As forecast in my previous thread, Aerial Raster for urban vegetation cover analysis, I have received some files to help me (hopefully) carry out a canopy cover analysis for a given area near Melbourne, Australia.
I would like to differentiate between vegetation layer height classes of my project area between 2 different years and determine percentage area changes.
What I want to achieve is a simple version of something like this report.
At the time I posted the previous thread I was not sure what form the data would arrive in, or if it would arrive at all. But, it did and it is ER Mapper Grid format - massive images with header files with the extension ers. I'm hoping it has the data I need for what I want.
To recap where the previous thread was going I've pasted Dimitri's help from the previous thread here where he is responding to my original post.
I'm hoping the data I get is already classified and has infrared data incorporated otherwise I see there are ways to do it, at least partly, in software such as Global Mapper and perhaps QGIS.
As I understand it, your task is simple, almost trivial, in Manifold: take a raster in ERS format that your client has provided and to color it by heights:
The analysis should ideally differentiate between layer height classes: grass (0-0.5m); shrub (0.5 - 3m); small tree (3 - 10m); medium tree (10 - 15m); and large tree (15m+) as can be seen here.
OK. Easy enough. Manifold reads ERS format, so once you have your surface, it sounds like the contour transform, set to areas, would do that in one step. Or, you could just style it, as in the Style: Contouring using Colors topic.
If there's something else, it's not clear what the task is. The "as can be seen here" link in the quote above jumps to an "Urban Monitor™" web page that has plenty of generalities but no specifics, so it's hard to tell exactly what they're selling. [Usually a sign they're trying to sell something simple for a too-high price... :-) ]
The only specific item is the illustration on that page showing a hill shaded surface that in addition to hill shading is colored by height with one palette in regions where land use indicates parks and vegetation and colored with a gray palette in regions where land use is structures and urban land cover.
If that's the task, that's simple to do in Manifold.
For example, it's easy to trace out polygonal areas from clumps of different raster pixels, classified as to land use (there's an example of that in the user manual). You can then use those polygonal areas as you like to manipulate the LiDAR points that fall or don't fall within them. Or, if you're working exclusively with raster layers you can do arbitrarily complex math between different raster layers if you like.
Once you know what data you're working with, if you run into any problems, ask here in the forum. State clearly the data you're working with and what you want to do, possibly posting links to examples to the data, and you'll get plenty of tips.
This will take a couple of posts to unpack so I'll start with issues getting the data into M9 in the correct location.
The files I received are around 4.4GB (a couple being 17.4GB) with no explanation or metadata beyond what is contained within them (and I'm unlikely to get a response from the source provider - I have put the question out there to them anyway - we live in hope).
The file names all contain various references to their projection and dates with one possible clue as to their contents being grs_raw, nsm_raw, tre_raw, vht_raw and msk_nod (the last one just appears to be rectangles off the coast of Melbourne). When I open them it can be seen that they are isolated height layers such as tree canopy, grass, etc. Some layers seem to have a mixture.
The files are expected to be in Australia GDA94 MGA55 as per the info in the ers header files with each reporting the dataum as "GDA94" and the projection as "MGA55".
This has been my work flow for a sample file so far:
After I link (File > Link) the ers file it imports the image with the default projection shown as Transverse Mercator. When opened it is about 90k to the east of where it should be.
If I try to reproject the image (Reproject Component) the dialog won't allow me to choose a Conversion Type to reproject it with to GDA94 55 so I allow the default.
Manifold chews on it for a while an then spits out an identically (as far as I can see) located copy.
If I link to the same file through QGIS it doesn't complain but does assign it a Papa New Guinea projection (EPSG:5551).
I can then use QGIS's 'Set CRS' to set the layer to GDA94 55 okay. It comes out in the correct position.
As an experiment in M9 I tried 'Repair Initial Coordinate System' to EPSG:5551 and then reproject it to GDA94 55. It moves but still nowhere near where it should be.
I exported a smaller clip of the file from QGIS in GDA94 55 and imported them into M9 but it still came out about 1800m north of where it should be. Interestingly the same file imported into M8 with the correct projection and in the correct place.
I experimented with importing the file into M9 rather then linking it and nothing changed as far as the above projection issue.
Global Mapper v.22 is available as a demo version so I tried that. I'm guessing that ers grid files are part of its staple diet because it imported whole files and clips, correctly projected them and applied colour to elevations. Looks pretty cool. I'm happy to buy Global Mapper if this is the best way to do this.
I'm sure M9 can do this too though so that's what I want to achieve as soon as I can get the projection issue sorted.
BTW, the tables only have x, y and geom data - no other fields.
Dale, if you read this, I did try several times to e-mail you but had no response. I'm sure you are a busy too!