GRASS GIS innovation platform

GRASS GIS as an open source innovation platform:

from dynamic visualizations to collaborative tangible modeling

Helena Mitasova, Anna Petrasova, Vaclav Petras, and Brendan Harmon

NCSU GeoForAll Lab at Center for Geospatial Analytics

September 14-16, 2016

Maps go digital with GRASS

Year 1987: Let William Shatner do the introduction

see full 15 min video here https://www.youtube.com/embed/U3Hf0qI4JLc

GRASS GIS for geospatial research

grass.osgeo.org

general purpose GIS with wxPython GUI and CLI

backend processing for QGIS, R statistics, WebGIS

powerful 2D/3D raster, imagery and vector processing

single integrated software with 30 years of development

GRASS GIS and innovation

GRASS has long history - known as a reliable geospatial number cruncher (Neteler 2014)
Developed as component of research projects - innovation through research
Many historically innovative tools serve today: our examples are just a small subset

Watersheds and stream extraction

First worldwide map of watersheds derived from a global DEM

Unique least cost path algorithm, no depression filling needed: r.watershed
Updated for massive data sets (SRTM, lidar DEMs)

Ehlschlaeger C., 1989, Using the AT Search Algorithm to Develop Hydrologic Models from Digital Elevation Data, Proc IGIS Symposium '89, 275-281.
Metz M., Mitasova H., and Harmon R.S., 2011, Efficient extraction of drainage networks from massive, radar-based elevation models with least cost path search, Hydrology and Earth System Sciences, 15, 667-678

Multivariate splines

2D, 3D and 4D interpolation with tuneable tension

Mitasova, H., L. Mitas, 1993, Interpolation by regularized spline with tension: I. Theory and implementation. Mathematical Geology 25, 641-655.

Splines with geometry analysis

simultaneous topo analysis: gradients, curvatures
tuneable level of detail, geometry preserving smoothing

Mitasova, H., Mitas, L., Harmon, R.S., 2005, Simultaneous spline interpolation and topographic analysis for lidar elevation data: methods for Open source GIS, IEEE GRSL 2(4), 375-379.

Dynamic volume modeling

Groundwater pollution plume interpolated from 10 years of well monitoring data using 4D spline function

WM Brown, M Astley, T Baker, H Mitasova, 1995, GRASS as an integrated GIS and visualization system for spatio-temporal modeling AUTOCARTO, 89-99

Dynamic water and sediment flow

Flow accumulation and sediment transport capacity visualized as dynamic surfaces

Mitasova, H., L. Mitas, B.M. Brown, D.P. Gerdes, I. Kosinovsky, 1995, Modeling spatially and temporally distributed phenomena: New methods and tools for GRASS GIS. IJGIS, 9 (4), 443-446.

Duality of particles and fields

Path sampling method for flow continuity equations

Mitas, L., Mitasova, H., 1998, Distributed erosion modeling for effective erosion prevention. Water Resources Research 34(3), pp. 505-516

GRASS 7 innovations

Space-Time Framework

Space-time 2D, 3D raster and vector datatypes
Time series datasets managed in temporal database
New modules: query, aggregation, conversion, statistics, gap filling
Temporal algebra: temporal relations, temporal buffer, spatio-temporal operators

Gebbert, S., Pebesma, E., 2014. TGRASS: A temporal GIS for field based environmental modeling. Environmental Modelling & Software 53, 1-12.

MODIS land surface temperature

14 years of 4/day (20K) maps, entire Europe, 250m res
advanced statistics to fill no-data and enhance resolution, multivariate regression includes elevation, solar angle, precipitation

EuroLST: http://gis.cri.fmach.it/eurolst/, Metz, Rocchini, Neteler, 2014: Rem Sens, 6(5): 3822-3840

DEM time series visualization

Jockey's Ridge migration 1974 - 2014, lidar time series

Hardin, E., Mitasova, H., Tateosian, L., Overton, M., 2014, GIS-based Analysis of Coastal Lidar Time-Series, Springer Briefs in Computer Science, Springer, New York, 84 p.

Space-Time Cube visualization

Jockey's Ridge 16m, 20m contour evolution isosurfaces

Space time cube for TLS series

Stream bank erosion from terrestrial lidar

Starek, M.J., Mitasova H., Wegmann, K, Lyons, N., 2013, Space-Time Cube Representation of Stream Bank Evolution Mapped by Terrestrial Laser Scanning, IEEE GRSL 10(6), p. 1369-1373

Mapping with sUAS

Trimble UX5 UAS flight plan analysis

Surface water flow modeling

SfM in Agisoft or OpenDroneMap > point cloud
DSM interpolation and path sampling-based surface runoff modeling in GRASS GIS

Jeziorska, J; Mitasova, H; Petrasova, A; Petras, V; Divakaran, D; Zajkowski, T., 2016, Overland flow analysis using time series of sUAS-derived elevation models, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol III-8, pp.159-166

Geomorphons

Basic landforms extracted for the entire US
Interactive search of similar landuse patterns
On-line geospatial analytics: http://sil.uc.edu/
Spatial Informatics Laboratory, University of Cincinnati

Tangible Landscape

Tangible user interface for GRASS GIS

Book: Petrasova, A., Harmon, B., Petras, V., Mitasova, H., 2015, Tangible Modeling with Open Source GIS, Springer International Publishing, 135 p.

Why tangible interfaces for GIS?

Interaction through mouse and display can be tedious
Manipulating 3D computer models requires specialized software and training, restricts creativity
Collaboration is limited as typically only one user at a time can navigate and modify models.

How does it work?

Tangible Landscape couples a digital and a physical model through a continuous cycle of 3D scanning, geospatial modeling, and projection

Software

User Interfaces

Tangible Graphical Command Line

Interactions

surface

points

lines

areas

3D soil moisture exploration

Wildfire spread

Coupling with contributed modules

TL coupled with models in GRASS GIS addons, R

Tangible Landscape + Immersive Virtual Reality

Resources for innovators

Workshops

GRASS GIS as platform for sustainable Open Science

Reproducibility: open source is the natural habitat for science and research
Return of Investment: many tools available since 80s, continuously developed
Auto-documentation: map and command history preserved “forever”
Reliability: testing and quality control system (in progress) integrated into the software itself
Longevity for Open Science: code integrated into GRASS “survives” even if original authors would not continue

NCSU Geoforall lab

geospatial.ncsu.edu/osgeorel/

NCSU Center for Geospatial Analytics, PhD program in Geospatial Analytics coming in fall 2017
Member of GeoForAll initiative and NA leading lab
GRASS GIS development, 3 members of GRASS PSC
Research: geocomputation and geovisualization
Courses on-campus and on-line with open source geospatial component

Integrating Free and Open Source Solutions into Geospatial Science Education. Petras, V., Petrasova, A., Harmon, B., Meentemeyer, R.K., Mitasova, H. ISPRS IJGI. 2015.