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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. Freiburg. Introduction of my Institution.

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slide1

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

slide2

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

slide3

Overview

  • IntroductiontoLiDAR in Forestry
  • Single treespecificapproaches
  • Single treedelineation
  • Treespeciesidentification
  • Forest stand specific approaches
  • Forest stand mapping
  • Forest road extraction
  • Outlook

LiDAR applications for forestry

slide4

1. Introduction

LiDAR applications for forestry

slide5

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

LiDAR applications for forestry

slide6

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

slide7

Derived data types

Digital terrain model (DTM)

Transect from LiDAR point cloud:

Digital surface model (DSM)

LiDAR applications for forestry

slide8

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

slide9

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

slide10

Informationsebenen

Terrain information

Forest Road Extraction

Average Slope

Tree Height

LiDAR applications for forestry

slide11

2. Single tree specific approaches

LiDAR applications for forestry

slide12

2. Single tree approaches

Automated single tree delineation

LiDAR applications for forestry

slide13

LiDAR-DSM based ‘watershed segmentation’

LiDAR applications for forestry

slide14

Locally adapted DSM smoothing

Improveddelineation

Texture based crown size estimation

Prior knowledge

Watershed segmentation

LiDAR applications for forestry

slide15

2. Single tree approaches

Treespeciesclassification

LiDAR applications for forestry

slide16

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

slide17

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

slide18

3. Forest stand specific approaches

LiDAR applications for forestry

slide19

3. Forest stand specific approaches

Automated forest stand mapping

LiDAR applications for forestry

slide20

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

slide21

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

slide22

Combination of all categories

  • Combination of:
  • Tree type (Deciduous/Conifers)
  • Vertical structure
  • Height classes
  • into 15 forest stand types

LiDAR applications for forestry

slide23

3. Forest stand specific approaches

Automated forest road extraction

LiDAR applications for forestry

slide25

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

slide26

5. Outlook

LiDAR applications for forestry

slide27

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

thank you
Thank you!

LiDAR applications for forestry