Individual Tree Detection (ITC)
From AWF-Wiki
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=Filter the CHM derived from LiDAR data= | =Filter the CHM derived from LiDAR data= | ||
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: (1) Gaussian filtering and (2) inversion of a CHM. |
* In the search engine of the Processing Toolbox, type {{typed|text=smooth}} and select '''Smoothing (gaussian)''' under Image filtering of the Orfeo Toolbox. | * In the search engine of the Processing Toolbox, type {{typed|text=smooth}} and select '''Smoothing (gaussian)''' under Image filtering of the Orfeo Toolbox. | ||
* Select the CHM raster data file in GeoTiff format as input layer. | * Select the CHM raster data file in GeoTiff format as input layer. | ||
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=Invert the CHM= | =Invert the CHM= | ||
| + | Now the smoothed CHM will be inverted. | ||
| + | * In the search engine of the Processing Toolbox, type {{typed|text=invert}} and select '''Invert data/no- data''' under Raster tools of SAGA. | ||
| + | * Select the smoothed CHM raster data file from previous step as input layer. | ||
| + | * The smoothing type is {{typed|text=gaussian}}. | ||
| + | * The circular structuring element has a radius of {{typed|text=2}} pixels. | ||
| + | * Enter name and path for an temporary output file. | ||
| + | * Click on {{button|text=Run}}. | ||
| + | [[File:Qgis_smooth_gauss2.png|300px]] | ||
| + | |||
| + | |||
=Watershed segmentation= | =Watershed segmentation= | ||
=Extracting tree heights= | =Extracting tree heights= | ||
Revision as of 23:20, 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 temporary 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 data/no- data under Raster tools of SAGA.
- Select the smoothed CHM raster data file from previous step 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.
