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

NCSU
GeoForAll Lab
at
Center for Geospatial Analytics

September 14-16, 2016

Year 1987: Let William Shatner do the introduction

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

- 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 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

- 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

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.

- 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.

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

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.

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

- 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.

- 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

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.

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

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

Trimble UX5 UAS flight plan analysis

- 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

- 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 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.

- 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.

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

surface | points | lines | areas |

Workshops

**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 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.

Tangible Landscape: tangible-landscape.github.io