I decided to make a detailed relief map of part of the area close to home. The data doesn't cover the whole country, only parts that are deemed at risk of flooding. All of Hull and the river Hull catchment area are included in this. I've only looked at my local area so other areas may vary.
The data is downloaded as Ordnance Survey 10km grid tiles. There are 2m, 1m, 50cm and 25cm options and digital terrain model and digital surface model options too, so let's look at these options, but first a bit about LIDAR.
LIDAR is a technology that uses laser light to measure a distance repeatedly over an area to create a 3D model of an area. If the LIDAR transceiver is mounted at a fixed point it can pan around to record a very detailed image in 3D of everything that can be seen from that point. It works very well in this way inside a building or a cave to make a very accurate model. The US Space Shuttle flew a mission to use a variant of LIDAR to record the height of the surface of the Earth from space. This is available as SRTM.
More recently LIDAR equipment has been flown in aircraft. The difficulty of making useful measurements from an aircraft should not be underestimated. The only data LIDAR returns is distance to the target, so knowing PRECISELY where the aircraft is in 3D is the real problem. GPS is hopeless at altitude measurement and scarcely good enough for lateral location, barometric height measurements vary over time and location and inertial dead-reckoning accuracy falls off with time. A combination of all of these plus post-processing can result in useful data.
The Environment Agency LIDAR distance options specify the distance between the sample points, the 2m option having less detail than the 25cm option. The area that these options cover varies with the highest detail covering the smallest area. I chose the 50cm option as it covered the area I wanted at the highest level of detail. The detail does make for larger datasets and more processing needed to do anything with it.
Clearly the LIDAR measures the distance to the first object it encounters from the aircraft, so it measures tree tops, building roofs and even vehicles. This is known as the digital surface model. This is often a composite from multiple images, as this data is, to compensate for location inaccuracies and to help remove things like vehicles. To get a useful model of the real landscape, without trees and buildings, the data is post processed to create the digital terrain model. This is the data I have used.
The OGL data was different from the data the Environment Agency originally supplied. The original data was in smaller grid squares and the height was rounded to the nearest centimetre. The OGL data is in bigger squares which makes it a bit easier to process but seems to use 18 decimal places of a metre, which is smaller than the diameter of an atom.
I wanted to create a relief map and make contours from the data and, not for the first time, GDAL had the tools. The data uses the UK Ordnance Survey projection, known as OSGB36 or ESPG:27700, so to use any OSM data with it I would need to reproject to WGS84 or EPSG:4326.
To make a relief map I used gdaldem with the hillshade option on each of the datafiles. These need to be joined together to make a larger image, so the option -compute-edges is also needed. The complete command is:
gdaldem hillshade -compute_edges infile relieftiff/outfile.tifThe output is geoTIFF files which can be merged into a single (large) geoTIFF with the command
gdal_merge.py -o big.tif *.tifThis creates a geoTIFF file which has the image of the relief in a TIFF image and also has the locations of the edges in the original OS projection.
The next step is to use gdalwarp to reproject the large tiff file to one in the WGS84 projection. The command describes the source and target projection and filenames. There are significant missing pieces in the large TIFF as the available data was not rectangular. The -srcnodata 0 and -dstalpha makes missing data transparent rather than black.
gdalwarp -s_srs epsg:27700 -t_srs epsg:4326 -srcnodata 0 -dstalpha big.tif bigr.tifThe new TIFF file is what we want to see, but now it needs turning into tiles to be displayed on a slippy map. I decided that zoom level 13 to 18 would give a useful display. To make these tiles I used gdal2tiles.py, specifying the reprojected TIFF image, the zoom levels and the folder to put the tiles into.
gdal2tiles.py -z13-19 bigrt.tif tilesThis makes a set of tiles in the TMS format in the specified folder, in this case tiles.
Another way to visualise the LIDAR data is contours. I decided to create a set of overlay tiles that are transparent except for the contours. These can have a different density of contours at each zoom level. I chose the smallest contour step to be shown at the highest zoom level to be 0.2 metres. The GDAL tool for the job is gdal_contour which makes a shapefile with a linestring for each contour. The command is
gdal_contour -i 0.2 -a height infile outshapefile.shpThe resulting shapefile needs to be reprojected to WGS84. The tool to reproject shapefiles is ogr2ogr
ogr2ogr -t_srs epsg:4326 -s_srs epsg:27700 outshape.shp new.shpI decided to use Mapnik to make the contour overlay tiles. Mapnik can use shapefiles but specifying the long list of shapefiles created above would be a problem so I loaded the shapefiles into a postgresql table in a database with PostGIS enabled. Postgresql comes with shp2pgsql to do this:
shp2pgsql -a -g way new.shp eacontours > new.sqlThis makes SQL to load the shapefile into eacontours table, putting the geometry in the field called way. To load this into a database called ukcontours which already has the postgis extension installed the command is
psql -d ukcontours -f new.sqlI then designed the overlay with Tilemill to create the transparent tiles with more contours at higher zoom levels.
You can see the results at http://relief.raggedred.net. I added a water overlay and a roads overlay (thanks to MapQuest for the roads) to help position the relief imagery.