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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):

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

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:

• BIO1: Annual mean temperature (°C*10): eurolst_clim.bio01.zip (MD5) 72MB
• BIO2: Mean diurnal range (Mean monthly (max – min tem)): eurolst_clim.bio02.zip (MD5) 72MB
• BIO3: Isothermality ((bio2/bio7)*100): eurolst_clim.bio03.zip (MD5) 72MB
• BIO4: Temperature seasonality (standard deviation * 100): eurolst_clim.bio04.zip (MD5) 160MB
• BIO5: Maximum temperature of the warmest month (°C*10): eurolst_clim.bio05.zip (MD5) 106MB
• BIO6: Minimum temperature of the coldest month (°C*10): eurolst_clim.bio06.zip (MD5) 104MB
• BIO7: Temperature annual range (bio5 – bio6) (°C*10): eurolst_clim.bio07.zip (MD5) 132MB
• BIO10: Mean temperature of the warmest quarter (°C*10): eurolst_clim.bio10.zip (MD5) 77MB
• BIO11: Mean temperature of the coldest quarter (°C*10): eurolst_clim.bio11.zip (MD5) 78MB

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.

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.

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/

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# 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

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

#!/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
)

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/

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

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.

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

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

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

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.

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:

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.

2. IHS transform

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

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″):

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

3. PCA transform

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

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.