MONITORING FOREST MANAGEMENT ACTIVTIES USING AIRBORNE LIDAR AND ALOS PALSAR.pptx

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MONITORING FOREST MANAGEMENT ACTIVTIES USING AIRBORNE LIDAR AND ALOS PALSAR.pptx

  1. 1. Monitoring Forest Management Activities using Airborne LiDAR and ALOS PALSAR<br />Akira Kato1, Manabu Watanabe2, Tatsuaki, Kobayashi1, <br />Yoshio Yamaguchi3,and Joji Iisaka4<br />1Graduate School of Horticulture, Chiba University, Japan<br />2Center for Northeast Asian Studies, Tohoku University, Japan<br />3Graduate School of Science & Technology, Niigata University,, Japan<br />4Department of Geography, University of Victoria, Canada<br />
  2. 2. ALOS PALSAR ⇔Airborne LiDAR<br />ALOS PALSAR<br /> - L-band radar -> polarization (indirect measurement)<br /> - Multi-temporal data<br /> - Lowcost<br /> - Globalacquisition<br /> - 15m~ resolution-> plot level estimation<br />Airborne LiDAR<br /> - Near-infrared red laser -> direct measurement<br /> - (Multi-) temporal data<br /> - Highcost <br /> - Local acquisition<br /> - 10cm ~ resolution-> single tree level estimation<br />
  3. 3. Problem ⇒ study frame<br /> ALOS PALSAR ⇔ limited field samples<br />Bottom-up approach<br />State Level: <br />Biomass change is monitored using PALSAR<br />as same quality as global scale.<br />District Level: <br />Biomass change is monitored using Airborne LiDAR<br />Stand Level: <br />Biomass change is monitored using <br /> Airborne or terrestorialLiDAR<br />
  4. 4. Forest Biomass ⇔ Volume Scattering<br />Past studies<br />1. Saturation level of forest biomass using L-band<br />100 ton/ha in homogeneous pine forest (Imhoffet al., 1995)<br />⇒ Approx. 5meters spacing of 20 m height trees.<br />40 ton/ha in broadleaf evergreen forest (Lucas et al., 2006)<br />2. HV polarization is higher correlation with forest biomass (Lucas et al., 2006)<br /> ALOS PALSAR is a good sensor to detect the forest <br />management activities, but correlation between <br /> backscattering coefficient and the change is still unknown.<br />
  5. 5. Volume Scattering ⇔stand condition<br />Stand condition is defined by<br /> - stem density<br /> - tree height<br /> - tree forms (the shape of tree crown)<br /> - tree age<br />⇒ airborne LiDARis used to bridge between field measurement and backscattering coefficient of ALOS PALSAR as the ground truth. <br />
  6. 6. Study frame ⇒forest management activities<br /><ul><li>2010Summer</li></ul>ALOS PALSAR data after thinning<br />The second airborne LiDARacquisiton<br />2009 Summer<br />ALOS PALSAR data before thinning<br />The first airborne LiDARacquisiton<br />Wider scale<br />biomass change<br />Ground Truth<br />Continuous samples<br />modeling<br />Discrete samples<br />field work<br />- measure trees.<br />2009& 2010 Winter We thinned trees.<br />
  7. 7. Terrestrial LiDAR (after thinning)<br />
  8. 8. Study Area<br />Sanmu City, Chiba Prefecture, JAPAN<br />-> Commercial timber production area<br />Research area is around 9 km2<br />- Dominant species is Japanese cedar <br />(Cryptomeria japonica)<br /><ul><li>Homogeneous stands </li></ul>- 30 plots (20m x 20m) were set<br />
  9. 9. Data – Airborne LiDAR<br />HH<br />HV<br />Before thinning<br />After thinning<br />
  10. 10. Data – ALOS PALSAR<br />L-band FBD (Fine beam Double Polarization)<br />Resolution: 20m <br />ALOS satellite endedat May 2011.<br />- 20 m resolution L-band SAR. <br />- 46 days observation cycle.<br />ALOS 2 will be launched at 2013. <br /><ul><li>1~3 m resolution L-band SAR.
  11. 11. 16 days observation cycle.</li></ul>Before <br />thinning<br />After <br />thinning<br />Backscattering coefficient<br />- σ0(dB, amplitude value) <br />HH<br />HV<br />
  12. 12. Preprocessing – ALOS PALSAR<br />1.Geometric and terrain correction<br />⇒MapReady (Alaska Satellite Facility, ver 2.3, 2010). <br />2. layover / shadow regions for the terrain correction<br />  ⇒5m resolution DEM provided by Geospatial Information Authority of Japan <br />3. Speckle filtering<br />⇒Averaging the values of multi-temporal data. The data before thinning (before August 2010) and after thinning (after August 2010) are averaged separately. <br />4. Pixel alignment<br />⇒Manual geo-referencing was applied to match the images with less than half pixel of error (10m) among the multi-temporal data <br />
  13. 13. Preprocessing – Airborne LiDAR<br />Digital Terrain Model<br />Digital Canopy Model<br />⇒Tree Top location<br />Digital Surface Model<br />
  14. 14. Preprocessing <br /> <br />DSM (50cm)<br />DTM (50cm)<br />      <br />Thinned area <br />⇒ white<br />2010 DCM (50cm)<br />
  15. 15. Methodology – Identify Tree Tops<br />Stem height and location have been identified by<br />(Bloomenthal et al., 1997)<br />Second order Taylor’s approximation<br />
  16. 16. Tree top location and height<br />   <br />Before Thinning (Aug 2009)<br />After Thinning (July 2010)<br />m<br />
  17. 17. Methodology<br />Biomass estimation<br />Biomass = (stem volume = f (tree height, dbh))<br />× (density factor)<br /> ×(expansion factor of branch) <br /> ×(expansion factor of stem) <br />Stem volume = α(stem density) +β(tree height) + C<br />
  18. 18. Results and Discussion<br />Airborne LiDAR<br /> Stem density Tree height <br />Stem density correction: <br /> y = 2.5034x - 12.41<br /> where x: the number of stems derived from airborne lidar<br /> y: the corrected number of stems <br />
  19. 19. Results and Discussion<br />V = 20.94 log(N) + 82.94 log(H) - 113.10<br />m<br />m<br />
  20. 20. Stem Volume Change (m3)<br />HH<br />HV<br />High: 137.03<br />Low: -116.04<br />m<br />
  21. 21. Results and Discussion<br />ALOS PALSAR<br />HV/HH is shifted in 9.8 degrees<br />Y-axis: HV backscattering <br />coefficients (σ0, dB)<br />X-axis: HH backscattering coefficients (σ0, dB)<br />Before Thinning After Thinning<br />The axis is rotated towards right (when trees are thinned) <br />
  22. 22. Future consideration<br />1. Full polarization data should be utilized for the biomass change analysis.<br />⇒ averaging speckle filtering requires data accumulation.<br />interferometric analysis needs the shorter observation cycle. <br />2. Full polarization interferometry analysis can raise the saturation level (more than 100 ton / ha). <br />⇒ registration among multi-temporal images should be accurate enough.<br />3. World biomass map shows the limitation to use the backscattering coefficient for the biomass stock, but the biomass change can be monitored.<br />
  23. 23. FAO global woody biomass map<br />
  24. 24. Future Study<br />Volume Scattering ⇒ Canopy Condition<br />Crown volume from<br /> wrapping method(m3)<br />Wrapping method - Kato et al., (2009) <br />Remote Sensing of Environment 113 : 1148-1162<br />Field measured crown volume (m3)<br />Green: Low density stands <br />Blue: High density stands<br />Quantifying the thickness of canopy from <br />crown volume derived by the wrapping method <br />
  25. 25. Thank you very much.<br />Any questions?<br />Contact:<br />Dr. Akira Kato<br />akiran@faculty.chiba-u.jp<br />Acknowledgement <br />This research was supported by the Environment Research and Technology Development Fund (RF-1006) of the Ministry of the Environment, Japan.<br />

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