1. Akira Kato 1, Manabu Watanabe 2, Tatsuaki, Kobayashi 1, Yoshio Yamaguchi 3,and Joji Iisaka 4 1 Graduate School of Horticulture, Chiba University, Japan 2 Center for Northeast Asian Studies, Tohoku University, Japan 3 Graduate School of Science & Technology, Niigata University,, Japan 4 Department of Geography, University of Victoria, Canada

2. ALOS PALSAR ⇔ Airborne LiDAR ALOS PALSAR - L-band radar → polarization (indirect measurement) - Multi-temporal data - Low cost - Global acquisition - 15m ～ resolution → plot level estimation Airborne LiDAR - Near-infrared red laser → direct measurement - (Multi-) temporal data - High cost - Local acquisition - 10cm ～ resolution → single tree level estimation

3. Problem ⇒ study frame ALOS PALSAR ⇔ limited field samples Bottom-up approach State Level: Biomass change is monitored using PALSAR as same quality as global scale. District Level: Biomass change is monitored using Airborne LiDAR Stand Level: Biomass change is monitored using Airborne or terrestorial LiDAR

4. Forest Biomass ⇔ Volume Scattering Past studies 1. Saturation level of forest biomass using L-band 100 ton/ha in homogeneous pine forest (Imhoff et al., 1995) ⇒ Approx. 5 meters spacing of 20 m height trees. 40 ton/ha in broadleaf evergreen forest (Lucas et al., 2006) 2. HV polarization is higher correlation with forest biomass (Lucas et al., 2006) ALOS PALSAR is a good sensor to detect the forest management activities, but correlation between backscattering coefficient and the change is still unknown.

5. Volume Scattering ⇔ stand condition Stand condition is defined by - stem density - tree height - tree forms (the shape of tree crown) - tree age ⇒ airborne LiDAR is used to bridge between field measurement and backscattering coefficient of ALOS PALSAR as the ground truth.

6. Study frame ⇒ forest management activities ２００９ Summer ALOS PALSAR data before thinning The first airborne LiDAR acquisiton Discrete samples field work - measure trees. Continuous samples modeling Wider scale biomass change Ground Truth ２０１０ Summer ALOS PALSAR data after thinning The second airborne LiDAR acquisiton ２００９ & ２０１０ Ｗｉｎｔｅｒ We thinned trees.

7. Terrestrial LiDAR (after thinning)

8. Study Area Sanmu City, Chiba Prefecture, JAPAN → Commercial timber production area NameNumberd.b.h(cm)Tree Height(m) Cryptomeria japonica 718 10.3 ～ 69.710.4 ～ 34.3 Chamaecyparis obtuse 179 8 ～ 722.9 ～ 31.8 Chamaecyparis pisifera 38 17.1 ～ 90.714.6 ～ 34.9 Quercus myrsinaefolia 9 4 ～ 67.75.9 ～ 29.8 Research area is around 9 km 2 - Dominant species is Japanese cedar (Cryptomeria japonica) -Homogeneous stands - 30 plots (20m x 20m) were set

9. Data – Airborne LiDAR Acquisition date 1 st Aug. 14 th, 2009 2 nd July 18 th, 2010 Laser sensorRiegl LMS-Q560 Laser wavelength1,550 nm (Near infrared red ) Average laser point20 points/m 2 HHHV Before thinningAfter thinning

10. Data – ALOS PALSAR ModePassWeatherAcquisition date FBD405Cloud2009/7/1 13:08 FBD404Sunny2009/7/30 13:06 FBD404Sunny2009/9/14 13:07 FBD405Sunny2009/10/1 13:09 FBD404Sunny2010/6/17 13:05 FBD405Sunny2010/7/4 13:07 FBD404Sunny2010/9/17 13:04 FBD405Sunny2010/10/4 13:06 FBD405Cloud2010/11/19 13:05 L-band FBD (Fine beam Double Polarization) Resolution: 20m Before thinning After thinning HHHV ALOS satellite ended at May 2011. - 20 m resolution L-band SAR. - 46 days observation cycle. ALOS 2 will be launched at 2013. -1 ～ 3 m resolution L-band SAR. -16 days observation cycle. Backscattering coefficient - σ 0 (dB, amplitude value)

11. Preprocessing – ALOS PALSAR １． Geometric and terrain correction ⇒ MapReady (Alaska Satellite Facility, ver 2.3, 2010). 2. layover / shadow regions for the terrain correction ⇒ 5m resolution DEM provided by Geospatial Information Authority of Japan 3. Speckle filtering ⇒ Averaging the values of multi-temporal data. The data before thinning (before August 2010) and after thinning (after August 2010) are averaged separately. 4. Pixel alignment ⇒ Manual geo-referencing was applied to match the images with less than half pixel of error (10m) among the multi-temporal data

12. Preprocessing – Airborne LiDAR Digital Terrain Model Digital Canopy Model ⇒ Tree Top location Digital Surface Model

13. Preprocessing DTM (50cm) DSM (50cm) 2010 DCM (50cm) Thinned area ⇒ white

14. Methodology – Identify Tree Tops Stem height and location have been identified by Second order Taylor’s approximation (Bloomenthal et al., 1997)

15. Tree top location and height Before Thinning (Aug 2009)After Thinning (July 2010) m

16. Methodology Biomass estimation Biomass = (stem volume = f (tree height, dbh)) × (density factor) ×(expansion factor of branch) ×(expansion factor of stem) Stem volume = α (stem density) + β (tree height) + C

17. Results and Discussion Airborne LiDAR Stem density Tree height Stem density correction: y = 2.5034x - 12.41 where x: the number of stems derived from airborne lidar y: the corrected number of stems

18. Results and Discussion V = 20.94 log(N) + 82.94 log(H) - 113.10 m m

19. Stem Volume Change (m 3 ) m High: 137.03 Low: -116.04 HH HV

20. Results and Discussion ALOS PALSAR HV/HH is shifted in 9.8 degrees X-axis: HH backscattering coefficients (σ 0, dB) Y-axis: HV backscattering coefficients (σ 0, dB) Before Thinning After Thinning The axis is rotated towards right (when trees are thinned)

21. Future consideration 1. Full polarization data should be utilized for the biomass change analysis. ⇒ averaging speckle filtering requires data accumulation. interferometric analysis needs the shorter observation cycle. 2. Full polarization interferometry analysis can raise the saturation level (more than 100 ton / ha). ⇒ registration among multi-temporal images should be accurate enough. 3. World biomass map shows the limitation to use the backscattering coefficient for the biomass stock, but the biomass change can be monitored.

22. FAO global woody biomass map

23. Future Study Volume Scattering ⇒ Canopy Condition Wrapping method - Kato et al., (2009) Remote Sensing of Environment 113 : 1148- 1162 Field measured crown volume (m 3 ) Crown volume from wrapping method(m 3 ) Quantifying the thickness of canopy from crown volume derived by the wrapping method Green: Low density stands Blue: High density stands

24. Thank you very much. Any questions? Contact: Dr. Akira Kato akiran@faculty.chiba-u.jp Acknowledgement This research was supported by the Environment Research and Technology Development Fund (RF-1006) of the Ministry of the Environment, Japan.