Category Archives: GIS

GRASS GIS 7.2.2 released

GRASS GIS 7.2.2 in action

What’s new in a nutshell

After three months of development the new update release GRASS GIS 7.2.2 is available. It provides more than 120 stability fixes and manual improvements compared to release version 7.2.1. An overview of new features in the 7.2 release series is available at New Features in GRASS GIS 7.2.

About GRASS GIS 7: Its graphical user interface supports the user to make complex GIS operations as simple as possible. The updated Python interface to the C library permits users to create new GRASS GIS-Python modules in a simple way while yet obtaining powerful and fast modules. Furthermore, the libraries were again significantly improved for speed and efficiency, along with support for huge files. A lot of effort has been invested to standardize parameter and flag names. Finally, GRASS GIS 7 comes with a series of new modules to analyse raster and vector data, along with a full temporal framework. For a detailed overview, see the list of new features. As a stable release series, 7.2.x enjoys long-term support.

Binaries/Installer download:

Source code download:

More details:

See also our detailed announcement:

First time users may explore the first steps tutorial after installation.

About GRASS GIS

The Geographic Resources Analysis Support System (https://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).

The GRASS Development Team, Sep 2017

GRASS GIS 7.2.1 released

We are pleased to announce the update release GRASS GIS 7.2.1

GRASS GIS 7.2.1 in actionWhat’s new in a nutshell

After four months of development the new update release GRASS GIS 7.2.1 is available. It provides more than 150 stability fixes and manual improvements compared to the first stable release version 7.2.0. An overview of new features in this release series is available at New Features in GRASS GIS 7.2.

About GRASS GIS 7: Its graphical user interface supports the user to make complex GIS operations as simple as possible. The updated Python interface to the C library permits users to create new GRASS GIS-Python modules in a simple way while yet obtaining powerful and fast modules. Furthermore, the libraries were again significantly improved for speed and efficiency, along with support for huge files. A lot of effort has been invested to standardize parameter and flag names. Finally, GRASS GIS 7 comes with a series of new modules to analyse raster and vector data, along with a full temporal framework. For a detailed overview, see the list of new features. As a stable release series, 7.2.x enjoys long-term support.

Binaries/Installer download:

Source code download:

More details:

See also our detailed announcement:

https://trac.osgeo.org/grass/wiki/Grass7/NewFeatures72 (overview of new 7.2 stable release series)

https://grass.osgeo.org/grass72/manuals/addons/ (list of available addons)

First time users may explore the first steps tutorial after installation.

About GRASS GIS

The Geographic Resources Analysis Support System (https://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).

The GRASS Development Team, May 2017

New major release: GRASS GIS 7.2.0 available

We are pleased to announce the stable release of GRASS GIS 7.2.0

What’s new in a nutshell

After almost two years of development the new stable major release GRASS GIS 7.2.0 is available. It provides more than 1950 stability fixes and manual improvements compared to the former stable release version 7.0.5. The new version includes a series of new modules to analyse raster and vector data along with new temporal algebra functionality.More than 50 new addons are also available. A summary of the new features is available at New Features in GRASS GIS 7.2.

About GRASS GIS 7: Its graphical user interface supports the user to make complex GIS operations as simple as possible. The updated Python interface to the C library permits users to create new GRASS GIS-Python modules in a simple way while yet obtaining powerful and fast modules. Furthermore, the libraries were again significantly improved for speed and efficiency, along with support for huge files. A lot of effort has been invested to standardize parameter and flag names. Finally, GRASS GIS 7 comes with a series of new modules to analyse raster and vector data, along with a full temporal framework. For a detailed overview, see the list of new features. As a stable release series, 7.2.x enjoys long-term support.

Binaries/Installer download:

Source code download:

More details:

See also our detailed announcement:

First time users may explore the first steps tutorial after installation.

About GRASS GIS

The Geographic Resources Analysis Support System (https://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).

The GRASS Development Team, December 2016

Markus Neteler joins the management of mundialis in Bonn

Press release

From March 2016 onwards, Dr. Markus Neteler, a prominent head of the Open Source GIS scene, will join the management board of mundialis GmbH & Co. KG in Bonn, Germany. Founded in 2015, mundialis combines remote sensing and satellite data analysis in the field of Big Data with Open Source WebGIS solutions.

Since 2008, Dr. Neteler was the head of the GIS and remote sensing unit at the Edmund Mach Foundation in Trento (Italy) and worked in this capacity on numerous projects related to biodiversity, environmental and agricultural research. He is also a founding member of the Open Source Geospatial Foundation (OSGeo), a nonprofit organization with headquarters in Delaware (USA), that promotes the development and use of free and open source geographic information systems (GIS). Since 1998 he coordinated the development of the well known GRASS GIS software project, a powerful Open Source GIS that supports processing of time series of several thousand raster, 3D raster or vector maps in a short time. Mongolia as seen by Sentinel-2A

Markus will keep his role as “Mr. GRASS” at mundialis, especially because the company also sees itself as a research and development enterprise that puts its focus on the open source interfaces between geoinformation and remote sensing. Although a new company, mundialis offers more than 50 years of experience in GIS, due to the background of its management. Besides Neteler, there are Till Adams and Hinrich Paulsen, both at the same time the founders and CEOs of terrestris in Bonn, a company that develops Open Source GIS solutions since 2002. These many years of experience in the construction of WebGIS and Geoportal architectures using free software as well as in the application of common OGC standards – are now combined with mundialis’ expertise in the processing of big data with spatial reference and remote sensing data.

Contact: http://www.mundialis.de/

GRASS GIS 7.0.1 released – 32 years of GRASS GIS

What’s new in a nutshellgrass7_logo_500px

This release addresses some minor issues found in the first GRASS GIS 7.0.0 release published earlier this year. The new release provides a series of stability fixes in the core system and the graphical user interface, PyGRASS improvements, some manual enhancements, and a few language translations.

This release is the 32nd birthday release of GRASS GIS.

New in GRASS GIS 7: Its new graphical user interface supports the user in making complex GIS operations as simple as possible. A new Python interface to the C library permits users to create new GRASS GIS-Python modules in a simple way while yet obtaining powerful and fast modules. Furthermore, the libraries were significantly improved for speed and efficiency, along with support for huge files. A lot of effort has been invested to standardize parameter and flag names. Finally, GRASS GIS 7 comes with a series of new modules to analyse raster and vector data, along with a full temporal framework. For a detailed overview, see the list of new features. As a stable release 7.0 enjoys long-term support.

Source code download:

Binaries download:

More details:

See also our detailed announcement:

  http://trac.osgeo.org/grass/wiki/Grass7/NewFeatures (overview of new stable release series)First time users may explore the first steps tutorial after installation.

About GRASS GIS

The Geographic Resources Analysis Support System (http://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).

The GRASS Development Team, July 2015

Inofficial QGIS 2.8 RPMs for EPEL 7: Fedora 20, Fedora 21, Centos 7, Scientific Linux 7

qgis-icon_smallThanks to the work of Devrim Gündüz, Volker Fröhlich, Dave Johansen, Rex Dieter and other Fedora/EPEL packagers I had an easy going to prepare RPM packages of QGIS 2.8 Wien for Fedora 20 and 21, Centos 7, and Scientific Linux 7.

The base SRPM package I copied from Fedora’s koji server, modified the SPEC file in order to remove the now outdated PyQwt bindings (see bugzilla) and compiled QGIS 2.8 via the great COPR platform.

Repo: https://copr.fedoraproject.org/coprs/neteler/QGIS-2.8-Wien/

The following packages can now be installed and tested on epel-7-x86_64 (Centos 7, Scientific Linux 7, etc.), Fedora-20-x86_64, and Fedora-21-x86_64:

  • qgis 2.8.1
  • qgis-debuginfo 2.8.1
  • qgis-devel 2.8.1
  • qgis-grass 2.8.1
  • qgis-python 2.8.1
  • qgis-server 2.8.1

Installation instructions (run as “root” user or use “sudo”):

# EPEL7:
yum -y install epel-release
yum -y install wget
# https://copr.fedoraproject.org/coprs/neteler/python-OWSLib/
wget -O /etc/yum.repos.d/neteler-python-OWSLib-epel-7.repo https://copr.fedoraproject.org/coprs/neteler/python-OWSLib/repo/epel-7/neteler-python-OWSLib-epel-7.repo
yum -y update
yum -y install python-OWSLib
wget -O /etc/yum.repos.d/qgis-epel-7.repo https://copr.fedoraproject.org/coprs/neteler/QGIS-2.8-Wien/repo/epel-7/neteler-QGIS-2.8-Wien-epel-7.repo
yum update
yum install qgis qgis-grass qgis-python qgis-server

# Fedora 20:
wget -O /etc/yum.repos.d/qgis-epel-7.repo https://copr.fedoraproject.org/coprs/neteler/QGIS-2.8-Wien/repo/fedora-20/neteler-QGIS-2.8-Wien-fedora-20.repo
yum update
yum install qgis qgis-grass qgis-python qgis-server

# Fedora 21:
wget -O /etc/yum.repos.d/qgis-epel-7.repo https://copr.fedoraproject.org/coprs/neteler/QGIS-2.8-Wien/repo/fedora-21/neteler-QGIS-2.8-Wien-fedora-21.repo
yum update
yum install qgis qgis-grass qgis-python qgis-server

The other packages are optional (well, also qgis-grass, qgis-python, and qgis-server…).

Enjoy!

PS: Of course I hope that QGIS 2.8 officially hits EPEL7 anytime soon! My COPR repo is just a temporary bridge towards that goal.

EDIT 30 April 2015:

  • updated EPEL7 installation for python-OWSLib dependency

New stable release of GRASS GIS 7.0.0!

The GRASS GIS Development team has announced the release of the new major version GRASS GIS 7.0.0. This version provides many new functionalities including spatio-temporal database support, image segmentation, estimation of evapotranspiration and emissivity from satellite imagery, automatic line vertex densification during reprojection, more LIDAR support and a strongly improved graphical user interface experience. GRASS GIS 7.0.0 also offers significantly improved performance for many raster and vector modules: “Many processes that would take hours now take less than a minute, even on my small laptop!” explains Markus Neteler, the coordinator of the development team composed of academics and GIS professionals from around the world. The software is available for Linux, MS-Windows, Mac OSX and other operating systems.

Detailed announcement and software download:
http://grass.osgeo.org/news/42/15/GRASS-GIS-7-0-0/

About GRASS GIS
The Geographic Resources Analysis Support System (http://grass.osgeo.org/), commonly referred to as GRASS GIS, is an open source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).

GRASS GIS 7: Vector data reprojection with automated vertex densification

GRASS GIS 7 just got better: When reprojecting vector data, now automated vertex densification is applied. This reduces the reprojection error for long lines (or polygon boundaries). The needed improvement has been kindly added in v.proj by Markus Metz.

1. Example

As an (extreme?) example, we generate a box in LatLong/WGS84 (EPSG: 4326) which is of 10 degree side length (see below for screenshot and at bottom for SHAPE file download of this “box” map):

[neteler@oboe ~]$ grass70 ~/grassdata/latlong/grass7/
# for the ease of generating the box, set computational region:
g.region n=60 s=40 w=0 e=30 res=10 -p
projection: 3 (Latitude-Longitude)
zone:       0
datum:      wgs84
ellipsoid:  wgs84
north:      60N
south:      40N
west:       0
east:       30E
nsres:      10
ewres:      10
rows:       2
cols:       3
cells:      6
# generate the box according to current computational region:
v.in.region box_latlong_10deg
exit

Next we start GRASS GIS in a metric projection, here the EU LAEA:

# EPSG 3035, metric EU LAEA:
grass70 ~/grassdata/europe_laea/user1/
GRASS 7.0.0svn (europe_laea): >

Now we first reproject the map the “traditional way” (no vertex densification as in most GIS, here enforced by smax=0):

v.proj box_latlong_10deg out=box_latlong_10deg_no_densification
location=latlong mapset=grass7 smax=0

Then we do a second reprojection with new automated vertex densification (here we use the default values for smax which is a 10km vertex distance in the reprojected map by default):

v.proj box_latlong_10deg out=box_latlong_10deg_yes_densification
location=latlong mapset=grass7

Eventually we can compare both reprojected maps:

g.region vect=box_latlong_10deg_no_densification

# compare:
d.mon wx0
d.vect box_latlong_10deg_no_densification color=red
d.vect box_latlong_10deg_yes_densification color=green fill_color=none
Comparison of the reprojection of a 10 degree large LatLong box to the metric EU LAEA (EPSG 3035): before in red and new in green. The grid is based on WGS84 at 5 degree spacing.

Comparison of the reprojection of a 10 degree large LatLong box to the metric EU LAEA (EPSG 3035): before in red and new in green. The grid is based on WGS84 at 5 degree spacing.

The result shows how nicely the projection is now performed in GRASS GIS 7: the red line output is old, the green color line is the new output (its area filling is not shown).

Consider to benchmark this with other GIS… the reprojected map should not become a simple trapezoid.

2. Sample dataset download

Download of box_latlong_10deg.shp for own tests (1kB).

50th ICA-OSGeo Lab established at Fondazione Edmund Mach (FEM)

We are pleased to announce that the 50th ICA-OSGeo Lab has been established at the GIS and Remote Sensing Unit (Piattaforma GIS & Remote Sensing, PGIS), Research and Innovation Centre (CRI), Fondazione Edmund Mach (FEM), Italy. CRI is a multifaceted research organization established in 2008 under the umbrella of FEM, a private research foundation funded by the government of Autonomous Province of Trento. CRI focuses on studies and innovations in the fields of agriculture, nutrition, and environment, with the aim to generate new sharing knowledge and to contribute to economic growth, social development and the overall improvement of quality of life.

The mission of the PGIS unit is to develop and provide multi-scale approaches for the description of 2-, 3- and 4-dimensional biological systems and processes. Core activities of the unit include acquisition, processing and validation of geo-physical, ecological and spatial datasets collected within various research projects and monitoring activities, along with advanced scientific analysis and data management. These studies involve multi-decadal change analysis of various ecological and physical parameters from continental to landscape level using satellite imagery and other climatic layers. The lab focuses on the geostatistical analysis of such information layers, the creation and processing of indicators, and the production of ecological, landscape genetics, eco-epidemiological and physiological models. The team pursues actively the development of innovative methods and their implementation in a GIS framework including the time series analysis of proximal and remote sensing data.

The GIS and Remote Sensing Unit (PGIS) members strongly support the peer reviewed approach of Free and Open Source software development which is perfectly in line with academic research. PGIS contributes extensively to the open source software development in geospatial (main contributors to GRASS GIS), often collaborating with various other developers and researchers around the globe. In the new ICA-OSGeo lab at FEM international PhD students, university students and trainees are present.

PGIS is focused on knowledge dissemination of open source tools through a series of courses designed for specific user requirement (schools, universities, research institutes), blogs, workshops and conferences. Their recent publication in Trends in Ecology and Evolution underlines the need on using Free and Open Source Software (FOSS) for completely open science. Dr. Markus Neteler, who is leading the group since its formation, has two decades of experience in developing and promoting open source GIS software. Being founding member of the Open Source Geospatial Foundation (OSGeo.org, USA), he served on its board of directors from 2006-2011. Luca Delucchi, focal point and responsible person for the new ICA-OSGeo Lab is member of the board of directors of the Associazione Italiana per l’Informazione Geografica Libera (GFOSS.it, the Italian Local Chapter of OSGeo). He contributes to several Free and Open Source software and open data projects as developer and trainer.

Details about the GIS and Remote Sensing Unit at http://gis.cri.fmach.it/

Open Source Geospatial Foundation (OSGeo) is a not-for-profit organisation founded in 2006 whose mission is to support and promote the collaborative development of open source geospatial technologies and data.

International Cartographic Association (ICA) is the world authoritative body for cartography and GIScience. See also the new ICA-OSGeo Labs website.

Processing Landsat 8 data in GRASS GIS 7: RGB composites and pan sharpening

banner_pansharpening

In our first blog post (“Processing Landsat 8 data in GRASS GIS 7: Import and visualization“) we imported a Landsat 8 scene (covering Raleigh, NC, USA). In this exercise we use Landsat 8 data converted to reflectance with i.landsat.toar as shown in the first posting.

Here we will try color balancing and pan-sharpening, i.e. applying the higher resolution panchromatic channel to the color channels, using i.colors.enhance (former i.landsat.rgb).

1. Landsat 8 – RGB color balancing: natural color composites

After import, the RGB (bands 4,3,2 for Landsat 8) may look initially less exciting than expected.This is easy to fix by a histogram based auto-balancing of the RGB color tables.

landsat8_rgb_composite_unbalanced

To brighten up the RGB composite, we can use the color balancing tool of GRASS GIS 7:

grass7_landsat_rgb0

As input, we specify the bands 4, 3, and 2:

grass7_landsat_rgb1

Using a “Cropping intensity (upper brightness level)” of 99 (percent), the result look as follows:

landsat10_rgb_composite_autobalance_99percent_crop

For special purposes or under certain atmospheric/ground conditions it may be useful to make use of the functions “Preserve relative colors, adjust brightness only” or “Extend colors to full range of data on each channel” in the “Optional” tab of i.colors.enhance (former i.landsat.rgb).

landsat9_rgb_composite_preserve_relative_colors

You will need to experiment since the results depend directly on the image data.

2. Landsat 8 pansharpening

Pansharpening is a technique to merge the higher geometrical pixel resolution of the panchromatic band (Band 8) with the lower resolution color bands (Bands 4, 3, 2).

GRASS GIS 7 offers several methods through the command i.pansharpen.

1) Brovey transform:

landsat8_pansharpen_brovey1

This module runs in multi-core mode parallelized. The management of the resolution (i.e., apply the higher resolution of the panchromatic band) is performed automatically.

landsat8_pansharpen_brovey2

2. IHS transform

Here we select as above the bands in the i.pansharpen interface but use the “ihs” method.

landsat8_pansharpen_ihs1

HINT: If the colors should look odd, then apply i.colors.enhance (former i.landsat.rgb) to the pan-sharpened bands (see above).

Color-adjusted IHS pansharpening (with “Cropping intensity: strength=99”):

landsat8_pansharpen_ihs_color_adjusted

Comparison of Landsat 8 RGB composite (39m) and IHS pansharpened RGB composite (15m):

landsat8_rgb432_color_adjusted_zoom landsat8_rgb432_pansharpen_ihs_color_adjusted_zoom

3. PCA transform

Here we select as above the bands in the i.pansharpen interface but use the “pca” method.

landsat8_pansharpen_pca1

Likewise other channels may be merged with i.pansharpen, even when originating from different sensors.

3. Conclusions

Overall, the IHS pansharpening method along with auto-balancing of colors appears to perform very well with Landsat 8.

Edit 2015: See also pansharpening with i.fusion.hpf!