Post on 20-Sep-2018
Presented on “International Conference on Sustainability Study (ICSS)”, Bali, January 11, 2012
Quantitative Measurement from Unifying Field and Airborne Hyperspectral (HyMap) for Diagnosing Peat Forest Degradation in
Central Kalimantan, Indonesia
by
Ohki Takahashi (MRI), Tomomi Takeda (ERSDAC), Muhammad Evri (BPPT)
Osamu Kashimura (ERSDAC), Mitsuru Osaki (HU), Kazuyo Hirose (HU), Hendrik Segah (HU)
BPPT ERSDAC
MRI
Hokkaido Univ.
METI Budget
Joint Research
Entrustment Collaboration
Research Project of Hyperspectral Technology for Tropical
Peat-Forest Mapping in Indonesia(Hyper PF MRV)
Rationale Statement of President Susilo Bambang Yudoyono in
the summit of G-20 in Pittsburgh (USA) on September
2009; to reduce greenhouse gases (GHG) 26%
through National Appropriate Mitigation and
Adaptation (NAMA) up to 2020 and become 41% with
international support.
Moratorium
Signed a decree (Inpres no. 10, 2011) on May 19, 2011; suspending
new concession permits and to improve good governance on
primary forest and peatland in Indonesia. Suspension of all new
concessions will be enforced for 2 years, and will be effective
immediately.
Indonesia is the third largest GHG emitter in the world.
It is estimated as about 2.1 GtCO2e in 2005.
Peat : 41%
(DNPI, 2010)
LULUCF: 37%
Agriculture: 6%
85% from Deforestation and
Degradation
To measure changes in carbon stocks caused by forest degradation, IPCC
(2006) recommends two approach method:
Intergovenmental Panel on Climate Change
Gain-Loss
∆𝐶 =𝐶𝑡2 − 𝐶𝑡1(𝑡2 − 𝑡1)
Where : ∆𝐶 = Annual carbon stock change in pool (tC/yr) ∆𝐶𝑡1= Carbon stock in pool at time 𝑡1 (tC) ∆𝐶𝑡2= Carbon stock in pool at time 𝑡2 (tC)
∆𝐶 = ∆𝐶𝑔𝑎𝑖𝑛 − ∆𝐶𝑙𝑜𝑠𝑠
Where : ∆𝐶 = Annual carbon stock change in pool (tC/yr) ∆𝐶𝑔𝑎𝑖𝑛= Annual gain in Carbon (tC)
∆𝐶𝑙𝑜𝑠𝑠= Annual loss in in Carbon (tC)
To estimate the net balance of
additions to and removals from a
carbon pool.
Used when annual data on
information such as growth rates and
wood harvest are available.
To estimate sequestration or emissions.
To measure the actual stock of biomass in
each carbon pool at two moments in time.
Suitable for estimating emissions caused
by both deforestation and degradation.
Can be applied to all carbon pools.
Stock-Difference
6
Hyperspectral for Forest Degradation
Number of bands
Acquisition of images in hundreds of calibrated, contiguous spectral bands, such that for each picture element it is possible to derive a complete reflectance spectrum
Definition
Unique discriminative power
Free band selection
Conducive for interdisciplinary collaboration
Advantages
Hyper-cube data
Huge dimension of hyperspectral data
Hyperspectral : excessiveness of the number of band
being employed in its sensor
(Airborne)
2,000 m
Landsat > 600 km Hyperion GOSAT
Supersite Supersite
Supersite
Supersite Biometric work Soil Repiration Ecophisiology
Terrain analysis
Obsv Tower Flux tower
Survey DGPS
MODIS ASTER ALOS PALSAR
LiDAR
Airborne-
hyperspectral
UAV
Airborne and Sensors
10
Sensor specification Bands : provides 126 bands across the reflective solar wavelength region of 450
- 2500 nm with contiguous spectral coverage (except in the atmospheric
water vapour bands) and bandwidths between 15 - 20 nm.
Platform : Light, twin engine aircraft,unpressurized
Altitudes : 2000 – 5000 m ALG
Ground speed : 110 – 180 knots
IFOV : 2.5 mr along track 2.0 mr aross track
FOV : 620 degrees (512 pixel)
Swath : 2.3 km at 5 m IFOV (along track) 4.6 km at 10 m IFOV (along track)
Sp
atia
l co
nfig
ura
tion
Typ
ical o
pe
ratio
nal
pa
ram
ete
rs
11
Central Kalimantan, Indonesia
– Two test sites → Hyperspectral sensor observation by aircraft
Test Site1
Test Site2
City of Palangkaraya
12
Peatland is rich soil carbon storage.
– It will become a large CO2 emission source
– Factor: Hydrological environment change ⇒Decrease of groundwater level and drying peat
“Forest degradation” in peatland forest
– Dissolved organic carbon from artificial canals, Poor growth vegetation, Forest disturbance, etc
Developing technology to assess forest degradation in peatland forest
Contributing MRV development of REDD+ activity in peatland forest
Canal
Degraded Forest
Groundwater
level
drying
Decrease
Healthy Forest
Disturbance by fire
Poor growth
Dying
Biomass
degradation
Peatland
C C C
C C
C
Emission
of DOC
Analysis of forest degradation
condition
Development of forest degradation monitoring method by satellite image
Development of DOC assessment method in canal by satellite image
Spectral analysis of dissolved
organic carbon (DOC) in canal
Reduction of CO2 sink capacity
Applying Hyperspectral sensor
13
Development of forest degradation monitoring
methods, focusing following indices
– Water stress
– Species
– Stand structures
– Biomass
Spectral analysis of DOC in canal
– Measurement of water quality and spectral
near water surface
– Analyze spectral characteristic of CDOM
※ CDOM:Colored Dissolved Organic Matter
Development of DOC assessment method in
canal.
– Qualitatively and quantitatively assessing
CDOM in canal
– Estimate DOC from CDOM analysis
Analysis of forest degradation condition and
spectral characteristics
– Identify the forest degradation condition
– Find the appropriate index
– Relation with soil moisture / underground
water level
Field data analysis
Image analysis
Consideration of MRV system using Hyperspectral Sensor
– Role of hyper spectral sensor (from monitoring target, area, frequency…)
14
Airborne campaign
– 15 – 16 July 2011
– Hyperspectral sensor : HyMAP (400 to 2500nm)
Field campaign
– 11 - 23 July 2011
– Ground reference measurement
• using FieldSpec
– Water quality measurement
• CDOM (Carbon Dissolve Organic Matter)
• Spectral near water surface using FieldSpec
– Forest Survey
• Tree and soil within the 20m×20m quadrat, 20 - 30 point
• Parameters
– Species
– DBH
– Tree Height
– Canopy Cover
– Soil Moisture
– Ground water level
Quadrat structure
Quadrat size are 20m : base area
10m sub-quadrat: A,B,C,D
5m sub-quadrat: 1,2,3,4
Setting the quadrat
Set 4 side of quadrat and 10m sub-quadrat,
along the azimuth direction
Set GPS point of 4 tips of quadrat
20m
10m 5m
SW SE
NE NW
A
B C
D
1
2 3
4
1
2 3
4 1
2 3
4
1
2 3
4 Quadrat condition
HU1: Condition of the disturbance with
forest fire and artificial logging
HU2: Condition of the drainage and
decreasing under ground water level
DBH & Tree species
Target tree
For 20m quadrat : all trees of “10cm =< DBH”
For A1,B1,C1,D1 : all trees of “5cm =< DBH < 10cm”
DBH are measured with caliper
Tree species are identified by LIPI expert
Genus, Family, and Local name
Tree Height
■ Target tree
20 trees are selected in the quadrat
Select 5 trees in each 10m sub-quadrat
Select the trees in a random manner to cover the variety of DBH and species.
■ Measured with VERTEX (Haglof Company Group)
■ Tree height model are made with DBH-Height relationship of 20 samples
=> All tree height in the quadrat are estimated with the model from DBH.
Soil moisture
Measured in 1 point per each quadrat
Data each point (depth) : 5cm, 15cm and 30cm.
Measured with HydroSense (Campbell Scientific Australia Pty. Ltd.)
Under ground water level
Measured in 1 point per each quadrat
Equipment : PVC pipe
Measurement : (1) the length from pipe edge to under ground
water level with plastic hose, (2) the length from pipe edge to
ground level.
Measurement day are passed more than 2 days from setting day
to balance the water level of inside pipe and outside.
Crown Cover
The picture of canopy taken with fish-eye camera, at the center of each 10m sub-quadrat
Total pictures : 4 points in the quadrat
Calculate the crown cover rate from the picture image with LIA for Win32 software
Under story Vegetation Measurement : coverage and height of under story vegetation
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Blue sheet
For correction
Water sampling Tree sampling
Soil sampling
FieldSpec
Water body Reference target
103
102 101 105
104 106
118
119
120
110 111
112 113
117
・Impact of forest fire
・Tumih is dominant
・Small number of trees
・Impact of forest fire
・Tumih is dominant but other tree
species exist.
・There are a lot of substances
with DBH 10cm or less
・Impact of Human activity
・Shorea is dominant
・High diversity of tree species
・Few large-diameter tree
・Impact of Humanty activity
・Shorea is dominant
・High diversity of tree species
・Few trees with large-diameter
・Extremely High degree of natural
・Shorea, and Calophyllum are dominant
・High diversity of tree species
・A lot of trees with large-diameter
・High degree of natural
・Shorea, and Acronychia are dominant
・High diversity of tree species
・There are trees with large or moderate
diameter
・Plenty substances with DBH 10cm or less
Analyzed Area
Classification of forest based on field survey :
Tree species structure, Stand structure, Growth status, etc.
201 202
203
209 208
207 206
221 220
119 215
214
・Impact of drain
・Shorea is dominant
・High diversity of tree species
・Large number of trees
・A lot of dead trees
・Small number of trees with large-diameter
Impact of forest fire
・Tumih is dominant
・Low diversity of tree species
・Large number of trees
・A lot of thin tree
・221 has tree species diversity
Impact of forest fire
・Tumih is dominant
・Low diversity of tree species
・Small number of trees
・Lower canopy cover ・High degree of natural
・Cratoxylum, Tristaniopsis are
dominant
・High diversity of tree species
・Large number of trees
・Small number of trees with large-
diameter
Analyzed Area
22
:Tumih
:Secondary Forest
20m
20m
Field survey quadrat
The number of Samples
Use the same size of four areas abutting on quadrat, which are
assumed to have the same tree species.
Use HyMAP images (Atmospheric and geometric correction)
The number of Bands
Use 86 bands of 126 band of 450nm-2490nm except :
O2 ,H2O and CO2 absorption band,
Area of low S/N: 2400-2490 nm
Classification Model
Classification model based on Sparse analysis
Optimize parameters by conducting 5-fold cross-validation
Test Site1
Test Site2
Test Site2
(Setia Alam)
Model’s Expressiveness
test
True Value
Secondary Tumih
Prediction Secondary 21 0
Tumih 0 21
Model’s prediction
capability test
True Value
Secondary Tumih
Prediction Secondary 14 1
Tumih 0 13
The number of samples: 42
Correct answer rate: 100%
Band Wave Length [nm] Coefficient
B2 469.7 0.1122
B6 531.2 -0.0587
B14 653.3 -0.0656
B27 851.1 0.0009
Band Wave Length [nm] Coefficient
B68 1515.8 0.0191
B70 1542.8 0.0050
B113 2315.6 0.0032
B118 2396.7 -0.0256
Band
used for
the c
lassific
ation m
odel
The number of samples: 28
Correct answer rate: 96.4%
Model’s Expressiveness
test
True Value
Secondary Tumih
Prediction Secondary 24 0
Tumih 0 9
Model’s prediction
capability test
True Value
Secondary Tumih
Prediction Secondary 24 0
Tumih 0 9
The classification : high accuracy
Band Wave Length [nm] Coefficient
B1 455.5 0.0953
B12 623.2 -0.0014
B16 683.9 -0.0601
B30 885.3 0.0049
The number of samples: 33
Correct answer rate: 100%
The number of samples: 22
Correct answer rate: 100%
Ban
d u
sed
for
the c
lassific
ation
mod
el
– Data used
• The number of samples
– Total: 26 points (Test Site1: 14 points, Test Site 2: 12 points)
– HyMAP data (atmospheric and geometric correction)
• The number of bands
– Use 86 bands selected from 126 bands of 450-2490nm by eliminating the large
S/N bands below.
» O2 absorption band: 750-770 nm
» H2O absorption band: 900-990, 1110-1180, 1330-1500, 1750-1950 nm
» CO2 absorption band: 1590-1630, 1950-2030 nm
» Area of low S/N: 2400-2490 nm
– Analysis
• Modeling Test Site-1 and Test Site-2 respectively.
– As for Test Site-2, data of Setia Alam area eliminated because of different
acquired date.
• Create estimation model from LASSO regression
• Optimize parameters from 3-fold cross-validation
26
Test Item Result
Model’s Expressiveness
test (Closed test) RMSE 2.78 [%]
Model’s prediction
capability test (Open test)
(CV average 1000 times)
RMSE 6.51[%]
Test Item Result
Model’s Expressiveness
test (Closed test) RMSE 5.57 [%]
Model’s prediction
capability test (Open test)
(CV average 1000 times)
RMSE 9.98[%]
Test Site1 Test Site2
※ Eliminate SetiaAlam
Estimation of canopy cover with several percent order.
Measured value
Estim
ate
d v
alu
e
Band Coefficient
B23 0.0543
B42 -0.0137
B55 -0.0511
B69 0.1370
B118 -0.2515
Band Coefficient
B9 -0.0326
B79 -0.2833
Test Site1
Test Site2
Bands used for estimation models
– Data used
The number of samples
Total: 21 points (Test Site1: 13 points, Test Site2: 8 points)
» Refer the data observed by Prof. Inoue at Hokkaido University for
Test Site2’s 206 and 214 observation points
» Except the above, obtain the groundwater level data from field
survey.
Conduct atmospheric correction, geometric correction, and equalization of
reflectance average between observation lines of HyMAP images
– Analysis
Mapping the below water stress index and the groundwater level results
of field survey.
Water Band Index(WBI)= R900/R970
Normalized Difference Water Index(NDWI)=(R857-
R1241)/(R857+R1241)
Modeling Test Site-1 and Test Site-2 respectively.
– Both WBI and NDWI may express groundwater level by using logarithmic function.
• Peat forest in the low groundwater level area has thick leaves with large moisture
content due to tackle drying stress. The efficiency of water usage is increased by
closing (not completely) pore.
• The water index is high (which means large moisture content) when the ground
water level is low. This corresponds with the above mentioned trend.
– Test Site2 can’t express relationships between water index and groundwater level.
• One factor of this is the limitation of correction of reflectance between observation
lines.
• Test Site2 isn’t suitable for water index analysis because there are great variability
of reflectance and extremely low values within the same observation lines.
– Constructing forest degradation monitoring system using
Hyperspectral sensor. • Forest degradation monitoring are difficult using existing satellite data and
method.
– REDD+ targets monitoring; forest enhancement,
sustainable forest management etc. • Potential to be applied for the change in the forest cover.
– Degradation; change worse
– Enhancement; change better
– Construct the new approach to assess the carbon emission
from peat-land soil to the river. • It is limited approach to evaluate the amount of such a carbon using
satellite.
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32
Hyperspectral remote sensing technology significantly improves the resolving power of remote sensing technology from discrimination to identification oriented problem solving.
Development of spectral library based on tree species is important as a baseline for further classification process in hyperspectral application for forest monitoring
Potential prediction for species classification, crown cover and water index
33
Adopting some techniques such as SAM (Single Angle
Mapper), SVM (Support Vector Machine), Neural Network
based classification, GA-PLSR (Genetic Algorithm-Partial
Least Square Regression) to yield more detail
classification of species, crown cover and water index
Carbon Accounting on peat-forest area
34
Sensor Characteristics (draft)
Parameter Requirement
Spatial Resolution 30 m
Swath Width 30 km
Spectral
Bands ~ 185 bands
Range 0.4 ~ 2.5μm
Resolution 10 nm (VNIR)
12.5 nm (SWIR)
Signal to Noise Ratio (S/N) ≥ 450 @600nm
≥ 300 @2,200nm
MTF ≥ 0.2
Dynamic Range ≥ 10 bits
Parameter Requirement
Spatial Resolution 5 m
Swath Width 90 km
Spectral Bands 4
Range 0.42~0.90μm
Signal to Noise Ratio (S/N) ≥ 200
MTF ≥ 0.3
Dynamic Range ≥ 8 bits
Hyper-spectrum
Multi-spectrum
30 km 30 km 30 km
Hyper-spectral sensor
Multi-spectral sensor
Thank You