Exploring TM Image Texture and its Relationship with Biomass

ACT Publication No. 05-13
Exploring T M Image Texture and its
Relationship with Biomass Estimation
in Rondenia, Brazilian Amazon.
Dengsheng Lu and Mateus Batistella
Reprinted from:
Acta Amazonica. 35(2) 2005: 249-257
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VOL 35(2)2005 I ii - 300
SUMMARY
BERGO. Ceko ; MENDONCA, H 6 l i ~: SII* M m o s -Effect of
time and kquency ofcutting in the essenti~loil pnxluction
Ill
of long peppcr (Piper hispidineruun~C. 1)C.)
.....................
IIORI1E,Atlriana; GOMB, Isr;~el;MIRANDA, Scbastiio; SII.V.4,
Maria;. Contribution to the hydrochemestry ofdrainages
in the municipality of mwaus -Am
119
.
..
LIMA, Jose; SAN'rOS, Joaquim dos: HIGIJCHI, Niro. - Lumher
industries in thc stateofAmazonas:situation in 2000. 125
AMARN, Bmndito; -Fisheries gmd Fichiie&)nat the indigenous
resemh~haninkaKaxinaxxi,
river Breu, B d Y e n r .
.. ...........
.......
... 133
COSTA, Maycira; - Use of radar imagery for estimating net
primary productivity of aquatic vegetation in t h e
Amazon floodplain.
145
~
SIIVA IXAS. Maria; COI-IEN,Julia; GANDU,Aclilson; - Clouds.
rain and biosphere interactions in Anxwon.
215
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S01!7A FILHO, Jose; RIBEIRO, Aristides; COSrA, Marcos;
COHEN, Julia; - Control mechanisms of the seasonal
variation of trnnspiration in a northeast amazonian
tropical rainforest .
223
.. -.....
. . .
.-.
WILI.lAMS, Earlc;DI\LL' AN'VONIA, Naor: DALC AN'TONIA,
Vitoria; ALMElD'A,Jorge;SUAIKL, Francisco; LIBBMANN,
Bnnt; MALHADO, A& -.She drought of the century in
the Amazon Basin. an analvsis of dle reeional variation of
r&nf;lll in South America in 1926.
231
.,
BATISELLA, Mateus; MORN, Emilio F.;- Hunlan dimensions
ofland use and lnntl cover in the Amazon: acontribution
.-~
fmm 1.DA.
239
..-..-.
-- ... - ...... - ... .- ....... .- ....
~~
EsP~wI'O-SANTO, F e n w ~ d
I.uiz; MACHADO, Evandro; -Analysis of the floristic and
phytosocioiogic composition of 'hpaj0s n;~tionalforest
with geognphic support ofsatellite images.
155
LU, Oengsheng; BPII'IS'I'EI.IA, Mateus;- lixploring TM image
texture and its relationships with biomass estimation
in RomlBnia, Brazilian Atnazon.
249
~
ALBIIECH?: Rachel; SLLVA D M , Mu*. - Microphysicalevidence
of the transition between predomin;int convective/
stratifor~nrainfall associated with the intraseasonal
oscillation in the Southwest Amazon.
175
....
.
.
.
.
~
ARTAXO, Paul<>;GATTI. Luciana V.; LEAL, h a M. C6rdova:
LONGO, Karla M.; PRCITAS, Saulo R.; LARA. Luciene I..;
PAULIQLWS, Thcotonio M.; ~ ~ 0 ~ 6 ~ 1 0 ,S.;~1<1220,
line
LucianaV: Atmospheric Chetnisuyin Anlmnia: d ~ forcst
e
and the biomass burning emissions controlling the
composition of the Anuazonian :itrnosphere.
185
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..........
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.
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KKUSCI IE. Alex: DAIIGSTGR. Maria: VICTORIA. Re~mldo:
~
NOVO, Evlyn: I:ERIU?lRA, Laerte; BARBOSA, Cliudio;
CARVALHO, Claudio; SANO, Edson I?.; SHIMABUKUIW.
Yosio; HUETI:, AITredo; I'O'I'I'ER, Christopher; KOBEI<l'S,
Dar A.; HESS. iaura I..; MtilACK,JohnJ.;\'OSI IIOKA, IJiiki:
KT.OOS'T1:R. Steven; KUMAK. Vipin; MYNliNl, Ranga;
KA'SANA. Piyachat: IIIDN, Kamel; MIURA, Ibmoaki; Aclvanccd rcmotc sensing tcchiniqucs for glol>alchanges
and Amazon
ecosystem functioning studies.
259
...........................
-.
. .
COKIEIA, Fmncis; A I Y A Regina;
~
MANU, Ant6nio: GIELOW;
Ralf; KUBO'SA, Pauio; -Calibration of the simplificul simplc
biosphere rnodel (SSiB) for Amazonian pasture and forest
sitcs using LBA data.
273
..............
~
INOUE. Llis;AFONSO, Luis B.;IWAMA, George K.; MORAES,
G i i k n o . - IiUects of clove oil o n the stress response o f
- E~ectcdflandu& changes in the bi~geoch~misu~offluvih matrirlxi (!jrycorr ce~halus)stibiected to transport. 289
~ ~ t e noftIleJi-Paran5
ls
tivet basin. R o n d 6 ~ a .
1%'
GIL-SANTANA, tlelcio R.; COLETTO-SILVA, Alexandre;.
BsrerLgeri~Jrufasli, n. pen., 11. sp. of Keduviinae from
MORAES, Bergson; COSTA, Jose: COSrA, Antonio; COSS'A,
Reserva Ducke, Anlazonas State, Brazil (HemipteraMarcos; - Spatial and tempomlvariation ofprecipis~tionin
Heteroptern, Reduviidae).
297
207
the State olPai-L.
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/ ACTA
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AMAZONICA
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Exploring TM Image Texture and its Relationships with
Biomass Estimation in Ronddnia, Brazilian Amazon.
Dengsheng LU1; Mateus BATISTELLA2
ABS'IRACT
Many twrmre measures have been dewloped and used for improving landaverdassification accuracy but d y has research
examined the role of textures in impmving the performance of aboveground biomass estimations. The relation5hip between
texture and biomass is poorly understood. 'l'his paper used Landsat Thematic Mapper (TM) data to explore relationships
between'l'M image textures and atmeground biomass in Ronddnia, Brazilian Amawn. Eight grey level co-occurrence matrix
(GLCM)based texture measures (i.e., mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and
correlation), associated with seven different window s i m (5~5,7~7,3x9,llxll,
15x15,19~19,
and 25x25), and five TM bands
(TM 2,3,4,5, and 7)wereandyed. Pearson's correlationmefficientwas used to analyze texture and hiomas relationships.This
research indicates that most t m u p s are weakly correlated with successional vegetation biomass, but some textures are
s ~ a n t ldy t e d with matun:forest biomass. In conmt, TMspfftral signaturesaresigru6candycorrelatedwith successional
vegetation biomass, but weakly correlated with mature forest biomass. Our findings imply that textures may be critical in
impmving mature forest biomass estimation,hut relativelylrss important hsuccessionalvegetation biomass estimation.
KEYWORDS
texture, aboveground biomass, TM image, correlation, Amazon
Explorando texturas de imagens TM e sum relaghes com
estimativas de Biomassa em Rondbnia.
RmMo
Muitas medidasde lextura t h sido desenvoluidase utilizudaspara melhorara acurdcia de c l m s i p ~de
s cobertura das
t m , mmrammmte t&m-seaualiadoa importdncia dessas medidas em estimatiuas de biomava. Este t r a b a l h utillzou
dadmLandsat TMpara explorur as relaqcies entre texturas de imugms ZU e bionwssa em Rondhnia, M n i a Forum
analisadas oito medidas de textura buseudus em m a t e e s de cwcorr&cia de tons de cinur (i.e., ddia, uaridncia,
'
,mfmste,
, -d
entrt@, segundomomfoemmh@o), ~ m m s e t e d t f ~
~ 7 x 7 , 9 x 9 , 1 1 x 1 1 , 1 . 5 x 1 5 , 1 9 x 1 9 e 2 5 x 2 5e ~) b a n d u s i W ( l M 2 , 3 , 4 , 5 e7).i n d i c e s d e c o m ~ d e p e a r s o n
foram utilizndospara analisar a s rel-s
entre textura e biomassa. Estapesquisa indica p e a maioria das medidasde
tertura sciopuco m I m a o o & c o m biomussa de vegeta@io senr&a,
mas dgumas medidasde tertura t5m comh@o
significativa com a biomassa deformcrgciesflorestais mudurus. Ao contrdrio, assinaturus espectrais de handas m s d o
~ ~ u i t i u a m e n t e m r r e ~ m m a h ~ d e v e g e ~mdmlf iiu a
u n,n e n t e a n r e l a c i ~ m a b i o m s a
deflomtas maduras. 0sresultados i n d i m que medidas de t a t u r a srio impntantes em esfimativasde b w m a w deflorestu
mudura, mas relatiwmente m a o s imporantespara estimatiuasde biomaw de vegekqio senmdriria
s
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EXPLORING TM IMAGE TEXTURE AND ITS REMTIONSHIPS WITH BIOMASS
ESTIMATIONIN ROND~NIA,BRAZILIAN AMAZON.
AMAZONICA
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effectively extract biomass information? During image
processing, what size of moving window is appropriate for a
specillc texture measure?This paper examines image texture
and vegetation aboveground biomass relationships using
Landsat Thematic Mapper (TM) data in RondBnia, Brazilian
Amazon, as an effort to find suitable textures for biomass
estimation.
INTRODUCTION
Aboveground biomass governs the potential carbon
emission that could be released to the atmosphere due to
deforestation. Accurate biomass estimation is necessary to
understand impacts of land-useAand-cover (LULC) change on
globalwanningandenvimnmental degradation. In recent years,
biomass estimation has attracted scientific interest because
regional changes in biomass havebeen aysociated with climate
DS
and ecosystem changes. 'She Large Scale BiosphereAtmosphere Experiment in AmazBnia (LBA) has driven its
attention to these issues contributing to the development of
study area
techniques for sustainable use of the land in the Amazon
environment. The results presented in this article belong to a
~
~has experienced
~
intensive
d
LULC
~
change
~ in thei
r m c h proiectletlb~IndianaUnivmityand E m b q Satellite past three decades. 'The deforestation rates in this State range
Monitoring with the support of LBA.
from 1.14 to 2.62% per year between 1991 and 2000, much
The advantages of remotely sensed data over traditional higher than the overall deforestation rate in the Brazilian
field inventory methods for biomass estimation have been Amazon region, which ranges from 0.37 to 0.80%per year at
indicated by a number of publications (Saderet a/., 1989; Roy & the same period (INPE, 2002). Following the national stntegy
Ravan, 1996;Boyd et a/., 1999; Nelson et al.,2000; Steininger, ofregional occupation and development,colonizationprojem
2000; Luetal., 2002a). However, previous researchhas shown initiated by the Brazilian government in the 1970s played a
the difficulty of biomass estimation based purely on remote- major role in this process (Moran, 1981). Most colonization
sensing spectral signatures because of the influence of projects in thestate weredesigned to settle landless migrants.
increased canopy shadowing within large stands, the The immigrants transformed the forested landscape into a
heterogeneity ofvegetation stand srmaures, and spectral data mosaic of cultivated crops, pastures, and different stages of
saturation (Spanneretal., 19% Roy&Ravan, 1996;Steininger, successional forests (Batistellaetal., 2003). Over the Amazon
2000). The complexity of forest stand
structure and species composition in the
BSI~
mw
moist tropical regions result in highly variable
,
standing stocky of biomass and even more
/'
/'
variable rate of biomass accu~nulation
/,'
following a deforestation event. This
I'
presents a challengefor biomass estimation,
/'
achallenge that must be addressed.
Texture often refers to the pattern of
intensityvariations in an image. Many texture
measures have been developed (Haralick et
d.,
1973;He &Wang, 19W U r n , 1995;Riou
RondOnls
& Seyler, 1997).In previous research, texture
measures were mainly used for LULC
classification (Franklin & Peddle, 1989;
Marceau etal., 1 9 9 ;Augusteijn etal., 1995;
Franklin et a/.. 2000: ndi Nvoungui et al..
2002; Podest & Saatchi, 2002). 0fjle many
texture measures, the grey-level cooccurrencematrix (GIKM) may be the most
common texture used for improving LULC
classification (Marceau et a/., 1990: Franklin
et al., 2000; 'ndi ~ ~ o u n g et
u ial., 2002).
MACHADINHO D'OESTE
However, rarely has research focused on
extraction of biomass information using
texture measures.?he relationships between
textures and aboveground biomass are
especially poorly understood. For example,
how are textures related to biomass
estimation? Which texture measure can Figure 1- Location of the study area in the State of RondBnia, Brazilian Amazon.
A
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VOL. 35(2) 2005: 249 - 257
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LU & BATISTELM
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EXPLORING TM IMAGE TEXTURE AND 11s REtATloNsHlPs WITH BoltvU6-S
ESTIMATIONIN R O N ~ N I A . BRAZILIANAMAZON.
AMAZONICA
region, a significant amount of the deforested area is in some
stage of secondary succession, requiring that we carefully
evaluate biomass and carbon dynamics represented by their
heterogeneous vegetation stands.
To ~laluatethe relationship between image texrures and
aboveground bionlass estimation, we chose the settlement of
Machadinho d'Oeste,located in northeastern RondBnia (Figure
1). This is anewercolonizationnmiect thanareas alonr BR-364
(kuiahh-PortoVelho ~i~hway).'Th;setdement of ~ac'hdinho
covers about 2,000 km2.Machadinho is adjacent to thehordes
with the Statesofhiwunas and Mato Crosso,which may offer
potentials and consmints for future initiativesof conservation
anddevelopment.~lheterrainundulat& ranginghm 100to
450m above sc-d level. The climate is classifiedaqequatorialhot
and humid, with tropicaltlansition.Thewelldefined+season
1astsfmmJunetoAugustthe anndanrxgcpmipitation is 2,016
mm,and the annuai a\.eragc trmpratw& is 2 5 . i (.(Kond6nia.
199898).Scvetal soil noes. includinealfisols. oxisols. ulti.wls. and
&d soils have &n identitimi (&nola & k,
1999)
Field vegetation Inventory and biomass
estimation
Fieldwork was conducted during the dry season of 1999.
Prrliminary image clasi6cation and band co~npositeprintoub
indicated candidate areastobe surveyed, and a flight overthe
amas provided visual insights a h u t the size, condition, and
accessibilityof each site. The surveys were conducted in m a s
with relatively homogeneous ecological conditions (e.g.,
topography, distance from water, and lancl use) and uniform
physiognomic characteristics. ffier defining the area to be
surveyed (plot sample), three subplots (1 m', 9 m2,and 100
m') were randomly selected to accurately represent the
variabilitywithin the plot sample. The center of each subplot
was randomly selected. 'lbtal tree height, stem height (the
height ofthe first main branch), and diameter at breast height
(DBH) were measured for all trees in the 100m'ma. Height
and DBH were measured for aU saplings in the 9 m2area.
(;round cover estimation and counting of individuals were
conducted for seedlings and herbaceous vegetation in the 1
m2m a . Here, seedlingswere defined as youngupes or shrubs
with a stem diameter smaller than 2 cm, saplings as young
trees with a stem DBH greater than 2 cm and smaller than 10
cm, and trees as woody plants with a DBH greater than or
equal to 10 cm. Adetailed description of field data collection
can be found in Batistella (2M)l)and Lu e t d . (2004).
A total of43 sample plots were inventoried, including 29
plots for d i i r e n t stages of successional vegetation and 14
plots for mature forests. Table 1 summarizes sample plot
distributions as well as the statistical ch~racteristicsof
abovegmund biomass. The vegetation ages of successional
forests range h m 2 to 13 years.
4d3rabnst.w huilt to integrate all vcgctation data collect~rl
dunt~etieldwork.Eauati(~nI 1 ) \rasuslrl tndculate inditidual
tree Gkass &el
al., 1%) and ~ q n t i o (2)
n for individual
saplingbiomac8(Horuaketd., 19%).
YT = 0.0326"' (DT)' '"Hand
(1)
YS = exp[-3.068 0.957 In (DS' "'H)], (2)
where D1' and DS are the tree and sapling DBH in
centimeters, respectivrly; ki is the total tree or sapling height
in meten; and YT and YS are the individual tree and sapling
biomassin kilograms, respaively,Abmqmund biomass (AGB:
w m 2 )was then calculated thmugh Eqwation (3).
+
where m is the tor4 tree number in a selected plot (three
100m2subplotsin aplot) ,and n is the total saplingnumber in
aselected plot (three9 m2subplntsin aplot). PAand SPAmthe
plot areas(in squarr-meters) for tree and sapling measurements.
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Table 1 Distribution of sample plots and sutistical characteristics.
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Successional for&
Sample plot distributions
Biomass (kglrn2)
Plots (N)
2-4
5
5
4-6
4
6-8
5
8-10
1
10-12
4
12-14
4
14-16
16.18
1
TO p l~o t ~
29
Mean
8.350
Minimum
Statistical characteristics
Maximum
Std. Dev.
4.455
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VOL. 35(2)2005: 249
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LU & BATISTELLA
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Mature forests
Biomass (kglrn2)
10.14
14-18
18-22
22-26
26-30
30.24
34-38
>38
2 -14
24.76r
~
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11.805
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