Remote sensing and Forest Management Planning in Denmark › ngdoc › forest › Konferanser ›...

Wall-to-wall mapping of forest resources in Denmark

Thomas Nord-LarsenTorben Riis-Nielsen Patrik Karlsson NyedMathias Bo Ottosen

Remote sensing and Forest Management Planning in Denmark

Laser scanning 2014-15

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Equipment

Airplane Fixed wing

Scanner Riegel LMS-680i

Flight specifications

Flying height 680 AGL

Flying speed 130 kts (240 km/h)

Side-lap (LiDAR) 30% (wrt 60 degrees FOV)

Strip distance 550 m

Minimum altitude 650 m AGL

Maximum altitude 710 m AGL

LiDAR

Scan angle (half) 30 degrees (60 degrees full)

Scanner pulse rate 400 kHz

Mirror frequency 152 lines /sec

Point density 4.6 points/m2

Horizontal accuracy 0.15 m

Vertical accuracy 0.05 m

GPS Base stations

Permanent stations Yes

Mobile stations No

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Remote sensing data

NFI data

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Species

type

Plots Canopy

height

Growing

stock

Above-ground

biomass

Total

biomass

dm m3/ha tons/ha tons/ha

Total 2334 177 198.3 106.3 129.1

86 182.1 97.8 118.9

Mixed 883 165 173.6 92.1 111.8

90 170.0 89.1 107.9

Broadleaf 823 203 232.8 130.1 159.1

85 195.3 113.0 137.5

Conifer 628 160 187.6 94.9 114.2

74 173.9 80.7 97.9

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Combining NFI data with remote sensing data

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Modelling forest resources

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Piece of cake?

• Huge amounts of data • 407 billion points = 8,7 points/m2 = >15 TB

• 92 billion points in forest = 14,5 points/m2

• Errors in LIDAR-data (date, format etc.)

• Ground truth data cleaning. • harvest and incomplete measurement, chimney smoke etc.

• The learning process: holes in the map• Below seawater level

• Errors in ground model (Bornholmian bump)

• Flight line gaps (angles)

• First version: 120 days of error-free PC time.

• Second version: 90 days of error-free PC time.

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Management planning at Frederiksdal 2014

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Forest management planning at estate level

Forest management planning at estate level

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Forest management planning at estate level

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Forest management planning at estate level

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Forest management planning at estate level

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Compartment Litra Harvest My estimate LiDAR estimate

23 e 113,0 120,0 932,8

24 d 180,6 287,0 166,6

40 c295,8 (+40) 399,0 339,5

41 f 69,3 56,0 80,8

41f

40c

Conclusions

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It damn well works!

Management planning with the new forestmap

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Forest inventory using airborne laser scanning in structurally rich forestsBachelorprojekt, Kristian Skau BjerreskovSkov- og Landskabsingeniøruddannelsen hold 2017

• Who to blame?

• NFI-method or ALS?

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Management plan for Hvilsom Plantage2017-2026 and the forest resource map’s capacity as

a taxation toolBachelorprojekt, Simon Tofte Lorentzen, 2017.Institut for Geovidenskab og Naturforvaltning

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• Conclusions:

• Useful as a management tool

• Overall: -1,6%

• Broadleaf: +11%

• Conifer: -3%

• Problems with small stands

204 ha skov

10/10/2017 18

• Developing methods for forest segmentation

• Developing/testing methods for species recognition

• Developing indicators of biodiversity with LiDAR

• Detecting individual trees from high density laser scanning

• ... and many, many more...