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Open education materials are critical for the advancement of open science and the development of open-source soft-ware. These accessible and transparent materials provide an important pathway for sharing both standard geospa-tial 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 analyt-ics. Specifically, Jupyter Notebooks are quickly becoming a standard format in open education. In this article, we intro-duce 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 devel-opment of grass.jupyter and demonstrate how the package facilitates teaching open-source geospatial mode-ling with a collection of Jupyter Notebooks designed for a graduate-level geospatial modeling course. The open educa-tion notebooks feature spatiotemporal data visualizations, hydrologic modeling, and spread simulations such as the spread of invasive species and urban growth

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.

With their complex topography and orographic effects, the amount of solar radiation reaching a surface (insolation) can vary over short distances and time frames in mountainous areas, affecting the spatio-temporal variability of hydrological and ecological processes and contributing to the biodiversity of mountain ecosystems. The combined effects of variable topography and meteorological conditions on insolation can complicate assessments of how land cover changes affect insolation in mountainous regions as measurement data is often limited to few locations. To incorporate the spatio-temporal variability of sky conditions as well as the spatial variability of terrain in estimates of solar radiation across a montane headwater basin over two summers, we extended an open-source geospatial model of surface solar radiation, r.sun.hourly, to permit the spatially and temporally explicit parameterization of atmospheric conditions at user-specified spatial and temporal resolutions with temporal raster datasets. Sensitivity analyses indicated that of the three atmospheric parameters in the model, the coefficient of real-sky direct beam solar radiation (coeff_bh) (an index of cloudiness to clear sky conditions) had the greatest influence on insolation estimates for our study area, located in the southern Appalachian Mountains of the southeastern USA, and we developed a workflow for estimating coeff_bh from publicly available geostationary meteorological satellite images (GOES-13, 1 km spatial and 15-minute temporal resolutions). The extended r.sun.hourly model was parameterized with a bare-earth digital elevation model (1/9 arc-second DEM obtained from the USGS National Map 3-D Elevation Program), the estimated coeff_bh temporal raster datasets (downscaled from 1 km to the DEM resolution), and monthly mean Linke Turbidity values to estimate global solar radiation across the basin at a 15-minute resolution over two summers. Estimates of instantaneous (15-minute interval) and cumulative total (for 12-hour period bracketing solar noon) global solar radiation were evaluated with pyranometer (Eppley 8-48) measurements of global solar radiation collected by the U.S. Department of Agriculture Forest Service Coweeta Hydrological Laboratory for a total of 144 days (dates with recorded precipitation during daylight hours or incomplete imagery datasets were excluded from analyses). Despite the low spatial resolution of the satellite images from which the real-sky direct beam radiation coefficient was estimated and the proximity of the ground measurement location to the edges of a GOES-13 cell (< 90 m north and < 230 m east), both instantaneous and daily total estimates of global solar radiation corresponded well with measurements (R2 = 0.81, p-value < 0.001, n = 7056 and R2 = 0.89, p-value < 0.001, n = 144). Estimate errors tended to be lower on cloudier days (MAPE = 6.8%, n = 61) than less cloudy days (MAPE = 13.6%, n = 83). Offsets in the timing and magnitude of peaks and troughs in insolation over time (with passing clouds) between measurements and estimates were also greater during partly cloudy conditions. Although these findings are for one location, they suggest the potential of the extended r.sun.hourly model to provide high-resolution estimates of solar radiation, over extensive areas and timeframes, in mountainous regions with remotely sensed elevation and meteorological data.

Ecological 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.

Models 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.

Recent advancements in mobile sensing and wearable technologies create new opportunities to improve our understanding of how people experience their environment. This understanding can inform urban design decisions. Currently, an important urban design issue is the adaptation of infrastructure to increasing cycle and e-bike use. Using data collected from 12 cyclists on a cycle highway between two municipalities in The Netherlands, we coupled location and wearable emotion data at a high spatiotemporal resolution to model and examine relationships between cyclists' emotional arousal (operationalized as skin conductance responses) and visual stimuli from the environment (operationalized as extent of visible land cover type). We specifically took a within-participants multilevel modeling approach to determine relationships between different types of viewable land cover area and emotional arousal, while controlling for speed, direction, distance to roads, and directional change. Surprisingly, our model suggests ride segments with views of larger natural, recreational, agricultural, and forested areas were more emotionally arousing for participants. Conversely, segments with views of larger developed areas were less arousing. The presented methodological framework, spatial-emotional analyses, and findings from multilevel modeling provide new opportunities for spatial, data-driven approaches to portable sensing and urban planning research. Furthermore, our findings have implications for design of infrastructure to optimize cycling experiences.

Viewscapes are the visible portions of a landscape that create a visual connection between a human observer and their 3-dimensional surroundings. However, most large area line-of-sight studies have modeled viewscapes using bare-earth digital elevation models, which exclude the 3-D elements of built and natural environments needed to comprehensively understand the scale, complexity and naturalness of an area. In this study, we compared viewscapes derived from LiDAR bare earth (BE) and top-of-canopy (ToC) surface models for 1000 exurban homes in a region of the Rocky Mountains, USA that is experiencing rapid low-density growth. We examined the extent to which the vertical structure of trees and neighboring houses in ToC models – not accounted for in BE models – affect the size and quality of each home’s viewscape. ToC models consistently produced significantly smaller viewscapes compared to BE models across five resolutions of LiDAR-derived models (1, 5, 10, 15, and 30-m). As resolution increased, both ToC and BE models produced increasingly larger, exaggerated viewscapes. Due to their exaggerated size, BE models overestimated the greenness and diversity of vegetation types in viewscapes and underestimated ruggedness of surrounding terrain compared to more realistic ToC models. Finally, ToC models also resulted in more private viewscapes, with exurban residents seeing almost three times fewer neighbors compared to BE models. These findings demonstrate that viewscape studies should consider both vertical and horizontal dimensions of built and natural environments in landscape and urban planning applications.

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.

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. Participatory modeling frameworks are needed to bridge research and management.With Tangible Landscape, users guided complex models using tangible gestures.Near real-time 3D visualizations and feedbacks supported collective decision making.Informed individuals engaged in trade-offs that facilitated solution building.

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, GIS-based topographic evolution model uses empirical models for soil erosion at watershed to regional scales and a physics-based model for shallow overland water flow and soil erosion at subwatershed scales to compute short-term topographic change. This either steady state or dynamic model simulates 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 Patterson Branch subwatershed on the Fort Bragg military installation in North Carolina, r.sim.terrain can realistically simulate the development of fine-scale morphological features including ephemeral gullies, rills, and hillslopes. Applications include land management, erosion control, landscape planning, and landscape restoration.

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.

Visual characteristics of urban environments influence human perception and behavior, including choices for living, recreation and modes of transportation. Although geospatial visualizations hold great potential to better inform urban planning and design, computational methods are lacking to realistically measure and model urban and parkland viewscapes at sufficiently fine-scale resolution. In this study, we develop and evaluate an integrative approach to measuring and modeling fine-scale viewscape characteristics of a mixed-use urban environment, a city park. Our viewscape approach improves the integration of geospatial and perception elicitation techniques by combining high-resolution lidar-based digital surface models, visual obstruction, and photorealistic immersive virtual environments (IVEs). We assessed the realism of our viewscape models by comparing metrics of viewscape composition and configuration to human subject evaluations of IVEs across multiple landscape settings. We found strongly significant correlations between viewscape metrics and participants&rsquo, perceptions of viewscape openness and naturalness, and moderately strong correlations with landscape complexity. These results suggest that lidar-enhanced viewscape models can adequately represent visual characteristics of fine-scale urban environments. Findings also indicate the existence of relationships between human perception and landscape pattern. Our approach allows urban planners and designers to model and virtually evaluate high-resolution viewscapes of urban parks and natural landscapes with fine-scale details never before demonstrated.

This paper presents novel and effective methods for teaching about topography--or shape of terrain--and assessing 3-dimensional spatial learning using tangibles. We used Tangible Landscape--a tangible interface for geospatial modeling--to teach multiple hands-on tangible lessons on the concepts of grading (i.e., earthwork), geomorphology, and hydrology. We examined students' ratings of the system's usability and user experience and tested students' acquisition and transfer of knowledge. Our results suggest the physicality of the objects enabled the participants to effectively interact with the system and each other, positively impacting ratings of usability and task-specific knowledge building. These findings can potentially advance the design and implementation of tangible teaching methods for the topics of geography, design, architecture, and engineering.

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.

Overland water flow can detach exposed soil and transport it over large distances, leading to soil loss and sediment deposition across landscape. Soil erosion can be effectively controlled by modifying topography to reduce concentrated overland flow or by planting vegetation to reduce soil detachment and transport. We used Tangible Landscape to analyze distribution of soil erosion and deposition potential in a small watershed and to design conservation measures by changing topography and planting vegetation in vulnerable locations. We iteratively adjusted and optimized our design based on real-time feedback from erosion and deposition maps projected over the modified 3D model. This feedback helped us to evaluate the effectiveness of our designs and develop better solutions.

The topography of the Earth’s surface controls the flow of water and mass over the landscape. Modifications to the surface geometry of the land redirect water and mass flows influencing ecosystems, crop growth, the built environment, and many other phenomena dependent on water. We used Tangible Landscape to explore the relationship between overland flow patterns and landscape topography by manually changing the landscape model, while getting near real-time feedback about changing flow patterns. We coupled Tangible Landscape with a sophisticated dam breach model to investigate flood scenarios after a dam breach.

People’s perception and experience of landscape plays a critical role in the social construction of these spaces—in how individuals and societies understand, value, and use landscapes. Perception and experience should, therefore, be an integral part of environmental modeling and geodesign. With the natural interaction afforded by Tangible Landscape and the realistic representations afforded by Immersive Virtual Environments (IVEs) experts and non-experts can collaboratively model landscapes and explore the environmental and experiential impacts of “what if” scenarios. We have paired GRASS GIS with Blender, a state-of-the-art 3D modeling and rendering program, to allow real-time 3D rendering and immersion. As users manipulate a tangible model with topography and objects, geospatial analyses and simulations are projected onto the tangible model and perspective views are realistically rendered on monitors and head-mounted displays (HMDs) in near real-time. Users can visualize in near real-time the changes they are making with either bird’s-eye views or perspective views from human vantage points. While geospatial data is typically visualized as maps, axonometric views, or bird’s-eye views, human-scale perspective views help us to understand how people would experience and perceive spaces within the landscape.

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.