Tag Archives: remote sensing

Convert 16bit to 8bit GeoTiff

Note to self: use the following command to scale 16 bit spatial raster data down to 8 bit.

gdal_translate -a_nodata 0 -of GTiff -ot Byte -scale 0 65535 0 255 input.tif 8bit_output.tif

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ARCSI

At the end of 2014 I used ARCSI (Atmospheric and Radiometric Correction of Satellite Imagery) for the first time. ARCSI is an open software project that provides a command line tool for the atmospheric correction of Earth Observation imagery. It provides a pretty much automatic way of running 6S.

Details of the software can be found here: https://bitbucket.org/petebunting/arcsi

At tutorial can be found here: https://spectraldifferences.wordpress.com/2014/05/27/arcsi/ 

This post is based largely on the tutorial linked to above but I also try to pull together some of the tips I read about in the help forums here: https://groups.google.com/forum/#!forum/rsgislib-support

Set up

All of the following instructions are run on an installation of the Ubuntu 14.04 operating system.

First, download the Anaconda 3.4 python distribution: http://docs.continuum.io/anaconda/

Install the Anaconda version of Python and the conda package manager using the command line. Once the bash script has run then install ARCSI and TuiView (a fast image viewer) using conda. If a warning comes up regarding gdal then install the gdal-data package.

bash Anaconda3-2.1.0-Linux-x86_64.sh

conda install -c https://conda.binstar.org/osgeo arcsi tuiview
conda update rsgislib arcsi

conda install -c jjhelmus gdal-data

For a successful installation I needed to change the default installation path for Anaconda from ~/anaconda3 to ~/anaconda

You should now be able to check your installation using the following command:


arcsi.py -h | less

To finalise the set up you need t point the GDAL driver path to the KEA drivers. Run the following to set up the path:


export GDAL_DRIVER_PATH=~/anaconda/gdalplugins:$GDAL_DRIVER_PATH

GDAL_DATA="/home/username/anaconda/share/gdal"

export GDAL_DATA

where username is the user account on the Ubuntu installation.

Run the code

When you run ARCSI, you’ll enter a command similar to the following:


arcsi.py -s ls8 -f KEA --stats -p RAD TOA SREF --aeropro NoAerosols --atmospro Tropical --aot 0.25 -o dir/to/outputs -i metadatafile_MTL.txt

To break this down a bit it first calls the arcsi.py script, passing to it the following parameters:

  • Sensor (-s) – landsat 8
  • Output image format (-f) – KEA
  • Parameters to compute (-p) – RAD Radiance conversion, TAO Top of atmosphere, SREF surface reflectance using 6S (for the full range please consult the official documentation)
  • Output directory (-o) – dir/to/outputs, into which the three computed outputs will be saved
  • Information (-i) – Landsat metadata file, using the format provided by the images available through Earth Explorer

 

Further information of all the parameters can be found in the help forums, the arcsi help command and in the code.

If an error is reported when you first run the ARCSI command, it might be that LIBGFORTRAN.SO.3 cannot be found. This will lead to the 6S model failing. You will need to install the required files using the following command:


sudo apt-get install libgfortran3

Dealing with outputs

The best way to view the output is to start the supplied viewer using the  ‘tuiview‘  command. This is an intuitive to use and very responsive viewer that handles the KEA format natively. To transform the KEA format outputs into a format readable by a wide range of GIS software, use the GDAL Translate command.


gdal_translate -of GTiff keafile.kea outputfile.tif

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Remote Sensing is everywhere!

Although the ongoing UK winter floods and storms of late 2013 and early 2014 must be an ordeal for those who are experiencing them first hand in their homes and businesses, they have also been a great showcase for the power and benefits of remote sensing. All over Twitter, LinkedIn and other social media are examples of maps showing either satellite imagery, or the extent of the floods derived from satellite imagery. People who haven’t been aware or interested in climate dynamics are now talking about the jet stream, and the feedback loops between it and North Atlantic low pressure systems! New methods of visualising and disseminating information (I’m thinking JavaScript libraries and web-mapping, specifically) that was created using atmospheric models, or derived from global satellite measurements, are helping inform and educate about the reasons behind this period of impressive weather.

But it isn’t just satellites that are getting press coverage. Land based remote sensing was mentioned on Radio 4’s PM programme on 11 Feb in an interview with the Coastal Processes Research Group (University of Plymouth) in the context of using laser scanning systems and video to monitor wave heights and to profile beaches. On the BBC website there are videos of flooded railway lines in the area around Windsor collected using unmanned aerial systems.

Remote sensing is becoming all pervasive as a method of rapidly collecting information across wide areas and quickly disseminating that out to the public. The general population may not even consciously register that this is the case, and for the correct information to be obtained, extracted and visualised in the most accessible and meaningful way there will be a continued requirement for well-trained RS experts.

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Maptember

It’s been a busy conference month: they didn’t rename it Maptember for nothing! Of all the conferences that were held (especially in the UK) I have attended three: the RSPSoc conference in Glasgow; AGI Geocommunity; and FOSS4G. The latter two were both held in Nottingham, re-branded Mappingham during FOSS4G.

These were three very different conferences, at least from my perspective. The RSPSoc conference had approximately 130 delegates, a fair number of whom I knew or recognised. Maybe this is why I felt most comfortable at this conference. The RSPSoc annual conference is very much an academic conference and a key part of the remote sensing calendar. It’s a brilliant opportunity to see what new methods and applications are being developed in the RS sector.

This was my first time at both the AGI GeoCommunity and FOSS4G conferences. To be honest, I wasn’t sure what to expect as my background is much more on the RS side, rather than the GIS side. I didn’t attend all of the events available at these conferences as funds are limited this year, so I only made the final day of the AGI GeoComm and the two main conference days of FOSS4G. I realise that there were loads more to both conferences and wish I could have made everything. For comparison, there must have been a couple of hundred attendees at GeoComm and there were about 850 at FOSS4G.

Some random points I have taken away from the conferences:

RSPSoc

  • There were two great keynote speeches given by Stewart Walker and Craig Clark.
  • I chaired a session on behalf of TOPSIG (Technical and Operational Procedures SIG) – there were some great presentations in that session and I’d like to thank all the speakers for their efforts.
  • The ARGUS-IS is a bonkers system – check out (http://www.youtube.com/watch?v=QGxNyaXfJsA) to see what I mean
  • Doreen Boyd presented some very cool work being undertaken at the University of Nottingham on using traffic cameras as data sources for validation work (http://www.mdpi.com/2072-4292/5/5/2200)
  • Glasgow was a lot warmer than I thought it would be!

AGI GeoCommunity

  • Maybe I was expecting more from the level of the talks at this conference or maybe I want to the wrong talks, but having come from the RSPSOc conference (and understanding that the AGI GeoComm was the flagship event in the UK of the GIS community), I was surprised not to be listening to reports about cutting edge developments. The talks were interesting in their own way, but they came across as very generic for what I assumed was going to be a specialist audience.
  • Anne Kemp did a really good job of chairing the day 2 opening session. She got audience members to identify themselves as groups of professionals who have worked in the sector for >20 years, 10-20 years and <10 years. She made the point that these groups should make sure they mix and pass on skills and experience to each other. It was a lot more effective than my description is making out!
  • Peter Batty gave a well timed keynote on Openness at the end of the day in readiness for FOSS4G (http://www.slideshare.net/pmbatty/agi-geocommunity-2013)

FOSS4G

  • I was nervous about attending this. I am not a developer. I can script in Python, but to me that is very different to being a software developer. I wondered whether the entire conference would be full of developers talking code rather than something for general users. I was steadied for rapidly getting lost in the detail, but it wasn’t like that at all. It was a totally inspiring experience, especially once I realised that I was brushing shoulders with some of the rock-stars of the FOSS world. The local organising committee did a great job of pulling together people from different (but related) technical backgrounds, cultures and languages and making the conference feeling one of cohesion.
  • The talks were brilliantly structured to take you from discussions of what ‘open’ means to hardcore technical breakdowns.
  • Arnulf Christl is an inspirational speaker and fully deserved to be presented with the Sol Katz Award

 

The main thing I learnt from all of the conferences – contribute in some way (as a developer, user, speaker, volunteer) and build your community. Collaborative research and development is fun, everyone has something to offer, and more often than not people are intrigued/excited/chuffed by what you say or do.

 

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Cool image

Whilst whiling away my life on the web I came across this stunning image: http://eijournal.com/showcase-articles/atmospheric-lows-and-highs-create-catalina-eddy

It reminds me of why I love remote sensing, and in particular why satellite earth observation will never be beaten. Physical geography, I bow to your awesomness.

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