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Mon Oct 24 23:50:11 2016

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

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
  • it performs bicubic or bilinear spline interpolation with Tykhonov regularization.


  • 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",

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:

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,

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

Compiling OTB Orfeo ToolBox software on Centos/Scientific Linux

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 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:

|-- 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 “” for easier (re-)use, then run it:


mkdir -p build
cd build

cmake -DCMAKE_INSTALL_PREFIX:PATH=/usr/local/otb-$OTBVER \

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.

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:

The Geographic Resources Analysis Support System (, 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.

EU-DEM: new Digital Surface Model at 25m


EU DEM 25m upper Garda Lake area with Riva del Garda and Arco (Italy). 3D view in wxNVIZ – GRASS GIS 7

The 25m European Digital Elevation Model (EU-DEM, Version 1) is a Digital Surface Model (DSM) representing the first surface as illuminated by the sensors:


EU DEM Rotaliana with Mezzocorona and S. Michele (Italy). Produced using Copernicus data and information funded by the European Union – EU-DEM layers.

Its elevations were captured at 1 arc second postings (2.78E-4 degrees). The tiles are provided at 25m resolution in EU-LAEA (EPSG. 3035) projection, temporal coverage: 2000, published in Oct 2013. It is a realisation of the Copernicus programme, managed by the European Commission, DG Enterprise and Industry. Metadata are provided here. According to their “Methodology” page it is a hybrid product based on SRTM and ASTER GDEM data fused by a weighted averaging approach and it has been generated as a contiguous dataset divided into 1 degree by 1 degree tiles, corresponding to the SRTM naming convention. In addition to the DEM data, a colour shaded relief image over Europe is provided.

From the metadata page: “The EU-DEM data are provided as is, i.e. without a formal validation yet. An independent statistical validation is scheduled as part of the GIO land monitoring service activities, and will be made available in the course of 2014.

1. Data download

Note that the GeoTIFF files are of major size, up to 5 GB:

Hint for GRASS GIS users: instead of importing the data, you can use r.external to simply register the GeoTIFF file within a EU LAEA projected location.


The post EU-DEM: new Digital Surface Model at 25m appeared first on GFOSS Blog | GRASS GIS Courses.

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