Individual Tree Detection (ITC)
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# A window showing the different input and outputs should appear, just as if you were using any other geoalgorithm of the Processing Toolbox. | # A window showing the different input and outputs should appear, just as if you were using any other geoalgorithm of the Processing Toolbox. | ||
# You will just need to provide the model with the LiDAR data input file in LAZ format. | # You will just need to provide the model with the LiDAR data input file in LAZ format. | ||
− | [[File:Qgis_graphical_model.png| | + | [[File:Qgis_graphical_model.png|500px]] |
[[Category:QGIS Tutorial]] | [[Category:QGIS Tutorial]] |
Latest revision as of 15:26, 8 January 2018
Contents |
[edit] Filter the CHM derived from LiDAR data
We use a Canopy Height Model (CHM) derived from LiDAR data as decribed here to detect Individual Tree Crowns (ITC). Two preprocessing steps prepare a watershed segmentation approach: (1) Gaussian filtering and (2) inversion of a CHM.
- In the search engine of the Processing Toolbox, type smooth and select Smoothing (gaussian) under Image filtering of the Orfeo Toolbox.
- Select the CHM raster data file in GeoTiff format as input layer.
- The smoothing type is gaussian.
- The circular structuring element has a radius of 2 pixels.
- Enter name and path for an output file.
- Click on Run.
[edit] Invert the CHM
Now the smoothed CHM will be inverted.
- In the search engine of the Processing Toolbox, type invert and select Invert grid under Raster tools of SAGA.
- Select the smoothed CHM raster data file from previous step as input layer.
- Enter name and path for an output file.
- Click on Run.
[edit] Watershed segmentation
- In the search engine of the Processing Toolbox, type watershed and select Watershed segmentation under Image Analysis of SAGA.
- Select the inverted and smoothed CHM raster data file as input Grid.
- The Output is Segment ID
- Select as Method the flow accumulation of Minima
- Seed points: enter name and path for a vector point output file.
- Click on Run.
[edit] Extracting tree heights
We extract normalized heights from the original CHM using the QGIS point sampling plugin.
- Click Plugins --> Manage and Install Plugins.
- Type in the search bar Point sampling tool, click on the plugin name and then on Install plugin.
- Load the single band raster file chm.tif.
- Load the vector point file Seed points.shp'.
- Make sure that both layers are ticked in the TOC.
- Open the Point Sampling Tool clicking .
- Specify the output of the resulting vector file marking column with CTRL + left click.
- Enter an output shapefile name seed_points_chm.shp and path by clicking Browse.
- Confirm with OK. The new layer is added to the Layer Panel .
- Right click on the layer name in the TOC and open the Attribute table of the new vector layer.
- Toggle editing on.
- Select features using an expression.
- Type in the expression editor: "chm" <= 3 and click Select. All points with heights below 3m are now selected.
- Click Delete selected features.
- Toggle editing off.
We produce a histogram of the tree height distribution in the forest stand.
- In the search engine of the Processing Toolbox, type histogram and select Vector layer histogram under Graphics of QGIS geoalgorithms.
- Select the shapefile name seed_points_chm.shp as input layer.
- Select as Attribute the column with the extracted height values chm.
- Select the number of histogram bins: 20.
- Enter name and path for a graphic output file.
- Click on Run.
You want to produce a statistical summary of tree heights in the stand? Use the QGIS Statistics Panel:
- Open View --> Statistical summary to open the Statistics Panel.
- The layer seed_points_chm.shp need to be loaded in the TOC.
- Choose seed_points_chm in the top combo box, then choose chm in the field selector box of the Statistics Panel.
- The statistical summary for tree heights of the forest stand is displayed in the Statistics Panel.
Report on the mean stand height. What means Q3?
[edit] Graphical Processing Tool Model
Exhausting to follow all these work steps? You can use a prepared graphical processing toolbox model in the lidar tutorial data set: ITC_detection.model.
- Open QGIS and go to the Processing Toolbox; if the Toolbox is not opened go to Processing --> Toolbox to activate it. Once the Toolbox is opened go to Models --> Tools --> Add model from file and load the previously downloaded model.
- The model should appear in the Models tab. Double click on it to execute.
- A window showing the different input and outputs should appear, just as if you were using any other geoalgorithm of the Processing Toolbox.
- You will just need to provide the model with the LiDAR data input file in LAZ format.