JAXA have recently released their global forest / non-forest map at 50 m resolution and the Advanced Land Orbiting Satellite (ALOS) Phased Array L-Band SAR (PALSAR) data from which they were derived. This is really exciting because SAR data provides a different view of the world than optical data, which we’re more used to viewing. A particularly interesting feature of L-band SAR for mapping vegetation is the ability to ‘see’ through clouds and the canopy layer of vegetation. A good introduction to SAR data, in the context of vegetation mapping, is provided in the following paper:
Rosenqvist, A., Finlayson, C. M., Lowry, J and Taylor, D., 2007. The potential of long- wavelength satellite-borne radar to support implementation of the Ramsar Wetlands Convention. Aquatic Conservation: Marine and Freshwater Ecosystems. 17:229–244.
You can download data from:
You need to sign up for an account but this is a quick and straightforward process.
You can download data in 1 x 1 degree tiles or batches of 5 x 5 degrees. Data are in ENVI format, and can be read with GDAL, or programs that use GDAL (e.g., QGIS). If you don’t already have a viewer, you can download TuiView to open them with. ArcMap can read them (as it uses GDAL) but it won’t recognise it if you go through the ‘Add Data’ dialogue. However, you can just drag the files (larger files without the ‘.hdr’ extension) from windows explorer to the ‘Table of Contents’.
To mosaic all files in a 5 x 5 degree batch (or any number of files), you can use a combination of GNU Parallel to untar and gdalbuildvrt. Assuming we want to mosaic the HH- and HV-polarisation PALSAR data the following commands can be used:
# Untar file tar -xf N60W105_07_MOS.tar.gz # Change into directory cd N60W105 # Untar all files, in parallel using GNU Parallel ls *.gz | parallel tar xf # Create a list of HH and HV files ls *_HH > hhfilelist.txt ls *_HV > hvfilelist.txt # Build VRT gdalbuildvrt -input_file_list hhfilelist.txt N60W105_HH.vrt gdalbuildvrt -input_file_list hvfilelist.txt N60W105_HV.vrt
This will create virtual rasters, which are text files containing references to the data. You can convert to real rasters (KEA, geotiff etc.,) using gdal_translate:
gdal_translate -of GTiff 60W105_HH.vrt 60W105_HH.tif gdal_translate -of GTiff 60W105_HV.vrt 60W105_HV.tif
The data are supplied as digital numbers, to calibrate and convert to dB, the following equation is used :
You can run this calibration in RSGISLib using the following Python script:
# Import RSGISLib import rsgislib from rsgislib import out image # Set input and output image inimage = 'N60W105_HH.vrt' outimage = 'N60W105_HH_db.kea' # Run image maths imagecalc.imageMath(inimage, outimage, '10*log10(b1^2) - 83.0, 'KEA', rsgislib.TYPE_32FLOAT) # Calculate stats and pyramids (for fast display) imageutils.popImageStats(outimage,True,0.,True)
SAR data takes a while to get your head into but once you do it provides a wealth of information.
Update – data are now available at 25 m resolution from the same place
 Shimada, M. and Ohtaki, T. 2010. Generating large-scale high-quality SAR mosaic datasets: Application to PALSAR data for global monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 3(4):637–656.