Gifu Univ.

Y. Awaya

Air Photo Digital Canopy Height (Airborne LiDAR)

© Mitake town © Nakanihon Air Service

Importance of precise biomass information for forest

managemant: A case study of management planning in Gifu, Japan

1) Gifu University, Gifu, Japan, 2) Gifu Prefectural Research Institute for Forests,

3) Kamo Forest Cooperation

Yoshio AWAYA1) Kuniaki, FURUKAWA2) Tomoyuki, KAWAKATA3)

Applying resource information by RS to forest management.

1

Gifu Univ.

Y. Awaya

1. Background

Necessity of resource information

Forest resource information in Japan and its accuracy.

Small compartment size

2. Resource mapping

Forest type by optical imagery

Stem biomass by LiDAR data

3. Usefulness of resource information by RS

An application for logging planning

4. For space-borne LiDAR

Coarse footprint size

Large area mapping

5. Summary

Contents

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Gifu Univ.

Y. Awaya

Forestry Agency 2009 Over mature artificial forest

Over mature

35%

Transition 10

years later 67%

March, 2007

Stand age of artificial forest in Japan

15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 100～

Necessity of resource information

Forest resource information:

Field measurement by either statistical or judgment sampling.

Aerial photo interpretation.

→ Accurate wall-to-wall biomass information is rare.

Problems: Over mature & Self sufficient rate of lumber: 27.8% in 2009.

Timber should be used effectively to reduce carbon emission.

→ Forestry agency requests forest entities to make logging plans by

organizing forest owners. → Need of resource information

March, 2017

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85～ (year) 3

Gifu Univ.

Y. Awaya

Forest base map in 1997

Forest base map & Forest register

Office comp. Area spp. Type density vol. logged slope soil elevation

Sub compartment information

Sub compartment boundaries (red)

on a orthophoto. （Oct. 23, 1997）

Forest register

Examples of

forest

information

in Yonaizawa

National

Forest

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Gifu Univ.

Y. Awaya

Volume prediction table

1. Trees are thinned regularly.

2. Hatching shows thinned timbers.

3. Seven thinning is scheduled by

age A.

4. C is stand volume at A and CB is

sub-harvest.

5. B is volume of primary trees

which are used for construction.

6. D is the total harvest which is the

sum of stand volume C and

thinned volume at 7 times.

7. Regular thinning is implicitly

designed in the volume

prediction table in the register.

Thinning was executed very

little before 2008.

→ Volume in register doesn’t

show volume in the field. 峰 一三, 1955, 収穫表に関する基礎的研究と信州地方カラマツ林収穫表の調整（収穫表調整業務研究資料 第12号）．林野庁林業試験場, 201pp.

材

積

林 齢

D

A

C

B

間伐 1 2 3 4 5 6 7 8

Conceptual diagram of growth prediction

Volume

in register

Volu

me

Thinning

Stand age

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Gifu Univ.

Y. Awaya

Accuracy of forest information Compartment boundaries are inaccurate in some places. Volume information is inaccurate.

Boundaries over an ortho-airphoto.

Boundaries over a forest type map.

Stem volume map by a register.

Stem volume map by LiDAR data.

It is very difficult to estimate annual logging works and incomes.

→ Tree species are identified and stem volume is estimated by field surveys.

750

500

250

0

(m3/ha)

Evergreen

conifer

Deciduous

broadleaf

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Gifu Univ.

Y. Awaya

1~5ha681,000 owners

(75%)

5~10ha119,000 owners

(13%)

5~10ha

0.78 mil. ha(15%)

10~50ha1.73 mil. ha

(33%)

1~5ha

1.42 mil. ha(27%)

100ha or more0.85 mil. ha

(16%)

100ha or larger

3,000 owners(0.4%)

10~50ha97,000 owners

(11%)

50~100ha

0.43 mil. ha(8%)

50~100ha7,000 owners

(1%)

(0.91 mil. owners)

(5.21 mil. ha)

Forest owners

Forest area

Difficulty of ground resolution selection

CategoryArea

mil. haArea %

Stockmil. m3

Stock %

National forest 7.7 30.6 1151.8 23.5

Public forest 2.9 11.6 557.7 11.4Private forest 14.5 57.8 3191.0 65.1Total 25.1 100.0 4900.5 100.0

Forestry Agency 2015

Sub-compartments are very small in Japanese private forest. About 17 %

forest area is divided into blocks less than 5 ha.

There are numerous sub-

compartments less than 1 ha.

Therefore ground resolution less

than 30 m is favorable.

Forestry Agency 2015

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Gifu Univ.

Y. Awaya

Forest resource information by remote sensing

Height measurement by laser beam

Direct measurement

Flight height is estimated by the time

lag between emission and receiving

of laser beam, then surface elevation

is estimated.

Location (X, Y, Z)

by GPS Laser

beam First return

Surface (DSM)

樹冠

高

樹冠

表面

の標

高

DSM DCHM

Last return

Ground (DTM)

DCHM = DSM - DTM

Classification by optical imagery

Indirect estimation

Spectra - color

Color composite

Classification by spectra

Cedar

Broadleaved

Cypress

Mitake, RapidEye 2013/05/03 ©JSI

Based on difference of color

by species. Seasonal change

is also important information.

Breen NIR Red

False color

Canopy

heig

ht E

levatio

n o

f

Canopy s

urfa

ce

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Gifu Univ.

Y. Awaya

Mapping procedure – forest type & volume

0

100

200

300

400

500

0 5 10 15 20 25

y = -32.0 + 15.2x R2= 0.811

材積

(m

3/h

a)

平均樹冠高 (m)

CH vs. Volume

Deciduous

0

200

400

600

800

1000

1200

0 200 400 600 800 10001200

y = 0.941x

推定

材積

(m

3 ha-1

)

地上調査　材積 (m3 ha-1)

Validation

Evergreen

Accurate DCHM makes precise volume

estimation possible.

Volume estimation models are applied for

cedar, cypress, broadleaves and so on.

GeoEye-1 2010/03/30 ©JSI

Sat. Imagery NDVI Classification

Cedar Cypress Deciduous

30

20

10

0 (m)

DCHM

1000

800

600

400

200

0 (m3/ha)

Stem volume

Forest type map Stem volume map

Average CH (m)

Field V (m3/ha)

Pre

dic

ted

V (

m3/h

a)

Ste

m v

olu

me

(m

3/h

a)

Sat. Image

- Canopy height1m ≧ : Forest

1m ＜ : Non-forest

Evergreen

Veg.Non-veg.

Non-for.For.

Deciduous

Cedar Cypress

- NDVI0.3≧ : Vegetation

0.3＜ : Non-vegetation

RuleFlow

Supervised

classify

- NDVI0.55≧ : Evergreen

0.55 ＜ : Deciduous

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Gifu Univ.

Y. Awaya

Forest type map (RS)

Register (Spp.)

Airphoto (© Nakanihon)

Register (Volume)

Cedar

Cypress Other

spp.

Cedar 1

Cypress 3

Red pine 6

Cedar 411 m3/ha

Cypress 224

Red pine 134

(m3/ha)

RS base information vs. forest register

Difference 1. Forest type distribution Cedar in valley Cypress on slope 2. Volume A comparison

Field ≧ RS ＞ Register

Register is cursory. RS information is accurate.

(m3/ha)

0

1500

Volume (RS) 10

Gifu Univ.

Y. Awaya

Natural Artificial Thinning Logging road 2012 2013 2014 2015

Register & base map Information by RS

Comparison of logging plans

The present forest resource information such as forest type distribution is inaccurate

and the logging plan should be replaced using resource information by RS.

Logging plans become reliable. → Accurate estimation of income is possible.

Additional field surveys is required less than before.

Retrieval of plans is required less than before.

Forest owners and foresters can share reliable resource information. 11

Gifu Univ.

Y. Awaya

Mesh size of volume data

Mesh size 10m Mesh size 50m - MOLI footprint

Fifty meter grids cannot show volume well in small sub-compartments.

Foot print size of MOLI is an obstacle in small forest compartments. MOLI

products would be useful in forests larger than 3 to 5 ha. 12

Gifu Univ.

Y. Awaya

0 200 400 600 800 1000 or more

Stem Volume (m3 / ha)

Necessity of large area map

A few prefecture governments

produced large area forest

resource maps by LiDAR data.

Airborne LiDAR data are

important information for

volume mapping, however,

airborne LiDAR data are costly.

Space-borne LiDAR data

would be cost effective and be

useful for deriving and renewal

of resource information in large

areas.

Large area resource maps can

be used for forest planning in

city, prefecture and national

levels.

10 km

Volume map over

Kamo, Gifu, Japan

711 km2

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Gifu Univ.

Y. Awaya

Artificial forest in the world

There are large artificial forest in the world and management planning

is an important task. MOLI products will be used in forest without

biomass information currently.

Artificial forest area in

the world (FAO 2010)

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Gifu Univ.

Y. Awaya

Thank you very much for your attention. The aerial photos and a part of LiDAR data were supplied by the Gifu prefecture. Field surveys were supported by staff members and students of Gifu University.

1. Harvest is an urgent matter in Japan, however, resource information is

inaccurate.

2. LiDAR data provides accurate volume distribution maps.

3. A logging plan is designed efficiently using resource information by RS.

4. Producing large area biomass maps is costly by airborne LiDAR,

therefore, space-borne LiDAR would be an important tool.

5. Fine ground resolution information is necessary due to small forest size in

Japan.

6. Biomass information by space-borne LiDAR data can be used for forest

planning by organizations managing large forest.

Summary

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