Dr. Johannes Heinzel (Dipl.-Geogr.)

1. Use of LiDAR data for automated forestry applications - Examples from central Europe Dr. Johannes Heinzel (Dipl.-Geogr.) University of Freiburg, Department of Remote Sensing and Landscape Information Systems, 79106 Freiburg, Germany

2. Freiburg Introduction of my Institution Albert Ludwigs-University of Freiburg (Germany) Faculty for Forest and Environmental Sciences Department for Remote Sensing and Landscape Information Systems (FELIS) Remote Sensing technology for forestry and related disciplines LiDAR applications for forestry

3. Overview • IntroductiontoLiDAR in Forestry • Single treespecificapproaches • Single treedelineation • Treespeciesidentification • Forest stand specific approaches • Forest stand mapping • Forest road extraction • Outlook LiDAR applications for forestry

4. 1. Introduction LiDAR applications for forestry

5. Why is airborne laser scanning (LiDAR) interesting for forestry? LiDAR applications for forestry

6. LiDAR in woodland • Benefits of LiDAR in woodland: • Exact extraction of terrain surface (DTM) below forest • Exact determination of vegetation height • Information on reflection within the tree crown • Additional information if full-waveform data is available • Spectral information from the near infrared (NIR) LiDAR applications for forestry

7. Derived data types Digital terrain model (DTM) Transect from LiDAR point cloud: Digital surface model (DSM) LiDAR applications for forestry

8. Tree Top Base of Crown Tree height Crown Diameter Levels of information Single tree level Tree Species Crown Volume Tree Height Single Tree Delineation LiDAR applications for forestry

9. 1 2 4 Top Height 3 Crown Closure Levels of information Forest stand level • Estimation of Average DBH (cm) • Estimation of Timber Volume(m³/ha) • Estimation of Biomass (t/ha) Percentage of conifers and deciduous trees Tree Numbers Tree Height LiDAR applications for forestry

10. Informationsebenen Terrain information Forest Road Extraction Average Slope Tree Height LiDAR applications for forestry

11. 2. Single tree specific approaches LiDAR applications for forestry

12. 2. Single tree approaches Automated single tree delineation LiDAR applications for forestry

13. LiDAR-DSM based ‘watershed segmentation’ LiDAR applications for forestry

14. Locally adapted DSM smoothing Improveddelineation Texture based crown size estimation Prior knowledge Watershed segmentation LiDAR applications for forestry

15. 2. Single tree approaches Treespeciesclassification LiDAR applications for forestry

16. LiDAR features: composed full-waveform parameters • Extraction of most important features: • Signal intensity • Signal width • Numbers of reflections within single beam 1. component 231 composed features Primary waveform-parameter LiDAR point information is projected onto a grid 2. component Statistical distributionwithingridcell 3. component Position in laser beam 4. component Position in space LiDAR applications for forestry

17. Results tree species classification Tree species (temperate forest): Pine (Pinussylvestris) Spruce (Piceaabies) Beech (Fagussylvatica) Oak (Quercuspetraea) Cherry (Prunusavium) Hornbeam (Carpinusbetulus) Main tree species All features 88.0 Full-waveform LiDAR 79.2 Hyperspectral 64.7 CIR 50.7 LiDAR height metrics 47.3 Overall accuracy (%) Texture 46.8 LiDAR applications for forestry

18. 3. Forest stand specific approaches LiDAR applications for forestry

19. 3. Forest stand specific approaches Automated forest stand mapping LiDAR applications for forestry

20. Forest stand mapping Definition: Identification of similar physical forest characteristics and grouping of trees in a logical and consistent manner • In Germany forestareaismanuallyclassifiedintomanagementunitsbased on: • Species composition • Age class / Height class • Vertical and horizontal structure LiDAR applications for forestry

21. LiDAR based automated approach Step 1: Modelling of deciduous and coniferous stands during winter (leaf-off) conditions Step 2: Classification based on the variation of height values (DSM) Step 3: Height classes based on Top Height Deciduous stand Coniferous stand Coefficient of variation (Cv): Deciduous stand Coniferous stand Winter: First Echo Winter: Last Echo Standard deviation arithm. mean LiDAR applications for forestry

22. Combination of all categories • Combination of: • Tree type (Deciduous/Conifers) • Vertical structure • Height classes • into 15 forest stand types LiDAR applications for forestry

23. 3. Forest stand specific approaches Automated forest road extraction LiDAR applications for forestry

24. Basic idea: change of slope on an LiDAR based DTM LiDAR applications for forestry

25. Method 3. Line following with "Lines Gauss“ algorithm after C. Steger (1996) • Computation of a digital terrain model (DTM) 4. Automatically derived forest roads 2. Computation of a gradient model • Extractedattributes (trafficability): • Road width • Slope • Curvature • Intersectionswithwaterrunoffline (erosion) Local Slope in %: low high LiDAR applications for forestry

26. 5. Outlook LiDAR applications for forestry

27. Outlook • Single tree specific approaches require high point density (> 7 pt/m²), stand specific approaches give good results with < 1 pt/m² • Full-waveform data has high potential for further technical improvements in pattern recognition (Information on reflection characteristics) • Combination with multispectral aerial photographs • Further important applications and possibilities: • Estimation of Biomass usingvegetation height (single trees and stands) • Deforestation and forest degradation • Tree crown features (Base of crown, volume, shape) • Standspecificvegetation layers • Good experiences in cooperating with aerial survey companies and system manufacturers LiDAR applications for forestry

28. Thank you! LiDAR applications for forestry