(or: going from 2 d to 3d)...height 1.3 6.2% 0.707 3.42 0.84 25 note: all regression coefficients...

Swedish University of Agricultural Sciences

Forest Remote Sensing

Nationwide forest estimates in Sweden using satellite data and airborne LiDAR

(Or: going from 2 D to 3D)

Håkan Olsson

Mats Nilsson, Johan Fransson, Henrik Persson and Mikael Egberth

Swedish University of Agricultural Sciences (SLU) Umeå

Svante Larsson

Swedish Forest Agency (Skogsstyrelsen)



Medverkande i förstudien

Från Skogsstyrelsen Svante Larsson (projektledare) Anders Persson Thomas Jonsson Marcus Larsson Peter Blombäck (projektägare) Johan Eriksson (ordf styrgrupp)

Från SLU Mats Nilsson Håkan Olsson Mikael Egberth Jörgen Wallerman Peder Axensten Jonas Jonzen



National laser scanning being made 2009 – about 2015 primarily for a new nationwide DEM 387 blocks with size 25 * 50 km. 0.5 – 1.0 returns / m2

Max scan angle 20 degrees



The scannings have been made during different seasons



And with different

laser scanners



Background 1.

Assignment from the goverment to the Forest Agency:

Work with SLU and other relevant agencies in order to utilise the national laser scanning for the forest sector.

A pre-study was made during January - March 2013.



Background 2. SLU perspective: nationwide satellite data based forest data produced every 5’th year

• 2000

– Landsat from 1997 - 2001

• 2005

– SPOT from 2005 - 2006

• 2010

– SPOT from 2008-2010

• 2015

– Landsat 8 + laser scanning?



• Nationwide prediction of woody biomass with ”2D” optical satellite data is used in the Nordic countries, but the accuracy is limited.

• Shadows provide the tree size related signal.



• CHM = DSM – DEM

• To be calibrated with field data

• Sensors that can provide (a semi?) DSM:

- laser,

- Point clouds from air photo

- multi view angle optical satellite,

- interferometric SAR,

- radargrammetry

DSM [m a.s.l.]

DEM [m a.s.l.]

Δ = CHM [m a. g.]

New possibilities for improved biomass estimates by use of 3D surface models and high accuracy DEM



Data sources for planned nationwide product

Airborne laser scanning data Satellite image

National forest inventory sample plots



•

Number of NFI plots within a 100 * 100 km area. In total about 30 000 NFI plots are available for training 22 M ha productive forest land = half the land area.



50km

Case study laser + satellite data trained with NFI plots

• 3 SPOT 5 images from 2010-06-04, same date and instrument settings

• 452 plots from the NFI, ranging from 2006 – 2010

• LiDAR data from 8 blocks scanned 2010 and 2011.



Stem volume at estate level



10

15

20

25

30

35

40

0 2 4 6 8 10 12 14 16 18 20

RM

SE%

k value

euclidean

mahalanobis

Random Forest

Stem volume accuracy as function of k



0

5

10

15

20

25

30

35

40

0 2 4 6 8 10 12 14 16 18 20

RM

SE%

k value

euclidean

mahalanobis

msn

Random Forest

Mean height accuracy as function of k



Decidous volume with and without satellite data

0

50

100

150

200

250

300

350

0 2 4 6 8 10 12 14 16 18 20

RM

SE%

k value

No satellite

Satellite included



Contribution from optical satellite data:

- Tree species - Age for young plantation - Uppdating of clear felled areas

But satellite scenes might not cover the same area as laserblocks useful for finding training areas for a specific block



Terra ASTER

SPOT HRS

ALOS PRISM

DMC air photo camera

TandDEM-X + TerraSAR X

Other tested 3D techniques



Tested along track stereo satellite sensors

• ASTER: 15 m pixels, 0° and -28°

• SPOT-5 HRS: 10 m pixels, +20° and -20°

• ALOS PRISM: 2.5 m, 0°, and +24° -24°



Canopy height models versus field measured tree heigths

SPOT HRS DMC camera

Still, error from SPOT HRG colour based estimates of stem volume reduced from 31 % to 23 % when this CHM data was added

Photogrammetry point cloud reduced estimation error from 31 % to 19 %.



Point cloud from digital photogrammetry over same area.

Point cloud from laser scanner

data



Height estimated from TandDEM-X versus validation data

RMSE = 6.2%



Comparison canopy heights from TanDEM-X versus LiDAR



Early Tandem-X results

• Results estimated at plot level (202 training plots and 25 validation plots with 10 m radius)

Biomass (tons ha-1) and height (m) estimation RMSE, adjusted coefficient of determination

(𝑅adj2 ), regression coefficients (𝛼0-𝛼𝟏) and the number of plots

Estimated RMSE RMSE% 𝑅adj2 𝛼0 𝛼1 𝑛

Biomass 43.9 23.1% 0.577 37.70 0.320 25

Height 1.3 6.2% 0.707 3.42 0.84 25

Note: all regression coefficients are significant at the 0.1% significance level (p ≤ 0.001).



Early ranking of some 3 D data for forest biomass retrieval in Sweden

from best to worst

Sensor Platform Type of sensor and data

Laserscanning (e.g. Leica) Aircraft 0,5 – 1 returns / m2

Digital Photogrammetry (DMC) Aircraft 4800 m, 60% overlap, point cloude

TanDEM-X Satellite X-band interferrometry

ALOS PRISM Satellite Optical 3 line puchbroom 2.5 m pixels

SPOT HRS Satellite Optical 2 line puchbroom 5 * 10 m pixels

Terra Aster Satellite Optical 2 line puchbroom 15 m pixels



Conclusions

• Forest biomass retrieval from nationwide laser scanning trained with national forest inventory plots is feasible. – But the production is a sensitive issue for the commercial sector

• Roles for optical satellite data: – Division into broad species classes – Age of plantations – Change detection

• In addition to laser are there several more techniques for obtaining 3D data related to the forest canopy (including also radargrammetry, to be studied in a planned EU FP 7 project)



Contributors

• Swedish National Space Board - funding

• Swedish National Land Survey – ALS and satellite data

• CNES and EC for data from the ISIS program

• SPOT Image for permission to use SPOT HRS raw data

• JAXA fors ALOS Prism data

• FOI for field data

• Joanneum Research, Graz, for permission to use the RSG software for 3D matching,

• Chalmers University of Technology radar remote sensing group



Medverkande i förstudien

Från Skogsstyrelsen Svante Larsson (projektledare) Anders Persson Thomas Jonsson Marcus Larsson Peter Blombäck (projektägare) Johan Eriksson (ordf styrgrupp)

Från SLU Mats Nilsson Håkan Olsson Mikael Egberth Jörgen Wallerman Peder Axensten Jonas Jonzen

Questions?



Additional material



Biomass

• Strong candidate for ESA

• Earth Explorer 7:

• Launching a P-band radar satellite 2019

• After the User Consultation Meeting in Graz on 5-6 April 2013, the mission candidate biomass was recommended by ESAC to become ESA’s 7th Earth Explorer

• Final decision will be taken by PBEO in May 2013



Kartering av skogstyper med satellit + 3D data

Hygge

Ungskog

Barr 5-15 m

Barr > 15 m

Lövkog

Blandskog

Indata Kart

Noggrannhet

satellitdata

67%

satellitdata + höjd från laser 77%

satellitdata + höjd från 3D flygbilder 76%

Satellitdata = SPOT-4 (20 m pixlar)

Skogsklasser som i Lantmäteriets Vegetationskartor och CORINE

Nordkvist et al, 2012. Remote Sensing Letters



ALOS PALSAR mosaic over

Scandinavia and Finland

Sweden

Finland

Norway

Denmark

ALOS PALSAR data used

Fine Beam Dual (FBD34)

63 strips from

43 orbital tracks

June – October 2009

Other data sources

Digital Elevation Model

JAXA’s Kyoto & Carbon Initiative: 2004-



Resultat skattning av trädhöjd

Fjärranalysdata RMSE

1. Krycklan, Västerbotten

SPOT HRG SPOT HRG + HRS SPOT HRG + DMC

13% 10% 7%

2. Remningstorp, Västergötland

SPOT HRG SPOT HRS ALOS PRISM SPOT HRG + HRS SPOT HRG + ALOS PRISM

16,1% 21,6% 15,3% 16,4% 12,9%

3. Remningstorp, Västergötland

SPOT HRG ALOS PRISM SPOT HRG + ALOS PRISM

13,6% 13,1% 10,5%

SPOT HRG = “Vanlig” SPOT bild i färg Grönt = enbart ytmodell Rött = Vanlig SPOT bild + någon ytmodell



Artikel och presentationer

Artikel Persson, H. Wallerman, J, Olsson, H och Fransson, J.E.S. 2013. Estimating forest biomass and height using optical stereo satellite data and DEM from laser scanning data. Artikel inskickad till Canadian Journal of Remote Sensing. Konferenspresentationer Wallerman, J. , Fransson, J.E.S, Reese, H., Bohlin, J., and Olsson, H. 2010. Forest mapping using 3D data from SPOT-5 HRS and Z/I DMC. In proceedings from IGARSS, Honolulu, Hawaii, July 25-30, pp. 64-67. + Muntlig presentation. Persson, H., Wallerman, J., Olsson, H. and Fransson, J.E.S. 2012. Estimating biomass and height using DSM from satellite data and DEM from high-resolution laser scanning data. In: Proceedings from IGARSS 2012, Munich, Germany, July 22-27. + Muntlig presentation. Olsson, H., Henrik Persson, Jörgen Wallerman, Jonas Bohlin, Johan Fransson. 2013. 3D data från optiska satelliter - Skogliga tillämpningar. Rymdstyrelsens fjärranalysdagar, Solna, 9-10 april, 2013.

(or: going from 2 d to 3d)...height 1.3 6.2% 0.707 3.42 0.84 25 note: all regression coefficients...

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