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This page is about work related to GRASS GIS presented at NCGIS2019, Winston-Salem, North Carolina, USA in February 27 - March 1, 2019 (


Creating Urbanization Scenarios with the FUTURES Model

Description: Urban growth scenario simulation is a powerful tool for exploring impacts of land use change due to urbanization and its effects on landscape. We present FUTURES (FUTure Urban - Regional Environment Simulation) which is a patch-based, stochastic, multi-level land change modeling framework. This model, which was once closed and inaccessible, is now integrated with an open source geospatial platfrom. We will describe our motivation for releasing this project as open source and the advantages of integrating it with GRASS GIS. Then we will show how GIS professionals can start using this tool to empower planners to make informed decisions. FUTURES model has a graphical user interface, pre-processing tools, and is available across all main operating systems. Besides the model itself, we provide free online tutorials and sample datasets so that the potential users can experiment with the model and explore its potential.

  • Speaker: Vaclav Petras, NCSU Center for Geospatial Analytics
  • Authors: Vaclav Petras, Anna Petrasova, Georgina M. Sanchez, Derek Van Berkel, Ross K. Meentemeyer

Geo For All: Blending OER, Open Data and FOSS in GIS Education

  • Description: Over the last several years there has been both a maturation of open source geospatial software and a convergence of open educational resources (OER) and open data for teaching and learning geographic information science. This presentation will focus on transitioning an introductory GIS course from using proprietary software and expensive texts to an open source GIS course using OER and open data. Open source software such as QGIS, GRASS, and the R statistical programming language will be covered, as will the “Geo For All” initiative by the Open Source Geospatial Foundation (OSGeo). The presentation will also address the ways in which a transition to OER and open source are having a positive impact on student outcomes in GIS education at a small liberal arts college, as well as how the open GIS course is being used to make a push for wider adoption of open educational resources institution-wide.
  • Speaker: David Abernathy, Warren Wilson College

Using a time series of 3D crop canopy models derived from sUAS imagery to identify geomorphic patterns related to crop stress

  • Description: Deriving crop information from remotely sensed data is a critical component of food security and sustainability efforts such as precision agriculture, soil conservation, and agroecological modeling. Small unmanned aerial systems (UAS) have emerged in recent years as a versatile remote sensing tool used by scientists and agricultural producers for collecting data at very high spatial and temporal resolutions. UAS can provide precisely-timed, fine-grained data for informing management responses to inter-field variability for maximizing crop productivity while minimizing natural resource degradation. Vegetation indices, like Normalized Difference Vegetation Index, calculated from remotely sensed spectral information have been shown to strongly correlate with crop health and are widely used in industry and throughout the literature. Many multispectral sensors for UAS, however, are limited by high cost and low spectral resolution. Furthermore, there has been very little exploration of the use of 3D models of canopy structure to provide information about crop health, population, and stand uniformity. Thus far, techniques for quantifying the geometric properties of morphological surfaces have been limited to terrain landforms. This research goes beyond spectral analysis for remote crop monitoring and investigates how patterns in canopy structure can be used to identify variability in crop health and productivity using remote sensing with low altitude small UAS. A time series of high resolution orthophotos (1cm/pix) and digital surface models (2cm/pix) was created using Structure from Motion (SfM) photogrammetry and analyzed using an open source GRASS GIS temporal framework to investigate the relationship between sorghum canopy morphology and yield variability. A bare earth digital elevation model created from a 2015 aerial LiDAR survey was used to identify and correct any systematic shifts in the crop surface models and calculate crop heights. Canopy structure was categorized using geomorphons and structural metrics to measure canopy roughness and uniformity were calculated to examine correlation with variability in yield. This approach for leveraging 3D canopy structure provided valuable information for characterizing crop status and productivity.
  • Speaker: Kellyn Montgomery, NCSU Center for Geospatial Analytics



NCSU Center for Geospatial Analytics