@article{white_petrasova_petras_tateosian_vukomanovic_mitasova_meentemeyer_2023, title={An open-source platform for geospatial participatory modeling in the cloud}, volume={167}, ISSN={["1873-6726"]}, url={https://doi.org/10.1016/j.envsoft.2023.105767}, DOI={10.1016/j.envsoft.2023.105767}, abstractNote={Participatory modeling facilitates the co-production of knowledge and action by engaging stakeholders in research. However, the spatial dimensions of socio-environmental systems and decision-making are challenging to incorporate in participatory models, as developing interactive geospatial models requires specialized knowledge. Yet, many of society's most pressing and complex socio-environmental problems require participatory modeling that is geospatial. Existing interactive online applications have broadened the audiences who can engage with geospatial models, but often do not provide a robust framework for interactive model development. Here, we develop an open-source platform, OpenPlains, to address barriers to participation in geospatial modeling by enabling researchers to develop interactive models that remove barriers to data aggregation and user engagement. OpenPlains consists of six new open-source libraries: OpenPlains, django-actina, grass-js-client, react-openplains, react-ol, and openplains-cli. We demonstrate OpenPlains through two web applications that work anywhere in the contiguous United States: a spatial–temporal watershed analysis application and an urban growth forecasting application.}, journal={ENVIRONMENTAL MODELLING & SOFTWARE}, publisher={Elsevier BV}, author={White, Corey T. and Petrasova, Anna and Petras, Vaclav and Tateosian, Laura G. and Vukomanovic, Jelena and Mitasova, Helena and Meentemeyer, Ross K.}, year={2023}, month={Sep} }
 @article{haedrich_petras_petrasova_blumentrath_mitasova_2023, title={Integrating GRASS GIS and Jupyter Notebooks to facilitate advanced geospatial modeling education}, volume={27}, url={https://doi.org/10.1111/tgis.13031}, DOI={10.1111/tgis.13031}, abstractNote={AbstractOpen education materials are critical for the advancement of open science and the development of open‐source software. These accessible and transparent materials provide an important pathway for sharing both standard geospatial analysis workflows and advanced research methods. Computational notebooks allow users to share live code with in‐line visualizations and narrative text, making them a powerful interactive teaching tool for geospatial analytics. Specifically, Jupyter Notebooks are quickly becoming a standard format in open education. In this article, we introduce a new GRASS GIS package, grass.jupyter, that enhances the existing GRASS Python API to allow Jupyter Notebook users to easily manage and visualize GRASS data including spatiotemporal datasets. While there are many Python‐based geospatial libraries available for use in Jupyter Notebooks, GRASS GIS has extensive geospatial functionality including support for multi‐temporal analysis and dynamic simulations, making it a powerful teaching tool for advanced geospatial analytics. We discuss the development of grass.jupyter and demonstrate how the package facilitates teaching open‐source geospatial modeling with a collection of Jupyter Notebooks designed for a graduate‐level geospatial modeling course. The open education notebooks feature spatiotemporal data visualizations, hydrologic modeling, and spread simulations such as the spread of invasive species and urban growth.}, number={3}, journal={Transactions in GIS}, publisher={Wiley}, author={Haedrich, Caitlin and Petras, Vaclav and Petrasova, Anna and Blumentrath, Stefan and Mitasova, Helena}, year={2023}, month={May}, pages={686–702} }
 @article{petras_petrasova_mccarter_mitasova_meentemeyer_2023, title={Point Density Variations in Airborne Lidar Point Clouds}, volume={23}, ISSN={["1424-8220"]}, url={https://doi.org/10.3390/s23031593}, DOI={10.3390/s23031593}, abstractNote={In spite of increasing point density and accuracy, airborne lidar point clouds often exhibit point density variations. Some of these density variations indicate issues with point clouds, potentially leading to errors in derived products. To highlight these issues, we provide an overview of point density variations and show examples in six airborne lidar point cloud datasets that we used in our topographic and geospatial modeling research. Using the published literature, we identified sources of point density variations and issues indicated or caused by these variations. Lastly, we discuss the reduction in point density variations using decimations, homogenizations, and their applicability.}, number={3}, journal={SENSORS}, author={Petras, Vaclav and Petrasova, Anna and McCarter, James B. and Mitasova, Helena and Meentemeyer, Ross K.}, year={2023}, month={Feb} }
 @article{karlovska_petrasova_petras_landa_2023, title={Redesigning Graphical User Interface of Open-Source Geospatial Software in a Community-Driven Way: A Case Study of GRASS GIS}, volume={12}, ISSN={["2220-9964"]}, url={https://doi.org/10.3390/ijgi12090376}, DOI={10.3390/ijgi12090376}, abstractNote={Learning to use geographic information system (GIS) software effectively may be intimidating due to the extensive range of features it offers. The GRASS GIS software, in particular, presents additional challenges for first-time users in terms of its complex startup procedure and unique terminology associated with its data structure. On the other hand, a substantial part of the GRASS user community including us as developers recognized and embraced the advantages of the current approach. Given the controversial nature of the whole issue, we decided to actively involve regular users by conducting several formal surveys and by performing usability testing. Throughout this process, we discovered that resolving specific software issues through pure user-centered design is not always feasible, particularly in the context of open-source scientific software where the boundary between users and developers is very fuzzy. To address this challenge, we adopted the user-centered methodology tailored to the requirements of open-source scientific software development, which we refer to as community-driven design. This paper describes the community-driven redesigning process on the GRASS GIS case study and sets a foundation for applying community-driven design in other open-source scientific projects by providing insights into effective software development practices driven by the needs and input of the project’s community.}, number={9}, journal={ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION}, author={Karlovska, Linda and Petrasova, Anna and Petras, Vaclav and Landa, Martin}, year={2023}, month={Sep} }
 @article{montgomery_petras_takeuchi_katsar_2022, title={Contaminated consignment simulation to support risk-based inspection design}, volume={5}, ISSN={["1539-6924"]}, url={https://doi.org/10.1111/risa.13943}, DOI={10.1111/risa.13943}, abstractNote={AbstractInvasive nonnative plant pests can cause extensive environmental and economic damage and are very difficult to eradicate once established. Phytosanitary inspections that aim to prevent biological invasions by limiting movement of nonnative plant pests across borders are a critical component of the biosecurity continuum. Inspections can also provide valuable information about when and where plant pests are crossing national boundaries. However, only a limited portion of the massive volume of goods imported daily can be inspected, necessitating a highly targeted, risk‐based strategy. Furthermore, since inspections must prioritize detection and efficiency, their outcomes generally cannot be used to make inferences about risk for cargo pathways as a whole. Phytosanitary agencies need better tools for quantifying pests going undetected and designing risk‐based inspection strategies appropriate for changing operational conditions. In this research, we present PoPS (Pest or Pathogen Spread) Border, an open‐source consignment inspection simulator for measuring inspection outcomes under various cargo contamination scenarios to support recommendations for inspection protocols and estimate pest slippage rates. We used the tool to estimate contamination rates of historical interception data, quantify tradeoffs in effectiveness and workload for inspection strategies, and identify vulnerabilities in sampling protocols as changes in cargo configurations and contamination occur. These use cases demonstrate how this simulation approach permits testing inspection strategies and measuring quantities that would otherwise be impossible in a field‐based setting. This work represents the first steps toward a decision support tool for creating dynamic inspection protocols that respond to changes in available resources, workload, and commerce trends.}, journal={RISK ANALYSIS}, author={Montgomery, Kellyn and Petras, Vaclav and Takeuchi, Yu and Katsar, Catherine S.}, year={2022}, month={May} }
 @article{jones_skrip_seliger_jones_wakie_takeuchi_petras_petrasova_meentemeyer_2022, title={Spotted lanternfly predicted to establish in California by 2033 without preventative management}, volume={5}, ISSN={["2399-3642"]}, url={https://doi.org/10.1038/s42003-022-03447-0}, DOI={10.1038/s42003-022-03447-0}, abstractNote={AbstractModels that are both spatially and temporally dynamic are needed to forecast where and when non-native pests and pathogens are likely to spread, to provide advance information for natural resource managers. The potential US range of the invasive spotted lanternfly (SLF, Lycorma delicatula) has been modeled, but until now, when it could reach the West Coast’s multi-billion-dollar fruit industry has been unknown. We used process-based modeling to forecast the spread of SLF assuming no treatments to control populations occur. We found that SLF has a low probability of first reaching the grape-producing counties of California by 2027 and a high probability by 2033. Our study demonstrates the importance of spatio-temporal modeling for predicting the spread of invasive species to serve as an early alert for growers and other decision makers to prepare for impending risks of SLF invasion. It also provides a baseline for comparing future control options.}, number={1}, journal={COMMUNICATIONS BIOLOGY}, author={Jones, Chris and Skrip, Megan M. and Seliger, Benjamin J. and Jones, Shannon and Wakie, Tewodros and Takeuchi, Yu and Petras, Vaclav and Petrasova, Anna and Meentemeyer, Ross K.}, year={2022}, month={Jun} }
 @article{gaydos_jones_jones_millar_petras_petrasova_mitasova_meentemeyer_2021, title={Evaluating online and tangible interfaces for engaging stakeholders in forecasting and control of biological invasions}, volume={9}, ISSN={["1939-5582"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85115251448&partnerID=MN8TOARS}, DOI={10.1002/eap.2446}, abstractNote={AbstractEcological forecasts will be best suited to inform intervention strategies if they are accessible to a diversity of decision‐makers. Researchers are developing intuitive forecasting interfaces to guide stakeholders through the development of intervention strategies and visualization of results. Yet, few studies to date have evaluated how user interface design facilitates the coordinated, cross‐boundary management required for controlling biological invasions. We used a participatory approach to develop complementary tangible and online interfaces for collaboratively forecasting biological invasions and devising control strategies. A diverse group of stakeholders evaluated both systems in the real‐world context of controlling sudden oak death, an emerging forest disease killing millions of trees in California and Oregon. Our findings suggest that while both interfaces encouraged adaptive experimentation, tangible interfaces are particularly well suited to support collaborative decision‐making. Reflecting on the strengths of both systems, we suggest workbench‐style interfaces that support simultaneous interactions and dynamic geospatial visualizations.}, number={8}, journal={ECOLOGICAL APPLICATIONS}, publisher={Wiley}, author={Gaydos, Devon A. and Jones, Chris M. and Jones, Shannon K. and Millar, Garrett C. and Petras, Vaclav and Petrasova, Anna and Mitasova, Helena and Meentemeyer, Ross K.}, year={2021}, month={Sep} }
 @article{jones_jones_petrasova_petras_gaydos_skrip_takeuchi_bigsby_meentemeyer_2021, title={Iteratively forecasting biological invasions with PoPS and a little help from our friends}, volume={6}, ISSN={["1540-9309"]}, url={http://dx.doi.org/10.1002/fee.2357}, DOI={10.1002/fee.2357}, abstractNote={Ecological forecasting has vast potential to support environmental decision making with repeated, testable predictions across management‐relevant timescales and locations. Yet resource managers rarely use co‐designed forecasting systems or embed them in decision making. Although prediction of planned management outcomes is particularly important for biological invasions to optimize when and where resources should be allocated, spatial–temporal models of spread typically have not been openly shared, iteratively updated, or interactive to facilitate exploration of management actions. We describe a species‐agnostic, open‐source framework – called the Pest or Pathogen Spread (PoPS) Forecasting Platform – for co‐designing near‐term iterative forecasts of biological invasions. Two case studies are presented to demonstrate that iterative calibration yields higher forecast skill than using only the earliest‐available data to predict future spread. The PoPS framework is a primary example of an ecological forecasting system that has been both scientifically improved and optimized for real‐world decision making through sustained participation and use by management stakeholders.}, number={7}, journal={FRONTIERS IN ECOLOGY AND THE ENVIRONMENT}, publisher={Wiley}, author={Jones, Chris M. and Jones, Shannon and Petrasova, Anna and Petras, Vaclav and Gaydos, Devon and Skrip, Megan M. and Takeuchi, Yu and Bigsby, Kevin and Meentemeyer, Ross K.}, year={2021}, month={Jun} }
 @article{petras_mitasova_petrasova_2021, title={Open Source Software Development}, url={https://doi.org/10.22224/gistbok/2021.2.4}, DOI={10.22224/gistbok/2021.2.4}, journal={Geographic Information Science & Technology Body of Knowledge}, author={Petras, Vaclav and Mitasova, Helena and Petrasova, Anna}, year={2021}, month={Apr} }
 @article{petrasova_gaydos_petras_jones_mitasova_meentemeyer_2020, title={Geospatial simulation steering for adaptive management}, volume={133}, url={https://doi.org/10.1016/j.envsoft.2020.104801}, DOI={10.1016/j.envsoft.2020.104801}, abstractNote={Spatio-temporal simulations are becoming essential tools for decision makers when forecasting future conditions and evaluating effectiveness of alternative decision scenarios. However, lack of interactive steering capabilities limits the value of advanced stochastic simulations for research and practice. To address this gap we identified conceptual challenges associated with steering stochastic, spatio-temporal simulations and developed solutions that better represent the realities of decision-making by allowing both reactive and proactive, spatially-explicit interventions. We present our approach, in a participatory modeling case study engaging stakeholders in developing strategies to contain the spread of a tree disease in Oregon, USA. Using intuitive interfaces, implemented through web-based and tangible platforms, stakeholders explored management options as the simulation progressed. Spatio-temporal steering allowed them to combine currently used management practices into novel adaptive management strategies, which were previously difficult to test and assess, demonstrating the utility of interactive simulations for decision-making.}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Petrasova, Anna and Gaydos, Devon A. and Petras, Vaclav and Jones, Chris M. and Mitasova, Helena and Meentemeyer, Ross K.}, year={2020}, month={Nov}, pages={104801} }
 @article{berkel_shashidharan_mordecai_vatsavai_petrasova_petras_mitasova_vogler_meentemeyer_2019, title={Projecting Urbanization and Landscape Change at Large Scale Using the FUTURES Model}, volume={8}, url={https://doi.org/10.3390/land8100144}, DOI={10.3390/land8100144}, abstractNote={Increasing population and rural to urban migration are accelerating urbanization globally, permanently transforming natural systems over large extents. Modelling landscape change over large regions, however, presents particular challenges due to local-scale variations in social and environmental factors that drive land change. We simulated urban development across the South Atlantic States (SAS), a region experiencing rapid population growth and urbanization, using FUTURES—an open source land change model that uses demand for development, local development suitability factors, and a stochastic patch growing algorithm for projecting alternative futures of urban form and landscape change. New advances to the FUTURES modelling framework allow for high resolution projections over large spatial extents by leveraging parallel computing. We simulated the adoption of different urban growth strategies that encourage settlement densification in the SAS as alternatives to the region’s increasing sprawl. Evaluation of projected patterns indicate a 15% increase in urban lands by 2050 given a status quo development scenario compared to a 14.8% increase for the Infill strategy. Status quo development resulted in a 3.72% loss of total forests, 2.97% loss of highly suitable agricultural land, and 3.69% loss of ecologically significant lands. An alternative Infill scenario resulted in similar losses of total forest (3.62%) and ecologically significant lands (3.63%) yet consumed less agricultural lands (1.23% loss). Moreover, infill development patterns differed qualitatively from the status quo and resulted in less fragmentation of the landscape.}, number={10}, journal={Land}, publisher={MDPI AG}, author={Berkel, Derek Van and Shashidharan, Ashwin and Mordecai, Rua and Vatsavai, Raju and Petrasova, Anna and Petras, Vaclav and Mitasova, Helena and Vogler, John and Meentemeyer, Ross}, year={2019}, month={Sep}, pages={144} }
 @article{harmon_mitasova_petrasova_petras_2019, title={r.sim.terrain 1.0: a landscape evolution model with dynamic hydrology}, volume={12}, ISSN={["1991-9603"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85068763744&partnerID=MN8TOARS}, DOI={10.5194/gmd-12-2837-2019}, abstractNote={Abstract. While there are numerical landscape evolution models that simulate how steady-state flows of water and sediment reshape topography over long periods of time, r.sim.terrain is the first to simulate short-term topographic change for both steady-state and dynamic flow regimes across a range of spatial scales. This free and open-source Geographic Information Systems (GIS)-based topographic evolution model uses empirical models for soil erosion and a physics-based model for shallow overland water flow and soil erosion to compute short-term topographic change. This model uses either a steady-state or unsteady representation of overland flow to simulate how overland sediment mass flows reshape topography for a range of hydrologic soil erosion regimes based on topographic, land cover, soil, and rainfall parameters. As demonstrated by a case study for the Patterson Branch subwatershed on the Fort Bragg military installation in North Carolina, r.sim.terrain simulates the development of fine-scale morphological features including ephemeral gullies, rills, and hillslopes. Applications include land management, erosion control, landscape planning, and landscape restoration.}, number={7}, journal={GEOSCIENTIFIC MODEL DEVELOPMENT}, author={Harmon, Brendan Alexander and Mitasova, Helena and Petrasova, Anna and Petras, Vaclav}, year={2019}, month={Jul}, pages={2837–2854} }
 @article{harmon_petrasova_petras_mitasova_meentemeyer_2018, title={Tangible topographic modeling for landscape architects}, volume={16}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85044342339&partnerID=MN8TOARS}, DOI={10.1177/1478077117749959}, abstractNote={ We present Tangible Landscape—a technology for rapidly and intuitively designing landscapes informed by geospatial modeling, analysis, and simulation. It is a tangible interface powered by a geographic information system that gives three-dimensional spatial data an interactive, physical form so that users can naturally sense and shape it. Tangible Landscape couples a physical and a digital model of a landscape through a real-time cycle of physical manipulation, three-dimensional scanning, spatial computation, and projected feedback. Natural three-dimensional sketching and real-time analytical feedback should aid landscape architects in the design of high performance landscapes that account for physical and ecological processes. We conducted a series of studies to assess the effectiveness of tangible modeling for landscape architects. Landscape architecture students, academics, and professionals were given a series of fundamental landscape design tasks—topographic modeling, cut-and-fill analysis, and water flow modeling. We assessed their performance using qualitative and quantitative methods including interviews, raster statistics, morphometric analyses, and geospatial simulation. With tangible modeling, participants built more accurate models that better represented morphological features than they did with either digital or analog hand modeling. When tangibly modeling, they worked in a rapid, iterative process informed by real-time geospatial analytics and simulations. With the aid of real-time simulations, they were able to quickly understand and then manipulate how complex topography controls the flow of water. }, number={1}, journal={International Journal of Architectural Computing}, author={Harmon, B. A. and Petrasova, A. and Petras, Vaclav and Mitasova, Helena and Meentemeyer, R.}, year={2018}, pages={4–21} }
 @article{löwe_petras_neteler_mitasova_2018, title={g.citation: Scientific citation for individual GRASS GIS software modules}, volume={9}, url={https://doi.org/10.7287/peerj.preprints.27206v1}, DOI={10.7287/peerj.preprints.27206v1}, abstractNote={The authors introduce the GRASS GIS add-on module g.citation. The module extends the existing citation capabilities of GRASS GIS, which until now only provide for automated citation of the software project as a whole, authored by the GRASS Development Team, without reference to individual persons. The functionalities of the new module enable individual code citation for each of the over 500 implemented functionalities, including add-on modules. Three different classes of citation output are provided in a variety human- and machine-readable formats. The implications of this reference implementation of scientific software citation for both for the GRASS GIS project and the OSGeo foundation are outlined.}, publisher={PeerJ}, author={Löwe, Peter and Petras, Vaclav and Neteler, Markus and Mitasova, Helena}, year={2018}, month={Sep} }
 @article{löwe_petras_neteler_mitasova_2018, title={g.citation: Scientific citation for individual GRASS GIS software modules}, volume={9}, url={https://doi.org/10.7287/peerj.preprints.27206}, DOI={10.7287/peerj.preprints.27206}, abstractNote={The authors introduce the GRASS GIS add-on module g.citation. The module extends the existing citation capabilities of GRASS GIS, which until now only provide for automated citation of the software project as a whole, authored by the GRASS Development Team, without reference to individual persons. The functionalities of the new module enable individual code citation for each of the over 500 implemented functionalities, including add-on modules. Three different classes of citation output are provided in a variety human- and machine-readable formats. The implications of this reference implementation of scientific software citation for both for the GRASS GIS project and the OSGeo foundation are outlined.}, publisher={PeerJ}, author={Löwe, Peter and Petras, Vaclav and Neteler, Markus and Mitasova, Helena}, year={2018}, month={Sep} }
 @article{rocchini_petras_petrasova_horning_furtkevicova_neteler_leutner_wegmann_2017, title={Open data and open source for remote sensing training in ecology}, volume={40}, ISSN={["1878-0512"]}, DOI={10.1016/j.ecoinf.2017.05.004}, abstractNote={Remote sensing is one of the most important tools in ecology and conservation for an effective monitoring of ecosystems in space and time. Hence, a proper training is crucial for developing effective conservation practices based on remote sensing data. In this paper we aim to highlight the potential of open access data and open source software and the importance of the inter-linkages between these and remote sensing training, with an interdisciplinary perspective. We will first deal with the importance of open access data and then we provide several examples of Free and Open Source Software (FOSS) for a deeper and more critical understanding of its application in remote sensing.}, journal={ECOLOGICAL INFORMATICS}, author={Rocchini, Duccio and Petras, Vaclav and Petrasova, Anna and Horning, Ned and Furtkevicova, Ludmila and Neteler, Markus and Leutner, Benjamin and Wegmann, Martin}, year={2017}, month={Jul}, pages={57–61} }
 @article{tonini_shoemaker_petrasova_harmon_petras_cobb_mitasova_meentemeyer_2017, title={Tangible geospatial modeling for collaborative solutions to invasive species management}, volume={92}, ISSN={["1873-6726"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85014320386&partnerID=MN8TOARS}, DOI={10.1016/j.envsoft.2017.02.020}, abstractNote={Managing landscape-scale environmental problems, such as biological invasions, can be facilitated by integrating realistic geospatial models with user-friendly interfaces that stakeholders can use to make critical management decisions. However, gaps between scientific theory and application have typically limited opportunities for model-based knowledge to reach the stakeholders responsible for problem-solving. To address this challenge, we introduce Tangible Landscape, an open-source participatory modeling tool providing an interactive, shared arena for consensus-building and development of collaborative solutions for landscape-scale problems. Using Tangible Landscape, stakeholders gather around a geographically realistic 3D visualization and explore management scenarios with instant feedback; users direct model simulations with intuitive tangible gestures and compare alternative strategies with an output dashboard. We applied Tangible Landscape to the complex problem of managing the emerging infectious disease, sudden oak death, in California and explored its potential to generate co-learning and collaborative management strategies among actors representing stakeholders with competing management aims.}, journal={ENVIRONMENTAL MODELLING & SOFTWARE}, author={Tonini, Francesco and Shoemaker, Douglas and Petrasova, Anna and Harmon, Brendan and Petras, Vaclav and Cobb, Richard C. and Mitasova, Helena and Meentemeyer, Ross K.}, year={2017}, month={Jun}, pages={176–188} }
 @article{tabrizian_petrasova_harmon_petras_mitasova_meentemeyer_2016, title={Immersive Tangible Geospatial Modeling}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85011015621&partnerID=MN8TOARS}, DOI={10.1145/2996913.2996950}, abstractNote={Tangible Landscape is a tangible interface for geographic information systems (GIS). It interactively couples physical and digital models of a landscape so that users can intuitively explore, model, and analyze geospatial data in a collaborative environment. Conceptually Tangible Landscape lets users hold a GIS in their hands so that they can feel the shape of the topography, naturally sculpt new landforms, and interact with simulations like water flow. Since it only affords a bird's-eye view of the landscape, we coupled it with an immersive virtual environment so that users can virtually walk around the modeled landscape and visualize it at a human-scale. Now as users shape topography, draw trees, define viewpoints, or route a walkthrough, they can see the results on the projection-augmented model, rendered on a display, or rendered on a head-mounted display. In this paper we present the Tangible Landscape Immersive Extension, describe its physical setup and software architecture, and demonstrate its features with a case study.}, journal={24TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2016)}, author={Tabrizian, Payam and Petrasova, Anna and Harmon, Brendan and Petras, Vaclav and Mitasova, Helena and Meentemeyer, Ross}, year={2016} }
 @inproceedings{jeziorska_mitasova_petrasova_petras_divakaran_zajkowski_2016, title={Overland flow analysis using time series of sUAS- derived elevation models}, volume={3}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84979525774&partnerID=MN8TOARS}, DOI={10.5194/isprs-annals-iii-8-159-2016}, abstractNote={Abstract. With the advent of the innovative techniques for generating high temporal and spatial resolution terrain models from Unmanned Aerial Systems (UAS) imagery, it has become possible to precisely map overland flow patterns. Furthermore, the process has become more affordable and efficient through the coupling of small UAS (sUAS) that are easily deployed with Structure from Motion (SfM) algorithms that can efficiently derive 3D data from RGB imagery captured with consumer grade cameras. We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data. The sUAS based data captures preferential flow along tillage and more accurately represents gullies. Furthermore the simulated water flow patterns over the sUAS based terrain models are consistent throughout the year. When terrain models are reconstructed only from sUAS captured RGB imagery, however, water flow modeling is only appropriate in areas with sparse or no vegetation cover.
                    }, number={8}, booktitle={International archives of the photogrammetry remote sensing and spatial}, author={Jeziorska, J. and Mitasova, Helena and Petrasova, A. and Petras, Vaclav and Divakaran, D. and Zajkowski, T.}, year={2016}, pages={159–166} }
 @article{rocchini_petras_petrasova_chemin_ricotta_frigeri_landa_marcantonio_bastin_metz_et al._2017, title={Spatio-ecological complexity measures in GRASS GIS}, volume={104}, ISSN={["1873-7803"]}, DOI={10.1016/j.cageo.2016.05.006}, abstractNote={Good estimates of ecosystem complexity are essential for a number of ecological tasks: from biodiversity estimation, to forest structure variable retrieval, to feature extraction by edge detection and generation of multifractal surface as neutral models for e.g. feature change assessment. Hence, measuring ecological complexity over space becomes crucial in macroecology and geography. Many geospatial tools have been advocated in spatial ecology to estimate ecosystem complexity and its changes over space and time. Among these tools, free and open source options especially offer opportunities to guarantee the robustness of algorithms and reproducibility. In this paper we will summarize the most straightforward measures of spatial complexity available in the Free and Open Source Software GRASS GIS, relating them to key ecological patterns and processes.}, journal={COMPUTERS & GEOSCIENCES}, author={Rocchini, Duccio and Petras, Vaclav and Petrasova, Anna and Chemin, Yann and Ricotta, Carlo and Frigeri, Alessandro and Landa, Martin and Marcantonio, Matteo and Bastin, Lucy and Metz, Markus and et al.}, year={2017}, month={Jul}, pages={166–176} }
 @inproceedings{harmon_petrasova_petras_mitasova_meentemeyer_2016, title={Tangible landscape: cognitively grasping the flow of water}, volume={41}, DOI={10.5194/isprs-archives-xli-b2-647-2016}, abstractNote={Abstract. Complex spatial forms like topography can be challenging to understand, much less intentionally shape, given the heavy cognitive load of visualizing and manipulating 3D form. Spatiotemporal processes like the flow of water over a landscape are even more challenging to understand and intentionally direct as they are dependent upon their context and require the simulation of forces like gravity and momentum. This cognitive work can be offloaded onto computers through 3D geospatial modeling, analysis, and simulation. Interacting with computers, however, can also be challenging, often requiring training and highly abstract thinking. Tangible computing – an emerging paradigm of human-computer interaction in which data is physically manifested so that users can feel it and directly manipulate it – aims to offload this added cognitive work onto the body. We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. Conceptually Tangible Landscape couples a malleable physical model with a digital model of a landscape through a continuous cycle of 3D scanning, geospatial modeling, and projection. We ran a flow modeling experiment to test whether tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies. We used hydrological simulations and statistics to quantitatively assess spatial performance. We found that Tangible Landscape enhanced 3D spatial performance and helped users understand water flow.
                    }, number={B2}, booktitle={International archives of the photogrammetry remote sensing and spatial}, author={Harmon, B. A. and Petrasova, A. and Petras, Vaclav and Mitasova, Helena and Meentemeyer, K.}, year={2016}, pages={647–653} }
 @article{petras_petrasova_harmon_meentemeyer_mitasova_2015, title={Integrating Free and Open Source Solutions into Geospatial Science Education}, volume={4}, ISSN={["2220-9964"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84948970902&partnerID=MN8TOARS}, DOI={10.3390/ijgi4020942}, abstractNote={While free and open source software becomes increasingly important in geospatial research and industry, open science perspectives are generally less reflected in universities’ educational programs. We present an example of how free and open source software can be incorporated into geospatial education to promote open and reproducible science. Since 2008 graduate students at North Carolina State University have the opportunity to take a course on geospatial modeling and analysis that is taught with both proprietary and free and open source software. In this course, students perform geospatial tasks simultaneously in the proprietary package ArcGIS and the free and open source package GRASS GIS. By ensuring that students learn to distinguish between geospatial concepts and software specifics, students become more flexible and stronger spatial thinkers when choosing solutions for their independent work in the future. We also discuss ways to continually update and improve our publicly available teaching materials for reuse by teachers, self-learners and other members of the GIS community. Only when free and open source software is fully integrated into geospatial education, we will be able to encourage a culture of openness and, thus, enable greater reproducibility in research and development applications.}, number={2}, journal={ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION}, author={Petras, Vaclav and Petrasova, Anna and Harmon, Brendan and Meentemeyer, Ross K. and Mitasova, Helena}, year={2015}, month={Jun}, pages={942–956} }