discriminant analysis quickbird multispectral panchromatic

Japan Society of Forest Planning

NII-Electronic Library Service

JapanSociety ofForest Planning

273

Article

Segmentation and Classification with Discriminant Analysis of

QuickBird Multispectral and Panchromatic Data to DistingriishCryptomeria J'aponica and Chamaeegparis obtusa Patches

Yasumasa Hirata*i, NaoynkiTomoaki Takahashi"'", YoshioFuruya'2,Awaya'`Atsushi

Sakai'Z,and Toru Sakai'5

ABSTRACT

Stands of CrpPtomerin jaPcmica and ChamaeopPan's obtusa haL,e different functions: in particular, diversity of

understory vegetation and preyent{on of soil erosion, but thelr distribution is not always delineated and distin-

guished en forest maps, 'Fhis

study investigated the relationsh{p between parameters in the image segmentatien

procedure for high-resolution sak/IIjte datu and generated object size in objoct-orienterl classification, and distin-

guished objects of C 1'aPonica and C obtusa by means of discriminant analysis. Thittsr-six sarnple plets were

established in Cj'mponica and C: obtttsa stands in the national forests of the Koisegawa watershed. QuickBirdpanchromatic and multispectral data was used for this study. Seginentatioii, which is the first step in object-ori-

ented class{fication, was applied to the image data of the sample plots, and species were assigned to corre-

sponding' objects generated from the image segnientation. For these objects, the aver[nge and standard deviation of

digital number for the feur rnultispectral bands und panchrematic band aiid the Nornialjzed IMfference Vegeta-

tion Index (NDVI) were calculated within them. Discriminant analysis to distinguish C,1'aPonica and Cobtttsa was

conducted using these twelve variables of average and standard deviation as independent vaidables. The correct

distinction was made for ICK)96 of CJ'aPoni(/a objects and 95.5Uh of C obtusa objects. The results clarified that it wus

possible to distingtiish C]'aPonicct and Cobtf･asa patehes using high-resolution satellite data.

KbJ,tvo}ds: CmpPtomen'a 1'aPonica, Chaniaeopan's obttrs4

tion, scale parameter

high-resolutionsatellite data, object-oriented classifica-

INTRODUCTION

In Japan, forests cover an area of about as million ha and

this estimate has remained stable for 40 vears. This constanov

of forested area is the result of the ba]ance between decreased

forest area b}, clevelopment due to expansion of urban areas

and increased area bv afforestation and/'or reforestation on

abandoned fannland due to people moving from the moun-

tains down to the towns. However, the ratio of plantation to

natural and secondary forest area has varied greatly. The

iricrease in tiinber demand for reconstruction after World WarII resulted in the conversion of natural forests to sugi (CnyyPto-men'a )'aponical and hinoki {Chantae()v)an's obtusa) plantations

in Japan, and the continuous natural forests becarne segi"en-

ted CMAITF, 20e3). Secondary forests near ui'ban areas finished

their role of providing fuel and many of them were also

eonverted te plantations. As a result, 42Yo of the forested area

or osVt/ ef the total land area is now covered with plantations

(FAO, 2oo6), fonning a mesuic of natural or secondary forests,

Correspondin.if. author/ Yasumusu HiratrAE-mail/ [email protected],jp'iBureau

of Climale Change, Forestrv and Forc/st

Products Research Institute,

1 Matsunosato, Tsukuba, 305-8os7, Japan"'

Forestry Division, Japan International Research Cen-

ter of Aghcultural Sciences,

1-1 OhwashL Tsul{uba, P,05-8686, Japan

'"Ilepartinent

of Forest Managcrmeiit, Fei'estry and

Forest }'roducts Research Institute,

1 Matsunosato, Tsukuba, :lms-Sifl87, Japan"River

Basin Research Center, Gifu University,

1-1 Yanagido, Gifu, 501-1193, JapuiiS'Research

Institute for Humanity and Nature,

os7-4 Motoyaina, Kainiga]ne, Kita-ku, Kyoto oo3-

W'n7, Japmi

f Phr Plann.1di 2Z3,284 (201 1.)




274

plantations and cleur-cutting areas, Restricted movement of

aniinals and loss of biodii,ersity and genetic diversity are a

growlng concem.

In Japari, the Forest Agenc}, and loca] govenirnents has,e

prepared forest maps at the scale of Ii5,ooO and forest regis-

ters by sub-compartment as management units conceniing, for-

est distribution and attiibutes such as location, area, species,

stand age, stand volume and regulatiun. Advances in comput-

er technolog'y as well as iinproved GIS funutions in the pastseveral years have led to tlie expansion of C'TIS utilization. The

rapid constiuction of GIS data related to forest rnanagement

units, which was generated from these forest niaps and forest

reg'isters, has provided additional spatial understaridiiig, and

demonstrates great ability for planning lorest road construc-

tion and final cutting, However, some species iu/e occasionalLv

recerded for a sub-compartment in the forest register becnuse

CJ'mponica and C. obtt{sa were planted in respectively suituble

sites on a slepe, Etnd some broad-!eaved trees are found along

the valley or the ridge. "rhile the mixed proporiion {s recor-

ded in the forest rcgister for such a sub-compartment, the in-

lormation on species distribution is not delineated on the

forest map. Stands of CJ'oponica and C vbtttsa have different

functiens/ in particular, diversity of understory vegetation und

prevention of soil erosion: therefore, the spatial understanding

of their distribution is hnportant.

In the application ol remote sensing to i'orestry, forest typemapping is essential in teinis of sustainable forest !nanage-

ment and ecologica] consewation, and has been required first

and foremost since the launch of the fhst Earth obsenration

satellite. Conventional remotely sensed dtita, such as from

Landsat and SI'OT satellites, has been used for forest typemapping making use of the differenoe in refiectance propenies

of the forest canopies (for eocample. WooDcooK et at., 1994; Jm);Get aL, 2C()4; GJERTsEN, 2oo7), However, there has been an issue

about the mixture of different forest or land-cover types in a

pixel leadin.as to misclassification in the case of a complex for-

est stand shape or s]nall stand area in comparison with the

pixel size of remotely sensed data.

The new generation of commercial high-resolution satellite

data with ground resolution finer th[m 1 × 1m such as IKONOS

and QuickBird opened a new era in digital analysis of forest

stand attributes. High-rcso]ution satellite data has almost the

smne spatial resolution as a conventienal aerial photograph, and

it {s possible to use the image for interpretation as an aeidal

photograph. In addition, high-resolution satellite data has a

wider d}ma!nic range than conventional satellite data accord-

ing to the degree of sensor sensitii,ity (HIRixT.1, 20C)9). These

advantag'es of high-resolution satellite data might make it

possible to distingniish stands of different age and species, On

the other hand, the irreguluidty of reilectance from the canop},

surface appears within a domain where the refiectance proper-

ty is regarded as unifonn (]L(uRAKAs{i, 2CD4), making it difficult

to apply conventional pixet-based classification to hi.orh-reselu-tion satellite data.

Jfiiuta et aJ.

Recentiy, the new "obj'ect-eriented''

classification method has

been used instead of pixel-based classification. Objoct-eriented

dassification is effectivc in segmentiiig aii area that consists of

varieus land-cover t}rpes into objects with extensions of simi-

lar properties (Li'u{oNAcA et aL, 2oo8). Previous studies have

app]ied this approach for land cover and forest rnapping us-

ing conventional satetlite data such as Landsat or SI'O'['

(GENELE'r'r[ and GoRTE, 20e3; HA[.L et aL, 2004; BocK et aL, 2005;

HAy et al., 2oo5; CoNcHEDDA et al., 2008) as ",ell as hi.uh-resolu-

tion satellite data such us IK()NOS or QuickBird (vAN Coll.LIEet al., 2oo7; Lvth･IoNAcA et aL, 2oo8: MixRTINEz-MoRA[.]r.s et al.,

2008}, 'l'he

object-eriented approach rnakes it possible to devel-

op a classificatien strategri that. employs sl)atial relationships

between ol]jects in addition to specti'al inferniation (PLAT'r and

SC}IoEN]gAGEI., 2oo9).

'1'he

ebject-oriented classification approach consists of tu,o

proccdures: image segmentation into objects and classification

of generated ebjects. This study investig.ated the relationship

between parameters i]i the irnage segmentation proceclure for

high-reselution satellite data and generated object size, and

d{stinguished objects of C1'aPonica and C, obtttsa by means of

discrimin[mt analvsis.

MATERIALS AND METHODS

Study Area and Samp]e Plots

The study area was located in the Koisegawa watershed,

Ibaraki prefecture, Japan {36'7'20'-ea'19'21"N, 14U'6'22"-1co'18'FS"

E, WGS M), which is an area of 2sa.5 kni". The watershed is

surrounded by Mt, Tsukuba, Mt. Ashio, Mt. Kaba Mt. Nan-

tai and rvft. Wagakuni, and farmland spreads out on the plain

below 10() m above sea level. Orchm'c[s are found around the

foot of the meuntuins, alld natural forests, secondary forests

and plantations are distributed north and west of the water-

shed, forrning a mosaic pattern.

The naticmal forests in thc watershed, which are maTiaged

t)v the fbaraki District Forest Office, were the targets of tliis

study. These forests cover an area of 1,8ee ha and plantations

occupy 7UV･i of the forests. The forests consist o[ plantations ot'

C.icipo}7ica and Cobtusa, broad-leaved forests, mixed foresbs and

smal1 patches of conifer species,

A total of ss circular sarnple p]ots of e.04 ha each were

cstablished in the stands, which ranged in age from 15 to 93

years {Fig. 1, Table 1). Fourteen plots were set up in C]'mponi-

ca stands Emd 22 plots in C obtusa stands. The coordinates at

the center of all saniple plots were positioned by DGPS for

supetimposing on high-resolution satellite data, DBH of all starid-

ing trees in thc sainple plots was measured and species were

recorded. Stand density jn the sample plots was calculated from

the number of standing trees per O.O'1 ha. Mcan DBH of sam-

ple plots ranged from 13.9 to 37.2 cm and stand density from

475 to 2,8oo trees/ha. Tree height ol inere than 40glo' of the stancl-

ing trees was measured in each sample plot Emd the (}thers were



JapanSociety ofForestPlanning

Sagmevttatinn and Classification t{itk lh'scn'mi}tant Analysi qt'QuickBint,lfi{ttk'Pex,tral and funchromcttic Data to Dds'ting"tsh Cryptomen'a j'aponica aiui Chainaeq}pari obtxtsa thtches 275

Fig.1

Q spom rm ww

Distribution of sarnple plotsTwo plots of Crp,Pfvnten'(i 1'aPonica with asterisk(n')were located in the

Chaswaet.mparis obtasa was dominant.

Table 1 Number of sample plots 1]y stand age

st

sub-compartinent where

class

Stand age dass Cyears) -20 21-30 31-･10 ･11-50 51-60 61- Total

C. 1'aPonica

C. obtz{sa

Total

2o2 o66 41014 325 4o4 145 142236

estiinated from the NdsIund forniula (NksuDgD, 19sa). The stand

height in the sarnple plots varied from 9.6 to li.8 m.

Sate11ite Data and GIS Data

QuickBird panchromat{c and multispectral data was ac-

quired on 27 February 2oo3. The spectral bund of panchromaV

ic data ranges from 450 to seO nm, and the spatial i'esolu-

tion is O.61 m at the nadir. Multispectral data consists of [our

separate spectral bands: blue (,lso-520 nm}, green (520-6oo nm),

red (630-6oo nm) and near infrared (760-910 nm), Emd tliey all

have 2.44-m spatial resolution at the nadir (DIGITi'll.GLoBE, 2oo6).

The data was geo-registered to the Universal Transverse Mer-

cator (UTM} coordinate system (WC]S an, Zone 54) with O.7-m

and 2.8-rri spatial resolution, respectiL,ely, using the neurest

neighbor method for resarnpliirg' to maintain the c)riginal re-

fiectance properties.

GIS data for the study area was generated from 1:5,Ooo

forest maps and digital forest registers compiled by the Kanto

f Fbx Plann. i6'2Z3'284 (201O

Regional Forest Office to establish sarnple plots and confirm

their stand age. This data was also used to gee-register

QuickBird data using forest type information.

Iniage Segmentation

Segmentation in the object-eriented approach is charucter-

ized by the integrated use of spectral and texturaHnformation

in uomparison to the single use of spectral jnfonnation in

conventional pixel-based classification, We investigated the

effect of the paranieter concerning heterogeneity in segrnenta-

tion in the object-oriented approach, on the number and aver-

age size of generated objects, The segmentatien of high-reselu-

tion satellite data was conducted using eCogn{tion software

(DEFIN]ENs IpsrAGINg 2001) fer object-oriented classification. The

heterogeneity Ui, which is defined as the addition of weighted

values ol the heterogeneity within the spectrum (in,,E.,) and that

within the shape (h.H,,,,.), is determined by the scale parameter

in eCognition, and is cxpressed as belew:



JapanSociety of

276

Forest Planning

f= Phcoior+(1-P) k.H"pe {1)

whereOgPS1and is decided by the analyst. The scaLe pa-

rameter relates the minimum size required to {dentiS, a par-

ticular objcwt, which depends on the resolution of the images.

Thei'e are twe other parameters: the heterogeneity of corri-

pactness (h,..,,.,t) and that of smeothness (lr..,,.,ih), which are

determ{ned by the analyst The heterogeneity within the shape

is dafined as the addition of their weighted x,alues.

h,h.p,- --

qh...p,.t +(1-q) I't..,,,,,h (2)

whereO '1( q<- 1 nnd is decided b}, the ana]yst. We investigated

the number and the average size of generated objects by scaleparameter fixing h,..,..t and h,.../h io CO.1, O.9), (O.5, O.5) and (O.9,

O.1), respectively.

Extraction of Plantations

In the study area, there were severat forest types such as

plantations ef C 1'aponica and C obtusa, broad-leaved forests,

mixed forests and small patches of conifer species. As the small

patches of conifer species were foundi only around the top of thc'.

mountains, only the broad-leai,ed forests and mixed forests

nee.ded to be distinguished from the plantutions. We prepared

the high-resolution satellite data in the leafless season for the

sake of Iacilitation. The Normalized Difference Vegetation ln-

dex (NDVI), which was one of the indiccs representing the

condition of vegetation, was calculated irom the red band (Band3) and infrared band (Band ,D as below:

NDVI =(Band4 -

Band 3) ,t (Band4 + Band 3} (3)

NDVJ was calcu]ated for the study area using QuickBird mul-

tispectral data.

The average NDVI was calculated for eauh object genera-

ted by image segmentatien. We used only those objects that

were included in the sub-compatunents of one forest type.

QuickBird panchromatic data as well as multispectral data was

used Ior image segnientation. The spatial resolution, nannely

pixel size, was dilferent: O.7 and 2.8 m. On the other hand,

NDIq was calculated using multispectral data; therefore, sorne

NDVI pixels within the beundaTy of objects included two dil

ierent ±'orest

types. In this analysis, we used only NDX･a pixelsthat were comp!etely included in the objects to calculate the

average. We compared the average NDVI b}, object in the

plantations with that in the broad-lcaved forests and mixed

forests.

Discriminant Anal},sis

In Japan, CJ'aPonica and C obtusa trees are often planted

in the same sub-compartment, and infonnation on their distri-

bution is not available. The distinction between CJ'mpeni(/a and

Hiruta et aL

C. obtusa patches was examined by means of discriminant

analysis. Obj'ects, which included sample plots, were used for

this analysis. The average and standard deviation in digital

number of panc/hrematic data and that ef the four bands of

multispe{Jtrul data for these obiects were calculated. The aver-

age and standard deviation of NDVI were also used for the

analysis. We cenducted the discriminant analysis using these

tu,elve averages and standaT'd deviations lor eaeh eb]'ect as

independent variables.

Test for Equality

While the forest type of generated objects can be con-

fimied by field surve},, another method is required for the

assessment of segmentation in comparison with the method for

assessing the results of conventional pixel-based dassification.

Verification of the accuracy of pixel-based elassifieatien

is conducted by pixel at a point where the forest type can be

collfirmed; however, in objeet-oriented classification, the

beundaries of objetrts, namely the segmentation results, are

inlportant.

In Japan, forest managemcnt units consist of compart-

ments and sub-compartments, and they are administered usiiig

forest registers and foi'est maps. Forest patches of the same

forest type are normally smaller tban a sub-compartment. The

boundaries of these forest patches are not delineated in forest

maps, but the proportion of each forest type in a sub-cem-

partment is recorded in the forest i'egisters fi'oin field observa-

tion. Therefore, the proportion of area by forest type obtained

from the forest register and that obtained from object-

oriented classifieation were cempared by cempartment. All

objects for the study area that were obtained from image

segrnentation were classified into broad-leaved and mixed for-

ests, CJ'tiPonica, and C obtusa using the coetT{cients ancl con-

stants of the linear discriminant function that were obtained inthe above analysis. Thc total area of objects of the same for-

est type was generated by compartment. Finally, six compart-

ments were used for the test for equality between proportionsof area by species obtained frern the forest register and those

obtained from object-oriented classification in a compartment,

and three coinpartments in the study area were excluded fromthe test due to cleud infiuence.

RESULTS AND DISCUSSION

]mage Segnlentntion

The results of image segrnentation with different scale

parameteis are shown in Fig. 2. From I;ig', 2, it is ebvious that

the choice ol scale parameter strongly effects on the generatedobject size. The relationship between scale parameter and the

number of generated objects with different ht,."pict ancl hs,nooth in

image segmenration is shown in Fig. 3. The relationship be-

tureen scale parameter and the average size of generatcd ob-




Saginentation and Ctass//ticatinn uu'th Dtsew'i}tinant Aiml.x'sis of QuicleBbrf im{tti)'Pe('trat and Ilancirmntfitic Data to Distiiiguish CoVinmena iuPvmica a}nt Chamaeo'Pan')' obtusa thtches277

Original

SP=60

SP=180

Fig.2

SP=80

SP=200

@DigitalGlobe,

Imagc sagrnentation with diiTerent scale

SP=100

lnc., All Rights Reserved

paraineters (SP)

f Fhr Pkinn,16･2Z3-284eOJI.)




278 th'vata et aL

ut-oomo".o#ooE=z

20000

lsooe

aoooo

seoo

o

ow

Rvaflm

pt

ite/t///p//t/ ilt/tt/t/t//

oo,g e.1:1 O.5 O.5xO.1 O.9

O 50 100 15D 200 250

Scaleparameter

Fig. :)) The relationship between scule paramder and the num-

ber ef generated objects -,ith different h,,,,,,,,,., and h,,..o in

image segnlentation

O.8

N=-.

e.6yoooo--o

O.4¢

.ijtntomS

O.2y<o

murewtiNesE

eeh,v/ttpat/ h.nb/,o/lt

Oo.9 OM-e.s o.sxO] O.9

o so foo lso 2eo

Scaleparameter

Fig.4 The relationship between scale paraineter and the

age size of generated obJ'ects with different h,..,,.-

h...,h ill illlag'e Segr11entatioll

25e

aver-

and

25

20uto=egts5tsoE

tO-z

5

o

e.t e.5 O.9 i.3 1.7 2.n 2.5

Patch size {ha}

Fig. 5 The frequeiicy of patch size consisting of

the study area

2.9 3.3

one forest ",pe in

jects with different h...,., and J't..,.,th in image segrnentation is

shown in I;ig. 4. It is clcar lrom these figures that diflerent

weight to 1'4,,,,,,,., and iu,,,,,,,,i, did net influence the average size

or number of generated objects and only the scale paranneteraffected them. However, we should notice that these parame-

ters infiuence the shape of generated c)bjects,

The freguency of patch size consistjng of one ferest type iv

the study area obtained from the forest register, which was

arran.cred 1'rom field investigation bv foresters, is sho"m in Fig.

5. While the averuge was 1.2 ha, the niode was O.3 ha. "'hen the

aL,erage sizc of .o. enerated objects was U,3 ha, thc scale param-

eter was between 120 and l4). Therefore, it is preferable that a

smaller parameter than this, such as loo or less, is used for

extracting forest patches with Em area of around O.3 ha, and that

patches smaller than C).3 ha are integrated accordin.o to forest

type.

When the scale parumeter was 20, objects consisting of 10

to 20 trees in mature stands of Cl'aPonica were generated by thc

image segrnentation procedure, This number was rather smal-

ler than the number that AwAyA et an C2007) obtained from

IKONOS data using the sanie scEde parameter. When the scale

parameter was ,10 to 60, rnixed 1'orest patches were reasonably

evctracted, but segmentation was observed for a plantntion patchin the same stand. When the scale parameter was 80 ro 1oo,

segrnentatien of a plantation patch in the same stand was al-

so feund, as well as inequality in objects. However, results

simi!ar to the interpretation of hjg'h-resolutien satellite image

were obtained using these scale parameters. It was. therefore,

uonsidered that the segrnentation using 80 to 1oo for the scale

parameter, with which a stand of the sanie forest type was

divided into three to five objects, was practically applicable to

forest type mapping using QuickBird data.

As forest patch size depends on the forestry practice and

topographic conditions, the average size of forest patches con-

sisting of one forest ty'pe shou]d be estimated using the forest

j'egister, and a sinaller scale pm'ameter, with which ebjects

s]naller than averag'e are generated, should be used for image

segmentation. It is necessary to pay attention to the dilference

in generated obiects using different sensors with different res-

olutions and different conditions for acquiring' satellite data

such as sun angle and senser angle.

Nevertheless the effectiveness of image segrnentation in

object-oriented dassilication for cxtracting forest patches from

high-resolution satellite data, it should be recogriized that the

rcsults of segnientation do not often correspond to the bound-

aries of sub-compartments. These gaps result from the d{ffer-

eiice between segrnentation of forest cover type deT'ived froin

high-resolution satellite image and forest inanagemeni un{t. For

extunple, it is difficult to segtnent imag'e area which consists of

multiple sub-compartments of same species with similar stand

age. '1'herefore,

the results ef imag'e segmentation cannot be

used as substitution ef forest GIS data conceming forest man-

agement unit. Mutual use of them is required to understand



JapanSociety of Forest Planning

Scgntentation aitri Classificatioit Lvith Lh'swiininant Analysis iV Qisif.'flBini ,lfitltisPecttnl aJsct I'anchnm!alic Data Vo Distijigvfish Cr.LPtoinen'a i[iPonica and Chamaef./l'Paiis obt!fsa lhtches

'1'able

2 Statistics on Norrna] I)ifference 1･regetation Index (NI)VI) of ebjects in broad-leavedAnixed ferests and plantations

279

Type Number of objects Mean Standard I)eviatjon Maximum Minimum

Broad-leavedf

mixed forest

PIantation

6,1152 O.44O.78 O.14O,03 O.56O.83 O.23O.70

Table 3The average

segrnentationand

standard deviation CSD.) of NDVI, inultispectral and paiichromatic dnta for the

which includes each sample plot

obiect inimage

l'lotSpecies NDVI

Average

Bandl Band2 Band3 Band4

SD. Average S.D. Average S.D. Average SJ), Averag'e SD.

Panehrematic

Average S.I).

21213212226293031323334363713,1567810ll1,1]516l7181920232425272835c.c.c.C.c.c.c.C.c.c.aC.c.c.C.c.c.c.c.c.c.c.c.c.c.c.c.c.c.c.c.c'.c.C.c.c.jaPonica.iaPonicaj'aPonica1'aPonicaJ'aPoni['aj'aPonit/a]'aPonica)'aPvnica1'aPonica.faPonica1'aPonicaj'aPvnit/a1'aPonica1'aPonicaobtusaobtusaobtt･tsaobtvfsctobtusaobtusaobtusaobtusaobtttsaobttfsavbtusctobtusaobtusaobtttsaoblttsavbtusaobtusaobtttsaobttt.saobtztsaobtt･fsaohtttsa80.278.075.976.273.978.677.578.279.076.078.078,O77.674.778,981.179.883.180.881.579.879.881.168.065.579.879.482.381.680.682.181.381.381,181.677,2 3.1

5.1

6.6

6.3

724.94.13.22.45.9

5.]5.15.28.24.62.14.3

1.8

6.I

3.24.44.2

3.89.9IL2

5.13.I

1.9

2.64.2

1.72.42.42.62.37.7

156,O155,1150.9155.1154.7155.5154.0155.4156.7154.6155,1155.1153.515tt.O154.3161.0155.].159.4153.7160.5156.0156,3158.6167.1160.8151,7159,O160.3156.7155.4161.3158.3158.3157.4157.4155.23.83.33.44.2,1.33.03.52.83.33.53.33.34.1･1.13.92.54.03.64.33.83.62.84.(]6.25.33.64.23.22.63.23.53.33.33.23.14.1178.1I73.4165,7172,9171.7175,4171.2175.3179.4172.l173.4173.4170.9170.9l74.9196.8175.6190.017Ll190.6177.7179,3186.6199.2182.9165.4185.3193.1183,1177.6191.5182.8182.8182.118I.4177.2 7.8

7.9

7.6

8.8IO.1

6.5

Z6

Z3

6.2

6.9

7.9

7.910.0'8.39.6

6.7IO.3

6.4

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f fox Pim･m.Ja･ 27 ?284 eOll)



JapanSociety of

280

ForestPlanning

present conditions ef sub-compartinenL

Extraction of Plantations

The frequency of NDV'I for objects in broad-leaved,/mixed

forests and plantations is showri in Fig. 6 and the statistics are

shown in Table 2, From Fig. 6 and Table 2, the maximurn

NDVI for objects in broad-leavedtm{xed forests was O.tt], und

the minimum NDVI for objects in plantations was U,70. As a

result, it was clear that objects in the broad-leaved/mixed for-

ests and those in the plantations could be distinguished easily

50

40e8e'

3oEB

2ogz

ao

o

Fig.6

O.2 i].3 e.4 e.5 06 O.7 G.8 O,9

NDVI

The frequency ef NDVI for objects in broad-lenved,/

mixed forcsts and plantations

Table 4 IMscriminant result between C y'aponica

Hi'tuta Et al.

{f the threshold for dividing them was set between the NDVI

maximum in broad-leaved/mixed forests and the minimum in

plantations when using the data from the leafless season,

Discriminant Analysis

The average and standard deviation (SD.) of NDVI, multi-

spectral and panchrematic data for the object in imtge seg-

mentation, which includes each sample plot, are shoum in

Table 3. Tlic results of discriminant analysis between C.

1'aPonica and Cobtvtsa are shown in Table 4, From the results

using the linear discriminant function, the percentage of cor-

rect C.j'aiponica discritnination was 100% and that for Cobtusa

discrimination was 95.5",in'. 'l'his

result indicates that stand

atttibutes of each species do not infiuence the distinction

1]etween C. 1'aPonica and C obtusa.

'l'his result amfirms that it

was possible to con'ectly distinguish between C,j'mponi(/a and C

obtvtsa objects using their corresponding spectral information,

Coefficients corresponding to the twelL,e variables and the

constants in the 1{near discriminant functien are shown in

'1'able

5. Values obtained f}'om the substitution of the average

and standard deviat.ion of panchrornatic data, feur bands ol

multispectral data and NI)VI in the objects, which includc

sarnple plots, into the linear discrim{nant function using the

coefficients and constant in Table 5 are shown in Table 6. In

Table 6, plots of Cy'apoi7ica indicates ncgative values except one

sample plot of C obtusa and other plots of C obt"sa indicate

positive values.

In this study, about ew "h ef plots' were establ{shed in stands

and C obtztsa using the ]inear discriminant function

Predicted group

C]'aPonica C. obtttsa

Total

Originaldata

Number of plotsC 1'aPonica

C. obtusa

141 o21 lg22

Rate C%) C y'aPonica

C obtttsa

100,O

･1.5

.o95.5 100.0100.0

Table5 Coefficientsand constant of the linear discriminanttunctlollin discriminant analysis between C J'aPonica and C obiusa

NI)VE.Average

NDVI S. 1).

Bandl Average

Bandl S. D.

Band2.Average

Band2 S. D.

Band3 Average

Band3 S. D.

O.936O.939

O.l54

O.588

e.621

1.198-O.195-O.197

Band4-Average

Band4 S. D.

panchro-Average

panchro-S.D.

Constant

-O.O05

-O.271

-o.e66

O.1,tl-181.446



JapanSociety of Forest Planning

Sagiimiitatfon and Classijication ttitl! Piscn)niimnt Aimb/.cis of Qficmaini thtltispectrai and flonchrotpiatic Data to th'stingiiis"i

Table 6 Values obtaaned from the substitution of the average and standard

bands of multispectral data und NDVI {n the objects. which {ndude

nant function using the coefficients and constant in 'I'able

5

Co'Ptonte'ha jpmnica and evzamaeq'Paris obtitsa Flrtches

dei,iation of panchromatic data, foursample plots, {nto the linear djscrimi-

281

Plot Speeies Value Plot Species Value

2121321222629303r323334C. 1'aPonica

C. iaPoilic/aC juPonic'a

C. iaponicaC. J'aPonica

C. J'aPonica

C. 1'aPonica

C. jaPoJ7ic'a

C 1'aPonica

C j'aPotzica

C. iciponicaC y'aPtJnica

-1.92-1.61-2.74-1.04-l.97-1.33-1.62-2,88-1.43-1,68-L61-1.611345678101114151617181920232,l2irt272835C, obttfsaC.

obtttsaC.

obtusaC.

obtusaC

obtusaC

obtusaC

obtttsaC

obtttsa

C obtusa

C. obtttsa

C. obtzfs'a

C. obtusa

C obtusa

C obtusa

C obtusa

C obtttsa

C obtusa

C. obtttsa

C obtusa

C. obtt{sa

C. obtusa

C. obtusa

e.83

3.25

O,Ol

2.46

2.05

1.44

O.27

O.76

2,N

2.37-O,88

e,o7

2.12

l.(}5

O.40

O.12

2.57

O.97

O,97

O.39

1.62

3,40

'1'able 7Testfromfer equality betweEn preportions

object-oriented classification in aef

area by species obtained

cornpartment

fl'Olllforestragisteralld those obtained

Compartment Pearsonchi-squure df Asyniptoticsignificance level

219220221225227229 2,628O.9460668O.9IOL5112.285222222 O.269O.623O.716O.634O,･170O.319

f R)x Pkinn.16.･2Z?284 eOIJ)




282 Hi'mtaet at

se-ge'

60itgEecoli=

4ee-g8'

2o

e

kx

+ltercslitcistcr

--Obieet-O:foTlteticlassificution

"･ /--'

80

8 6ons9cug.c

40.=o"g8'

2o

C/,jcpnnica C.ohtusa Aroad-teavud

n]ixedfb"esl

Species

Compartment 2 l 9

o

+FeresLreghlcr

-e-Object-ovicnteclthassifeation

-x- --

--..x

C',./'aponica Cohrttsa Broadleave[ff

nittrEtiJb"'c'st

Species

Compartment220

80

8 6olg,L･o[

40g'tg8'

2o

oC.iaptmiea Cobtu,sa Bt'itad-teavevttt

nlirelfY})resr

Species

Compartment221

80

ge 6ets9ru5.

40.=o-g8'

2o

e

u.x

-tvFerestregtter･deObjcct-oiientedclassilicaLion

.-7

C i[vmniea C/. obtusu tlreadleai'edi

n/ixedjb)'est

Speoie$

Compartment 225

80

ge 60A9re6

40,sg8'

2o

o

-.-

+Forcstrcghter

+Obj'ccr-orbmcdcinssificatlan

st--/

ee

8 6oG9tu-oc

4e9'toQ9za

2e

C,jcmponica C,t)btusa Bs'oad-ieaveti,

rnixedforest

Species

Compartment227

o

m+"l"ovestregkstei"-'Obiecl-orientedclassificathm

x

(',J'"ponica {tohtu,xtt :/'outd-Iea/,ed'

inixed.forc,vt

Species

Compartment 229

Fig.7TheforcstInelltresults from

res.ister andthc

comparison

those obtainedbetureen

proportions of area

frorn image seglnentation ofby

species

QuickBirdobtaineddata in a1'roin

theconlpart-




Saginentation a}ttt Cinss4'ti('atiwt tm'tk Discn'niitmnt Amal.x'sis of Otficmeini ,vafJlispe'cimtand 1'anchromalic Data to Di3'ting,ttisk CoPtotneiin .iaPoizica aiid Chantaec.iPan's obliLya thtches 283

of 21 years to 6e years of age, and this distribution almost

corresponds to the stand age distribution of Japanese artificial

forests (FoREsTRy AGENcy ok' JApAN, 2oo5). Further investiga-tion is required to distinguish between Cl'aPonica and C obtu-

sa stands of mo years and under of age, where canopy dose notclose enoug'h, or those of oo years ot age and over, where sorne

broad-leaved trees invade,

Test for Equality

The results from the comparison between proportions ef

area by species obtained from the forest register and those

obtained from image segmentalion and classification of

QuickBird data in a compartnient are shown in Fig. 7. It is

clear that the proportion of area by species obtained from the'forest

register and that obtained from image segrnentation and

classification in a compatment are very similar. The results of

the test for equality between their proportions are shewn in

Table 7. The proportien of forest type in each compambnent

obtained from forest register and that obtained freni ima.ue

seginentation and classification show good agreement, indica-

ting the nffectiveness of object-oriented classiiication in practi-

cal use. It is expected that rcusonable forest type tnapping byobject-eriented classification for high-resolution satcllite data

can be applied to suitable forest managemenL

CONCLUSIONS

In this study, we investigated the relationship between pa-rameters {n image segnientation applied to high-resolution sat-

ellite data and generated object size, and we distinguished ob-

jects of C J'mponica and C obtusa by ineans ol discrimillantanalysis. The findings are summarized below;

1. It is considered that seginentation using sc to 1oo lor the scale parameter, with which a stand ol the sanie forest type

was divided into three to five olziects, is practically applica-

ble to forest type mapping using QuickBird data.2. Broad-leavedfmixed forests and plantations could be cor-

rectly distinguished using the NDVI threshold when we used

high-reso]ution satellite data obtained during the leafless season.

3. Ptttches of C]'aponica and C obtusa could be distinguished

with the linear discTiminant function using' the average and

standard deviation of QuickBird panchromatic data, four

bands of multispectral data and NDVI as independent valti-

ables, The percentage of correct results for C1'aponica dis-

crimination was 100% and that for C obtttsa discriminEttion

was 95.5%.4. As a result of the test for equality between proponions o[

area by species obtained from the forest registei' and those

obtained from image seginentation of QuickBird data in a

cornpartment, the propertions were in good agreement, indi-

cating the effectiveness of object-oriented classification for

f R)n Pimtn. 1a2Z?i284 (2011)

practical use.

Object-oriented classification for satellite data is increas-ingly used in forest type mapping and further progress isexpected in detennining purameters and eN,aluating the classi-

fication results. High-resolution satellite data is also being ap-

plied Ior estimating stand a"ributes such as stand density andstand volume in C,1'aiponica and Cobtttsa plantations (HiRATA,2008; HiRATA et al., 2oo9), and the integrated use of spatial

inforniation obtained from objecVoriented dassilicatien and

stand attributes estimated from panchromatic clata analysis wil]

be required.

ACKNOWLEDGEMENTS

This study was funded by the project entitled "Develop-

ment of Eco-lriendly Management Method for Water and Agro-Forested-Aqua-Ecosystems in Watershed and Estuary Areas" of

the Ministry of Agriculture, Forestiy and Fisheries.

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