Monitoring Forest Management Activities using Airborne LiDAR and ALOS PALSAR

Akira Kato1, Manabu Watanabe2, Tatsuaki, Kobayashi1, Yoshio Yamaguchi3,and Joji Iisaka4

1Graduate School of Horticulture, Chiba University, Japan2Center for Northeast Asian Studies, Tohoku University, Japan

3Graduate School of Science & Technology, Niigata University,, Japan

4Department of Geography, University of Victoria, Canada

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

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.

Study frame forest management activities⇒

２００９ Summer

ALOS PALSAR data before thinning

The first airborne LiDAR acquisiton

Discrete samplesfield work

- measure trees.

Continuous samplesmodeling

Wider scalebiomass change

Ground Truth

２０１０ Summer

ALOS PALSAR data after thinning

The second airborne LiDAR acquisiton

２００９ & ２０１０　Ｗｉｎｔｅｒ We thinned trees.

Terrestrial LiDAR (after thinning)

Study AreaSanmu City, Chiba Prefecture, JAPAN → Commercial timber production area

Name Number d.b.h(cm) Tree Height(m)Cryptomeria japonica

718 10.3～ 69.7 10.4～ 34.3

Chamaecyparis obtuse

179 8～ 72 2.9～ 31.8

Chamaecyparis pisifera

38 17.1～ 90.7 14.6～ 34.9

Quercus myrsinaefolia

9 4～ 67.7 5.9～ 29.8

Research area is around 9 km2

- Dominant species is Japanese cedar (Cryptomeria japonica)-Homogeneous stands - 30 plots (20m x 20m) were set

Data – Airborne LiDARAcquisition date 1st Aug. 14th, 2009

2nd July 18th, 2010 Laser sensor Riegl LMS-Q560 Laser wavelength 1,550 nm

(Near infrared red ) Average laser point 20 points/m2

HH HV

Before thinning After thinning

Data – ALOS PALSARMode Pass Weather Acquisition dateFBD 405 Cloud 2009/7/1 13:08FBD 404 Sunny 2009/7/30 13:06FBD 404 Sunny 2009/9/14 13:07FBD 405 Sunny 2009/10/1 13:09FBD 404 Sunny 2010/6/17 13:05FBD 405 Sunny 2010/7/4 13:07FBD 404 Sunny 2010/9/17 13:04FBD 405 Sunny 2010/10/4 13:06FBD 405 Cloud 2010/11/19 13:05

L-band FBD (Fine beam Double Polarization)Resolution: 20m

Before thinning

After thinning

HH HV

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- σ0 (dB, amplitude value)

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)

0 75 150 225 30037.5メートルm

MethodologyBiomass estimationBiomass = (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

Results and DiscussionAirborne 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

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

m m

Stem Volume Change (m3)

m

High: 137.03

Low: -116.04

HH

HV

Results and DiscussionALOS PALSAR

HV/HH is shifted in 9.8 degrees

X-axis: HH backscattering coefficients (σ0, dB)

Y-a

xis:

HV

bac

ksca

tter

ing

coef

fici

ents

(σ 0,

dB)

Before Thinning After Thinning

The axis is rotated towards right (when trees are thinned)

Future consideration1. 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.

FAO global woody biomass map

Future StudyVolume Scattering ⇒ Canopy Condition

Wrapping method - Kato et al., (2009) Remote Sensing of Environment 113 : 1148-1162

Field measured crown volume (m3)

Cro

wn

volu

me f

rom

wra

pp

ing

meth

od

(m3)

Quantifying the thickness of canopy from crown volume derived by the wrapping method

Green: Low density stands Blue: High density stands

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.