25 Years of GRASS GIS

Meeting
Present and Future of the GRASS User community in Japan

Markus Neteler
Fondazione E Mach IASMA Center of Research and Innovation
Italy, markus.neteler@iasma.it

OSGeo Japan OSGeo.jpTokyo, Nomura Research Institute, Marunouchi Center

1987USA(GRASS 2.0,
no Web!)

1995
USA

1997,Univ. ofHannover,
Germany

2002ITC-irst Trento+ Baylor

ITC-irst
2002

Today...

GRASS: 25 years of GFOSS

GNU/LinuxMacOSXMS-Windows

iPAQ (2002)Member of OSGeo.org
(Open Source Geospatial Foundation)

Developed since 1984 as
Open Source Software,
since 1999 under
GPL License

Written in C language,
800k SLOC, portable
source code (32/64bit;
various operating systems
and hardware platforms),
modular

GRASS: The portable GIS

Open Source GIS:
GRASS

Communities: www.grass-verein.de,www.gfoss.it, www.osgeo.jp,
www.osgeo.it, www.osgeo.de ...

New Windows Installers: OSGeo4W
and winGRASS stand-alone

GRASS: command line and
graphical user interfaces

1982.... 1995

Since 2007..
today

GRASS GIS: new graphical user interface

Written in Python/wxWidgets Intuitive usage

Powerful

Future:

Cartography tool

Location Wizard

Attribute manager

New GRASS Digitizer:Topological editing: visual quality feedback

Object and vector transfer from background maps

Vector objects editing:Add, Break, Copy, Connect, Convert Type, Delete,
Flip direction, Merge, Move, Select lines by query,
Snap, Split

Categories/Attributes:Copy, Delete, Z-bulk labeling for contour lines

Vertex:Insert, Delete, Move

Undo function

GRASS GIS: new digitizer

Ongoing: Localization of GRASS GIS

Status:
Several hundred messages have been translated
in January 2010 (MEXT project)

Geostatistics with GRASS and R-stats

R statistical software is run inside a GRASS session:
R reads and writes data directly from/to GRASS

A comparison of hiking paths in Val di Fassa, Italy Rot: existing path Blau: calculated with r.walk

r.walk improved again in GRASS 6.5+

Arrival

Start

Autor:
M. Franchi, Young researchers wanted award (PBZ, 2006)

r.walk: Walking connectivity

r.walk: Terrain accessibility

Hiking incomplex
terrain

GRASS terrain data elaboration

Topographic index

Solar energy

Geomorphometry

Overlay of geocoded historical maps
(1840) to recent orthophotos

LANDSAT ETM+
color composite
(from free archive)

GRASS image processing

GRASS image
processing:

Atmospheric
correction

Haze reduction in
LANDSAT ETM+ withTasseled Cap transform

Also available:6S integration in GRASS
(i.atcorr)

METEOSAT, GOES east/west,
NOAA AVHRR, SPOT HRV,
LANDSAT TM and ETM+,IRS 1C-LISS, ASTER

(ALOS + Ikonos in prep.)

GRASS visualization:
Orthophotos over Lidar-DEM/DSM

Visualization in
GRASS-NVIZ tool

GIS and Remote sensing unit at FEM: Spatial modelling
of disease vectors, biodiversity and beyond

http://gis.fem-environment.eu

CLAND SURFACE TEMPERATURE (LST)
Data enhancements in complex Alpine terrain

http://gis.fem-environment.eu

111111111111111111

5101520

0.10.20.30.40.50.60.7

10day periods (2003)

2222222222222222222

33333333333333333333

123Cavedine (570m a.s.l)Val di Non (610m a.s.l)Levico (760m a.s.l)

AprilEVI

10km

3

1

2

Enhanced Vegetation Index (EVI)

Spring/autumn detection: Trentino 2003

Effect of valley orientation
and expositionGIS Modelling and Remote Sensing

Time series elaboration of satellite data for
disease dynamics and ecological modelling

GIS modelling for risk analysis

SELECTED PUBLICATIONS:Rizzoli, A., Hauffe, H.C., Tagliapietra, V., Neteler, M., and Ros, R. (2009). Forest structure and roe deer abundance predict tick-borne encephalitis risk in Italy. PLoS ONE, 4(2):e4336+.

Carpi G., Cagnacci F., Neteler M., Rizzoli A, 2008: Tick infestation on roe deer in relation to geographic and remotely-sensed climatic variables in a tick-borne encephalitis endemic area. Epidem. and Infection, 136, pp. 1416-1424

M. Neteler, 2005: Time series processing of MODIS satellite data for landscape epidemiological applications. Intl J Geoinf., 1(1), pp. 133-138

Tiger mosquito (Ae. Albopictus) risk

Original MODIS LST map,
QA layer applied as filter

Second and third filter stage
applied to MODIS LST map

MODIS LST (Aqua satellite)1st June 2003, 13:30 solar time

[C]

[C]

Undetected
outliers (clouds)

Original MODIS LST map,
QA layer applied as filter

Reconstructed MODIS LST map

[C]

[C]

MODIS LST (Aqua satellite)1st June 2003, 13:30 solar time

Reconstructed LST time series
versus meteo data

Missingdata dueto cloudsetc.

Available now: > 11000 LST maps (4/day)

LST and meteo-dataare two independentdata sets

Examples for Daily mean data

16-day period aggregated means

Linear regression for trend analysis

Neteler, M., 2010: Estimating daily LST in mount. env. by reconstr. MODIS LST data. Rem. Sens. 2(1), 333-351

Infrastructure:
GIS & RS Platform cluster for GIS-HPC

12 single-blades and 2 double-blades

In total 128 nodes with 400 Gb RAM

Circa 1.7 Tflops/s

Linux operating system, blades diskless

Used for heavy GIS
data processing

GRASS and R-stats

Queue system for
job management
(Grid Engine)

Flow tracing and watershed analysis: r.watershed: D8, SFD, MFD, shortest path

r.flow: Dinf, SFD

r.terraflow: D8, SFD/MFD

Process-based modeling: r.sim.water:overland water flow

r.sim.sediment: soil erosion/deposition

r.topmodel: hydrologic simulation

Flood modelling: r.lake

HydroFOSS, JGRASS

Watershed analysis and hydrologic modeling

Web Processing Service - WPSJ Cepicky, 2008

Controlling an analytical GISfrom Web

Current GRASS-WPS implementationsPyWPS: Python

52N WPS: Java

vtkGRASSBridge: VTK

ZOO project - various languages

...

Web

Ch. Schwartze,
Geoinformatics FCE CTU 2008

GRASS 6.4.0 News

Modules improvements
Rewritten for being fast!... g.mlist, g.mremove: scripts rewritten as faster C version

r.cost: 50x faster

r.watershed: time consumption reduced from n to log(n)

r.horizon (new) + r.sun (rewritten): faster, improved

Improved...

v.out.ascii: attribute export added

v.out.gpsbabel: new, export to GPS

v.buffer, v.delaunay, v.parallel: rewritten (Google SoC 2008 projects)

wxPython digitizer: Undo function added

wxPython GUI: MS-Windows portable

Additionally:
> 230 bug tickets fixedR-GRASS-Interface:
now fast data exchange

GRASS 6.4.0 News

New modules

NVIZ for wxPython: new

r.external: link raster maps instead of import (think WPS!)

v.colors: new, easy vector color tables

v.generalize: new, 10 vector generalization algorithms

v.out.gpsbabel: new, export to GPS

and even more...

d.split.frame, r.colors.stddev, r.grow.distance, v.to.3d

...

GRASS 7 News

Image processing: i.albedo , i.biomass , i.cca , i.eb.eta , i.eb.evapfr , i.eb.h_SEBAL01 , i.eb.soilheatflux , i.emissivity , i.evapo.time_integration , i.latlong , i.modis.qc , i.sunhours , i.vi

Raster processing: .cost, r.walk, r.watershed: faster by orders of magnitude (10x 1000x)

Vector processing: v.in.ogr: faster data import

v.krige: new kriging (using gstat/R)

new spatial index: faster queries (>10x for large vectors), less memory for opening an existing vector

General/API: Large File Support (LFS) globally enabled

Improved Python API, read/write GRASS rasters to/from NumPy

WPS support implemented in parser (c, c++, python, shell)

www.grassbook.org

Translated by Tetsuji Uemura

ARIGATO....

thanks to OSGeo.JP
for making this trip
possible!

2010, Markus Neteler