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 ID: 422172
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Slide1
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, CanadaSlide2
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
-
Loca
l acquisition
-
1
0cm
~
resolution
→
single
tree level estimationSlide3
Problem ⇒ study frame
ALOS PALSAR
⇔
limited field samples
Bottom-up approach
State Level:
Biomass
change is monitored using
PALSAR
a
s same quality as global scale.
District Level:
Biomass
change is monitored using
Airborne
LiDAR
Stand Level:
Biomass
change is monitored using
Airborne or
terrestorial
LiDARSlide4
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. Slide5
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
. Slide6
Study frame
⇒
forest management activities
2009
Summer
ALOS PALSAR data before thinning
The first airborne
LiDAR
acquisiton
Discrete samples
f
ield work
- measure trees.
Continuous samples
modeling
Wider
scale
b
iomass change
Ground Truth
2010
Summer
ALOS PALSAR data after thinning
The second airborne
LiDAR
acquisiton
2009
&
2010
Winter
W
e thinned trees.Slide7
Terrestrial LiDAR (after thinning)Slide8
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 km
2
- Dominant
species is Japanese cedar
(
Cryptomeria
japonica
)
Homogeneous
stands
- 30 plots (20m x 20m) were setSlide9
Data – Airborne LiDAR
Acquisition date
1
st
Aug
. 14
th
, 2009
2
nd
July
18
th
, 2010
Laser sensor
Riegl LMS-Q560
Laser wavelength
1,550 nm
(Near infrared red )
Average laser point
20 points/m
2
HH
HV
Before thinning
After thinningSlide10
Data – ALOS PALSAR
Mode
Pass
Weather
Acquisition date
FBD
405
Cloud
2009/7/1 13:08
FBD
404
Sunny
2009/7/30 13:06
FBD
404
Sunny
2009/9/14 13:07
FBD
405
Sunny
2009/10/1 13:09
FBD
404
Sunny
2010/6/17 13:05
FBD
405
Sunny
2010/7/4 13:07
FBD
404
Sunny
2010/9/17 13:04
FBD
405
Sunny
2010/10/4 13:06
FBD
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.
B
ackscattering coefficient
-
σ
0
(dB,
amplitude value) Slide11
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 Slide12
Preprocessing – Airborne LiDAR
Digital Terrain Model
Digital
Canopy Model
⇒
Tree
Top location
Digital
Surface
ModelSlide13
Preprocessing
DTM (50cm)
DSM (50cm)
2010 DCM
(50cm)
Thinned area
⇒
whiteSlide14
Methodology – Identify Tree Tops
Stem
height and location have
been
identified by
Second order Taylor’s approximation
(
Bloomenthal
et al.
,
1997)Slide15
Tree top location
and height
Before Thinning (
Aug 2009)
After Thinning (
July 2010)
mSlide16
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
) + CSlide17
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 Slide18
Results and Discussion
V
= 20.94 log(N) + 82.94 log(H) - 113.10
m
mSlide19
Stem
V
olume Change (m
3
)
m
H
igh: 137.03
Low
: -116.04
HH
HVSlide20
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) Slide21
Future consideration
1. Full polarization data should be utilized for the biomass change analysis.
⇒
averaging speckle filtering requires
data accumulation
.
i
nterferometric 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.Slide22
FAO global woody biomass mapSlide23
Future StudyVolume Scattering ⇒
Canopy Condition
Wrapping method - Kato
et al.
, (2009)
Remote Sensing of Environment
113 : 1148-1162
Field measured crown volume (m
3
)
C
rown 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 standsSlide24
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.