as bathymetry and topography. In addition, an automated system that runs concurrently with the models on high performance machines matches routinely available observations to modelled values to form a database of matchups with which statistics can be calculated and displayed, to facilitate validation of forecast state and derived variables. ArcMAP, developed by Environmental Systems Research Institute, is a GIS application used by the Naval Research Laboratory (NRL) and naval operational meteorological and oceanographic centers to analyse the environment in support of a range of Navy missions. For example, acoustic propagation in the ocean is described with a three-dimensional analysis of sound speed that depends on profiles of temperature, pressure and salinity predicted by the Navy Coastal Ocean Model. The data and model output must include geo-referencing information suitable for accurately placing the data within the ArcMAP framework. NRL has developed tools that facilitate merging these geophysical data and their analyses, including intercomparisons between model predictions as well as comparison to validation data. This methodology produces new insights and facilitates identification of potential problems in ocean prediction.

Investigating Surface Bias Errors in the Weather Research and Forecasting (WRF) Model using a Geographic Information System (GIS)

DTIC Science & Technology

2015-02-01

WRF ) Model using a Geographic Information System (GIS) by Jeffrey A Smith, Theresa A Foley, John W Raby, and Brian Reen...ARL-TR-7212 ● FEB 2015 US Army Research Laboratory Investigating Surface Bias Errors in the Weather Research and Forecasting ( WRF ) Model...SUBTITLE Investigating surface bias errors in the Weather Research and Forecasting ( WRF ) Model using a Geographic Information System (GIS) 5a

The prediction of leaf area index from forest polygons decomposed through the integration of remote sensing, GIS, UNIX, and C

NASA Astrophysics Data System (ADS)

Wulder, M. A.

1998-03-01

Forest stand data are normally stored in a geographic information system (GIS) on the basis of areas of similar species combinations. Polygons are created based upon species assemblages and given labels relating the percentage of areal coverage by each significant species type within the specified area. As a result, estimation of leaf area index (LAI) from the digital numbers found within GIS-stored polygons lack accuracy as the predictive equations for LAI are normally developed for individual species, not species assemblages. A Landsat TM image was acquired to enable a classification which allows for the decomposition of forest-stand polygons into greater species detail. Knowledge of the actual internal composition of the stand polygons provides for computation of LAI values based upon the appropriate predictive equation resulting in higher accuracy of these estimates. To accomplish this goal it was necessary to extract, for each cover type in each polygon, descriptive values to represent the digital numbers located in that portion of the polygon. The classified image dictates the species composition of the various portions of the polygon and within these areas the raster pixel values are tabulated and averaged. Due to a lack of existing software tools to assess the raster values occurring within GIS polygons a combination of remote sensing, GIS, UNIX, and specifically coded C programs were necessary. Such tools are frequently used by the spatial analyst and indicate the complexity of what may appear to be a straight-forward spatial analysis problem.

Prediction of agriculture derived groundwater nitrate distribution in North China Plain with GIS-based BPNN

NASA Astrophysics Data System (ADS)

Wang, M. X.; Liu, G. D.; Wu, W. L.; Bao, Y. H.; Liu, W. N.

2006-07-01

In recent years, nitrate contamination of groundwater has become a growing concern for people in rural areas in North China Plain (NCP) where groundwater is used as drinking water. The objective of this study was to simulate agriculture derived groundwater nitrate pollution patterns with artificial neural network (ANN), which has been proved to be an effective tool for prediction in many branches of hydrology when data are not sufficient to understand the physical process of the systems but relative accurate predictions is needed. In our study, a back propagation neural network (BPNN) was developed to simulate spatial distribution of NO3-N concentrations in groundwater with land use information and site-specific hydrogeological properties in Huantai County, a typical agriculture dominated region of NCP. Geographic information system (GIS) tools were used in preparing and processing input-output vectors data for the BPNN. The circular buffer zones centered on the sampling wells were designated so as to consider the nitrate contamination of groundwater due to neighboring field. The result showed that the GIS-based BPNN simulated groundwater NO3-N concentration efficiently and captured the general trend of groundwater nitrate pollution patterns. The optimal result was obtained with a learning rate of 0.02, a 4-7-1 architecture and a buffer zone radius of 400 m. Nitrogen budget combined with GIS-based BPNN can serve as a cost-effective tool for prediction and management of groundwater nitrate pollution in an agriculture dominated regions in North China Plain.

OpenClimateGIS - A Web Service Providing Climate Model Data in Commonly Used Geospatial Formats

NASA Astrophysics Data System (ADS)

Erickson, T. A.; Koziol, B. W.; Rood, R. B.

2011-12-01

The goal of the OpenClimateGIS project is to make climate model datasets readily available in commonly used, modern geospatial formats used by GIS software, browser-based mapping tools, and virtual globes.The climate modeling community typically stores climate data in multidimensional gridded formats capable of efficiently storing large volumes of data (such as netCDF, grib) while the geospatial community typically uses flexible vector and raster formats that are capable of storing small volumes of data (relative to the multidimensional gridded formats). OpenClimateGIS seeks to address this difference in data formats by clipping climate data to user-specified vector geometries (i.e. areas of interest) and translating the gridded data on-the-fly into multiple vector formats. The OpenClimateGIS system does not store climate data archives locally, but rather works in conjunction with external climate archives that expose climate data via the OPeNDAP protocol. OpenClimateGIS provides a RESTful API web service for accessing climate data resources via HTTP, allowing a wide range of applications to access the climate data.The OpenClimateGIS system has been developed using open source development practices and the source code is publicly available. The project integrates libraries from several other open source projects (including Django, PostGIS, numpy, Shapely, and netcdf4-python).OpenClimateGIS development is supported by a grant from NOAA's Climate Program Office.

Spatial epidemiological techniques in cholera mapping and analysis towards a local scale predictive modelling

NASA Astrophysics Data System (ADS)

Rasam, A. R. A.; Ghazali, R.; Noor, A. M. M.; Mohd, W. M. N. W.; Hamid, J. R. A.; Bazlan, M. J.; Ahmad, N.

2014-02-01

Cholera spatial epidemiology is the study of the spread and control of the disease spatial pattern and epidemics. Previous studies have shown that multi-factorial causation such as human behaviour, ecology and other infectious risk factors influence the disease outbreaks. Thus, understanding spatial pattern and possible interrelationship factors of the outbreaks are crucial to be explored an in-depth study. This study focuses on the integration of geographical information system (GIS) and epidemiological techniques in exploratory analyzing the cholera spatial pattern and distribution in the selected district of Sabah. Spatial Statistic and Pattern tools in ArcGIS and Microsoft Excel software were utilized to map and analyze the reported cholera cases and other data used. Meanwhile, cohort study in epidemiological technique was applied to investigate multiple outcomes of the disease exposure. The general spatial pattern of cholera was highly clustered showed the disease spread easily at a place or person to others especially 1500 meters from the infected person and locations. Although the cholera outbreaks in the districts are not critical, it could be endemic at the crowded areas, unhygienic environment, and close to contaminated water. It was also strongly believed that the coastal water of the study areas has possible relationship with the cholera transmission and phytoplankton bloom since the areas recorded higher cases. GIS demonstrates a vital spatial epidemiological technique in determining the distribution pattern and elucidating the hypotheses generating of the disease. The next research would be applying some advanced geo-analysis methods and other disease risk factors for producing a significant a local scale predictive risk model of the disease in Malaysia.

Use of wild bird surveillance, human case data and GIS spatial analysis for predicting spatial distributions of West Nile virus in Greece.

PubMed

Valiakos, George; Papaspyropoulos, Konstantinos; Giannakopoulos, Alexios; Birtsas, Periklis; Tsiodras, Sotirios; Hutchings, Michael R; Spyrou, Vassiliki; Pervanidou, Danai; Athanasiou, Labrini V; Papadopoulos, Nikolaos; Tsokana, Constantina; Baka, Agoritsa; Manolakou, Katerina; Chatzopoulos, Dimitrios; Artois, Marc; Yon, Lisa; Hannant, Duncan; Petrovska, Liljana; Hadjichristodoulou, Christos; Billinis, Charalambos

2014-01-01

West Nile Virus (WNV) is the causative agent of a vector-borne, zoonotic disease with a worldwide distribution. Recent expansion and introduction of WNV into new areas, including southern Europe, has been associated with severe disease in humans and equids, and has increased concerns regarding the need to prevent and control future WNV outbreaks. Since 2010, 524 confirmed human cases of the disease have been reported in Greece with greater than 10% mortality. Infected mosquitoes, wild birds, equids, and chickens have been detected and associated with human disease. The aim of our study was to establish a monitoring system with wild birds and reported human cases data using Geographical Information System (GIS). Potential distribution of WNV was modelled by combining wild bird serological surveillance data with environmental factors (e.g. elevation, slope, land use, vegetation density, temperature, precipitation indices, and population density). Local factors including areas of low altitude and proximity to water were important predictors of appearance of both human and wild bird cases (Odds Ratio = 1,001 95%CI = 0,723-1,386). Using GIS analysis, the identified risk factors were applied across Greece identifying the northern part of Greece (Macedonia, Thrace) western Greece and a number of Greek islands as being at highest risk of future outbreaks. The results of the analysis were evaluated and confirmed using the 161 reported human cases of the 2012 outbreak predicting correctly (Odds = 130/31 = 4,194 95%CI = 2,841-6,189) and more areas were identified for potential dispersion in the following years. Our approach verified that WNV risk can be modelled in a fast cost-effective way indicating high risk areas where prevention measures should be implemented in order to reduce the disease incidence.

SiSeRHMap v1.0: a simulator for mapped seismic response using a hybrid model

NASA Astrophysics Data System (ADS)

Grelle, Gerardo; Bonito, Laura; Lampasi, Alessandro; Revellino, Paola; Guerriero, Luigi; Sappa, Giuseppe; Guadagno, Francesco Maria

2016-04-01

The SiSeRHMap (simulator for mapped seismic response using a hybrid model) is a computerized methodology capable of elaborating prediction maps of seismic response in terms of acceleration spectra. It was realized on the basis of a hybrid model which combines different approaches and models in a new and non-conventional way. These approaches and models are organized in a code architecture composed of five interdependent modules. A GIS (geographic information system) cubic model (GCM), which is a layered computational structure based on the concept of lithodynamic units and zones, aims at reproducing a parameterized layered subsoil model. A meta-modelling process confers a hybrid nature to the methodology. In this process, the one-dimensional (1-D) linear equivalent analysis produces acceleration response spectra for a specified number of site profiles using one or more input motions. The shear wave velocity-thickness profiles, defined as trainers, are randomly selected in each zone. Subsequently, a numerical adaptive simulation model (Emul-spectra) is optimized on the above trainer acceleration response spectra by means of a dedicated evolutionary algorithm (EA) and the Levenberg-Marquardt algorithm (LMA) as the final optimizer. In the final step, the GCM maps executor module produces a serial map set of a stratigraphic seismic response at different periods, grid solving the calibrated Emul-spectra model. In addition, the spectra topographic amplification is also computed by means of a 3-D validated numerical prediction model. This model is built to match the results of the numerical simulations related to isolate reliefs using GIS morphometric data. In this way, different sets of seismic response maps are developed on which maps of design acceleration response spectra are also defined by means of an enveloping technique.

Analyzing rasters, vectors and time series using new Python interfaces in GRASS GIS 7

NASA Astrophysics Data System (ADS)

Petras, Vaclav; Petrasova, Anna; Chemin, Yann; Zambelli, Pietro; Landa, Martin; Gebbert, Sören; Neteler, Markus; Löwe, Peter

2015-04-01

GRASS GIS 7 is a free and open source GIS software developed and used by many scientists (Neteler et al., 2012). While some users of GRASS GIS prefer its graphical user interface, significant part of the scientific community takes advantage of various scripting and programing interfaces offered by GRASS GIS to develop new models and algorithms. Here we will present different interfaces added to GRASS GIS 7 and available in Python, a popular programming language and environment in geosciences. These Python interfaces are designed to satisfy the needs of scientists and programmers under various circumstances. PyGRASS (Zambelli et al., 2013) is a new object-oriented interface to GRASS GIS modules and libraries. The GRASS GIS libraries are implemented in C to ensure maximum performance and the PyGRASS interface provides an intuitive, pythonic access to their functionality. GRASS GIS Python scripting library is another way of accessing GRASS GIS modules. It combines the simplicity of Bash and the efficiency of the Python syntax. When full access to all low-level and advanced functions and structures from GRASS GIS library is required, Python programmers can use an interface based on the Python ctypes package. Ctypes interface provides complete, direct access to all functionality as it would be available to C programmers. GRASS GIS provides specialized Python library for managing and analyzing spatio-temporal data (Gebbert and Pebesma, 2014). The temporal library introduces space time datasets representing time series of raster, 3D raster or vector maps and allows users to combine various spatio-temporal operations including queries, aggregation, sampling or the analysis of spatio-temporal topology. We will also discuss the advantages of implementing scientific algorithm as a GRASS GIS module and we will show how to write such module in Python. To facilitate the development of the module, GRASS GIS provides a Python library for testing (Petras and Gebbert, 2014) which helps researchers to ensure the robustness of the algorithm, correctness of the results in edge cases as well as the detection of changes in results due to new development. For all modules GRASS GIS automatically creates standardized command line and graphical user interfaces and documentation. Finally, we will show how GRASS GIS can be used together with powerful Python tools such as the NumPy package and the IPython Notebook. References: Gebbert, S., Pebesma, E., 2014. A temporal GIS for field based environmental modeling. Environmental Modelling & Software 53, 1-12. Neteler, M., Bowman, M.H., Landa, M. and Metz, M., 2012. GRASS GIS: a multi-purpose Open Source GIS. Environmental Modelling & Software 31: 124-130. Petras, V., Gebbert, S., 2014. Testing framework for GRASS GIS: ensuring reproducibility of scientific geospatial computing. Poster presented at: AGU Fall Meeting, December 15-19, 2014, San Francisco, USA. Zambelli, P., Gebbert, S., Ciolli, M., 2013. Pygrass: An Object Oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). ISPRS International Journal of Geo-Information 2, 201-219.

On-line applications of numerical models in the Black Sea GIS

NASA Astrophysics Data System (ADS)

Zhuk, E.; Khaliulin, A.; Zodiatis, G.; Nikolaidis, A.; Nikolaidis, M.; Stylianou, Stavros

2017-09-01

The Black Sea Geographical Information System (GIS) is developed based on cutting edge information technologies, and provides automated data processing and visualization on-line. Mapserver is used as a mapping service; the data are stored in MySQL DBMS; PHP and Python modules are utilized for data access, processing, and exchange. New numerical models can be incorporated in the GIS environment as individual software modules, compiled for a server-based operational system, providing interaction with the GIS. A common interface allows setting the input parameters; then the model performs the calculation of the output data in specifically predefined files and format. The calculation results are then passed to the GIS for visualization. Initially, a test scenario of integration of a numerical model into the GIS was performed, using software, developed to describe a two-dimensional tsunami propagation in variable basin depth, based on a linear long surface wave model which is legitimate for more than 5 m depth. Furthermore, the well established oil spill and trajectory 3-D model MEDSLIK (http://www.oceanography.ucy.ac.cy/medslik/) was integrated into the GIS with more advanced GIS functionality and capabilities. MEDSLIK is able to forecast and hind cast the trajectories of oil pollution and floating objects, by using meteo-ocean data and the state of oil spill. The MEDSLIK module interface allows a user to enter all the necessary oil spill parameters, i.e. date and time, rate of spill or spill volume, forecasting time, coordinates, oil spill type, currents, wind, and waves, as well as the specification of the output parameters. The entered data are passed on to MEDSLIK; then the oil pollution characteristics are calculated for pre-defined time steps. The results of the forecast or hind cast are then visualized upon a map.

State of the Art of the Landscape Architecture Spatial Data Model from a Geospatial Perspective

NASA Astrophysics Data System (ADS)

Kastuari, A.; Suwardhi, D.; Hanan, H.; Wikantika, K.

2016-10-01

Spatial data and information had been used for some time in planning or landscape design. For a long time, architects were using spatial data in the form of topographic map for their designs. This method is not efficient, and it is also not more accurate than using spatial analysis by utilizing GIS. Architects are sometimes also only accentuating the aesthetical aspect for their design, but not taking landscape process into account which could cause the design could be not suitable for its use and its purpose. Nowadays, GIS role in landscape architecture has been formalized by the emergence of Geodesign terminology that starts in Representation Model and ends in Decision Model. The development of GIS could be seen in several fields of science that now have the urgency to use 3 dimensional GIS, such as in: 3D urban planning, flood modeling, or landscape planning. In this fields, 3 dimensional GIS is able to support the steps in modeling, analysis, management, and integration from related data, that describe the human activities and geophysics phenomena in more realistic way. Also, by applying 3D GIS and geodesign in landscape design, geomorphology information can be better presented and assessed. In some research, it is mentioned that the development of 3D GIS is not established yet, either in its 3D data structure, or in its spatial analysis function. This study literature will able to accommodate those problems by providing information on existing development of 3D GIS for landscape architecture, data modeling, the data accuracy, representation of data that is needed by landscape architecture purpose, specifically in the river area.

Bring NASA Scientific Data into GIS

NASA Astrophysics Data System (ADS)

Xu, H.

2016-12-01

NASA's Earth Observation System (EOS) and many other missions produce data of huge volume and near real time which drives the research and understanding of climate change. Geographic Information System (GIS) is a technology used for the management, visualization and analysis of spatial data. Since it's inception in the 1960s, GIS has been applied to many fields at the city, state, national, and world scales. People continue to use it today to analyze and visualize trends, patterns, and relationships from the massive datasets of scientific data. There is great interest in both the scientific and GIS communities in improving technologies that can bring scientific data into a GIS environment, where scientific research and analysis can be shared through the GIS platform to the public. Most NASA scientific data are delivered in the Hierarchical Data Format (HDF), a format is both flexible and powerful. However, this flexibility results in challenges when trying to develop supported GIS software - data stored with HDF formats lack a unified standard and convention among these products. The presentation introduces an information model that enables ArcGIS software to ingest NASA scientific data and create a multidimensional raster - univariate and multivariate hypercubes - for scientific visualization and analysis. We will present the framework how ArcGIS leverages the open source GDAL (Geospatial Data Abstract Library) to support its raster data access, discuss how we overcame the GDAL drivers limitations in handing scientific products that are stored with HDF4 and HDF5 formats and how we improve the way in modeling the multidimensionality with GDAL. In additional, we will talk about the direction of ArcGIS handling NASA products and demonstrate how the multidimensional information model can help scientists work with various data products such as MODIS, MOPPIT, SMAP as well as many data products in a GIS environment.

RockFall analyst: A GIS extension for three-dimensional and spatially distributed rockfall hazard modeling

NASA Astrophysics Data System (ADS)

Lan, Hengxing; Derek Martin, C.; Lim, C. H.

2007-02-01

Geographic information system (GIS) modeling is used in combination with three-dimensional (3D) rockfall process modeling to assess rockfall hazards. A GIS extension, RockFall Analyst (RA), which is capable of effectively handling large amounts of geospatial information relative to rockfall behaviors, has been developed in ArcGIS using ArcObjects and C#. The 3D rockfall model considers dynamic processes on a cell plane basis. It uses inputs of distributed parameters in terms of raster and polygon features created in GIS. Two major components are included in RA: particle-based rockfall process modeling and geostatistics-based rockfall raster modeling. Rockfall process simulation results, 3D rockfall trajectories and their velocity features either for point seeders or polyline seeders are stored in 3D shape files. Distributed raster modeling, based on 3D rockfall trajectories and a spatial geostatistical technique, represents the distribution of spatial frequency, the flying and/or bouncing height, and the kinetic energy of falling rocks. A distribution of rockfall hazard can be created by taking these rockfall characteristics into account. A barrier analysis tool is also provided in RA to aid barrier design. An application of these modeling techniques to a case study is provided. The RA has been tested in ArcGIS 8.2, 8.3, 9.0 and 9.1.

Holocene thinning of the Greenland ice sheet.

PubMed

Vinther, B M; Buchardt, S L; Clausen, H B; Dahl-Jensen, D; Johnsen, S J; Fisher, D A; Koerner, R M; Raynaud, D; Lipenkov, V; Andersen, K K; Blunier, T; Rasmussen, S O; Steffensen, J P; Svensson, A M

2009-09-17

On entering an era of global warming, the stability of the Greenland ice sheet (GIS) is an important concern, especially in the light of new evidence of rapidly changing flow and melt conditions at the GIS margins. Studying the response of the GIS to past climatic change may help to advance our understanding of GIS dynamics. The previous interpretation of evidence from stable isotopes (delta(18)O) in water from GIS ice cores was that Holocene climate variability on the GIS differed spatially and that a consistent Holocene climate optimum-the unusually warm period from about 9,000 to 6,000 years ago found in many northern-latitude palaeoclimate records-did not exist. Here we extract both the Greenland Holocene temperature history and the evolution of GIS surface elevation at four GIS locations. We achieve this by comparing delta(18)O from GIS ice cores with delta(18)O from ice cores from small marginal icecaps. Contrary to the earlier interpretation of delta(18)O evidence from ice cores, our new temperature history reveals a pronounced Holocene climatic optimum in Greenland coinciding with maximum thinning near the GIS margins. Our delta(18)O-based results are corroborated by the air content of ice cores, a proxy for surface elevation. State-of-the-art ice sheet models are generally found to be underestimating the extent and changes in GIS elevation and area; our findings may help to improve the ability of models to reproduce the GIS response to Holocene climate.

Modeling waterfowl habitat selection in the Central Valley of California to better understand the spatial relationship between commercial poultry and waterfowl

USGS Publications Warehouse

Matchett, Elliott L.; Casazza, Michael L.; Fleskes, Joseph; Kelman, T.; Cadena, M.; Pitesky, M.

2017-01-01

Wildlife researchers frequently study resource and habitat selection of wildlife to understand their potential habitat requirements and to conserve their populations. Understanding wildlife spatial-temporal distributions related to habitat have other applications such as to model interfaces between wildlife and domestic food animals in order to mitigate disease transmission to food animals. The highly pathogenic avian influenza (HPAI) virus represents a significant risk to the poultry industry. The Central Valley of California offers a unique geographical confluence of commercial poultry and wild waterfowl, which are thought to be a key reservoir of avian influenza (AI). Therefore, understanding spatio-temporal distributions of waterfowl could improve our understanding of potential risk of HPAI exposure from a commercial poultry perspective. Using existing radio-telemetry data on waterfowl (U.S. Geological Survey) in combination with habitat and vegetation data based on Geographic Information Systems (GIS), we are developing GIS-based statistical models that predict the probability of waterfowl presence (Habitat Suitability Mapping). Near-real-time application can be developed using recent habitat data derived from Landsat imagery (acquired by satellites and publically available through the U.S. Geological Survey) to predict temporally- and spatially-varying distributions of waterfowl in the Central Valley. These results could be used to provide decision support for the poultry industry in addressing potential risk of HPAI exposure related to waterfowl proximity.

Modeling of natural risks in GIS, decision support in the Civil Protection and Emergency Planning

NASA Astrophysics Data System (ADS)

Santos, M.; Martins, L.; Moreira, S.; Costa, A.; Matos, F.; Teixeira, M.; Bateira, C.

2012-04-01

The assessment of natural hazards in Civil Protection is essential in the prevention and mitigation of emergency situations. This paper presents the results of the development of mapping susceptibility to landslides, floods, forest fires and soil erosion, using GIS (Geographic Information System) tools in two municipalities - Santo Tirso and Trofa - in the district of Oporto, in the northwest of Portugal. The mapping of natural hazards fits in the legislative plan of the Municipal Civil Protection (Law No. 65/2007 of 12 November) and it provides the key elements to planning and preparing an appropriate response in case some of the processes / phenomena occur, thus optimizing the procedures for protection and relief provided by the Municipal Civil Protection Service. Susceptibility mapping to landslides, floods, forest fires and soil erosion was performed with GIS tools resources. The methodology used to compile the mapping of landslides, forest fires and soil erosion was based on the modeling of different conditioning factors and validated with field work and event log. The mapping of susceptibility to floods and flooding was developed through mathematical parameters (statistical, hydrologic and hydraulic), supported by field work and the recognition of individual characteristics of each sector analysis and subsequently analyzed in a GIS environment The mapping proposal was made in 1:5000 scale which allows not only the identification of large sets affected by the spatial dynamics of the processes / phenomena, but also a more detailed analysis, especially when combined with geographic information systems (GIS) thus allowing to study more specific situations that require a quick response. The maps developed in this study are fundamental to the understanding, prediction and prevention of susceptibility and risks present in the municipalities, being a valuable tool in the process of Emergency Planning, since it identifies priority areas of intervention for farther detail analysis, promote and safeguard mechanisms to prevent injury and it anticipates the possibility of potential interventions that can minimize the risk.

A GIS-Enabled, Michigan-Specific, Hierarchical Groundwater Modeling and Visualization System

NASA Astrophysics Data System (ADS)

Liu, Q.; Li, S.; Mandle, R.; Simard, A.; Fisher, B.; Brown, E.; Ross, S.

2005-12-01

Efficient management of groundwater resources relies on a comprehensive database that represents the characteristics of the natural groundwater system as well as analysis and modeling tools to describe the impacts of decision alternatives. Many agencies in Michigan have spent several years compiling expensive and comprehensive surface water and groundwater inventories and other related spatial data that describe their respective areas of responsibility. However, most often this wealth of descriptive data has only been utilized for basic mapping purposes. The benefits from analyzing these data, using GIS analysis functions or externally developed analysis models or programs, has yet to be systematically realized. In this talk, we present a comprehensive software environment that allows Michigan groundwater resources managers and frontline professionals to make more effective use of the available data and improve their ability to manage and protect groundwater resources, address potential conflicts, design cleanup schemes, and prioritize investigation activities. In particular, we take advantage of the Interactive Ground Water (IGW) modeling system and convert it to a customized software environment specifically for analyzing, modeling, and visualizing the Michigan statewide groundwater database. The resulting Michigan IGW modeling system (IGW-M) is completely window-based, fully interactive, and seamlessly integrated with a GIS mapping engine. The system operates in real-time (on the fly) providing dynamic, hierarchical mapping, modeling, spatial analysis, and visualization. Specifically, IGW-M allows water resources and environmental professionals in Michigan to: * Access and utilize the extensive data from the statewide groundwater database, interactively manipulate GIS objects, and display and query the associated data and attributes; * Analyze and model the statewide groundwater database, interactively convert GIS objects into numerical model features, automatically extract data and attributes, and simulate unsteady groundwater flow and contaminant transport in response to water and land management decisions; * Visualize and map model simulations and predictions with data from the statewide groundwater database in a seamless interactive environment. IGW-M has the potential to significantly improve the productivity of Michigan groundwater management investigations. It changes the role of engineers and scientists in modeling and analyzing the statewide groundwater database from heavily physical to cognitive problem-solving and decision-making tasks. The seamless real-time integration, real-time visual interaction, and real-time processing capability allows a user to focus on critical management issues, conflicts, and constraints, to quickly and iteratively examine conceptual approximations, management and planning scenarios, and site characterization assumptions, to identify dominant processes, to evaluate data worth and sensitivity, and to guide further data-collection activities. We illustrate the power and effectiveness of the M-IGW modeling and visualization system with a real case study and a real-time, live demonstration.

Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS

NASA Astrophysics Data System (ADS)

Tien Bui, Dieu; Pradhan, Biswajeet; Lofman, Owe; Revhaug, Inge; Dick, Oystein B.

2012-08-01

The objective of this study is to investigate a potential application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a relatively new approach for landslide susceptibility mapping in the Hoa Binh province of Vietnam. Firstly, a landslide inventory map with a total of 118 landslide locations was constructed from various sources. Then the landslide inventory was randomly split into a testing dataset 70% (82 landslide locations) for training the models and the remaining 30% (36 landslides locations) was used for validation purpose. Ten landslide conditioning factors such as slope, aspect, curvature, lithology, land use, soil type, rainfall, distance to roads, distance to rivers, and distance to faults were considered in the analysis. The hybrid learning algorithm and six different membership functions (Gaussmf, Gauss2mf, Gbellmf, Sigmf, Dsigmf, Psigmf) were applied to generate the landslide susceptibility maps. The validation dataset, which was not considered in the ANFIS modeling process, was used to validate the landslide susceptibility maps using the prediction rate method. The validation results showed that the area under the curve (AUC) for six ANFIS models vary from 0.739 to 0.848. It indicates that the prediction capability depends on the membership functions used in the ANFIS. The models with Sigmf (0.848) and Gaussmf (0.825) have shown the highest prediction capability. The results of this study show that landslide susceptibility mapping in the Hoa Binh province of Vietnam using the ANFIS approach is viable. As far as the performance of the ANFIS approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.

Integrating R with GIS for innovative geocomputing - the examples of RQGIS and RSAGA

NASA Astrophysics Data System (ADS)

Muenchow, Jannes; Schratz, Patrick; Bangs, Donovan; Brenning, Alexander

2017-04-01

While Geographical information systems (GIS) are good at efficiently manipulating and processing large amounts of geospatial data, the programming language R excels in (geo-)statistical analyses. Thus, bringing GIS algorithms to the R console combines the best of the two worlds, and paves the way for innovative geocomputing. To promote this approach, we will contrast the new RQGIS package with the established RSAGA package in terms of architecture, functionality and ease-of-use. Both packages use already existing Application Programming Interfaces (API), namely the QGIS Python API and SAGA API, to access GIS functionality from within R. Overall, RQGIS has the advantage of providing a unified interface to several GIS toolboxes (GRASS, SAGA, GDAL, etc.) bringing more than 1000 geocomputing algorithms to the R console (though only a subset of the full functionality of a specific third-party provider might be available). To further support the unified interface, QGIS automatically converts the input data into the formats supported by the respective third-party module. Moreover, RQGIS is easier to use than RSAGA due to special convenience functions (open_help, get_args_man, run_qgis). Nevertheless, the experienced SAGA user will most likely prefer the RSAGA package since it lets the user access all SAGA algorithms for a wide range of SAGA versions (currently 2.0.4 - 2.2.3). Additionally, RSAGA includes numerous user-friendly wrapper functions with arguments and meaningful default values (e.g., rsaga.slope). What is more, RSAGA provides the user with special geocomputing R functions, i.e. functions which solely run in R without using SAGA in the background (e.g., pick.from.ascii.grid, grid.predict and multi.local.function). To demonstrate the advantages of each package, we will derive terrain attributes from digital elevation models to model species richness and landslide susceptibility using non-linear generalized linear or generalized additive models. In the end, the choice of RQGIS or RSAGA depends on the user's preferences, expertise and tasks at hand. But both packages will benefit anyone working with large spatio-temporal data in R.

Forest Ecosystem Analysis Using a GIS

Treesearch

S.G. McNulty; W.T. Swank

1996-01-01

Forest ecosystem studies have expanded spatially in recent years to address large scale environmental issues. We are using a geographic information system (GIS) to understand and integrate forest processes at landscape to regional spatial scales. This paper presents three diverse research studies using a GIS. First, we used a GIS to develop a landscape scale model to...

Teaching Practical Science Online Using GIS: A Cautionary Tale of Coping Strategies

ERIC Educational Resources Information Center

Argles, Tom

2017-01-01

Strong demand for GIS and burgeoning cohorts have encouraged the delivery of GIS teaching via online distance education models. This contribution reviews a brief foray (2012-2014) into this field by the Open University, deploying open source GIS software to enable students to perform practical science investigations online. The "Remote…

Application of GIS Technology for Town Planning Tasks Solving

NASA Astrophysics Data System (ADS)

Kiyashko, G. A.

2017-11-01

For developing territories, one of the most actual town-planning tasks is to find out the suitable sites for building projects. The geographic information system (GIS) allows one to model complex spatial processes and can provide necessary effective tools to solve these tasks. We propose several GIS analysis models which can define suitable settlement allocations and select appropriate parcels for construction objects. We implement our models in the ArcGIS Desktop package and verify by application to the existing objects in Primorsky Region (Primorye Territory). These suitability models use several variations of the analysis method combinations and include various ways to resolve the suitability task using vector data and a raster data set. The suitability models created in this study can be combined, and one model can be integrated into another as its part. Our models can be updated by other suitability models for further detailed planning.

An Overview of GIS-Based Modeling and Assessment of Mining-Induced Hazards: Soil, Water, and Forest

PubMed Central

Kim, Sung-Min; Yi, Huiuk; Choi, Yosoon

2017-01-01

In this study, current geographic information system (GIS)-based methods and their application for the modeling and assessment of mining-induced hazards were reviewed. Various types of mining-induced hazard, including soil contamination, soil erosion, water pollution, and deforestation were considered in the discussion of the strength and role of GIS as a viable problem-solving tool in relation to mining-induced hazards. The various types of mining-induced hazard were classified into two or three subtopics according to the steps involved in the reclamation procedure, or elements of the hazard of interest. Because GIS is appropriated for the handling of geospatial data in relation to mining-induced hazards, the application and feasibility of exploiting GIS-based modeling and assessment of mining-induced hazards within the mining industry could be expanded further. PMID:29186922

An Overview of GIS-Based Modeling and Assessment of Mining-Induced Hazards: Soil, Water, and Forest.

PubMed

Suh, Jangwon; Kim, Sung-Min; Yi, Huiuk; Choi, Yosoon

2017-11-27

In this study, current geographic information system (GIS)-based methods and their application for the modeling and assessment of mining-induced hazards were reviewed. Various types of mining-induced hazard, including soil contamination, soil erosion, water pollution, and deforestation were considered in the discussion of the strength and role of GIS as a viable problem-solving tool in relation to mining-induced hazards. The various types of mining-induced hazard were classified into two or three subtopics according to the steps involved in the reclamation procedure, or elements of the hazard of interest. Because GIS is appropriated for the handling of geospatial data in relation to mining-induced hazards, the application and feasibility of exploiting GIS-based modeling and assessment of mining-induced hazards within the mining industry could be expanded further.

Application of GIS Rapid Mapping Technology in Disaster Monitoring

NASA Astrophysics Data System (ADS)

Wang, Z.; Tu, J.; Liu, G.; Zhao, Q.

2018-04-01

With the rapid development of GIS and RS technology, especially in recent years, GIS technology and its software functions have been increasingly mature and enhanced. And with the rapid development of mathematical statistical tools for spatial modeling and simulation, has promoted the widespread application and popularization of quantization in the field of geology. Based on the investigation of field disaster and the construction of spatial database, this paper uses remote sensing image, DEM and GIS technology to obtain the data information of disaster vulnerability analysis, and makes use of the information model to carry out disaster risk assessment mapping.Using ArcGIS software and its spatial data modeling method, the basic data information of the disaster risk mapping process was acquired and processed, and the spatial data simulation tool was used to map the disaster rapidly.

Sensitivity of wildlife habitat models to uncertainties in GIS data

NASA Technical Reports Server (NTRS)

Stoms, David M.; Davis, Frank W.; Cogan, Christopher B.

1992-01-01

Decision makers need to know the reliability of output products from GIS analysis. For many GIS applications, it is not possible to compare these products to an independent measure of 'truth'. Sensitivity analysis offers an alternative means of estimating reliability. In this paper, we present a CIS-based statistical procedure for estimating the sensitivity of wildlife habitat models to uncertainties in input data and model assumptions. The approach is demonstrated in an analysis of habitat associations derived from a GIS database for the endangered California condor. Alternative data sets were generated to compare results over a reasonable range of assumptions about several sources of uncertainty. Sensitivity analysis indicated that condor habitat associations are relatively robust, and the results have increased our confidence in our initial findings. Uncertainties and methods described in the paper have general relevance for many GIS applications.

Ecology, distribution, and predictive occurrence modeling of Palmers chipmunk (Tamias palmeri): a high-elevation small mammal endemic to the Spring Mountains in southern Nevada, USA

USGS Publications Warehouse

Lowrey, Chris E.; Longshore, Kathleen M.; Riddle, Brett R.; Mantooth, Stacy

2016-01-01

Although montane sky islands surrounded by desert scrub and shrub steppe comprise a large part of the biological diversity of the Basin and Range Province of southwestern North America, comprehensive ecological and population demographic studies for high-elevation small mammals within these areas are rare. Here, we examine the ecology and population parameters of the Palmer’s chipmunk (Tamias palmeri) in the Spring Mountains of southern Nevada, and present a predictive GIS-based distribution and probability of occurrence model at both home range and geographic spatial scales. Logistic regression analyses and Akaike Information Criterion model selection found variables of forest type, slope, and distance to water sources as predictive of chipmunk occurrence at the geographic scale. At the home range scale, increasing population density, decreasing overstory canopy cover, and decreasing understory canopy cover contributed to increased survival rates.

Predicting fecal indicator organism contamination in Oregon coastal streams.

PubMed

Pettus, Paul; Foster, Eugene; Pan, Yangdong

2015-12-01

In this study, we used publicly available GIS layers and statistical tree-based modeling (CART and Random Forest) to predict pathogen indicator counts at a regional scale using 88 spatially explicit landscape predictors and 6657 samples from non-estuarine streams in the Oregon Coast Range. A total of 532 frequently sampled sites were parsed down to 93 pathogen sampling sites to control for spatial and temporal biases. This model's 56.5% explanation of variance, was comparable to other regional models, while still including a large number of variables. Analysis showed the most important predictors on bacteria counts to be: forest and natural riparian zones, cattle related activities, and urban land uses. This research confirmed linkages to anthropogenic activities, with the research prediction mapping showing increased bacteria counts in agricultural and urban land use areas and lower counts with more natural riparian conditions. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

NASA Astrophysics Data System (ADS)

Tien Bui, Dieu; Hoang, Nhat-Duc

2017-09-01

In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.

Real-time flood forecasting

USGS Publications Warehouse

Lai, C.; Tsay, T.-K.; Chien, C.-H.; Wu, I.-L.

2009-01-01

Researchers at the Hydroinformatic Research and Development Team (HIRDT) of the National Taiwan University undertook a project to create a real time flood forecasting model, with an aim to predict the current in the Tamsui River Basin. The model was designed based on deterministic approach with mathematic modeling of complex phenomenon, and specific parameter values operated to produce a discrete result. The project also devised a rainfall-stage model that relates the rate of rainfall upland directly to the change of the state of river, and is further related to another typhoon-rainfall model. The geographic information system (GIS) data, based on precise contour model of the terrain, estimate the regions that were perilous to flooding. The HIRDT, in response to the project's progress, also devoted their application of a deterministic model to unsteady flow of thermodynamics to help predict river authorities issue timely warnings and take other emergency measures.

Tracking acid mine-drainage in Southeast Arizona using GIS and sediment delivery models

USGS Publications Warehouse

Norman, L.M.; Gray, F.; Guertin, D.P.; Wissler, C.; Bliss, J.D.

2008-01-01

This study investigates the application of models traditionally used to estimate erosion and sediment deposition to assess the potential risk of water quality impairment resulting from metal-bearing materials related to mining and mineralization. An integrated watershed analysis using Geographic Information Systems (GIS) based tools was undertaken to examine erosion and sediment transport characteristics within the watersheds. Estimates of stream deposits of sediment from mine tailings were related to the chemistry of surface water to assess the effectiveness of the methodology to assess the risk of acid mine-drainage being dispersed downstream of abandoned tailings and waste rock piles. A watershed analysis was preformed in the Patagonia Mountains in southeastern Arizona which has seen substantial mining and where recent water quality samples have reported acidic surface waters. This research demonstrates an improvement of the ability to predict streams that are likely to have severely degraded water quality as a result of past mining activities. ?? Springer Science+Business Media B.V. 2007.

A fast topographic characterization of seismic station locations in Iran through integrated use of digital elevation models and GIS

NASA Astrophysics Data System (ADS)

Karimzadeh, Sadra; Miyajima, Masakatsu; Kamel, Batoul; Pessina, Vera

2015-10-01

We present topographic slope positions of seismic stations within four independent networks (IGUT, IIEES, GSI, and BHRC) in Iran through integrated use of digital elevation models and GIS. Since topographic amplification factor (TAF) due to ground surface irregularity could be one of the reasons of earthquake wave amplification and unexpected damage of structures located on the top of ridges in many previous studies, the ridge stations in the study area are recognized using topographic position index (TPI) as a spatial-based scale-dependent approach that helps in classification of topographic positions. We also present the correlation between local topographic positions and V {/s 30} along with Voronoi tiles of two networks (IGUT and IIEES). The obtained results can be profitably used in seismology to establish homogeneous subnetworks based on Voronoi tiles with precise feedback and in the formulation of new ground motion prediction equations with respect to topographic position and topographic amplification factor.

Effect of Climatic Factors and Population Density on the Distribution of Dengue in Sri Lanka: A GIS Based Evaluation for Prediction of Outbreaks.

PubMed

Sirisena, Pdnn; Noordeen, Faseeha; Kurukulasuriya, Harithra; Romesh, Thanuja Alar; Fernando, LakKumar

2017-01-01

Dengue is one of the major hurdles to the public health in Sri Lanka, causing high morbidity and mortality. The present study focuses on the use of geographical information systems (GIS) to map and evaluate the spatial and temporal distribution of dengue in Sri Lanka from 2009 to 2014 and to elucidate the association of climatic factors with dengue incidence. Epidemiological, population and meteorological data were collected from the Epidemiology Unit, Department of Census and Statistics and the Department of Meteorology of Sri Lanka. Data were analyzed using SPSS (Version 20, 2011) and R studio (2012) and the maps were generated using Arc GIS 10.2. The dengue incidence showed a significant positive correlation with rainfall (p<0.0001). No positive correlation was observed between dengue incidence and temperature (p = 0.107) or humidity (p = 0.084). Rainfall prior to 2 and 5 months and a rise in the temperature prior to 9 months positively correlated with dengue incidence as based on the auto-correlation values. A rise in humidity prior to 1 month had a mild positive correlation with dengue incidence. However, a rise in humidity prior to 9 months had a significant negative correlation with dengue incidence based on the auto-correlation values. Remote sensing and GIS technologies give near real time utility of climatic data together with the past dengue incidence for the prediction of dengue outbreaks. In that regard, GIS will be applicable in outbreak predictions including prompt identification of locations with dengue incidence and forecasting future risks and thus direct control measures to minimize major outbreaks.

Effect of Climatic Factors and Population Density on the Distribution of Dengue in Sri Lanka: A GIS Based Evaluation for Prediction of Outbreaks

PubMed Central

Sirisena, PDNN; Noordeen, Faseeha; Kurukulasuriya, Harithra; Romesh, Thanuja ALAR; Fernando, LakKumar

2017-01-01

Dengue is one of the major hurdles to the public health in Sri Lanka, causing high morbidity and mortality. The present study focuses on the use of geographical information systems (GIS) to map and evaluate the spatial and temporal distribution of dengue in Sri Lanka from 2009 to 2014 and to elucidate the association of climatic factors with dengue incidence. Epidemiological, population and meteorological data were collected from the Epidemiology Unit, Department of Census and Statistics and the Department of Meteorology of Sri Lanka. Data were analyzed using SPSS (Version 20, 2011) and R studio (2012) and the maps were generated using Arc GIS 10.2. The dengue incidence showed a significant positive correlation with rainfall (p<0.0001). No positive correlation was observed between dengue incidence and temperature (p = 0.107) or humidity (p = 0.084). Rainfall prior to 2 and 5 months and a rise in the temperature prior to 9 months positively correlated with dengue incidence as based on the auto-correlation values. A rise in humidity prior to 1 month had a mild positive correlation with dengue incidence. However, a rise in humidity prior to 9 months had a significant negative correlation with dengue incidence based on the auto-correlation values. Remote sensing and GIS technologies give near real time utility of climatic data together with the past dengue incidence for the prediction of dengue outbreaks. In that regard, GIS will be applicable in outbreak predictions including prompt identification of locations with dengue incidence and forecasting future risks and thus direct control measures to minimize major outbreaks. PMID:28068339

ArcGIS Framework for Scientific Data Analysis and Serving

NASA Astrophysics Data System (ADS)

Xu, H.; Ju, W.; Zhang, J.

2015-12-01

ArcGIS is a platform for managing, visualizing, analyzing, and serving geospatial data. Scientific data as part of the geospatial data features multiple dimensions (X, Y, time, and depth) and large volume. Multidimensional mosaic dataset (MDMD), a newly enhanced data model in ArcGIS, models the multidimensional gridded data (e.g. raster or image) as a hypercube and enables ArcGIS's capabilities to handle the large volume and near-real time scientific data. Built on top of geodatabase, the MDMD stores the dimension values and the variables (2D arrays) in a geodatabase table which allows accessing a slice or slices of the hypercube through a simple query and supports animating changes along time or vertical dimension using ArcGIS desktop or web clients. Through raster types, MDMD can manage not only netCDF, GRIB, and HDF formats but also many other formats or satellite data. It is scalable and can handle large data volume. The parallel geo-processing engine makes the data ingestion fast and easily. Raster function, definition of a raster processing algorithm, is a very important component in ArcGIS platform for on-demand raster processing and analysis. The scientific data analytics is achieved through the MDMD and raster function templates which perform on-demand scientific computation with variables ingested in the MDMD. For example, aggregating monthly average from daily data; computing total rainfall of a year; calculating heat index for forecasting data, and identifying fishing habitat zones etc. Addtionally, MDMD with the associated raster function templates can be served through ArcGIS server as image services which provide a framework for on-demand server side computation and analysis, and the published services can be accessed by multiple clients such as ArcMap, ArcGIS Online, JavaScript, REST, WCS, and WMS. This presentation will focus on the MDMD model and raster processing templates. In addtion, MODIS land cover, NDFD weather service, and HYCOM ocean model will be used to illustrate how ArcGIS platform and MDMD model can facilitate scientific data visualization and analytics and how the analysis results can be shared to more audience through ArcGIS Online and Portal.

Real-time GIS data model and sensor web service platform for environmental data management.

PubMed

Gong, Jianya; Geng, Jing; Chen, Zeqiang

2015-01-09

Effective environmental data management is meaningful for human health. In the past, environmental data management involved developing a specific environmental data management system, but this method often lacks real-time data retrieving and sharing/interoperating capability. With the development of information technology, a Geospatial Service Web method is proposed that can be employed for environmental data management. The purpose of this study is to determine a method to realize environmental data management under the Geospatial Service Web framework. A real-time GIS (Geographic Information System) data model and a Sensor Web service platform to realize environmental data management under the Geospatial Service Web framework are proposed in this study. The real-time GIS data model manages real-time data. The Sensor Web service platform is applied to support the realization of the real-time GIS data model based on the Sensor Web technologies. To support the realization of the proposed real-time GIS data model, a Sensor Web service platform is implemented. Real-time environmental data, such as meteorological data, air quality data, soil moisture data, soil temperature data, and landslide data, are managed in the Sensor Web service platform. In addition, two use cases of real-time air quality monitoring and real-time soil moisture monitoring based on the real-time GIS data model in the Sensor Web service platform are realized and demonstrated. The total time efficiency of the two experiments is 3.7 s and 9.2 s. The experimental results show that the method integrating real-time GIS data model and Sensor Web Service Platform is an effective way to manage environmental data under the Geospatial Service Web framework.

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.

Predicting the distribution and ecological niche of unexploited snow crab (Chionoecetes opilio) populations in Alaskan waters: a first open-access ensemble model.

PubMed

Hardy, Sarah M; Lindgren, Michael; Konakanchi, Hanumantharao; Huettmann, Falk

2011-10-01

Populations of the snow crab (Chionoecetes opilio) are widely distributed on high-latitude continental shelves of the North Pacific and North Atlantic, and represent a valuable resource in both the United States and Canada. In US waters, snow crabs are found throughout the Arctic and sub-Arctic seas surrounding Alaska, north of the Aleutian Islands, yet commercial harvest currently focuses on the more southerly population in the Bering Sea. Population dynamics are well-monitored in exploited areas, but few data exist for populations further north where climate trends in the Arctic appear to be affecting species' distributions and community structure on multiple trophic levels. Moreover, increased shipping traffic, as well as fisheries and petroleum resource development, may add additional pressures in northern portions of the range as seasonal ice cover continues to decline. In the face of these pressures, we examined the ecological niche and population distribution of snow crabs in Alaskan waters using a GIS-based spatial modeling approach. We present the first quantitative open-access model predictions of snow-crab distribution, abundance, and biomass in the Chukchi and Beaufort Seas. Multi-variate analysis of environmental drivers of species' distribution and community structure commonly rely on multiple linear regression methods. The spatial modeling approach employed here improves upon linear regression methods in allowing for exploration of nonlinear relationships and interactions between variables. Three machine-learning algorithms were used to evaluate relationships between snow-crab distribution and environmental parameters, including TreeNet, Random Forests, and MARS. An ensemble model was then generated by combining output from these three models to generate consensus predictions for presence-absence, abundance, and biomass of snow crabs. Each algorithm identified a suite of variables most important in predicting snow-crab distribution, including nutrient and chlorophyll-a concentrations in overlying waters, temperature, salinity, and annual sea-ice cover; this information may be used to develop and test hypotheses regarding the ecology of this species. This is the first such quantitative model for snow crabs, and all GIS-data layers compiled for this project are freely available from the authors, upon request, for public use and improvement.

A GIS-based atmospheric dispersion model for pollutants emitted by complex source areas.

PubMed

Teggi, Sergio; Costanzini, Sofia; Ghermandi, Grazia; Malagoli, Carlotta; Vinceti, Marco

2018-01-01

Gaussian dispersion models are widely used to simulate the concentrations and deposition fluxes of pollutants emitted by source areas. Very often, the calculation time limits the number of sources and receptors and the geometry of the sources must be simple and without holes. This paper presents CAREA, a new GIS-based Gaussian model for complex source areas. CAREA was coded in the Python language, and is largely based on a simplified formulation of the very popular and recognized AERMOD model. The model allows users to define in a GIS environment thousands of gridded or scattered receptors and thousands of complex sources with hundreds of vertices and holes. CAREA computes ground level, or near ground level, concentrations and dry deposition fluxes of pollutants. The input/output and the runs of the model can be completely managed in GIS environment (e.g. inside a GIS project). The paper presents the CAREA formulation and its applications to very complex test cases. The tests shows that the processing time are satisfactory and that the definition of sources and receptors and the output retrieval are quite easy in a GIS environment. CAREA and AERMOD are compared using simple and reproducible test cases. The comparison shows that CAREA satisfactorily reproduces AERMOD simulations and is considerably faster than AERMOD. Copyright © 2017 Elsevier B.V. All rights reserved.

A Modular GIS-Based Software Architecture for Model Parameter Estimation using the Method of Anchored Distributions (MAD)

NASA Astrophysics Data System (ADS)

Ames, D. P.; Osorio-Murillo, C.; Over, M. W.; Rubin, Y.

2012-12-01

The Method of Anchored Distributions (MAD) is an inverse modeling technique that is well-suited for estimation of spatially varying parameter fields using limited observations and Bayesian methods. This presentation will discuss the design, development, and testing of a free software implementation of the MAD technique using the open source DotSpatial geographic information system (GIS) framework, R statistical software, and the MODFLOW groundwater model. This new tool, dubbed MAD-GIS, is built using a modular architecture that supports the integration of external analytical tools and models for key computational processes including a forward model (e.g. MODFLOW, HYDRUS) and geostatistical analysis (e.g. R, GSLIB). The GIS-based graphical user interface provides a relatively simple way for new users of the technique to prepare the spatial domain, to identify observation and anchor points, to perform the MAD analysis using a selected forward model, and to view results. MAD-GIS uses the Managed Extensibility Framework (MEF) provided by the Microsoft .NET programming platform to support integration of different modeling and analytical tools at run-time through a custom "driver." Each driver establishes a connection with external programs through a programming interface, which provides the elements for communicating with core MAD software. This presentation gives an example of adapting the MODFLOW to serve as the external forward model in MAD-GIS for inferring the distribution functions of key MODFLOW parameters. Additional drivers for other models are being developed and it is expected that the open source nature of the project will engender the development of additional model drivers by 3rd party scientists.

Comparison of perceived and modelled geographical access to accident and emergency departments: a cross-sectional analysis from the Caerphilly Health and Social Needs Study.

PubMed

Fone, David L; Christie, Stephen; Lester, Nathan

2006-04-13

Assessment of the spatial accessibility of hospital accident and emergency departments as perceived by local residents has not previously been investigated. Perceived accessibility may affect where, when, and whether potential patients attend for treatment. Using data on 11,853 respondents to a population survey in Caerphilly county borough, Wales, UK, we present an analysis comparing the accessibility of accident and emergency departments as reported by local residents and drive-time to the nearest accident and emergency department modelled using a geographical information system (GIS). Median drive-times were significantly shorter in the lowest perceived access category and longer in the best perceived access category (p < 0.001). The perceived access and GIS modelled drive-time variables were positively correlated (Spearman's rank correlation coefficient, r = 0.38, p < 0.01). The strongest correlation was found for respondents living in areas in which nearly all households had a car or van (r = 0.47, p < 0.01). Correlations were stronger among respondents reporting good access to public transport and among those reporting a recent accident and emergency attendance for injury treatment compared to other respondents. Correlation coefficients did not vary substantially by levels of household income. Drive-time, road distance and straight-line distance were highly inter-correlated and substituting road distance or straight-line distance as the GIS modelled spatial accessibility measure only marginally decreased the magnitude of the correlations between perceived and GIS modelled access. This study provides evidence that the accessibility of hospital-based health care services as perceived by local residents is related to measures of spatial accessibility modelled using GIS. For studies that aim to model geographical separation in a way that correlates well with the perception of local residents, there may be minimal advantage in using sophisticated measures. Straight-line distance, which can be calculated without GIS, may be as good as GIS-modelled drive-time or distance for this purpose. These findings will be of importance to health policy makers and local planners who seek to obtain local information on access to services through focussed assessments of residents' concerns over accessibility and GIS modelling.

Digital shoreline analysis system-based change detection along the highly eroding Krishna-Godavari delta front

NASA Astrophysics Data System (ADS)

Kallepalli, Akhil; Kakani, Nageswara Rao; James, David B.; Richardson, Mark A.

2017-07-01

Coastal regions are highly vulnerable to rising sea levels due to global warming. Previous Intergovernmental Panel on Climate Change (2013) predictions of 26 to 82 cm global sea level rise are now considered conservative. Subsequent investigations predict much higher levels which would displace 10% of the world's population living less than 10 m above sea level. Remote sensing and GIS technologies form the mainstay of models on coastal retreat and inundation to future sea-level rise. This study estimates the varying trends along the Krishna-Godavari (K-G) delta region. The rate of shoreline shift along the 330-km long K-G delta coast was estimated using satellite images between 1977 and 2008. With reference to a selected baseline from along an inland position, end point rate and net shoreline movement were calculated using a GIS-based digital shoreline analysis system. The results indicated a net loss of about 42.1 km2 area during this 31-year period, which is in agreement with previous literature. Considering the nature of landforms and EPR, the future hazard line (or coastline) is predicted for the area; the predication indicates a net erosion of about 57.6 km2 along the K-G delta coast by 2050 AD.

Reducing tick bite risk in Finland - combining citizen science and GIS for predictive modelling of tick occurrence

NASA Astrophysics Data System (ADS)

Sormunen, Jani; Kulha, Niko; Klemola, Tero

2017-04-01

Ticks (Acari: Ixodidae) and tick-borne diseases constitute a growing welfare problem in northern Europe and Russia. Surveys conducted in Russia, Sweden and Norway have revealed a northwards shift in distribution and an increase in tick abundance over the past few decades. In southwestern Finland, surveys have revealed a similar increase in tick abundance, as well as the presence of novel tick-borne pathogens. As avoiding risk areas and removing attached ticks as quickly as possible are the best available methods for preventing tick-borne diseases, accessible and up-to-date data on tick occurrence is essential. However, consistently tracking the nationwide distribution of ticks is impossible using traditional collection methods. Therefore, GIS-based predictive modelling for tick occurrence is required. In May 2015, a national tick collection campaign was launched by the University of Turku tick project, with the objective of mapping the current geographical distribution of the two tick species responsible for tick-borne infections in Finland, Ixodes ricinus and Ixodes persulcatus. During the collection campaign, citizens were asked to send any ticks they found to the University of Turku by letter, along with information on the collection locality. The campaign ended in September 2015 and was a great success, with nearly 7000 letters delivered to the University. These letters contained more than 20 000 individual ticks from all around Finland. The geographic data from the letters was converted into coordinate points after the campaign was concluded. Data from the national tick collection campaign revealed not only a northwards shift in the distribution of I. ricinus, but also novel foci for I. persulcatus in Finland. Strikingly, while they were otherwise found throughout Finland, I. persulcatus were absent from the south-southwestern coast, where I. ricinus is nevertheless abundant. The exact cause for this phenomenon is unclear, as I. persulcatus are found further south in nearby Estonia and Russia. Using the location and tick species data from the collection campaign, as well as nationwide data sets regarding several different environmental factors (e.g. temperature sum, soil type), we seek to identify potential environmental causes for the realized geographical distributions of these two tick species in Finland. Particularly, we seek to identify factors limiting tick occurrence in certain areas, especially I. persulcatus occurrence in southern Finland. The ultimate goal is to determine whether quantifiable environmental factors linked to tick occurrence can be found, and, if found, use them to apply GIS models to map and predict changes in tick distribution in Finland. In the poster presented here, we showcase the methodology used in assessing effects of different environmental factors on tick occurrence, and present preliminary results from GIS analysis of coordinate, tick species and environmental data.

Topsoil pollution forecasting using artificial neural networks on the example of the abnormally distributed heavy metal at Russian subarctic

NASA Astrophysics Data System (ADS)

Tarasov, D. A.; Buevich, A. G.; Sergeev, A. P.; Shichkin, A. V.; Baglaeva, E. M.

2017-06-01

Forecasting the soil pollution is a considerable field of study in the light of the general concern of environmental protection issues. Due to the variation of content and spatial heterogeneity of pollutants distribution at urban areas, the conventional spatial interpolation models implemented in many GIS packages mostly cannot provide appreciate interpolation accuracy. Moreover, the problem of prediction the distribution of the element with high variability in the concentration at the study site is particularly difficult. The work presents two neural networks models forecasting a spatial content of the abnormally distributed soil pollutant (Cr) at a particular location of the subarctic Novy Urengoy, Russia. A method of generalized regression neural network (GRNN) was compared to a common multilayer perceptron (MLP) model. The proposed techniques have been built, implemented and tested using ArcGIS and MATLAB. To verify the models performances, 150 scattered input data points (pollutant concentrations) have been selected from 8.5 km2 area and then split into independent training data set (105 points) and validation data set (45 points). The training data set was generated for the interpolation using ordinary kriging while the validation data set was used to test their accuracies. The networks structures have been chosen during a computer simulation based on the minimization of the RMSE. The predictive accuracy of both models was confirmed to be significantly higher than those achieved by the geostatistical approach (kriging). It is shown that MLP could achieve better accuracy than both kriging and even GRNN for interpolating surfaces.

Schistosomes, snails and satellites.

PubMed

Brooker, S

2002-05-01

This paper gives an overview of the recent progress made in the use and application of geographical information systems (GIS) and remotely sensed (RS) satellite sensor data for the epidemiology and control of schistosomiasis in sub-Saharan Africa. Details are given of the use of GIS to collate, map and analyse available parasitological data. The use of RS data to understand better the broad scale environmental factors influencing schistosome distribution is defined and examples detailed for the prediction of schistosomiasis in unsampled areas. Finally, the current practical application of GIS and remote sensing are reviewed in the context of national control programmes.

Ecological Niche Modelling using satellite data for assessing distribution of threatened species Ceropegia bulbosa Roxb.

NASA Astrophysics Data System (ADS)

Kumar, S.; Kulloli, R. N.; Tewari, J. C.; Singh, J. P.; Singh, A.

2014-11-01

Ceropegia bulbosa Roxb. is a narrow endemic, tuberous twiner of Asclepiadaceae family. It is medicinally important: tubers are nutritive and edible, leaves are digestive and a cure for dysentery and diarrhea. Exploitation for its tubers and poor regeneration of this species has shrunk its distribution. In order to know its present status, we report here the results of its appraisal in Rajasthan, using remote sensing and ground truthing in the past five years (2009-14). A base map of C. bulbosa was prepared using Geographical Information System (GIS), open source software Quantum GIS, SAGA. The Landsat Enhanced Thematic Mapper (ETM) +Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Global Digital Elevation Model (GDEM) Satellite Data were used in this study. ASTER and GDEM Data was clipped with district boundary and provided color range to get elevation information. A digital elevation model of Rajasthan physiography was developed from ASTER GDEM of 30-m resolution. GIS layers of Area of occurrences for C. bulbosa plant and elevation were created. This map along with topographic sheets of 1:50000 were used for field traversing and ground truthing as per GPS location inferred from map. Its geographic distribution was assessed using MaxEnt distribution modelling algorithm that employed 12 presence locality data, 19 bioclimatic variables, and elevation data. Results of this modelling predicted occurrence of C. bulbosa in the districts of Sirohi, Jalore, Barmer, Pali, Ajmer, Jhalawar, Dungarpur, Banswara, Baran, Kota, Bundi and Chittorgarh. Ground validation in these districts revealed its presence only at four places in three districts confirming its rarity. Analysis of dominance at their sites of occurrence revealed their poor populations and sub dominant status (RIV = 20-32) and very low density (2-12 plants per tenth ha).

How well do modelled routes to school record the environments children are exposed to?: a cross-sectional comparison of GIS-modelled and GPS-measured routes to school

PubMed Central

2014-01-01

Background The school journey may make an important contribution to children’s physical activity and provide exposure to food and physical activity environments. Typically, Geographic Information Systems (GIS) have been used to model assumed routes to school in studies, but these may differ from those actually chosen. We aimed to identify the characteristics of children and their environments that make the modelled route more or less representative of that actually taken. We compared modelled GIS routes and actual Global Positioning Systems (GPS) measured routes in a free-living sample of children using varying travel modes. Methods Participants were 175 13-14 yr old children taking part in the Sport, Physical activity and Eating behaviour: Environmental Determinants in Young people (SPEEDY) study who wore GPS units for up to 7 days. Actual routes to/from school were extracted from GPS data, and shortest routes between home and school along a road network were modelled in a GIS. Differences between them were assessed according to length, percentage overlap, and food outlet exposure using multilevel regression models. Results GIS routes underestimated route length by 21.0% overall, ranging from 6.1% among walkers to 23.2% for bus users. Among pedestrians food outlet exposure was overestimated by GIS routes by 25.4%. Certain characteristics of children and their neighbourhoods that improved the concordance between GIS and GPS route length and overlap were identified. Living in a village raised the odds of increased differences in length (odds ratio (OR) 3.36 (1.32-8.58)), while attending a more urban school raised the odds of increased percentage overlap (OR 3.98 (1.49-10.63)). However none were found for food outlet exposure. Journeys home from school increased the difference between GIS and GPS routes in terms of food outlet exposure, and this measure showed considerable within-person variation. Conclusions GIS modelled routes between home and school were not truly representative of accurate GPS measured exposure to obesogenic environments, particularly for pedestrians. While route length may be fairly well described, especially for urban populations, those living close to school, and those travelling by foot, the additional expense of acquiring GPS data seems important when assessing exposure to route environments. PMID:24529075

How well do modelled routes to school record the environments children are exposed to? A cross-sectional comparison of GIS-modelled and GPS-measured routes to school.

PubMed

Harrison, Flo; Burgoine, Thomas; Corder, Kirsten; van Sluijs, Esther M F; Jones, Andy

2014-02-14

The school journey may make an important contribution to children's physical activity and provide exposure to food and physical activity environments. Typically, Geographic Information Systems (GIS) have been used to model assumed routes to school in studies, but these may differ from those actually chosen. We aimed to identify the characteristics of children and their environments that make the modelled route more or less representative of that actually taken. We compared modelled GIS routes and actual Global Positioning Systems (GPS) measured routes in a free-living sample of children using varying travel modes. Participants were 175 13-14 yr old children taking part in the Sport, Physical activity and Eating behaviour: Environmental Determinants in Young people (SPEEDY) study who wore GPS units for up to 7 days. Actual routes to/from school were extracted from GPS data, and shortest routes between home and school along a road network were modelled in a GIS. Differences between them were assessed according to length, percentage overlap, and food outlet exposure using multilevel regression models. GIS routes underestimated route length by 21.0% overall, ranging from 6.1% among walkers to 23.2% for bus users. Among pedestrians food outlet exposure was overestimated by GIS routes by 25.4%. Certain characteristics of children and their neighbourhoods that improved the concordance between GIS and GPS route length and overlap were identified. Living in a village raised the odds of increased differences in length (odds ratio (OR) 3.36 (1.32-8.58)), while attending a more urban school raised the odds of increased percentage overlap (OR 3.98 (1.49-10.63)). However none were found for food outlet exposure. Journeys home from school increased the difference between GIS and GPS routes in terms of food outlet exposure, and this measure showed considerable within-person variation. GIS modelled routes between home and school were not truly representative of accurate GPS measured exposure to obesogenic environments, particularly for pedestrians. While route length may be fairly well described, especially for urban populations, those living close to school, and those travelling by foot, the additional expense of acquiring GPS data seems important when assessing exposure to route environments.

Predicting floodplain boundary changes following the Cerro Grande wildfire

NASA Astrophysics Data System (ADS)

McLin, Stephen G.; Springer, Everett P.; Lane, Leonard J.

2001-10-01

A combined ArcView GIS-HEC modelling application for floodplain analysis of pre- and post-burned watersheds is described. The burned study area is located on Pajarito Plateau near Los Alamos National Laboratory (the Laboratory), where the Cerro Grande Wildfire burned 42 878 acres (17 352 ha) in May 2000. This area is dominated by rugged mountains that are dissected by numerous steep canyons having both ephemeral and perennial channel reaches. Vegetation consists of pinon-juniper woodlands located between 6000 and 7000 ft (1829-2134 m) above mean sea level (MSL), and Ponderosa pine stands between 7000 and 10000 ft MSL (2134-3048 m). Approximately 17% of the burned area is located within the Laboratory, and the remainder is located in upstream or adjacent watersheds. Pre-burn floodplains were previously mapped in 1990-91 using early HEC models as part of the hazardous waste site permitting process. Precipitation and stream gauge data provide essential information characterizing rainfall-runoff relationships before and after the fire. They also provide a means of monitoring spatial and temporal changes as forest recovery progresses. The 2000 summer monsoon began in late June and provided several significant runoff events for model calibration. HEC-HMS modelled responses were sequentially refined so that observed and predicted hydrograph peaks were matched at numerous channel locations. The 100 year, 6 h design storm was eventually used to predict peak hydrographs at critical sites. These results were compared with pre-fire simulations so that new flood-prone areas could be systematically identified. Stream channel cross-sectional geometries were extracted from a gridded 1 ft (0·3 m) digital elevation model (DEM) using ArcView GIS. Then floodpool topwidths, depths, and flow velocities were remapped using the HEC-RAS model. Finally, numerous surveyed channel sections were selectively made at crucial sites for DEM verification. These evaluations provided timely guidance that influenced the decision to construct several flood detention structures that were completed in September 2000. Published in 2001 John Wiley & Sons, Ltd.

Performance of Gout Impact Scale in a longitudinal observational study of patients with gout

PubMed Central

Wallace, Beth; Khanna, Dinesh; Aquino-Beaton, Cleopatra; Singh, Jasvinder A.; Duffy, Erin; Elashoff, David

2016-01-01

Abstract Objective. The aim was to evaluate the reliability, validity and responsiveness to change of the Gout Impact Scale (GIS), a disease-specific measure of patient-reported outcomes, in a multicentre longitudinal prospective cohort of gout patients. Methods. Subjects completed the GIS, a 24-item instrument with five scales: Concern Overall, Medication Side Effects, Unmet Treatment Need, Well-Being during Attack, and Concern Over Attack. The total GIS score was calculated by averaging the GIS scale scores. HAQ-Disability Index (HAQ-DI), Short Form (SF)-36 physical and mental component summaries (PCS and MCS) and physician and patient gout severity assessments were also completed. Reliability was assessed with Cronbach’s α. Baseline GIS scores were compared in subjects with and without gout attacks in the past 3 months using Wilcoxon rank sum tests. Multivariate linear regression was used to evaluate predictors of total GIS. Pearson’s correlation coefficients 0.24–0.36 were considered moderate and >0.37 considered large. The effect size for responsiveness to change was interpreted as follows: 0.20–0.49 small, 0.50–0.79 medium and >0.79 large. Results. In 147 subjects, reliability was acceptable for total GIS (0.93) and all GIS scales (0.82–0.94) except Medication Side Effects and Unmet Treatment Need. Total GIS and all scales except Medication Side Effects discriminated between subjects with and without recent gout attacks (P < 0.05). Total GIS showed moderate-to-large correlations with HAQ-DI, SF-36 PCS and MCS (0.33–0.46). Improvement in total GIS tracked with improved physician and patient severity scores. Worsening physician severity score and recent gout attack predicted worsening total GIS. Conclusion. Total GIS score is reliable, valid and responsive to change in patients with gout, and differentiates between subjects with and without recent gout attacks. PMID:26888852

A case study for the integration of predictive mineral potential maps

NASA Astrophysics Data System (ADS)

Lee, Saro; Oh, Hyun-Joo; Heo, Chul-Ho; Park, Inhye

2014-09-01

This study aims to elaborate on the mineral potential maps using various models and verify the accuracy for the epithermal gold (Au) — silver (Ag) deposits in a Geographic Information System (GIS) environment assuming that all deposits shared a common genesis. The maps of potential Au and Ag deposits were produced by geological data in Taebaeksan mineralized area, Korea. The methodological framework consists of three main steps: 1) identification of spatial relationships 2) quantification of such relationships and 3) combination of multiple quantified relationships. A spatial database containing 46 Au-Ag deposits was constructed using GIS. The spatial association between training deposits and 26 related factors were identified and quantified by probabilistic and statistical modelling. The mineral potential maps were generated by integrating all factors using the overlay method and recombined afterwards using the likelihood ratio model. They were verified by comparison with test mineral deposit locations. The verification revealed that the combined mineral potential map had the greatest accuracy (83.97%), whereas it was 72.24%, 65.85%, 72.23% and 71.02% for the likelihood ratio, weight of evidence, logistic regression and artificial neural network models, respectively. The mineral potential map can provide useful information for the mineral resource development.

geoKepler Workflow Module for Computationally Scalable and Reproducible Geoprocessing and Modeling

NASA Astrophysics Data System (ADS)

Cowart, C.; Block, J.; Crawl, D.; Graham, J.; Gupta, A.; Nguyen, M.; de Callafon, R.; Smarr, L.; Altintas, I.

2015-12-01

The NSF-funded WIFIRE project has developed an open-source, online geospatial workflow platform for unifying geoprocessing tools and models for for fire and other geospatially dependent modeling applications. It is a product of WIFIRE's objective to build an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction and visualization of wildfire behavior. geoKepler includes a set of reusable GIS components, or actors, for the Kepler Scientific Workflow System (https://kepler-project.org). Actors exist for reading and writing GIS data in formats such as Shapefile, GeoJSON, KML, and using OGC web services such as WFS. The actors also allow for calling geoprocessing tools in other packages such as GDAL and GRASS. Kepler integrates functions from multiple platforms and file formats into one framework, thus enabling optimal GIS interoperability, model coupling, and scalability. Products of the GIS actors can be fed directly to models such as FARSITE and WRF. Kepler's ability to schedule and scale processes using Hadoop and Spark also makes geoprocessing ultimately extensible and computationally scalable. The reusable workflows in geoKepler can be made to run automatically when alerted by real-time environmental conditions. Here, we show breakthroughs in the speed of creating complex data for hazard assessments with this platform. We also demonstrate geoKepler workflows that use Data Assimilation to ingest real-time weather data into wildfire simulations, and for data mining techniques to gain insight into environmental conditions affecting fire behavior. Existing machine learning tools and libraries such as R and MLlib are being leveraged for this purpose in Kepler, as well as Kepler's Distributed Data Parallel (DDP) capability to provide a framework for scalable processing. geoKepler workflows can be executed via an iPython notebook as a part of a Jupyter hub at UC San Diego for sharing and reporting of the scientific analysis and results from various runs of geoKepler workflows. The communication between iPython and Kepler workflow executions is established through an iPython magic function for Kepler that we have implemented. In summary, geoKepler is an ecosystem that makes geospatial processing and analysis of any kind programmable, reusable, scalable and sharable.

r.avaflow, the GIS simulation model for avalanche and debris flows: new developments and challenges

NASA Astrophysics Data System (ADS)

Mergili, Martin; Queiroz de Oliveira, Gustavo; Fischer, Jan-Thomas; Krenn, Julia; Kulisch, Helmut; Malcherek, Andreas; Pudasaini, Shiva P.

2016-04-01

We present the latest developments and discuss some of the key challenges with regard to the novel and unified computational tool r.avaflow, representing an advanced, comprehensive, GIS-based open source simulation environment for two-phase geophysical mass flows such as avalanches of snow or rock, flows of debris or mud, and related process chains. r.avaflow is freely available and adoptable as a raster module of the GRASS GIS software (http://www.avaflow.org). We focus on the following issues: (1) We back-calculate a laboratory-scale debris flow experiment with r.avaflow and thereby show that different types of drag may govern the evolving flow dynamics, depending on the initial flow configuratiuon. In particular, it appears necessary to consider viscous ambient drag in order to achieve simulation results in line with experimentally measurements. (2) We employ a set of well-documented rock avalanche events to illustrate the use of a built-in functionality for parameter sensitivity analysis and optimization. To do so, we demonstrate possible strategies going beyond the deficient one-at-a-time simulation approach. They allow us to test three or more parameters at once with a limited number of model runs. Computational times are kept at an acceptable level by multi-core processing strategies and use of the Vienna Scientific Cluster. We further discuss a number of key issues with regard to (i) arbitrary mountain topography; and (ii) entrainment and deposition of material. Most tests indicate a good model performance when the affected areas predicted for a late stage of the flow simulation are compared with observed affected areas. However, we note that such a validation is not fully justified without the implementation of a physically correct model for the deposition process. Acknowledgement: The work was conducted as part of the international cooperation project "A GIS simulation model for avalanche and debris flows (avaflow)" supported by the Austrian Science Fund (FWF, project number I 1600-N30) and the German Research Foundation (DFG, project number PU 386/3-1).

A GIS modeling method applied to predicting forest songbird habitat

USGS Publications Warehouse

Dettmers, Randy; Bart, Jonathan

1999-01-01

We have developed an approach for using a??presencea?? data to construct habitat models. Presence data are those that indicate locations where the target organism is observed to occur, but that cannot be used to define locations where the organism does not occur. Surveys of highly mobile vertebrates often yield these kinds of data. Models developed through our approach yield predictions of the amount and the spatial distribution of good-quality habitat for the target species. This approach was developed primarily for use in a GIS context; thus, the models are spatially explicit and have the potential to be applied over large areas. Our method consists of two primary steps. In the first step, we identify an optimal range of values for each habitat variable to be used as a predictor in the model. To find these ranges, we employ the concept of maximizing the difference between cumulative distribution functions of (1) the values of a habitat variable at the observed presence locations of the target organism, and (2) the values of that habitat variable for all locations across a study area. In the second step, multivariate models of good habitat are constructed by combining these ranges of values, using the Boolean operators a??anda?? and a??or.a?? We use an approach similar to forward stepwise regression to select the best overall model. We demonstrate the use of this method by developing species-specific habitat models for nine forest-breeding songbirds (e.g., Cerulean Warbler, Scarlet Tanager, Wood Thrush) studied in southern Ohio. These models are based on speciesa?? microhabitat preferences for moisture and vegetation characteristics that can be predicted primarily through the use of abiotic variables. We use slope, land surface morphology, land surface curvature, water flow accumulation downhill, and an integrated moisture index, in conjunction with a land-cover classification that identifies forest/nonforest, to develop these models. The performance of these models was evaluated with an independent data set. Our tests showed that the models performed better than random at identifying where the birds occurred and provided useful information for predicting the amount and spatial distribution of good habitat for the birds we studied. In addition, we generally found positive correlations between the amount of habitat, as predicted by the models, and the number of territories within a given area. This added component provides the possibility, ultimately, of being able to estimate population sizes. Our models represent useful tools for resource managers who are interested in assessing the impacts of alternative management plans that could alter or remove habitat for these birds.

Gis-Based Spatial Statistical Analysis of College Graduates Employment

NASA Astrophysics Data System (ADS)

Tang, R.

2012-07-01

It is urgently necessary to be aware of the distribution and employment status of college graduates for proper allocation of human resources and overall arrangement of strategic industry. This study provides empirical evidence regarding the use of geocoding and spatial analysis in distribution and employment status of college graduates based on the data from 2004-2008 Wuhan Municipal Human Resources and Social Security Bureau, China. Spatio-temporal distribution of employment unit were analyzed with geocoding using ArcGIS software, and the stepwise multiple linear regression method via SPSS software was used to predict the employment and to identify spatially associated enterprise and professionals demand in the future. The results show that the enterprises in Wuhan east lake high and new technology development zone increased dramatically from 2004 to 2008, and tended to distributed southeastward. Furthermore, the models built by statistical analysis suggest that the specialty of graduates major in has an important impact on the number of the employment and the number of graduates engaging in pillar industries. In conclusion, the combination of GIS and statistical analysis which helps to simulate the spatial distribution of the employment status is a potential tool for human resource development research.

Structure and data consistency of a GIS database for geological risk analysis in S. Miguel Island (Azores)

NASA Astrophysics Data System (ADS)

Queiroz, G.; Goulart, C.; Gaspar, J. L.; Gomes, A.; Resendes, J. P.; Marques, R.; Gonçalves, P.; Silveira, D.; Valadão, P.

2003-04-01

The Geographic Information Systems (GIS) are becoming a major tool in the domain of geological hazard assessment and risk mitigation. When available, hazard and vulnerability data can easily be represented in a GIS and a great diversity of risk maps can be produced following the implementation of specific predicting models. A major difficulty for those that deal with GIS is to obtain high quality, well geo-referenced and validated data. This situation is particularly evident in the scope of risk analysis due to the diversity of data that need to be considered. In order to develop a coherent database for the geological risk analysis of the Azores archipelago it was decided to use the digital maps edited in 2001 by the Instituto Geográfico do Exército de Portugal (scale 1:25000), comprising altimetry, urban areas, roads and streams network. For the particular case of S. Miguel Island the information contained in these layers was revised and rectifications were made whenever needed. Moreover basic additional layers were added to the system, including counties and parishes administrative limits, agriculture and forested areas. For detailed studies all the edifices (e.g. houses, public buildings, monuments) are being individualized and characterized taking in account several parameters that can become crucial to assess their direct vulnerability to geological hazards (e.g. type of construction, number of floors, roof stability). Geological data obtained (1) through the interpretation of historical documents, (2) during recent fieldwork campaigns (e.g. mapping of volcanic centres and associated deposits, faults, dikes, soil degassing anomalies, landslides) and (3) by the existent monitoring networks (e.g. seismic, geodetic, fluid geochemistry) are also being digitised. The acquisition, storage and maintenance of all this information following the same criteria of quality are critical to guarantee the accuracy and consistency of the GIS database through time. In this work we notice the GIS-based methodologies aimed to assure the development of a GIS database directed to the geological risk analysis in S. Miguel Island. In a long-term programme the same strategy is being extended to the other Azorean islands.

Development of a Carbon Management Geographic Information System (GIS) for the United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Howard Herzog; Holly Javedan

In this project a Carbon Management Geographical Information System (GIS) for the US was developed. The GIS stored, integrated, and manipulated information relating to the components of carbon management systems. Additionally, the GIS was used to interpret and analyze the effect of developing these systems. This report documents the key deliverables from the project: (1) Carbon Management Geographical Information System (GIS) Documentation; (2) Stationary CO{sub 2} Source Database; (3) Regulatory Data for CCS in United States; (4) CO{sub 2} Capture Cost Estimation; (5) CO{sub 2} Storage Capacity Tools; (6) CO{sub 2} Injection Cost Modeling; (7) CO{sub 2} Pipeline Transport Costmore » Estimation; (8) CO{sub 2} Source-Sink Matching Algorithm; and (9) CO{sub 2} Pipeline Transport and Cost Model.« less

Comparative assessment of GIS-based methods and metrics for estimating long-term exposures to air pollution

NASA Astrophysics Data System (ADS)

Gulliver, John; de Hoogh, Kees; Fecht, Daniela; Vienneau, Danielle; Briggs, David

2011-12-01

The development of geographical information system techniques has opened up a wide array of methods for air pollution exposure assessment. The extent to which these provide reliable estimates of air pollution concentrations is nevertheless not clearly established. Nor is it clear which methods or metrics should be preferred in epidemiological studies. This paper compares the performance of ten different methods and metrics in terms of their ability to predict mean annual PM 10 concentrations across 52 monitoring sites in London, UK. Metrics analysed include indicators (distance to nearest road, traffic volume on nearest road, heavy duty vehicle (HDV) volume on nearest road, road density within 150 m, traffic volume within 150 m and HDV volume within 150 m) and four modelling approaches: based on the nearest monitoring site, kriging, dispersion modelling and land use regression (LUR). Measures were computed in a GIS, and resulting metrics calibrated and validated against monitoring data using a form of grouped jack-knife analysis. The results show that PM 10 concentrations across London show little spatial variation. As a consequence, most methods can predict the average without serious bias. Few of the approaches, however, show good correlations with monitored PM 10 concentrations, and most predict no better than a simple classification based on site type. Only land use regression reaches acceptable levels of correlation ( R2 = 0.47), though this can be improved by also including information on site type. This might therefore be taken as a recommended approach in many studies, though care is needed in developing meaningful land use regression models, and like any method they need to be validated against local data before their application as part of epidemiological studies.

Open Source GIS Connectors to NASA GES DISC Satellite Data

NASA Technical Reports Server (NTRS)

Kempler, Steve; Pham, Long; Yang, Wenli

2014-01-01

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) houses a suite of high spatiotemporal resolution GIS data including satellite-derived and modeled precipitation, air quality, and land surface parameter data. The data are valuable to various GIS research and applications at regional, continental, and global scales. On the other hand, many GIS users, especially those from the ArcGIS community, have difficulties in obtaining, importing, and using our data due to factors such as the variety of data products, the complexity of satellite remote sensing data, and the data encoding formats. We introduce a simple open source ArcGIS data connector that significantly simplifies the access and use of GES DISC data in ArcGIS.

Spatial modeling of environmental vulnerability of marine finfish aquaculture using GIS-based neuro-fuzzy techniques.

PubMed

Navas, Juan Moreno; Telfer, Trevor C; Ross, Lindsay G

2011-08-01

Combining GIS with neuro-fuzzy modeling has the advantage that expert scientific knowledge in coastal aquaculture activities can be incorporated into a geospatial model to classify areas particularly vulnerable to pollutants. Data on the physical environment and its suitability for aquaculture in an Irish fjard, which is host to a number of different aquaculture activities, were derived from a three-dimensional hydrodynamic and GIS models. Subsequent incorporation into environmental vulnerability models, based on neuro-fuzzy techniques, highlighted localities particularly vulnerable to aquaculture development. The models produced an overall classification accuracy of 85.71%, with a Kappa coefficient of agreement of 81%, and were sensitive to different input parameters. A statistical comparison between vulnerability scores and nitrogen concentrations in sediment associated with salmon cages showed good correlation. Neuro-fuzzy techniques within GIS modeling classify vulnerability of coastal regions appropriately and have a role in policy decisions for aquaculture site selection. Copyright © 2011 Elsevier Ltd. All rights reserved.

Microbial genomic island discovery, visualization and analysis.

PubMed

Bertelli, Claire; Tilley, Keith E; Brinkman, Fiona S L

2018-06-03

Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.

Scaling up from field to region for wind erosion prediction using a field-scale wind erosion model and GIS

USGS Publications Warehouse

Zobeck, T.M.; Parker, N.C.; Haskell, S.; Guoding, K.

2000-01-01

Factors that affect wind erosion such as surface vegetative and other cover, soil properties and surface roughness usually change spatially and temporally at the field-scale to produce important field-scale variations in wind erosion. Accurate estimation of wind erosion when scaling up from fields to regions, while maintaining meaningful field-scale process details, remains a challenge. The objectives of this study were to evaluate the feasibility of using a field-scale wind erosion model with a geographic information system (GIS) to scale up to regional levels and to quantify the differences in wind erosion estimates produced by different scales of soil mapping used as a data layer in the model. A GIS was used in combination with the revised wind erosion equation (RWEQ), a field-scale wind erosion model, to estimate wind erosion for two 50 km2 areas. Landsat Thematic Mapper satellite imagery from 1993 with 30 m resolution was used as a base map. The GIS database layers included land use, soils, and other features such as roads. The major land use was agricultural fields. Data on 1993 crop management for selected fields of each crop type were collected from local government agency offices and used to 'train' the computer to classify land areas by crop and type of irrigation (agroecosystem) using commercially available software. The land area of the agricultural land uses was overestimated by 6.5% in one region (Lubbock County, TX, USA) and underestimated by about 21% in an adjacent region (Terry County, TX, USA). The total estimated wind erosion potential for Terry County was about four times that estimated for adjacent Lubbock County. The difference in potential erosion among the counties was attributed to regional differences in surface soil texture. In a comparison of different soil map scales in Terry County, the generalised soil map had over 20% more of the land area and over 15% greater erosion potential in loamy sand soils than did the detailed soil map. As a result, the wind erosion potential determined using the generalised soil map Was about 26% greater than the erosion potential estimated by using the detailed soil map in Terry County. This study demonstrates the feasibility of scaling up from fields to regions to estimate wind erosion potential by coupling a field-scale wind erosion model with GIS and identifies possible sources of error with this approach.

Malaria Modeling using Remote Sensing and GIS Technologies

NASA Technical Reports Server (NTRS)

Kiang, Richard

2004-01-01

Malaria has been with the human race since the ancient time. In spite of the advances of biomedical research and the completion of genomic mapping of Plasmodium falciparum, the exact mechanisms of how the various strains of parasites evade the human immune system and how they have adapted and become resistant to multiple drugs remain elusive. Perhaps because of these reasons, effective vaccines against malaria are still not available. Worldwide, approximately one to three millions deaths are attributed to malaria annually. With the increased availability of remotely sensed data, researchers in medical entomology, epidemiology and ecology have started to associate environmental and ecological variables with malaria transmission. In several studies, it has been shown that transmission correlates well with certain environmental and ecological parameters, and that remote sensing can be used to measure these determinants. In a NASA project, we have taken a holistic approach to examine how remote sensing and GIs can contribute to vector and malaria controls. To gain a better understanding of the interactions among the possible promoting factors, we have been developing a habitat model, a transmission model, and a risk prediction model, all using remote sensing data as input. Our objectives are: 1) To identify the potential breeding sites of major vector species and the locations for larvicide and insecticide applications in order to reduce costs, lessen the chance of developing pesticide resistance, and minimize the damage to the environment; 2) To develop a malaria transmission model characterizing the interactions among hosts, vectors, parasites, landcover and environment in order to identify the key factors that sustain or intensify malaria transmission, and 3) To develop a risk model to predict the occurrence of malaria and its transmission intensity using epidemiological data and satellite-derived or ground-measured environmental and meteorological data.

Modelling topographic potential for erosion and deposition using GIS

Treesearch

Helena Mitasova; Louis R. Iverson

1996-01-01

Modelling of erosion and deposition in complex terrain within a geographical information system (GIS) requires a high resolution digital elevation model (DEM), reliable estimation of topographic parameters, and formulation of erosion models adequate for digital representation of spatially distributed parameters. Regularized spline with tension was integrated within a...

Study on the key technology of grain logistics tracking system

NASA Astrophysics Data System (ADS)

Zhen, Tong; Ge, Hongyi; Jiang, Yuying; Che, Yi

2010-07-01

In recent year, with the rapid development of GIS technology, more and more programming problems depend on the GIS technology and professional model system. The solution of auxiliary programming problem by using GIS technology, which has become very popular. GIS is an important tool and technology, that captures, stores, analyzes, manages, and presents data that are linked to location. A grain logistics distribution system based on GIS is established, which provides a visualization scheme during the process of grain circulation and supports users making decision and analyzing for grain logistics enterprise.

Forest and wildlife habitat analysis using remote sensing and geographic information systems. M.S. Thesis, 26 May 1992 Abstract Only

NASA Technical Reports Server (NTRS)

Fiorella, Maria

1995-01-01

Forest and wildlife habitat analyses were conducted at the H.J. Andrews Experimental Forest in the Central Cascade Mountains of Oregon using remotely sensed data and a geographic information system (GIS). Landsat Thematic Mapper (TM) data were used to determine forest successional stages, and to analyze the structure of both old and young conifer forests. Two successional stage maps were developed. One was developed from six TM spectral bands alone, and the second was developed from six TM spectral bands and a relative sun incidence band. Including the sun incidence band in the classification improved the mapping accuracy in the two youngest successional stages, but did not improve overall accuracy or accuracy of the two oldest successional stages. Mean spectral values for old-growth and mature stands were compared in seven TM bands and seven band transformations. Differences between mature and old-growth successional stages were greatest for the band ratio of TM 4/5 (P = 0.00005) and the multiband transformation of wetness (P = 0.00003). The age of young conifer stands had the highest correlation to TM 4/5 values (r = 0.9559) of any of the TM band or band transformations used. TM 4/5 ratio values of poorly regenerated conifer stands were significantly different from well regenerated conifer stands after age 15 (P = 0.0000). TM 4/5 was named a 'Successional Stage Index' (SSI) because of its ability to distinguish forest successional stages. The forest successional stage map was used as input into a vertebrate richness model using GIS. The three variables of (1) successional stage, (2) elevation, and (3) site moisture were used in the GIS to predict the spatial occurrence of small mammal, amphibian, and reptile species based on primary and secondary habitat requirements. These occurrence or habitat maps were overlayed to tally the predicted number of vertebrate at any given point in the study area. Overall, sixty-three and sixty-seven percent of the model predictions for vertebrate occurrence matched the vertebrates that were trapped in the field in eight forested stands. Of the three model variables, site moisture appeared to have the greatest influence on the pattern of high vertebrate richness in all vertebrate classes.

GIS-based spatial statistical analysis of risk areas for liver flukes in Surin Province of Thailand.

PubMed

Rujirakul, Ratana; Ueng-arporn, Naporn; Kaewpitoon, Soraya; Loyd, Ryan J; Kaewthani, Sarochinee; Kaewpitoon, Natthawut

2015-01-01

It is urgently necessary to be aware of the distribution and risk areas of liver fluke, Opisthorchis viverrini, for proper allocation of prevention and control measures. This study aimed to investigate the human behavior, and environmental factors influencing the distribution in Surin Province of Thailand, and to build a model using stepwise multiple regression analysis with a geographic information system (GIS) on environment and climate data. The relationship between the human behavior, attitudes (<50%; X111), environmental factors like population density (148-169 pop/km2; X73), and land use as wetland (X64), were correlated with the liver fluke disease distribution at 0.000, 0.034, and 0.006 levels, respectively. Multiple regression analysis, by equations OV=-0.599+0.005(population density (148-169 pop/km2); X73)+0.040 (human attitude (<50%); X111)+0.022 (land used (wetland; X64), was used to predict the distribution of liver fluke. OV is the patients of liver fluke infection, R Square=0.878, and, Adjust R Square=0.849. By GIS analysis, we found Si Narong, Sangkha, Phanom Dong Rak, Mueang Surin, Non Narai, Samrong Thap, Chumphon Buri, and Rattanaburi to have the highest distributions in Surin province. In conclusion, the combination of GIS and statistical analysis can help simulate the spatial distribution and risk areas of liver fluke, and thus may be an important tool for future planning of prevention and control measures.

Utilizing environmental, socioeconomic data and GIS techniques to estimate the risk for ascariasis and trichuriasis in Minas Gerais, Brazil.

PubMed

Scholte, Ronaldo G C; Freitas, Corina C; Dutra, Luciano V; Guimaraes, Ricardo J P S; Drummond, Sandra C; Oliveira, Guilherme; Carvalho, Omar S

2012-02-01

The impact of intestinal helminths on human health is well known among the population and health authorities because of their wide geographic distribution and the serious problems they cause. Geohelminths are highly prevalent and have a big impact on public health, mainly in underdeveloped and developing countries. Geohelminths are responsible for the high levels of debility found in the younger population and are often related to cases of chronic diarrhea and malnutrition, which put the physical and intellectual development of children at risk. These geohelminths have not been sufficiently studied. One obstacle in implementing a control program is the lack of knowledge of the prevalence and geographical distribution. Geographical information systems (GIS) and remote sensing (RS) have been utilized to improve understanding of infectious disease distribution and climatic patterns. In this study, GIS and RS technologies, as well as meteorological, social, and environmental variables were utilized for the modeling and prediction of ascariasis and trichuriasis. The GIS and RS technologies specifically used were those produced by orbital sensing including the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Shuttle Radar Topography Mission (SRTM). The results of this study demonstrated important factors related to the transmission of ascariasis and trichuriasis and confirmed the key association between environmental variables and the poverty index, which enabled us to identify priority areas for intervention planning in the state of Minas Gerais in Brazil. Copyright © 2011 Elsevier B.V. All rights reserved.

A web GIS based integrated flood assessment modeling tool for coastal urban watersheds

NASA Astrophysics Data System (ADS)

Kulkarni, A. T.; Mohanty, J.; Eldho, T. I.; Rao, E. P.; Mohan, B. K.

2014-03-01

Urban flooding has become an increasingly important issue in many parts of the world. In this study, an integrated flood assessment model (IFAM) is presented for the coastal urban flood simulation. A web based GIS framework has been adopted to organize the spatial datasets for the study area considered and to run the model within this framework. The integrated flood model consists of a mass balance based 1-D overland flow model, 1-D finite element based channel flow model based on diffusion wave approximation and a quasi 2-D raster flood inundation model based on the continuity equation. The model code is written in MATLAB and the application is integrated within a web GIS server product viz: Web Gram Server™ (WGS), developed at IIT Bombay, using Java, JSP and JQuery technologies. Its user interface is developed using open layers and the attribute data are stored in MySQL open source DBMS. The model is integrated within WGS and is called via Java script. The application has been demonstrated for two coastal urban watersheds of Navi Mumbai, India. Simulated flood extents for extreme rainfall event of 26 July, 2005 in the two urban watersheds of Navi Mumbai city are presented and discussed. The study demonstrates the effectiveness of the flood simulation tool in a web GIS environment to facilitate data access and visualization of GIS datasets and simulation results.

A Geothermal GIS for Nevada: Defining Regional Controls and Favorable Exploration Terrains for Extensional Geothermal Systems

USGS Publications Warehouse

Coolbaugh, M.F.; Taranik, J.V.; Raines, G.L.; Shevenell, L.A.; Sawatzky, D.L.; Bedell, R.; Minor, T.B.

2002-01-01

Spatial analysis with a GIS was used to evaluate geothermal systems in Nevada using digital maps of geology, heat flow, young faults, young volcanism, depth to groundwater, groundwater geochemistry, earthquakes, and gravity. High-temperature (>160??C) extensional geothermal systems are preferentially associated with northeast-striking late Pleistocene and younger faults, caused by crustal extension, which in most of Nevada is currently oriented northwesterly (as measured by GPS). The distribution of sparse young (160??C) geothermal systems in Nevada are more likely to occur in areas where the groundwater table is shallow (<30m). Undiscovered geothermal systems may occur where groundwater levels are deeper and hot springs do not issue at the surface. A logistic regression exploration model was developed for geothermal systems, using young faults, young volcanics, positive gravity anomalies, and earthquakes to predict areas where deeper groundwater tables are most likely to conceal geothermal systems.

Schistosomiasis: Geospatial Surveillance and Response Systems in Southeast Asia

NASA Astrophysics Data System (ADS)

Malone, John; Bergquist, Robert; Rinaldi, Laura; Xiao-nong, Zhou

2016-10-01

Geographic information system (GIS) and remote sensing (RS) from Earth-observing satellites offer opportunities for rapid assessment of areas endemic for vector-borne diseases including estimates of populations at risk and guidance to intervention strategies. This presentation deals with GIS and RS applications for the control of schistosomiasis in China and the Philippines. It includes large-scale risk mapping including identification of suitable habitats for Oncomelania hupensis, the intermediate host snail of Schistosoma japonicum. Predictions of infection risk are discussed with reference to ecological transformations and the potential impact of climate change and the potential for long-term temperature increases in the North as well as the impact on rivers, lakes and water resource developments. Potential integration of geospatial mapping and modeling in schistosomiasis surveillance and response systems in Asia within Global Earth Observation System of Systems (GEOSS) guidelines in the health societal benefit area is discussed.

From BIM to GIS at the Smithsonian Institution

NASA Astrophysics Data System (ADS)

Günther-Diringer, Detlef

2018-05-01

BIM-files (Building Information Models) are in modern architecture and building management a basic prerequisite for successful creation of construction engineering projects. At the facilities department of the Smithsonian Institution more than six hundred buildings were maintained. All facilities were digital available in an ESRI ArcGIS-environment with connection to the database information about single rooms with the usage and further maintenance information. These data are organization wide available by an intranet viewer, but only in a two-dimensional representation. Goal of the carried out project was the development of a workflow from available BIM-models to the given GIS-structure. The test-environment were the BIM-models of the buildings of the Smithsonian museums along the Washington Mall. Based on new software editions of Autodesk Revit, FME and ArcGIS Pro the workflow from BIM to the GIS-data structure of the Smithsonian was successfully developed and may be applied for the setup of the future 3D intranet viewer.

Depth data research of GIS based on clustering analysis algorithm

NASA Astrophysics Data System (ADS)

Xiong, Yan; Xu, Wenli

2018-03-01

The data of GIS have spatial distribution. Geographic data has both spatial characteristics and attribute characteristics, and also changes with time. Therefore, the amount of data is very large. Nowadays, many industries and departments in the society are using GIS. However, without proper data analysis and mining scheme, GIS will not exert its maximum effectiveness and will waste a lot of data. In this paper, we use the geographic information demand of a national security department as the experimental object, combining the characteristics of GIS data, taking into account the characteristics of time, space, attributes and so on, and using cluster analysis algorithm. We further study the mining scheme for depth data, and get the algorithm model. This algorithm can automatically classify sample data, and then carry out exploratory analysis. The research shows that the algorithm model and the information mining scheme can quickly find hidden depth information from the surface data of GIS, thus improving the efficiency of the security department. This algorithm can also be extended to other fields.

GenGIS 2: Geospatial Analysis of Traditional and Genetic Biodiversity, with New Gradient Algorithms and an Extensible Plugin Framework

PubMed Central

Parks, Donovan H.; Mankowski, Timothy; Zangooei, Somayyeh; Porter, Michael S.; Armanini, David G.; Baird, Donald J.; Langille, Morgan G. I.; Beiko, Robert G.

2013-01-01

GenGIS is free and open source software designed to integrate biodiversity data with a digital map and information about geography and habitat. While originally developed with microbial community analyses and phylogeography in mind, GenGIS has been applied to a wide range of datasets. A key feature of GenGIS is the ability to test geographic axes that can correspond to routes of migration or gradients that influence community similarity. Here we introduce GenGIS version 2, which extends the linear gradient tests introduced in the first version to allow comprehensive testing of all possible linear geographic axes. GenGIS v2 also includes a new plugin framework that supports the development and use of graphically driven analysis packages: initial plugins include implementations of linear regression and the Mantel test, calculations of alpha-diversity (e.g., Shannon Index) for all samples, and geographic visualizations of dissimilarity matrices. We have also implemented a recently published method for biomonitoring reference condition analysis (RCA), which compares observed species richness and diversity to predicted values to determine whether a given site has been impacted. The newest version of GenGIS supports vector data in addition to raster files. We demonstrate the new features of GenGIS by performing a full gradient analysis of an Australian kangaroo apple data set, by using plugins and embedded statistical commands to analyze human microbiome sample data, and by applying RCA to a set of samples from Atlantic Canada. GenGIS release versions, tutorials and documentation are freely available at http://kiwi.cs.dal.ca/GenGIS, and source code is available at https://github.com/beiko-lab/gengis. PMID:23922841

Towards evidence-based, GIS-driven national spatial health information infrastructure and surveillance services in the United Kingdom

PubMed Central

Boulos, Maged N Kamel

2004-01-01

The term "Geographic Information Systems" (GIS) has been added to MeSH in 2003, a step reflecting the importance and growing use of GIS in health and healthcare research and practices. GIS have much more to offer than the obvious digital cartography (map) functions. From a community health perspective, GIS could potentially act as powerful evidence-based practice tools for early problem detection and solving. When properly used, GIS can: inform and educate (professionals and the public); empower decision-making at all levels; help in planning and tweaking clinically and cost-effective actions, in predicting outcomes before making any financial commitments and ascribing priorities in a climate of finite resources; change practices; and continually monitor and analyse changes, as well as sentinel events. Yet despite all these potentials for GIS, they remain under-utilised in the UK National Health Service (NHS). This paper has the following objectives: (1) to illustrate with practical, real-world scenarios and examples from the literature the different GIS methods and uses to improve community health and healthcare practices, e.g., for improving hospital bed availability, in community health and bioterrorism surveillance services, and in the latest SARS outbreak; (2) to discuss challenges and problems currently hindering the wide-scale adoption of GIS across the NHS; and (3) to identify the most important requirements and ingredients for addressing these challenges, and realising GIS potential within the NHS, guided by related initiatives worldwide. The ultimate goal is to illuminate the road towards implementing a comprehensive national, multi-agency spatio-temporal health information infrastructure functioning proactively in real time. The concepts and principles presented in this paper can be also applied in other countries, and on regional (e.g., European Union) and global levels. PMID:14748927

A Behavioral Model of Landscape Change in the Amazon Basin: The Colonist Case

NASA Technical Reports Server (NTRS)

Walker, R. A.; Drzyzga, S. A.; Li, Y. L.; Wi, J. G.; Caldas, M.; Arima, E.; Vergara, D.

2004-01-01

This paper presents the prototype of a predictive model capable of describing both magnitudes of deforestation and its spatial articulation into patterns of forest fragmentation. In a departure from other landscape models, it establishes an explicit behavioral foundation for algorithm development, predicated on notions of the peasant economy and on household production theory. It takes a 'bottom-up' approach, generating the process of land-cover change occurring at lot level together with the geography of a transportation system to describe regional landscape change. In other words, it translates the decentralized decisions of individual households into a collective, spatial impact. In so doing, the model unites the richness of survey research on farm households with the analytical rigor of spatial analysis enabled by geographic information systems (GIs). The paper describes earlier efforts at spatial modeling, provides a critique of the so-called spatially explicit model, and elaborates a behavioral foundation by considering farm practices of colonists in the Amazon basin. It then uses, insight from the behavioral statement to motivate a GIs-based model architecture. The model is implemented for a long-standing colonization frontier in the eastern sector of the basin, along the Trans-Amazon Highway in the State of Para, Brazil. Results are subjected to both sensitivity analysis and error assessment, and suggestions are made about how the model could be improved.

Summaries of Minnehaha Creek Watershed District Plans/Studies/Reports

DTIC Science & Technology

2004-01-30

34+ Management of all wetland functional assessment data in a Microsoft Access© database "+ Development of a GIS wetland data management system "+ Recommendations...General Task B Design GIS -Based Decision Making Model: Scenario-Based $125,000 $125,000 Model of Landuse Hydro Data Monitoring Task C Water Quality...Landuse and Land cover data + Watershed GIS data layers + Flood Insurance Rate Maps + Proposed project locations + Stream miles, reaches and conditions

Composite use of numerical groundwater flow modeling and geoinformatics techniques for monitoring Indus Basin aquifer, Pakistan.

PubMed

Ahmad, Zulfiqar; Ashraf, Arshad; Fryar, Alan; Akhter, Gulraiz

2011-02-01

The integration of the Geographic Information System (GIS) with groundwater modeling and satellite remote sensing capabilities has provided an efficient way of analyzing and monitoring groundwater behavior and its associated land conditions. A 3-dimensional finite element model (Feflow) has been used for regional groundwater flow modeling of Upper Chaj Doab in Indus Basin, Pakistan. The approach of using GIS techniques that partially fulfill the data requirements and define the parameters of existing hydrologic models was adopted. The numerical groundwater flow model is developed to configure the groundwater equipotential surface, hydraulic head gradient, and estimation of the groundwater budget of the aquifer. GIS is used for spatial database development, integration with a remote sensing, and numerical groundwater flow modeling capabilities. The thematic layers of soils, land use, hydrology, infrastructure, and climate were developed using GIS. The Arcview GIS software is used as additive tool to develop supportive data for numerical groundwater flow modeling and integration and presentation of image processing and modeling results. The groundwater flow model was calibrated to simulate future changes in piezometric heads from the period 2006 to 2020. Different scenarios were developed to study the impact of extreme climatic conditions (drought/flood) and variable groundwater abstraction on the regional groundwater system. The model results indicated a significant response in watertable due to external influential factors. The developed model provides an effective tool for evaluating better management options for monitoring future groundwater development in the study area.

Risk analysis for dengue suitability in Africa using the ArcGIS predictive analysis tools (PA tools).

PubMed

Attaway, David F; Jacobsen, Kathryn H; Falconer, Allan; Manca, Germana; Waters, Nigel M

2016-06-01

Risk maps identifying suitable locations for infection transmission are important for public health planning. Data on dengue infection rates are not readily available in most places where the disease is known to occur. A newly available add-in to Esri's ArcGIS software package, the ArcGIS Predictive Analysis Toolset (PA Tools), was used to identify locations within Africa with environmental characteristics likely to be suitable for transmission of dengue virus. A more accurate, robust, and localized (1 km × 1 km) dengue risk map for Africa was created based on bioclimatic layers, elevation data, high-resolution population data, and other environmental factors that a search of the peer-reviewed literature showed to be associated with dengue risk. Variables related to temperature, precipitation, elevation, and population density were identified as good predictors of dengue suitability. Areas of high dengue suitability occur primarily within West Africa and parts of Central Africa and East Africa, but even in these regions the suitability is not homogenous. This risk mapping technique for an infection transmitted by Aedes mosquitoes draws on entomological, epidemiological, and geographic data. The method could be applied to other infectious diseases (such as Zika) in order to provide new insights for public health officials and others making decisions about where to increase disease surveillance activities and implement infection prevention and control efforts. The ability to map threats to human and animal health is important for tracking vectorborne and other emerging infectious diseases and modeling the likely impacts of climate change. Copyright © 2016 Elsevier B.V. All rights reserved.

The Multi-factor Predictive Seis &Gis Model of Ecological, Genetical, Population Health Risk and Bio-geodynamic Processes In Geopathogenic Zones

NASA Astrophysics Data System (ADS)

Bondarenko, Y.

I. Goal and Scope. Human birth rate decrease, death-rate growth and increase of mu- tagenic deviations risk take place in geopathogenic and anthropogenic hazard zones. Such zones create unfavourable conditions for reproductive process of future genera- tions. These negative trends should be considered as a protective answer of the com- plex biosocial system to the appearance of natural and anthropogenic risk factors that are unfavourable for human health. The major goals of scientific evaluation and de- crease of risk of appearance of hazardous processes on the territory of Dnipropetrovsk, along with creation of the multi-factor predictive Spirit-Energy-Information Space "SEIS" & GIS Model of ecological, genetical and population health risk in connection with dangerous bio-geodynamic processes, were: multi-factor modeling and correla- tion of natural and anthropogenic environmental changes and those of human health; determination of indicators that show the risk of destruction structures appearance on different levels of organization and functioning of the city ecosystem (geophys- ical and geochemical fields, soil, hydrosphere, atmosphere, biosphere); analysis of regularities of natural, anthropogenic, and biological rhythms' interactions. II. Meth- ods. The long spatio-temporal researches (Y. Bondarenko, 1996, 2000) have proved that the ecological, genetic and epidemiological processes are in connection with de- velopment of dangerous bio-geophysical and bio-geodynamic processes. Mathemat- ical processing of space photos, lithogeochemical and geophysical maps with use of JEIS o and ERDAS o computer systems was executed at the first stage of forma- tion of multi-layer geoinformation model "Dnipropetrovsk ARC View GIS o. The multi-factor nonlinear correlation between solar activity and cosmic ray variations, geophysical, geodynamic, geochemical, atmospheric, technological, biological, socio- economical processes and oncologic case rate frequency, general and primary popula- tion sickness cases in Dnipropetrovsk City (1.2 million persons) are described by the multi-factor predictive SEIS & GIS model of geopathogenic zones that determines the human health risk and hazards. Results and Conclusions. We have created the SEIS system and multi-factor predictive SEIS model for the analysis of phase-metric spatio- 1 temporal nonlinear correlation and variations of rhythms of human health, ecological, genetic, epidemiological risks, demographic, socio-economic, bio-geophysical, bio- geodynamic processes in geopathogenic hazard zones. Cosmophotomaps "CPM" of vegetation index, anthropogenic-landscape and landscape-geophysical human health risk of Dnipropetrovsk City present synthesis-based elements of multi-layer GIS, which include multispectral images SPOT o, maps of different geophysical, geochem- ical, anthropogenic and citogenic risk factors, maps of integral oncologic case rate frequency, general and primary population sickness cases for administrative districts. Results of multi-layer spatio-temporal correlation of geophysical field parameters and variations of population sickness rate rhythms have enabled us to state grounds and to develop medico-biological and bio-geodynamic classification of geopathogenic zones. Bio-geodynamic model has served to define contours of anthropogenic-landscape and landscape-geophysical human health risk in Dnipropetrovsk City. Biorhythmic vari- ations give foundation for understanding physiological mechanisms of organism`s adaptation to extreme helio-geophysical and bio-geodynamic environmental condi- tions, which are dictated by changes in Multi-factor Correlation Stress Field "MCSF" with deformation of 5D SEIS. Interaction between organism and environment results in continuous superpositioning of external (exogenic) Nuclear-Molecular-Cristallic "NMC" MCSF rhythms on internal (endogenic) Nuclear-Molecular-Cellular "NMCl" MCSF rhythms. Their resonance wave (energy-information) integration and disinte- gration are responsible for structural and functional state of different physiological systems. Herewith, complex restructurization of defense functions blocks the adapta- tion process and may turn to be the primary reason for phase shifting, process and biorhythms hindering, appearance of different deseases. Interaction of biorhythms with natural and anthropogenic rhythms specify the peculiar features of environ- mental adaptation of living species. Such interaction results in correlation of sea- sonal rhythms in variations of thermo-baro-geodynamic "TBG" parameters of am- bient air with toxic concentration and human health risk in Dnipropetrovsk City. Bio-geodynamic analysis of medical and demographic situations has provided for search of spatio-temporal correlation between rhythms of general and primary pop- ulation sickness cases and oncologic case rate frequency, other medico-demographic rhythms, natural processes (helio-geophysical, thermodynamic, geodynamic) and an- thropogenic processes (industrial and houschold waste disposal, toxic emissions and their concentration in ambient air). The year of 1986, the year of minimum helio- geophysical activity "2G1dG1" and maximum anthropogenic processes associated with changes in sickness and death rates of the population of Earth were synchronized. With account of quantum character of SEIS rhythms, 5 reference levels of desyn- chronized helio-geophysical and bio-geodynamic processes affecting population sick- ness rate have been specified within bio-geodynamic models. The first reference level 2 of SEIS desynchronization includes rhythms with period of 22,5 years: ... 1958,2; 1980,7; 2003,2; .... The second reference level of SEIS desynchronization includes rhythms with period of 11,25 years: ... 1980,7; 1992; 2003,2;.... The third reference level covers 5,625-years periodic rhythms2:... 1980,7; 1986,3; 1992; 1997,6; 2003,2; .... The fourth quantum reference level includes rhythms 3 with period of 2,8125 years: ... 1980,7; 1983,5; 1986,3; 1989,1; 1992; 1994,8; 1997,6; 2000,4; 2003,2; .... Rhythms with 1,40625-years period fall is fifth reference level of SEIS desynchro- nization: ...1980,7; 1982,1; 1983,5; 1984,9; 1986,3; 1987,7; 1989,1; 1990,5; 1992; 1993,3; 1994,8; 1996,2; 1997,6; 1999; 2000,4; 2001,8; 2003,2;.... Analysis of alternat- ing medical and demographic situation in Ukraine (1981-1992)and in Dnipropetrovsk (1988-1995)has allowed to back up theoretical model of various-level rhythm quan- tum, with non-linear regularities due to phase-metric spatio-temporal deformation be- ing specified. Application of new technologies of Risk Analysis, Sinthesis and SEIS Modeling at the choice of a burial place for dangerous radioactive wastes in the zone of Chernobyl nuclear disaster (Shestopalov V., Bondarenko Y...., 1998) has shown their very high efficiency in comparison with GIS Analysis. IV.Recommendations and Outlook. In order to draw a conclusion regarding bio-geodynamic modeling of spatio-temporal structure of areas where common childhood sickness rate exists, it is necessary to mention that the only thing that can favour to exact predicting of where and when important catastrophes and epidemies will take place is correct and complex bio-geodynamic modeling. Imperfection of present GIS is the result of the lack of interactive facilities for multi-factor modeling of nonlinear natural and an- thropogenic processes. Equations' coefficients calculated for some areas are often irrelevant when applied to others. In this connection there arises a number of prob- lems concerning practical application and reliability of GIS-models that are used to carry out efficient ecological monitoring. References Bondarenko Y., 1997, Drawing up Cosmophotomaps and Multi-factor Forecasting of Hazard of Development of Dan- gerous Geodynamic Processes in Dnipropetrovsk,The Technically-Natural Problems of failures and catastrophes in connection with development of dangerous geological processes, Kiev, Ukraine, 1997. Bondarenko Y., 1997, The Methodology of a State the Value of Quality of the Ground and the House Level them Ecology-Genetic-Toxic of the human health risk based on multi-layer cartographical model", Experience of application GIS - Technologies for creating Cadastral Systems, Yalta, Ukraine, 1997, p. 39-40. Shestopalov V., Bondarenko Y., Zayonts I., Rudenko Y. , Bohuslavsky A., 1998, Complexation of Structural-Geodynamical and Hydrogeological Methods of Studying Areas to Reveal Geological Structural Perspectives for Deep Isolation of Radioactive Wastes, Field Testing and Associated Modeling of Potential High-Level Nuclear Waste Geologic Disposal Sites, Berkeley, USA, 1998, p.81-82. 3

Investigating flow sensitivity of Greenland outlet glaciers using a time-evolving calving model in Elmer FEM.

NASA Astrophysics Data System (ADS)

Todd, Joe; Christoffersen, Poul

2013-04-01

It is becoming increasingly evident that the marine margins of the Greenland Ice Sheet (GIS) are highly sensitive to local and regional scale climate change, with significant changes in mass balance occurring on sub-decadal timescales. The majority of this mass loss is hypothesised to have been triggered at the termini of calving glaciers. Recent studies suggest that increased calving rate is being driven through some combination of increased submarine undercutting, increased surface hydrofracturing, and changes in the strength and seasonal duration of sikussak. This project aims to improve understanding of these physical processes, in order to better predict how the GIS will respond to future climate change. Two glaciers in the Uummannaq region, Store Gletscher and Rink Isbræ, have been modelled in 2D using the Finite Element modelling package "Elmer FEM". The model produces a time-evolving solution to the coupled Navier-Stokes/heat equations; this allows the dynamic response of these glaciers to external forcing at their termini to be investigated. Furthermore, the model includes a water-depth calving criterion, and is able to simulate realistic calving events, and the subsequent stress/dynamic response of the glacier. Preliminary results suggest that both sikussak backstress and submarine undercutting may represent significant factors in calving terminus stability.

A predictive numerical model for potential mapping of the gas hydrate stability zone in the Gulf of Cadiz

NASA Astrophysics Data System (ADS)

Leon, R.; Somoza, L.

2009-04-01

This comunication presents a computational model for mapping the regional 3D distribution in which seafloor gas hydrates would be stable, that is carried out in a Geographical Information System (GIS) environment. The construction of the model is comprised of three primary steps, namely (1) the construction of surfaces for the various variables based on available 3D data (seafloor temperature, geothermal gradient and depth-pressure); (2) the calculation of the gas function equilibrium functions for the various hydrocarbon compositions reported from hydrate and sediment samples; and (3) the calculation of the thickness of the hydrate stability zone. The solution is based on a transcendental function, which is solved iteratively in a GIS environment. The model has been applied in the northernmost continental slope of the Gulf of Cadiz, an area where an abundant supply for hydrate formation, such as extensive hydrocarbon seeps, diapirs and fault structures, is combined with deep undercurrents and a complex seafloor morphology. In the Gulf of Cadiz, model depicts the distribution of the base of the gas hydrate stability zone for both biogenic and thermogenic gas compositions, and explains the geometry and distribution of geological structures derived from gas venting in the Tasyo Field (Gulf of Cadiz) and the generation of BSR levels on the upper continental slope.

Use of watershed factors to predict consumer surfactant risk, water quality, and habitat quality in the upper Trinity River, Texas.

PubMed

Atkinson, S F; Johnson, D R; Venables, B J; Slye, J L; Kennedy, J R; Dyer, S D; Price, B B; Ciarlo, M; Stanton, K; Sanderson, H; Nielsen, A

2009-06-15

Surfactants are high production volume chemicals that are used in a wide assortment of "down-the-drain" consumer products. Wastewater treatment plants (WWTPs) generally remove 85 to more than 99% of all surfactants from influents, but residual concentrations are discharged into receiving waters via wastewater treatment plant effluents. The Trinity River that flows through the Dallas-Fort Worth metropolitan area, Texas, is an ideal study site for surfactants due to the high ratio of wastewater treatment plant effluent to river flow (>95%) during late summer months, providing an interesting scenario for surfactant loading into the environment. The objective of this project was to determine whether surfactant concentrations, expressed as toxic units, in-stream water quality, and aquatic habitat in the upper Trinity River could be predicted based on easily accessible watershed characteristics. Surface water and pore water samples were collected in late summer 2005 at 11 sites on the Trinity River in and around the Dallas-Fort Worth metropolitan area. Effluents of 4 major waste water treatment plants that discharge effluents into the Trinity River were also sampled. General chemistries and individual surfactant concentrations were determined, and total surfactant toxic units were calculated. GIS models of geospatial, anthropogenic factors (e.g., population density) and natural factors (e.g., soil organic matter) were collected and analyzed according to subwatersheds. Multiple regression analyses using the stepwise maximum R(2) improvement method were performed to develop prediction models of surfactant risk, water quality, and aquatic habitat (dependent variables) using the geospatial parameters (independent variables) that characterized the upper Trinity River watershed. We show that GIS modeling has the potential to be a reliable and inexpensive method of predicting water and habitat quality in the upper Trinity River watershed and perhaps other highly urbanized watersheds in semi-arid regions.

SERVIR: Connecting Space to Village

NASA Technical Reports Server (NTRS)

Limaye, Ashutosh S.; Flores Cordova, Africa Ixmucan

2018-01-01

From space, we can view our planet in new ways. SERVIR empowers people in developing countries to use that view for gaining knowledge and insights about their environments and adaptation to a changing climate. We work with regional decision-makers to foster use of Earth observation satellite data, GIS, and predictive models for addressing water and land use, natural disasters, agricultural problems, biodiversity, and more. These tools can improve the lives, livelihoods, safety, and future of people in communities around the world.

GIS Application System Design Applied to Information Monitoring

NASA Astrophysics Data System (ADS)

Qun, Zhou; Yujin, Yuan; Yuena, Kang

Natural environment information management system involves on-line instrument monitoring, data communications, database establishment, information management software development and so on. Its core lies in collecting effective and reliable environmental information, increasing utilization rate and sharing degree of environment information by advanced information technology, and maximizingly providing timely and scientific foundation for environmental monitoring and management. This thesis adopts C# plug-in application development and uses a set of complete embedded GIS component libraries and tools libraries provided by GIS Engine to finish the core of plug-in GIS application framework, namely, the design and implementation of framework host program and each functional plug-in, as well as the design and implementation of plug-in GIS application framework platform. This thesis adopts the advantages of development technique of dynamic plug-in loading configuration, quickly establishes GIS application by visualized component collaborative modeling and realizes GIS application integration. The developed platform is applicable to any application integration related to GIS application (ESRI platform) and can be as basis development platform of GIS application development.

Processing and utilization of LiDAR data as a support for a good management of DDBR

NASA Astrophysics Data System (ADS)

Nichersu, I.; Grigoras, I.; Constantinescu, A.; Mierla, M.; Tifanov, C.

2012-04-01

Danube Delta Biosphere Reserve (DDBR) has 5,800 km2 as surface and it is situated in the South-East of Europe, in the East of Romania. The paper is taking into account the data related to the elevation surfaces of the DDBR (Digital Terrain Model DTM and Digital Surface Model DSM). To produce such kind of models of elevation for the entire area of DDBR it was used the most modern method that utilizes the Light Detection And Ranging (LiDAR). The raw LiDAR data (x, y, z) for each point were transformed into grid formats for DTM and DSM. Based on these data multiple GIS analyses can be done for management purposes : hydraulic modeling 1D2D scenarios, flooding regime and protection, biomass volume estimation, GIS biodiversity processing. These analyses are very useful in the management planning process. The hydraulic modeling 1D2D scenarios are used by the administrative authority to predict the sense of the fluvial water flow and also to predict the places where the flooding could occur. Also it can be predicted the surface of the terrain that will be occupied by the water from floods. Flooding regime gives information about the frequency of the floods and also the intensity of these. In the same time it could be predicted the time of water remanence period. The protection face of the flooding regime is in direct relation with the socio-cultural communities and all their annexes those that are in risk of being flooded. This raises the problem of building dykes and other flooding protection systems. The biomass volume contains information derived from the LiDAR cloud points that describes only the vegetation. The volume of biomass is an important item in the management of a Biosphere Reserve. Also the LiDAR cloud points that refer to vegetation could help in identifying the groups of vegetal association. All these information corroborated with other information build good premises for a good management. Keywords: Danube Delta Biosphere Reserve, LiDAR data, DTM, DSM, flooding, management

New Nuclear Emergency Prognosis system in Korea

NASA Astrophysics Data System (ADS)

Lee, Hyun-Ha; Jeong, Seung-Young; Park, Sang-Hyun; Lee, Kwan-Hee

2016-04-01

This paper reviews the status of assessment and prognosis system for nuclear emergency response in Korea, especially atmospheric dispersion model. The Korea Institute of Nuclear Safety (KINS) performs the regulation and radiological emergency preparedness of the nuclear facilities and radiation utilizations. Also, KINS has set up the "Radiological Emergency Technical Advisory Plan" and the associated procedures such as an emergency response manual in consideration of the IAEA Safety Standards GS-R-2, GS-G-2.0, and GS-G-2.1. The Radiological Emergency Technical Advisory Center (RETAC) organized in an emergency situation provides the technical advice on radiological emergency response. The "Atomic Computerized Technical Advisory System for nuclear emergency" (AtomCARE) has been developed to implement assessment and prognosis by RETAC. KINS developed Accident Dose Assessment and Monitoring (ADAMO) system in 2015 to reflect the lessons learned from Fukushima accident. It incorporates (1) the dose assessment on the entire Korean peninsula, Asia region, and global region, (2) multi-units accident assessment (3) applying new methodology of dose rate assessment and the source term estimation with inverse modeling, (4) dose assessment and monitoring with the environmental measurements result. The ADAMO is the renovated version of current FADAS of AtomCARE. The ADAMO increases the accuracy of the radioactive material dispersion with applying the LDAPS(Local Data Assimilation Prediction System, Spatial resolution: 1.5 km) and RDAPS(Regional Data Assimilation Prediction System, Spatial resolution: 12km) of weather prediction data, and performing the data assimilation of automatic weather system (AWS) data from Korea Meteorological Administration (KMA) and data from the weather observation tower at NPP site. The prediction model of the radiological material dispersion is based on the set of the Lagrangian Particle model and Lagrangian Puff model. The dose estimation methodology incorporate the dose assessment methods of IAEA, WHO, and USNRC. The dose assessment result will express on the GIS (GIS (Geographic Information System) to provide to the local- governments and the central government. Acknowledgements This research has been supported by the Nuclear Safety and Security Commission [Reference No.1305020-0315-SB110

Water Resource Assessment in KRS Reservoir Using Remote Sensing and GIS Modelling

NASA Astrophysics Data System (ADS)

Manubabu, V. H.; Gouda, K. C.; Bhat, N.; Reddy, A.

2014-12-01

In the recent time the fresh water resource becomes very important because of various reasons like population growth, pollution, over exploitation of the ground water resources etc. As there is no efficient and proper measures for recharging ground water exists and also the climatological impacts on water resources like global warming exacerbating water shortages, growing populations and rising demand for freshwater in agriculture, industry, and energy production. There is a need and challenging task for analyzing the future changes in regional water availability and it is also very much necessary to asses and predict the fresh water present in a lake or reservoir to make better decision making in the optimal usage of surface water. In the present study is intended to provide a practical discussion of methodology that deals with how to asses and predict amount of surface water available in the future using Remote Sensing(RS) data , Geographical Information System(GIS) techniques, and GCM (Global Circulation Model). Basically the study emphasized over one of the biggest reservoir i.e. the Krishna Raja Sagara (KRS) reservoir situated in the state of Karnataka in India. Multispectral satellite images like IRS LISS III and Landsat L8 from different open source web portals like NRSC-Bhuvan and NASA Earth Explorer respectively are used for the present analysis. The multispectral satellite images are used to identify the temporal changes of the water quantity in the reservoir for the period 2000 to 2014. Also the water volume are being calculated using Advances Space born Thermal Emission and Reflection Radiometer (ASTER) Global DEM over the reservoir basin. The hydro meteorological parameters are also studied using multi-source observed data and the empirical water budget models for the reservoir in terms of rainfall, temperature, run off, water inflow and outflow etc. are being developed and analyzed. Statistical analysis are also carried out to quantify the relation between reservoir water volume and the hydrological parameters (Figure 1). A general circulation model (GCM) is used for the prediction of major hydro meteorological parameters like rainfall and using the GCM predictions the water availability in terms of water volume in future are simulated using the empirical water budget model.

Modification of a rainfall-runoff model for distributed modeling in a GIS and its validation

NASA Astrophysics Data System (ADS)

Nyabeze, W. R.

A rainfall-runoff model, which can be inter-faced with a Geographical Information System (GIS) to integrate definition, measurement, calculating parameter values for spatial features, presents considerable advantages. The modification of the GWBasic Wits Rainfall-Runoff Erosion Model (GWBRafler) to enable parameter value estimation in a GIS (GISRafler) is presented in this paper. Algorithms are applied to estimate parameter values reducing the number of input parameters and the effort to populate them. The use of a GIS makes the relationship between parameter estimates and cover characteristics more evident. This paper has been produced as part of research to generalize the GWBRafler on a spatially distributed basis. Modular data structures are assumed and parameter values are weighted relative to the module area and centroid properties. Modifications to the GWBRafler enable better estimation of low flows, which are typical in drought conditions.

Resource materials for a GIS spatial analysis course

USGS Publications Warehouse

Raines, Gary L.

2001-01-01

This report consists of materials prepared for a GIS spatial analysis course offered as part of the Geography curriculum at the University of Nevada, Reno and the University of California at Santa Barbara in the spring of 2000. The report is intended to share information with instructors preparing spatial-modeling training and scientists with advanced GIS expertise. The students taking this class had completed each universities GIS curriculum and had a foundation in statistics as part of a science major. This report is organized into chapters that contain the following: Slides used during lectures, Guidance on the use of Arcview, Introduction to filtering in Arcview, Conventional and spatial correlation in Arcview, Tools for fuzzification in Arcview, Data and instructions for creating using ArcSDM for simple weights-of-evidence, fuzzy logic, and neural network models for Carlin-type gold deposits in central Nevada, Reading list on spatial modeling, and Selected student spatial-modeling posters from the laboratory exercises.

Open cyberGIS software for geospatial research and education in the big data era

NASA Astrophysics Data System (ADS)

Wang, Shaowen; Liu, Yan; Padmanabhan, Anand

CyberGIS represents an interdisciplinary field combining advanced cyberinfrastructure, geographic information science and systems (GIS), spatial analysis and modeling, and a number of geospatial domains to improve research productivity and enable scientific breakthroughs. It has emerged as new-generation GIS that enable unprecedented advances in data-driven knowledge discovery, visualization and visual analytics, and collaborative problem solving and decision-making. This paper describes three open software strategies-open access, source, and integration-to serve various research and education purposes of diverse geospatial communities. These strategies have been implemented in a leading-edge cyberGIS software environment through three corresponding software modalities: CyberGIS Gateway, Toolkit, and Middleware, and achieved broad and significant impacts.

San Mateo County Geographic Information Systems (GIS) project

USGS Publications Warehouse

Brabb, E.E.

1986-01-01

Earthquakes and ground failures in the United States cause billions of dollars of damages each year, but techniques for predicting and reducing these hazardous geologic processes remain elusive. geologists, geophysicists, hydrologists, engineers, cartographers, and computer specialists from the U.S geological Survey in Menlo Park, California, are working together on a project involving GIS techniques to determine how to predict the consequences of earthquakes and landslides, using San Mateo County as a subject area. Together with members of the Planning and Emergency Serivces Departments of San Mateo County and the Association of Bay Area Governments, They are also determining how to reduce the losses caused by hazards. 

Using SCADA Data, Field Studies, and Real-Time Modeling to ...

EPA Pesticide Factsheets

EPA has been providing technical assistance to the City of Flint and the State of Michigan in response to the drinking water lead contamination incident. Responders quickly recognized the need for a water distribution system hydraulic model to provide insight on flow patterns and water quality as well as to evaluate changes being made to the system operation to enhance corrosion control and improve chlorine residuals. EPA partnered with the City of Flint and the Michigan Department of Environmental Quality to update and calibrate an existing hydraulic model. The City provided SCADA data, GIS data, customer billing data, valve status data, design diagrams, and information on operations. Team members visited all facilities and updated pump and valve types, sizes, settings, elevations, and pump discharge curves. Several technologies were used to support this work including the EPANET-RTX based Polaris real-time modeling software, WaterGEMS, ArcGIS, EPANET, and RTX:LINK. Field studies were conducted to collect pressure and flow data from more than 25 locations throughout the distribution system. An assessment of the model performance compared model predictions for flow, pressure, and tank levels to SCADA and field data, resulting in error measurements for each data stream over the time period analyzed. Now, the calibrated model can be used with a known confidence in its performance to evaluate hydraulic and water quality problems, and the model can be easily

Estimating regional plant biodiversity with GIS modelling

Treesearch

Louis R. Iverson; Anantha M. Prasad; Anantha M. Prasad

1998-01-01

We analyzed a statewide species database together with a county-level geographic information system to build a model based on well-surveyed areas to estimate species richness in less surveyed counties. The model involved GIS (Arc/Info) and statistics (S-PLUS), including spatial statistics (S+SpatialStats).

Bim and Gis: when Parametric Modeling Meets Geospatial Data

NASA Astrophysics Data System (ADS)

Barazzetti, L.; Banfi, F.

2017-12-01

Geospatial data have a crucial role in several projects related to infrastructures and land management. GIS software are able to perform advanced geospatial analyses, but they lack several instruments and tools for parametric modelling typically available in BIM. At the same time, BIM software designed for buildings have limited tools to handle geospatial data. As things stand at the moment, BIM and GIS could appear as complementary solutions, notwithstanding research work is currently under development to ensure a better level of interoperability, especially at the scale of the building. On the other hand, the transition from the local (building) scale to the infrastructure (where geospatial data cannot be neglected) has already demonstrated that parametric modelling integrated with geoinformation is a powerful tool to simplify and speed up some phases of the design workflow. This paper reviews such mixed approaches with both simulated and real examples, demonstrating that integration is already a reality at specific scales, which are not dominated by "pure" GIS or BIM. The paper will also demonstrate that some traditional operations carried out with GIS software are also available in parametric modelling software for BIM, such as transformation between reference systems, DEM generation, feature extraction, and geospatial queries. A real case study is illustrated and discussed to show the advantage of a combined use of both technologies. BIM and GIS integration can generate greater usage of geospatial data in the AECOO (Architecture, Engineering, Construction, Owner and Operator) industry, as well as new solutions for parametric modelling with additional geoinformation.

Environmental factor analysis of cholera in China using remote sensing and geographical information systems.

PubMed

Xu, M; Cao, C X; Wang, D C; Kan, B; Xu, Y F; Ni, X L; Zhu, Z C

2016-04-01

Cholera is one of a number of infectious diseases that appears to be influenced by climate, geography and other natural environments. This study analysed the environmental factors of the spatial distribution of cholera in China. It shows that temperature, precipitation, elevation, and distance to the coastline have significant impact on the distribution of cholera. It also reveals the oceanic environmental factors associated with cholera in Zhejiang, which is a coastal province of China, using both remote sensing (RS) and geographical information systems (GIS). The analysis has validated the correlation between indirect satellite measurements of sea surface temperature (SST), sea surface height (SSH) and ocean chlorophyll concentration (OCC) and the local number of cholera cases based on 8-year monthly data from 2001 to 2008. The results show the number of cholera cases has been strongly affected by the variables of SST, SSH and OCC. Utilizing this information, a cholera prediction model has been established based on the oceanic and climatic environmental factors. The model indicates that RS and GIS have great potential for designing an early warning system for cholera.

Use of Wild Bird Surveillance, Human Case Data and GIS Spatial Analysis for Predicting Spatial Distributions of West Nile Virus in Greece

PubMed Central

Valiakos, George; Papaspyropoulos, Konstantinos; Giannakopoulos, Alexios; Birtsas, Periklis; Tsiodras, Sotirios; Hutchings, Michael R.; Spyrou, Vassiliki; Pervanidou, Danai; Athanasiou, Labrini V.; Papadopoulos, Nikolaos; Tsokana, Constantina; Baka, Agoritsa; Manolakou, Katerina; Chatzopoulos, Dimitrios; Artois, Marc; Yon, Lisa; Hannant, Duncan; Petrovska, Liljana; Hadjichristodoulou, Christos; Billinis, Charalambos

2014-01-01

West Nile Virus (WNV) is the causative agent of a vector-borne, zoonotic disease with a worldwide distribution. Recent expansion and introduction of WNV into new areas, including southern Europe, has been associated with severe disease in humans and equids, and has increased concerns regarding the need to prevent and control future WNV outbreaks. Since 2010, 524 confirmed human cases of the disease have been reported in Greece with greater than 10% mortality. Infected mosquitoes, wild birds, equids, and chickens have been detected and associated with human disease. The aim of our study was to establish a monitoring system with wild birds and reported human cases data using Geographical Information System (GIS). Potential distribution of WNV was modelled by combining wild bird serological surveillance data with environmental factors (e.g. elevation, slope, land use, vegetation density, temperature, precipitation indices, and population density). Local factors including areas of low altitude and proximity to water were important predictors of appearance of both human and wild bird cases (Odds Ratio = 1,001 95%CI = 0,723–1,386). Using GIS analysis, the identified risk factors were applied across Greece identifying the northern part of Greece (Macedonia, Thrace) western Greece and a number of Greek islands as being at highest risk of future outbreaks. The results of the analysis were evaluated and confirmed using the 161 reported human cases of the 2012 outbreak predicting correctly (Odds = 130/31 = 4,194 95%CI = 2,841–6,189) and more areas were identified for potential dispersion in the following years. Our approach verified that WNV risk can be modelled in a fast cost-effective way indicating high risk areas where prevention measures should be implemented in order to reduce the disease incidence. PMID:24806216

An efficient approach for site-specific scenery prediction in surveillance imaging near Earth's surface

NASA Astrophysics Data System (ADS)

Jylhä, Juha; Marjanen, Kalle; Rantala, Mikko; Metsäpuro, Petri; Visa, Ari

2006-09-01

Surveillance camera automation and camera network development are growing areas of interest. This paper proposes a competent approach to enhance the camera surveillance with Geographic Information Systems (GIS) when the camera is located at the height of 10-1000 m. A digital elevation model (DEM), a terrain class model, and a flight obstacle register comprise exploited auxiliary information. The approach takes into account spherical shape of the Earth and realistic terrain slopes. Accordingly, considering also forests, it determines visible and shadow regions. The efficiency arises out of reduced dimensionality in the visibility computation. Image processing is aided by predicting certain advance features of visible terrain. The features include distance from the camera and the terrain or object class such as coniferous forest, field, urban site, lake, or mast. The performance of the approach is studied by comparing a photograph of Finnish forested landscape with the prediction. The predicted background is well-fitting, and potential knowledge-aid for various purposes becomes apparent.

Developing Predictive Models for Algal Bloom Occurrence and Identifying Factors Controlling their Occurrence in the Charlotte County and Surroundings

NASA Astrophysics Data System (ADS)

Karki, S.; Sultan, M.; Elkadiri, R.; Chouinard, K.

2017-12-01

Numerous occurrences of harmful algal blooms (Karenia Brevis) were reported from Southwest Florida along the coast of Charlotte County, Florida. We are developing data-driven (remote sensing, field, and meteorological data) models to accomplish the following: (1) identify the factors controlling bloom development, (2) forecast bloom occurrences, and (3) make recommendations for monitoring variables that are found to be most indicative of algal bloom occurrences and for identifying optimum locations for monitoring stations. To accomplish these three tasks we completed/are working on the following steps. Firstly, we developed an automatic system for downloading and processing of ocean color data acquired through MODIS Terra and MODIS Aqua products using SeaDAS ocean color processing software. Examples of extracted variables include: chlorophyll a (OC3M), chlorophyll a Generalized Inherent Optical Property (GIOP), chlorophyll a Garver-Siegel- Maritorena (GSM), sea surface temperature (SST), Secchi disk depth, euphotic depth, turbidity index, wind direction and speed, colored dissolved organic material (CDOM). Secondly we are developing a GIS database and a web-based GIS to host the generated remote sensing-based products in addition to relevant meteorological and field data. Examples of the meteorological and field inputs include: precipitation amount and rates, concentrations of nitrogen, phosphorous, fecal coliform and Dissolved Oxygen (DO). Thirdly, we are constructing and validating a multivariate regression model and an artificial neural network model to simulate past algal bloom occurrences using the compiled archival remote sensing, meteorological, and field data. The validated model will then be used to predict the timing and location of algal bloom occurrences. The developed system, upon completion, could enhance the decision making process, improve the citizen's quality of life, and strengthen the local economy.

A GIS Tool for evaluating and improving NEXRAD and its application in distributed hydrologic modeling

NASA Astrophysics Data System (ADS)

Zhang, X.; Srinivasan, R.

2008-12-01

In this study, a user friendly GIS tool was developed for evaluating and improving NEXRAD using raingauge data. This GIS tool can automatically read in raingauge and NEXRAD data, evaluate the accuracy of NEXRAD for each time unit, implement several geostatistical methods to improve the accuracy of NEXRAD through raingauge data, and output spatial precipitation map for distributed hydrologic model. The geostatistical methods incorporated in this tool include Simple Kriging with varying local means, Kriging with External Drift, Regression Kriging, Co-Kriging, and a new geostatistical method that was newly developed by Li et al. (2008). This tool was applied in two test watersheds at hourly and daily temporal scale. The preliminary cross-validation results show that incorporating raingauge data to calibrate NEXRAD can pronouncedly change the spatial pattern of NEXRAD and improve its accuracy. Using different geostatistical methods, the GIS tool was applied to produce long term precipitation input for a distributed hydrologic model - Soil and Water Assessment Tool (SWAT). Animated video was generated to vividly illustrate the effect of using different precipitation input data on distributed hydrologic modeling. Currently, this GIS tool is developed as an extension of SWAT, which is used as water quantity and quality modeling tool by USDA and EPA. The flexible module based design of this tool also makes it easy to be adapted for other hydrologic models for hydrological modeling and water resources management.

3D subsurface geological modeling using GIS, remote sensing, and boreholes data

NASA Astrophysics Data System (ADS)

Kavoura, Katerina; Konstantopoulou, Maria; Kyriou, Aggeliki; Nikolakopoulos, Konstantinos G.; Sabatakakis, Nikolaos; Depountis, Nikolaos

2016-08-01

The current paper presents the combined use of geological-geotechnical insitu data, remote sensing data and GIS techniques for the evaluation of a subsurface geological model. High accuracy Digital Surface Model (DSM), airphotos mosaic and satellite data, with a spatial resolution of 0.5m were used for an othophoto base map compilation of the study area. Geological - geotechnical data obtained from exploratory boreholes and the 1:5000 engineering geological maps were digitized and implemented in a GIS platform for a three - dimensional subsurface model evaluation. The study is located at the North part of Peloponnese along the new national road.

Using GPS, GIS, and Accelerometer Data to Predict Transportation Modes.

PubMed

Brondeel, Ruben; Pannier, Bruno; Chaix, Basile

2015-12-01

Active transportation is a substantial source of physical activity, which has a positive influence on many health outcomes. A survey of transportation modes for each trip is challenging, time-consuming, and requires substantial financial investments. This study proposes a passive collection method and the prediction of modes at the trip level using random forests. The RECORD GPS study collected real-life trip data from 236 participants over 7 d, including the transportation mode, global positioning system, geographical information systems, and accelerometer data. A prediction model of transportation modes was constructed using the random forests method. Finally, we investigated the performance of models on the basis of a limited number of participants/trips to predict transportation modes for a large number of trips. The full model had a correct prediction rate of 90%. A simpler model of global positioning system explanatory variables combined with geographical information systems variables performed nearly as well. Relatively good predictions could be made using a model based on the 991 trips of the first 30 participants. This study uses real-life data from a large sample set to test a method for predicting transportation modes at the trip level, thereby providing a useful complement to time unit-level prediction methods. By enabling predictions on the basis of a limited number of observations, this method may decrease the workload for participants/researchers and provide relevant trip-level data to investigate relations between transportation and health.

Are Places Concepts? Familarity and Expertise Effects in Neighborhood Cognition

NASA Astrophysics Data System (ADS)

Davies, Clare

Named urban neighborhoods (localities) are often examples of vague place extents. These are compared with current knowledge of vagueness in concepts and categories within semantic memory, implying graded membership and typicality. If places are mentally constructed and used like concepts, this might account for their cognitive variability, and help us choose suitable geospatial (GIS) data models. An initial within-subjects study with expert geographic surveyors tested specific predictions about the role of central tendency, ideals, context specificity, familiarity and expertise in location judgements - theoretically equivalent to categorization. Implications for spatial data models and a further research agenda are suggested.

Adapting the iSNOBAL model for improved visualization in a GIS environment

NASA Astrophysics Data System (ADS)

Johansen, W. J.; Delparte, D.

2014-12-01

Snowmelt is a primary means of crucial water resources in much of the western United States. Researchers are developing models that estimate snowmelt to aid in water resource management. One such model is the image snowcover energy and mass balance (iSNOBAL) model. It uses input climate grids to simulate the development and melting of snowpack in mountainous regions. This study looks at applying this model to the Reynolds Creek Experimental Watershed in southwestern Idaho, utilizing novel approaches incorporating geographic information systems (GIS). To improve visualization of the iSNOBAL model, we have adapted it to run in a GIS environment. This type of environment is suited to both the input grid creation and the visualization of results. The data used for input grid creation can be stored locally or on a web-server. Kriging interpolation embedded within Python scripts are used to create air temperature, soil temperature, humidity, and precipitation grids, while built-in GIS and existing tools are used to create solar radiation and wind grids. Additional Python scripting is then used to perform model calculations. The final product is a user-friendly and accessible version of the iSNOBAL model, including the ability to easily visualize and interact with model results, all within a web- or desktop-based GIS environment. This environment allows for interactive manipulation of model parameters and visualization of the resulting input grids for the model calculations. Future work is moving towards adapting the model further for use in a 3D gaming engine for improved visualization and interaction.

Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan.

PubMed

Dou, Jie; Tien Bui, Dieu; Yunus, Ali P; Jia, Kun; Song, Xuan; Revhaug, Inge; Xia, Huan; Zhu, Zhongfan

2015-01-01

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner.

Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan

PubMed Central

Dou, Jie; Tien Bui, Dieu; P. Yunus, Ali; Jia, Kun; Song, Xuan; Revhaug, Inge; Xia, Huan; Zhu, Zhongfan

2015-01-01

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner. PMID:26214691

Advanced GIS Exercise: Performing Error Analysis in ArcGIS ModelBuilder

ERIC Educational Resources Information Center

Hall, Steven T.; Post, Christopher J.

2009-01-01

Knowledge of Geographic Information Systems is quickly becoming an integral part of the natural resource professionals' skill set. With the growing need of professionals with these skills, we created an advanced geographic information systems (GIS) exercise for students at Clemson University to introduce them to the concept of error analysis,…

GIS-NaP1 zeolite microspheres as potential water adsorption material: Influence of initial silica concentration on adsorptive and physical/topological properties

PubMed Central

Sharma, Pankaj; Song, Ju-Sub; Han, Moon Hee; Cho, Churl-Hee

2016-01-01

GIS-NaP1 zeolite samples were synthesized using seven different Si/Al ratios (5–11) of the hydrothermal reaction mixtures having chemical composition Al2O3:xSiO2:14Na2O:840H2O to study the impact of Si/Al molar ratio on the water vapour adsorption potential, phase purity, morphology and crystal size of as-synthesized GIS-NaP1 zeolite crystals. The X-ray diffraction (XRD) observations reveal that Si/Al ratio does not affect the phase purity of GIS-NaP1 zeolite samples as high purity GIS-NaP1 zeolite crystals were obtained from all Si/Al ratios. Contrary, Si/Al ratios have remarkable effect on the morphology, crystal size and porosity of GIS-NaP1 zeolite microspheres. Transmission electron microscopy (TEM) evaluations of individual GIS-NaP1 zeolite microsphere demonstrate the characteristic changes in the packaging/arrangement, shape and size of primary nano crystallites. Textural characterisation using water vapour adsorption/desorption, and nitrogen adsorption/desorption data of as-synthesized GIS-NaP1 zeolite predicts the existence of mix-pores i.e., microporous as well as mesoporous character. High water storage capacity 1727.5 cm3 g−1 (138.9 wt.%) has been found for as-synthesized GIS-NaP1 zeolite microsphere samples during water vapour adsorption studies. Further, the total water adsorption capacity values for P6 (1299.4 mg g−1) and P7 (1388.8 mg g−1) samples reveal that these two particular samples can absorb even more water than their own weights. PMID:26964638

GIS-NaP1 zeolite microspheres as potential water adsorption material: Influence of initial silica concentration on adsorptive and physical/topological properties.

PubMed

Sharma, Pankaj; Song, Ju-Sub; Han, Moon Hee; Cho, Churl-Hee

2016-03-11

GIS-NaP1 zeolite samples were synthesized using seven different Si/Al ratios (5-11) of the hydrothermal reaction mixtures having chemical composition Al2O3:xSiO2:14Na2O:840H2O to study the impact of Si/Al molar ratio on the water vapour adsorption potential, phase purity, morphology and crystal size of as-synthesized GIS-NaP1 zeolite crystals. The X-ray diffraction (XRD) observations reveal that Si/Al ratio does not affect the phase purity of GIS-NaP1 zeolite samples as high purity GIS-NaP1 zeolite crystals were obtained from all Si/Al ratios. Contrary, Si/Al ratios have remarkable effect on the morphology, crystal size and porosity of GIS-NaP1 zeolite microspheres. Transmission electron microscopy (TEM) evaluations of individual GIS-NaP1 zeolite microsphere demonstrate the characteristic changes in the packaging/arrangement, shape and size of primary nano crystallites. Textural characterisation using water vapour adsorption/desorption, and nitrogen adsorption/desorption data of as-synthesized GIS-NaP1 zeolite predicts the existence of mix-pores i.e., microporous as well as mesoporous character. High water storage capacity 1727.5 cm(3) g(-1) (138.9 wt.%) has been found for as-synthesized GIS-NaP1 zeolite microsphere samples during water vapour adsorption studies. Further, the total water adsorption capacity values for P6 (1299.4 mg g(-1)) and P7 (1388.8 mg g(-1)) samples reveal that these two particular samples can absorb even more water than their own weights.

The benefits of GIS to land use planning

NASA Astrophysics Data System (ADS)

Strielko, Irina; Pereira, Paulo

2014-05-01

The development of information technologies has significantly changed the approach to land use and spatial planning, management of natural resources. GIS considerably simplifies territorial planning operating analyzing necessary data concerning their spatial relationship that allows carrying out complex assessment of the situation and creates a basis for adoption of more exact and scientifically reasonable decisions in the course of land use. To assess the current land use situation and the possibility of modeling possible future changes associated with complex of adopted measures GIS allows the integration of diverse spatial data, for example, data about soils, climate, vegetation, and other and also to visualize available information in the form of maps, graphs or charts, 3D models. For the purposes of land use GIS allow using data of remote sensing, which allows to make monitoring of anthropogenic influence in a particular area and estimate scales and rates of degradation of green cover, flora and fauna. Assessment of land use can be made in complex or componentwise, indicating the test sites depending on the goals. GIS make it easy to model spatial distribution of various types of pollution of stationary and mobile sources in soil, atmosphere and the hydrological network. Based on results of the analysis made by GIS choose the optimal solutions of land use that provide the minimum impact on environment, make optimal decisions of conflict associated with land use and control of their using. One of the major advantages of using GIS is possibility of the complex analysis in concrete existential aspect. Analytical opportunities of GIS define conditionality of spatial distribution of objects and interrelation communication between them. For a variety of land management objectives analysis method is chosen based on the parameters of the problem and parameters of use of its results.

Fort Collins Science Center

USGS Publications Warehouse

Banowetz, Michele

2004-01-01

FORT serves all Department of the Interior land management bureaus and other natural resource agencies. In addition, FORT scientists partner with DOI and other federal entities such as CDC, DOE, EPA, NASA, NIH, and USDA to share expertise and resources. FORT also partners with several universities and works cooperatively with states and nongovernmental organizations. Products and services include reports and publications, predictive models and software, maps and GIS products, and other technical assistance in the form of meetings, workshops, training, field visits, and needs assessments.

A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates.

PubMed

Lindström, Johan; Szpiro, Adam A; Sampson, Paul D; Oron, Assaf P; Richards, Mark; Larson, Tim V; Sheppard, Lianne

2014-09-01

The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system (GIS) covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of NO x in the Los Angeles area during a ten year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated R 2 of approximately 0.7 at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy.

A large scale GIS geodatabase of soil parameters supporting the modeling of conservation practice alternatives in the United States

USDA-ARS?s Scientific Manuscript database

Water quality modeling requires across-scale support of combined digital soil elements and simulation parameters. This paper presents the unprecedented development of a large spatial scale (1:250,000) ArcGIS geodatabase coverage designed as a functional repository of soil-parameters for modeling an...

Predicting Seagrass Occurrence in a Changing Climate Using Random Forests

NASA Astrophysics Data System (ADS)

Aydin, O.; Butler, K. A.

2017-12-01

Seagrasses are marine plants that can quickly sequester vast amounts of carbon (up to 100 times more and 12 times faster than tropical forests). In this work, we present an integrated GIS and machine learning approach to build a data-driven model of seagrass presence-absence. We outline a random forest approach that avoids the prevalence bias in many ecological presence-absence models. One of our goals is to predict global seagrass occurrence from a spatially limited training sample. In addition, we conduct a sensitivity study which investigates the vulnerability of seagrass to changing climate conditions. We integrate multiple data sources including fine-scale seagrass data from MarineCadastre.gov and the recently available globally extensive publicly available Ecological Marine Units (EMU) dataset. These data are used to train a model for seagrass occurrence along the U.S. coast. In situ oceans data are interpolated using Empirical Bayesian Kriging (EBK) to produce globally extensive prediction variables. A neural network is used to estimate probable future values of prediction variables such as ocean temperature to assess the impact of a warming climate on seagrass occurrence. The proposed workflow can be generalized to many presence-absence models.

Limitations to mapping habitat-use areas in changing landscapes using the Mahalanobis distance statistic

USGS Publications Warehouse

Knick, Steven T.; Rotenberry, J.T.

1998-01-01

We tested the potential of a GIS mapping technique, using a resource selection model developed for black-tailed jackrabbits (Lepus californicus) and based on the Mahalanobis distance statistic, to track changes in shrubsteppe habitats in southwestern Idaho. If successful, the technique could be used to predict animal use areas, or those undergoing change, in different regions from the same selection function and variables without additional sampling. We determined the multivariate mean vector of 7 GIS variables that described habitats used by jackrabbits. We then ranked the similarity of all cells in the GIS coverage from their Mahalanobis distance to the mean habitat vector. The resulting map accurately depicted areas where we sighted jackrabbits on verification surveys. We then simulated an increase in shrublands (which are important habitats). Contrary to expectation, the new configurations were classified as lower similarity relative to the original mean habitat vector. Because the selection function is based on a unimodal mean, any deviation, even if biologically positive, creates larger Malanobis distances and lower similarity values. We recommend the Mahalanobis distance technique for mapping animal use areas when animals are distributed optimally, the landscape is well-sampled to determine the mean habitat vector, and distributions of the habitat variables does not change.

Cloud GIS Based Watershed Management

NASA Astrophysics Data System (ADS)

Bediroğlu, G.; Colak, H. E.

2017-11-01

In this study, we generated a Cloud GIS based watershed management system with using Cloud Computing architecture. Cloud GIS is used as SAAS (Software as a Service) and DAAS (Data as a Service). We applied GIS analysis on cloud in terms of testing SAAS and deployed GIS datasets on cloud in terms of DAAS. We used Hybrid cloud computing model in manner of using ready web based mapping services hosted on cloud (World Topology, Satellite Imageries). We uploaded to system after creating geodatabases including Hydrology (Rivers, Lakes), Soil Maps, Climate Maps, Rain Maps, Geology and Land Use. Watershed of study area has been determined on cloud using ready-hosted topology maps. After uploading all the datasets to systems, we have applied various GIS analysis and queries. Results shown that Cloud GIS technology brings velocity and efficiency for watershed management studies. Besides this, system can be easily implemented for similar land analysis and management studies.

Enhancing GIS Capabilities for High Resolution Earth Science Grids

NASA Astrophysics Data System (ADS)

Koziol, B. W.; Oehmke, R.; Li, P.; O'Kuinghttons, R.; Theurich, G.; DeLuca, C.

2017-12-01

Applications for high performance GIS will continue to increase as Earth system models pursue more realistic representations of Earth system processes. Finer spatial resolution model input and output, unstructured or irregular modeling grids, data assimilation, and regional coordinate systems present novel challenges for GIS frameworks operating in the Earth system modeling domain. This presentation provides an overview of two GIS-driven applications that combine high performance software with big geospatial datasets to produce value-added tools for the modeling and geoscientific community. First, a large-scale interpolation experiment using National Hydrography Dataset (NHD) catchments, a high resolution rectilinear CONUS grid, and the Earth System Modeling Framework's (ESMF) conservative interpolation capability will be described. ESMF is a parallel, high-performance software toolkit that provides capabilities (e.g. interpolation) for building and coupling Earth science applications. ESMF is developed primarily by the NOAA Environmental Software Infrastructure and Interoperability (NESII) group. The purpose of this experiment was to test and demonstrate the utility of high performance scientific software in traditional GIS domains. Special attention will be paid to the nuanced requirements for dealing with high resolution, unstructured grids in scientific data formats. Second, a chunked interpolation application using ESMF and OpenClimateGIS (OCGIS) will demonstrate how spatial subsetting can virtually remove computing resource ceilings for very high spatial resolution interpolation operations. OCGIS is a NESII-developed Python software package designed for the geospatial manipulation of high-dimensional scientific datasets. An overview of the data processing workflow, why a chunked approach is required, and how the application could be adapted to meet operational requirements will be discussed here. In addition, we'll provide a general overview of OCGIS's parallel subsetting capabilities including challenges in the design and implementation of a scientific data subsetter.

Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand

NASA Astrophysics Data System (ADS)

Oh, Hyun-Joo; Lee, Saro; Chotikasathien, Wisut; Kim, Chang Hwan; Kwon, Ju Hyoung

2009-04-01

For predictive landslide susceptibility mapping, this study applied and verified probability model, the frequency ratio and statistical model, logistic regression at Pechabun, Thailand, using a geographic information system (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and maps of the topography, geology and land cover were constructed to spatial database. The factors that influence landslide occurrence, such as slope gradient, slope aspect and curvature of topography and distance from drainage were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite image. The frequency ratio and logistic regression coefficient were overlaid for landslide susceptibility mapping as each factor’s ratings. Then the landslide susceptibility map was verified and compared using the existing landslide location. As the verification results, the frequency ratio model showed 76.39% and logistic regression model showed 70.42% in prediction accuracy. The method can be used to reduce hazards associated with landslides and to plan land cover.

Evolution of a Greenland Ice sheet Including Shelves and Regional Sea Level Variations

NASA Astrophysics Data System (ADS)

Bradley, Sarah; Reerink, Thomas; van de Wal, Roderik S. W.; Helsen, Michiel; Goelzer, Heiko

2016-04-01

Observational evidence, including offshore moraines and marine sediment cores infer that at the Last Glacial maximum (LGM) the Greenland ice sheet (GIS) grounded out across the Davis Strait into Baffin Bay, with fast flowing ice streams extending out to the continental shelf break along the NW margin. These observations lead to a number of questions as to weather the GIS and Laurentide ice sheet (LIS) coalesced during glacial maximums, and if so, did a significant ice shelf develop across Baffin Bay and how would such a configuration impact on the relative contribution of these ice sheets to eustatic sea level (ESL). Most previous paleo ice sheet modelling simulations of the GIS recreated an ice sheet that either did not extend out onto the continental shelf or utilised a simplified marine ice parameterisation to recreate an extended GIS, and therefore did not fully include ice shelf dynamics. In this study we simulate the evolution of the GIS from 220 kyr BP to present day using IMAU-ice; a 3D thermodynamical ice sheet model which fully accounts for grounded and floating ice, calculates grounding line migration and ice shelf dynamics. As there are few observational estimates of the long-term (yrs) sub marine basal melting rates (mbm) for the GIS, we developed a mbm parameterization within IMAU-ice controlled primarily by changes in paleo water depth. We also investigate the influence of the LIS on the GIS evolution by including relative sea level forcing's derived from a Glacial Isostatic Adjustment model. We will present results of how changes in the mbm directly impacts on the ice sheet dynamics, timing and spatial extent of the GIS at the glacial maximums, but also on the rate of retreat and spatial extent at the Last interglacial (LIG) minimum. Results indicate that with the inclusion of ice shelf dynamics, a larger GIS is generated which is grounded out into Davis strait, up to a water depth of -750 m, but significantly reduces the GIS contribution to Last interglacial ESL.

Mapping environmental injustices: pitfalls and potential of geographic information systems in assessing environmental health and equity.

PubMed Central

Maantay, Juliana

2002-01-01

Geographic Information Systems (GIS) have been used increasingly to map instances of environmental injustice, the disproportionate exposure of certain populations to environmental hazards. Some of the technical and analytic difficulties of mapping environmental injustice are outlined in this article, along with suggestions for using GIS to better assess and predict environmental health and equity. I examine 13 GIS-based environmental equity studies conducted within the past decade and use a study of noxious land use locations in the Bronx, New York, to illustrate and evaluate the differences in two common methods of determining exposure extent and the characteristics of proximate populations. Unresolved issues in mapping environmental equity and health include lack of comprehensive hazards databases; the inadequacy of current exposure indices; the need to develop realistic methodologies for determining the geographic extent of exposure and the characteristics of the affected populations; and the paucity and insufficiency of health assessment data. GIS have great potential to help us understand the spatial relationship between pollution and health. Refinements in exposure indices; the use of dispersion modeling and advanced proximity analysis; the application of neighborhood-scale analysis; and the consideration of other factors such as zoning and planning policies will enable more conclusive findings. The environmental equity studies reviewed in this article found a disproportionate environmental burden based on race and/or income. It is critical now to demonstrate correspondence between environmental burdens and adverse health impacts--to show the disproportionate effects of pollution rather than just the disproportionate distribution of pollution sources. PMID:11929725

GIS and Geodatabase Disaster Risk for Spatial Planning

NASA Astrophysics Data System (ADS)

Hendriawan Nur, Wawan; Kumoro, Yugo; Susilowati, Yuliana

2018-02-01

The spatial planning in Indonesia needs to consider the information on the potential disaster. That is because disaster is a serious and detrimental problem that often occurs and causes casualties in some areas in Indonesia as well as inhibits the development. Various models and research were developed to calculate disaster risk assessment. GIS is a system for assembling, storing, analyzing, and displaying geographically referenced disaster. The information can be collaborated with geodatabases to model and to estimate disaster risk in an automated way. It also offers the possibility to customize most of the parameters used in the models. This paper describes a framework which can improve GIS and Geodatabase for the vulnerability, capacity or disaster risk assessment to support the spatial planning activities so they can be more adaptable. By using this framework, GIS application can be used in any location by adjusting variables or calculation methods without changing or rebuilding system from scratch.

Development of a GIS-based spill management information system.

PubMed

Martin, Paul H; LeBoeuf, Eugene J; Daniel, Edsel B; Dobbins, James P; Abkowitz, Mark D

2004-08-30

Spill Management Information System (SMIS) is a geographic information system (GIS)-based decision support system designed to effectively manage the risks associated with accidental or intentional releases of a hazardous material into an inland waterway. SMIS provides critical planning and impact information to emergency responders in anticipation of, or following such an incident. SMIS couples GIS and database management systems (DBMS) with the 2-D surface water model CE-QUAL-W2 Version 3.1 and the air contaminant model Computer-Aided Management of Emergency Operations (CAMEO) while retaining full GIS risk analysis and interpretive capabilities. Live 'real-time' data links are established within the spill management software to utilize current meteorological information and flowrates within the waterway. Capabilities include rapid modification of modeling conditions to allow for immediate scenario analysis and evaluation of 'what-if' scenarios. The functionality of the model is illustrated through a case study of the Cheatham Reach of the Cumberland River near Nashville, TN.

A GIS-based modeling system for petroleum waste management. Geographical information system.

PubMed

Chen, Z; Huang, G H; Li, J B

2003-01-01

With an urgent need for effective management of petroleum-contaminated sites, a GIS-aided simulation (GISSIM) system is presented in this study. The GISSIM contains two components: an advanced 3D numerical model and a geographical information system (GIS), which are integrated within a general framework. The modeling component undertakes simulation for the fate of contaminants in subsurface unsaturated and saturated zones. The GIS component is used in three areas throughout the system development and implementation process: (i) managing spatial and non-spatial databases; (ii) linking inputs, model, and outputs; and (iii) providing an interface between the GISSIM and its users. The developed system is applied to a North American case study. Concentrations of benzene, toluene, and xylenes in groundwater under a petroleum-contaminated site are dynamically simulated. Reasonable outputs have been obtained and presented graphically. They provide quantitative and scientific bases for further assessment of site-contamination impacts and risks, as well as decisions on practical remediation actions.

Towards AN Integration of GIS and Bim Data: what are the Geometric and Topological Issues?

NASA Astrophysics Data System (ADS)

Arroyo Ohori, K.; Biljecki, F.; Diakité, A.; Krijnen, T.; Ledoux, H.; Stoter, J.

2017-10-01

Geographic information and building information modelling both model buildings and infrastructure, but the way in which they are modelled is usually complimentary and BIM-GIS integration is widely considered as a way forward for both domains. For one, more detailed BIM data can feed more general GIS data and GIS data can provide the context that is necessary to BIM data. While previous studies have focused on the theoretical aspects of such an integration at a schema level, in this paper we focus on explaining the geometric and topological issues we have found while trying to develop software to realise such an integration in practice and at a data level. In our preliminary results, which are presented here, we have found that many issues for such an integration remain: handling the geometric and topological problems in BIM models, dealing with bad georeferencing and figuring out the best way to convert data between IFC and CityGML are all open issues.

Automating the Fireshed Assessment Process with ArcGIS

Treesearch

Alan Ager; Klaus Barber

2006-01-01

A library of macros was developed to automate the Fireshed process within ArcGIS. The macros link a number of vegetation simulation and wildfire behavior models (FVS, SVS, FARSITE, and FlamMap) with ESRI geodatabases, desktop software (Access, Excel), and ArcGIS. The macros provide for (1) an interactive linkage between digital imagery, vegetation data, FVS-FFE, and...

Ecological criteria, participant preferences and location models: A GIS approach toward ATV trail planning

Treesearch

Stephanie A. Snyder; Jay H. Whitmore; Ingrid E. Schneider; Dennis R. Becker

2008-01-01

This paper presents a geographic information system (GIS)-based method for recreational trail location for all-terrain vehicles (ATVs) which considers environmental factors, as well as rider preferences for trail attributes. The method utilizes the Least-Cost Path algorithm within a GIS framework to optimize trail location. The trail location algorithm considered trail...

Developing Library GIS Services for Humanities and Social Science: An Action Research Approach

ERIC Educational Resources Information Center

Kong, Ningning; Fosmire, Michael; Branch, Benjamin Dewayne

2017-01-01

In the academic libraries' efforts to support digital humanities and social science, GIS service plays an important role. However, there is no general service model existing about how libraries can develop GIS services to best engage with digital humanities and social science. In this study, we adopted the action research method to develop and…

A spatiotemporal data model for incorporating time in geographic information systems (GEN-STGIS)

NASA Astrophysics Data System (ADS)

Narciso, Flor Eugenia

Temporal Geographic Information Systems (TGIS) is a new technology, which is being developed to work with Geographic Information Systems (GIS) that deal with geographic phenomena that change over time. The capabilities of TGIS depend on the underlying data model. However, a literature review of current spatiotemporal GIS data models has shown that they are not adequate for managing time when representing temporal data. In addition, the majority of these data models have been designed to support the requirements of specific-purpose applications. In an effort to resolve this problem, the related literature has been explored. A comparative investigation of the current spatiotemporal GIS data models has been made to identify their characteristics, advantages and disadvantages, similarities and differences, and to determine why they do not work adequately. A new object-oriented General-purpose Spatiotemporal GIS (GEN-STGIS) data model is proposed here. This model provides better representation, storage and management of data related to geographic phenomena that change over time and overcomes some of the problems detected in the reviewed data models. The proposed data model has four key benefits. First, it provides the capabilities of a standard vector-based GIS embedded in the 2-D Euclidean space. Second, it includes the two temporal dimensions, valid time and transaction time, supported by temporal databases. Third, it inherits, from the object oriented approach, the flexibility, modularity and ability to handle the complexities introduced by spatial and temporal dimensions. Fourth, it improves the geographic query capabilities of current TGIS with the introduction of the concept of bounding box while providing temporal and spatiotemporal query capabilities. The data model is then evaluated in order to assess its strengths and weaknesses as a spatiotemporal GIS data model, and to determine how well the model satisfies the requirements imposed by TGIS applications. The practicality of the data model is demonstrated by the creation of a TGIS example and the partial implementation of the model using the POET Java software for developing the object-oriented database. the object-oriented database.

Preparing Teachers to Use GIS: The Impact of a Hybrid Professional Development Program on Teachers' Use of GIS

NASA Astrophysics Data System (ADS)

Moore, Steven; Haviland, Don; Moore, William; Tran, Michael

2016-12-01

This article reports the findings of a 3-year study of a hybrid professional development program designed to prepare science and mathematics teachers to implement GIS in their classrooms. The study was conducted as part of the CoastLines Innovative Technology Experiences for Students and Teachers project funded by the National Science Foundation. Three cohorts of teachers participated in the program, with each participant receiving 40 h of synchronous online instruction and 80 h of in-person instruction and support over an 8-month period. Data from surveys of participants both before and after the program were analyzed using correlation, ordinary least squares, and ordered logit regression analyses. The analyses revealed increases in the self-reported frequency of GIS use and enhanced feelings of preparation, competence, community, and comfort with respect to using GIS for instruction. A composite index of all impact variables was positively influenced as well. The statistical analyses found a strong relationship between self-reported feelings of preparation and use of GIS. Some support was found for the idea that feelings of competence, community, and comfort were related to the teachers' sense of preparation. The findings suggest that a robust hybrid model of teacher professional development can prepare teachers to use GIS in their classrooms. More research is needed to understand how hybrid models influence the sociopsychological and other dimensions that support teachers' feelings of preparation to implement GIS.

GI-SVM: A sensitive method for predicting genomic islands based on unannotated sequence of a single genome.

PubMed

Lu, Bingxin; Leong, Hon Wai

2016-02-01

Genomic islands (GIs) are clusters of functionally related genes acquired by lateral genetic transfer (LGT), and they are present in many bacterial genomes. GIs are extremely important for bacterial research, because they not only promote genome evolution but also contain genes that enhance adaption and enable antibiotic resistance. Many methods have been proposed to predict GI. But most of them rely on either annotations or comparisons with other closely related genomes. Hence these methods cannot be easily applied to new genomes. As the number of newly sequenced bacterial genomes rapidly increases, there is a need for methods to detect GI based solely on sequences of a single genome. In this paper, we propose a novel method, GI-SVM, to predict GIs given only the unannotated genome sequence. GI-SVM is based on one-class support vector machine (SVM), utilizing composition bias in terms of k-mer content. From our evaluations on three real genomes, GI-SVM can achieve higher recall compared with current methods, without much loss of precision. Besides, GI-SVM allows flexible parameter tuning to get optimal results for each genome. In short, GI-SVM provides a more sensitive method for researchers interested in a first-pass detection of GI in newly sequenced genomes.

Introducing a new open source GIS user interface for the SWAT model

USDA-ARS?s Scientific Manuscript database

The Soil and Water Assessment Tool (SWAT) model is a robust watershed modelling tool. It typically uses the ArcSWAT interface to create its inputs. ArcSWAT is public domain software which works in the licensed ArcGIS environment. The aim of this paper was to develop an open source user interface ...

WATGIS: A GIS-Based Lumped Parameter Water Quality Model

Treesearch

Glenn P. Fernandez; George M. Chescheir; R. Wayne Skaggs; Devendra M. Amatya

2002-01-01

A Geographic Information System (GIS)­based, lumped parameter water quality model was developed to estimate the spatial and temporal nitrogen­loading patterns for lower coastal plain watersheds in eastern North Carolina. The model uses a spatially distributed delivery ratio (DR) parameter to account for nitrogen retention or loss along a drainage network. Delivery...

Modular GIS Framework for National Scale Hydrologic and Hydraulic Modeling Support

NASA Astrophysics Data System (ADS)

Djokic, D.; Noman, N.; Kopp, S.

2015-12-01

Geographic information systems (GIS) have been extensively used for pre- and post-processing of hydrologic and hydraulic models at multiple scales. An extensible GIS-based framework was developed for characterization of drainage systems (stream networks, catchments, floodplain characteristics) and model integration. The framework is implemented as a set of free, open source, Python tools and builds on core ArcGIS functionality and uses geoprocessing capabilities to ensure extensibility. Utilization of COTS GIS core capabilities allows immediate use of model results in a variety of existing online applications and integration with other data sources and applications.The poster presents the use of this framework to downscale global hydrologic models to local hydraulic scale and post process the hydraulic modeling results and generate floodplains at any local resolution. Flow forecasts from ECMWF or WRF-Hydro are downscaled and combined with other ancillary data for input into the RAPID flood routing model. RAPID model results (stream flow along each reach) are ingested into a GIS-based scale dependent stream network database for efficient flow utilization and visualization over space and time. Once the flows are known at localized reaches, the tools can be used to derive the floodplain depth and extent for each time step in the forecast at any available local resolution. If existing rating curves are available they can be used to relate the flow to the depth of flooding, or synthetic rating curves can be derived using the tools in the toolkit and some ancillary data/assumptions. The results can be published as time-enabled spatial services to be consumed by web applications that use floodplain information as an input. Some of the existing online presentation templates can be easily combined with available online demographic and infrastructure data to present the impact of the potential floods on the local community through simple, end user products. This framework has been successfully used in both the data rich environments as well as in locales with minimum available spatial and hydrographic data.

ESTIMATION OF GROUNDWATER POLLUTION POTENTIAL BY PESTICIDES IN MID-ATLANTIC COASTAL PLAIN WATERSHEDS

EPA Science Inventory

A simple GIS-based transport model to estimate the potential for groundwater pollution by pesticides has been developed within the ArcView GIS environment. The pesticide leaching analytical model, which is based on one-dimensional advective-dispersive-reactive (ADR) transport, ha...

Conceptual Model for Basement and Surface Structure Relationships in an Oblique Collision, Sawtooth Range, MT

NASA Astrophysics Data System (ADS)

Palu, J. M.; Burberry, C. M.

2014-12-01

The reactivation potential of pre-existing basement structures affects the geometry of subsequent deformation structures. A conceptual model depicting the results of these interactions can be applied to multiple fold-thrust systems and lead to valuable deformation predictions. These predictions include the potential for hydrocarbon traps or seismic risk in an actively deforming area. The Sawtooth Range, Montana, has been used as a study area. A model for the development of structures close to the Augusta Syncline in the Sawtooth Range is being developed using: 1) an ArcGIS map of the basement structures of the belt based on analysis of geophysical data indicating gravity anomalies and aeromagnetic lineations, seismic data indicating deformation structures, and well logs for establishing lithologies, previously collected by others and 2) an ArcGIS map of the surface deformation structures of the belt based on interpretation of remote sensing images and verification through the collection of surface field data indicating stress directions and age relationships, resulting in a conceptual model based on the understanding of the interaction of the two previous maps including statistical correlations of data and development of balanced cross-sections using Midland Valley's 2D/3D Move software. An analysis of the model will then indicate viable deformation paths where prominent basement structures influenced subsequently developed deformation structures and reactivated faults. Preliminary results indicate that the change in orientation of thrust faults observed in the Sawtooth Range, from a NNW-SSE orientation near the Gibson Reservoir to a WNW-ESE trend near Haystack Butte correlates with pre-existing deformation structures lying within the Great Falls Tectonic Zone. The Scapegoat-Bannatyne trend appears to be responsible for this orientation change and rather than being a single feature, may be composed of up to 4 NE-SW oriented basement strike-slip faults. This indicates that the pre-existing basement features have a profound effect on the geometry of the later deformation. This conceptual model can also be applied to other deformed belts to provide a prediction for the potential hydrocarbon trap locations of the belt as well as their seismic risk.

Sources of endocrine-disrupting compounds in North Carolina waterways: a geographic information systems approach

USGS Publications Warehouse

Sackett, Dana K.; Pow, Crystal Lee; Rubino, Matthew J.; Aday, D.D.; Cope, W. Gregory; Kullman, Seth W.; Rice, J.A.; Kwak, Thomas J.; Law, L.M.

2015-01-01

The presence of endocrine-disrupting compounds (EDCs), particularly estrogenic compounds, in the environment has drawn public attention across the globe, yet a clear understanding of the extent and distribution of estrogenic EDCs in surface waters and their relationship to potential sources is lacking. The objective of the present study was to identify and examine the potential input of estrogenic EDC sources in North Carolina water bodies using a geographic information system (GIS) mapping and analysis approach. Existing data from state and federal agencies were used to create point and nonpoint source maps depicting the cumulative contribution of potential sources of estrogenic EDCs to North Carolina surface waters. Water was collected from 33 sites (12 associated with potential point sources, 12 associated with potential nonpoint sources, and 9 reference), to validate the predictive results of the GIS analysis. Estrogenicity (measured as 17β-estradiol equivalence) ranged from 0.06 ng/L to 56.9 ng/L. However, the majority of sites (88%) had water 17β-estradiol concentrations below 1 ng/L. Sites associated with point and nonpoint sources had significantly higher 17β-estradiol levels than reference sites. The results suggested that water 17β-estradiol was reflective of GIS predictions, confirming the relevance of landscape-level influences on water quality and validating the GIS approach to characterize such relationships.

Sources of endocrine-disrupting compounds in North Carolina waterways: a geographic information systems approach.

PubMed

Sackett, Dana K; Pow, Crystal Lee; Rubino, Matthew J; Aday, D Derek; Cope, W Gregory; Kullman, Seth; Rice, James A; Kwak, Thomas J; Law, Mac

2015-02-01

The presence of endocrine-disrupting compounds (EDCs), particularly estrogenic compounds, in the environment has drawn public attention across the globe, yet a clear understanding of the extent and distribution of estrogenic EDCs in surface waters and their relationship to potential sources is lacking. The objective of the present study was to identify and examine the potential input of estrogenic EDC sources in North Carolina water bodies using a geographic information system (GIS) mapping and analysis approach. Existing data from state and federal agencies were used to create point and nonpoint source maps depicting the cumulative contribution of potential sources of estrogenic EDCs to North Carolina surface waters. Water was collected from 33 sites (12 associated with potential point sources, 12 associated with potential nonpoint sources, and 9 reference), to validate the predictive results of the GIS analysis. Estrogenicity (measured as 17β-estradiol equivalence) ranged from 0.06 ng/L to 56.9 ng/L. However, the majority of sites (88%) had water 17β-estradiol concentrations below 1 ng/L. Sites associated with point and nonpoint sources had significantly higher 17β-estradiol levels than reference sites. The results suggested that water 17β-estradiol was reflective of GIS predictions, confirming the relevance of landscape-level influences on water quality and validating the GIS approach to characterize such relationships. © 2014 SETAC.

Modelling Multi Hazard Mapping in Semarang City Using GIS-Fuzzy Method

NASA Astrophysics Data System (ADS)

Nugraha, A. L.; Awaluddin, M.; Sasmito, B.

2018-02-01

One important aspect of disaster mitigation planning is hazard mapping. Hazard mapping can provide spatial information on the distribution of locations that are threatened by disaster. Semarang City as the capital of Central Java Province is one of the cities with high natural disaster intensity. Frequent natural disasters Semarang city is tidal flood, floods, landslides, and droughts. Therefore, Semarang City needs spatial information by doing multi hazard mapping to support disaster mitigation planning in Semarang City. Multi Hazards map modelling can be derived from parameters such as slope maps, rainfall, land use, and soil types. This modelling is done by using GIS method with scoring and overlay technique. However, the accuracy of modelling would be better if the GIS method is combined with Fuzzy Logic techniques to provide a good classification in determining disaster threats. The Fuzzy-GIS method will build a multi hazards map of Semarang city can deliver results with good accuracy and with appropriate threat class spread so as to provide disaster information for disaster mitigation planning of Semarang city. from the multi-hazard modelling using GIS-Fuzzy can be known type of membership that has a good accuracy is the type of membership Gauss with RMSE of 0.404 the smallest of the other membership and VAF value of 72.909% of the largest of the other membership.

Comparison of three GIS-based models for predicting rockfall runout zones at a regional scale

NASA Astrophysics Data System (ADS)

Dorren, Luuk K. A.; Seijmonsbergen, Arie C.

2003-11-01

Site-specific information about the level of protection that mountain forests provide is often not available for large regions. Information regarding rockfalls is especially scarce. The most efficient way to obtain information about rockfall activity and the efficacy of protection forests at a regional scale is to use a simulation model. At present, it is still unknown which forest parameters could be incorporated best in such models. Therefore, the purpose of this study was to test and evaluate a model for rockfall assessment at a regional scale in which simple forest stand parameters, such as the number of trees per hectare and the diameter at breast height, are incorporated. Therefore, a newly developed Geographical Information System (GIS)-based distributed model is compared with two existing rockfall models. The developed model is the only model that calculates the rockfall velocity on the basis of energy loss due to collisions with trees and on the soil surface. The two existing models calculate energy loss over the distance between two cell centres, while the newly developed model is able to calculate multiple bounces within a pixel. The patterns of rockfall runout zones produced by the three models are compared with patterns of rockfall deposits derived from geomorphological field maps. Furthermore, the rockfall velocities modelled by the three models are compared. It is found that the models produced rockfall runout zone maps with rather similar accuracies. However, the developed model performs best on forested hillslopes and it also produces velocities that match best with field estimates on both forested and nonforested hillslopes irrespective of the slope gradient.

GIS-aided Statistical Landslide Susceptibility Modeling And Mapping Of Antipolo Rizal (Philippines)

NASA Astrophysics Data System (ADS)

Dumlao, A. J.; Victor, J. A.

2015-09-01

Slope instability associated with heavy rainfall or earthquake is a familiar geotechnical problem in the Philippines. The main objective of this study is to perform a detailed landslide susceptibility assessment of Antipolo City. The statistical method of assessment used was logistic regression. Landslide inventory was done through interpretation of aerial photographs and satellite images with corresponding field verification. In this study, morphologic and non-morphologic factors contributing to landslide occurrence and their corresponding spatial relationships were considered. The analysis of landslide susceptibility was implemented in a Geographic Information System (GIS). The 17320 randomly selected datasets were divided into training and test data sets. K- cross fold validation is done with k= 5. The subsamples are then fitted five times with k-1 training data set and the remaining fold as the validation data set. The AUROC of each model is validated using each corresponding data set. The AUROC of the five models are; 0.978, 0.977, 0.977, 0.974, and 0.979 respectively, implying that the models are effective in correctly predicting the occurrence and nonoccurrence of landslide activity. Field verification was also done. The landslide susceptibility map was then generated from the model. It is classified into four categories; low, moderate, high and very high susceptibility. The study also shows that almost 40% of Antipolo City has been assessed to be potentially dangerous areas in terms of landslide occurrence.

Economic value of big game habitat production from natural and prescribed fire

Treesearch

Armando Gonzalez-Caban; John B. Loomis; Dana Griffin; Elen Wu; Daniel McCollum; Jane McKeever; Diane Freeman

2003-01-01

A macro time-series model and a micro GIS model were used to estimate a production function relating deer harvest response to prescribed fire, holding constant other environmental variables. The macro time-series model showed a marginal increase in deer harvested of 33 for an increase of 1,100 acres of prescribed burn. The marginal deer increase for the micro GIS model...

A geographic information system forecast model for strategic control of fasciolosis in Ethiopia.

PubMed

Yilma, J M; Malone, J B

1998-07-31

A geographic information system (GIS) forecast model based on moisture and thermal regime was developed to assess the risk of Fasciola hepatica, a temperate species, and its tropical counterpart, Fasciola gigantica, in Ethiopia. Agroecological map zones and corresponding environmental features that control the distribution and abundance of the disease and its snail intermediate hosts were imported from the Food and Agriculture Organization (FAO) Crop Production System Zones (CPSZ) database on east Africa and used to construct a GIS using ATLAS GIS 3.0 software. Base temperatures of 10 degrees C and 16 degrees C were used for F. hepatica and F. gigantica, respectively, to calculate growing degree days in a previously developed climate forecast system that was modified to allow use of monthly climate data values. The model was validated by comparison of risk indices and environmental features to available survey data on fasciolosis. Monthly Fasciola risk indices of four climatic regions in Ethiopia were used to project infection transmission patterns under varying climatic conditions and strategic chemotherapeutic fasciolosis control schemes. Varying degrees of F. hepatica risk occurred in most parts of the country and distinct regional F. hepatica transmission patterns could be identified. In the humid west, cercariae-shedding was predicted to occur from May to October. In the south it occurred from April to May and September to October, depending on the annual abundance of rain. In the north-central and central regions, risk was highest during heavy summer rains and pasture contamination with metacercariae was predicted to occur during August-September, except in wet years, when it may start as early as July and extend up to October. At cooler sites above altitude of 2800 m, completion of an infection cycle may require more than a year. Fasciola gigantica risk was present in the western, southern and north-central regions of the country at altitudes of 1440-2560 m. However, a transmission cycle could be completed in a single year only at elevations below 1700 m. The greatest risk of F. gigantica infection was in the humid western region. Regional strategic chemotherapy schemes of two or three treatments per year were developed. Results suggest that the model can be extrapolated to all CPSZ in the country and adapted for use in control of other vector-borne diseases of economic and public health importance.

Modelling soil erosion and associated sediment yield for small headwater catchments of the Daugava spillway valley, Latvia

NASA Astrophysics Data System (ADS)

Soms, Juris

2015-04-01

The accelerated soil erosion by water and associated fine sediment transfer in river catchments has various negative environmental as well as economic implications in many EU countries. Hence, the scientific community had recognized and ranked soil erosion among other environmental problems. Moreover, these matters might worsen in the near future in the countries of the Baltic Region, e.g. Latvia considering the predicted climate changes - more precisely, the increase in precipitation and shortening of return periods of extreme rainfall events, which in their turn will enable formation of surface runoff, erosion and increase of sediment delivery to receiving streams. Thereby it is essential to carry out studies focused on these issues in order to obtain reliable data in terms of both scientific and applied aims, e.g. environmental protection and sustainable management of soils as well as water resources. During the past decades, many of such studies of soil erosion had focused on the application of modelling techniques implemented in a GIS environment, allowing indirectly to estimate the potential soil losses and to quantify related sediment yield. According to research results published in the scientific literature, this approach currently is widely used all over the world, and most of these studies are based on the USLE model and its revised and modified versions. Considering that, the aim of this research was to estimate soil erosion rates and sediment transport under different hydro-climatic conditions in south-eastern Latvia by application of GIS-based modelling. For research purposes, empirical RUSLE model and ArcGIS software were applied, and five headwater catchments were chosen as model territories. The selected catchments with different land use are located in the Daugava spillway valley, which belongs to the upper Daugava River drainage basin. Considering lithological diversity of Quaternary deposits, a variety of soils can be identified, i.e., Stagnic Albeluvisols, Albic Rubic Arenosols and Albic Stagnic Podzols with stony loamy - clayey diamicton to coarse sand textures prevail in the selected catchments. The results of modelling were validated through obtaining data on suspended sediment load directly during episodic runoff events caused by different scenarios of runoff formation. In order to get comparable values of suspended sediment load from gully catchments that differ in size, an area-specific daily suspended sediment yield was derived. The obtained results indicate that modelled area-specific sediment yield from the catchments to river greatly varies from 0.001 to 97.2 t ha-1 yr-1; the average soil loss predicted by RUSLE for the each of five catchments calculated for a 1 × 1 m cell grid totals 0.81; 1.36; 0.96; 1.05 and 1.55 t ha-1 yr-1 respectively. Notably, despite the presence of forest vegetation that cover more than 40% of area of three of these catchments, sizable plots of soils are potentially prone to erosion rates above the tolerable threshold, i.e. 0.3 t ha-1 yr-1. Comparison of modelled vs. measured values indicates that the applied RUSLE model underestimates real sediment delivery, which shortly can reach values 213.75 kg ha-1 day-1 during intense snow melting in spring. Nevertheless, results of GIS modelling can be reasonably used to estimate the spatial distribution of soil erosion risk and to identify potential erosion hotspots.

Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods

NASA Astrophysics Data System (ADS)

Pham, Binh Thai; Tien Bui, Dieu; Pourghasemi, Hamid Reza; Indra, Prakash; Dholakia, M. B.

2017-04-01

The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.

A GIS-based decision support system for regional eco-security assessment and its application on the Tibetan Plateau.

PubMed

Xiaodan, Wang; Xianghao, Zhong; Pan, Gao

2010-10-01

Regional eco-security assessment is an intricate, challenging task. In previous studies, the integration of eco-environmental models and geographical information systems (GIS) usually takes two approaches: loose coupling and tight coupling. However, the present study used a full coupling approach to develop a GIS-based regional eco-security assessment decision support system (ESDSS). This was achieved by merging the pressure-state-response (PSR) model and the analytic hierarchy process (AHP) into ArcGIS 9 as a dynamic link library (DLL) using ArcObjects in ArcGIS and Visual Basic for Applications. Such an approach makes it easy to capitalize on the GIS visualization and spatial analysis functions, thereby significantly supporting the dynamic estimation of regional eco-security. A case study is presented for the Tibetan Plateau, known as the world's "third pole" after the Arctic and Antarctic. Results verified the usefulness and feasibility of the developed method. As a useful tool, the ESDSS can also help local managers to make scientifically-based and effective decisions about Tibetan eco-environmental protection and land use. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Design of an UML conceptual model and implementation of a GIS with metadata information for a seismic hazard assessment cooperative project.

NASA Astrophysics Data System (ADS)

Torres, Y.; Escalante, M. P.

2009-04-01

This work illustrates the advantages of using a Geographic Information System in a cooperative project with researchers of different countries, such as the RESIS II project (financed by the Norwegian Government and managed by CEPREDENAC) for seismic hazard assessment of Central America. As input data present different formats, cover distinct geographical areas and are subjected to different interpretations, data inconsistencies may appear and their management get complicated. To achieve data homogenization and to integrate them in a GIS, it is required previously to develop a conceptual model. This is accomplished in two phases: requirements analysis and conceptualization. The Unified Modeling Language (UML) is used to compose the conceptual model of the GIS. UML complies with ISO 19100 norms and allows the designer defining model architecture and interoperability. The GIS provides a frame for the combination of large geographic-based data volumes, with an uniform geographic reference and avoiding duplications. All this information contains its own metadata following ISO 19115 normative. In this work, the integration in the same environment of active faults and subduction slabs geometries, combined with the epicentres location, has facilitated the definition of seismogenetic regions. This is a great support for national specialists of different countries to make easier their teamwork. The GIS capacity for making queries (by location and by attributes) and geostatistical analyses is used to interpolate discrete data resulting from seismic hazard calculations and to create continuous maps as well as to check and validate partial results of the study. GIS-based products, such as complete, homogenised databases and thematic cartography of the region, are distributed to all researchers, facilitating cross-national communication, the project execution and results dissemination.

Integrating Transportation Modeling and Desktop GIS: A Practical and Affordable Analysis Tool for Small and Medium Sized Communities

DOT National Transportation Integrated Search

1998-09-16

This paper and presentation discuss some of the benefits of integrating travel : demand models and desktop GIS (ArchInfo and ArcView for PCs) as a : cost-effective and staff saving tool, as well as specific improvements to : transportation planning m...

Spatio-temporal modeling with GIS and remote sensing for schistosomiasis control in Sichuan, China

NASA Astrophysics Data System (ADS)

Xu, Bing

Schistosomiasis is a water-borne parasitic disease endemic in tropical and subtropical areas. Its transmission requires certain kind of snail as the intermediate host. Some efforts have been made to mapping snail habitats with remote sensing and schistosomiasis transmission modeling. However, the modeling is limited to isolated residential groups and does not include spatial interaction among those groups. Remotely sensed data are only used in snail habitat classification, not in estimation of snail abundance that is an important parameter in schistosomiasis transmission modeling. This research overcomes the above two problems using innovative geographic information system (GIS) and remote sensing technology. A mountainous environment near Xichang, China, is chosen as the test site. Environmental and epidemiological data are stored in a GIS to support modeling. Snail abundance is estimated from land-cover and land-use fractions derived from high spatial resolution IKONOS satellite data. Spatial interaction is determined in consideration of neighborhoods, group areas, relative slopes among groups, and natural barriers. Land-cover and land-use information extracted from 4 m high resolution IKONOS data is used as reference in scaling up to the regional level. The scale-up is done with coarser resolution satellite data including Landsat Thematic Mapper (TM), EO-1 Advanced Land Imager (ALI) and Hyperion data all at 30 m resolution. Snail abundance is estimated by regressing snail survey data with land-cover and land-use fractions. An R2 of 0.87 is obtained between the average snail density predicted and that surveyed at the group level. With such a model, a snail density map is generated for all residential groups in the study area. A spatio-temporal model of schistosomiasis transmission is finally built to incorporate the spatial interaction caused by miracidia and cercaria migration. Comparing the model results with and without spatial interaction has revealed a number of advantages of the spatio-temporal model. Particularly, with the inclusion of spatial interaction, more effective control of schistosomiasis transmission over the whole study area can be achieved.

Integration agent-based models and GIS as a virtual urban dynamic laboratory

NASA Astrophysics Data System (ADS)

Chen, Peng; Liu, Miaolong

2007-06-01

Based on the Agent-based Model and spatial data model, a tight-coupling integrating method of GIS and Agent-based Model (ABM) is to be discussed in this paper. The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena such as urban dynamic. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, the agent-based model and spatial data model are discussed, and then the relationships affecting spatial data model and agent-based process models interaction. After that, a realistic crowd flow simulation experiment is presented. Using some tools provided by general GIS systems and a few specific programming languages, a new software system integrating GIS and MAS as a virtual laboratory applicable for simulating pedestrian flows in a crowd activity centre has been developed successfully. Under the environment supported by the software system, as an applicable case, a dynamic evolution process of the pedestrian's flows (dispersed process for the spectators) in a crowds' activity center - The Shanghai Stadium has been simulated successfully. At the end of the paper, some new research problems have been pointed out for the future.

Environmental analysis using integrated GIS and remotely sensed data - Some research needs and priorities

NASA Technical Reports Server (NTRS)

Davis, Frank W.; Quattrochi, Dale A.; Ridd, Merrill K.; Lam, Nina S.-N.; Walsh, Stephen J.

1991-01-01

This paper discusses some basic scientific issues and research needs in the joint processing of remotely sensed and GIS data for environmental analysis. Two general topics are treated in detail: (1) scale dependence of geographic data and the analysis of multiscale remotely sensed and GIS data, and (2) data transformations and information flow during data processing. The discussion of scale dependence focuses on the theory and applications of spatial autocorrelation, geostatistics, and fractals for characterizing and modeling spatial variation. Data transformations during processing are described within the larger framework of geographical analysis, encompassing sampling, cartography, remote sensing, and GIS. Development of better user interfaces between image processing, GIS, database management, and statistical software is needed to expedite research on these and other impediments to integrated analysis of remotely sensed and GIS data.

Regional Assessment of the Relationship Between Landscape Attributes and Water Quality in Five National Parks of the Rocky Mountains

NASA Astrophysics Data System (ADS)

Nanus, L.; Williams, M. W.; Campbell, D. H.

2005-12-01

Atmospheric deposition of pollutants threatens pristine environments around the world. However, scientifically-based decisions regarding management of these environments has been confounded by spatial variability of atmospheric deposition, particularly across regional scales at which resource management is typically considered. A statistically based methodology coupled within GIS is presented that builds on small alpine lake and sub-alpine catchments scale to identify deposition-sensitive lakes across larger watershed and regional scales. The sensitivity of 874 alpine and subalpine lakes to acidification from atmospheric deposition of nitrogen and sulfur was estimated using statistical models relating water quality and landscape attributes in Glacier National Park, Yellowstone National Park, Grand Teton National Park, Rocky Mountain National Park and Great Sand Dunes National Park and Preserve. Water-quality data measured during synoptic lake surveys were used to calibrate statistical models of lake sensitivity. In the case of nitrogen deposition, water quality data were supplemented with dual isotopic measurements of d15N and d18O of nitrate. Landscape attributes for the lake basins were derived from GIS including the following explanatory variables; topography (basin slope, basin aspect, basin elevation), bedrock type, vegetation type, and soil type. Using multivariate logistic regression analysis, probability estimates were developed for acid-neutralizing capacity, nitrate, sulfate and DOC concentrations, and lakes with a high probability of being sensitive to atmospheric deposition were identified. Water-quality data collected at 60 lakes during fall 2004 were used to validate statistical models. Relationships between landscape attributes and water quality vary by constituent, due to spatial variability in landscape attributes and spatial variation in the atmospheric deposition of pollutants within and among the five National Parks. Predictive ability, model fit and sensitivity were first assessed for each of the five National Parks individually, to evaluate the utility of this methodology for prediction of alpine and sub-alpine lake sensitivity across the catchment scale. A similar assessment was then performed, treating the five parks as a group. Validation results showed that 85 percent of lakes sampled were accurately identified by the model as having a greater than 60 percent probability of acid-neutralizing capacity concentrations less than 200 microequivalents per liter. Preliminary findings indicate good predictive ability and reasonable model fit and sensitivity, suggesting that logistic regression modeling coupled within a GIS framework is an appropriate approach for remote identification of deposition-sensitive lakes across the Rocky Mountain region. To assist resource management decisions regarding alpine and sub-alpine lakes across this region, screening procedures were developed based on terrain and landscape attribute information available to all participating parks. Since the screening procedure is based on publicly available data, our methodology and similar screening procedures may be applicable to other National Parks with deposition-sensitive surface waters.

Towards an integrated approach to natural hazards risk assessment using GIS: with reference to bushfires.

PubMed

Chen, Keping; Blong, Russell; Jacobson, Carol

2003-04-01

This paper develops a GIS-based integrated approach to risk assessment in natural hazards, with reference to bushfires. The challenges for undertaking this approach have three components: data integration, risk assessment tasks, and risk decision-making. First, data integration in GIS is a fundamental step for subsequent risk assessment tasks and risk decision-making. A series of spatial data integration issues within GIS such as geographical scales and data models are addressed. Particularly, the integration of both physical environmental data and socioeconomic data is examined with an example linking remotely sensed data and areal census data in GIS. Second, specific risk assessment tasks, such as hazard behavior simulation and vulnerability assessment, should be undertaken in order to understand complex hazard risks and provide support for risk decision-making. For risk assessment tasks involving heterogeneous data sources, the selection of spatial analysis units is important. Third, risk decision-making concerns spatial preferences and/or patterns, and a multicriteria evaluation (MCE)-GIS typology for risk decision-making is presented that incorporates three perspectives: spatial data types, data models, and methods development. Both conventional MCE methods and artificial intelligence-based methods with GIS are identified to facilitate spatial risk decision-making in a rational and interpretable way. Finally, the paper concludes that the integrated approach can be used to assist risk management of natural hazards, in theory and in practice.

An integrated GIS-based data model for multimodal urban public transportation analysis and management

NASA Astrophysics Data System (ADS)

Chen, Shaopei; Tan, Jianjun; Ray, C.; Claramunt, C.; Sun, Qinqin

2008-10-01

Diversity is one of the main characteristics of transportation data collected from multiple sources or formats, which can be extremely complex and disparate. Moreover, these multimodal transportation data are usually characterised by spatial and temporal properties. Multimodal transportation network data modelling involves both an engineering and research domain that has attracted the design of a number of spatio-temporal data models in the geographic information system (GIS). However, the application of these specific models to multimodal transportation network is still a challenging task. This research addresses this challenge from both integrated multimodal data organization and object-oriented modelling perspectives, that is, how a complex urban transportation network should be organized, represented and modeled appropriately when considering a multimodal point of view, and using object-oriented modelling method. We proposed an integrated GIS-based data model for multimodal urban transportation network that lays a foundation to enhance the multimodal transportation network analysis and management. This modelling method organizes and integrates multimodal transit network data, and supports multiple representations for spatio-temporal objects and relationship as both visual and graphic views. The data model is expressed by using a spatio-temporal object-oriented modelling method, i.e., the unified modelling language (UML) extended to spatial and temporal plug-in for visual languages (PVLs), which provides an essential support to the spatio-temporal data modelling for transportation GIS.

Sustainable and Smart City Planning Using Spatial Data in Wallonia

NASA Astrophysics Data System (ADS)

Stephenne, N.; Beaumont, B.; Hallot, E.; Wolff, E.; Poelmans, L.; Baltus, C.

2016-09-01

Simulating population distribution and land use changes in space and time offer opportunities for smart city planning. It provides a holistic and dynamic vision of fast changing urban environment to policy makers. Impacts, such as environmental and health risks or mobility issues, of policies can be assessed and adapted consequently. In this paper, we suppose that "Smart" city developments should be sustainable, dynamic and participative. This paper addresses these three smart objectives in the context of urban risk assessment in Wallonia, Belgium. The sustainable, dynamic and participative solution includes (i) land cover and land use mapping using remote sensing and GIS, (ii) population density mapping using dasymetric mapping, (iii) predictive modelling of land use changes and population dynamics and (iv) risk assessment. The comprehensive and long-term vision of the territory should help to draw sustainable spatial planning policies, to adapt remote sensing acquisition, to update GIS data and to refine risk assessment from regional to city scale.

[Study on ecological suitability regionalization of Corni Fructus based on Maxent and ArcGIS model].

PubMed

Zhang, Fei; Chen, Sui-Qing; Wang, Li-Li; Zhang, Tao; Zhang, Xiao-Bo; Zhu, Shou-Dong

2017-08-01

Through planting regionalization the scientific basis for planting area of high-quality medicinal materials was predicted. Through interview investigation and field survey, the distribution information of Corni Fructus in China was collected,and 89 sampling point from 14 producing areas were collected. Climate and topography of Corni Fructus were analyzed, the ecological adaptability of study was conducted based on ArcGIS and Maxent. Different suitability grade at potential areas and regionalization map were formulated. There are nine ecological factors affecting the growth of Corni Fructus, for example precipitation in November and March and vegetation type. The results showed that the most suitable habitats are Henan, Shaanxi, Zhejiang, Chongqing, Hubei, Sichuan, Anhui, Hunan and Shandong province. Using the spatial analysis method,the study not only illustrates the most suitable for the surroundings of Corni Fructus,but also provides a scientific reference for wild resource tending, introduction and cultivation, and artificial planting base and directing production layout. Copyright© by the Chinese Pharmaceutical Association.

Distribution of black-tailed jackrabbit habitat determined by GIS in southwestern Idaho

USGS Publications Warehouse

Knick, Steven T.; Dyer, D.L.

1997-01-01

We developed a multivariate description of black-tailed jackrabbit (Lepus californicus) habitat associations from Geographical Information Systems (GIS) signatures surrounding known jackrabbit locations in the Snake River Birds of Prey National Conservation Area (NCA), in southwestern Idaho. Habitat associations were determined for characteristics within a 1-km radius (approx home range size) of jackrabbits sighted on night spotlight surveys conducted from 1987 through 1995. Predictive habitat variables were number of shrub, agriculture, and hydrography cells, mean and standard deviation of shrub patch size, habitat richness, and a measure of spatial heterogeneity. In winter, jackrabbits used smaller and less variable sizes of shrub patches and areas of higher spatial heterogeneity when compared to summer observations (P 0.05), differed significantly between high and low population phase. We used the Mahalanobis distance statistic to rank all 50-m cells in a 440,000-ha region relative to the multivariate mean habitat vector. On verification surveys to test predicted models, we sighted jackrabbits in areas ranked close to the mean habitat vector. Areas burned by large-scale fires between 1980 and 1992 or in an area repeatedly burned by military training activities had greater Mahalanobis distances from the mean habitat vector than unburned areas and were less likely to contain habitats used by jackrabbits.

Geographic information system as country-level development and monitoring tool, Senegal example

USGS Publications Warehouse

Moore, Donald G.; Howard, Stephen M.; ,

1990-01-01

Geographic information systems (GIS) allow an investigator the capability to merge and analyze numerous types of country-level resource data. Hypothetical resource analysis applications in Senegal were conducted to illustrate the utility of a GIS for development planning and resource monitoring. Map and attribute data for soils, vegetation, population, infrastructure, and administrative units were merged to form a database within a GIS. Several models were implemented using a GIS to: analyze development potential for sustainable dryland agriculture; prioritize where agricultural development should occur based upon a regional food budget; and monitor dynamic events with remote sensing. The steps for implementing a GIS analysis are described and illustrated, and the use of a GIS for conducting an economic analysis is outlined. Using a GIS for analysis and display of results opens new methods of communication between resource scientists and decision makers. Analyses yielding country-wide map output and detailed statistical data for each level of administration provide the advantage of a single system that can serve a variety of users.

WebGIS based on semantic grid model and web services

NASA Astrophysics Data System (ADS)

Zhang, WangFei; Yue, CaiRong; Gao, JianGuo

2009-10-01

As the combination point of the network technology and GIS technology, WebGIS has got the fast development in recent years. With the restriction of Web and the characteristics of GIS, traditional WebGIS has some prominent problems existing in development. For example, it can't accomplish the interoperability of heterogeneous spatial databases; it can't accomplish the data access of cross-platform. With the appearance of Web Service and Grid technology, there appeared great change in field of WebGIS. Web Service provided an interface which can give information of different site the ability of data sharing and inter communication. The goal of Grid technology was to make the internet to a large and super computer, with this computer we can efficiently implement the overall sharing of computing resources, storage resource, data resource, information resource, knowledge resources and experts resources. But to WebGIS, we only implement the physically connection of data and information and these is far from the enough. Because of the different understanding of the world, following different professional regulations, different policies and different habits, the experts in different field will get different end when they observed the same geographic phenomenon and the semantic heterogeneity produced. Since these there are large differences to the same concept in different field. If we use the WebGIS without considering of the semantic heterogeneity, we will answer the questions users proposed wrongly or we can't answer the questions users proposed. To solve this problem, this paper put forward and experienced an effective method of combing semantic grid and Web Services technology to develop WebGIS. In this paper, we studied the method to construct ontology and the method to combine Grid technology and Web Services and with the detailed analysis of computing characteristics and application model in the distribution of data, we designed the WebGIS query system driven by ontology based on Grid technology and Web Services.

Modeling post-fire hydro-geomorphic recovery in the Waldo Canyon Fire

NASA Astrophysics Data System (ADS)

Kinoshita, Alicia; Nourbakhshbeidokhti, Samira; Chin, Anne

2016-04-01

Wildfire can have significant impacts on watershed hydrology and geomorphology by changing soil properties and removing vegetation, often increasing runoff and soil erosion and deposition, debris flows, and flooding. Watershed systems may take several years or longer to recover. During this time, post-fire channel changes have the potential to alter hydraulics that influence characteristics such as time of concentration and increase time to peak flow, flow capacity, and velocity. Using the case of the 2012 Waldo Canyon Fire in Colorado (USA), this research will leverage field-based surveys and terrestrial Light Detection and Ranging (LiDAR) data to parameterize KINEROS2 (KINematic runoff and EROSion), an event oriented, physically-based watershed runoff and erosion model. We will use the Automated Geospatial Watershed Assessment (AGWA) tool, which is a GIS-based hydrologic modeling tool that uses commonly available GIS data layers to parameterize, execute, and spatially visualize runoff and sediment yield for watersheds impacted by the Waldo Canyon Fire. Specifically, two models are developed, an unburned (Bear Creek) and burned (Williams) watershed. The models will simulate burn severity and treatment conditions. Field data will be used to validate the burned watersheds for pre- and post-fire changes in infiltration, runoff, peak flow, sediment yield, and sediment discharge. Spatial modeling will provide insight into post-fire patterns for varying treatment, burn severity, and climate scenarios. Results will also provide post-fire managers with improved hydro-geomorphic modeling and prediction tools for water resources management and mitigation efforts.

Using a Temperature Model and GIS Analysis of Landscape Features to Assess Headwater Resilience to Climate Change in the Driftless Area of Wisconsin

NASA Astrophysics Data System (ADS)

Schuster, Z.; Potter, K. W.

2015-12-01

Cold groundwater discharges in the headwaters of streams in the Driftless Area of Wisconsin help support cold-water fisheries that are valued by anglers throughout the Midwestern U.S. With climate change expected to increase temperatures and threaten the cold-water habitat of species such as brook and brown trout, the Wisconsin Department of Natural Resources is focusing resources on restoration as means of adapting to climate change. One of the challenges they face is a lack of site-specific temperature data in the headwaters streams that they are targeting for restoration activities. Previous work has shown that there is a strong relationship between air and stream temperature. In this study, we calculated weekly mean air-stream temperature relationships for Driftless region headwaters streams and used air temperature projections from a set of statistically-downscaled GCM models to model thermal metrics relevant to fish species suitability described by Lyons et al. (2009) for historical (1961-2000) and future (2046-2065) conditions. We then combined the stream temperature projections with a GIS analysis of physiographic and geologic features to attempt to develop a way of predicting ungaged headwaters streams in the region that are likely to be resilient to temperature increases due to climate change.

Real-time distribution of pelagic fish: combining hydroacoustics, GIS and spatial modelling at a fine spatial scale.

PubMed

Muška, Milan; Tušer, Michal; Frouzová, Jaroslava; Mrkvička, Tomáš; Ricard, Daniel; Seďa, Jaromír; Morelli, Federico; Kubečka, Jan

2018-03-29

Understanding spatial distribution of organisms in heterogeneous environment remains one of the chief issues in ecology. Spatial organization of freshwater fish was investigated predominantly on large-scale, neglecting important local conditions and ecological processes. However, small-scale processes are of an essential importance for individual habitat preferences and hence structuring trophic cascades and species coexistence. In this work, we analysed the real-time spatial distribution of pelagic freshwater fish in the Římov Reservoir (Czechia) observed by hydroacoustics in relation to important environmental predictors during 48 hours at 3-h interval. Effect of diurnal cycle was revealed of highest significance in all spatial models with inverse trends between fish distribution and predictors in day and night in general. Our findings highlighted daytime pelagic fish distribution as highly aggregated, with general fish preferences for central, deep and highly illuminated areas, whereas nighttime distribution was more disperse and fish preferred nearshore steep sloped areas with higher depth. This turnover suggests prominent movements of significant part of fish assemblage between pelagic and nearshore areas on a diel basis. In conclusion, hydroacoustics, GIS and spatial modelling proved as valuable tool for predicting local fish distribution and elucidate its drivers, which has far reaching implications for understanding freshwater ecosystem functioning.

Using GIS in risk analysis: a case study of hazardous waste transport.

PubMed

Lovett, A A; Parfitt, J P; Brainard, J S

1997-10-01

This paper provides an illustration of how a geographic information system (GIS) can be used in risk analysis. It focuses on liquid hazardous waste transport and utilizes records archived by the London Waste Regulatory Authority. This data source provides information on the origin and destination of each waste stream, but not the route followed during transport. A GIS was therefore employed to predict the paths used, taking into account different routing criteria and characteristics of the available road network. Details were also assembled on population distribution and ground-water vulnerability, thus providing a basis for evaluating the potential consequences of a waste spillage during transport. Four routing scenarios were implemented to identify sections of road which consistently saw heavy traffic. These simulations also highlighted that some interventions could lead to risk tradeoffs rather than hazard mitigation. Many parts of the research would not have been possible without a GIS, and the study demonstrates the considerable potential of such software in environmental risk assessment and management.

Risk Zone Modelling and Early Warning System for Visceral Leishmaniasis Kala-Azar Disease in Bihar, India Using Remote Sensing and GIS

NASA Astrophysics Data System (ADS)

Jeyaram, A.; Kesari, S.; Bajpai, A.; Bhunia, G. S.; Krishna Murthy, Y. V. N.

2012-07-01

Visceral Leishmaniasis (VL) commonly known as Kala-azar is one of the most neglected tropical disease affecting approximately 200 million poorest populations 'at risk in 109 districts of three endemic countries namely Bangladesh, India and Nepal at different levels. This tropical disease is caused by the protozoan parasite Leishmania donovani and transmitted by female Phlebotomus argentipes sand flies. The analysis of disease dynamics indicate the periodicity at seasonal and inter-annual temporal scale which forms the basis for development of advanced early warning system. Study area of highly endemic Vaishali district, Bihar, India has been taken for model development. A Systematic study of geo-environmental parameters derived from satellite data in conjunction with ground intelligence enabled modelling of infectious disease and risk villages. High resolution Indian satellites data of IRS LISS IV (multi-spectral) and Cartosat-1 (Pan) have been used for studying environmentally risk parameters viz. peri-domestic vegetation, dwelling condition, wetland ecosystem, cropping pattern, Normalised Difference Vegetation Index (NDVI), detailed land use etc towards risk assessment. Univariate analysis of the relationship between vector density and various land cover categories and climatic variables suggested that all the variables are significantly correlated. Using the significantly correlated variables with vector density, a seasonal multivariate regression model has been carried out incorporating geo-environmental parameters, climate variables and seasonal time series disease parameters. Linear and non-linear models have been applied for periodicity and interannual temporal scale to predict Man-hour-density (MHD) and 'out-of-fit' data set used for validating the model with reasonable accuracy. To improve the MHD predictive approach, fuzzy model has also been incorporated in GIS environment combining spatial geo-environmental and climatic variables using fuzzy membership logic. Based on the perceived importance of the geoenvironmental parameters assigned by epidemiology expert, combined fuzzy membership has been calculated. The combined fuzzy membership indicate the predictive measure of vector density in each village. A γ factor has been introduced to have increasing effect in the higher side and decreasing effect in the lower side which facilitated for prioritisation of the villages. This approach is not only to predict vector density but also to prioritise the villages for effective control measures. A software package for modelling the risk villages integrating multivariate regression and fuzzy membership analysis models have been developed to estimate MHD (vector density) as part of the early warning system.

LAV@HAZARD: a Web-GIS Framework for Real-Time Forecasting of Lava Flow Hazards

NASA Astrophysics Data System (ADS)

Del Negro, C.; Bilotta, G.; Cappello, A.; Ganci, G.; Herault, A.

2014-12-01

Crucial to lava flow hazard assessment is the development of tools for real-time prediction of flow paths, flow advance rates, and final flow lengths. Accurate prediction of flow paths and advance rates requires not only rapid assessment of eruption conditions (especially effusion rate) but also improved models of lava flow emplacement. Here we present the LAV@HAZARD web-GIS framework, which combines spaceborne remote sensing techniques and numerical simulations for real-time forecasting of lava flow hazards. By using satellite-derived discharge rates to drive a lava flow emplacement model, LAV@HAZARD allows timely definition of parameters and maps essential for hazard assessment, including the propagation time of lava flows and the maximum run-out distance. We take advantage of the flexibility of the HOTSAT thermal monitoring system to process satellite images coming from sensors with different spatial, temporal and spectral resolutions. HOTSAT was designed to ingest infrared satellite data acquired by the MODIS and SEVIRI sensors to output hot spot location, lava thermal flux and discharge rate. We use LAV@HAZARD to merge this output with the MAGFLOW physics-based model to simulate lava flow paths and to update, in a timely manner, flow simulations. Thus, any significant changes in lava discharge rate are included in the predictions. A significant benefit in terms of computational speed was obtained thanks to the parallel implementation of MAGFLOW on graphic processing units (GPUs). All this useful information has been gathered into the LAV@HAZARD platform which, due to the high degree of interactivity, allows generation of easily readable maps and a fast way to explore alternative scenarios. We will describe and demonstrate the operation of this framework using a variety of case studies pertaining to Mt Etna, Sicily. Although this study was conducted on Mt Etna, the approach used is designed to be applicable to other volcanic areas around the world.

Prediction of household and commercial BMW generation according to socio-economic and other factors for the Dublin region.

PubMed

Purcell, M; Magette, W L

2009-04-01

Both planning and design of integrated municipal solid waste management systems require accurate prediction of waste generation. This research predicted the quantity and distribution of biodegradable municipal waste (BMW) generation within a diverse 'landscape' of residential areas, as well as from a variety of commercial establishments (restaurants, hotels, hospitals, etc.) in the Dublin (Ireland) region. Socio-economic variables, housing types, and the sizes and main activities of commercial establishments were hypothesized as the key determinants contributing to the spatial variability of BMW generation. A geographical information system (GIS) 'model' of BMW generation was created using ArcMap, a component of ArcGIS 9. Statistical data including socio-economic status and household size were mapped on an electoral district basis. Historical research and data from scientific literature were used to assign BMW generation rates to residential and commercial establishments. These predictions were combined to give overall BMW estimates for the region, which can aid waste planning and policy decisions. This technique will also aid the design of future waste management strategies, leading to policy and practice alterations as a function of demographic changes and development. The household prediction technique gave a more accurate overall estimate of household waste generation than did the social class technique. Both techniques produced estimates that differed from the reported local authority data; however, given that local authority reported figures for the region are below the national average, with some of the waste generated from apartment complexes being reported as commercial waste, predictions arising from this research are believed to be closer to actual waste generation than a comparison to reported data would suggest. By changing the input data, this estimation tool can be adapted for use in other locations. Although focusing on waste in the Dublin region, this method of waste prediction can have significant potential benefits if a universal method can be found to apply it effectively.

Application of geo-spatial technology in schistosomiasis modelling in Africa: a review.

PubMed

Manyangadze, Tawanda; Chimbari, Moses John; Gebreslasie, Michael; Mukaratirwa, Samson

2015-11-04

Schistosomiasis continues to impact socio-economic development negatively in sub-Saharan Africa. The advent of spatial technologies, including geographic information systems (GIS), Earth observation (EO) and global positioning systems (GPS) assist modelling efforts. However, there is increasing concern regarding the accuracy and precision of the current spatial models. This paper reviews the literature regarding the progress and challenges in the development and utilization of spatial technology with special reference to predictive models for schistosomiasis in Africa. Peer-reviewed papers identified through a PubMed search using the following keywords: geo-spatial analysis OR remote sensing OR modelling OR earth observation OR geographic information systems OR prediction OR mapping AND schistosomiasis AND Africa were used. Statistical uncertainty, low spatial and temporal resolution satellite data and poor validation were identified as some of the factors that compromise the precision and accuracy of the existing predictive models. The need for high spatial resolution of remote sensing data in conjunction with ancillary data viz. ground-measured climatic and environmental information, local presence/absence intermediate host snail surveys as well as prevalence and intensity of human infection for model calibration and validation are discussed. The importance of a multidisciplinary approach in developing robust, spatial data capturing, modelling techniques and products applicable in epidemiology is highlighted.

Using digital databases to create geologic maps for the 21st century : a GIS model for geologic, environmental, cultural and transportation data from southern Rhode Island

DOT National Transportation Integrated Search

2002-05-01

Knowledge of surface and subsurface geology is fundamental to the planning and development of new or modified transportation systems. Toward this : end, we have compiled a model GIS database consisting of important geologic, cartographic, environment...

Using GIS Models to Identify Relative Nitrogen Attenuation by Riparian Buffers in the Coastal Plain of North Carolina

EPA Science Inventory

Riparian areas have demonstrated the ability to attenuate nutrients and provide water quality services at the field scale, but services of riparian buffers for downstream users should be assessed at watershed scales. GIS-based riparian models have been developed to connect ripari...

Exchanging transportation networks between two GISs via the SDTS

DOT National Transportation Integrated Search

1997-05-01

Performing meaningful network analyses is greatly dependent upon accurate and : complete transportation network models, which are digitized into a Geographic : Information System (GIS) or, more often, imported from another GIS. : Transportation netwo...

NE Ohio Urban Growth Monitoring and Modeling Prototype. Revised

NASA Technical Reports Server (NTRS)

Siebert, Loren; Klosterman, Richard E.

2001-01-01

At the University of Akron, Dr. Loren Siebert, Dr. Richard Klosterman, and their graduate research assistants (Jung-Wook Kim, Mohammed Hoque, Aziza Parveen, and Ben Stabler) worked on the integration of remote sensing and GIs-based planning support systems. The primary goal of the project was to develop methods that use remote sensing land cover mapping and GIs-based modeling to monitor and project urban growth and farmland loss in northeast Ohio. Another research goal has been to use only GIS data that are accessible via the World Wide Web, to determine whether Ohio's small counties and townships that do not currently have parcel-level GIS systems can apply these techniques. The project was jointly funded by NASA and USGS OhioView grants during the 2000-2001 academic year; the work is now being continued under a USGS grant.

An integrated assessment of soil erosion dynamics with special emphasis on gully erosion: Case studies from South Africa and Iran

NASA Astrophysics Data System (ADS)

Maerker, Michael; Sommer, Christian; Zakerinejad, Reza; Cama, Elena

2017-04-01

Soil erosion by water is a significant problem in arid and semi arid areas of large parts of Iran. Water erosion is one of the most effective phenomena that leads to decreasing soil productivity and pollution of water resources. Especially in semiarid areas like in the Mazayjan watershed in the Southwestern Fars province as well as in the Mkomazi catchment in Kwa Zulu Natal, South Africa, gully erosion contributes to the sediment dynamics in a significant way. Consequently, the intention of this research is to identify the different types of soil erosion processes acting in the area with a stochastic approach and to assess the process dynamics in an integrative way. Therefore, we applied GIS, and satellite image analysis techniques to derive input information for the numeric models. For sheet and rill erosion the Unit Stream Power-based Erosion Deposition Model (USPED) was utilized. The spatial distribution of gully erosion was assessed using a statistical approach which used three variables (stream power index, slope, and flow accumulation) to predict the spatial distribution of gullies in the study area. The eroded gully volumes were estimated for a multiple years period by fieldwork and Google Earth high resolution images as well as with structure for motion algorithm. Finally, the gully retreat rates were integrated into the USPED model. The results show that the integration of the SPI approach to quantify gully erosion with the USPED model is a suitable method to qualitatively and quantitatively assess water erosion processes in data scarce areas. The application of GIS and stochastic model approaches to spatialize the USPED model input yield valuable results for the prediction of soil erosion in the test areas. The results of this research help to develop an appropriate management of soil and water resources in the study areas.

Providing open-access online materials and hands-on sessions for GIS exercises

NASA Astrophysics Data System (ADS)

Oguchi, T.; Yamauchi, H.; Hayakawa, Y. S.

2017-12-01

Researchers of GIS (Geographical Information Systems/Sciences) in Japan have collaborated to provide materials for GIS lecture classes in universities for the last 20 years. The major outcomes include 1) a GIS core curriculum, 2) a GIS "body of knowledge" explaining the details of the curriculum, 3) a series of PowerPoint presentations, and 4) a comprehensive GIS textbook. However, materials for GIS exercises at university classes using GIS software have been limited in Japan. Therefore, we launched a project to provide such materials which will be available online and accessible by anybody. The materials cover broad basic aspects of GIS including geoscientific applications such as terrain analysis using digital elevation models. The materials utilize public-domain and open-source software packages such as QGIS and GRASS. The data used are also freely available ones such as those from the Geospatial Information Authority of Japan. The use of the GitHub platform to distribute the materials allow easier online interactions by both material producers and users. Selected sets of the materials have been utilized for hands-on activities including both official university classes and public instructions. We have been updating the materials based on the opinions of people who took the hands-on courses for better GIS education. The current materials are in Japanese, but we plan to translate some of them into English.

Assessment of Managed Aquifer Recharge Site Suitability Using a GIS and Modeling.

PubMed

Russo, Tess A; Fisher, Andrew T; Lockwood, Brian S

2015-01-01

We completed a two-step regional analysis of a coastal groundwater basin to (1) assess regional suitability for managed aquifer recharge (MAR), and (2) quantify the relative impact of MAR activities on groundwater levels and sea water intrusion. The first step comprised an analysis of surface and subsurface hydrologic properties and conditions, using a geographic information system (GIS). Surface and subsurface data coverages were compiled, georeferenced, reclassified, and integrated (including novel approaches for combining related datasets) to derive a spatial distribution of MAR suitability values. In the second step, results from the GIS analysis were used with a regional groundwater model to assess the hydrologic impact of potential MAR placement and operating scenarios. For the region evaluated in this study, the Pajaro Valley Groundwater Basin, California, GIS results suggest that about 7% (15 km2) of the basin may be highly suitable for MAR. Modeling suggests that simulated MAR projects placed near the coast help to reduce sea water intrusion more rapidly, but these projects also result in increased groundwater flows to the ocean. In contrast, projects placed farther inland result in more long-term reduction in sea water intrusion and less groundwater flowing to the ocean. This work shows how combined GIS analysis and modeling can assist with regional water supply planning, including evaluation of options for enhancing groundwater resources. © 2014, National Ground Water Association.

Diverting the tourists: a spatial decision-support system for tourism planning on a developing island

NASA Astrophysics Data System (ADS)

Beedasy, Jaishree; Whyatt, Duncan

Mauritius is a small island (1865 km 2) in the Indian Ocean. Tourism is the third largest economic sector of the country, after manufacturing and agriculture. A limitation of space and the island's vulnerable ecosystem warrants a rational approach to tourism development. The main problems so far have been to manipulate and integrate all the factors affecting tourism planning and to match spatial data with their relevant attributes. A Spatial Decision Support System (SDSS) for sustainable tourism planning is therefore proposed. The proposed SDSS design would include a GIS as its core component. A first GIS model has already been constructed with available data. Supporting decision-making in a spatial context is implicit in the use of GIS. However the analytical capability of the GIS has to be enhanced to solve semi-structured problems, where subjective judgements come into play. The second part of the paper deals with the choice, implementation and customisation of a relevant model to develop a specialised SDSS. Different types of models and techniques are discussed, in particular a comparison of compensatory and non-compensatory approaches to multicriteria evaluation (MCE). It is concluded that compensatory multicriteria evaluation techniques increase the scope of the present GIS model as a decision-support tool. This approach gives the user or decision-maker the flexibility to change the importance of each criterion depending on relevant objectives.

A validation of ground ambulance pre-hospital times modeled using geographic information systems.

PubMed

Patel, Alka B; Waters, Nigel M; Blanchard, Ian E; Doig, Christopher J; Ghali, William A

2012-10-03

Evaluating geographic access to health services often requires determining the patient travel time to a specified service. For urgent care, many research studies have modeled patient pre-hospital time by ground emergency medical services (EMS) using geographic information systems (GIS). The purpose of this study was to determine if the modeling assumptions proposed through prior United States (US) studies are valid in a non-US context, and to use the resulting information to provide revised recommendations for modeling travel time using GIS in the absence of actual EMS trip data. The study sample contained all emergency adult patient trips within the Calgary area for 2006. Each record included four components of pre-hospital time (activation, response, on-scene and transport interval). The actual activation and on-scene intervals were compared with those used in published models. The transport interval was calculated within GIS using the Network Analyst extension of Esri ArcGIS 10.0 and the response interval was derived using previously established methods. These GIS derived transport and response intervals were compared with the actual times using descriptive methods. We used the information acquired through the analysis of the EMS trip data to create an updated model that could be used to estimate travel time in the absence of actual EMS trip records. There were 29,765 complete EMS records for scene locations inside the city and 529 outside. The actual median on-scene intervals were longer than the average previously reported by 7-8 minutes. Actual EMS pre-hospital times across our study area were significantly higher than the estimated times modeled using GIS and the original travel time assumptions. Our updated model, although still underestimating the total pre-hospital time, more accurately represents the true pre-hospital time in our study area. The widespread use of generalized EMS pre-hospital time assumptions based on US data may not be appropriate in a non-US context. The preference for researchers should be to use actual EMS trip records from the proposed research study area. In the absence of EMS trip data researchers should determine which modeling assumptions more accurately reflect the EMS protocols across their study area.

Modeling wind energy potential in a data-poor region: A geographic information systems model for Iraq

NASA Astrophysics Data System (ADS)

Khayyat, Abdulkareem Hawta Abdullah Kak Ahmed

Scope and Method of Study: Most developing countries, including Iraq, have very poor wind data. Existing wind speed measurements of poor quality may therefore be a poor guide to where to look for the best wind resources. The main focus of this study is to examine how effectively a GIS spatial model estimates wind power potential in regions where high-quality wind data are very scarce, such as Iraq. The research used a mixture of monthly and hourly wind data from 39 meteorological stations. The study applied spatial analysis statistics and GIS techniques in modeling wind power potential. The model weighted important human, environmental and geographic factors that impact wind turbine siting, such as roughness length, land use⪉nd cover type, airport locations, road access, transmission lines, slope and aspect. Findings and Conclusions: The GIS model provided estimations for wind speed and wind power density and identified suitable areas for wind power projects. Using a high resolution (30*30m) digital elevation model DEM improved the GIS wind suitability model. The model identified areas suitable for wind farm development on different scales. The model showed that there are many locations available for large-scale wind turbines in the southern part of Iraq. Additionally, there are many places in central and northern parts (Kurdistan Region) for smaller scale wind turbine placement.

Gis-Based Smart Cartography Using 3d Modeling

NASA Astrophysics Data System (ADS)

Malinverni, E. S.; Tassetti, A. N.

2013-08-01

3D City Models have evolved to be important tools for urban decision processes and information systems, especially in planning, simulation, analysis, documentation and heritage management. On the other hand existing and in use numerical cartography is often not suitable to be used in GIS because not geometrically and topologically correctly structured. The research aim is to 3D structure and organize a numeric cartography for GIS and turn it into CityGML standardized features. The work is framed around a first phase of methodological analysis aimed to underline which existing standard (like ISO and OGC rules) can be used to improve the quality requirement of a cartographic structure. Subsequently, from this technical specifics, it has been investigated the translation in formal contents, using an owner interchange software (SketchUp), to support some guide lines implementations to generate a GIS3D structured in GML3. It has been therefore predisposed a test three-dimensional numerical cartography (scale 1:500, generated from range data captured by 3D laser scanner), tested on its quality according to the previous standard and edited when and where necessary. Cad files and shapefiles are converted into a final 3D model (Google SketchUp model) and then exported into a 3D city model (CityGML LoD1/LoD2). The GIS3D structure has been managed in a GIS environment to run further spatial analysis and energy performance estimate, not achievable in a 2D environment. In particular geometrical building parameters (footprint, volume etc.) are computed and building envelop thermal characteristics are derived from. Lastly, a simulation is carried out to deal with asbestos and home renovating charges and show how the built 3D city model can support municipal managers with risk diagnosis of the present situation and development of strategies for a sustainable redevelop.

The Integration of GPR, GIS, and GPS for 3D Soil Morphologic Models

NASA Astrophysics Data System (ADS)

Tischler, M.; Collins, M. E.

2005-05-01

Ground-Penetrating Radar (GPR) has become a useful and efficient instrument for gathering information about subsurface diagnostic horizons in Florida soils. Geographic Information Systems (GIS) are a popular and valuable tool for spatial data analysis of real world features in a digital environment. Ground-Penetrating Radar can be linked to GIS by using Global Positioning Systems (GPS). By combining GPR, GPS, and GIS technologies, a more detailed geophysical survey can be completed for an area of interest by integratinghydrologic, pedologic, and geologic data. Thus, the objectives of this research were to identify subsurface soil layers using GPR and their geographic position with a highly accurate GPS; to develop a procedure to import GPR data into a popular software package, such as ArcGIS, and; to create 3D subsurface models based on the imported GPR data. The site for this study was the Plant Science Research and Education Center in Marion County, Florida. The soils are characterized by Recent-Pleistocene-age sand over the clayey, marine deposited Plio-Miocene-age Hawthorn Formation which drapes the Eocene-age Ocala Limestone. Consequently, soils in the research area vary from deep quartz sands (Typic Quartzipsamments) to shallow outcrops of the Hawthorn Formation (Arenic Hapludalfs). A GPR survey was performed on a 160 m x 320 m grid to gather data for processing. Four subsurface models estimating the depth to argillic horizon were created using a variety of specialized GPR data filters and geostatistical data analyses. The models were compared with ground-truth points that measured the depth to argillic horizon to validate each model and calculate error metrics. These models may assist research station personnel to determine best management practices (including experimental plot placement, irrigation management, fertilizer treatment, and pesticide applications). In addition, the developed methodology exploits the potential of combining GPR and GIS.

Integrating remote sensing, geographic information systems and global positioning system techniques with hydrological modeling

NASA Astrophysics Data System (ADS)

Thakur, Jay Krishna; Singh, Sudhir Kumar; Ekanthalu, Vicky Shettigondahalli

2017-07-01

Integration of remote sensing (RS), geographic information systems (GIS) and global positioning system (GPS) are emerging research areas in the field of groundwater hydrology, resource management, environmental monitoring and during emergency response. Recent advancements in the fields of RS, GIS, GPS and higher level of computation will help in providing and handling a range of data simultaneously in a time- and cost-efficient manner. This review paper deals with hydrological modeling, uses of remote sensing and GIS in hydrological modeling, models of integrations and their need and in last the conclusion. After dealing with these issues conceptually and technically, we can develop better methods and novel approaches to handle large data sets and in a better way to communicate information related with rapidly decreasing societal resources, i.e. groundwater.

Transient deterministic shallow landslide modeling: Requirements for susceptibility and hazard assessments in a GIS framework

USGS Publications Warehouse

Godt, J.W.; Baum, R.L.; Savage, W.Z.; Salciarini, D.; Schulz, W.H.; Harp, E.L.

2008-01-01

Application of transient deterministic shallow landslide models over broad regions for hazard and susceptibility assessments requires information on rainfall, topography and the distribution and properties of hillside materials. We survey techniques for generating the spatial and temporal input data for such models and present an example using a transient deterministic model that combines an analytic solution to assess the pore-pressure response to rainfall infiltration with an infinite-slope stability calculation. Pore-pressures and factors of safety are computed on a cell-by-cell basis and can be displayed or manipulated in a grid-based GIS. Input data are high-resolution (1.8??m) topographic information derived from LiDAR data and simple descriptions of initial pore-pressure distribution and boundary conditions for a study area north of Seattle, Washington. Rainfall information is taken from a previously defined empirical rainfall intensity-duration threshold and material strength and hydraulic properties were measured both in the field and laboratory. Results are tested by comparison with a shallow landslide inventory. Comparison of results with those from static infinite-slope stability analyses assuming fixed water-table heights shows that the spatial prediction of shallow landslide susceptibility is improved using the transient analyses; moreover, results can be depicted in terms of the rainfall intensity and duration known to trigger shallow landslides in the study area.

Construction schedule simulation of a diversion tunnel based on the optimized ventilation time.

PubMed

Wang, Xiaoling; Liu, Xuepeng; Sun, Yuefeng; An, Juan; Zhang, Jing; Chen, Hongchao

2009-06-15

Former studies, the methods for estimating the ventilation time are all empirical in construction schedule simulation. However, in many real cases of construction schedule, the many factors have impact on the ventilation time. Therefore, in this paper the 3D unsteady quasi-single phase models are proposed to optimize the ventilation time with different tunneling lengths. The effect of buoyancy is considered in the momentum equation of the CO transport model, while the effects of inter-phase drag, lift force, and virtual mass force are taken into account in the momentum source of the dust transport model. The prediction by the present model for airflow in a diversion tunnel is confirmed by the experimental values reported by Nakayama [Nakayama, In-situ measurement and simulation by CFD of methane gas distribution at a heading faces, Shigen-to-Sozai 114 (11) (1998) 769-775]. The construction ventilation of the diversion tunnel of XinTangfang power station in China is used as a case. The distributions of airflow, CO and dust in the diversion tunnel are analyzed. A theory method for GIS-based dynamic visual simulation for the construction processes of underground structure groups is presented that combines cyclic operation network simulation, system simulation, network plan optimization, and GIS-based construction processes' 3D visualization. Based on the ventilation time the construction schedule of the diversion tunnel is simulated by the above theory method.

Using a Glacial Isostatic Adjustment model to investigate the contribution of the Antarctic and Greenland Ice sheet to the Last Interglacial Sea Level.

NASA Astrophysics Data System (ADS)

Bradley, Sarah; Hindmarsh, Richard C. A.

2014-05-01

Eustatic Sea Level during the Last interglacial (LIG) is likely to have been 4- 6 m higher than present day, with the observed relative sea level (RSL) at numerous far-field sites even higher [Dutton and Lambeck, 2012]. It has been suggested to generate this higher than present day sea level requires a retreat of both the Antarctic (AIS) and Greenland (GIS) Ice sheets beyond the present day extent, but the exact contribution of these two global ice sheets has yet to be resolved. By combing a Glacial Isostatic Adjustment (GIA) model with a suite of LIG ice-loading histories we will address a number of outstanding issues (i) What was the contribution of the AIS and GIS to ESL, (ii) Was the AIS or the GIS smaller during the LIG than the present interglacial? (iii) Can we generate the observed higher LIG RSL at a range of far-field sites? The suite of AIS and GIS ice-loading histories is constrained using the most recent near-field evidence, LIG stable isotope ice core data [Dahl-Jensen et al., 2013; Masson-Delmotte et al., 2011] and the output from ice sheet and climate models [Helsen et al., 2013; Pollard and DeConto, 2009; Stone et al., 2013]. Comparing the predicted RSL to a recent database of observed LIG far-field sea level [Dutton and Lambeck, 2012] allows for an assessment of the plausibility of the suite of ice loading histories. With this study, we aim to provide insight into the LIG history of the AIS and GIS. Dahl-Jensen, D., et al. (2013), Eemian interglacial reconstructed from a Greenland folded ice core, Nature, 493(7433), 489-494. Dutton, A., and K. Lambeck (2012), Ice Volume and Sea Level During the Last Interglacial, Science, 337(6091), 216-219. Helsen, M. M., W. J. van de Berg, R. S. W. van de Wal, M. R. van den Broeke, and J. Oerlemans (2013), Coupled regional climate-ice-sheet simulation shows limited Greenland ice loss during the Eemian, Clim Past, 9(4), 1773-1788. Masson-Delmotte, V., et al. (2011), A comparison of the present and last interglacial periods in six Antarctic ice cores, Clim Past, 7(2), 397-423. Pollard, D., and R. M. DeConto (2009), Modelling West Antarctic ice sheet growth and collapse through the past five million years, Nature, 458(7236), 329-U389. Stone, E. J., D. J. Lunt, J. D. Annan, and J. C. Hargreaves (2013), Quantification of the Greenland ice sheet contribution to Last Interglacial sea level rise, Clim Past, 9(2), 621-639.

Northern Forest Ecosystem Dynamics Using Coupled Models and Remote Sensing

NASA Technical Reports Server (NTRS)

Ranson, K. J.; Sun, G.; Knox, R. G.; Levine, E. R.; Weishampel, J. F.; Fifer, S. T.

1999-01-01

Forest ecosystem dynamics modeling, remote sensing data analysis, and a geographical information system (GIS) were used together to determine the possible growth and development of a northern forest in Maine, USA. Field measurements and airborne synthetic aperture radar (SAR) data were used to produce maps of forest cover type and above ground biomass. These forest attribute maps, along with a conventional soils map, were used to identify the initial conditions for forest ecosystem model simulations. Using this information along with ecosystem model results enabled the development of predictive maps of forest development. The results obtained were consistent with observed forest conditions and expected successional trajectories. The study demonstrated that ecosystem models might be used in a spatial context when parameterized and used with georeferenced data sets.

Evacuation Simulation in Kalayaan Residence Hall, up Diliman Using Gama Simulation Software

NASA Astrophysics Data System (ADS)

Claridades, A. R. C.; Villanueva, J. K. S.; Macatulad, E. G.

2016-09-01

Agent-Based Modeling (ABM) has recently been adopted in some studies for the modelling of events as a dynamic system given a set of events and parameters. In principle, ABM employs individual agents with assigned attributes and behaviors and simulates their behavior around their environment and interaction with other agents. This can be a useful tool in both micro and macroscale-applications. In this study, a model initially created and applied to an academic building was implemented in a dormitory. In particular, this research integrates three-dimensional Geographic Information System (GIS) with GAMA as the multi-agent based evacuation simulation and is implemented in Kalayaan Residence Hall. A three-dimensional GIS model is created based on the floor plans and demographic data of the dorm, including respective pathways as networks, rooms, floors, exits and appropriate attributes. This model is then re-implemented in GAMA. Different states of the agents and their effect on their evacuation time were then observed. GAMA simulation with varying path width was also implemented. It has been found out that compared to their original states, panic, eating and studying will hasten evacuation, and on the other hand, sleeping and being on the bathrooms will be impedances. It is also concluded that evacuation time will be halved when path widths are doubled, however it is recommended for further studies for pathways to be modeled as spaces instead of lines. A more scientific basis for predicting agent behavior in these states is also recommended for more realistic results.

[Application of GIS and integrated mathematic models on estimating forest land wood productiveness and solar energy use efficiency].

PubMed

Xing, Shihe; Lin, Dexi; Shen, Jinquan; Cao, Rongbin

2005-10-01

Based on the meteorological elements observation and mountain soil survey in Fujian Province, this paper approached the application of geographic information system (GIS) and integrated mathematic models on estimating the grid wood productiveness and solar energy use efficiency (SEUE) of regional forest land. The results showed that there was a significant quadratic correlation of annual mean temperature, precipitation and total solar radiation energy(TSRE) with longitude, latitude and altitude, and their multiple correlation coefficients ranged from 0.692 to 0.981. The regional annual mean TSRE, temperature and precipitation could be well estimated by GIS and integrated models of quadratic tendency curve, and linear, quadratic and quartic inverse distance weighted interpolation. These annual means estimated by the models did not differ greatly from observed data, and the t test values were 1.29, 0.12 and 0.06, respectively. The grid wood productiveness and SEUE of regional forest land in Fujian could also be well estimated with the aid of GIS and integrated models, which ranged from 2.32 m3 x hm(-2) yr(-1) to 18.61 m3 x hm(-2) yr(-1) and from 0.11% to 0.91%, respectively.

Collapse susceptibility mapping in karstified gypsum terrain (Sivas basin - Turkey) by conditional probability, logistic regression, artificial neural network models

NASA Astrophysics Data System (ADS)

Yilmaz, Isik; Keskin, Inan; Marschalko, Marian; Bednarik, Martin

2010-05-01

This study compares the GIS based collapse susceptibility mapping methods such as; conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) applied in gypsum rock masses in Sivas basin (Turkey). Digital Elevation Model (DEM) was first constructed using GIS software. Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index- TWI, stream power index- SPI, Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from CP, LR and ANN models, and they were then compared by means of their validations. Area Under Curve (AUC) values obtained from all three methodologies showed that the map obtained from ANN model looks like more accurate than the other models, and the results also showed that the artificial neural networks is a usefull tool in preparation of collapse susceptibility map and highly compatible with GIS operating features. Key words: Collapse; doline; susceptibility map; gypsum; GIS; conditional probability; logistic regression; artificial neural networks.

Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling

USGS Publications Warehouse

Keane, Robert E.; Burgan, Robert E.; Van Wagtendonk, Jan W.

2001-01-01

Fuel maps are essential for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, fuel mapping is an extremely difficult and complex process requiring expertise in remotely sensed image classification, fire behavior, fuels modeling, ecology, and geographical information systems (GIS). This paper first presents the challenges of mapping fuels: canopy concealment, fuelbed complexity, fuel type diversity, fuel variability, and fuel model generalization. Then, four approaches to mapping fuels are discussed with examples provided from the literature: (1) field reconnaissance; (2) direct mapping methods; (3) indirect mapping methods; and (4) gradient modeling. A fuel mapping method is proposed that uses current remote sensing and image processing technology. Future fuel mapping needs are also discussed which include better field data and fuel models, accurate GIS reference layers, improved satellite imagery, and comprehensive ecosystem models.

A GIS-based approach for comparative analysis of potential fire risk assessment

NASA Astrophysics Data System (ADS)

Sun, Ying; Hu, Lieqiu; Liu, Huiping

2007-06-01

Urban fires are one of the most important sources of property loss and human casualty and therefore it is necessary to assess the potential fire risk with consideration of urban community safety. Two evaluation models are proposed, both of which are integrated with GIS. One is the single factor model concerning the accessibility of fire passage and the other is grey clustering approach based on the multifactor system. In the latter model, fourteen factors are introduced and divided into four categories involving security management, evacuation facility, construction resistance and fire fighting capability. A case study on campus of Beijing Normal University is presented to express the potential risk assessment models in details. A comparative analysis of the two models is carried out to validate the accuracy. The results are approximately consistent with each other. Moreover, modeling with GIS promotes the efficiency the potential risk assessment.

Application of GIS in Modeling Zilberchai Basin Runoff

NASA Astrophysics Data System (ADS)

Malekani, L.; Khaleghi, S.; Mahmoodi, M.

2014-10-01

Runoff is one of most important hydrological variables that are used in many civil works, planning for optimal use of reservoirs, organizing rivers and warning flood. The runoff curve number (CN) is a key factor in determining runoff in the SCS (Soil Conservation Service) based hydrologic modeling method. The traditional SCS-CN method for calculating the composite curve number consumes a major portion of the hydrologic modeling time. Therefore, geographic information systems (GIS) are now being used in combination with the SCS-CN method. This work uses a methodology of determining surface runoff by Geographic Information System model and applying SCS-CN method that needs the necessary parameters such as land use map, hydrologic soil groups, rainfall data, DEM, physiographic characteristic of the basin. The model is built by implementing some well known hydrologic methods in GIS like as ArcHydro, ArcCN-Runoff for modeling of Zilberchai basin runoff. The results show that the high average weighted of curve number indicate that permeability of the basin is low and therefore likelihood of flooding is high. So the fundamental works is essential in order to increase water infiltration in Zilberchai basin and to avoid wasting surface water resources. Also comparing the results of the computed and observed runoff value show that use of GIS tools in addition to accelerate the calculation of the runoff also increase the accuracy of the results. This paper clearly demonstrates that the integration of GIS with the SCS-CN method provides a powerful tool for estimating runoff volumes in large basins.

Developing of operational hydro-meteorological simulating and displaying system

NASA Astrophysics Data System (ADS)

Wang, Y.; Shih, D.; Chen, C.

2010-12-01

Hydrological hazards, which often occur in conjunction with extreme precipitation events, are the most frequent type of natural disaster in Taiwan. Hence, the researchers at the Taiwan Typhoon and Flood Research Institute (TTFRI) are devoted to analyzing and gaining a better understanding of the causes and effects of natural disasters, and in particular, typhoons and floods. The long-term goal of the TTFRI is to develop a unified weather-hydrological-oceanic model suitable for simulations with local parameterizations in Taiwan. The development of a fully coupled weather-hydrology interaction model is not yet completed but some operational hydro-meteorological simulations are presented as a step in the direction of completing a full model. The predicted rainfall data from Weather Research Forecasting (WRF) are used as our meteorological forcing on watershed modeling. The hydrology and hydraulic modeling are conducted by WASH123D numerical model. And the WRF/WASH123D coupled system is applied to simulate floods during the typhoon landfall periods. The daily operational runs start at 04UTC, 10UTC, 16UTC and 22UTC, about 4 hours after data downloaded from NCEP GFS. This system will execute 72-hr weather forecasts. The simulation of WASH123D will sequentially trigger after receiving WRF rainfall data. This study presents the preliminary framework of establishing this system, and our goal is to build this earlier warning system to alert the public form dangerous. The simulation results are further display by a 3D GIS web service system. This system is established following the Open Geospatial Consortium (OGC) standardization process for GIS web service, such as Web Map Service (WMS) and Web Feature Service (WFS). The traditional 2D GIS data, such as high resolution aerial photomaps and satellite images are integrated into 3D landscape model. The simulated flooding and inundation area can be dynamically mapped on Wed 3D world. The final goal of this system is to real-time forecast flood and the results can be visually displayed on the virtual catchment. The policymaker can easily and real-time gain visual information for decision making at any site through internet.

Drainpipe network management information system design based on GIS and SCADA technique

NASA Astrophysics Data System (ADS)

Gu, Ze-Yu; Zhao, De-An

2011-02-01

Achieving urban drainpipe network integration of geographical information system (GIS) and supervisory control and data acquisition (SCADA) technology is described in this paper. The system design's plans are put forward, which have realized GIS and SCADA system supplementary in the technology and strengthened the model visible analysis ability. It is verified by practical cases that the system has more practical values and a good prospect.

Application of a Geographic Information System for regridding a ground-water flow model of the Columbia Plateau Regional Aquifer System, Walla Walla River basin, Oregon-Washington

USGS Publications Warehouse

Darling, M.E.; Hubbard, L.E.

1994-01-01

Computerized Geographic Information Systems (GIS) have become viable and valuable tools for managing,analyzing, creating, and displaying data for three-dimensional finite-difference ground-water flow models. Three GIS applications demonstrated in this study are: (1) regridding of data arrays from an existing large-area, low resolution ground-water model to a smaller, high resolution grid; (2) use of GIS techniques for assembly of data-input arrays for a ground-water model; and (3) use of GIS for rapid display of data for verification, for checking of ground-water model output, and for the cre.ation of customized maps for use in reports. The Walla Walla River Basin was selected as the location for the demonstration because (1) data from a low resolution ground-water model (Columbia Plateau Regional Aquifer System Analysis [RASA]) were available and (2) concern for long-term use of water resources for irrigation in the basin. The principal advantage of regridding is that it may provide the ability to more precisely calibrate a model, assuming chat a more detailed coverage of data is available, and to evaluate the numerical errors associated with a particular grid design.Regridding gave about an 8-fold increase in grid-node density.Several FORTRAN programs were developed to load the regridded ground-water data into a finite-difference modular model as model-compatible input files for use in a steady-state model run.To facilitate the checking and validating of the GIS regridding process, maps and tabular reports were produced for each of eight ground-water parameters by model layer. Also, an automated subroutine that was developed to view the model-calculated water levels in cross-section will aid in the synthesis and interpretation of model results.

Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data

NASA Astrophysics Data System (ADS)

Sampson, Paul D.; Szpiro, Adam A.; Sheppard, Lianne; Lindström, Johan; Kaufman, Joel D.

2011-11-01

Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in "land use" regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM 2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation.

Modeling of phosphorus fluxes produced by wild fires at watershed scales.

NASA Astrophysics Data System (ADS)

Matyjasik, M.; Hernandez, M.; Shaw, N.; Baker, M.; Fowles, M. T.; Cisney, T. A.; Jex, A. P.; Moisen, G.

2017-12-01

River runoff is one of the controlling processes in the terrestrial phosphorus cycle. Phosphorus is often a limiting factor in fresh water. One of the factors that has not been studied and modeled in detail is phosporus flux produced from forest wild fires. Phosphate released by weathering is quickly absorbed in soils. Forest wild fires expose barren soils to intensive erosion, thus releasing relatively large fluxes of phosphorus. Measurements from three control burn sites were used to correlate erosion with phosphorus fluxes. These results were used to model phosphorus fluxes from burned watersheds during a five year long period after fires occurred. Erosion in our model is simulated using a combination of two models: the WEPP (USDA Water Erosion Prediction Project) and the GeoWEPP (GIS-based Water Erosion Prediction Project). Erosion produced from forest disturbances is predicted for any watershed using hydrologic, soil, and meteorological data unique to the individual watersheds or individual slopes. The erosion results are modified for different textural soil classes and slope angles to model fluxes of phosphorus. The results of these models are calibrated using measured concentrations of phosphorus for three watersheds located in the Interior Western United States. The results will help the United States Forest Service manage phosporus fluxes in national forests.

Utilizing remote sensing to supplement ground monitoring of Diorhabda elongata as a control agent for Tamarix ramosissima in Dinosaur National Monument

NASA Astrophysics Data System (ADS)

Archambault, V.; Auch, J.; Landy, J.; Rudy, G.; Seifert, C.; Schmidt, C.; Skiles, J.

2008-12-01

The plant Tamarix ramosissima has invaded significant riparian habitat along the Green River in Dinosaur National Monument. Commonly known as salt cedar or tamarisk, it was introduced from Eurasia to the Southwestern United States to prevent soil erosion along riverbanks and as an ornamental plant. It has since come to affect water resources, recreation, wildlife, and ecosystem services. Various methods used to control tamarisk's spread have had moderate success but have drained National Park Service of human and monetary resources. In June 2006, the salt cedar leaf beetle (Diorhabda elongata) was released as a biological control agent within the park to defoliate and ultimately eradicate the invasive species. This study examines the efficacy of using Landsat TM imagery to supplement ground monitoring of the beetle's spread and its effects on tamarisk in Dinosaur National Monument, and discusses the development of a GIS model to predict annual change in tamarisk cover and beetle populations. Through fieldwork, we determined four areas of interest with favorable attributes for satellite detection. A change detection model was created by layering 2005-2008 data and quantifying mean NDVI. Results show that intra-year NDVI trends may be more effective for accurate detection than single-image year-to-year comparisons largely because intra-year environmental variability is significantly smaller. Additionally, our GIS model predicted significant growth of beetle population, implying that defoliation will become more apparent in future years. However, challenges to detecting this defoliation include the year-to-year variability of environmental factors, low spatial resolution of Landsat TM data, low visibility into parts of the Green River canyon, and the spectral mixing of tamarisk and native vegetation.

Global Distribution of Outbreaks of Water-Associated Infectious Diseases

PubMed Central

Yang, Kun; LeJeune, Jeffrey; Alsdorf, Doug; Lu, Bo; Shum, C. K.; Liang, Song

2012-01-01

Background Water plays an important role in the transmission of many infectious diseases, which pose a great burden on global public health. However, the global distribution of these water-associated infectious diseases and underlying factors remain largely unexplored. Methods and Findings Based on the Global Infectious Disease and Epidemiology Network (GIDEON), a global database including water-associated pathogens and diseases was developed. In this study, reported outbreak events associated with corresponding water-associated infectious diseases from 1991 to 2008 were extracted from the database. The location of each reported outbreak event was identified and geocoded into a GIS database. Also collected in the GIS database included geo-referenced socio-environmental information including population density (2000), annual accumulated temperature, surface water area, and average annual precipitation. Poisson models with Bayesian inference were developed to explore the association between these socio-environmental factors and distribution of the reported outbreak events. Based on model predictions a global relative risk map was generated. A total of 1,428 reported outbreak events were retrieved from the database. The analysis suggested that outbreaks of water-associated diseases are significantly correlated with socio-environmental factors. Population density is a significant risk factor for all categories of reported outbreaks of water-associated diseases; water-related diseases (e.g., vector-borne diseases) are associated with accumulated temperature; water-washed diseases (e.g., conjunctivitis) are inversely related to surface water area; both water-borne and water-related diseases are inversely related to average annual rainfall. Based on the model predictions, “hotspots” of risks for all categories of water-associated diseases were explored. Conclusions At the global scale, water-associated infectious diseases are significantly correlated with socio-environmental factors, impacting all regions which are affected disproportionately by different categories of water-associated infectious diseases. PMID:22348158

Development of a geotechnical GIS for subsurface characterization with three dimensional modeling capabilities.

DOT National Transportation Integrated Search

2006-06-01

The New Hampshire Department of Transportation initiated this research to develop a geographical information system (GIS) that : visualizes subsurface conditions three dimensionally by pulling together geotechnical data containing spatial references....

Greenland was nearly ice-free for extended periods during the Pleistocene

NASA Astrophysics Data System (ADS)

Schaefer, Joerg M.; Finkel, Robert C.; Balco, Greg; Alley, Richard B.; Caffee, Marc W.; Briner, Jason P.; Young, Nicolas E.; Gow, Anthony J.; Schwartz, Roseanne

2016-12-01

The Greenland Ice Sheet (GIS) contains the equivalent of 7.4 metres of global sea-level rise. Its stability in our warming climate is therefore a pressing concern. However, the sparse proxy evidence of the palaeo-stability of the GIS means that its history is controversial (compare refs 2 and 3 to ref. 4). Here we show that Greenland was deglaciated for extended periods during the Pleistocene epoch (from 2.6 million years ago to 11,700 years ago), based on new measurements of cosmic-ray-produced beryllium and aluminium isotopes (10Be and 26Al) in a bedrock core from beneath an ice core near the GIS summit. Models indicate that when this bedrock site is ice-free, any remaining ice is concentrated in the eastern Greenland highlands and the GIS is reduced to less than ten per cent of its current volume. Our results narrow the spectrum of possible GIS histories: the longest period of stability of the present ice sheet that is consistent with the measurements is 1.1 million years, assuming that this was preceded by more than 280,000 years of ice-free conditions. Other scenarios, in which Greenland was ice-free during any or all Pleistocene interglacials, may be more realistic. Our observations are incompatible with most existing model simulations that present a continuously existing Pleistocene GIS. Future simulations of the GIS should take into account that Greenland was nearly ice-free for extended periods under Pleistocene climate forcing.

Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis

NASA Astrophysics Data System (ADS)

Tang, Zhongqian; Zhang, Hua; Yi, Shanzhen; Xiao, Yangfan

2018-03-01

GIS-based multi-criteria decision analysis (MCDA) is increasingly used to support flood risk assessment. However, conventional GIS-MCDA methods fail to adequately represent spatial variability and are accompanied with considerable uncertainty. It is, thus, important to incorporate spatial variability and uncertainty into GIS-based decision analysis procedures. This research develops a spatially explicit, probabilistic GIS-MCDA approach for the delineation of potentially flood susceptible areas. The approach integrates the probabilistic and the local ordered weighted averaging (OWA) methods via Monte Carlo simulation, to take into account the uncertainty related to criteria weights, spatial heterogeneity of preferences and the risk attitude of the analyst. The approach is applied to a pilot study for the Gucheng County, central China, heavily affected by the hazardous 2012 flood. A GIS database of six geomorphological and hydrometeorological factors for the evaluation of susceptibility was created. Moreover, uncertainty and sensitivity analysis were performed to investigate the robustness of the model. The results indicate that the ensemble method improves the robustness of the model outcomes with respect to variation in criteria weights and identifies which criteria weights are most responsible for the variability of model outcomes. Therefore, the proposed approach is an improvement over the conventional deterministic method and can provides a more rational, objective and unbiased tool for flood susceptibility evaluation.

Communication by Fire (and Smoke) Signals in the Kingdom of Judah

NASA Technical Reports Server (NTRS)

Borowski, Oded; Howell, Burgess F.; Sever, Thomas L.

1998-01-01

This paper examines the use of ancient fire and smoke signals for communication in the Kingdom of Judah. Historical and biblical references are cited that discuss this communication system. The current physical and political landscape of Israel precludes testing of hypotheses using traditional techniques. The use of a GIS is enlisted to overcome these obstacles and predict line-of-sight patterns that are conducive for a fire signal communication system. Final demonstration of this predictive model will incorporate state-of-the-art technology and in-field data acquisition to provide the sufficient accuracy that is required for proof-of-concept. This research will provide insight into the technical capability of the ancient Israelites for communication across a mountainous, desert environment.

From data to information: Tools and techniques educators can use to enhance Google Earth imagery with geographic information systems data and three dimensional models

NASA Astrophysics Data System (ADS)

Simms, M.

2007-12-01

As with any educational technology, moving beyond basic information delivery to dynamic use can be a challenge and Google Earth (GE) is no exception. Moving beyond annotated placemarks and pictures, educators can utilize free, free-to-educators, and low cost tools to develop learning experiences within GE to facilitate dynamic interaction with real world data in the form of three dimensional models (3D) and geographic information systems data (GIS). Students take an active role in knowledge construction through self-directed navigation in 3D, seeing features of the landscape not in snapshot views found in textbooks, but in situ and in context. By incorporating categorized data, such as what is commonly found in GIS, an added dimension of human interaction can be incorporated. For example, GIS layers such as landuse, soil type, etc. provide students with data tools for investigating the role their community plays in supporting migrating Monarch butterfly habitat. Functionality for changing the appearance of layers in GE facilitates interaction with geospatial data in a manner that creates a type of "visual" GIS and can serve as an advanced organizer for later use of more powerful GIS software. GE can also be used as a metaphor to create a new context for an otherwise abstract concept, for example, scaling 3D models of the sun and planets to the size of a well known football stadium and placing each planet at the corresponding scaled distances from that location. Photorealistic 3D models created using SketchUp and Anim8or may help students relate to an otherwise abstract concept of planetary size and distances. Finally, digital elevation models (DEM) draped with imagery not available in GE or with GIS data can be used to make topic-specific 3D models either used within GE or in a 3D model viewer embedded in a website or email. With a little instruction, students can quickly learn how to make their own models as well. Procedures and software to accomplish each of these examples will be demonstrated.

Phylogeny predicts future habitat shifts due to climate change.

PubMed

Kuntner, Matjaž; Năpăruş, Magdalena; Li, Daiqin; Coddington, Jonathan A

2014-01-01

Taxa may respond differently to climatic changes, depending on phylogenetic or ecological effects, but studies that discern among these alternatives are scarce. Here, we use two species pairs from globally distributed spider clades, each pair representing two lifestyles (generalist, specialist) to test the relative importance of phylogeny versus ecology in predicted responses to climate change. We used a recent phylogenetic hypothesis for nephilid spiders to select four species from two genera (Nephilingis and Nephilengys) that match the above criteria, are fully allopatric but combined occupy all subtropical-tropical regions. Based on their records, we modeled each species niche spaces and predicted their ecological shifts 20, 40, 60, and 80 years into the future using customized GIS tools and projected climatic changes. Phylogeny better predicts the species current ecological preferences than do lifestyles. By 2080 all species face dramatic reductions in suitable habitat (54.8-77.1%) and adapt by moving towards higher altitudes and latitudes, although at different tempos. Phylogeny and life style explain simulated habitat shifts in altitude, but phylogeny is the sole best predictor of latitudinal shifts. Models incorporating phylogenetic relatedness are an important additional tool to predict accurately biotic responses to global change.

Evaluating the Potential of Commercial GIS for Accelerator Configuration Management

DOE Office of Scientific and Technical Information (OSTI.GOV)

T.L. Larrieu; Y.R. Roblin; K. White

2005-10-10

The Geographic Information System (GIS) is a tool used by industries needing to track information about spatially distributed assets. A water utility, for example, must know not only the precise location of each pipe and pump, but also the respective pressure rating and flow rate of each. In many ways, an accelerator such as CEBAF (Continuous Electron Beam Accelerator Facility) can be viewed as an ''electron utility''. Whereas the water utility uses pipes and pumps, the ''electron utility'' uses magnets and RF cavities. At Jefferson lab we are exploring the possibility of implementing ESRI's ArcGIS as the framework for buildingmore » an all-encompassing accelerator configuration database that integrates location, configuration, maintenance, and connectivity details of all hardware and software. The possibilities of doing so are intriguing. From the GIS, software such as the model server could always extract the most-up-to-date layout information maintained by the Survey & Alignment for lattice modeling. The Mechanical Engineering department could use ArcGIS tools to generate CAD drawings of machine segments from the same database. Ultimately, the greatest benefit of the GIS implementation could be to liberate operators and engineers from the limitations of the current system-by-system view of machine configuration and allow a more integrated regional approach. The commercial GIS package provides a rich set of tools for database-connectivity, versioning, distributed editing, importing and exporting, and graphical analysis and querying, and therefore obviates the need for much custom development. However, formidable challenges to implementation exist and these challenges are not only technical and manpower issues, but also organizational ones. The GIS approach would crosscut organizational boundaries and require departments, which heretofore have had free reign to manage their own data, to cede some control and agree to a centralized framework.« less

A comparison of self reported air pollution problems and GIS-modeled levels of air pollution in people with and without chronic diseases

PubMed Central

Piro, Fredrik Niclas; Madsen, Christian; Næss, Øyvind; Nafstad, Per; Claussen, Bjørgulf

2008-01-01

Objective To explore various contributors to people's reporting of self reported air pollution problems in area of living, including GIS-modeled air pollution, and to investigate whether those with respiratory or other chronic diseases tend to over-report air pollution problems, compared to healthy people. Methods Cross-sectional data from the Oslo Health Study (2000–2001) were linked with GIS-modeled air pollution data from the Norwegian Institute of Air Research. Multivariate regression analyses were performed. 14 294 persons aged 30, 40, 45, 60 or 75 years old with complete information on modeled and self reported air pollution were included. Results People who reported air pollution problems were exposed to significantly higher GIS-modeled air pollution levels than those who did not report such problems. People with chronic disease, reported significantly more air pollution problems after adjustment for modeled levels of nitrogen dioxides, socio-demographic variables, smoking, depression, dwelling conditions and an area deprivation index, even if they had a non-respiratory disease. No diseases, however, were significantly associated with levels of nitrogen dioxides. Conclusion Self reported air pollution problems in area of living are strongly associated with increased levels of GIS-modeled air pollution. Over and above this, those who report to have a chronic disease tend to report more air pollution problems in area of living, despite no significant difference in air pollution exposure compared to healthy people, and no associations between these diseases and NO2. Studies on the association between self reported air pollution problems and health should be aware of the possibility that disease itself may influence the reporting of air pollution. PMID:18307757

Assessment of vulnerability zones for ground water pollution using GIS-DRASTIC-EC model: A field-based approach

NASA Astrophysics Data System (ADS)

Anantha Rao, D.; Naik, Pradeep K.; Jain, Sunil K.; Vinod Kumar, K.; Dhanamjaya Rao, E. N.

2018-06-01

Assessment of groundwater vulnerability to pollution is an essential pre-requisite for better planning of an area. We present the groundwater vulnerability assessment in parts of the Yamuna Nagar District, Haryana State, India in an area of about 800 km2, considered to be a freshwater zone in the foothills of the Siwalik Hill Ranges. Such areas in the Lower Himalayas form good groundwater recharge zones, and should always be free from contamination. But, the administration has been trying to promote industrialization along these foothill zones without actually assessing the environmental consequences such activities may invite in the future. GIS-DRASTIC model has been used with field based data inputs for studying the vulnerability assessment. But, we find that inclusion electrical conductivity (EC) as a model parameter makes it more robust. Therefore, we rename it as GIS-DRASTIC-EC model. The model identifies three vulnerability zones such as low, moderate and high with an areal extent of 5%, 80% and 15%, respectively. On the basis of major chemical parameters alone, the groundwater in the foothill zones apparently looks safe, but analysis with the help of GIS-DRASTIC-EC model gives a better perspective of the groundwater quality in terms of identifying the vulnerable areas.

Analysis of Distribution of Vector-Borne Diseases Using Geographic Information Systems.

PubMed

Nihei, Naoko

2017-01-01

The distribution of vector-borne diseases is changing on a global scale owing to issues involving natural environments, socioeconomic conditions and border disputes among others. Geographic information systems (GIS) provide an important method of establishing a prompt and precise understanding of local data on disease outbreaks, from which disease eradication programs can be established. Having first defined GIS as a combination of GPS, RS and GIS, we showed the processes through which these technologies were being introduced into our research. GIS-derived geographical information attributes were interpreted in terms of point, area, line, spatial epidemiology, risk and development for generating the vector dynamic models associated with the spread of the disease. The need for interdisciplinary scientific and administrative collaboration in the use of GIS to control infectious diseases is highly warranted.

GLABROUS INFLORESCENCE STEMS (GIS) is required for trichome branching through gibberellic acid signaling in Arabidopsis.

PubMed

An, Lijun; Zhou, Zhongjing; Su, Sha; Yan, An; Gan, Yinbo

2012-02-01

Cell differentiation generally corresponds to the cell cycle, typically forming a non-dividing cell with a unique differentiated morphology, and Arabidopsis trichome is an excellent model system to study all aspects of cell differentiation. Although gibberellic acid is reported to be involved in trichome branching in Arabidopsis, the mechanism for such signaling is unclear. Here, we demonstrated that GLABROUS INFLORESCENCE STEMS (GIS) is required for the control of trichome branching through gibberellic acid signaling. The phenotypes of a loss-of-function gis mutant and an overexpressor showed that GIS acted as a repressor to control trichome branching. Our results also show that GIS is not required for cell endoreduplication, and our molecular and genetic study results have shown that GIS functions downstream of the key regulator of trichome branching, STICHEL (STI), to control trichome branching through the endoreduplication-independent pathway. Furthermore, our results also suggest that GIS controls trichome branching in Arabidopsis through two different pathways and acts either upstream or downstream of the negative regulator of gibbellic acid signaling SPINDLY (SPY).

Managing Data in a GIS Environment

NASA Technical Reports Server (NTRS)

Beltran, Maria; Yiasemis, Haris

1997-01-01

A Geographic Information System (GIS) is a computer-based system that enables capture, modeling, manipulation, retrieval, analysis and presentation of geographically referenced data. A GIS operates in a dynamic environment of spatial and temporal information. This information is held in a database like any other information system, but performance is more of an issue for a geographic database than a traditional database due to the nature of the data. What distinguishes a GIS from other information systems is the spatial and temporal dimensions of the data and the volume of data (several gigabytes). Most traditional information systems are usually based around tables and textual reports, whereas GIS requires the use of cartographic forms and other visualization techniques. Much of the data can be represented using computer graphics, but a GIS is not a graphics database. A graphical system is concerned with the manipulation and presentation of graphical objects whereas a GIS handles geographic objects that have not only spatial dimensions but non-visual, i e., attribute and components. Furthermore, the nature of the data on which a GIS operates makes the traditional relational database approach inadequate for retrieving data and answering queries that reference spatial data. The purpose of this paper is to describe the efficiency issues behind storage and retrieval of data within a GIS database. Section 2 gives a general background on GIS, and describes the issues involved in custom vs. commercial and hybrid vs. integrated geographic information systems. Section 3 describes the efficiency issues concerning the management of data within a GIS environment. The paper ends with a summary of the main concerns of this paper.

GRASS GIS: The first Open Source Temporal GIS

NASA Astrophysics Data System (ADS)

Gebbert, Sören; Leppelt, Thomas

2015-04-01

GRASS GIS is a full featured, general purpose Open Source geographic information system (GIS) with raster, 3D raster and vector processing support[1]. Recently, time was introduced as a new dimension that transformed GRASS GIS into the first Open Source temporal GIS with comprehensive spatio-temporal analysis, processing and visualization capabilities[2]. New spatio-temporal data types were introduced in GRASS GIS version 7, to manage raster, 3D raster and vector time series. These new data types are called space time datasets. They are designed to efficiently handle hundreds of thousands of time stamped raster, 3D raster and vector map layers of any size. Time stamps can be defined as time intervals or time instances in Gregorian calendar time or relative time. Space time datasets are simplifying the processing and analysis of large time series in GRASS GIS, since these new data types are used as input and output parameter in temporal modules. The handling of space time datasets is therefore equal to the handling of raster, 3D raster and vector map layers in GRASS GIS. A new dedicated Python library, the GRASS GIS Temporal Framework, was designed to implement the spatio-temporal data types and their management. The framework provides the functionality to efficiently handle hundreds of thousands of time stamped map layers and their spatio-temporal topological relations. The framework supports reasoning based on the temporal granularity of space time datasets as well as their temporal topology. It was designed in conjunction with the PyGRASS [3] library to support parallel processing of large datasets, that has a long tradition in GRASS GIS [4,5]. We will present a subset of more than 40 temporal modules that were implemented based on the GRASS GIS Temporal Framework, PyGRASS and the GRASS GIS Python scripting library. These modules provide a comprehensive temporal GIS tool set. The functionality range from space time dataset and time stamped map layer management over temporal aggregation, temporal accumulation, spatio-temporal statistics, spatio-temporal sampling, temporal algebra, temporal topology analysis, time series animation and temporal topology visualization to time series import and export capabilities with support for NetCDF and VTK data formats. We will present several temporal modules that support parallel processing of raster and 3D raster time series. [1] GRASS GIS Open Source Approaches in Spatial Data Handling In Open Source Approaches in Spatial Data Handling, Vol. 2 (2008), pp. 171-199, doi:10.1007/978-3-540-74831-19 by M. Neteler, D. Beaudette, P. Cavallini, L. Lami, J. Cepicky edited by G. Brent Hall, Michael G. Leahy [2] Gebbert, S., Pebesma, E., 2014. A temporal GIS for field based environmental modeling. Environ. Model. Softw. 53, 1-12. [3] Zambelli, P., Gebbert, S., Ciolli, M., 2013. Pygrass: An Object Oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). ISPRS Intl Journal of Geo-Information 2, 201-219. [4] Löwe, P., Klump, J., Thaler, J. (2012): The FOSS GIS Workbench on the GFZ Load Sharing Facility compute cluster, (Geophysical Research Abstracts Vol. 14, EGU2012-4491, 2012), General Assembly European Geosciences Union (Vienna, Austria 2012). [5] Akhter, S., Aida, K., Chemin, Y., 2010. "GRASS GIS on High Performance Computing with MPI, OpenMP and Ninf-G Programming Framework". ISPRS Conference, Kyoto, 9-12 August 2010

Modifications of the U.S. Geological Survey modular, finite-difference, ground-water flow model to read and write geographic information system files

USGS Publications Warehouse

Orzol, Leonard L.; McGrath, Timothy S.

1992-01-01

This report documents modifications to the U.S. Geological Survey modular, three-dimensional, finite-difference, ground-water flow model, commonly called MODFLOW, so that it can read and write files used by a geographic information system (GIS). The modified model program is called MODFLOWARC. Simulation programs such as MODFLOW generally require large amounts of input data and produce large amounts of output data. Viewing data graphically, generating head contours, and creating or editing model data arrays such as hydraulic conductivity are examples of tasks that currently are performed either by the use of independent software packages or by tedious manual editing, manipulating, and transferring data. Programs such as GIS programs are commonly used to facilitate preparation of the model input data and analyze model output data; however, auxiliary programs are frequently required to translate data between programs. Data translations are required when different programs use different data formats. Thus, the user might use GIS techniques to create model input data, run a translation program to convert input data into a format compatible with the ground-water flow model, run the model, run a translation program to convert the model output into the correct format for GIS, and use GIS to display and analyze this output. MODFLOWARC, avoids the two translation steps and transfers data directly to and from the ground-water-flow model. This report documents the design and use of MODFLOWARC and includes instructions for data input/output of the Basic, Block-centered flow, River, Recharge, Well, Drain, Evapotranspiration, General-head boundary, and Streamflow-routing packages. The modification to MODFLOW and the Streamflow-Routing package was minimized. Flow charts and computer-program code describe the modifications to the original computer codes for each of these packages. Appendix A contains a discussion on the operation of MODFLOWARC using a sample problem.

Lowering the barriers to computational modeling of Earth's surface: coupling Jupyter Notebooks with Landlab, HydroShare, and CyberGIS for research and education.

NASA Astrophysics Data System (ADS)

Bandaragoda, C.; Castronova, A. M.; Phuong, J.; Istanbulluoglu, E.; Strauch, R. L.; Nudurupati, S. S.; Tarboton, D. G.; Wang, S. W.; Yin, D.; Barnhart, K. R.; Tucker, G. E.; Hutton, E.; Hobley, D. E. J.; Gasparini, N. M.; Adams, J. M.

2017-12-01

The ability to test hypotheses about hydrology, geomorphology and atmospheric processes is invaluable to research in the era of big data. Although community resources are available, there remain significant educational, logistical and time investment barriers to their use. Knowledge infrastructure is an emerging intellectual framework to understand how people are creating, sharing and distributing knowledge - which has been dramatically transformed by Internet technologies. In addition to the technical and social components in a cyberinfrastructure system, knowledge infrastructure considers educational, institutional, and open source governance components required to advance knowledge. We are designing an infrastructure environment that lowers common barriers to reproducing modeling experiments for earth surface investigation. Landlab is an open-source modeling toolkit for building, coupling, and exploring two-dimensional numerical models. HydroShare is an online collaborative environment for sharing hydrologic data and models. CyberGIS-Jupyter is an innovative cyberGIS framework for achieving data-intensive, reproducible, and scalable geospatial analytics using the Jupyter Notebook based on ROGER - the first cyberGIS supercomputer, so that models that can be elastically reproduced through cloud computing approaches. Our team of geomorphologists, hydrologists, and computer geoscientists has created a new infrastructure environment that combines these three pieces of software to enable knowledge discovery. Through this novel integration, any user can interactively execute and explore their shared data and model resources. Landlab on HydroShare with CyberGIS-Jupyter supports the modeling continuum from fully developed modelling applications, prototyping new science tools, hands on research demonstrations for training workshops, and classroom applications. Computational geospatial models based on big data and high performance computing can now be more efficiently developed, improved, scaled, and seamlessly reproduced among multidisciplinary users, thereby expanding the active learning curriculum and research opportunities for students in earth surface modeling and informatics.

Prediction of River Flooding using Geospatial and Statistical Analysis in New York, USA and Kent, UK

NASA Astrophysics Data System (ADS)

Marsellos, A.; Tsakiri, K.; Smith, M.

2014-12-01

Flooding in the rivers normally occurs during periods of excessive precipitation (i.e. New York, USA; Kent, UK) or ice jams during the winter period (New York, USA). For the prediction and mapping of the river flooding, it is necessary to evaluate the spatial distribution of the water (volume) in the river as well as study the interaction between the climatic and hydrological variables. Two study areas have been analyzed; one in Mohawk River, New York and one in Kent, United Kingdom (UK). A high resolution Digital Elevation Model (DEM) of the Mohawk River, New York has been used for a GIS flooding simulation to determine the maximum elevation value of the water that cannot continue to be restricted in the trunk stream and as a result flooding in the river may be triggered. The Flooding Trigger Level (FTL) is determined by incremental volumetric and surface calculations from Triangulated Irregular Network (TIN) with the use of GIS software and LiDAR data. The prediction of flooding in the river can also be improved by the statistical analysis of the hydrological and climatic variables in Mohawk River and Kent, UK. A methodology of time series analysis has been applied for the decomposition of the hydrological (water flow and ground water data) and climatic data in both locations. The KZ (Kolmogorov-Zurbenko) filter is used for the decomposition of the time series into the long, seasonal, and short term components. The explanation of the long term component of the water flow using the climatic variables has been improved up to 90% for both locations. Similar analysis has been performed for the prediction of the seasonal and short term component. This methodology can be applied for flooding of the rivers in multiple sites.

Research and implementation on 3D modeling of geological body

NASA Astrophysics Data System (ADS)

Niu, Lijuan; Li, Ligong; Zhu, Renyi; Huang, Man

2017-10-01

This study based on GIS thinking explores the combination of the mixed spatial data model and GIS model to build three-dimensional(3d) model of geological bodies in the Arc Engine platform, describes the interface and method used in the construction of 3d geological body in Arc Engine component platform in detail, and puts forward an indirect method which constructs a set of geological grid layers through Rigging interpolation by the borehole data and then converts it into the geological layers of TIN, which improves the defect in building the geological layers of TIN directly and makes it better to complete the simulation of the real geological layer. This study makes a useful attempt to build 3d model of the geological body based on the GIS, and provides a certain reference value for simulating geological bodies in 3d and constructing the digital system of underground space.

An integrated user-friendly ArcMAP tool for bivariate statistical modeling in geoscience applications

NASA Astrophysics Data System (ADS)

Jebur, M. N.; Pradhan, B.; Shafri, H. Z. M.; Yusof, Z.; Tehrany, M. S.

2014-10-01

Modeling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modeling. Bivariate statistical analysis (BSA) assists in hazard modeling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, BSM (bivariate statistical modeler), for BSA technique is proposed. Three popular BSA techniques such as frequency ratio, weights-of-evidence, and evidential belief function models are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and is created by a simple graphical user interface, which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications.

An integrated user-friendly ArcMAP tool for bivariate statistical modelling in geoscience applications

NASA Astrophysics Data System (ADS)

Jebur, M. N.; Pradhan, B.; Shafri, H. Z. M.; Yusoff, Z. M.; Tehrany, M. S.

2015-03-01

Modelling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modelling. Bivariate statistical analysis (BSA) assists in hazard modelling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time-consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, bivariate statistical modeler (BSM), for BSA technique is proposed. Three popular BSA techniques, such as frequency ratio, weight-of-evidence (WoE), and evidential belief function (EBF) models, are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and created by a simple graphical user interface (GUI), which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve (AUC) is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications.

Techniques for estimating flood-peak discharges of rural, unregulated streams in Ohio

USGS Publications Warehouse

Koltun, G.F.

2003-01-01

Regional equations for estimating 2-, 5-, 10-, 25-, 50-, 100-, and 500-year flood-peak discharges at ungaged sites on rural, unregulated streams in Ohio were developed by means of ordinary and generalized least-squares (GLS) regression techniques. One-variable, simple equations and three-variable, full-model equations were developed on the basis of selected basin characteristics and flood-frequency estimates determined for 305 streamflow-gaging stations in Ohio and adjacent states. The average standard errors of prediction ranged from about 39 to 49 percent for the simple equations, and from about 34 to 41 percent for the full-model equations. Flood-frequency estimates determined by means of log-Pearson Type III analyses are reported along with weighted flood-frequency estimates, computed as a function of the log-Pearson Type III estimates and the regression estimates. Values of explanatory variables used in the regression models were determined from digital spatial data sets by means of a geographic information system (GIS), with the exception of drainage area, which was determined by digitizing the area within basin boundaries manually delineated on topographic maps. Use of GIS-based explanatory variables represents a major departure in methodology from that described in previous reports on estimating flood-frequency characteristics of Ohio streams. Examples are presented illustrating application of the regression equations to ungaged sites on ungaged and gaged streams. A method is provided to adjust regression estimates for ungaged sites by use of weighted and regression estimates for a gaged site on the same stream. A region-of-influence method, which employs a computer program to estimate flood-frequency characteristics for ungaged sites based on data from gaged sites with similar characteristics, was also tested and compared to the GLS full-model equations. For all recurrence intervals, the GLS full-model equations had superior prediction accuracy relative to the simple equations and therefore are recommended for use.

Assessing critical source areas in watersheds for conservation buffer planning and riparian restoration.

PubMed

Qiu, Zeyuan

2009-11-01

A science-based geographic information system (GIS) approach is presented to target critical source areas in watersheds for conservation buffer placement. Critical source areas are the intersection of hydrologically sensitive areas and pollutant source areas in watersheds. Hydrologically sensitive areas are areas that actively generate runoff in the watershed and are derived using a modified topographic index approach based on variable source area hydrology. Pollutant source areas are the areas in watersheds that are actively and intensively used for such activities as agricultural production. The method is applied to the Neshanic River watershed in Hunterdon County, New Jersey. The capacity of the topographic index in predicting the spatial pattern of runoff generation and the runoff contribution to stream flow in the watershed is evaluated. A simple cost-effectiveness assessment is conducted to compare the conservation buffer placement scenario based on this GIS method to conventional riparian buffer scenarios for placing conservation buffers in agricultural lands in the watershed. The results show that the topographic index reasonably predicts the runoff generation in the watershed. The GIS-based conservation buffer scenario appears to be more cost-effective than the conventional riparian buffer scenarios.

A GIS planning model for urban oil spill management.

PubMed

Li, J

2001-01-01

Oil spills in industrialized cities pose a significant threat to their urban water environment. The largest city in Canada, the city of Toronto, has an average 300-500 oil spills per year with an average total volume of about 160,000 L/year. About 45% of the spills was eventually cleaned up. Given the enormous amount of remaining oil entering into the fragile urban ecosystem, it is important to develop an effective pollution prevention and control plan for the city. A Geographic Information System (GIS) planning model has been developed to characterize oil spills and determine preventive and control measures available in the city. A database of oil spill records from 1988 to 1997 was compiled and geo-referenced. Attributes to each record such as spill volume, oil type, location, road type, sector, source, cleanup percentage, and environmental impacts were created. GIS layers of woodlots, wetlands, watercourses, Environmental Sensitive Areas, and Areas of Natural and Scientific Interest were obtained from the local Conservation Authority. By overlaying the spill characteristics with the GIS layers, evaluation of preventive and control solutions close to these environmental features was conducted. It was found that employee training and preventive maintenance should be improved as the principal cause of spills was attributed to human errors and equipment failure. Additionally, the cost of using oil separators at strategic spill locations was found to be $1.4 million. The GIS model provides an efficient planning tool for urban oil spill management. Additionally, the graphical capability of GIS allows users to integrate environmental features and spill characteristics in the management analysis.

Monitoring spatial variations in soil organic carbon using remote sensing and geographic information systems

NASA Astrophysics Data System (ADS)

Jaber, Salahuddin M.

Soil organic carbon (SOC) sequestration is a component of larger strategies to control the accumulation of greenhouse gases that may be causing global warming. To implement this approach, it is necessary to improve the methods of measuring SOC content. Among these methods are indirect remote sensing and geographic information systems (GIS) techniques that are required to provide non-intrusive, low cost, and spatially continuous information that cover large areas on a repetitive basis. The main goal of this study is to evaluate the effects of using Hyperion hyperspectral data on improving the existing remote sensing and GIS-based methodologies for rapidly, efficiently, and accurately measuring SOC content on farmland. The study area is Big Creek Watershed (BCW) in Southern Illinois. The methodology consists of compiling a GIS database (consisting of remote sensing and soil variables) for 303 composite soil samples collected from representative pixels along the Hyperion coverage area of the watershed. Stepwise procedures were used to calibrate and validate linear multiple regression models where SOC was regarded as the response and the other remote sensing and soil variables as the predictors. Two models were selected. The first was the best all variables model and the second was the best only raster variables model. Map algebra was implemented to extrapolate the best only raster variables model and produce a SOC map for the BGW. This study concluded that Hyperion data marginally improved the predictability of the existing SOC statistical models based on multispectral satellite remote sensing sensors with correlation coefficient of 0.37 and root mean square error of 3.19 metric tons/hectare to a 15-cm depth. The total SOC pool of the study area is about 225,232 metric tons to 15-cm depth. The nonforested wetlands contained the highest SOC density (34.3 metric tons/hectare/15cm) with total SOC content of about 2,003.5 metric tons to 15-cm depth, where croplands had the lowest SOC density (21.6 metric tons/hectare/15cm) with total SOC content of about 44,571.2 metric tons to 15-cm depth.

Integration of modern statistical tools for the analysis of climate extremes into the web-GIS “CLIMATE”

NASA Astrophysics Data System (ADS)

Ryazanova, A. A.; Okladnikov, I. G.; Gordov, E. P.

2017-11-01

The frequency of occurrence and magnitude of precipitation and temperature extreme events show positive trends in several geographical regions. These events must be analyzed and studied in order to better understand their impact on the environment, predict their occurrences, and mitigate their effects. For this purpose, we augmented web-GIS called “CLIMATE” to include a dedicated statistical package developed in the R language. The web-GIS “CLIMATE” is a software platform for cloud storage processing and visualization of distributed archives of spatial datasets. It is based on a combined use of web and GIS technologies with reliable procedures for searching, extracting, processing, and visualizing the spatial data archives. The system provides a set of thematic online tools for the complex analysis of current and future climate changes and their effects on the environment. The package includes new powerful methods of time-dependent statistics of extremes, quantile regression and copula approach for the detailed analysis of various climate extreme events. Specifically, the very promising copula approach allows obtaining the structural connections between the extremes and the various environmental characteristics. The new statistical methods integrated into the web-GIS “CLIMATE” can significantly facilitate and accelerate the complex analysis of climate extremes using only a desktop PC connected to the Internet.

Approach to spatial information security based on digital certificate

NASA Astrophysics Data System (ADS)

Cong, Shengri; Zhang, Kai; Chen, Baowen

2005-11-01

With the development of the online applications of geographic information systems (GIS) and the spatial information services, the spatial information security becomes more important. This work introduced digital certificates and authorization schemes into GIS to protect the crucial spatial information combining the techniques of the role-based access control (RBAC), the public key infrastructure (PKI) and the privilege management infrastructure (PMI). We investigated the spatial information granularity suited for sensitivity marking and digital certificate model that fits the need of GIS security based on the semantics analysis of spatial information. It implements a secure, flexible, fine-grained data access based on public technologies in GIS in the world.

Temporal Dynamics and Spatial Patterns of Aedes aegypti Breeding Sites, in the Context of a Dengue Control Program in Tartagal (Salta Province, Argentina).

PubMed

Espinosa, Manuel; Weinberg, Diego; Rotela, Camilo H; Polop, Francisco; Abril, Marcelo; Scavuzzo, Carlos Marcelo

2016-05-01

Since 2009, Fundación Mundo Sano has implemented an Aedes aegypti Surveillance and Control Program in Tartagal city (Salta Province, Argentina). The purpose of this study was to analyze temporal dynamics of Ae. aegypti breeding sites spatial distribution, during five years of samplings, and the effect of control actions over vector population dynamics. Seasonal entomological (larval) samplings were conducted in 17,815 fixed sites in Tartagal urban area between 2009 and 2014. Based on information of breeding sites abundance, from satellite remote sensing data (RS), and by the use of Geographic Information Systems (GIS), spatial analysis (hotspots and cluster analysis) and predictive model (MaxEnt) were performed. Spatial analysis showed a distribution pattern with the highest breeding densities registered in city outskirts. The model indicated that 75% of Ae. aegypti distribution is explained by 3 variables: bare soil coverage percentage (44.9%), urbanization coverage percentage(13.5%) and water distribution (11.6%). This results have called attention to the way entomological field data and information from geospatial origin (RS/GIS) are used to infer scenarios which could then be applied in epidemiological surveillance programs and in the determination of dengue control strategies. Predictive maps development constructed with Ae. aegypti systematic spatiotemporal data, in Tartagal city, would allow public health workers to identify and target high-risk areas with appropriate and timely control measures. These tools could help decision-makers to improve health system responses and preventive measures related to vector control.

Temporal Dynamics and Spatial Patterns of Aedes aegypti Breeding Sites, in the Context of a Dengue Control Program in Tartagal (Salta Province, Argentina)

PubMed Central

Espinosa, Manuel; Weinberg, Diego; Rotela, Camilo H.; Polop, Francisco; Abril, Marcelo; Scavuzzo, Carlos Marcelo

2016-01-01

Background Since 2009, Fundación Mundo Sano has implemented an Aedes aegypti Surveillance and Control Program in Tartagal city (Salta Province, Argentina). The purpose of this study was to analyze temporal dynamics of Ae. aegypti breeding sites spatial distribution, during five years of samplings, and the effect of control actions over vector population dynamics. Methodology/Principal Findings Seasonal entomological (larval) samplings were conducted in 17,815 fixed sites in Tartagal urban area between 2009 and 2014. Based on information of breeding sites abundance, from satellite remote sensing data (RS), and by the use of Geographic Information Systems (GIS), spatial analysis (hotspots and cluster analysis) and predictive model (MaxEnt) were performed. Spatial analysis showed a distribution pattern with the highest breeding densities registered in city outskirts. The model indicated that 75% of Ae. aegypti distribution is explained by 3 variables: bare soil coverage percentage (44.9%), urbanization coverage percentage(13.5%) and water distribution (11.6%). Conclusions/Significance This results have called attention to the way entomological field data and information from geospatial origin (RS/GIS) are used to infer scenarios which could then be applied in epidemiological surveillance programs and in the determination of dengue control strategies. Predictive maps development constructed with Ae. aegypti systematic spatiotemporal data, in Tartagal city, would allow public health workers to identify and target high-risk areas with appropriate and timely control measures. These tools could help decision-makers to improve health system responses and preventive measures related to vector control. PMID:27223693

Geo-environmental model for the prediction of potential transmission risk of Dirofilaria in an area with dry climate and extensive irrigated crops. The case of Spain.

PubMed

Simón, Luis; Afonin, Alexandr; López-Díez, Lucía Isabel; González-Miguel, Javier; Morchón, Rodrigo; Carretón, Elena; Montoya-Alonso, José Alberto; Kartashev, Vladimir; Simón, Fernando

2014-03-01

Zoonotic filarioses caused by Dirofilaria immitis and Dirofilaria repens are transmitted by culicid mosquitoes. Therefore Dirofilaria transmission depends on climatic factors like temperature and humidity. In spite of the dry climate of most of the Spanish territory, there are extensive irrigated crops areas providing moist habitats favourable for mosquito breeding. A GIS model to predict the risk of Dirofilaria transmission in Spain, based on temperatures and rainfall data as well as in the distribution of irrigated crops areas, is constructed. The model predicts that potential risk of Dirofilaria transmission exists in all the Spanish territory. Highest transmission risk exists in several areas of Andalucía, Extremadura, Castilla-La Mancha, Murcia, Valencia, Aragón and Cataluña, where moderate/high temperatures coincide with extensive irrigated crops. High risk in Balearic Islands and in some points of Canary Islands, is also predicted. The lowest risk is predicted in Northern cold and scarcely or non-irrigated dry Southeastern areas. The existence of irrigations locally increases transmission risk in low rainfall areas of the Spanish territory. The model can contribute to implement rational preventive therapy guidelines in accordance with the transmission characteristics of each local area. Moreover, the use of humidity-related factors could be of interest in future predictions to be performed in countries with similar environmental characteristics. Copyright © 2014 Elsevier B.V. All rights reserved.

Integration of Watershed Model AnnAGNPS and Stream Network Model CCHE1D for the Development of a New GIS-Based BMP Planning Tool

USDA-ARS?s Scientific Manuscript database

This paper presents a new GIS-based Best Management Practice (BMP) Tool developed for watershed managers to assist in the decision making process by simulating various scenarios using various combinations of Best Management Practices (BMPs). The development of this BMPTool is based on the integratio...

Indigenous Waters: Applying the SWAT Hydrological Model to the Lumbee River Watershed

NASA Astrophysics Data System (ADS)

Painter, J.; Singh, N.; Martin, K. L.; Vose, J. M.; Wear, D. N.; Emanuel, R. E.

2016-12-01

Hydrological modeling can reveal insight about how rainfall becomes streamflow in a watershed comprising heterogeneous soils, terrain and land cover. Modeling can also help disentangle predicted impacts of climate and land use change on hydrological processes. We applied a hydrological model to the Lumbee River watershed, also known as the Lumber River Watershed, in the coastal plain of North Carolina (USA) to better understand how streamflow may be impacted by predicted climate and land use change in the mid-21st century. The Lumbee River flows through a predominantly Native American community, which may be affected by changing water resources during this period. The long-term goal of our project is to predict the effects of climate and land use change on the Lumbee River watershed and on the Native community that relies upon the river. We applied the Soil & Water Assessment Tool for ArcGIS (ArcSWAT), which was calibrated to historical climate and USGS streamflow data during the late 20th century, and we determined frequency distributions for key model parameters that best predicted streamflow during this time period. After calibrating and validating the model during the historical period, we identified land use and climate projections to represent a range of future conditions in the watershed. Specifically, we selected downscaled climate forcing data from four general circulation models running the RCP8.5 scenario. We also selected land use projections from a cornerstone scenario of the USDA Forest Service's Southern Forest Futures Project. This presentation reports on our methods for propagating parameter and climatic uncertainty through model predictions, and it reports on spatial patterns of land use change predicted by the cornerstone scenario.

A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)

NASA Astrophysics Data System (ADS)

Gholami, V.; Khaleghi, M. R.; Sebghati, M.

2017-11-01

The process of water quality testing is money/time-consuming, quite important and difficult stage for routine measurements. Therefore, use of models has become commonplace in simulating water quality. In this study, the coactive neuro-fuzzy inference system (CANFIS) was used to simulate groundwater quality. Further, geographic information system (GIS) was used as the pre-processor and post-processor tool to demonstrate spatial variation of groundwater quality. All important factors were quantified and groundwater quality index (GWQI) was developed. The proposed model was trained and validated by taking a case study of Mazandaran Plain located in northern part of Iran. The factors affecting groundwater quality were the input variables for the simulation, whereas GWQI index was the output. The developed model was validated to simulate groundwater quality. Network validation was performed via comparison between the estimated and actual GWQI values. In GIS, the study area was separated to raster format in the pixel dimensions of 1 km and also by incorporation of input data layers of the Fuzzy Network-CANFIS model; the geo-referenced layers of the effective factors in groundwater quality were earned. Therefore, numeric values of each pixel with geographical coordinates were entered to the Fuzzy Network-CANFIS model and thus simulation of groundwater quality was accessed in the study area. Finally, the simulated GWQI indices using the Fuzzy Network-CANFIS model were entered into GIS, and hence groundwater quality map (raster layer) based on the results of the network simulation was earned. The study's results confirm the high efficiency of incorporation of neuro-fuzzy techniques and GIS. It is also worth noting that the general quality of the groundwater in the most studied plain is fairly low.

Evaluation of Wind Energy Production in Texas using Geographic Information Systems (GIS)

NASA Astrophysics Data System (ADS)

Ferrer, L. M.

2017-12-01

Texas has the highest installed wind capacity in the United States. The purpose of this research was to estimate the theoretical wind turbine energy production and the utilization ratio of wind turbines in Texas. Windfarm data was combined applying Geographic Information System (GIS) methodology to create an updated GIS wind turbine database, including location and technical specifications. Applying GIS diverse tools, the windfarm data was spatially joined with National Renewable Energy Laboratory (NREL) wind data to calculate the wind speed at each turbine hub. The power output for each turbine at the hub wind speed was evaluated by the GIS system according the respective turbine model power curve. In total over 11,700 turbines are installed in Texas with an estimated energy output of 60 GWh per year and an average utilization ratio of 0.32. This research indicates that applying GIS methodologies will be crucial in the growth of wind energy and efficiency in Texas.

Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS

NASA Astrophysics Data System (ADS)

Tien Bui, Dieu; Pradhan, Biswajeet; Nampak, Haleh; Bui, Quang-Thanh; Tran, Quynh-An; Nguyen, Quoc-Phi

2016-09-01

This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE = 0.306, MAE = 0.094, AUC = 0.962) and validation dataset (RMSE = 0.362, MAE = 0.130, AUC = 0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas.

Using classification tree analysis to predict oak wilt distribution in Minnesota and Texas

Treesearch

Marla c. Downing; Vernon L. Thomas; Jennifer Juzwik; David N. Appel; Robin M. Reich; Kim Camilli

2008-01-01

We developed a methodology and compared results for predicting the potential distribution of Ceratocystis fagacearum (causal agent of oak wilt), in both Anoka County, MN, and Fort Hood, TX. The Potential Distribution of Oak Wilt (PDOW) utilizes a binary classification tree statistical technique that incorporates: geographical information systems (GIS...

Integration of GIS, Geostatistics, and 3-D Technology to Assess the Spatial Distribution of Soil Moisture

NASA Technical Reports Server (NTRS)

Betts, M.; Tsegaye, T.; Tadesse, W.; Coleman, T. L.; Fahsi, A.

1998-01-01

The spatial and temporal distribution of near surface soil moisture is of fundamental importance to many physical, biological, biogeochemical, and hydrological processes. However, knowledge of these space-time dynamics and the processes which control them remains unclear. The integration of geographic information systems (GIS) and geostatistics together promise a simple mechanism to evaluate and display the spatial and temporal distribution of this vital hydrologic and physical variable. Therefore, this research demonstrates the use of geostatistics and GIS to predict and display soil moisture distribution under vegetated and non-vegetated plots. The research was conducted at the Winfred Thomas Agricultural Experiment Station (WTAES), Hazel Green, Alabama. Soil moisture measurement were done on a 10 by 10 m grid from tall fescue grass (GR), alfalfa (AA), bare rough (BR), and bare smooth (BS) plots. Results indicated that variance associated with soil moisture was higher for vegetated plots than non-vegetated plots. The presence of vegetation in general contributed to the spatial variability of soil moisture. Integration of geostatistics and GIS can improve the productivity of farm lands and the precision of farming.

Anaerobic co-digestion plants for the revaluation of agricultural waste: Sustainable location sites from a GIS analysis.

PubMed

Villamar, Cristina Alejandra; Rivera, Diego; Aguayo, Mauricio

2016-04-01

The aim of this study was to establish sustainably feasible areas for the implementation of anaerobic co-digestion plants for agricultural wastes (cattle/swine slurries and cereal crop wastes). The methodology was based on the use of geographic information systems (GIS), the analytic hierarchy process (AHP) and map algebra generated from hedges related to environmental, social and economic constraints. The GIS model obtained was applied to a region of Chile (Bío Bío Region) as a case study showing the energy potential (205 MW-h) of agricultural wastes (swine/cattle manures and cereal crop wastes) and thereby assessing its energy contribution (3.5%) at country level (Chile). From this model, it was possible to spatially identify the influence of each factor (environmental, economic and social) when defining suitable areas for the siting of anaerobic co-digestion plants. In conclusion, GIS-based models establish appropriate areas for the location of anaerobic co-digestion plants in the revaluation of agricultural waste from the production of energy through biogas production. © The Author(s) 2016.

Spatial distribution of block falls using volumetric GIS-decision-tree models

NASA Astrophysics Data System (ADS)

Abdallah, C.

2010-10-01

Block falls are considered a significant aspect of surficial instability contributing to losses in land and socio-economic aspects through their damaging effects to natural and human environments. This paper predicts and maps the geographic distribution and volumes of block falls in central Lebanon using remote sensing, geographic information systems (GIS) and decision-tree modeling (un-pruned and pruned trees). Eleven terrain parameters (lithology, proximity to fault line, karst type, soil type, distance to drainage line, elevation, slope gradient, slope aspect, slope curvature, land cover/use, and proximity to roads) were generated to statistically explain the occurrence of block falls. The latter were discriminated using SPOT4 satellite imageries, and their dimensions were determined during field surveys. The un-pruned tree model based on all considered parameters explained 86% of the variability in field block fall measurements. Once pruned, it classifies 50% in block falls' volumes by selecting just four parameters (lithology, slope gradient, soil type, and land cover/use). Both tree models (un-pruned and pruned) were converted to quantitative 1:50,000 block falls' maps with different classes; starting from Nil (no block falls) to more than 4000 m 3. These maps are fairly matching with coincidence value equal to 45%; however, both can be used to prioritize the choice of specific zones for further measurement and modeling, as well as for land-use management. The proposed tree models are relatively simple, and may also be applied to other areas (i.e. the choice of un-pruned or pruned model is related to the availability of terrain parameters in a given area).

Modelling Soil Erosion in the Densu River Basin Using RUSLE and GIS Tools.

PubMed

Ashiagbori, G; Forkuo, E K; Laari, P; Aabeyir, R

2014-07-01

Soil erosion involves detachment and transport of soil particles from top soil layers, degrading soil quality and reducing the productivity of affected lands. Soil eroded from the upland catchment causes depletion of fertile agricultural land and the resulting sediment deposited at the river networks creates river morphological change and reservoir sedimentation problems. However, land managers and policy makers are more interested in the spatial distribution of soil erosion risk than in absolute values of soil erosion loss. The aim of this paper is to model the spatial distribution of soil erosion in Densu River Basin of Ghana using RUSLE and GIS tools and to use the model to explore the relationship between erosion susceptibility, slope and land use/land cover (LULC) in the Basin. The rainfall map, digital elevation model, soil type map, and land cover map, were input data in the soil erosion model developed. This model was then categorized into four different erosion risk classes. The developed soil erosion map was then overlaid with the slope and LULC maps of the study area to explore their effects on erosion susceptibility of the soil in the Densu River Basin. The Model, predicted 88% of the basin as low erosion risk and 6% as moderate erosion risk, 3% as high erosion risk and 3% as severe risk. The high and severe erosion areas were distributed mainly within the areas of high slope gradient and also sections of the moderate forest LULC class. Also, the areas within the moderate forest LULC class found to have high erosion risk, had an intersecting high erodibility soil group.

Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways

PubMed Central

Boucher, Benjamin; Lee, Anna Y.; Hallett, Michael; Jenna, Sarah

2016-01-01

A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults, and also into the link existing between genotype and phenotype. While a significant conservation of GIs and GI network structure has been reported between distant yeast species, such a conservation is not clear between unicellular and multicellular organisms. Structural and functional characterization of a GI network in these latter organisms is consequently of high interest. In this study, we present an in-depth characterization of ~1.5K GIs in the nematode Caenorhabditis elegans. We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network, including co-expression, phenotypical manifestations, relationship with protein-protein interaction dense subnetworks (PDS) and pathways, molecular and biological functions, gene essentiality and pleiotropy. Our study shows that GI classes link genes within pathways and display distinctive properties, specifically towards PDS. It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors. Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism. It also suggests a model to understand better how GIs control system robustness and evolution. PMID:26871911

Landscape modeling for Everglades ecosystem restoration

USGS Publications Warehouse

DeAngelis, D.L.; Gross, L.J.; Huston, M.A.; Wolff, W.F.; Fleming, D.M.; Comiskey, E.J.; Sylvester, S.M.

1998-01-01

A major environmental restoration effort is under way that will affect the Everglades and its neighboring ecosystems in southern Florida. Ecosystem and population-level modeling is being used to help in the planning and evaluation of this restoration. The specific objective of one of these modeling approaches, the Across Trophic Level System Simulation (ATLSS), is to predict the responses of a suite of higher trophic level species to several proposed alterations in Everglades hydrology. These include several species of wading birds, the snail kite, Cape Sable seaside sparrow, Florida panther, white-tailed deer, American alligator, and American crocodile. ATLSS is an ecosystem landscape-modeling approach and uses Geographic Information System (GIS) vegetation data and existing hydrology models for South Florida to provide the basic landscape for these species. A method of pseudotopography provides estimates of water depths through time at 28 ?? 28-m resolution across the landscape of southern Florida. Hydrologic model output drives models of habitat and prey availability for the higher trophic level species. Spatially explicit, individual-based computer models simulate these species. ATLSS simulations can compare the landscape dynamic spatial pattern of the species resulting from different proposed water management strategies. Here we compare the predicted effects of one possible change in water management in South Florida with the base case of no change. Preliminary model results predict substantial differences between these alternatives in some biotic spatial patterns. ?? 1998 Springer-Verlag.

Carbon footprint estimation of municipal water cycle

NASA Astrophysics Data System (ADS)

Bakhshi, Ali A.

2009-11-01

This research investigates the embodied energy associated with water use. A geographic information system (GIS) was tested using data from Loudoun County, Virginia. The objective of this study is to estimate the embodied energy and carbon emission levels associated with water service at a geographical location and to improve for sustainability planning. Factors that affect the carbon footprint were investigated and the use of a GIS based model as a sustainability planning framework was evaluated. The carbon footprint metric is a useful tool for prediction and measurement of a system's sustainable performance over its expected life cycle. Two metrics were calculated: tons of carbon dioxide per year to represent the contribution to global warming and watt-hrs per gallon to show the embodied energy associated with water consumption. The water delivery to the building, removal of wastewater from the building and associated treatment of water and wastewater create a sizable carbon footprint; often the energy attributed to this water service is the greatest end use of electrical energy. The embodied energy in water depends on topographical characteristics of the area's local water supply, the efficiency of the treatment systems, and the efficiency of the pumping stations. The questions answered by this research are: What is the impact of demand side sustainable water practices on the embodied energy as represented by a comprehensive carbon footprint? What are the major energy consuming elements attributed to the system? What is a viable and visually identifiable tool to estimate the carbon footprint attributed to those Greenhouse Gas (GHG) producing elements? What is the embodied energy and emission associated with water use delivered to a building? Benefits to be derived from a standardized GIS applied carbon footprint estimation approach include: (1) Improved environmental and economic information for the developers, water and wastewater processing and municipal planners; (2) Improved energy use reporting and conservation planning; (3) Establishment of a benchmark for GHG emissions attributed to the water and wastewater industry; (4) Ability to quantify relative impacts of building design options using carbon emission equivalents. The GIS based model was applied to the Dulles South and Brambelton regions in Loudoun County, Virginia. The GIS revealed the customer's embodied energy to be in the range of 4.41MWh/Mgal to 8.0 MWh/Mgal. The customer's carbon footprint is between 0.008 and18.0 Tons of CO2 for year 2008. The results of this study contributed to development of a standardized approach to estimate the GHG impact of a total water cycle, and provided a viable GIS tool resulting in visual maps as a decision support. It also showed the use of derived empirical formulas in predication of GHG impact for end users in a specific geographical area. The embodied energy in delivered water can be estimated using the devised model and be considered by the building sustainability ranking programs such as the USGBC LEED rating system. KEYWORDS. Water Life Cycle, Embodied Energy, Global Warming Potential, Energy Intensity, Energy Intensity Matrix, Emission Intensity, Emission Coefficient, Carbon Dioxide Emission, Water and Wastewater, Collection, Treatment and Distribution, Carbon Footprint, Topography, Municipality, Environmental Indicator, ArcGIS, LEED, GHG, ESI, LCA, LCEA, LCI, Sustainability, End Use, Potable Water

Traveller Information System for Heterogeneous Traffic Condition: A Case Study in Thiruvananthapuram City, India

NASA Astrophysics Data System (ADS)

Satyakumar, M.; Anil, R.; Sreeja, G. S.

2017-12-01

Traffic in Kerala has been growing at a rate of 10-11% every year, resulting severe congestion especially in urban areas. Because of the limitation of spaces it is not always possible to construct new roads. Road users rely on travel time information for journey planning and route choice decisions, while road system managers are increasingly viewing travel time as an important network performance indicator. More recently Advanced Traveler Information Systems (ATIS) are being developed to provide real-time information to roadway users. For ATIS various methodologies have been developed for dynamic travel time prediction. For this work the Kalman Filter Algorithm was selected for dynamic travel time prediction of different modes. The travel time data collected using handheld GPS device were used for prediction. Congestion Index were calculated and Range of CI values were determined according to the percentage speed drop. After prediction using Kalman Filter, the predicted values along with the GPS data was integrated to GIS and using Network Analysis of ArcGIS the offline route navigation guide was prepared. Using this database a program for route navigation based on travel time was developed. This system will help the travelers with pre-trip information.

Integrating remote sensing and GIS for prediction of winter wheat (Triticum aestivum) protein contents in Linfen (Shanxi), China.

PubMed

Feng, Mei-chen; Xiao, Lu-jie; Zhang, Mei-jun; Yang, Wu-de; Ding, Guang-wei

2014-01-01

In this study, relationships between normalized difference vegetation index (NDVI) and plant (winter wheat) nitrogen content (PNC) and between PNC and grain protein content (GPC) were investigated using multi-temporal moderate-resolution imaging spectroradiometer (MODIS) data at the different stages of winter wheat in Linfen (Shanxi, P. R. China). The anticipating model for GPC of winter wheat was also established by the approach of NDVI at the different stages of winter wheat. The results showed that the spectrum models of PNC passed F test. The NDVI4.14 regression effect of PNC model of irrigated winter wheat was the best, and that in dry land was NDVI4.30. The PNC of irrigated and dry land winter wheat were significantly (P<0.01) and positively correlated to GPC. Both of protein spectral anticipating model of irrigated and dry land winter wheat passed a significance test (P<0.01). Multiple anticipating models (MAM) were established by NDVI from two periods of irrigated and dry land winter wheat and PNC to link GPC anticipating model. The coefficient of determination R(2) (R) of MAM was greater than that of the other two single-factor models. The relative root mean square error (RRMSE) and relative error (RE) of MAM were lower than those of the other two single-factor models. Therefore, test effects of multiple proteins anticipating model were better than those of single-factor models. The application of multiple anticipating models for predication of protein content (PC) of irrigated and dry land winter wheat was more accurate and reliable. The regionalization analysis of GPC was performed using inverse distance weighted function of GIS, which is likely to provide the scientific basis for the reasonable winter wheat planting in Linfen city, China.

Integrating Remote Sensing and GIS for Prediction of Winter Wheat (Triticum aestivum) Protein Contents in Linfen (Shanxi), China

PubMed Central

Feng, Mei-chen; Xiao, Lu-jie; Zhang, Mei-jun; Yang, Wu-de; Ding, Guang-wei

2014-01-01

In this study, relationships between normalized difference vegetation index (NDVI) and plant (winter wheat) nitrogen content (PNC) and between PNC and grain protein content (GPC) were investigated using multi-temporal moderate-resolution imaging spectroradiometer (MODIS) data at the different stages of winter wheat in Linfen (Shanxi, P. R. China). The anticipating model for GPC of winter wheat was also established by the approach of NDVI at the different stages of winter wheat. The results showed that the spectrum models of PNC passed F test. The NDVI4.14 regression effect of PNC model of irrigated winter wheat was the best, and that in dry land was NDVI4.30. The PNC of irrigated and dry land winter wheat were significantly (P<0.01) and positively correlated to GPC. Both of protein spectral anticipating model of irrigated and dry land winter wheat passed a significance test (P<0.01). Multiple anticipating models (MAM) were established by NDVI from two periods of irrigated and dry land winter wheat and PNC to link GPC anticipating model. The coefficient of determination R2 (R) of MAM was greater than that of the other two single-factor models. The relative root mean square error (RRMSE) and relative error (RE) of MAM were lower than those of the other two single-factor models. Therefore, test effects of multiple proteins anticipating model were better than those of single-factor models. The application of multiple anticipating models for predication of protein content (PC) of irrigated and dry land winter wheat was more accurate and reliable. The regionalization analysis of GPC was performed using inverse distance weighted function of GIS, which is likely to provide the scientific basis for the reasonable winter wheat planting in Linfen city, China. PMID:24404124

Development of a GIS-based integrated framework for coastal seiches monitoring and forecasting: A North Jiangsu shoal case study

NASA Astrophysics Data System (ADS)

Qin, Rufu; Lin, Liangzhao

2017-06-01

Coastal seiches have become an increasingly important issue in coastal science and present many challenges, particularly when attempting to provide warning services. This paper presents the methodologies, techniques and integrated services adopted for the design and implementation of a Seiches Monitoring and Forecasting Integration Framework (SMAF-IF). The SMAF-IF is an integrated system with different types of sensors and numerical models and incorporates the Geographic Information System (GIS) and web techniques, which focuses on coastal seiche events detection and early warning in the North Jiangsu shoal, China. The in situ sensors perform automatic and continuous monitoring of the marine environment status and the numerical models provide the meteorological and physical oceanographic parameter estimates. A model outputs processing software was developed in C# language using ArcGIS Engine functions, which provides the capabilities of automatically generating visualization maps and warning information. Leveraging the ArcGIS Flex API and ASP.NET web services, a web based GIS framework was designed to facilitate quasi real-time data access, interactive visualization and analysis, and provision of early warning services for end users. The integrated framework proposed in this study enables decision-makers and the publics to quickly response to emergency coastal seiche events and allows an easy adaptation to other regional and scientific domains related to real-time monitoring and forecasting.

A global organism detection and monitoring system for non-native species

USGS Publications Warehouse

Graham, J.; Newman, G.; Jarnevich, C.; Shory, R.; Stohlgren, T.J.

2007-01-01

Harmful invasive non-native species are a significant threat to native species and ecosystems, and the costs associated with non-native species in the United States is estimated at over $120 Billion/year. While some local or regional databases exist for some taxonomic groups, there are no effective geographic databases designed to detect and monitor all species of non-native plants, animals, and pathogens. We developed a web-based solution called the Global Organism Detection and Monitoring (GODM) system to provide real-time data from a broad spectrum of users on the distribution and abundance of non-native species, including attributes of their habitats for predictive spatial modeling of current and potential distributions. The four major subsystems of GODM provide dynamic links between the organism data, web pages, spatial data, and modeling capabilities. The core survey database tables for recording invasive species survey data are organized into three categories: "Where, Who & When, and What." Organisms are identified with Taxonomic Serial Numbers from the Integrated Taxonomic Information System. To allow users to immediately see a map of their data combined with other user's data, a custom geographic information system (GIS) Internet solution was required. The GIS solution provides an unprecedented level of flexibility in database access, allowing users to display maps of invasive species distributions or abundances based on various criteria including taxonomic classification (i.e., phylum or division, order, class, family, genus, species, subspecies, and variety), a specific project, a range of dates, and a range of attributes (percent cover, age, height, sex, weight). This is a significant paradigm shift from "map servers" to true Internet-based GIS solutions. The remainder of the system was created with a mix of commercial products, open source software, and custom software. Custom GIS libraries were created where required for processing large datasets, accessing the operating system, and to use existing libraries in C++, R, and other languages to develop the tools to track harmful species in space and time. The GODM database and system are crucial for early detection and rapid containment of invasive species. ?? 2007 Elsevier B.V. All rights reserved.

Using remote sensing to predict earthquake impacts

NASA Astrophysics Data System (ADS)

Fylaktos, Asimakis; Yfantidou, Anastasia

2017-09-01

Natural hazards like earthquakes can result to enormous property damage, and human casualties in mountainous areas. Italy has always been exposed to numerous earthquakes, mostly concentrated in central and southern regions. Last year, two seismic events near Norcia (central Italy) have occurred, which led to substantial loss of life and extensive damage to properties, infrastructure and cultural heritage. This research utilizes remote sensing products and GIS software, to provide a database of information. We used both SAR images of Sentinel 1A and optical imagery of Landsat 8 to examine the differences of topography with the aid of the multi temporal monitoring technique. This technique suits for the observation of any surface deformation. This database is a cluster of information regarding the consequences of the earthquakes in groups, such as property and infrastructure damage, regional rifts, cultivation loss, landslides and surface deformations amongst others, all mapped on GIS software. Relevant organizations can implement these data in order to calculate the financial impact of these types of earthquakes. In the future, we can enrich this database including more regions and enhance the variety of its applications. For instance, we could predict the future impacts of any type of earthquake in several areas, and design a preliminarily model of emergency for immediate evacuation and quick recovery response. It is important to know how the surface moves, in particular geographical regions like Italy, Cyprus and Greece, where earthquakes are so frequent. We are not able to predict earthquakes, but using data from this research, we may assess the damage that could be caused in the future.

The GIS Weasel: An interface for the development of geographic information used in environmental simulation modeling

USGS Publications Warehouse

Viger, R.J.

2008-01-01

The GIS Weasel is a freely available, open-source software package built on top of ArcInfo Workstation?? [ESRI, Inc., 2001, ArcInfo Workstation (8.1 ed.), Redlands, CA] for creating maps and parameters of geographic features used in environmental simulation models. The software has been designed to minimize the need for GIS expertise and automate the preparation of the geographic information as much as possible. Although many kinds of data can be exploited with the GIS Weasel, the only information required is a raster dataset of elevation for the user's area of interest (AOI). The user-defined AOI serves as a starting point from which to create maps of many different types of geographic features, including sub-watersheds, streams, elevation bands, land cover patches, land parcels, or anything else that can be discerned from the available data. The GIS Weasel has a library of over 200 routines that can be applied to any raster map of geographic features to generate information about shape, area, or topological association with other features of the same or different maps. In addition, a wide variety of parameters can be derived using ancillary data layers such as soil and vegetation maps.

A validation of ground ambulance pre-hospital times modeled using geographic information systems

PubMed Central

2012-01-01

Background Evaluating geographic access to health services often requires determining the patient travel time to a specified service. For urgent care, many research studies have modeled patient pre-hospital time by ground emergency medical services (EMS) using geographic information systems (GIS). The purpose of this study was to determine if the modeling assumptions proposed through prior United States (US) studies are valid in a non-US context, and to use the resulting information to provide revised recommendations for modeling travel time using GIS in the absence of actual EMS trip data. Methods The study sample contained all emergency adult patient trips within the Calgary area for 2006. Each record included four components of pre-hospital time (activation, response, on-scene and transport interval). The actual activation and on-scene intervals were compared with those used in published models. The transport interval was calculated within GIS using the Network Analyst extension of Esri ArcGIS 10.0 and the response interval was derived using previously established methods. These GIS derived transport and response intervals were compared with the actual times using descriptive methods. We used the information acquired through the analysis of the EMS trip data to create an updated model that could be used to estimate travel time in the absence of actual EMS trip records. Results There were 29,765 complete EMS records for scene locations inside the city and 529 outside. The actual median on-scene intervals were longer than the average previously reported by 7–8 minutes. Actual EMS pre-hospital times across our study area were significantly higher than the estimated times modeled using GIS and the original travel time assumptions. Our updated model, although still underestimating the total pre-hospital time, more accurately represents the true pre-hospital time in our study area. Conclusions The widespread use of generalized EMS pre-hospital time assumptions based on US data may not be appropriate in a non-US context. The preference for researchers should be to use actual EMS trip records from the proposed research study area. In the absence of EMS trip data researchers should determine which modeling assumptions more accurately reflect the EMS protocols across their study area. PMID:23033894

Effective Alphas and Mixing for Disks with Gravitational Instabilities: Convergence Testing in Global 3D Simulations

NASA Astrophysics Data System (ADS)

Michael, Scott A.; Steiman-Cameron, T.; Durisen, R.; Boley, A.

2008-05-01

Using 3D simulations of a cooling disk undergoing gravitational instabilities (GIs), we compute the effective Shakura and Sunyaev (1973) alphas due to gravitational torques and compare them to predictions from an analytic local theory for thin disks by Gammie (2001). Our goal is to determine how accurately a locally defined alpha can characterize mass and angular momentum transport by GIs in disks. Cases are considered both with cooling by an imposed constant global cooling time (Mejia et al. 2005) and with realistic radiative transfer (Boley et al. 2007). Grid spacing in the azimuthal direction is varied to investigate how the computed alpha is affected by numerical resolution. The azimuthal direction is particularly important, because higher resolution in azimuth allows GI power to spread to higher-order (multi-armed) modes that behave more locally. We find that, in many important respects, the transport of mass and angular momentum by GIs is an intrinsically global phenomenon. Effective alphas are variable on a dynamic time scale over global spatial scales. Nevertheless, preliminary results at the highest resolutions for an imposed cooling time show that our computed alphas, though systematically higher, tend on average to follow Gammie's prediction to within perhaps a factor of two. Our computed alphas include only gravitational stresses, while in Gammie's treatment the effective alpha is due equally to hydrodynamic (Reynolds) and gravitational stresses. So Gammie's prediction may significantly underestimate the true average stresses in a GI-active disk. Our effective alphas appear to be reasonably well converged for 256 and 512 azimuthal zones. We also have a high-resolution simulation under way to test the extent of radial mixing by GIs of gas and its entrained dust for comparison with Stardust observations. Results will be presented if available at the time of the meeting.

Development and deployment of a water-crop-nutrient simulation model embedded in a web application

NASA Astrophysics Data System (ADS)

Langella, Giuliano; Basile, Angelo; Coppola, Antonio; Manna, Piero; Orefice, Nadia; Terribile, Fabio

2016-04-01

It is long time by now that scientific research on environmental and agricultural issues spent large effort in the development and application of models for prediction and simulation in spatial and temporal domains. This is fulfilled by studying and observing natural processes (e.g. rainfall, water and chemicals transport in soils, crop growth) whose spatiotemporal behavior can be reproduced for instance to predict irrigation and fertilizer requirements and yield quantities/qualities. In this work a mechanistic model to simulate water flow and solute transport in the soil-plant-atmosphere continuum is presented. This desktop computer program was written according to the specific requirement of developing web applications. The model is capable to solve the following issues all together: (a) water balance and (b) solute transport; (c) crop modelling; (d) GIS-interoperability; (e) embedability in web-based geospatial Decision Support Systems (DSS); (f) adaptability at different scales of application; and (g) ease of code modification. We maintained the desktop characteristic in order to further develop (e.g. integrate novel features) and run the key program modules for testing and validation purporses, but we also developed a middleware component to allow the model run the simulations directly over the web, without software to be installed. The GIS capabilities allows the web application to make simulations in a user-defined region of interest (delimited over a geographical map) without the need to specify the proper combination of model parameters. It is possible since the geospatial database collects information on pedology, climate, crop parameters and soil hydraulic characteristics. Pedological attributes include the spatial distribution of key soil data such as soil profile horizons and texture. Further, hydrological parameters are selected according to the knowledge about the spatial distribution of soils. The availability and definition in the geospatial domain of these attributes allow the simulation outputs at a different spatial scale. Two different applications were implemented using the same framework but with different configurations of the software pieces making the physically based modelling chain: an irrigation tool simulating water requirements and their dates and a fertilization tool for optimizing in particular mineral nitrogen adds.

Mapping toxicology: Using ARC info to protect the public health

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morrison, M.

1998-07-01

There are many issues in dealing with the problems of the release of toxic chemicals into the environment. Those issues include determining the extent of the contamination, identifying possible receptor populations, and estimating the amount of exposure to those populations. Geographic Information Systems (GIS) are useful tools to address those issues. With a GIS, a professional can plot the location of areas of contamination, populations can be estimated by linking census data with the graphical data in the GIS or addresses of individuals can be plotted, sampling results can be used to create contours of exposure levels, and overlays ofmore » other relevant information (cancer rates) can all be done using a GIS. The spatial features in a GIS can be used with diffusion and other models to estimate spread of contamination from a source. This paper will use a case study method to demonstrate various aspects of using GIS to answer questions of public health relevance from the potential exposures to toxic substances in the environment. The case study will include a discussion of data identification and gathering, spatial analysis, and a full discussion of the possible conclusions that could be reached using the data in the GIS.« less

A 125 year history of topographic mapping and GIS in the U.S. Geological Survey 1884-2009, part 2: 1980-2009

USGS Publications Warehouse

Usery, E. Lynn; Varanka, Dalia; Finn, Michael P.

2009-01-01

The United States Geological Survey (USGS) entered the mainstream of developments in computer-assisted technology for mapping during the 1970s. The introduction by USGS of digital line graphs (DLGs), digital elevation models (DEMs), and land use data analysis (LUDA) nationwide land-cover data provided a base for the rapid expansion of the use of GIS in the 1980s. Whereas USGS had developed the topologically structured DLG data and the Geographic Information Retrieval and Analysis System (GIRAS) for land-cover data, the Map Overlay Statistical System (MOSS), a nontopologically structured GIS software package developed by Autometric, Inc., under contract to the U.S. Fish and Wildlife Service, dominated the use of GIS by federal agencies in the 1970s. Thus, USGS data was used in MOSS, but the topological structure, which later became a requirement for GIS vector datasets, was not used in early GIS applications. The introduction of Esri's ARC/INFO in 1982 changed that, and by the end of the 1980s, topological structure for vector data was essential, and ARC/INFO was the dominant GIS software package used by federal agencies.

Numerical simulation of the paleohydrology of glacial Lake Oshkosh, eastern Wisconsin, USA

USGS Publications Warehouse

Clark, J.A.; Befus, K.M.; Hooyer, T.S.; Stewart, P.W.; Shipman, T.D.; Gregory, C.T.; Zylstra, D.J.

2008-01-01

Proglacial lakes, formed during retreat of the Laurentide ice sheet, evolved quickly as outlets became ice-free and the earth deformed through glacial isostatic adjustment. With high-resolution digital elevation models (DEMs) and GIS methods, it is possible to reconstruct the evolution of surface hydrology. When a DEM deforms through time as predicted by our model of viscoelastic earth relaxation, the entire surface hydrologic system with its lakes, outlets, shorelines and rivers also evolves without requiring assumptions of outlet position. The method is applied to proglacial Lake Oshkosh in Wisconsin (13,600 to 12,900??cal yr BP). Comparison of predicted to observed shoreline tilt indicates the ice sheet was about 400??m thick over the Great Lakes region. During ice sheet recession, each of the five outlets are predicted to uplift more than 100??m and then subside approximately 30??m. At its maximum extent, Lake Oshkosh covered 6600??km2 with a volume of 111??km3. Using the Hydrologic Engineering Center-River Analysis System model, flow velocities during glacial outburst floods up to 9??m/s and peak discharge of 140,000??m3/s are predicted, which could drain 33.5??km3 of lake water in 10??days and transport boulders up to 3??m in diameter. ?? 2007 University of Washington.

Construction of a Distributed-network Digital Watershed Management System with B/S Techniques

NASA Astrophysics Data System (ADS)

Zhang, W. C.; Liu, Y. M.; Fang, J.

2017-07-01

Integrated watershed assessment tools for supporting land management and hydrologic research are becoming established tools in both basic and applied research. The core of these tools are mainly spatially distributed hydrologic models as they can provide a mechanism for investigating interactions among climate, topography, vegetation, and soil. However, the extensive data requirements and the difficult task of building input parameter files for driving these distributed models, have long been an obstacle to the timely and cost-effective use of such complex models by watershed managers and policy-makers. Recently, a web based geographic information system (GIS) tool to facilitate this process has been developed for a large watersheds of Jinghe and Weihe catchments located in the loess plateau of the Huanghe River basin in north-western China. A web-based GIS provides the framework within which spatially distributed data are collected and used to prepare model input files of these two watersheds and evaluate model results as well as to provide the various clients for watershed information inquiring, visualizing and assessment analysis. This Web-based Automated Geospatial Watershed Assessment GIS (WAGWA-GIS) tool uses widely available standardized spatial datasets that can be obtained via the internet oracle databank designed with association of Map Guide platform to develop input parameter files for online simulation at different spatial and temporal scales with Xing’anjiang and TOPMODEL that integrated with web-based digital watershed. WAGWA-GIS automates the process of transforming both digital data including remote sensing data, DEM, Land use/cover, soil digital maps and meteorological and hydrological station geo-location digital maps and text files containing meteorological and hydrological data obtained from stations of the watershed into hydrological models for online simulation and geo-spatial analysis and provides a visualization tool to help the user interpret results. The utility of WAGWA-GIS in jointing hydrologic and ecological investigations has been demonstrated on such diverse landscapes as Jinhe and Weihe watersheds, and will be extended to be utilized in the other watersheds in China step by step in coming years

Using GIS to Estimate Lake Volume from Limited Data

EPA Science Inventory

Estimates of lake volume are necessary for estimating residence time or modeling pollutants. Modern GIS methods for calculating lake volume improve upon more dated technologies (e.g. planimeters) and do not require potentially inaccurate assumptions (e.g. volume of a frustum of ...

When Theory Meets Data: Comparing Model Predictions Of Hillslope Sediment Size With Field Measurements.

NASA Astrophysics Data System (ADS)

Mahmoudi, M.; Sklar, L. S.; Leclere, S.; Davis, J. D.; Stine, A.

2017-12-01

The size distributions of sediment produced on hillslopes and supplied to river channels influence a wide range of fluvial processes, from bedrock river incision to the creation of aquatic habitats. However, the factors that control hillslope sediment size are poorly understood, limiting our ability to predict sediment size and model the evolution of sediment size distributions across landscapes. Recently separate field and theoretical investigations have begun to address this knowledge gap. Here we compare the predictions of several emerging modeling approaches to landscapes where high quality field data are available. Our goals are to explore the sensitivity and applicability of the theoretical models in each field context, and ultimately to provide a foundation for incorporating hillslope sediment size into models of landscape evolution. The field data include published measurements of hillslope sediment size from the Kohala peninsula on the island of Hawaii and tributaries to the Feather River in the northern Sierra Nevada mountains of California, and an unpublished data set from the Inyo Creek catchment of the southern Sierra Nevada. These data are compared to predictions adapted from recently published modeling approaches that include elements of topography, geology, structure, climate and erosion rate. Predictive models for each site are built in ArcGIS using field condition datasets: DEM topography (slope, aspect, curvature), bedrock geology (lithology, mineralogy), structure (fault location, fracture density), climate data (mean annual precipitation and temperature), and estimates of erosion rates. Preliminary analysis suggests that models may be finely tuned to the calibration sites, particularly when field conditions most closely satisfy model assumptions, leading to unrealistic predictions from extrapolation. We suggest a path forward for developing a computationally tractable method for incorporating spatial variation in production of hillslope sediment size distributions in landscape evolution models. Overall, this work highlights the need for additional field data sets as well as improved theoretical models, but also demonstrates progress in predicting the size distribution of sediments produced on hillslopes and supplied to channels.

GIS as a tool for efficient management of transport streams

NASA Astrophysics Data System (ADS)

Zatserkovnyi, V. I.; Kobrin, O. V.

2015-10-01

The transport network, which is an ideal object for the automation and the increase of efficiency using geographic information systems (GIS), is considered. The transport problems, which have a lot of mathematical models of the traffic flow for their solution, are enumerated. GIS analysis tools that allow one to build optimal routes in the real road network with its capabilities and limitations are presented. They can solve the extremely important problem of modern Ukraine - the rapid increase of the number of cars and the glut of road network vehicles. The intelligent transport systems, which are created and developed on the basis of GPS, GIS, modern communications and telecommunications facilities, are considered.

Rethinking GIS Towards The Vision Of Smart Cities Through CityGML

NASA Astrophysics Data System (ADS)

Guney, C.

2016-10-01

Smart cities present a substantial growth opportunity in the coming years. The role of GIS in the smart city ecosystem is to integrate different data acquired by sensors in real time and provide better decisions, more efficiency and improved collaboration. Semantically enriched vision of GIS will help evolve smart cities into tomorrow's much smarter cities since geospatial/location data and applications may be recognized as a key ingredient of smart city vision. However, it is need for the Geospatial Information communities to debate on "Is 3D Web and mobile GIS technology ready for smart cities?" This research places an emphasis on the challenges of virtual 3D city models on the road to smarter cities.

Using game engine for 3D terrain visualisation of GIS data: A review

NASA Astrophysics Data System (ADS)

Che Mat, Ruzinoor; Shariff, Abdul Rashid Mohammed; Nasir Zulkifli, Abdul; Shafry Mohd Rahim, Mohd; Hafiz Mahayudin, Mohd

2014-06-01

This paper reviews on the 3D terrain visualisation of GIS data using game engines that are available in the market as well as open source. 3D terrain visualisation is a technique used to visualise terrain information from GIS data such as a digital elevation model (DEM), triangular irregular network (TIN) and contour. Much research has been conducted to transform the 2D view of map to 3D. There are several terrain visualisation softwares that are available for free, which include Cesium, Hftool and Landserf. This review paper will help interested users to better understand the current state of art in 3D terrain visualisation of GIS data using game engines.

Requirements and principles for the implementation and construction of large-scale geographic information systems

NASA Technical Reports Server (NTRS)

Smith, Terence R.; Menon, Sudhakar; Star, Jeffrey L.; Estes, John E.

1987-01-01

This paper provides a brief survey of the history, structure and functions of 'traditional' geographic information systems (GIS), and then suggests a set of requirements that large-scale GIS should satisfy, together with a set of principles for their satisfaction. These principles, which include the systematic application of techniques from several subfields of computer science to the design and implementation of GIS and the integration of techniques from computer vision and image processing into standard GIS technology, are discussed in some detail. In particular, the paper provides a detailed discussion of questions relating to appropriate data models, data structures and computational procedures for the efficient storage, retrieval and analysis of spatially-indexed data.

Hillslope chemical weathering across Paraná, Brazil: a data mining-GIS hybrid approach

USGS Publications Warehouse

Iwashita, Fabio; Friedel, Michael J.; Filho, Carlos Roberto de Souza; Fraser, Stephen J.

2011-01-01

Self-organizing map (SOM) and geographic information system (GIS) models were used to investigate the nonlinear relationships associated with geochemical weathering processes at local (~100 km2) and regional (~50,000 km2) scales. The data set consisted of 1) 22 B-horizon soil variables: P, C, pH, Al, total acidity, Ca, Mg, K, total cation exchange capacity, sum of exchangeable bases, base saturation, Cu, Zn, Fe, B, S, Mn, gammaspectrometry (total count, potassium, thorium, and uranium) and magnetic susceptibility measures; and 2) six topographic variables: elevation, slope, aspect, hydrological accumulated flux, horizontal curvature and vertical curvature. It is characterized at 304 locations from a quasi-regular grid spaced about 24 km across the state of Paraná. This data base was split into two subsets: one for analysis and modeling (274 samples) and the other for validation (30 samples) purposes. The self-organizing map and clustering methods were used to identify and classify the relations among solid-phase chemical element concentrations and GIS derived topographic models. The correlation between elevation and k-means clusters related the relative position inside hydrologic macro basins, which was interpreted as an expression of the weathering process reaching a steady-state condition at the regional scale. Locally, the chemical element concentrations were related to the vertical curvature representing concave–convex hillslope features, where concave hillslopes with convergent flux tends to be a reducing environment and convex hillslopes with divergent flux, oxidizing environments. Stochastic cross validation demonstrated that the SOM produced unbiased classifications and quantified the relative amount of uncertainty in predictions. This work strengthens the hypothesis that, at B-horizon steady-state conditions, the terrain morphometry were linked with the soil geochemical weathering in a two-way dependent process: the topographic relief was a factor on environmental geochemistry while chemical weathering was for terrain feature delineation.

Hillslope chemical weathering across Paraná, Brazil: A data mining-GIS hybrid approach

NASA Astrophysics Data System (ADS)

Iwashita, Fabio; Friedel, Michael J.; Filho, Carlos Roberto de Souza; Fraser, Stephen J.

2011-09-01

Self-organizing map (SOM) and geographic information system (GIS) models were used to investigate the nonlinear relationships associated with geochemical weathering processes at local (~100 km 2) and regional (~50,000 km 2) scales. The data set consisted of 1) 22 B-horizon soil variables: P, C, pH, Al, total acidity, Ca, Mg, K, total cation exchange capacity, sum of exchangeable bases, base saturation, Cu, Zn, Fe, B, S, Mn, gammaspectrometry (total count, potassium, thorium, and uranium) and magnetic susceptibility measures; and 2) six topographic variables: elevation, slope, aspect, hydrological accumulated flux, horizontal curvature and vertical curvature. It is characterized at 304 locations from a quasi-regular grid spaced about 24 km across the state of Paraná. This data base was split into two subsets: one for analysis and modeling (274 samples) and the other for validation (30 samples) purposes. The self-organizing map and clustering methods were used to identify and classify the relations among solid-phase chemical element concentrations and GIS derived topographic models. The correlation between elevation and k-means clusters related the relative position inside hydrologic macro basins, which was interpreted as an expression of the weathering process reaching a steady-state condition at the regional scale. Locally, the chemical element concentrations were related to the vertical curvature representing concave-convex hillslope features, where concave hillslopes with convergent flux tends to be a reducing environment and convex hillslopes with divergent flux, oxidizing environments. Stochastic cross validation demonstrated that the SOM produced unbiased classifications and quantified the relative amount of uncertainty in predictions. This work strengthens the hypothesis that, at B-horizon steady-state conditions, the terrain morphometry were linked with the soil geochemical weathering in a two-way dependent process: the topographic relief was a factor on environmental geochemistry while chemical weathering was for terrain feature delineation.

Global land ice measurements from space (GLIMS): remote sensing and GIS investigations of the Earth's cryosphere

USGS Publications Warehouse

Bishop, Michael P.; Olsenholler, Jeffrey A.; Shroder, John F.; Barry, Roger G.; Rasup, Bruce H.; Bush, Andrew B. G.; Copland, Luke; Dwyer, John L.; Fountain, Andrew G.; Haeberli, Wilfried; Kääb, Andreas; Paul, Frank; Hall, Dorothy K.; Kargel, Jeffrey S.; Molnia, Bruce F.; Trabant, Dennis C.; Wessels, Rick L.

2004-01-01

Concerns over greenhouse‐gas forcing and global temperatures have initiated research into understanding climate forcing and associated Earth‐system responses. A significant component is the Earth's cryosphere, as glacier‐related, feedback mechanisms govern atmospheric, hydrospheric and lithospheric response. Predicting the human and natural dimensions of climate‐induced environmental change requires global, regional and local information about ice‐mass distribution, volumes, and fluctuations. The Global Land‐Ice Measurements from Space (GLIMS) project is specifically designed to produce and augment baseline information to facilitate glacier‐change studies. This requires addressing numerous issues, including the generation of topographic information, anisotropic‐reflectance correction of satellite imagery, data fusion and spatial analysis, and GIS‐based modeling. Field and satellite investigations indicate that many small glaciers and glaciers in temperate regions are downwasting and retreating, although detailed mapping and assessment are still required to ascertain regional and global patterns of ice‐mass variations. Such remote sensing/GIS studies, coupled with field investigations, are vital for producing baseline information on glacier changes, and improving our understanding of the complex linkages between atmospheric, lithospheric, and glaciological processes.

Prediction of groundwater inrush into coal mines from aquifers underlying the coal seams in China: application of vulnerability index method to Zhangcun Coal Mine, China

NASA Astrophysics Data System (ADS)

Wu, Qiang; Zhou, Wanfang; Wang, Jinhua; Xie, Shuhan

2009-05-01

Groundwater inrush is a geohazard that can significantly impact safe operations of the coal mines in China. Its occurrence is controlled by many factors and processes are often not amenable to mathematical expressions. To evaluate the water inrush risk, Professor Wu and his colleagues have proposed the vulnerability index approach by coupling the artificial neural network (ANN) and geographic information system (GIS). The detailed procedures of using this innovative approach are shown in a case study. Firstly, the powerful spatial data analysis functions of GIS was used to establish the thematic layer of each of the main factors that control the water inrush, and then to choose the training sample on the thematic layer with the ANN-BP Arithmetic. Secondly, the ANN evaluation model of the water inrush was established to determine the threshold value for each risk level with a histogram of the water inrush vulnerability index. As a result, the mine area was divided into four regions with different vulnerability levels and they served as the general guidelines for the mine operations.

Assessment method for epithermal gold deposits in Northeast Washington State using weights-of-evidence GIS modeling

USGS Publications Warehouse

Boleneus, D.E.; Raines, G.L.; Causey, J.D.; Bookstrom, A.A.; Frost, T.P.; Hyndman, P.C.

2001-01-01

The weights-of-evidence analysis, a quantitative mineral resource mapping tool, is used to delineate favorable areas for epithermal gold deposits and to predict future exploration activity of the mineral industry for similar deposits in a four-county area (222 x 277 km), including the Okanogan and Colville National Forests of northeastern Washington. Modeling is applied in six steps: (1) building a spatial digital database, (2) extracting predictive evidence for a particular deposit, based on an exploration model, (3) calculating relative weights for each predictive map, (4) combining the geologic evidence maps to predict the location of undiscovered mineral resources and (5) measuring the intensity of recent exploration activity by use of mining claims on federal lands, and (6) combining mineral resource and exploration activity into an assessment model of future mining activity. The analysis is accomplished on a personal computer using ArcView GIS platform with Spatial Analyst and Weights-of-Evidence software. In accord with the descriptive model for epithermal gold deposits, digital geologic evidential themes assembled include lithologic map units, thrust faults, normal faults, and igneous dikes. Similarly, geochemical evidential themes include placer gold deposits and gold and silver analyses from stream sediment (silt) samples from National Forest lands. Fifty mines, prospects, or occurrences of epithermal gold deposits, the training set, define the appropriate a really-associated terrane. The areal (or spatial) correlation of each evidential theme with the training set yield predictor theme maps for lithology, placer sites and normal faults. The weights-of-evidence analysis disqualified the thrust fault, dike, and gold and silver silt analyses evidential themes because they lacked spatial correlation with the training set. The decision to accept or reject evidential themes as predictors is assisted by considering probabilistic data consisting of weights and contrast values calculated for themes according to areal correlation with the training sites. Predictor themes having acceptable weights and contrast values are combined into a preliminary model to predict the locations of undiscovered epithermal gold deposits. This model facilitates ranking of tracts as non-permissive, permissive or favorable categories based on exclusionary, passive, and active criteria through evaluation of probabilistic data provided by interaction of predictor themes. The method is very similar to the visual inspection method of drawing conclusions from anomalies on a manually overlain system of maps. This method serves as a model for future mineral assessment procedures because of its objective nature. To develop a model to predict future exploration activity, the locations of lode mining claims were summarized for 1980, 1985, 1990, and 1996. Land parcels containing historic claims were identified either as those with mining claims present in 1980 or valid claims present in 1985. Current claim parcels were identified as those containing valid lode claims in either 1990 or 1996. A consistent parcel contains both historic and current claims. The epithermal gold and mining claim activity models were combined into an assessment (or mineral resource-activity) model to assist in land use decisions by providing a prediction of mineral exploration activity on federal land in the next decade. Ranks in the assessment model are: (1) no activity, (2) low activity, (3) low to moderate activity, (4) moderate activity and (5) high activity.

Topographic models for predicting malaria vector breeding habitats: potential tools for vector control managers.

PubMed

Nmor, Jephtha C; Sunahara, Toshihiko; Goto, Kensuke; Futami, Kyoko; Sonye, George; Akweywa, Peter; Dida, Gabriel; Minakawa, Noboru

2013-01-16

Identification of malaria vector breeding sites can enhance control activities. Although associations between malaria vector breeding sites and topography are well recognized, practical models that predict breeding sites from topographic information are lacking. We used topographic variables derived from remotely sensed Digital Elevation Models (DEMs) to model the breeding sites of malaria vectors. We further compared the predictive strength of two different DEMs and evaluated the predictability of various habitat types inhabited by Anopheles larvae. Using GIS techniques, topographic variables were extracted from two DEMs: 1) Shuttle Radar Topography Mission 3 (SRTM3, 90-m resolution) and 2) the Advanced Spaceborne Thermal Emission Reflection Radiometer Global DEM (ASTER, 30-m resolution). We used data on breeding sites from an extensive field survey conducted on an island in western Kenya in 2006. Topographic variables were extracted for 826 breeding sites and for 4520 negative points that were randomly assigned. Logistic regression modelling was applied to characterize topographic features of the malaria vector breeding sites and predict their locations. Model accuracy was evaluated using the area under the receiver operating characteristics curve (AUC). All topographic variables derived from both DEMs were significantly correlated with breeding habitats except for the aspect of SRTM. The magnitude and direction of correlation for each variable were similar in the two DEMs. Multivariate models for SRTM and ASTER showed similar levels of fit indicated by Akaike information criterion (3959.3 and 3972.7, respectively), though the former was slightly better than the latter. The accuracy of prediction indicated by AUC was also similar in SRTM (0.758) and ASTER (0.755) in the training site. In the testing site, both SRTM and ASTER models showed higher AUC in the testing sites than in the training site (0.829 and 0.799, respectively). The predictability of habitat types varied. Drains, foot-prints, puddles and swamp habitat types were most predictable. Both SRTM and ASTER models had similar predictive potentials, which were sufficiently accurate to predict vector habitats. The free availability of these DEMs suggests that topographic predictive models could be widely used by vector control managers in Africa to complement malaria control strategies.

How effective is albedo modification (solar radiation management geoengineering) in preventing sea-level rise from the Greenland Ice Sheet?

NASA Astrophysics Data System (ADS)

Applegate, Patrick J.; Keller, Klaus

2015-08-01

Albedo modification (AM) is sometimes characterized as a potential means of avoiding climate threshold responses, including large-scale ice sheet mass loss. Previous work has investigated the effects of AM on total sea-level rise over the present century, as well as AM’s ability to reduce long-term (≫103 yr) contributions to sea-level rise from the Greenland Ice Sheet (GIS). These studies have broken new ground, but neglect important feedbacks in the GIS system, or are silent on AM’s effectiveness over the short time scales that may be most relevant for decision-making (<103 yr). Here, we assess AM’s ability to reduce GIS sea-level contributions over decades to centuries, using a simplified ice sheet model. We drive this model using a business-as-usual base temperature forcing scenario, as well as scenarios that reflect AM-induced temperature stabilization or temperature drawdown. Our model results suggest that (i) AM produces substantial near-term reductions in the rate of GIS-driven sea-level rise. However, (ii) sea-level rise contributions from the GIS continue after AM begins. These continued sea level rise contributions persist for decades to centuries after temperature stabilization and temperature drawdown begin, unless AM begins in the next few decades. Moreover, (iii) any regrowth of the GIS is delayed by decades or centuries after temperature drawdown begins, and is slow compared to pre-AM rates of mass loss. Combined with recent work that suggests AM would not prevent mass loss from the West Antarctic Ice Sheet, our results provide a nuanced picture of AM’s possible effects on future sea-level rise.

Rainfall-induced landslide vulnerability Assessment in urban area reflecting Urban structure and building characteristics

NASA Astrophysics Data System (ADS)

Park, C.; Cho, M.; Lee, D.

2017-12-01

Landslide vulnerability assessment methodology of urban area is proposed with urban structure and building charateristics which can consider total damage cost of climate impacts. We used probabilistic analysis method for modeling rainfall-induced shallow landslide susceptibility by slope stability analysis and Monte Carlo simulations. And We combined debris flows with considering spatial movements under topographical condition and built environmental condition. Urban vulnerability of landslide is assessed by two categories: physical demages and urban structure aspect. Physical vulnerability is related to buildings, road, other ubran infra. Urban structure vulnerability is considered a function of the socio-economic factors, trigger factor of secondary damage, and preparedness level of the local government. An index-based model is developed to evaluate the life and indirect damage under landslide as well as the resilience ability against disasters. The analysis was performed in a geographic information system (GIS) environment because GIS can deal efficiently with a large volume of spatial data. The results of the landslide susceptibility assessment were compared with the landslide inventory, and the proposed approach demonstrated good predictive performance. The general trend found in this study indicates that the higher population density areas under a weaker fiscal condition that are located at the downstream of mountainous areas are more vulnerable than the areas in opposite conditions.

Topographic context of glaciers and perennial snowfields, Glacier National Park, Montana

NASA Astrophysics Data System (ADS)

Allen, Thomas R.

1998-01-01

Equilibrium-line altitudes (ELAs) of modem glaciers in the northern Rocky Mountains are known to correspond with regional climate, but strong subregional gradients such as across the Continental Divide in Glacier National Park, Montana, also exert topoclimatic influences on the ELA. This study analyzed the relationships between glacier and snowfield morphology, ELA, and surrounding topography. Glaciers and perennial snowfields were mapped using multitemporal satellite data from the Landsat Thematic Mapper and aerial photography within an integrated Geographic Information System (GIS). Relationships between glacier morphology and ELA were investigated using discriminant analysis. Four morphological categories of perennial snow and ice patches were identified: cirque glacier, niche glacier, ice cap, and snowfield. ELA was derived from overlaid glacier boundaries and Digital Elevation Models (DEMs) within the GIs. DEMs provided topographic variables and models of solar radiation and wind exposure/shelteredness. Regression analysis showed the effects of exposure; on snow accumulation, the strong influence of local topography through upslope zone morphology such as cirque backwalls, and the tendency for glaciers with high ELAs to exhibit compactness in morphology. Results highlight the relatively compact shape and larger area of glaciers adjacent to the Continental Divide. Discriminant analysis correctly predicted the type of glacier morphology in more than half the observations using factored variables of glacier shape, elevation range, and upslope area.

Estimation of River Discharge at Ungauged Catchment using GIS Map Correlation Method as Applied in Sta. Lucia River in Mauban, Quezon, Philippines

NASA Astrophysics Data System (ADS)

Monjardin, Cris Edward F.; Uy, Francis Aldrine A.; Tan, Fibor J.

2017-06-01

This paper presents use of GIS Map Correlation Method, a novel method of Prediction of Ungauged Basin, which is used to estimate the river flow at an ungauged catchment. The PUB Method used here intends to reduce the time and costs of data gathering procedure since it will just rely on a reference calibrated watershed that has almost the same characteristics in terms of slope, curve number, land cover, climatic condition, and average basin elevation. Furthermore, this utilized a set of modelling software which used digital elevation models (DEM), rainfall and discharge data. The researchers estimated the river flow of Sta. Lucia River in Quezon province, which is the ungauged catchment. The researchers assessed 11 gauged catchments and determined which basin could be correlated to Sta. Lucia. After finding the most correlated basin, the researchers used the data considering adjusted parameters of the gauged catchment. In evaluating the accuracy of the method, the researchers simulated a rainfall event in the said catchment and compared the actual discharge and the generated discharge from HEC-HMS. The researchers found out that method showed a good fit in the compared results, proving GMC Method is effective for use in the calibration of ungauged catchments.

Predicting the impacts of bioenergy production on farmland birds.

PubMed

Rivas Casado, Monica; Mead, Andrew; Burgess, Paul J; Howard, David C; Butler, Simon J

2014-04-01

Meeting European renewable energy production targets is expected to cause significant changes in land use patterns. With an EU target of obtaining 20% of energy consumption from renewable sources by 2020, national and local policy makers need guidance on the impact of potential delivery strategies on ecosystem goods and services to ensure the targets are met in a sustainable manner. Within agroecosystems, models are available to explore consequences of such policy decisions for food, fuel and fibre production but few can describe the effect on biodiversity. This paper describes the integration and application of a farmland bird population model within a geographical information system (GIS) to explore the consequences of land use changes arising from different strategies to meet renewable energy production targets. Within a 16,000 ha arable dominated case study area in England, the population growth rates of 19 farmland bird species were predicted under baseline land cover, a scenario maximising wheat production for bioethanol, and a scenario focused on mix of bioenergy sources. Both scenarios delivered renewable energy production targets for the region (>12 kWh per person per day) but, despite differences in resultant landscape composition, the response of the farmland bird community as a whole to each scenario was small and broadly similar. However, this similarity in overall response masked significant intra- and inter-specific variations across the study area and between scenarios suggesting contrasting mechanisms of impact and highlighting the need for context dependent, species-level assessment of land use change impacts. This framework provides one of the first systematic attempts to spatially model the effect of policy driven land use change on the population dynamics of a suite of farmland birds. The GIS framework also facilitates its integration with other ecosystem service models to explore wider synergies and trade offs arising from national or local policy interventions. Copyright © 2013 Elsevier B.V. All rights reserved.

The FORWARD Project: Incorporating Long-Term Hydrologic Datasets Into Detailed Forest Management Plans for the Canadian Boreal Forest

NASA Astrophysics Data System (ADS)

Dinsmore, P.; Prepas, E.; Putz, G.; Smith, D.

2008-12-01

The Forest Watershed and Riparian Disturbance (FORWARD) Project has collected data on weather, soils, vegetation, streamflow and stream water quality under relatively undisturbed conditions, as well as after experimental forest harvest, in partnership with industrial forest operations within the Boreal Plain and Boreal Shield ecozones of Canada. Research-based contributions from FORWARD were integrated into our Boreal Plain industry partner's 2007-2016 Detailed Forest Management Plan. These contributions consisted of three components: 1) A GIS watershed and stream layer that included a hydrological network, a Digital Elevation Model, and Strahler classified streams and watersheds for 1st- and 3rd-order watersheds; 2) a combined soil and wetland GIS layer that included maps and associated datasets for relatively coarse mineral soils (which drain quickly) and wetlands (which retain water), which were the key features that needed to be identified for the FORWARD modelling effort; and 3) a lookup table was developed that permits planners to determine runoff coefficients (the variable selected for hydrological modelling) for 1st-order watersheds, based upon slope, vegetation and soil attributes in forest polygons. The lookup table was populated with output from the deterministic Soil and Water Assessment Tool (SWAT), adapted for boreal forest vegetation with a version of the plant growth model, ALMANAC. The runoff coefficient lookup table facilitated integration of predictions of hydrologic impacts of forest harvest into planning. This pilot-scale effort will ultimately be extended to the Boreal Shield study area.

Perception Modelling of Visitors in Vargas Museum Using Agent-Based Simulation and Visibility Analysis

NASA Astrophysics Data System (ADS)

Carcellar, B. G., III

2017-10-01

Museum exhibit management is one of the usual undertakings of museum facilitators. Art works must be strategically placed to achieve maximum viewing from the visitors. The positioning of the artworks also highly influences the quality of experience of the visitors. One solution in such problems is to utilize GIS and Agent-Based Modelling (ABM). In ABM, persistent interacting objects are modelled as agents. These agents are given attributes and behaviors that describe their properties as well as their motion. In this study, ABM approach that incorporates GIS is utilized to perform analyticcal assessment on the placement of the artworks in the Vargas Museum. GIS serves as the backbone for the spatial aspect of the simulation such as the placement of the artwork exhibits, as well as possible obstructions to perception such as the columns, walls, and panel boards. Visibility Analysis is also done to the model in GIS to assess the overall visibility of the artworks. The ABM is done using the initial GIS outputs and GAMA, an open source ABM software. Visitors are modelled as agents, moving inside the museum following a specific decision tree. The simulation is done in three use cases: the 10 %, 20 %, and 30 % chance of having a visitor in the next minute. For the case of the said museum, the 10 % chance is determined to be the closest simulation case to the actual and the recommended minimum time to achieve a maximum artwork perception is 1 hour and 40 minutes. Initial assessment of the results shows that even after 3 hours of simulation, small parts of the exhibit show lack of viewers, due to its distance from the entrance. A more detailed decision tree for the visitor agents can be incorporated to have a more realistic simulation.

Development of an Integrated Moisture Index for predicting species composition

Treesearch

Louis R. Iverson; Charles T. Scott; Martin E. Dale; Anantha Prasad

1996-01-01

A geographic information system (GIS) approach was used to develop an Integrated Moisture Index (IMI), which was used to predict species composition for Ohio forests. Several landscape features (a slope-aspect shading index, cumulative flow of water downslope, curvature of the landscape, and the water-holding capacity of the soil) were derived from elevation and soils...

Risk of congenital anomalies around a municipal solid waste incinerator: a GIS-based case-control study

PubMed Central

Vinceti, Marco; Malagoli, Carlotta; Fabbi, Sara; Teggi, Sergio; Rodolfi, Rossella; Garavelli, Livia; Astolfi, Gianni; Rivieri, Francesca

2009-01-01

Background Waste incineration releases into the environment toxic substances having a teratogenic potential, but little epidemiologic evidence is available on this topic. We aimed at examining the relation between exposure to the emissions from a municipal solid waste incinerator and risk of birth defects in a northern Italy community, using Geographical Information System (GIS) data to estimate exposure and a population-based case-control study design. By modelling the incinerator emissions, we defined in the GIS three areas of increasing exposure according to predicted dioxins concentrations. We mapped the 228 births and induced abortions with diagnosis of congenital anomalies observed during the 1998–2006 period, together with a corresponding series of control births matched for year and hospital of birth/abortion as well as maternal age, using maternal address in the first three months of pregnancy to geocode cases and controls. Results Among women residing in the areas with medium and high exposure, prevalence of anomalies in the offspring was substantially comparable to that observed in the control population, nor dose-response relations for any of the major categories of birth defects emerged. Furthermore, odds ratio for congenital anomalies did not decrease during a prolonged shut-down period of the plant. Conclusion Overall, these findings do not lend support to the hypothesis that the environmental contamination occurring around an incineration plant such as that examined in this study may induce major teratogenic effects. PMID:19208225

Laharz_py: GIS tools for automated mapping of lahar inundation hazard zones

USGS Publications Warehouse

Schilling, Steve P.

2014-01-01

Laharz_py is written in the Python programming language as a suite of tools for use in ArcMap Geographic Information System (GIS). Primarily, Laharz_py is a computational model that uses statistical descriptions of areas inundated by past mass-flow events to forecast areas likely to be inundated by hypothetical future events. The forecasts use physically motivated and statistically calibrated power-law equations that each has a form A = cV2/3, relating mass-flow volume (V) to planimetric or cross-sectional areas (A) inundated by an average flow as it descends a given drainage. Calibration of the equations utilizes logarithmic transformation and linear regression to determine the best-fit values of c. The software uses values of V, an algorithm for idenitifying mass-flow source locations, and digital elevation models of topography to portray forecast hazard zones for lahars, debris flows, or rock avalanches on maps. Laharz_py offers two methods to construct areas of potential inundation for lahars: (1) Selection of a range of plausible V values results in a set of nested hazard zones showing areas likely to be inundated by a range of hypothetical flows; and (2) The user selects a single volume and a confidence interval for the prediction. In either case, Laharz_py calculates the mean expected A and B value from each user-selected value of V. However, for the second case, a single value of V yields two additional results representing the upper and lower values of the confidence interval of prediction. Calculation of these two bounding predictions require the statistically calibrated prediction equations, a user-specified level of confidence, and t-distribution statistics to calculate the standard error of regression, standard error of the mean, and standard error of prediction. The portrayal of results from these two methods on maps compares the range of inundation areas due to prediction uncertainties with uncertainties in selection of V values. The Open-File Report document contains an explanation of how to install and use the software. The Laharz_py software includes an example data set for Mount Rainier, Washington. The second part of the documentation describes how to use all of the Laharz_py tools in an example dataset at Mount Rainier, Washington.

On the long-term memory of the Greenland Ice Sheet

NASA Astrophysics Data System (ADS)

Rogozhina, I.; Martinec, Z.; Hagedoorn, J. M.; Thomas, M.; Fleming, K.

2011-03-01

In this study, the memory of the Greenland Ice Sheet (GIS) with respect to its past states is analyzed. According to ice core reconstructions, the present-day GIS reflects former climatic conditions dating back to at least 250 thousand years before the present (kyr BP). This fact must be considered when initializing an ice sheet model. The common initialization techniques are paleoclimatic simulations driven by atmospheric forcing inferred from ice core records and steady state simulations driven by the present-day or past climatic conditions. When paleoclimatic simulations are used, the information about the past climatic conditions is partly reflected in the resulting present-day state of the GIS. However, there are several important questions that need to be clarified. First, for how long does the model remember its initial state? Second, it is generally acknowledged that, prior to 100 kyr BP, the longest Greenland ice core record (GRIP) is distorted by ice-flow irregularities. The question arises as to what extent do the uncertainties inherent in the GRIP-based forcing influence the resulting GIS? Finally, how is the modeled thermodynamic state affected by the choice of initialization technique (paleo or steady state)? To answer these questions, a series of paleoclimatic and steady state simulations is carried out. We conclude that (1) the choice of an ice-covered initial configuration shortens the initialization simulation time to 100 kyr, (2) the uncertainties in the GRIP-based forcing affect present-day modeled ice-surface topographies and temperatures only slightly, and (3) the GIS forced by present-day climatic conditions is overall warmer than that resulting from a paleoclimatic simulation.

Possible contribution of ice-sheet/lithosphere interactions to past glaciological changes in Greenland

NASA Astrophysics Data System (ADS)

Alley, R. B.; Parizek, B. R.; Anandakrishnan, S.; Pollard, D.; Stevens, N. T.; Pourpoint, M.

2017-12-01

Ice-lithosphere interactions may have influenced the history of ice-sheet sensitivity to climate change. The Greenland ice sheet (GIS) is sensitive to warming, and is likely to be largely removed if subjected to relatively small additional temperature increases. The recent report (Schaefer et al., 2016, Nature) of near-complete GIS removal under modest Pleistocene forcing suggests that GIS sensitivity may be even greater than generally modeled, but lack of major Holocene retreat is more consistent with existing models. As shown by Stevens et al. (2016, JGR), peak lithospheric flexural stresses associated with ice-age GIS cycling are of the same order as dike-driving stresses in plutonic systems, and migrate over ice-age cycles. The full analysis by Stevens et al. suggests the possibility that the onset of cyclic ice-sheet loading allowed deep melt associated with the passage of the Icelandic hot spot beneath Greenland to work up though the crust to or near the base of the ice sheet, helping explain the anomalous geothermal heat fluxes observed at the head of the Northeast Greenland Ice Stream and elsewhere in the northern part of GIS. If ice-age cycling aided extraction of an existing reservoir of melted rock, then geothermal heat flux would have risen with the onset of extraction and migration, but with a subsequent fall associated with reservoir depletion. Simple parameterized flow-model simulations confirm intuition that a higher geothermal flux makes deglaciation easier, with the northern part of the ice sheet especially important. Large uncertainties remain in quantification, but we suggest the hypothesis that, following the onset of ice-age cycling, deglaciation of the GIS first became easier and then more difficult in response to feedbacks involving the ice sheet and the geological system beneath. In turn, this suggests that evidence of past deglaciation under moderate forcing is consistent with existing ice-sheet models.

Coupling urban growth scenarios with nearshore biophysical change models to inform coastal restoration planning in Puget Sound, Washington

NASA Astrophysics Data System (ADS)

Byrd, K. B.; Kreitler, J.; Labiosa, W.

2010-12-01

A scenario represents an account of a plausible future given logical assumptions about how conditions change over discrete bounds of space and time. Development of multiple scenarios provides a means to identify alternative directions of urban growth that account for a range of uncertainty in human behavior. Interactions between human and natural processes may be studied by coupling urban growth scenario outputs with biophysical change models; if growth scenarios encompass a sufficient range of alternative futures, scenario assumptions serve to constrain the uncertainty of biophysical models. Spatially explicit urban growth models (map-based) produce output such as distributions and densities of residential or commercial development in a GIS format that can serve as input to other models. Successful fusion of growth model outputs with other model inputs requires that both models strategically address questions of interest, incorporate ecological feedbacks, and minimize error. The U.S. Geological Survey (USGS) Puget Sound Ecosystem Portfolio Model (PSEPM) is a decision-support tool that supports land use and restoration planning in Puget Sound, Washington, a 35,500 sq. km region. The PSEPM couples future scenarios of urban growth with statistical, process-based and rule-based models of nearshore biophysical changes and ecosystem services. By using a multi-criteria approach, the PSEPM identifies cross-system and cumulative threats to the nearshore environment plus opportunities for conservation and restoration. Sub-models that predict changes in nearshore biophysical condition were developed and existing models were integrated to evaluate three growth scenarios: 1) Status Quo, 2) Managed Growth, and 3) Unconstrained Growth. These decadal scenarios were developed and projected out to 2060 at Oregon State University using the GIS-based ENVISION model. Given land management decisions and policies under each growth scenario, the sub-models predicted changes in 1) fecal coliform in shellfish growing areas, 2) sediment supply to beaches, 3) State beach recreational visits, 4) eelgrass habitat suitability, 5) forage fish habitat suitability, and 6) nutrient loadings. In some cases thousands of shoreline units were evaluated with multiple predictive models, creating a need for streamlined and consistent database development and data processing. Model development over multiple disciplines demonstrated the challenge of merging data types from multiple sources that were inconsistent in spatial and temporal resolution, classification schemes, and topology. Misalignment of data in space and time created potential for error and misinterpretation of results. This effort revealed that the fusion of growth scenarios and biophysical models requires an up-front iterative adjustment of both scenarios and models so that growth model outputs provide the needed input data in the correct format. Successful design of data flow across models that includes feedbacks between human and ecological systems was found to enhance the use of the final data product for decision making.

Model of Higher GIS Education

ERIC Educational Resources Information Center

Jakab, Imrich; Ševcík, Michal; Grežo, Henrich

2017-01-01

The methods of geospatial data processing are being continually innovated, and universities that are focused on educating experts in Environmental Science should reflect this reality with an elaborate and purpose-built modernization of the education process, education content, as well as learning conditions. Geographic Information Systems (GIS)…

GIS Learning Objects: An Approach to Content Aggregation

ERIC Educational Resources Information Center

Govorov, Michael; Gienko, Gennady

2013-01-01

Content development and maintenance of geographic information systems (GIS) related courses, especially designed for distance and online delivery, could be a tedious task even for an experienced instructor. The paper outlines application of abstract instructional design techniques for modeling course structure and developing corresponding course…

Business models for implementing geospatial technologies in transportation decision-making : GIS-T symposium, April 8, 2009.

DOT National Transportation Integrated Search

2009-04-08

In 2005 and 2006, the Federal Highway Administration (FHWA) Office of Interstate and Border Planning (HEPI), along with several state transportation executives, conducted a series of site visits to transportation agencies and GIS vendors to identify ...

Estimating Lake Volume from Limited Data: A Simple GIS Approach

EPA Science Inventory

Lake volume provides key information for estimating residence time or modeling pollutants. Methods for calculating lake volume have relied on dated technologies (e.g. planimeters) or used potentially inaccurate assumptions (e.g. volume of a frustum of a cone). Modern GIS provid...

Integrating and Managing Bim in GIS, Software Review

NASA Astrophysics Data System (ADS)

El Meouche, R.; Rezoug, M.; Hijazi, I.

2013-08-01

Since the advent of Computer-Aided Design (CAD) and Geographical Information System (GIS) tools, project participants have been increasingly leveraging these tools throughout the different phases of a civil infrastructure project. In recent years the number of GIS software that provides tools to enable the integration of Building information in geo context has risen sharply. More and more GIS software are added tools for this purposes and other software projects are regularly extending these tools. However, each software has its different strength and weakness and its purpose of use. This paper provides a thorough review to investigate the software capabilities and clarify its purpose. For this study, Autodesk Revit 2012 i.e. BIM editor software was used to create BIMs. In the first step, three building models were created, the resulted models were converted to BIM format and then the software was used to integrate it. For the evaluation of the software, general characteristics was studied such as the user interface, what formats are supported (import/export), and the way building information are imported.

Comparison of regression coefficient and GIS-based methodologies for regional estimates of forest soil carbon stocks.

PubMed

Campbell, J Elliott; Moen, Jeremie C; Ney, Richard A; Schnoor, Jerald L

2008-03-01

Estimates of forest soil organic carbon (SOC) have applications in carbon science, soil quality studies, carbon sequestration technologies, and carbon trading. Forest SOC has been modeled using a regression coefficient methodology that applies mean SOC densities (mass/area) to broad forest regions. A higher resolution model is based on an approach that employs a geographic information system (GIS) with soil databases and satellite-derived landcover images. Despite this advancement, the regression approach remains the basis of current state and federal level greenhouse gas inventories. Both approaches are analyzed in detail for Wisconsin forest soils from 1983 to 2001, applying rigorous error-fixing algorithms to soil databases. Resulting SOC stock estimates are 20% larger when determined using the GIS method rather than the regression approach. Average annual rates of increase in SOC stocks are 3.6 and 1.0 million metric tons of carbon per year for the GIS and regression approaches respectively.

A geographic information system-based 3D city estate modeling and simulation system

NASA Astrophysics Data System (ADS)

Chong, Xiaoli; Li, Sha

2015-12-01

This paper introduces a 3D city simulation system which is based on geographic information system (GIS), covering all commercial housings of the city. A regional- scale, GIS-based approach is used to capture, describe, and track the geographical attributes of each house in the city. A sorting algorithm of "Benchmark + Parity Rate" is developed to cluster houses with similar spatial and construction attributes. This system is applicable for digital city modeling, city planning, housing evaluation, housing monitoring, and visualizing housing transaction. Finally, taking Jingtian area of Shenzhen as an example, the each unit of 35,997 houses in the area could be displayed, tagged, and easily tracked by the GIS-based city modeling and simulation system. The match market real conditions well and can be provided to house buyers as reference.

Using Geographic Information System-based Ecologic Niche Models to Forecast the Risk of Hantavirus Infection in Shandong Province, China

PubMed Central

Wei, Lan; Qian, Quan; Wang, Zhi-Qiang; Glass, Gregory E.; Song, Shao-Xia; Zhang, Wen-Yi; Li, Xiu-Jun; Yang, Hong; Wang, Xian-Jun; Fang, Li-Qun; Cao, Wu-Chun

2011-01-01

Hemorrhagic fever with renal syndrome (HFRS) is an important public health problem in Shandong Province, China. In this study, we combined ecologic niche modeling with geographic information systems (GIS) and remote sensing techniques to identify the risk factors and affected areas of hantavirus infections in rodent hosts. Land cover and elevation were found to be closely associated with the presence of hantavirus-infected rodent hosts. The averaged area under the receiver operating characteristic curve was 0.864, implying good performance. The predicted risk maps based on the model were validated both by the hantavirus-infected rodents' distribution and HFRS human case localities with a good fit. These findings have the applications for targeting control and prevention efforts. PMID:21363991

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

PubMed Central

Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

2015-01-01

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi’an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO2, and NO2, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors’ variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas. PMID:26426030

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS.

PubMed

Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

2015-09-29

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO₂, and NO₂, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

The Vertical Flux Method (VFM) for regional estimates of temporally and spatially varying nitrate fluxes in unsaturated zone and groundwater

NASA Astrophysics Data System (ADS)

Green, C. T.; Liao, L.; Nolan, B. T.; Juckem, P. F.; Ransom, K.; Harter, T.

2017-12-01

Process-based modeling of regional NO3- fluxes to groundwater is critical for understanding and managing water quality. Measurements of atmospheric tracers of groundwater age and dissolved-gas indicators of denitrification progress have potential to improve estimates of NO3- reactive transport processes. This presentation introduces a regionalized version of a vertical flux method (VFM) that uses simple mathematical estimates of advective-dispersive reactive transport with regularization procedures to calibrate estimated tracer concentrations to observed equivalents. The calibrated VFM provides estimates of chemical, hydrologic and reaction parameters (source concentration time series, recharge, effective porosity, dispersivity, reaction rate coefficients) and derived values (e.g. mean unsaturated zone travel time, eventual depth of the NO3- front) for individual wells. Statistical learning methods are used to extrapolate parameters and predictions from wells to continuous areas. The regional VFM was applied to 473 well samples in central-eastern Wisconsin. Chemical measurements included O2, NO3-, N2 from denitrification, and atmospheric tracers of groundwater age including carbon-14, chlorofluorocarbons, tritium, and triogiogenic helium. VFM results were consistent with observed chemistry, and calibrated parameters were in-line with independent estimates. Results indicated that (1) unsaturated zone travel times were a substantial portion of the transit time to wells and streams (2) fractions of N leached to groundwater have changed over time, with increasing fractions from manure and decreasing fractions from fertilizer, and (3) under current practices and conditions, 60% of the shallow aquifer will eventually be affected by NO3- contamination. Based on GIS coverages of variables related to soils, land use and hydrology, the VFM results at individual wells were extrapolated regionally using boosted regression trees, a statistical learning approach, that related the GIS variables to the VFM parameters and predictions. Future work will explore applications at larger scales with direct integration of the statistical prediction model with the mechanistic VFM.