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ESTIMATING THE SPATIAL DISTRIBUTION OF THE ABOVE GROUND BIOMASS DERIVED FROM TERRESTRIAL-BASED FOREST INVENTORY

I Nengah Surati Jaya Fauziah Dwi Hayati

Putu Arimbawa & M. Buce Saleh

INTRODUCTION

METHODS

RESULTS

CONCLUSION

Fac. of Forestry, IPB

INTRODUCTION 1:

International concern

on GHG issues:

C stock of Indonesia, India & Myanmar for (70%) of the Asian tropical forest

Global Concerns

Defor Degrad

Conserv.

SFM

Increase C. stock .

Need •C St. measurement

Support •SFM, Research, MRV

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INTRODUCTION 2

One

• Many techniques forest biomass terrestrial or remote sensing technique

Two

• Periodically performed forest inventory, IHMB by each forest concession holders (IUPHHK), in natural or plant. Forests

Three

• spatial information of forest biomass is an important for supporting SFM

Four

• Very few researches were found related to biomass estimation using terrestrial data. Almost no research for biomass estimation using IHMB data in Indonesia

ONE

• (1) What is the characteristics of spatial distribution for each forest ecosystem type?

TWO

• (2) Is the spatial distribution pattern of old forest biomass similar to the newly logged over forests or disturbed logged-over forest or degraded forest due to fires?

INTRODUCTION: RESEARCH QUESTION & OBJECTIVE

OBJECTIVE

1. Identify the most accurate INT. METHOD for estimating the spatial distribution of forest biomass

in the dry forest.

(add) 2. Determine the spatial distribution patterns of forest stand biomass dry land

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SITES • SITE 1 LABANAN

2O0’N;117005’)

•

• SITE 2 LAMANDAU TSI: 1°45’S 111°37’30E

1050 m 1000 m

1450 m 1450 m

899 m 1000 m

1345 m 1345 m

1

2

(1) site 1, PT Inhutani I, in Labanan District, Berau

Regency, East Kalimantan Prov.

(2) site 2, PT TSI , Lamandau District, Kota

Waringin Barat regency, C. Kalimantan Prov.

Methods Approaches Interpola

tion

method

Deterministic

Splines

Thiessen P.

IDW

Geostatistic Ordinary Kriging

Universal Kriging

Combination

METHODS

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ANALYSIS

4. MODEL EVALUATION: ACTUAL VS PREDICTED COMP.

MEAN DEV (MD); RMSE; Chi-sq; BIAS

n

i

p

i

p

i

i

d

d

1

/1

/1

)]()(var[2

1)(),( 000 xZxZhxx i

1. IDW

2. KRIGING

LIN

Gaus

Sper

Exp

Cir

3. Eq AGB IHMB AGB all (Jaya, et al, 2012)

AGBAll = 7.355 + 1.003*AGBIHMB

RESULTS ERROR VALUE & SCORE FOR SITE 1

• LABANAN SITE, BERAU EAST KALIMANTAN

,0000

,005000

,010000

,015000

,020000

,025000

,030000

,0

20,0

40,0

60,0

80,0

100,0

120,0

P05 P1 P2 P3 P4 P5 P6 SP CR EX GS LN

valu

e o

f er

ror

Validation value for each interpolation method

Chi-sq

RMSE (%)

BIAS (%)

MD (%)

AD

interpolation methods

-

2,00

4,00

6,00

8,00

10,00

12,00

P05 P1 P2 P3 P4 P5 P6 SP CR EX GS LN

Sco

res

Scores for each interpolation method

Chi-sqRMSEAD(SA)BIAS

Interpolation method

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ERROR VALUE & SCORE FOR SITE 2

• LAMANDAU – CENTRAL KALIMANTAN

-,050000

,0000

,050000

,10000

,150000

,20000

,250000

,0

10,0

20,0

30,0

40,0

50,0

60,0

P05 P1 P2 P3 P4 P5 P6 K_S K_C K_E K_G K_L

valu

e o

f er

ror

Validation value for each interpolation method in PT TSI

Chi-sq

RMSE (%)

BIAS (%)

MD (%)

AD

Interpolation method va

lue

of

AD

-1,90

,10

2,10

4,10

6,10

8,10

10,10

P05 P1 P2 P3 P4 P5 P6 K_S K_C K_E K_G K_L

Sco

re

Score value for each interpolation method in PT TSI

Chi-sq

RMSE (%)

AD

BIAS (%)

MD (%)

Average

Interpolation method

SPATIAL DISTRIBUTION SITE 1

• IDW, P=2

• ORD KRIG - EXPONENTIAL

SITE 2

• IDW, p=2

• ORD KRIG - EXPONENTIAL

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The IDW give more accurate estimation than Kriging

For Labanan (with 1450 m x 1450 m: the best IDW with p= 2, having MD = 15% >> Kriging MD=21%

For Lamandau ( dist 1345 m x 1345 m: the best is IDW p=2, provide MD = 17% >> The Kriging MD = 27%.

The IHMB data is quite useful for estimating biomass with low error

CONCLUSION

COMPARTMENT OF INHUTANI I

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TSI – ALOS PALSAR

TSI – biomass measurement

Sample plots