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GRASS GIS 7.2.1 released

May 10, 2017 Markus Neteler

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

The post GRASS GIS 7.2.1 released appeared first on GFOSS Blog | GRASS GIS Courses.

by neteler at 7:38 PM under blog , fossgis , gis , grass , osgeo , remote sensing (Comments)

The EuroLST seamless and gap-free daily European maps of land surface temperatures

September 13, 2016 Markus Neteler

The EuroLST dataset is seamless and gap-free with a temporal resolution of four records per day and enhanced spatial resolution of 250 m. This newly developed reconstruction method (Metz et al, 2014) has been applied to Europe and neighbouring countries, resulting in complete daily coverage from 2001 onwards. To our knowledge, this new reconstructed LST time series exceeds the level of detail of comparable reconstructed LST datasets by several orders of magnitude. Studies on emerging diseases, parasite risk assessment and temperature anomalies can now be performed on the continental scale, maintaining high spatial and temporal detail. In their paper, the authors provide examples for implications and applications of the new LST dataset, such as disease risk assessment, epidemiology, environmental monitoring, and temperature anomalies.

Reconstructed MODIS Land Surface Temperature Dataset, at 250m pixel resolution (click figure to enlarge):
MODIS lst time series reconstructed

Section 1. Article and data citation:

EuroLST has been produced by the former PGIS group at Fondazione Edmund Mach, DBEM based on daily MODIS LST (Product of NASA) maps.

Metz, M.; Rocchini, D.; Neteler, M. 2014: Surface temperatures at the continental scale: Tracking changes with remote sensing at unprecedented detail. Remote Sensing. 2014, 6(5): 3822-3840 (DOI | HTML | PDF)

Section 2. Used software

Open Source commands used in processing (GRASS GIS 7):
links to the related manual pages involved in the data preparation

  • i.pca: Principal Components Analysis (PCA) for image processing.
  • r.regression.multi: it calculates multiple linear regression from raster maps
  • v.surf.bspline: it performs bicubic or bilinear spline interpolation with Tykhonov regularization.

Furthermore:

  • r.bioclim: calculates various bioclimatic indices from monthly temperature and optional precipitation time series (install in GRASS GIS 7 with “g.extention r.bioclim”)
  • pyModis: Free and Open Source Python based library to work with MODIS data

Section 3. Metadata

Map projection: EPSG 3035, prj file
PROJCS["Lambert Azimuthal Equal Area",
    GEOGCS["grs80",
        DATUM["European_Terrestrial_Reference_System_1989",
            SPHEROID["Geodetic_Reference_System_1980",6378137,298.257222101]],
        PRIMEM["Greenwich",0],
        UNIT["degree",0.0174532925199433]],
    PROJECTION["Lambert_Azimuthal_Equal_Area"],
    PARAMETER["latitude_of_center",52],
    PARAMETER["longitude_of_center",10],
    PARAMETER["false_easting",4321000],
    PARAMETER["false_northing",3210000],
    UNIT["Meter",1]]

1. Selected open data derived from EuroLST

Section 1. BIOCLIM derived from reconstructed MODIS LST at 250m pixel resolution

BIO1: Annual mean temperature (°C*10) BIO2: Mean diurnal range (Mean monthly (max - min tem)) BIO3: Isothermality ((bio2/bio7)*100) BIO4: Temperature seasonality (standard deviation * 100) BIO5: Maximum temperature of the warmest month (°C*10) BIO6: Minimum temperature of the coldest month (°C*10) BIO7: Temperature annual range (bio5 - bio6) (°C*10) BIO10: Mean temperature of the warmest quarter (°C*10) BIO11: Mean temperature of the coldest quarter (°C*10)

BIOCLIM-like European LST maps following the “Bioclim” definition (Hutchinson et al., 2009) – derived from 10 years of reconstructed MODIS LST (download to be completed) as GeoTIFF files, 250m pixel resolution, in EU LAEA projection:

Each ZIP file contains the respective GeoTIFF file (for cell value units, see below), the color table as separate ASCII file and a README.txt with details.

Section 2. WMS/WCS Server

Using this URL, you can read the EuroLST BIOCLIM data directly via OGC WMS and WCS protocol:

http://geodati.fmach.it/production/ows_europe_lst

Section 3. OpenData License

The data published in this page are open data and released under the ODbL (Open Database License).

The full EuroLST dataset is not released online as open data (size: 18TB), please ask Luca Delucchi or Roberto Zorer for more info

2. Acknowledgments

The MOD11A1.005, MYD11A1.005 were retrieved from the online web site, courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, http://e4ftl01.cr.usgs.gov/

The post The EuroLST seamless and gap-free daily European maps of land surface temperatures appeared first on GFOSS Blog | GRASS GIS Courses.

by neteler at 5:11 PM under bioclim , blog , data , geodata , lst , modis , raster maps , remote sensing , temperature

Markus Neteler joins the management of mundialis in Bonn

January 25, 2016 Markus Neteler

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/

The post Markus Neteler joins the management of mundialis in Bonn appeared first on GFOSS Blog | GRASS GIS Courses.

by neteler at 5:06 PM under blog , geodata , gis , grass , hpc , landsat , ogc , osgeo , remote sensing

Compiling OTB Orfeo ToolBox software on Centos/Scientific Linux

February 25, 2015 Markus Neteler

The Orfeo ToolBox (OTB), an open-source C++ library for remote sensing images processing, is offering a wealth of algorithms to perform Image manipulation, Data pre-processing, Features extraction, Image Segmentation and Classification, Change detection, Hyperspectral processing, and SAR processing.

Since there is no (fresh) RPM package available for Centos or Scientific Linux, here some quick hints (no full tutorial, though) how to get OTB easily locally compiled. We are following the Installation Chapter.

Importantly, you need to have some libraries installed including GDAL. Be sure that it has been compiled with the “–with-rename-internal-libtiff-symbols” and ” –with-rename-internal-libgeotiff-symbols” flags to avoid namespace collision a.k.a segmentation fault of OTB as per “2.2.4 Building your own qualified Gdal“. We’ll configure and build with the GDAL-internal Tiff and Geotiff libraries that supports BigTiff files

# configure GDAL
./configure \
 --without-libtool \
 --with-geotiff=internal --with-libtiff=internal \
 --with-rename-internal-libtiff-symbols=yes \
 --with-rename-internal-libgeotiff-symbols=yes \
...
make
make install

The compilation of the OTB source code requires “cmake” and some other requirements which you can install via “yum install …”. Be sure to have the following structure for compiling OTB, i.e. store the source code in a subdirectory. The binaries will then be compiled in a “build” directory parallel to the OTB-SRC directory:

OTB-4.4.0/
|-- build/
`-- OTB-SRC/
    |-- Applications/
    |-- CMake/
    |-- CMakeFiles/
    |-- Code/
    |-- Copyright/
    |-- Examples/
    |-- Testing/
    `-- Utilities/

Now it is time to configure everything for OTB. Since I didn’t want to bother with “ccmake”, below the magic lines to compile and install OTB into its own subdirectory within /usr/local/. We’ll use as many internal libraries as possible according to the table in the installation guide. The best way is to save the following lines as a text script “cmake_otb.sh” for easier (re-)use, then run it:

#!/bin/sh

OTBVER=4.4.0
(
mkdir -p build
cd build

cmake -DCMAKE_INSTALL_PREFIX:PATH=/usr/local/otb-$OTBVER \
      -DOTB_USE_EXTERNAL_ITK=OFF -DOTB_USE_EXTERNAL_OSSIM=OFF \
      -DOTB_USE_EXTERNAL_EXPAT=OFF -DOTB_USE_EXTERNAL_BOOST=OFF \
      -DOTB_USE_EXTERNAL_TINYXML=OFF -DOTB_USE_EXTERNAL_LIBKML=OFF \
      -DOTB_USE_EXTERNAL_MUPARSER=OFF \
       ../OTB-SRC/

make -j4
# note: we assume to have write permission in /usr/local/otb-$OTBVER
make install
)

That’s it!

In order to use the freshly compiled OTB, be sure to add the new directories for the binaries and the libraries to your PATH and LD_LIBRARY_PATH variables, e.g. in $HOME/.bashrc:

export PATH=$PATH:/usr/local/bin:/usr/local/otb-4.4.0/bin
export LD_LIBRARY_PATH=/usr/local/lib:/usr/local/lib64/:/usr/local/otb-4.4.0/lib/otb/

Enjoy OTB! And thanks to the OTB developers for making it available.

The post Compiling OTB Orfeo ToolBox software on Centos/Scientific Linux appeared first on GFOSS Blog | GRASS GIS Courses.

by neteler at 9:19 PM under blog , centos , fedora , linux , orfeo , otb , raster maps , remote sensing (Comments)

New stable release of GRASS GIS 7.0.0!

February 22, 2015 Markus Neteler

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).

The post New stable release of GRASS GIS 7.0.0! appeared first on GFOSS Blog | GRASS GIS Courses.

by neteler at 10:15 PM under blog , fossgis , gis , grass , opensource , osgeo , raster maps , remote sensing , vector , visualization , voxel , wps (Comments)

Landsat 8 captures Trentino in November 2014

November 27, 2014 Markus Neteler

The beautiful days in early November 2014 allowed to get some nice views of the Trentino (Northern Italy) – thanks to Landsat 8 and NASA’s open data policy:

Landsat 8: Northern Italy 1 Nov 2014
Landsat 8: Northern Italy 1 Nov 2014

Trento captured by Landsat8
Trento captured by Landsat8

Landsat 8: San Michele - 1 Nov 2014
Landsat 8: San Michele – 1 Nov 2014

The beauty of the landscape but also the human impact (landscape and condensation trails of airplanes) are clearly visible.

All data were processed in GRASS GIS 7 and pansharpened with i.fusion.hpf written by Nikos Alexandris.

The post Landsat 8 captures Trentino in November 2014 appeared first on GFOSS Blog | GRASS GIS Courses.

by neteler at 5:21 PM under blog , grass , landsat , osgeo , remote sensing (Comments)

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

October 4, 2013 Markus Neteler

banner_pansharpening

Table Of Content

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

2. Landsat 8 pansharpening

3. Conclusions

In our first posting (“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 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.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.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.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.

by neteler at 11:32 PM under blog , gis , grass , landsat , remote sensing (Comments)
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