DR. JOHANNES HEINZEL (Dipl.-Geogr.) University of Freiburg, Department of Remote Sensing and Landscape Information Systems, Freiburg, Germany Use of LiDAR data for automated forestry applications - Examples from central Europe

Albert Ludwigs-University of Freiburg (Germany) 16:59:29 LiDAR applications for forestry 2 Introduction of my Institution Freiburg Faculty for Forest and Environmental Sciences Department for Remote Sensing and Landscape Information Systems (FELIS) Remote Sensing technology for forestry and related disciplines

16:59:29 LiDAR applications for forestry 3 1.Introduction to LiDAR in Forestry 2.Single tree specific approaches Single tree delineation Tree species identification 3.Forest stand specific approaches Forest stand mapping Forest road extraction 4.Outlook Overview

16:59: Introduction LiDAR applications for forestry

16:59:295 LiDAR applications for forestry Why is airborne laser scanning (LiDAR) interesting for forestry?

16:59:296 LiDAR in woodland LiDAR applications for forestry 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)

Transect from LiDAR point cloud: Digital surface model (DSM) Digital terrain model (DTM) 16:59:297 LiDAR applications for forestry Derived data types

16:59:298 LiDAR applications for forestry Levels of information Single tree level Tree Height Tree Top Crown Diameter Crown Volume Base of Crown Single Tree Delineation Tree height Tree Species

16:59:299 LiDAR applications for forestry Forest stand level Tree Height  Estimation of Average DBH (cm)  Estimation of Timber Volume(m³/ha)  Estimation of Biomass (t/ha) Percentage of conifers and deciduous trees Top Height Crown Closure Tree Numbers Levels of information

16:59:2910 LiDAR applications for forestry Informationsebenen Terrain information Tree Height Average Slope Forest Road Extraction

16:59: Single tree specific approaches LiDAR applications for forestry

12 Aerial survey Selection of the study area Collection of field reference data Acquisition of full-waveform LiDAR, CIR- and RGB-Data 16:59:29 LiDAR applications for forestry ≈ 10 km² Decidious, coniferous and mixed forest stands All main tree species of temperate forest Dense and heterogenous stand structure Harrier 56 Full-waveform LiDAR Matrix-Camera Falcon II Discrete LiDAR Line scanner

13 Aerial survey Selection of the study area (temperat forest in central Europe) Collection of field reference data Acquisition of full-waveform LiDAR, CIR- and RGB-Data 16:59:29 LiDAR applications for forestry Harrier 56 Full-waveform LiDAR Matrix-Camera Falcon II Discrete LiDAR Line scanner ≈ 10 km² Decidious, coniferous and mixed forest stands All main tree species of temperate forest Dense and heterogenous stand structure

14 Aerial survey Selection of the study area (temperat forest in central Europe) Acquisition of full-waveform LiDAR, CIR- and RGB-Data 16:59:29 LiDAR applications for forestry Harrier 56 Full-waveform LiDAR Matrix-Camera Falcon II Discrete LiDAR Line scanner ≈ 10 km² Decidious, coniferous and mixed forest stands

15 Automated single tree delineation 16:59:29 LiDAR applications for forestry 2. Single tree approaches

16 LiDAR-DSM based ‘watershed segmentation’ 16:59:29 LiDAR applications for forestry

17 Locally adapted DSM smoothing 16:59:29 Texture based crown size estimation

LiDAR applications for forestry 18 Locally adapted DSM smoothing 16:59:29 Texture based crown size estimation Watershed segmentation

LiDAR applications for forestry 19 Results single tree delineation With prior crownsize Deciduous Conifers Mixed (User‘s accuracy (%)) No DSM smoothing 30% average enhancement 16:59:29

20 Tree species classification 16:59:29 LiDAR applications for forestry 2. Single tree approaches

Extraction of most important features: 1.Signal intensity 2.Signal width 3.Numbers of reflections within single beam Position in space Position in laser beam Statistical distribution within grid cell Primary waveform- parameter 21 LiDAR features: composed full-waveform parameters 16:59:29 LiDAR applications for forestry 231 composed features LiDAR point information is projected onto a grid

22 Classifier: Support Vector Machine 16:59:29 Ranking of feature relevance LiDAR applications for forestry

Main tree species All features Full-waveform LiDAR Hyperspectral CIR LiDAR height metrics Texture Tree species (temperate forest): Pine (Pinus sylvestris) Spruce (Picea abies) Beech (Fagus sylvatica) Oak (Quercus petraea) Cherry (Prunus avium) Hornbeam (Carpinus betulus) 23 Results tree species classification 16:59:29 LiDAR applications for forestry Overall accuracy (%)

Main tree species All features Full-waveform LiDAR Hyperspectral CIR LiDAR height metrics Texture Tree species (temperate forest): Pine (Pinus sylvestris) Spruce (Picea abies) Beech (Fagus sylvatica) Oak (Quercus petraea) Cherry (Prunus avium) Hornbeam (Carpinus betulus) 24 Results tree species classification 16:59: features SVM-ranking 14 features LiDAR applications for forestry Overall accuracy (%)

Main tree species All features Full-waveform LiDAR Hyperspectral CIR LiDAR height metrics Texture Tree species (temperate forest): Pine (Pinus sylvestris) Spruce (Picea abies) Beech (Fagus sylvatica) Oak (Quercus petraea) Cherry (Prunus avium) Hornbeam (Carpinus betulus) 25 Results tree species classification 16:59:29 LiDAR applications for forestry Overall accuracy (%)

26 Combination of delineation and classification 16:59:29 LiDAR applications for forestry 2. Single tree approaches

27 Combination of delineation with classification 1.Intersection of: LiDAR based delineation with: tree species classification result:corrected delineation 2.Polygons including more than one species are splitted 16:59:29 LiDAR applications for forestry

16:59: Forest stand specific approaches LiDAR applications for forestry

16:59:2929 Automated forest stand mapping LiDAR applications for forestry 3. Forest stand specific approaches

16:59:2930 LiDAR applications for forestry Definition: Identification of similar physical forest characteristics and grouping of trees in a logical and consistent manner In Germany forest area is manually classified into management units based on: Species composition Age class / Height class Vertical and horizontal structure Forest stand mapping

16:59:2931 LiDAR applications for forestry 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 Winter: First Echo Deciduous stand Coniferous stand Winter: Last Echo Deciduous stand Coniferous stand Standard deviation arithm. mean Coefficient of variation (C v ): juvenile:h < 3m sapling:3 =< h <10m pole:10 =< h <15m mature trees:15 =< h <25m old trees:25m = < h

16:59:2932 LiDAR applications for forestry Combination of all categories Combination of: Tree type (Deciduous/Conifers) Vertical structure Height classes into 15 forest stand types

16:59:2933 Automated forest road extraction LiDAR applications for forestry 3. Forest stand specific approaches

16:59:2934 LiDAR applications for forestry Basic idea: change of slope on an LiDAR based DTM

16:59:2935 LiDAR applications for forestry 2. Computation of a gradient model Local Slope in %: low high 1.Computation of a digital terrain model (DTM) 3. Line following with "Lines Gauss“ algorithm after C. Steger (1996) Method 4. Automatically derived forest roads Extracted attributes (trafficability): Road width Slope Curvature Intersections with water runoff line (erosion)

16:59:2936 Forest cover mapping LiDAR applications for forestry 3. Forest stand specific approaches

16:59:2937 LiDAR applications for forestry Basic idea for forest / non-forest classification

16:59:2938 LiDAR applications for forestry Forest cover mapping Automatisierte Klassifikation Forest Non-forest (tree groups) Non-forest (single trees)

16:59: Outlook LiDAR applications for forestry

16:59:2940 LiDAR applications for forestry 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 using vegetation height (single trees and stands)  Deforestation and forest degradation  Tree crown features (Base of crown, volume, shape)  Standspecific vegetation layers Good experiences in cooperating with aerial survey companies and system manufacturers Outlook

Thank you! 4116:59:29 LiDAR applications for forestry