prediction of productivity indices using remotely sensed data · 3/5/2015 · lidar derived si av....
TRANSCRIPT
![Page 1: Prediction of productivity indices using remotely sensed data · 3/5/2015 · LiDAR derived SI Av. spr. air temp Band 5 ME-25 Red reflectance NIR refectance Age Blue reflectance](https://reader035.vdocument.in/reader035/viewer/2022071214/6042115edef696787e46bb3a/html5/thumbnails/1.jpg)
Prediction of productivity indices using remotely sensed data
Michael Watt, Jonathan Dash, Pete Watt, Santosh Bhandari
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Introduction
• Site Index, 300 Index commonly used metrics to describe plantation productivity
• Typically estimates of these properties made at the stand level by foresters
• Estimates of these productivity surfaces have also been made using environmental variables
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Introduction• Little research has investigated the utility of
remotely sensed data sources for predicting productivity metrics
• Use of remotely sensed data could : improve prediction accuracy allow fine scale spatial prediction
• Combinations of the following three data sources could be useful : Environmental Surfaces Satellite Imagery LiDAR
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Data sources - Environmental surfaces
• Data mostly at 100 m2
resolution
• Air temperature• Solar radiation • Rainfall• Relative humidity• Slope• Soil fertility
MW2
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Slide 4
MW2 Pete, Santosh, if you have any additional information or pictures please add these. Michael Watt, 4/03/2015
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Data sources - satellite imagery
• RapidEye most useful satellite 5 m resolution, 0.01-0.02 $/ha
• 5 spectral bands• Vegetation ratios that describe
photosynthetic activity can be derived.
• Textural measures that describe vegetation surfaces can be derived.
• Easily incorporated into GIS
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Data sources - Light detection and ranging (LiDAR)Active remote sensing technology
LiDAR emits ~ 200,000 pulses per secondRecords up to 4 returns per pulseEach return is converted to 3D point
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Site Index from LiDAR
• LiDAR can be used to accurately predict height
• Using stand age and a age-height equation wecan estimate Site Index
• Site Index predicted from LiDAR was usedfor modelling
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Objective
• Develop models of 300 Index and Site Index using different combinations of LiDAR, satellite imagery and environmental surfaces
• Models developed with and without stand age using both non-parametric and parametric methods
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Methods
• LiDAR acquired early 2014 ~ 11 points m2
• RapidEye acquired January 2014
• Total of 493 plots installed early 2014
• 433 plots used for model fitting, 60 plots set aside for model validation
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Methods
• Total of 14 models developed using various combinations of the three datasets.
• Parametric method used was multiple regression
• Non-parametric method used was k-nearest neighbour• Random forest or k-MSN used to assign neighbours• Values of k were optimised to minimise model error
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Results - Site Index
Rap
idE
ye (R
E) s
pect
ral
RE
ratio
s
RE
text
ure
RE
spec
tal +
ratio
s +
text
ure
Envi
ronm
enta
l var
iabl
es
Env
ironm
enta
l var
iabl
es +
RE
LiD
AR
pre
dict
ed S
ite In
dex
(SI)
LiD
AR
Pre
d. S
I + E
nviro
n. +
RE
Roo
t mea
n sq
uare
err
or (m
)
1.0
1.5
2.0
2.5
3.0
ParametricNon-parametric k-NN
1.0
1.5
2.0
2.5
3.0
3.5
Models used to predict Site Index
Rap
idE
ye (R
E) s
pect
ral
RE
ratio
s
RE
text
ure
RE
spec
tal +
ratio
s +
text
ure
Envi
ronm
enta
l var
iabl
es
Env
ironm
enta
l var
iabl
es +
RE
LiD
AR
pre
dict
ed S
ite In
dex
(SI)
LiD
AR
Pre
d. S
I + E
nviro
n. +
RE
Coe
ffici
ent o
f det
erm
inat
ion
(R2 )
0.5
0.6
0.7
0.8
0.9
0.5
0.6
0.7
0.8
0.9
Without age
With age
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Results - 300 Index
Rap
idE
ye (R
E) s
pect
ral
RE
ratio
s
RE
text
ure
RE
spe
ctal
+ ra
tios
+ te
xtur
e
Env
ironm
enta
l var
iabl
es
Envi
ronm
enta
l var
iabl
es +
RE
LiD
AR
pre
dict
ed S
ite In
dex
(SI)
LiD
AR P
red.
SI +
Env
iron.
+ R
E
Roo
t mea
n sq
uare
err
or (m
3 ha-1
yr-1
)
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Parametric Non-parametric k-NN
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Models used to predict 300 Index
Rap
idE
ye (R
E) s
pect
ral
RE
ratio
s
RE
text
ure
RE
spe
ctal
+ ra
tios
+ te
xtur
e
Env
ironm
enta
l var
iabl
es
Envi
ronm
enta
l var
iabl
es +
RE
LiD
AR
pre
dict
ed S
ite In
dex
(SI)
LiD
AR P
red.
SI +
Env
iron.
+ R
E
Coe
ffici
ent o
f det
erm
inat
ion
(R2 )
0.4
0.5
0.6
0.7
0.4
0.5
0.6
0.7
0.8
Without age
With age
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Results – Best Models
Metric Age? Type Variables R2 RMSESite Index Y NP LiDAR + Environ. + RapidEye 0.91 1.40 m
N P Environmental + RapidEye 0.79 2.46 m
300 Index Y P LiDAR 0.79 2.45 m3 ha-1 yr-1
N NP Environmental + RapidEye 0.65 3.21 m3 ha-1 yr-1
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Site Index 300 Index
Variable importance
LiD
AR
der
ived
SI
Av.
spr
ing
air t
emp.
Av.
spr
ing
sola
r rad
.E
VI
Band
5 M
E-2
5A
geN
IR re
fect
ance
Sim
ple
ratio
ND
VI
Ban
d 5
CO
R-1
5B
1 V
AR
-25
Band
4 M
E-2
5Bl
ue re
flect
ance
Red
refle
ctan
ceB
and
4 SD
-25
Gre
en re
flect
ance GR
Slo
pe
Mea
n Im
porta
nce
0
1
2
3
4
5
Variable included in the model
LiD
AR
der
ived
SI
Av.
spr
. air
tem
pBa
nd 5
ME
-25
Red
refle
ctan
ceN
IR re
fect
ance
Age
Blue
refle
ctan
ce EV
IBa
nd 5
EN
T-15
B
and
4 SD
-25
ND
VI
Sim
ple
ratio
Band
4 A
SM
-25
Band
5 C
OR
-15
Band
4 M
E-2
5A
v. s
pr. s
olar
rad
Ban
d 5
CO
N-1
5 A
v. s
olar
rad.
Mea
n F-
valu
e 0
300
600
900(a)
(b)
LiD
AR d
eriv
ed S
IAv
. spr
. air
tem
pAv
. sum
. VPD Age
Perc
. ret
. > m
ean
Can
opy
relie
f rat
ioN
DVI
NIR
refle
ctan
ce EVI
Brig
htne
ssB
and
5 M
E-2
5Le
ngth
and
slo
pe fa
ctor
Red
refle
ctan
ceG
reen
refle
ctan
ceIn
tLku
rtosi
sC
N ra
tioR
ed e
dge
ratio
Ban
d 5
EN
T-5
Mea
n Im
porta
nce
-1
0
1
2
3
4
Variable included in the model Li
DA
R d
eriv
ed S
IAv
. spr
. air
tem
pB
and
5 M
E-25
ND
VI
Can
opy
relie
f rat
ioE
VI
NIR
refe
ctan
ceAg
eP
erc.
ret.
> m
ean
Brig
htne
ssR
ed re
flect
ance
Band
4 E
NT-
25
Ban
d 2
VAR
- 25
Red
edg
e ra
tioG
reen
refle
ctan
ceA
v. s
olar
rad.
In
tLku
rtosi
sC
N ra
tio
Mea
n F-
valu
e
0
100
200
300
400(a)
(b)
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Results – Top three variables
Metric Type VariablesSite Index P LiDAR derived Site Index; Av. spring air temp; NIR mean texture
NP LiDAR derived Site Index; Av. spring air temp; Av. spring solar rad.
300 Index P LiDAR derived Site Index; Av. spring air temp; NIR mean textureNP LiDAR derived Site Index; Av. spring air temp; Av. summer VPD
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Research gains
Predicted Site Index (m)20 25 30 35 40
20
25
30
35
40
20
25
30
35
40
45
Predicted 300 Index (m3 ha-1 yr-1)10 15 20 25 30 35
10
15
20
25
30
35
Mea
sure
d Si
te In
dex
(m)
10
15
20
25
30
35
40
Mea
sure
d 30
0 In
dex
(m3 h
a-1 y
r-1)
Using Environmental Surfaces (previous method)
Using all variables, including LiDAR
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Research gains
Predicted Site Index (m)20 25 30 35 40
20
25
30
35
40
20
25
30
35
40
45
Predicted 300 Index (m3 ha-1 yr-1)10 15 20 25 30 35
10
15
20
25
30
35
Mea
sure
d Si
te In
dex
(m)
10
15
20
25
30
35
40
Mea
sure
d 30
0 In
dex
(m3 h
a-1 y
r-1)
Using Environmental Surfaces (previous method)
Using all variables, including LiDAR
R2 = 0.57RMSE = 3.55 m3 ha-1 yr-1
R2 = 0.79RMSE = 2.45 m3 ha-1 yr-1
R2 = 0.76RMSE = 2.30 m
R2 = 0.91RMSE = 1.40 m
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Summary
• Models of Site Index and 300 Index created
• Significant gains in precision possible using information from LiDAR and RapidEye
• For models without age, best data source were environmental surfaces + RapidEye
• For models with age, best data source was LiDAR
• RapidEye and environmental surfaces useful cost effective alternative to LiDAR
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Acknowledgements
• GCFF programme jointly funded by MBIE and NZ Forest Growers Levy Trust
• Timberlands
• Professional contribution of all field teams involved
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http://research.nzfoa.org.nz/www.scionresearch.com/gcff
Michael WattResearch Leader – Forest Operations and Monitoring
Date:24 March 2015