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Mining for the Timber-Volume for a State-Wide Forest Information SystemLIDAR Mapping Forum, February, 15th, 2017

Dr.-Ing. Arno Buecken

Mining for the Timber-Volume for a State-Wide Forest Information System | February, 15th, 2017

Institute for Man-Machine Interaction | www.mmi.rwth-aachen.de

Dr.-Ing. Arno Bücken| [email protected]

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Key Idea: Do not harvest more than the timber volume that grows back.

Sustainable Usage of the Forest

We need a precise inventory of the forest.

Image: Pixabay




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Traditional forest surveying

Terrestrial Survey Measuring DBH Manual Notes Images: J. D

ietz




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1) Perform an angular sample count to determine the basal area of all trees of the unit

Image: J. D

ietz




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2) Determine top or mean height of the trees




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3) Determine the age of the trees

Image: P

ixabay




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4) Estimate the timber volume by using the yield tables

The yield tables are

a characteristic map

for forestry units.

Age Height

Yield Class

Yield

Table




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5) Update the map of the unit.

Image: W

. H

art

mann




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6) Take the collected data to the office.

This has to be repeated every

seven years for each forestry

unit.Im

age: J. D

ietz




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Survey

Basal Area

Species Distribution

Height

Yield Classes

Age

Forest Surveyor




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Remote Sensing




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Forest Surveyor 2.0

Image: A

. B

öhm




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Survey

Basal Area


Height

Yield Classes

Age

Forest Surveyor Forest Surveyor 2.0

(individual trees)

Basal Area


Height

Yield Classes

Age




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LIDAR point clouds

Point Clouds in Testareas provide a

detailed view of the forestry unit.




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From Testareas to States

State Northrhine-Westfalia

1

1

1

BIG DATA0

0

0

0

0

0 011

1

• Heterogenous Pointcloud

• 250 Billion Points

• 1-10 Points/m²

• 32.000 km²

• Several Flight Campaigns

• Different YearsMap: Wikipedia




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Open data in Northrhine-Westfalia

Since January 1st, 2017, Northrhine-Westfalia

provides open-data. All geodata collected by

the state are now available for free for

everybody.

Map: Wikipedia




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Open data in Northrhine-Westfalia

First Pulse

Last Pulse

Single Return

Artificial Point

Classification

Image:

GE

Ob

asis

.NR

W




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Open data – LIDAR point clouds

Near Espelkamp

34470000 5800000

Near Winterberg

34469500 5677000

Near Bad Münstereifel

34336000 5602000

5+

Points/m²

4

2-3

1

0




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Survey

Basal Area


Height

Yield Classes

Age


(stand wise)

Basal Area


Height

Yield Classes

Age




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Calculate the tree species

distribution from the classification

map.




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Survey

Basal Area


Height

Yield Classes

Age


(stand wise)

Basal Area


Height

Yield Classes

Age




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Top Height

For each species: Weighted average

of the highest points for raster cell




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Survey

Basal Area


Height

Yield Classes

Age


(stand wise)

Basal Area


Height

Yield Classes

Age




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Basal Area

The basal area is a measure for the density of a forestry unit.

It is associated with the yield factor.

There is a correlation between the yield factor and a density in

the LIDAR data.

Desired

Basal Area Basal Area

Yield Factor

Yield

Table




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Basal Area

But… Density in the LIDAR data depends on the

flight and scanner parameters!




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Yield Factor

Collection of ground truth and remote sensing data are not synchronous.

Units may be fully stocked in one and empty in the other dataset.




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Yield Factor




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Yield Factor

Height of the Decision Plane0% 100%

Co

rre

lati

on

0,0

1,0

0,5

1) Calculate Correlation




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Yield Factor


Co

rre

lati

on

0,0

1,0

0,5 Me

an

Sq

ua

re E

rro

r


2) Perform Regression Analysis

3) Calculate Mean Square Error




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Yield Factor


Yie

ldF

ac

tor

Co

rre

lati

on

0,0

1,0

0,5

0,0

1,0

0,5

1,5

Me

an

Sq

ua

re E

rro

r




4) Calculate resulting Yield Factors for a Canopy

Cover between 0% and 100%




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Yield Factor


Yie

ldF

ac

tor

Co

rre

lati

on

0,0

1,0

0,5

0,0

1,0

0,5

1,5

Me

an

Sq

ua

re E

rro

r




4) Calculate resulting Yield Factors for a Canopy

Cover between 0% and 100%

5) Decide which Decision Plane has to be used




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Survey

Basal Area


Height

Yield Classes

Age


(stand wise)

Basal Area


Height

Yield Classes

Age

Desired

Basal Area Basal Area

Yield Factor

Yield

Table




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Conclusions

• From testareas towards „real-world data“

• Data collection on the same quality level than the traditional surveyor, but faster

• No need to post process collected data in the office

• Available for Forest Information Systems as well as for online Solutions




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Thank you!This project is supported by:

mining for the timber-volume for a state-wide forest ... · institute for man-machine interaction |...

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