Individual Tree Detection (ITC)
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We use a Canopy Height Model (CHM) derived from LiDAR data as decribed [http://wiki.awf.forst.uni-goettingen.de/wiki/index.php/Canopy_Height_Model_based_on_Airborne_Laserscanning_using_LAStools#Create_a_CHM_directly_from_height-normalized_points:_lasthin_and_las2dem here] to detect individual tree crowns. | We use a Canopy Height Model (CHM) derived from LiDAR data as decribed [http://wiki.awf.forst.uni-goettingen.de/wiki/index.php/Canopy_Height_Model_based_on_Airborne_Laserscanning_using_LAStools#Create_a_CHM_directly_from_height-normalized_points:_lasthin_and_las2dem here] to detect individual tree crowns. | ||
Two preprocessing steps prepare a watershed segmentation approach: | Two preprocessing steps prepare a watershed segmentation approach: | ||
Revision as of 23:15, 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:
- 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 temporary output file.
- Click on Run.
