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.
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).
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.colors.enhance (former i.landsat.rgb).
You will need to experiment since the results depend directly on the image data.
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.colors.enhance (former i.landsat.rgb) to the pan-sharpened bands (see above).
The Landsat 8 mission is a collaboration between the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration (NASA) which continues the acquisition of high-quality data for observing land use and land cover change.
The Landsat 8 spacecraft which was launched in 2013 carries they following key instruments:
OLI: the Operational Land Imager which collects data in the visible, near infrared, and shortwave infrared wavelength regions as well as a panchromatic band. With respect to Landsat 7 two new spectral bands have been added: a deep-blue band for coastal water and aerosol studies (band 1), and a band for cirrus cloud detection (band 9). Furthermore, a Quality Assurance band (BQA) is also included to indicate the presence of terrain shadowing, data artifacts, and clouds.
TIRS: The Thermal Infrared Sensor continues thermal imaging and is also intended to support emerging applications such as modeling evapotranspiration for monitoring water use consumption over irrigated lands.
The data from Landsat 8 are available for download at no charge and with no user restrictions.
Using the “Download options”, you can download the data set (requires login). Select the choice:
[x] Level 1 GeoTIFF Data Product (842.4 MB)
You will receive the file “LC80160352013134LGN03.tar.gz”.
Unpacking the downloaded Landsat 8 dataset
To unpack the data, run (or use a graphical tool at your choice):
tar xvfz LC80160352013134LGN03.tar.gz
A series of GeoTIFF files will be extracted: LC80160352013134LGN03_B1.TIF, LC80160352013134LGN03_B2.TIF, LC80160352013134LGN03_B3.TIF, LC80160352013134LGN03_B4.TIF, LC80160352013134LGN03_B5.TIF, LC80160352013134LGN03_B6.TIF, LC80160352013134LGN03_B7.TIF, LC80160352013134LGN03_B8.TIF, LC80160352013134LGN03_B9.TIF, LC80160352013134LGN03_B10.TIF, LC80160352013134LGN03_B11.TIF, LC80160352013134LGN03_BQA.TIF
Note: While this Landsat 8 scene covers the area of the North Carolina (NC) sample dataset, it is delivered in UTM rather than the NC’s state plane metric projection. Hence we preprocess the data first in its original UTM projection prior to the reprojection to NC SPM.
Using the Location Wizard, we can import the dataset easily into a new location (in case you don’t have UTM17N not already created earlier):
Now start GRASS GIS 7 and you will find the first band already imported (the others will follow shortly!).
For the lazy folks among us, we can also create a new GRASS GIS Location right away from the dataset on command line:
Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images consist of nine spectral bands with a spatial resolution of 30 meters for Bands 1 to 7 and 9. New band 1 (ultra-blue) is useful for coastal and aerosol studies. New band 9 is useful for cirrus cloud detection. The resolution for Band 8 (panchromatic) is 15 meters. Thermal bands 10 and 11 are useful in providing more accurate surface temperatures and are collected at 100 meters. Approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi).
Band 1 – Coastal aerosol
0.43 – 0.45
Band 1 – Blue
0.45 – 0.52
Band 2 – Blue
0.45 – 0.51
Band 2 – Green
0.52 – 0.60
Band 3 – Green
0.53 – 0.59
Band 3 – Red
0.63 – 0.69
Band 4 – Red
0.64 – 0.67
Band 4 (NIR)
0.77 – 0.90
Band 5 – Near Infrared (NIR)
0.85 – 0.88
Band 5 (SWIR 1)
1.55 – 1.75
Band 6 – SWIR 1
1.57 – 1.65
Band 7 (SWIR 2)
2.09 – 2.35
Band 7 – SWIR 2
2.11 – 2.29
Band 8 – Panchromatic
0.52 – 0.90
Band 8 – Panchromatic
0.50 – 0.68
Band 9 – Cirrus
Band 6 – Thermal Infrared (TIR)
Band 10 – Thermal Infrared (TIRS) 1
10.60 – 11.19
Band 11 – Thermal Infrared (TIRS) 2
* ETM+ Band 6 is acquired at 60-meter resolution. Products processed after February 25, 2010 are resampled to 30-meter pixels.
* TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.
Natural color view (RGB composite)
Due to the introduction of a new “Cirrus” band (#1), the BGR bands are now 2, 3, and 4, respectively. See also “Common band combinations in RGB” for Landsat 7 or Landsat 5, and Landsat 8.
From Digital Numer (DN) to reflectance:
Before creating an RGB composite, it is important to convert the digital number data (DN) to reflectance (or optionally radiance). Otherwise the colors of a “natural” RGB composite do not look convincing but rather hazy (see background in the next screenshot). This conversion is easily done using the metadata file which is included in the data set with i.landsat.toar:
Now we are ready to create a nice RGB composite (hint 2015: i.landsat.rgb has been renamed to i.colors.enhance):
Select the bands to be visually combined:
… and voilà !
Applying the Landsat 8 Quality Assessment (QA) Band
One of the bands of a Landsat 8 scene is named “BQA” which contains for each pixel a decimal value representing a bit-packed combination of surface, atmosphere, and sensor conditions found during the overpass. It can be used to judge the overall usefulness of a given pixel.
We can use this information to easily eliminate e.g. cloud contaminated pixels. In short, the QA concept is (cited here from the USGS page):
For the single bits (0, 1, 2, and 3):
0 = No, this condition does not exist
1 = Yes, this condition exists.
The double bits (4-5, 6-7, 8-9, 10-11, 12-13, and 14-15) represent levels of confidence that a condition exists:
00 = Algorithm did not determine the status of this condition
01 = Algorithm has low confidence that this condition exists (0-33 percent confidence)
10 = Algorithm has medium confidence that this condition exists (34-66 percent confidence)
11 = Algorithm has high confidence that this condition exists (67-100 percent confidence).
Detailed bit patterns (d: double bits; s: single bits):
d – Bit 15 = 0 = cloudy
d – Bit 14 = 0 = cloudy
d – Bit 13 = 0 = not a cirrus cloud
d – Bit 12 = 0 = not a cirrus cloud
d – Bit 11 = 0 = not snow/ice
d – Bit 10 = 0 = not snow/ice
d – Bit 9 = 0 = not populated
d – Bit 8 = 0 = not populated
d – Bit 7 = 0 = not populated
d – Bit 6 = 0 = not populated
d – Bit 5 = 0 = not water
d – Bit 4 = 0 = not water
s – Bit 3 = 0 = not populated
s – Bit 2 = 0 = not terrain occluded
s – Bit 1 = 0 = not a dropped frame
s – Bit 0 = 0 = not fill
Usage example 1: Creating a mask from a bitpattern
We can create a cloud mask (bit 15+14 are set) from this pattern:
Using the Python shell tab, we can easily convert this into the corresponding decimal number for r.mapcalc:
Welcome to wxGUI Interactive Python Shell 0.9.8
Type "help(grass)" for more GRASS scripting related information.
Type "AddLayer()" to add raster or vector to the layer tree.
Python 2.7.5 (default, Aug 22 2013, 09:31:58)
[GCC 4.8.1 20130603 (Red Hat 4.8.1-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Using this decimal value of 49152, we can create a cloud mask:
# set NULL for cloudy pixels, 1 elsewhere:
r.mapcalc "cloudmask = if(LC80160352013134LGN03_BQA == 49152, null(), 1 )"
# apply this mask
In our sample scene, there are only tiny clouds in the north-east, so no much to be seen. Some spurious cloud pixels are scattered over the scene, too, which could be eliminated (in case of false positives) or kept.
Usage example 2: Querying the Landsat 8 BQA map and retrieve the bitpattern
Perhaps you prefer to query the BQA map itself (overlay the previously created RGB composite and query the BSA map by selecting it in the Layer Manager). In our example, we query the BQA value of the cloud:
Using again the Python shell tab, we can easily convert the decimal number (used for r.mapcalc) into the corresponding binary representation to verify with the table values above.
“Although the USGS does not have detailed records since the mission’s inception in 1972, there is good evidence that more data have been distributed in the last 6 months than in the entire first 36 years of the Landsat missions combined.”
Amazing, no? Don’t have to say more when blinking at EU data sources…
Thermal map of Valsugana near Trento, Italy (30 July 2003, ~ 9:30)
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