Individual Tree Detection (ITC)
From AWF-Wiki
(Difference between revisions)
(→Invert the CHM) |
(→Filter the CHM derived from LiDAR data) |
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* The smoothing type is {{typed|text=gaussian}}. | * The smoothing type is {{typed|text=gaussian}}. | ||
* The circular structuring element has a radius of {{typed|text=2}} pixels. | * The circular structuring element has a radius of {{typed|text=2}} pixels. | ||
− | * Enter name and path for an | + | * Enter name and path for an output file. |
* Click on {{button|text=Run}}. | * Click on {{button|text=Run}}. | ||
[[File:Qgis_smooth_gauss2.png|300px]] | [[File:Qgis_smooth_gauss2.png|300px]] |
Revision as of 23:30, 6 January 2018
Contents |
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. 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.
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.