Monitoring Forest Management Activities using Airborne LiDAR and ALOS PALSAR 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
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MONITORING FOREST MANAGEMENT ACTIVTIES USING AIRBORNE LIDAR AND ALOS PALSAR.pptx
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Monitoring Forest Management Activities using Airborne LiDAR and ALOS PALSAR
Airborne LiDAR - Near-infrared red laser → direct measurement - (Multi-) temporal data - High cost - Local acquisition - 10cm ~ resolution → single tree level estimation
Problem study frame⇒ ALOS PALSAR ⇔ limited field samplesBottom-up approach
State Level: Biomass change is monitored using PALSARas same quality as global scale.
District Level: Biomass change is monitored using Airborne LiDARStand Level: Biomass change is monitored using Airborne or terrestorial LiDAR
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.
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.
Preprocessing – ALOS PALSAR1.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
Preprocessing – Airborne LiDARDigital Terrain Model Digital Canopy Model
⇒Tree Top locationDigital Surface Model
Preprocessing
DTM (50cm) DSM (50cm)
2010 DCM (50cm) Thinned area ⇒ white
Methodology – Identify Tree TopsStem height and location have been identified
by 0,;02 yyxxyyxxxy fffff
xx
yyxxxyxy
f
ffff
2
tan
Second order Taylor’s approximation
yyxyxx fyyfyyxxfxxyxfyxf 2000
2000 ))(2/1())(())(2/1(),(),(
~
sin)( 0 rxx cos)( 0 ryy
(Bloomenthal et al., 1997)
Tree top location and height
Before Thinning (Aug 2009) After Thinning (July 2010)