(or: going from 2 d to 3d)...height 1.3 6.2% 0.707 3.42 0.84 25 note: all regression coefficients...
TRANSCRIPT
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)
Swedish University of Agricultural Sciences
Forest Remote Sensing
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
Swedish University of Agricultural Sciences
Forest Remote Sensing
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
Swedish University of Agricultural Sciences
Forest Remote Sensing
The scannings have been made during different seasons
Swedish University of Agricultural Sciences
Forest Remote Sensing
And with different
laser scanners
Swedish University of Agricultural Sciences
Forest Remote Sensing
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.
Swedish University of Agricultural Sciences
Forest Remote Sensing
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?
Swedish University of Agricultural Sciences
Forest Remote Sensing
• 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.
Swedish University of Agricultural Sciences
Forest Remote Sensing
• 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
Swedish University of Agricultural Sciences
Forest Remote Sensing
Data sources for planned nationwide product
Airborne laser scanning data Satellite image
National forest inventory sample plots
Swedish University of Agricultural Sciences
Forest Remote Sensing
•
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.
Swedish University of Agricultural Sciences
Forest Remote Sensing
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.
Swedish University of Agricultural Sciences
Forest Remote Sensing
Stem volume at estate level
Swedish University of Agricultural Sciences
Forest Remote Sensing
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
Swedish University of Agricultural Sciences
Forest Remote Sensing
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
Swedish University of Agricultural Sciences
Forest Remote Sensing
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
Swedish University of Agricultural Sciences
Forest Remote Sensing
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
Swedish University of Agricultural Sciences
Forest Remote Sensing
Terra ASTER
SPOT HRS
ALOS PRISM
DMC air photo camera
TandDEM-X + TerraSAR X
Other tested 3D techniques
Swedish University of Agricultural Sciences
Forest Remote Sensing
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°
Swedish University of Agricultural Sciences
Forest Remote Sensing
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 %.
Swedish University of Agricultural Sciences
Forest Remote Sensing
Point cloud from digital photogrammetry over same area.
Point cloud from laser scanner
data
Swedish University of Agricultural Sciences
Forest Remote Sensing
Height estimated from TandDEM-X versus validation data
RMSE = 6.2%
Swedish University of Agricultural Sciences
Forest Remote Sensing
Comparison canopy heights from TanDEM-X versus LiDAR
Swedish University of Agricultural Sciences
Forest Remote Sensing
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).
Swedish University of Agricultural Sciences
Forest Remote Sensing
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
Swedish University of Agricultural Sciences
Forest Remote Sensing
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)
Swedish University of Agricultural Sciences
Forest Remote Sensing
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
Swedish University of Agricultural Sciences
Forest Remote Sensing
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?
Swedish University of Agricultural Sciences
Forest Remote Sensing
Additional material
Swedish University of Agricultural Sciences
Forest Remote Sensing
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
Swedish University of Agricultural Sciences
Forest Remote Sensing
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
Swedish University of Agricultural Sciences
Forest Remote Sensing
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-
Swedish University of Agricultural Sciences
Forest Remote Sensing
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
Swedish University of Agricultural Sciences
Forest Remote Sensing
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.