I would encourage you to investigate the spatial wavelet analysis (SWA) method. This is an automated object oriented approach used to identifying individual tree canopies. The method has the potential to identify both tree height and canopy diameter from LiDAR derived canopy height models. The output is usually composed of a table with tree centroid coords, ...
First, for the answers proposed here, you probably do not want to use the ferry filter to push HeightAboveGround to Z, at least not prior to segmentation, as the act of normalizing heights involves subtracting an interpolated estimate of the ground elevation from each return. Something planar in the original X, Y, Z space may no longer be planar in the ...
The most often used method that I've encountered in the literature involves a "local maxima" identification and subsequent inverted watershed creation. This link gives one example using LiDAR data and the free USFS FUSION software
A simple Google scholar or other database search for "local maxima tree canopy" will yield many other peer-reviewed remote ...
I ended up with this solution with PDAL and GDAL:
First I used liblas to create LAS containing only ground points. Then I used PDAL similar to the "Basic Example" but with output_type: max to create a DTM from the terrain LAS and a DSM from the original LAS.
Then just gdal_calc.py with these to elevation models to get a DHM.
gdal_calc.py -A DSM.tif -B ...
In addition to what has been mentioned above, SPDLib is another powerful set of open-source tools for processing LiDAR data (LAS files). It is cross-platform and supports Mac.
The spdinterp program has the capability to generate Canopy Height Models as well as DTMs and DSMs.
1) You can use the the little known Whitebox GAT (Mac version, Download Whitebox Geospatial Analysis Tools), developed for the Java platform (Java Runtime Environment (JRE) version 8.0 or higher installed) and Open Source (GNU General Public License version 3)
Look at Working with LiDAR data in Whitebox GAT
2) You can also use
GRASS GIS (GRASS GIS for ...
Well, it is more complicated than that.
Can you read a CHM from ASCII file? Yes, but this is related to the raster package not to lidR. https://stackoverflow.com/questions/20177581/reading-an-asc-file-into-r
Can you segment trees from a raster in lidR? Yes some algorithm are raster-based. This is not the case of li2012 you mentioned that is point-cloud based....
If I have interpreted your question correctly, I believe that what you are trying to do is to remove the underlying ground surface from your digital surface model (DSM). I would recommend using a white top-hat transform for this operation, rather than subtracting a median filtered DEM. You can perform this operation in QGIS using the Whitebox for Processing ...
There are two additional options to identify trees from canopy height models. Both of these options will identify (mostly) trees, which you can then use as a mask to isolate buildings.
lastrees in the R lidR package. There is a good tutorial on tree
segmentation from the author of the package.
FUSION's TreeSeg algorithm (p.138 documentation)
Using the Trend tool does not work because it will only smooth the DSM surface, while what it is necessary is a DEM (bare-earth surface).
What you want is a Canopy Height Model (CHM) (sometimes known as a normalized DSM) which is a raster expressing heights from objects relative to ground (i.e., all ground points/pixels are set to the same level, a ...
A good place to ask what algorithms people use in practice to extract tree locations and tree heights and/or to segment tree crowns from a raster CHM or a raster DSM would be in the LAStools user forum. There seem to be a number of forestry people that are doing plot-scale analysis as well as single-tree analysis for actual production work.
I'm responding to the comments here which will hopefully sort of answer the question.
I went on an LCCS training course in Rome a few years ago. LCCS is a methodology for creating a legend for your land classification. You build a rule set that says land cover X must have vegetation Y, in semi-arid areas and a periodic brackish water supply, etc etc. You ...
I think the equation in the raster calculator should be written as follows:
(("TOA_B5.tif"+1) * (256-"TOA_B4.tif") * ("TOA_B5.tif"-"TOA_B4.tif"))**(1.0/3.0)
**: means power
convert (1/3) integer division into float division (1.0/3.0)
You need to change "TOA_B5.tif" and "TOA_B4.tif" to the corresponding layer names exist in the table of content in ArcMap
In order to answer, let’s put aside important, but broad issues:
The fact that identifying and segmenting trees is a very complex analysis which depends on many things (things related to the type of vegetation, and quality and amount of available data, for example).
That processing 'large point clouds' in R is a real concern (due to memory limitation), and ...
.exp() computes the "Euler's number e raised to the power of the input" and does not take an any arguments.
What you want to do is I guess
var Biomass = CHMfinal.pow(ee.Number(2.895))
or if CHMfinal is an image
var Biomass = CHMfinal.pow(ee.Image(2.895))
It is always advisable to filter your points prior to generating a canopy height model. The following is a processing pipeline that filters normalized points.
Read point cloud data
Normalize point cloud so that ground = 0
Filter points, keeping points where Z >= 0m & Z <= 40m
Generate the canopy height model
Your idea is correct, however you might incur into some practical issues.
The first is that the DTM and the stereos you are using seem to have a different height reference system.
For example, as you suggest, one might use ellipsoidic heights, the other geoidic heights.
While it is possible to convert ellipsoidic <-> geoidic heights using geoid models, ...
Unfortunately without point cloud data you're going to have a hard time creating any sort of bare earth model. Do you know of any organizations in the area that do aerial photography or survey? I would suggest directly asking them if they've collected any such data within your study area as I doubt there will be any free data that will suffice for your needs....
To obtain a Canopy Height Model (CHM), subtract the DEM from a DSM using the Subtract Model tool:
Take a look at Quick Terrain Modeler: Importing LAS to DEM/DSM tutorial. The part you are interested begins in 4:58, but I advise watching the full video (the above picture was taken from it).
I also find the Quick Terrain Modeler: Above Ground Analysis ...
You can get the elevation of individual points in Google Earth by hovering your mouse pointer over the location and looking at the status bar at the bottom, which should indicate the altitude at that point.
You can get an "elevation profile" graph in Google Earth Pro by drawing a line right-clicking on it and selecting "show elevation profile".
To get ...
In order not to receive super small polygons after the conversion, try to first smooth / generalise your raster, i.e. using the Filter() or FocalStatistics() out of the Spatial Anaylist Toolbox. You'd have to play around using those in order to find the solution fitting your needs. The FocalStatistics() tool basically does the same as Filter(), except you ...