quantitative measurement from unifying field and … files/s2-evri.pdf · quantitative measurement...

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

18

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



・Few trees with large-diameter

・Extremely High degree of natural

・Shorea, and Calophyllum are dominant


・A lot of trees with large-diameter

・High degree of natural

・Shorea, and Acronychia are dominant


・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



・Large number of trees

・A lot of dead trees

・Small number of trees with large-diameter

Impact of forest fire


・Low diversity of tree species


・A lot of thin tree

・221 has tree species diversity

Impact of forest fire


・Low diversity of tree species

・Small number of trees

・Lower canopy cover ・High degree of natural

・Cratoxylum, Tristaniopsis are

dominant



・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


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


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


Correct answer rate: 96.4%


test

True Value

Secondary Tumih


Tumih 0 9


capability test

True Value

Secondary Tumih


Tumih 0 9

The classification : high accuracy


B1 455.5 0.0953

B12 623.2 -0.0014

B16 683.9 -0.0601

B30 885.3 0.0049





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


test (Closed test) RMSE 2.78 [%]


capability test (Open test)

(CV average 1000 times)

RMSE 6.51[%]

Test Item Result


test (Closed test) RMSE 5.57 [%]


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.

31

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

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