„Combined Tree Segmentation and Stem Detection using Full ... › ls2007 › presentations ›...

Josef Reitberger (FHM)Peter Krzystek (FHM)

Uwe Stilla (TUM)

„Combined TreeSegmentation and Stem

Detection using FullWaveform Lidar Data“

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COMBINED TREE SEGMENTATION AND STEM DETECTION USING FULL WAVEFORM LIDAR DATA

Reitberger, Krzystek, Stilla September 2007

Outline

MotivationFull waveform data

AcquisitionDecomposition

Segmentation of tree crownsDetection of tree stemsResults of segmentation and stem detectionAnalysis of full waveform data

ExperimentsResults

Conclusions and future work

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MotivationSegmentation

Basis for the derivation of forest parameters on single tree levelState of the art: Use of a CHM => The information below thesurface is ignoredProblem: Limited detection rate in lower tree layers

Stem detectionBasic idea: Use of information below the canopy surfaceStem reflections are registered by full waveform laser scanningCan tree detection be improved by using stem reflections?

Do stem reflections have specific characteristics? Compared to ground and crown reflectionsWith respect to pulse width and intensity derived by waveformdecomposition

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Acquisition of full waveform data (1)

Flight in Mai 2006 (Riegl LMS-Q560)

Scan angle: 0° - 22.5°Wavelength: 1550 nmPulse rate: 45 kHzPulse length: 4 nsBeam divergence: 0.5 mradFlying height: 400 m25 points/m2

Full waveform data• Variable length• Sample rate: 15 cm• Recording of the emitted pulse

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Bergmischwald

TestgebieteBergfichtenwald

Aufichtenwald

Test site

Waveformdata

© Bavarian Forest National Park

Mixed mountain forest

Sub alpine spruce forest

Alluvial spruce forest

Acquisition of full waveform data (2)

Bavarian Forest National Park

Reference areas

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sX

X

Y

Z

Y

sr

Decomposition of full waveform data

Waveform model: Sum of Gaussian functions

W2 I2

3X

2X

1X

4X

5X

3D coordinates of the reflections:

),1()( pssmsm NmrttXX =−+=

Width and intensity of the return pulse as attributes:

mmW σ⋅= 2mmm AI ⋅⋅⋅= σπ2

Point classes: first, middle, last, single

first

last

middle

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Segmentation of tree crowns (1)

Derivation of a CHM by least squares surface reconstruction

Interpolated CHM with local maxima (=tree positions)All points Highest points

Extraction of the highest points

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Delineation of tree crowns with watershed algorithm

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Problem: Sometimes several trees in one segment, which cannot beseparated by only using the CHM

Reference trees

Local maximum

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Detection of tree stems (1)

Separation of points between ground and begin of crownHierarchical clustering by using the horizontal distances between theseparated points

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Detection of tree stems (2)

Fitting of lines using the „Ransac“ algorithm to eliminate outliers

Additional rules: Angle of inclination < 7°

Minimum number of 3 pointsPositions found by stem

detection

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Results of segmentation and stem detection

Evaluation of 11 reference areas (2.6 ha, 1012 trees) Overall detection rate of tree crown segmentation: 49 %Improvement of detection rate by stem detection

Very different for the individual areas

Works best at sparse understorey and a large crown base height

Best reference area: 15 %

Improvement in the intermediate layer for all areas: 8 %

Improvement including all layers for all areas: 4 %

Improvement of tree position: 24 cm (= 21 %)

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Analysis of full waveform data

Do the stem reflections have specific characteristics?

Pulse widthIntensity

Crown points

Stem points

Ground points

Angle of incidence

β

s

(calibrated w.r.t. emitted signal)(calibrated w.r.t. emitted signal and run length s)

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Analysis: Stem, crown and ground

Average pulse width dependingon angle of incidence

Single + last points

Average intensity dependingon angle of incidence

Single points

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Analysis: Different tree species (crown)

Single points


Single + first + middle + last points

Average intensity depending on angle of incidence

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Analysis: Different tree species (stem)

Single + first + middle + last points


Single points

Average intensity depending on angle of incidence

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Results of waveform analysis

The intensity and pulse width of stem points depend on theangle of incidence

Ground points differ considerably from stem points and crownpoints

Crown points and ground points show no angle dependence

The mean intensity of stem points differs from crown points foran incidence angle larger than 10°

Crown points of coniferous trees differ from crown points of deciduous trees

The mean intensity of stem points differs considerably for thetwo tree species

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Conclusions and future workConclusions

The detection rate and position of single trees can be improvedby stem detectionThe intensity and pulse width can be used for forestclassifications if the order of the reflections in the waveforms isconsidered

Future work: 3D segmentation by using waveform information

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Thank youfor your

attention!

Acknowledgement: We thank Dr. Marco Heurich from the Bavarian Forest National Park for the productive contributions und for giving us the opportunity to use the remotesensing test sites.

„Combined Tree Segmentation and Stem Detection using Full ... › ls2007 › presentations ›...

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