NCGIS2019

This page is about work related to GRASS GIS presented at NCGIS2019, Winston-Salem, North Carolina, USA in February 27 - March 1, 2019 (https://ncgisconference.com/).

Presentations

GRASS GIS: What's New?

• Description: GRASS GIS is a geospatial analysis and remote sensing software aiming at providing its users with an all-in-one tool. GRASS GIS is open source which makes it both free to use but also, for the more adventurous, hackable. This talk will give an overview of what the GRASS GIS community provides to the public and will highlight the latest additions to GRASS GIS. These include improvements in import and handling of vector data formats including messy datasets, extended Python capabilities, and easier native data manipulation. Algorithms for interpolation, solar radiation, water flow, and sediment transport now come in parallelized versions, while all raster operations now benefit from new compression algorithms. List of new algorithms includes point clustering, vector topology cleaning, turn support for network analysis, landform and shape detection, image segmentation and clumping, atmospheric corrections for more satellites, and temporal algebra. Experimental features available for download now include concave hull, vector algebra, ground point cloud classification, DEM fusion and blending, object-based classification, Sentinel data handling, and sky-view factor visualization.
• Speaker: Vaclav Petras, NCSU Center for Geospatial Analytics
• Authors: Vaclav Petras, Doug Newcomb (USFWS), Corey White (NCSU Center for Geospatial Analytics)

GRASS GIS: Getting Started

• Description: GRASS GIS is a software for geospatial analysis and remote sensing. It comes with features covering broad spectrum of topics from network analysis to surface hydrology. In many ways, GRASS GIS is similar to other GIS packages, but it also comes with several unique concepts such as computational region, integrated command line, locations and mapsets. These come with advantages praised by advanced users, but at the same time, these concepts often feel unfamiliar to those accustomed to other GIS software. Nevertheless, for over 30 years now, GIS and RS analysts had reasons to learn GRASS GIS often on their own. Reasons include the breadth of functionality, long-term compatibility and the open source nature of GRASS GIS. For GRASS GIS, being open source means more than just escape from recurring license fees. The GRASS GIS development is characterized by high involvement of interested users in the development process hand-in-hand with programmers and scientists. This session aims at providing basis for self-learning GRASS GIS and will be useful whether you have already tried running GRASS GIS or whether you are just checking it out.
• Speaker: Vaclav Petras, NCSU Center for Geospatial Analytics
• Authors: Vaclav Petras, Corey White (NCSU Center for Geospatial Analytics), Doug Newcomb (USFWS)

Navigating the Geospatial Open Source Software Landscape

• Description: Does open source mean for free? Is it free, libre, or open source? Can I use it commercially? What software to choose? Where to start learning about geospatial open source software? Do I have to use Linux? Do I have to share my data online? Do I have to start programing? If you are wondering about any of these questions or you are just curious about open source software in general, this talk is for you. This talk will help you understand the geospatial open source software ecosystem and community and additionally, it will provide an overview of available tools and give hints on how to choose between them.
• Speaker: Vaclav Petras, NCSU Center for Geospatial Analytics
• Authors: Vaclav Petras, Corey White (NCSU Center for Geospatial Analytics), Randal Hale (North River Geographic Systems, Inc)
• Relevance to GRASS GIS: GRASS GIS was used in examples of tickets, commits, and other open source concepts. It was also included in overviews of available software.

Construction of Landscape Level QL2 LiDAR Data Sets for Species Habitat Assessment in Eastern North Carolina

• Description: In response to Hurricane Matthew, the State of North Carolina partnered with USGS to undertake a LiDARdata collection updated to 3DEP QL2 standards. This data collection effort has so far generated 360 billionpoints of multiple return LiDAR data in 59 counties in eastern North Carolina. Using native 64-bit GRASSGIS on Ubuntu Linux, forest canopy heights and other structural metrics were generated on a 6.096m (20foot) grid from county LAZ LiDAR datasets and aggregated to 3.5 billion pixel raster layers denotingcanopy heights, point Z value skewness, relative point density at 1-3m and 3-7m, and building footprints.These data layers were compared to 25m buffered point observations of bird species of interest to theUSFWS in eastern North Carolina, 12 digit USGS Hydrologic Unit (HU) polygons, and the 30m resolution2011 National Landcover Dataset (NLCD). Vegetation structure differences between bird species werenoted, as well as geographic differences in structure for Red-cockaded woodpecker (Picoides borealis) ineastern North Carolina. Aggregate vegetation and building statistics were generated for the 12-digitHydrologic Units (HU) for use in aquatic species habitat assessment. Summary statistics for percentagesof Forest, Non-forest, and Building area for each 2011 NLCD class for eastern North Carolina weregenerated to show the level of agreement between NLCD and the LiDAR data classification.
• Speaker: Doug Newcomb (USFWS)
• Authors: Doug Newcomb (USFWS), Vaclav Petras (NCSU Center for Geospatial Analytics)

New Open Source Tool for Plant Pest and Pathogen Spread Modeling

Plant diseases and pests directly threaten food production and production of plant-based materials and can also influence agricultural and industrial trade in an area after establishment of necessary quarantine zones. Understanding the potential spread of pests and pathogens is thus crucial for protecting the economy and food security. We present a new software for modeling the spread of pests and pathogens over a landscape. The core of the model is implemented as a C++ library for performance reasons, while the model users can choose from a variety of convenient interfaces. We provide an R package and a GRASS GIS module. The R package provides integration with the well-know statistical tool R and the GRASS GIS provides the module with graphical user interface, Python interface, and preprocessing and visualization tools. The model as well as all its dependencies and integrations are open source. This removes any licensing barriers for application of the model and gives us an opportunity to openly collaborate on improving the model. It also opens opportunities for the model to be scrutinized not only by the traditional peer-review of scientific publications, but also by a review performed by anybody who is interested in using the model.

• Speaker: Vaclav Petras, NCSU Center for Geospatial Analytics
• Authors: Vaclav Petras, Chris Jones, Anna Petrasova, Devon Gaydos, Kellyn Montgomery, Ross K. Meentemeyer

Citizen Science - Processing LiDAR data at Home to compare to the NLCD

• Description: An example of citizen science you can reproduce at home right now. Private citizen processes open data using open source software. Classifications from LiDAR data were processed using GRASS GIS at a computer at home and compared to the NLCD.
• Speaker: Doug Newcomb

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
• Relevance to GRASS GIS: GRASS GIS is taught through QGIS Processing plugin and also directly in more advanced classes.

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

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