Publications

@book{Petrasova2015,
title = {Tangible Modeling with Open Source GIS},
author = {Anna Petrasova and Brendan Harmon and Vaclav Petras and Helena Mitasova},
url = {http://www.springer.com/us/book/9783319257730},
doi = {10.1007/978-3-319-25775-4},
isbn = {978-3-319-25775-4},
year = {2015},
date = {2015-12-15},
publisher = {Springer International Publishing},
abstract = {This book presents a new type of modeling environment where users interact with geospatial simulations using 3D physical models of studied landscapes. Multiple users can alter the physical model by hand during scanning, thereby providing input for simulation of geophysical processes in this setting.

The authors have developed innovative techniques and software that couple this hardware with open source GRASS GIS, making the system instantly applicable to a wide range of modeling and design problems. Since no other literature on this topic is available, this Book fills a gap for this new technology that continues to grow.

Tangible Modeling with Open Source GIS will appeal to advanced-level students studying geospatial science, computer science and earth science such as landscape architecture and natural resources. It will also benefit researchers and professionals working in geospatial modeling applications, computer graphics, hazard risk management, hydrology, solar energy, coastal and fluvial flooding, fire spread, landscape, park design and computer games.},
keywords = {GIS, open source, Tangible Landscape},
pubstate = {published},
tppubtype = {book}
}

@inbook{Petras2015b,
title = {Mapping gradient fields of landform migration},
author = {Vaclav Petras and Helena Mitasova and Anna Petrasova},
editor = {Jaroslaw Jasiewicz and Zbigniew Zwolinski and Helena Mitasova and Tomislav Hengl},
url = {http://www.geomorphometry.org/Petras2015},
isbn = {978-83-7986-059-3},
year = {2015},
date = {2015-01-02},
pages = {173-176},
publisher = {Bogucki Wydawnictwo Naukowe, Adam Mickiewicz University in Poznań - Institute of Geoecology and Geoinformation},
address = {Poland},
abstract = {Geospatial analytics techniques describing changes of unstable landscapes provide critical information for hazard management and mitigation. We propose a method for quantifying horizontal migration of complex landforms based on the analysis of contour evolution. When applied to a set of elevations this technique provides comprehensive information on magnitude and direction of landform migration at any point in space and time. The method is based on the concept of space-time cube combined with GIS-based analysis applied to spatio-temporal surface. The result of the analysis is a vector field representing the movement and deformation of contours. We also present several approaches to visualization of these vector fields as space-time gradient lines, vectors or dynamic ”comets”. We demonstrate the method on a laboratory model and an elevation time series capturing evolution of a coastal sand dune.},
keywords = {geospatial analytics, GIS, gradient fields, landform migration},
pubstate = {published},
tppubtype = {inbook}
}

@article{Meentemeyer2011,
title = {Epidemiological modeling of invasion in heterogeneous landscapes: Spread of sudden oak death in California (1990-2030)},
author = {Ross K. Meentemeyer and Nik J. Cunniffe and Alex R. Cook and Joao A. Filipe and Richard D. Hunter and David M. Rizzo and Christopher A. Gilligan },
url = {http://dx.doi.org/10.1890/ES10-00192.1},
year = {2011},
date = {2011-02-16},
journal = {Ecosphere},
volume = {2},
number = {art17},
abstract = {The spread of emerging infectious diseases (EIDs) in natural environments poses substantial risks to biodiversity and ecosystem function. As EIDs and their impacts grow, landscape- to regional-scale models of disease dynamics are increasingly needed for quantitative prediction of epidemic outcomes and design of practicable strategies for control. Here we use spatio-temporal, stochastic epidemiological modeling in combination with realistic geographical modeling to predict the spread of the sudden oak death pathogen (Phytophthora ramorum) through heterogeneous host populations in wildland forests, subject to fluctuating weather conditions. The model considers three stochastic processes: (1) the production of inoculum at a given site; (2) the chance that inoculum is dispersed within and among sites; and (3) the probability of infection following transmission to susceptible host vegetation. We parameterized the model using Markov chain Monte Carlo (MCMC) estimation from snapshots of local- and regional-scale data on disease spread, taking account of landscape heterogeneity and the principal scales of spread. Our application of the model to Californian landscapes over a 40-year period (1990–2030), since the approximate time of pathogen introduction, revealed key parameters driving the spatial spread of disease and the magnitude of stochastic variability in epidemic outcomes. Results show that most disease spread occurs via local dispersal (<250 m) but infrequent long-distance dispersal events can substantially accelerate epidemic spread in regions with high host availability and suitable weather conditions. In the absence of extensive control, we predict a ten-fold increase in disease spread between 2010 and 2030 with most infection concentrated along the north coast between San Francisco and Oregon. Long-range dispersal of inoculum to susceptible host communities in the Sierra Nevada foothills and coastal southern California leads to little secondary infection due to lower host availability and less suitable weather conditions. However, a shift to wetter and milder conditions in future years would double the amount of disease spread in California through 2030. This research illustrates how stochastic epidemiological models can be applied to realistic geographies and used to increase predictive understanding of disease dynamics in large, heterogeneous regions.},
keywords = {computational biology, emerging infectious disease, GIS, landscape epidemiology, Phytophthora ramorum, spatial heterogeneity, species distribution model},
pubstate = {published},
tppubtype = {article}
}