i
THE USE OF REMOTELY SENSED DATA FOR FOREST
BIOMASS MONITORING
A case o o es si es in No h-Eas e n A menia
Man el Khudinyan
ii
THE USE OF REMOTELY SENSED DATA FOR FOREST
BIOMASS MONITORING
A case o o es si es in No h-Eas e n A menia
Disse a ion supe ised by:
Joel Sil a, PhD. In o ma ion Managemen School, No a Uni e si y o Lisbon,
Lisbon, Po ugal
Goha Ghaza yan, MSc. Cen e o Remo e Sensing o Land Su aces (ZFL),
Uni e si y o Bonn, Bonn, Ge many
Ignacio Gue e o, PhD. Uni e si a Jaume I, Cas ellón de la Plana, Spain
Feb ua y, 2019
iii
ACKNOWLEDGMENTS
I am especially indeb ed o my supe iso PhD Joel Sil a o he g ea suppo and
echnical guidance he has o e ed. I would also like o pa icula ly hank my co-
supe iso PhD Goha Ghaza yan o he con inuous suppo and cons uc i e
commen s. I am also e y hank ul o co-supe iso P o . Ignacio o he ime he has
de o ed o my hesis.
This wo k would no ha e been possible wi hou P o . D . Ma co Painho, P o . B ox
and e e y membe o s a a UNL and i gi who I ha e o hank o my wonde ul
s udy expe ience.
I would especially like o hank P o . Ho ik Sayadyan who p o ided he ield da a (no
da a – no hesis), o suppo ing my ideas and o he encou agemen . I am also
g a e ul o PhD Go ik A e isyan and A man Kanda yan o he da a hey p o ided, as
well as my iends, o es e s PhD Vahe Ma i osyan and PhD Vahe Ma sakyan o he
consul a ions on he o es s and hei help.
Las bu no leas I would like o hank my amily, my iends and my classma es o
hei cons an suppo and ca e.
i
THE USE OF REMOTELY SENSED DATA FOR FOREST
BIOMASS MONITORING
A case o o es si es in No h-Eas e n A menia
ABSTRACT
In ecen yea s he e has been an inc easing in e es in he use o syn he ic ape u e
ada (SAR) da a and geospa ial echnologies o en i onmen al moni o ing․
Pa icula ly, o es biomass e alua ion was o high impo ance, as o es s ha e a
c ucial ole in global ca bon emission. Wi hin his s udy we e alua e he use o
Sen inel 1 C-band mul i empo al SAR da a wi h combina ion o Alos Palsa L-band
SAR and Sen inel 2 mul ispec al emo e sensing (RS) da a o mapping o es
abo eg ound biomass (AGB) o d y sub opical o es s in moun ainous a eas. Field
obse a ion om Na ional Fo es In en o y was used as a g ound u h da a. As he
SAR da a su e s g ea ly by he complex opog aphy, a simple app oach o aspec and
slope in o ma ion as o es y ancilla y da a was implemen ed di ec ly in he eg ession
model o he i s ime o mi iga e he opog aphy e ec on ada backsca e ing
alue․ Dense ime-se ies analysis allowed us o o e come he SAR sa u a ion by he
o es phenology and selec he op imal C-band scene. Image ex u e measu es o
SAR da a has been s ongly ela ed o he biomass dis ibu ion and has obus ly
con ibu ed o he p edic ion․ Mul ilinea S epwise Reg ession allowed o selec and
e alua e he mos ele an a iables o AGB. The p edic ion model combining RS
wi h ancilla y da a explained he 62 % o a iance wi h oo -mean-squa e e o o
56.6 ha¯¹. The s udy also e eals ha C-band SAR da a on o es biomass p edic ion
is limi ed due o hei sho wa eleng h. Fu he , he moun ainous condi ion is a majo
cons ain o AGB es ima ion. Addi ionally, his esea ch demons a es a posi i e
ou come in o es AGB p edic ion wi h eely accessible RS da a.
KEYWORDS
Syn he ic Ape u e Rada
Fo es
Abo eg ound Fo es Biomass
Mul ilinea S epwise eg ession
Sen inel 1
Sen inel 2
ALOS PALSAR
GLCM

i
ACRONYMS
AGB – Abo eg ound Biomass
SAR – Syn he ic Ape u e Rada
REDD+ – Reducing Emission om De o es a ion and o es Deg ada ion
UN – Eu opean Union
NDVI – No malized Di e ence Vege a ion Index
NDMI – No malized Di e ence Mois u e Index
RVI – Simple Ra io Vege a ion Index
FAO – Food and Ag icul u e O ganiza ion o he Uni ed Na ions
NFI – Na ional Fo es In en o y
ESA – Eu opean Space Agency
SRTM - Shu le Rada Topog aphy Mission
SWR – S ep Wise Reg ession
JAXA – Japanese Ae ospace Explo a ion Agency
DBHOB – Diame e on B es Heigh O e Ba k
GEE – Google Ea h Engie
UNDP – Uni ed Na ion De elopmen P og am
GEF – Global En i onmen al Facili y
ii
INDEX OF THE TEXT
Page
ACKNOWLEDGMENTS .......................................................................................... iii
ABSTRACT ................................................................................................................. i
KEYWORDS ................................................................................................................
ACRONYMS ............................................................................................................... i
INDEX OF THE TEXT ............................................................................................. ii
INDEX OF TABLES .................................................................................................. ix
INDEX OF FIGURES ................................................................................................. x
1.1 Theo e ical F amewo k ........................................................................................... 1
1.2 S a emen o he P oblem ........................................................................................ 3
1.3 Aims and Objec i es ............................................................................................... 5
1.4 Ou line ..................................................................................................................... 6
LITERATURE REVIEW ........................................................................................... 7
2.1 In oduc ion o Abo eg ound Fo es Biomass ...................................................... 7
2.2 SAR and Op ical emo e sensing da a o AGB es ima ion .................................. 12
2.2.1 Combina ion o Mul isou ce Da a o AGB Moni o ing ................................... 14
2.2.2 SAR D awbacks ............................................................................................................ 15
2.2.3 SAR Dense Time-Se ies Analysis ........................................................................... 17
2.3 Fo es AGB p edic ion models: Model diagnos ics .............................................. 18
DATA AND STUDY AREA ..................................................................................... 21
3.1 In oduc ion o he S udy A ea .............................................................................. 21
3.2 In oduc ion o Da a .............................................................................................. 23
3.2.1 Field Da a ........................................................................................................................ 23
3.2.2 Sen inel 1 Da a ............................................................................................................... 24
3.2.3 Sen inel 2 Da a ............................................................................................................... 25
3.2.4 Alos Palsa Da a ............................................................................................................ 26
3.2.5 Fo es y Ancilla y Da a ............................................................................................... 27
APPROACHES, METHODOLOGY, DATA PREPARATION ........................... 28
4.1 App oach and Gene al Me hodology .................................................................... 28
iii
4.2 Tools ..................................................................................................................... 31
4.3 Field Da a Calcula ion .......................................................................................... 32
4.4 Sen inel 1 P e-p ocessing ...................................................................................... 34
4.5 Palsa P e-p ocessing ............................................................................................ 36
4.6 Sen inel 2 P e-p ocessing ...................................................................................... 36
4.7 Ancilla y Da a P e-p ocessing .............................................................................. 37
4.8 GLCM ex u e analysis ......................................................................................... 38
DATA PROCESSING․ FOREST BIOMASS MONITORING ............................. 41
5.1 Fo es /Non-Fo es classi ica ion ........................................................................... 41
5.2 Resul s o Reg ession analysis .............................................................................. 44
5.2.1 Model Compa ison and P edic o E alua ion....................................................... 46
5.2.2 Model Compa ison on Si e Le el ............................................................................ 49
5.3 Discussion ............................................................................................................. 52
5.4 Limi a ions and Recommenda ions o Fu u e Resea ch ....................................... 56
CONCLUSION .......................................................................................................... 58
BIBLIOGRAPHIC REFERENCES ......................................................................... 59
ix
INDEX OF TABLES
Table 1. Bene i s and limi a ions o a ailable me hods o es ima e na ional-le el o es
ca bon s ocks (da a sou ce: Gibbs e al., 2007) ....................................................... 8
Table 2. Common ada bands used in Remo e Sensing (da a sou ce: Pohl. 2017) .... 12
Table 3. Field sampling da a pe s a a ........................................................................ 23
Table 4. Tools and ela ed p ocessing .......................................................................... 31
Table 5. AGB allome ic equa ions o dominan ee species .................................... 32
Table 6. Fo mulas o GLCM ex u e measu es .......................................................... 40
Table 7. Valida ion con usion ma ix o o es non- o es classi ica ion ................... 42
Table 8. Inpu a iables o eg ession analysis ........................................................... 44
Table 9. E alua ion o s a is ics o eg ession analysis o each model ...................... 47
6
1.4 Ou line
The s uc u e o he disse a ion is as ollows: Chap e 2 p esen s he e iew o
exis ing li e a u e on he opic o his esea ch. Chap e 3 desc ibes he condi ions
and speci ica ion o he s udy a ea and he da a used wi hin his s udy. Chap e 4
discusses he app oaches and me hodology, also desc ibes he da a p e-p ocessing
s eps in e y de ails. Chap e 5 p esen s and discusses he esul s o o es /non- o es
classi ica ion and o eg ession analysis, also ema ks he limi a ions and u u e
possible esea ches. Sec ion Conclusion sums up he esul s o his esea ch and
e alua es i s con ibu ions.

7
LITERATURE REVIEW
2.1 In oduc ion o Abo eg ound Fo es Biomass
Be o e s a ing he analysis o emo ely sensed da a o o es abo eg ound biomass
es ima ion, i is essen ial o unde s and he concep o o es AGB, he adi ional
echniques and me hods o es ima ion, which gi es be e unde s anding and ision o
he pa ame e s and condi ions should be conside ed be o e applying emo e sensing
me hods.
Abo eg ound li e biomass includes he li ing biomass o ees, sh ubs and he bs
abo e he soil including s em, s ump, b anches, ba k, lea es (UNDP-GEF 00091048,
2015), and ep esen s he la ges pool o ca bon s ock (Gibbs e al., 2007). AGB is
widely used o co ela ions and es ima ions o ca bon s o age in some o he o he
pools such as oo biomass and consequen ly he ca bon s ocks (app oxima ely 20 %
o AGB (Gibbs e al., 2007)) and dead wood o li e ca bon pool (app . 10-20 % in
na u al o es s (UNDP-GEF 00091048, 2015)). Thus, he o es AGB es ima ion is an
essen ial s ep in quan i ying he ca bon s ocks in he o es s.
The e a e se e al me hods de eloped o AGB es ima ion wi h di e en demands and
le el o accu acy. The mos accu a e and s aigh o wa d me hod o quan i y he o es
biomass is es ablishing sampling si es on he ield and ha es ing all he ees, d ying
hem (in pa icula , o ca bon s ock es ima ion) and weigh ing he biomass (Gibbs e
al., 2007). This me hod is e y expensi e and has des uc i e a ec ion on he
expe imen al si e. I is p ecise o ha one loca ion only, and imp ac ical in o he
egions o coun y le el analysis (Chen e al., 2018).
Such me hods canno be applied ac oss he landscape. Consequen ly, many
in es men s we e pu in o de elopmen o models ha a e able o “scale up” he ield
measu emen esul s o e la ge a eas. Gibbs did sum up in glance he main me hods
used o o es AGB es ima ion wi h hei ad an ages and limi a ions (Table 1)
(Gibbs e al., 2007).
8
Me hod
Bene i s
Limi a ions
Unce ain y
Biome
a e ages
• Immedia ely a ailable could
inc ease accu acy • Da a
e inemen s could inc ease
accu acy • Globally consis en
• Fai ly gene alized • Da a
sou ces no p ope ly sampled o
desc ibe la ge a eas
High
Fo es
in en o y
• Gene ic ela ionships eadily
a ailable • Low- ech me hod
widely unde s ood • Can be
ela i ely expensi e as ield-
labo is la ges cos
• Gene ic ela ionships widely
unde s ood • Low- ech me hod
inexpensi e as • Can be ela i ely
ield-labo is la ges cos
Low
Op ical
emo e
senso s
• Sa elli e da a ou inely
a ailable a global scale •
Globally consis en
• Limi ed abili y o de elop good
models o opical • Spec al
indices sa u a e a ela i ely low
ca bon s ocks • Can be echnically
demanding
High
Rada
emo e
senso s
• Sa elli e da a a e no always
ee •New sys ems launched
a e expec ed o p o ide
imp o ed da a • accu a e o
young o spa se o es
• Less accu a e in ma u e o es s
because signal sa u a es also
inc eases e o s • Moun ainous
e ain also inc eases e o s • Can
be expensi e
Medium
Table 1. Bene i s and limi a ions o a ailable me hods o es ima e na ional-le el o es ca bon
s ocks (da a sou ce: Gibbs e al., 2007)
Bou e and Gibbs a e di iding biome-a e age me hod in o wo main s eps (Bou e e
al., 2018; Gibbs e al., 2007). Fi s , da a ga he ing is being done h ough NFIs. Un il
now he bes esul s a e gained by ield calcula ions using expe imen al plo s,
ha es ing and ac ual o es olume es ima ion (Kachamba e al., 2016). In second
s ep allome ic models a e applied o calcula e a e age biomass o a sampling plo
loca ed wi hin a ce ain s a um. La e , na ional le el o ca bon s ock can be p edic ed
by applying he a e age biomass and ca bon densi y alues (de Bad s, 2002) o e he
egion o he same o es s a a (Bou e e al., 2018; Kachamba e al., 2016). Unlike
he high le el o unce ain y compa ed o he o es ha es ing me hod, his me hod
can be immedia ely a ailable and p o ide in o ma ion abou AGB on a wide scale
landscape.
G ound-based o es in en o y ocused on ield campaigns and o es in en o y
measu emen s o es ima ion o o es AGB. Measu emen s include diame e a b eas
heigh (DBH), a e age ee heigh and ee ype, and h ough which o es biomass is
being calcula ed using allome ic ela ionships (Cha e e al., 2005; Gibbs e al., 2007).
Only DBH is desc ibing AGB wi h 95 % accu acy (Cha e e al., 2005). Zhang o e s
o collec da a abou he ege a ion ype and soil ype as well (Zhang e al., 2018). As
he AGB consis o s em, ba k, b anches and lea es o needles depending on he o es
9
ype, he assumes o o ganize ield da a acquisi ion di e en ly o coni e ous and
deciduous o es s and exclude lea e biomass o win e calcula ions in deciduous
o es s (Cle ici e al., 2016).
Fo ield da a collec ion he e a e se e al app oaches o sampling poin s selec ion.
Sys ema ic and andom sampling designs a e he mos used ones. Sys ema ic
sampling uses? egula g id o plo loca ion selec ion, and he andom selec ion is
applied o andomly alloca ed sampling poin s. Bo h o he app oaches a e no
conside ing o es ype s a i ica ion and o es dis ibu ion. This, bo h schemes can
unde - o o e -sample as a as he pa e ns in na u e ha e na u ally clumpy and
andom dis ibu ion is less likely (Gibbs e al., 2007).
One o he unce ain ies o AGB es ima ion by con e ing ee measu emen s da a is
he lack o s anda d me hod. Li e a u e e iew shows di e en app oaches by
di e en au ho s o AGB es ima ion e en o he same ype o o es s (Cha e e al.,
2005; Cle ici e al., 2016; Gibbs e al., 2007). Gibbs p esen s a comple e o e iew o
o es AGB es ima ions by di e en au ho s o he same coun ies, and as he a icle
poin s ou , o Sh i-Lanka he AGB es ima ion by di e en me hods can al e in he
ange o 138–509 Mg/m2. The unce ain y occu s when shi ing om one o es ype
o ano he . In opical o es s 1 ha o es a ea can con ain 300 di e en ee species.
This means ha one canno use species-speci ic eg ession model, which is common
o apply while wo king wi h empe a e o bo eal o es s. In such condi ions be e
esul s a e achie ed by mixed-species eg ession model (Cha e e al., 2005).
Amongs exis ing numbe o s a is ical me hods o AGB acquisi ion based on
sampled allome ic measu emen s, Cha es sugges s “biome-diame e -heigh
eg ession” and “biome-diame e eg ession” models o hei simplici y and wide
applica ion (Cha e e al., 2005). Biome-diame e -heigh eg ession includes
in o ma ion abou ee heigh (H), diame e (D), and wood speci ic g a i y (ρ)
(Dawakin’s eg ession model):
ln(𝐴𝐺𝐵) = α + ln(D²Hρ) (1)
Bo h o he eg ession and he es a e applied using linea models.
Field sampling da a does no always include he ee heigh in o ma ion. In his case
Cha es assumes ela ionship be ween loga i hm o heigh , In(H), and he loga i hm o
diame e , In(D). A polynomial ela ionship be ween log. heigh and log. diame e
10
gi es a easonable gene aliza ion o he powe -low model. Hence, he model will ha e
ollowing equa ion:
ln(𝐴𝐺𝐵) = a + b*ln(D²Hρ) + c*ln(D²) + d*ln(D³)+ β*3ln(ρ) (2)
The powe -low is pa ame ized as c=d=0 (Cha e e al., 2005).
All hose con en ional models a e accu a e o he loca ion o sampling si e bu a e
di icul o ex end o e la ge a ea and a e being done no mo e han once o ce ain
a ea because o pa amoun esou ce demand. Ins ead, emo ely sensed da a p o ides
use ul o AGB es ima ion da a o he whole globe and wi h empo al esolu ion, bu
canno measu e he biomass di ec ly, hus equi e g ound- u h da a (Gibbs e al.,
2007; Kuma e al., 2015). Ano he impo an ad an age o emo e sensing is he
abili y o easily collec da a on he places whe e g ound access is limi ed.
Op ical emo e sensing da a om di e en senso s (see chap e 1.1) ha e been
implemen ed ( ied) o o es AGB indi ec es ima ion ia s a is ical ela ionship
be ween ield measu ed da a and sa elli e-obse ed ege a ion indices (Fassnach e
al., 2014). As he op ical senso eco ds he in o ma ion e lec ed om he o es
canopy and is dependen on he lea s uc u e, pigmen a ion and mois u e and ha e
week pene a ion possibili ies, hus he unce ain y is high, especially, in dense
opical o es s wi h big amoun o biomass (Chen e al., 2018; Joshi e al., 2016;
T euha e al., 2017). In he con a y, compa a i ely low unce ain y and highe
co ela ion be ween senso -da a and ield-da a a e eco ded in bo eal o es s whe e he
hie a chical s uc u e is absen in he o es s and indi idual ees can be ixed on he
pho os (Gibbs e al., 2007; Kachamba e al., 2016). Cle ici a i ms ha e y high-
esolu ion (VHR) op ical image y imp o es he AGB es ima ions in o es s (Cle ici e
al., 2016) using a io ege a ion index (RVI), no malized ege a ion index (NDVI),
ans o med ege a ion index (TVI), while he Vege a ion Index Numbe (VIN)
eco ded he wo s pe o ming. Kuma o e s a use o hype spec al senso da a,
pa icula ly unde lying he impo ance o mid-in a ed (MIR) e lec ance as he bes
desc ibe o o es biomass. He s a es ha MIR has ad an ages o e isible and nea -
in a ed (NIR) e lec ance (L. Kuma e al., 2015). Signal sa u a ion in he o es s is
he main cons ain o op ical emo e sensing o AGB es ima ion and almos all he
e iewed a icles de ec ed o be su e ing wi h his issue.
11
Wi h he launch o di e en SAR senso s wi h eely dis ibu ed da a, he e is
inc easing in e es in he use o hese obse a ions o o es AGB es ima ion. This
in e es s a ed in 2000s when Te aSAR-X, Alos, Palsa L-band we e launched, and
become mo e popula , when in 2014 i s Sen inel 1 C-band da a became eely
a ailable. Fo ins ance, he majo i y o he s udies o SAR in AGB es ima ions we e
published in he las 3-5 yea s (Be engue e al., 2018; Be ninge e al., 2018; Cle ici
e al., 2016; Joshi e al., 2016; Kuma e al., 2015; Na a o e al., 2016; Reiche e al.,
2018; San i e al., 2017; San o o & Ca us, 2018; Va aei e al., 2018; Yu & Saa chi,
2016).
Gene ally, he mos egula ly used me hods o o es AGB es ima ion using SAR
da a can be classi ied in o se e al g oups. The simples and subsequen ly he mos
used me hod is he measu ing backsca e ing coe icien om Pola ime ic SAR
(PolSAR) o e eal he oughness o he su ace. This is called 2D PolSAR me hod
(Zhang e al., 2018). As i was men ioned be o e, he bes esul s a e ob ained wi h
c oss-pola iza ion dual pola iza ion which is mo e sensi i e o he o es AGB. Mos
o hem applied loga i hm o biomass and used he backsca e ing coe icien o
o es biomass p edic ion (Lau in e al., 2018; Zhang e al., 2018). This me hod s ill
has some limi a ions such as sa u a ion, which depends on he wa eleng h and he
incidence angel (Joshi e al., 2016). The capabili ies, ad an ages and d awbacks o
SAR backsca e da a in o es AGB es ima ion, will be discussed in de ails in he nex
subchap e s
In e e ome ic SAR (InSAR) o Pola ime ic InSAR (PolInSAR) by using
in e e ome ic phase o cohe ence omog aphy o InSAR a e able o eco d he
ele a ion o he g ound and he op o he o es , and hus, hey can de i e he o es
heigh which hen can be ans o med in o o es biomass by allome ic equa ion
models. This model is p omising and has mo e po en ial, and i can educe he
sa u a ion in some ex an (Chen e al., 2018). A numbe o s udies show imp o ed
AGB es ima ion esul s by using InSAR da a (Huang, Zini i, To bick, & Ducey, 2018;
San o o & Ca us, 2018; Zhang e al., 2018).
The e a e also o he me hods o AGB es ima ion h ough Lase scanning and LiDAR
scanning (Joshi e al., 2016), which a e no i ing in he scopes o his esea ch,
he e o e hey will no be discussed in de ails.

12
2.2 SAR and Op ical emo e sensing da a o AGB es ima ion
SAR a e ac i e mic owa e ada s ope a ing in be ween 1 cen ime e and se e al
me e s o elec omagne ic spec um. A pa o he impulse ene gy om ada , once
mee ing he Ea h’s su ace, i is being sca e ed back owa ds he senso . This is wha
he Rada eco ds and measu es. Depending on he su ace ype and s uc u e,
di e en amoun o ene gy is sca e ed back and hus, di e en pa ame e s o he
su ace a e eco ded on he image. The image in ensi ies depend on he backsca e ed
signal cha ac e is ics such as wa eleng h, incidence angle, signal pola iza ion, scan
di ec ion, in addi ion o such pa ame e s as su ace oughness, mois u e, dielec ic
p ope ies, geome ic shape, o ien a ion (Pohl C., 2017). The equency, incidence
angle, pola iza ion and scan di ec ion a e p ope ies o sys em and he dielec ic
cha ac e is ics, o ien a ion, su ace oughness and he mois u e a e a ge p ope ies
(Pe iasamy e al., 2018). Those a e he pa ame e s ha a e in luencing on he
backsca e ed signal and hus, desc ibing he su ace objec s, hey a e co e o ada
image p ocessing.
SAR images o he same a ea a y as he sys em pa ame e s change. Fo ins ance, he
pene a ion capabili y o he signal is dependen on he wa eleng h: The longe
wa eleng h he la ge objec s hey pene a e. Acco ding o he ule o humb he
pene a ion leng h is he hal o he wa eleng h. Acco ding o he same ule, highe
he backsca e in ensi y he oughe he su ace ha is being imaged (CRISP 2001,
n.d.).
Rada Band
Wa eleng h (cm)
F equency (GHz)
X
2.5–3.75
8–12
C
3.75–7.5
4–8
S
11.11–7.69
2.7–3.9
L
15–30
1–2
P
100
0.3
Table 2. Common ada bands used in Remo e Sensing (da a sou ce: Pohl. 2017)
Depending on he used wa eleng h, SAR senso s a e di ided in o se e al bands
(namely: K, X, S, C, L, P, VHF) (Timo hy e al., 2016). Table 2 desc ibes he
wa eleng h and app op ia e equencies o he SAR bands mos commonly used o
land co e su eys.
L and P- band (15-100 cm oge he ) ha e p i ileges o e C-band (3.75-7.5 cm) in
e ms o o es e ical alues desc ip ion. C-band can pene a e only lea es and
13
needles and small b anches, while L and P-bands p o ide s onge backsca e o m
unk and la ge b anches (Figu e 1) (Joshi e al., 2016). This leads o a di e en esul
when calcula ing o es AGB ough abo e-men ioned SAR bands. Acco ding o he
e iewed li e a u e L-band analysis eco ds highe co ela ion wi h g ound da a
compa ed wi h he C-band (Bou e e al., 2018; Lau in e al., 2016, 2018; Me moz e
al., 2015; Yu & Saa chi, 2016).
Figu e 1. Vege a ion pene a ion capabili ies o X, C and L ada bands (da a sou ce: Pohl.
2017)
Pola ime ic SAR senso s a e capable o ansmi and de ec he e ical (V) and
ho izon al (H) componen s o he backsca e ed adia ion (Pohl C., 2017). Hence,
he e a e ou possible pola iza ion con igu a ions: co-pola iza ion- HH, VV, c oss-
pola iza ion- HV, VH, and hei combina ions (Aula d-Macle M., R. Ba s ow, 2011).
Pola ime ic SAR backsca e ed ene gy is di ec ly depending on he physical
p ope ies o he ege a ion elemen s and in luences on backsca e ing mechanism
(Pohl C., 2017). The ough su ace signi ican ly depola izes he signal in a g ea e
magni ude while smoo he su ace depola izes he signal a he lowe magni ude.
Thus, deg ee o pola iza ion can p o ide aluable s uc u al in o ma ion abou he
o es s (Pe iasamy, 2018). Lau in and San o o in hei a icles a i m ha c oss-
pola iza ion has p io i ies o e co-pola ized backsca e in o es biomass in o ma ion
e ie al (Lau in e al., 2018; San o o & Ca us, 2018). A numbe o s udies poin ou
he VH backsca e om Sen inel 1 C-band and HV backsca e o m Alos Palsa and
Alos2 Palsa 2 L-band SAR o ha e mo e eliable co ela ion wi h AGB da a o e he
co-pola iza ion and o he pola iza ion con igu a ions (Huang e al., 2018; Lau in e
al., 2016)
Li e a u e e iew shows se e al a emp s o image ex u e cha ac e is ics
implemen a ion o biomass es ima ion using op ical image y back in he pas (Ecke ,
14
2012). In he mo e esen li e a u e e iew by San o o & Ca us (2018) he
in es iga ion o ex u e cha ac e is ics and pola iza ion decomposi ions o SAR
image y a e b inging smalle e ie al e o s in o es AGB es ima ion compa ed o
only backsca e alues. Pape s published by Be ninge , Thapa and Huang depic
imp o ed co ela ions be ween image ex u e (namely, homogenei y, con as ,
en opy, olume sca e ing) and he ield da a om allome ic calcula ions (Be ninge
e al., 2018; Huang e al., 2018; Thapa e al., 2016). In case o SAR da a, he ex u e is
a measu e o he spa ial homogenei y o he backsca e ing and desc ibes he
p ope ies o su ace, such as smoo hness, egula i y and onal a ia ion, and so,
should enclose in o ma ion abou o es s uc u e (San o o & Ca us, 2018). Chen e
al. also show ha Sen inel 1 image ex u es a e he mos ela i e and impo an
p edic o s o AGB es ima ion compa ed o he o iginal backsca e ing da a (Chen e
al., 2018). This e iew shows ha he implemen a ion o ex u e me hods o o es
biomass e ie al a e unde s udied ye and seem o ha e high po en ial in imp o ing
he es ima ion accu acy.
2.2.1 Combina ion o Mul isou ce Da a o AGB Moni o ing
Combina ion o SAR and op ical da a is being in ensi ely implemen ed mos ly in he
las couple o yea s and shows high po en ial o imp o e he o es biomass p edic ion
accu acy (Joshi e al., 2016; Kuma e al, 2016; Lau in e al., 2018). Joshi e al 2016,
in his e iew o exis ing li e a u e inds ha di e en SAR da a wi h conjunc ion o
op ical da a ha e compa a i ely highe accu acy han SAR and op ical da a alone
(Joshi e al., 2016).
Besides he p edic ion accu acy imp o emen , combina ion o globally a ailable
op ical senso s such as Landsa 8, Sen inel-2, and SAR senso s like Palsa -2 and
Sen inel-1 wi h high empo al esolu ion, is becoming a e y impo an ool in a way
ha ensu es ope a ional and con inuous global o es co e moni o ing in consis en
and obus manne (Reiche e al., 2016). Joshi e al. (2016) and Reiche e al. (2106)
assume ha ope a ing on di e en physical p inciples, hence, he SAR and op ical
senso s p o ide syne gis ic in o ma ion on he o es p ope ies. As we al eady saw,
he i s is dependen on he size, o ien a ion and dielec ic p ope ies, densi y, while
he second is dependen on he lea s uc u e, mois u e and pigmen a ion. This le s
hem conside ha combina ion o hose di e en sa elli e image y should be
15
p omising app oach o inc easing he accu acy o AGB es ima ion in o es s (Joshi e
al., 2016; Reiche e al., 2016). The e is g owing ocus on his app oach and he
majo i y o he esea ches e iewed a e om 2018 (Chen e al., 2018; Kuma e al.,
2016; Lau in e al., 2018; Pe iasamy, 2018; Va aei e al., 2018). The mos o en used
op ical senso is Sen inel-2 because o he high esolu ion, ee a ailabili y and he
global co e wi h high equency e isi cycle. Lau in e al. in hei pape (2018)
showed ha combina ion o Sen inel-1 and Sen inel-2 s ongly imp o ed he o es
AGB es ima ion in Medi e anean sh ublands (a ound 14% wi h espec o he senso s
sepa a ely). As an inpu o he p edic ion model all he bands o m Sen inel 2 as
di e en a iables we e used. Chen e al. (2018) gene a ed numbe o ege a ion
indices and biophysical a iables as inpu a iables o he p edic ion models. The
esul s show ha he ege a ion biophysical a iables a e ou s anding compa ed o he
o he Sen inel-2 p oduc s while combining wi h Sen inel-1 p oduc s (Chen e al.,
2018).
Lau in e al (2018) and Chen e al (2018) highligh ha combina ion o mul i-sou ce
sa elli e image y imp o es he sa u a ion le el o o es AGB es ima ion. Lau in e al
in hei esea ch (2018) go up o 400 ones/ha accu a e es ima ion combining Sen inel
1,2 and Palsa 2 sa elli e image y. Zhang assumes ha in e e ome ic phase and
cohe ence me hods ha e po en ial o o e come he sa u a ion in some ex ends (Zhang
e al., 2018).
2.2.2 SAR D awbacks
The e a e numbe o challenges while analyzing and in e p e ing SAR images o land
applica ions and pa icula ly o o es y. Th ee main d awbacks o SAR da a can be
sepa a ed ha se e ely a ec he measu emen accu acy, namely, speckle noise, he
bias on he backsca e alue because o he moun ain elie and sa u a ion o
elec omagne ic signal.
Speckle noises: The majo p oblems a e he speckle noises on SAR images ha migh
cause o poo classi ica ion (Joshi e al., 2016). Unlike he op ical emo e sensing,
ada scanning is cohe en in e ac ion o he signal wi h he su ace objec s. As he
esul o cohe en summa ion o he signal sca e ed o m, he g ound sca e e s has
andom dis ibu ion wi hin each pixel and is called speckle noise (CRISP 2001, n.d.).
22
co e ed (UNDP-GEF, 2015). Annual p ecipi a ion is 500-540 mm and he clima e is
desc ibed wi h wa m and d y summe s and empe a e win e s (FMP, 2018).
Figu e 2. Loca ion o he s udy a ea. a) Loca ion o he Ta ush p o ince, b) o es
en e p ises in Ta ush p o ince, c) "Noyembe yan" o es en e p ise
This esea ch ocuses on he o es si es loca ed in Ta ush p o ince (A s abe d,
Ije an, Se qa , Noyembe yan and “Dilijan” Na ional Pa k), The main ocus is
Noyembe yan o es en e p ise o se e al easons:
• The pilo p ojec o na ional o es ca bon in en o y has been implemen ed in
Noyembe yan o es en e p ise and can be as a g ound base o e alua ion o
esul s,
• The ield sampling densi y is much highe in Noyembe yan o es en e p ise (55
sampling poin s ou o 115),
• Some o emo e sensing da a has huge dis o ions o e he o he o es si es which
p ac ically makes hose da a no use ul o o es moni o ing on he whole a ea,
• All he o es s in he NE A menia ha e e y simila cha ac e is ics in e ms o
o es ype, s uc u e, amoun o biomass, e ain condi ions, and so, we do no pu

23
unce ain y while using he ield sampling da a om one si e o model aining
and applying on he o he one.
Fo s a is ical analysis and model aining in o ma ion was collec ed om all he 5
o es sigh s bu he p ojec ’s main ocus a ea is conside ed he o es s o
Noyembe yan. The e o e, all he objec i es and hypo hesis a e applied and he inal
maps and e alua ion a e done o Noyembe yan o es s only.
The o es s in Noyembe yan en e p ise is 29334 ha and is loca ed in be ween 600-
1850 MASL al i udes and he dis ibu ion is as ollowing: <800m – 4.2%, 801-1200-
47.9%, 1201-1600-37.8%, 1601> - 10.1%. Fo es s uc u e, quali y and dominan
species a y based on he al i ude - sp ead low densi y in he low al i udes o high
densi y in he middle. The opog aphy is highly agmen ed and ema kable wi h s eep
slopes and aspec composi ion a ia ions. This s ongly a ec s he o es dis ibu ion.
The e a e mo e o es s loca ed on he no h slopes (58.6%) han on he sou h slopes
(41.4%) (FMP, 2018).
3.2 In oduc ion o Da a
3.2.1 Field Da a
Field da a a e gained om he UNDP-GEF ongoing p ojec . The ini ial numbe o
plo s we e 115, ou o which 55 we e om Noyembe yan en e p ise collec ed in 2017,
and he es a e om he o he o es si es o Ta ush p o ince collec ed in he
beginning o 2018. The ield sampling plo s’ loca ions a e chosen based on a
sys ema ic sampling plo design (Figu e 3).
S a a
No o samples
Beech
18
Ho nbeam
22
Oak
18
Pine
12
O he
19
Dis u bed
26
To al
115
Table 3. Field sampling da a pe s a a
24
Figu e 3. Field sampling plo s dis ibu ion
The e was also used a o es s a i ica ion map o ensu e ha he sampling plo s
include all he o es ypes (Table 3). Fixed size o 0,1 ha ci cula plo s we e
es ablished wi h 17.84 m adius (depending on he slope deg ee i migh change in
o de o ensu e a 0,1 ha plo size when p ojec ed on a plane). All he la ge ees (>8
cm in diame e ) a e assessed o he ollowing p ope ies: species, DBHOB, ee
s a us, decay class, c own class and many o he pa ame e s, ha a e no ele an o
his s udy, hus will no be epo ed.
3.2.2 Sen inel 1 Da a
Fo his s udy Sen inel 1A and Sen inel 1B C-band SAR da a om Cope nicus p ojec
o ESA was used. The da a is in e e ome ic wide swa h (IW) scanning mode, wi h
25
250 km swa h wid h. We used Le el-1 G ound Range De ec ed (GRD) p oduc which
is al eady Mul i-looked (one look in ange and i e in azimu h), Geocoded and i is
a ailable wi h 10x10m pixel spacing bo h in Cope nicus Open Access Hub¹, ( he
online sys em o he ESA), and in Google Ea h Engine Ja aSc ip API² (GEE). Bo h
sa elli es o bi in nea pola , sun-synch onized o bi a 693 km al i ude and in he same
o bi al plane (To es e al., 2017). Sen inel 1 C-band SAR has 5.405 GHz equency
(co esponding o a wa eleng h o ~5.6 cm) and p o ides images in wo pola iza ion
modes: VV co-pola iza ion and VH c oss-pola iza ion.
The incidence angle is be ween ~31 and ~46 deg ees. The esolu ion was se o 10 m.
Each image con ains 3 bands: wo o backsca e ing in ensi y (VV, VH) and one o
incidence angle. Because o he e ogenei y in he pa ame e s o a ailable images he e
is a need o il e o down he da a o a homogeneous subse . In o de o ha e he
same incidence angle o all he images he same ela i e o bi numbe 152 was
selec ed. The e we e 72 scenes acqui ed o he whole yea o 2017 (30 om Sen inel
1A, 42 om Sen inel 1B), 6 images pe mon h in a e age.
Fo Sen inel 1 da a a ailable in GEE pla o m he backsca e ing coe icien om
na u al alues o sigma naugh (σ◦) is con e ed in o dB alues.
σ◦ = 10⁎log10 𝐷𝑁 (3)
Whe e he DN is he digi al numbe o he na u al alues.
(σ0 (dB) = 10*log10(absolu e (σ0))).
3.2.3 Sen inel 2 Da a
The Sen inel 2 image y was ob ained om 2 sa elli es: Sen inel 2A and Sen inel 2B.
Da a is a ailable ee o cha ge in Cope nicus Open Access Hub and GEE pla o m.
Sen inel 2 da a a e cha ac e ized by13 spec al bands wi h 10-m, 20-m, and 60-m
spa ial esolu ion and a adiome ic esolu ion o 12 bi . Two o m o da a a e
a ailable: Le el 1C (L1C)– op-o -a mosphe ic e lec ance p oduc , and Le el 2A
(L2A) – bo om-o -a mosphe ic e lec ance p oduc .
All he acqui ed images a e om 2017: June and July (16 L1C p oduc s wi h cloud
co e less han 15%), Sep embe (8 L1C p oduc s wi h cloud co e less han 25%)
and Oc obe (2 L1C p oduc s wi h cloud co e less han 10%). Only 10m and 20m
26
esolu ion bands we e used o he analysis: B2 (490nm), B3 (560nm), B4 (665nm),
and B8 (842nm) 10m spa ial esolu ion bands, B5 (705 nm), B11 (1610nm), and B12
(2190nm) 20 m spa ial esolu ion bands (ESA, 2018).
3.2.4 Alos Palsa Da a
Global Palsa -2/ Palsa L-band SAR da a we e accessed a a 25 m scale in GEE
pla o m. The da ase is gene a ed by applying Japanese Ae ospace Explo a ion
Agency’s (JAXA) p ocessing and analysis echnique o a lo o images ob ained wi h
Japanese (Palsa and Palsa -2) ada s on Ad anced Land Obse ing Sa elli e (ALOS
and ALOS-2) sa elli es/ca ie s (JAXA, 2018).
Palsa / Palsa -2 images a e L-band SAR (~23.5 cm wa eleng h) and he images a e
acqui ed in Fine Beam Dual pola iza ion (FBD, 70 km swa h wid h) mode: HH-
ho izon al ansmi , ho izon al ecei e, HV- ho izon al ansmi , e ical ecei e. The
incidence angle is be ween 28.6 deg ee and 32.9 deg ee (CEOS, 2016; JAXA, 2018).
The empo al in e al o he images con ained by he mosaic is gene ally 1 yea , and
no in o ma ion is a ailable abou he scanning da e o a pa icula a ea. Acco ding o
he u o ial o Global 25 m Palsa p oduc , he images a e selec ed aking in o
conside a ion he wea he in o ma ion, in o de o a oid ada backsca e ing
sa u a ion e ec caused by he mois u e (JAXA, 2018).
O ho- ec i ica ion and opog aphic co ec ions on he SAR da a a e applied using
90m SRTM DEM, which is no p e e able o his esea ch, as ou s udy a ea is highly
agmen ed and his can cause o impo an in o ma ion lose.
Backsca e ing da a a e s o ed in digi al numbe (DN) o unsigned 16 bi . These
alues can be con e ed o gamma naugh (γ◦) na u al alues in decibel uni (dB) by
he ollowing equa ion:
γ◦ = 10⁎log10(𝐷𝑁²)−𝐶𝐹 (dB) (4)
Whe e CF is he calib a ion ac o and o p oduc o Palsa / Palsa -2 is measu ed o
be ~83.0 dB.
27
3.2.5 Fo es y Ancilla y Da a
The o es y ancilla y da a used in he scopes o his esea ch a e he o es ype
in o ma ion om o es s a i ica ion map, and he opog aphic pa ame e s o he
sampling plo s, namely, he aspec and he slope in o ma ion. Though his in o ma ion
is a ailable om he o es in en o y da a, we used he necessa y equi alen
in o ma ion e ie ed om DEM. The SRTM DEM which was gained om U.S.
Geological Su ey po al (USGS, Sep embe , 2018) ee o cha ge. DEM da a is low
spa ial esolu ion (30 m).

28
APPROACHES, METHODOLOGY, DATA
PREPARATION
4.1 App oach and Gene al Me hodology
Di e en pixel-based me hods we e used in o de o answe he esea ch ques ions
and achie e he objec i es. The pixel size is chosen 30m quad a o be equal o he
SRTM DEM p oduc , which was used o e ain co ec ions. Also, ce ain
ci cums ances we e conside ed: he minimum mapping uni (MMU) is 0,09ha, which
is compe i i e wi h he a ea o ield sampling plo s (0,1 ha), and a e applying a
scaling ac o o 0,9 on he ield ABG es ima ion, we eplaced he ci cula sampling
plo s wi h squa ed pixels. P ojec ion sys em o he whole p ojec was selec ed WGS-
84 38N local o he s udy a ea p ojec ion. All he as e s we e aligned in o de o
ma ch he pixels o as e analysis.
Field da a was ca e ully examined as ce ain condi ions should ha e been ensu ed
be o e using SAR da a o eg ession analysis. E en i a pa o he ield sampling
da a we e om he beginning o 2018, we kep using he emo ely sensed da a om
2017. As he annual g ow h o wood o he o es s in A menia is es ima ed as 1.4
m³/ha (Rio+20, 2012), which we conside o be no signi ican o his s udy.
Li e a u e e iew shows, ha wo king wi h SAR da a demands deepe unde s anding
o he physics behind i and mo e ca e ul p e-p ocessing and p ocessing o he da a,
which is c ucial o imp o ing he quali y and e ie ing he ele an in o ma ion om
SAR backsca e ing. Implemen a ion o some ypical p ocessing s eps, such as
adiome ic co ec ion and speckle il e ing a e equi ed e en i we a e wo king wi h
images o m he same senso bu wi h di e en ime s amps (Na a o e al., 2016).
E en mo e, wi hin he scope o his s udy we combined da a om di e en senso s,
which assumes mo e p e-p ocessing s eps such as iden ical backsca e ing naugh
e ie al.
As he s udy a ea is su e ing om complex and highly segmen ed opog aphy and
aking in o conside a ion he ac ha in such case e en he adiome ic e ain
co ec ion is no su icien o mi iga ing he bias and he gain on he SAR
backsca e ing da a caused by high opog aphic a ia ion (Pohl C., 2017), we
implemen ed ancilla y opog aphic in o ma ion o he sampling si es di ec ly in o
29
eg ession model. Simple emo al o he s eep slopes and masking o he
o esho ening and shadows on he SAR da a is no possible, as hose a eas a e
occupying he mos pa o he s udy a ea.
Besides he ee co e ed a eas, o es si es include also non- o es ed o es lands
(meadows, pas u es, he bs and o es dis u bance a eas) a e some imes signi ican ly
big and can mislead he o es biomass es ima ion. Fo he FNF map gene a ion in
pu pose o p ecise delinea ion o o es s, Sen inel 2 image y and Random Fo es (RF)
classi ica ion me hod was used. This non-pa ame ic classi ie was selec ed since i
does no equi e any s a is ical analysis o inpu a iable and can handle wi h
mul icollinea i y e ec .
Recen S udies show success ul implemen a ion o Ha alic ex u e analysis (GLCM
ma ices) o biomass p edic ion, when di e en pa ame e s o he local a iance o
he pixel alues is calcula ed (Chen e al., 2018; Huang e al.,, 2018). Image ex u e
analysis echnique was also applied by choosing he mo e ele an ex u e pa ame e s.
Fo he biomass p edic ion and AGB mapping backwa d S epwise Mul iple Linea
Reg ession (SWR) model was used o au oma e he bes explana o y a iable
selec ion. E en i he s udied s a e some be e esul s o non-pa ame ic models o e
he pa ame ic ones o biomass p edic ion, he use o SWR allows us o e alua e and
compa e each o he inpu a iables (i.e. he inpu da a and he en i onmen al
condi ions), which is one o he main ocus a eas o his esea ch. Va iables wi h
pa ame e s o p- alue < 0.05 and VIF > 5 we e excluded o m eg ession model
(Be ninge e al., 2018; Lau in e al., 2018).
The Figu e 4 p o ides he lowcha o he o e all me hodology (The comple e low
cha o he me hodology see in APPENDIX B). The low cha d is designed
ollowing way: aw inpu da a (g ay), in o ma ion o unp ocessed da a (blue), da a
p e-p ocessing and p ocessing s eps (whi e), p ocessed- eady o analysis da a
(g een), classi ica ion and eg ession analysis (b own). Red dashed boxes a e
indica ing he main sub-p ocesses which will be discussed in a de ailed manne in he
nex chap e s. Those sub-p ocesses a e:
• Field da a calcula ion
• Sen inel 1 da a p e-p ocessing
• PALSAR da a p e-p ocessing
30
• GLCM ex u e analysis
• Sen inel 2 da a p e-p ocessing
• Ancilla y da a p e-p ocessing
• Fo es /Non-Fo es classi ica ion
The eg ession analysis, model diagnos ics and e alua ion a e discussed in he
Chap e 5.
Figu e 4. Flow cha o gene al me hodology
31
4.2 Tools
The ools used o he da a p e-p ocessing and p ocessing a e p esen ed in he Table
3. The ocus is on he open so wa e and ee o cha ge cloud en i onmen . Sa elli e
image p e-p ocessing and p ocessing is mos ly ca ied ou in he Google Ea h Engine
cloud-based en i onmen by using Ja aSc ip API.
P ocess
Tools
S1 p e-p ocessing
GEE Ja aSc ip
API
Palsa mosaic p e-p ocessing
S2 ege a ion indices calcula ions
Hansen Global o es map implemen a ion
FNF classi ica ion wi h RF
Classi ica ion accu acy assessmen
GLCM ex u e analysis
S2 L1C o L2A p ocessing
SNAP, Sen inel
Toolbox,
Sen2Co plugin
S2 ege a ion indices calcula ions
Reg ession analysis
R
C oss- alida ion and model diagnos ics
AGB mapping on he s udy a ea
P ojec ion ans o ma ion
QGIS
Ras e e-scaling and aligning
Field sampling da a p ocessing
Shape o as e con e sions
Map designing
A cGIS Desk op
Aspec and Slope p ocessing
Field da a calcula ions
MS O ice Excel
Table 4. Tools and ela ed p ocessing
GEE is cloud based geospa ial p ocessing pla o m and p o ides huge compu a ional
powe , which made possible he p ocessing o eno mous amoun o sa elli e image y
and conduc dense ime-se ies analysis. GEE is an en i onmen o plane a y-scale
en i onmen al da a analysis and con ains he a chi es o many publicly a ailable
emo e sensing image y (Go elick e al., 2017).
38
Because he aspec da a is ca ego ical, we combined i in o 8 segmen s, each g oup
consis ing o 45 deg ees o aspec . Comple ely la a eas consis 0.8% o he o al a e,
so we dis ega ded ha and included in he 1 h segmen . The numbe ing is designed
s epwise. A e wa ds, we implemen ed one-ho -encoding echnique o ans o m he
ca ego ical a iable in o 8 di e en inpu a iables wi h nume ical alues using R (R
Co e Team, 2018). Thus, we made i possible o his in o ma ion o be used in linea
eg ession analysis as an inpu a iable. “Encoding” package was used o his.
Figu e 10. DEM, Aspec and Slope maps o he s udy a ea
4.8 GLCM ex u e analysis
Image ex u al ea u es, de eloped by Ha alic e al. (1973), measu e he spa ial
homogenei y o he backsca e ing and con ain in o ma ion abou o es s uc u e.
Se e al s udies ha e p o en ha GLCM ex u e om SAR can be be e desc ibing
he biomass dis ibu ion han SAR backsca e ing i sel (Chap e 2.2). GLCM
analysis we e applied only on he SAR image y wi h a selec i e app oach (Figu e
11), aiming o dec ease he numbe o inpu a iables o he eg ession analysis.

39
Figu e 11. Flow cha o GLCM ex u e analysis s eps
In o de o de e mine he mo e ele an SAR a iables, he eg ession model was un
wi h he inpu a iables o S1 (22 a iable om ime-se ies s acks o each VV and
VH pola iza ion), Palsa (2 a iable o HH and HV pola iza ion), Vege a ion indices
(3 a iable – NDVI, NDMI, RVI) and ancilla y da a (3 a iable - ee species ype,
aspec , slope). Mo e impo an a iables we e selec ed based on hei co ela ion
signi icance acco ding o he s epwise linea eg ession es (SWR). In he i s
i e a ion he mo e signi ican SAR a iable (Palsa HV) was selec ed, hen 14 ex u e
measu es we e calcula ed o Palsa HV in GEE (Angula Second Momen ,
Con as , Homogenei y, Co ela ion, Va iance, Sum a e age, Sum a iance,
En opy, Sum en opy, Di e ence en opy, Ene gy, Di e ence a iance,
Di e ence en opy, Maximum co ela ion). Those masseu s as inpu a iables we e
added o he same a iables o ano he i e a ion o eg ession.
This ime he bes ex u e measu es wi h highes co ela ion wi h he biomass we e
de e mined again based on he a iable signi icance om SWR es . Those measu es
a e Va iance, En opy, Co ela ion, Sum o a e age (Table 6).
GLCM ex u e analysis we e ca ied wi h GEE cloud en i onmen . Fo hese analysis
SAR da a in gamma naugh (con e ed o na u al alues) a e used. To ha e he eal
s a is ics o he image pixels, ex u e measu es a e calcula ed on he images be o e
applying a speckle il e . 4x4 window size was se up o he calcula ions.
40
GLCM
ex u e
Fo mula
Desc ip ion
No o eq.
Va iance
Measu es he dispe sion (wi h ega d
o he mean) o he g ay le el
dis ibu ion
(12)
En opy
Measu es he deg ee o diso de
among pixels in he image; i is
(app oxima ely) in e sely co ela ed
wi h uni o mi y; images wi h a la ge
numbe o g ay le els ha e la ge
en opy
(13)
Co ela ion
Measu es he linea dependency o g ay
le els on hose o neighbo ing pixels; i
p o ides a measu e simila o
au oco ela ion me hods
(14)
Sum a e age
Measu es he mean o he g ay le el sum
dis ibu ion o he image
(15)
Table 6. Fo mulas o GLCM ex u e measu es. Fo mulas o GLCM ex u e measu es. Fo
all he equa ions, p(i, j) is he (i, j)- h en y o he no malized g ay-le el co-occu ence ma ix,
ha means, p(i, j) = P(i, j) / ∑P(i,j)
𝑖𝑗 , whe e P(i, j) is he (i, j)- h en y o he compu ed
GLCM; 𝑁𝑔 is he o al numbe o g ay le els on he image; and 𝜇𝑥, 𝜇𝑦 and 𝜎𝑥,d𝜎𝑦 s and o
he Mean and S anda d De ia ion o he ow and column sums o he GLCM, espec i ely
(da a sou ce: Ha alic e al., 1973).
∑ ∑ (𝑖−𝜇)2
𝑁𝑔
𝑗=1 𝑝(𝑖,𝑗)
𝑁𝑔
𝑖=1
−∑ ∑ 𝑝(𝑖,𝑗)
𝑁𝑔
𝑗=1 log𝑝[(𝑖,𝑗)]
𝑁𝑔
𝑖=1
∑ ∑ 𝑖𝑗𝑝(𝑖,𝑗)− 𝜇𝑥𝜇𝑦
𝜎𝑥𝜎𝑦
𝑁𝑔
𝑗=1
𝑁𝑔
𝑖=1
∑ 𝑖𝑝𝑥+𝑦 −(𝑖)
2𝑁𝑔
𝑖=2
41
DATA PROCESSING․ FOREST BIOMASS MONITORING
5.1 Fo es /Non-Fo es classi ica ion
Be o e s a ing he o es moni o ing and biomass calcula ions, i ’s signi ican o
dis inguish he main concep s ega ding o es de ini ion, which will b ing be e
insigh in o he ield da a we ha e, and on he ex en s o he span ha should be
explo ed wi hin his esea ch. As he o es y ield in o ma ion is p o ided by each
o es en e p ise (bigges o es si e uni s), i is wo h o de ine he o es en e p ise o
his case. Acco ding o he Fo es Code o A menia:
Fo es en e p ise – a p oduc ion uni wi h he aim o sus ainable o es managemen .
As he o es en e p ise is o es -economic uni , i consis s o o es ed and non-
o es ed lands. Acco ding o he same Code:
Fo es Lands - o es ed lands and lands alloca ed o en isaged o lo a and auna
p o ec ion, na u e p o ec ion as well as non- o es ed lands alloca ed o en isaged o
he unning o o es economy.
On he o he hand, ecen s udies show ha he non-homogeneous o es ed a eas can
cause o SAR backsca e ing alue dis o ion on he image. To a oid om his kind
o unce ain ies, we o e o delinea e he bounda ies o o es s only, inside he o es
en e p ises and conside hose a eas as he limi o he ex en s o his s udy.
Acco ding o he same o es Code:
Fo es - in e connec ed and in e ac ing in eg i y o biological di e si y domina ed by
ee-bush ege a ion and o componen s o na u al en i onmen on o es lands o
o he lands alloca ed o a o es a ion wi h he minimal a ea o 0,1 ha, minimal wid h
o 10 m and wi h ee c owns co e ing a leas 30% o he a ea, as well as non-
o es ed a eas o p e iously o es ed o es lands (Fo es Code, 2005).
Thus, wi hin he scope o his esea ch we closely aligned he o es de ini ion o he
one o Fo es Code A menia, meaning, ha he o es s a e de ined as lands o mo e
han 0.09 ha wi h he ee canopy co e o mo e han 30%. Fo his pu pose, Hansen
o es co e p oduc o 2000 was adop ed, as well as o es /non- o es (FNF) bina y
classi ica ion was pe o med using S2 p oduc and Random Fo es classi ie (Figu e
12).
42
Figu e 12. Flow cha o o es /non- o es classi ica ion s eps
This FNF classi ica ion wi h a new da a was necessa y due o he li e a u e indica ing
he scales o illegal elling (Chap e 1.3) and he Global Fo es Wa ch online
pla o m (Global Fo es Wa ch), a i ming ha he e a e signi ican dis u bance spo s
in he o es s and he e is a need o upda ing he Hansen map a e 17 yea s. On he
o he hand, a emp s o adjus Hansen Global map wi h simple h eshold o o es
non- o es delinea ion ailed o հis s udy a ea.
The aining and es ing da ase s we e gene a ed and a bina y classi ica ion was
ca ied ou in GEE cloud en i onmen using Ja aSc ip API and was pe o med in
wo s eps. Fi s , o es canopy co e h eshold o 30% was se up using Hansen global
o es co e map, as each pixel on his map ep esen s he canopy co e pe cen age.
This way we de ined o es and non- o es a eas o he yea o 2000. Tha was
pa icula ly done o aining da ase gene a ion. Fo ha pu pose, “s a i iedSample”
ool was used o gene a e andomly dis ibu ed poin s wi hin a g id wi h a scale o
30m. 200 hund ed poin s we e gene a ed his way: 100 poin s wi hin he o es a ea
and 100 poin s wi hin he non- o es a ea.
Valida ion con usion ma ix
P edic ion
n =200
Posi i e
Nega i e
To al
Ac ual
Posi i e
TP = 97
FP = 3
100
Nega i e
FN = 4
TN = 96
100
To al
101
99
193
Table 7. Valida ion con usion ma ix o o es non- o es classi ica ion
43
In he second s ep Random Fo es classi ie wi h 300 ees was ained using he S2
da a om 2017 and he aining da ase . FNF classi ica ion was d i en and he
allowing accu acy pa ame e s we e assessed again in GEE en i onmen (Tables 7 ).
The RF pe o mance on bina y classi ica ion shows high sensi i i y: 0.96 (p opo ion
o ac ual posi i es ha a e co ec ly iden i ied as such) and speci ici y: 0.97
(p opo ion o ac ual nega i es ha a e co ec ly iden i ied as such) using S2 da a.
Thus, he o e all accu acy o classi ica ion is 0.965 and we use his classi ica ion
esul as a base map o ou esea ch o delinea e only o es ed a eas. Kappa s a is ics
o he classi ica ion was 0.93. As he esul , 86% (25184.4 ha) wi hin he s udy a ea
was classi ied as o es and 14% (4149.6 ha) was classi ied as non- o es (Figu e 13).
Majo i y o ing il e was applied on he ou pu as e map once.
Figu e 13. Map o FNF bina y classi ica ion. Non- o es ed si e examples on he snapsho s on
he igh (g een line shows he classi ied o es bo de , ed line shows he o es en e p ise
bo de ).

44
5.2 Resul s o Reg ession analysis
The eg ession analysis was conduc ed using AGB da a (adjus ed by scaling ac o o
0.09 ha MMU) as a dependen a iable and he es o he inpu s as independen
a iables (p edic o s). The sequence o he s eps a e ollowing he gene al low cha
in he sec ion o me hodology (Chap e 4.1). Di e en combina ion o p edic o
a iables we e calcula ed and he eg ession model has been e alua ed o
mul icollinea i y in p edic o s (VIF es ), no mali y (Shapi o-Wilk no mali y es ) and
au oco ela ion (co elog am) in esiduals o each ime.
Reg ession analysis we e ca ied ou wi h R p og amming language and “Akaike
c i e ion” unc ion, which s ands o mul ilinea s epwise eg ession. Fo he
eg ession analysis o al o 60 inpu a iables we e used (Table 8).
Sou ce
Numbe o
a iables
Explana ion Va iable
To al
Sen inel 1
12
VV mon hly s ack - 2017
32
12
VH mon hly s ack - 2017
4
GLCM
VV10 (Co , En , Va , Sa g)
4
VH12 (Co , En , Va , Sa g)
Sen inel 2
3
NDVI, NDMI, RVI
3
Alos Palsa
2
HH, HV - 2017
10
4
GLCM
HH (Co , En , Va , Sa g)
4
HV (Co , En , Va , Sa g)
Ancilla y da a
Aspec
8
aspec ca ego ies
15
Slope
1
slope alues
Fo es ype
6
dominan ee ype
To al amoun o a iables
60
Table 8. Inpu a iables o eg ession analysis
Ini ially, only 24 obse a ions om S1 ime se ies (VV and VH) we e used o
eg ession analysis in o de o selec he bes scenes om S1 wi h highe impo ance
in AGB p edic ion. This was done o wo main pu poses: Fi s , o e alua e he
p e e ence o using S1 dense ime se ies ins ead o choosing one scene o o es AGB
calcula ions and second, o dec ease he numbe o inpu a iables o GLCM ex u e
analysis o he u he use. This was pa icula ly impo an o keeping he numbe o
inpu a iables as less as possible, as he aining da ase ( ield da a) was no la ge.
45
F om he pe spec i e o he phy oclima ic seasonali y in he o es in ou s udy a ea,
he co ela ion be ween ield AGB and Sen inel 1 C-band s ack ime se ies we e
explo ed i s using Pea son’s co ela ion coe icien (Figu e 14). This shows he
co ela ion o be almos always highe o VH c oss-pola iza ion compa ed wi h VV
co-pola iza ion. Ne e heless, o bo h pola iza ion modes i is lowe han 0.5, and
ge s o i s maximum o he s ack a e ages o Oc obe and Decembe (bo h o VH
backsca e ). The assump ion is ha C-band backsca e ing be e ep esen s he o es
biomass du ing he lea es-o season, and when he soil is no ozen.
Figu e 14. Pea son’s co ela ion coe icien calcula ed be ween Sen inel 1 s ack ime se ies
and AGB om he 79 plo s and o each o he ime se ies.
The eg ession analysis we e epea ed using he o es ype in o ma ion as a
ca ego ical a iable. The esul s showed ha among he S1 ime se ies only he s acks
o VH pola iza ion om Oc obe and Decembe a e passing he minimum limi o p-
alue < 0.05. Also, bo h HH and HV pola iza ion mosaics om Palsa we e chosen by
eg ession model o be impo an p edic o s o AGB. The o es ype in o ma ion was
ne e selec ed as impo an , he e o e was d opped om la e analysis. Hence, we
deduce ha SAR backsca e ing is no sensi i e o he ee ype.
Those wo s acks o m S1 and HH, HV Palsa mosaics we e chosen o GLCM
ex u e measu es and o u he eg ession analysis. The GLCM analysis we e done
as i is desc ibed in he Chap e 4.8. Fu he , du ing his eg ession analysis, 2
samples wi h ex eme biomass alues o he s udy a e (680 ℎ𝑎−1 and 500 ℎ𝑎−1),
as well as 1 sample wi h unusual ex eme pixel alues on Palsa scenes we e excluded
om he u he analysis, as hey we e causing majo unce ain y in he p edic ion. As
ollows, 79 ield sampling poin s we e conside ed o he inal eg ession analysis.
46
5.2.1 Model Compa ison and P edic o E alua ion
Since he objec i e o his s udy is o e alua e he di e en o igin da a o o es AGB
p edic ion, 4 eg ession models we e p oposed wi h di e en p edic o combina ions
(Table 10). In his sec ion we compa ed p edic ion models wi h a ocus o de ec ing
ela i e di e ence in he p edic ion accu acy. Fo his pu pose, mul iple linea
eg ession analysis was ca ied ou wi h di e en p edic o usion aiming o
de e mine he bes models using di e en ype o da a in di e en combina ions.
Acco dingly, 4 models wi h he ollowing combina ions we e de eloped: Model 1 –
only SAR da a, Model 2 – SAR+op ical da a, Model 3 – SAR+ancilla y da a, Model
4 – SAR+op ical+ancilla y da a.
As many o he pa ame e s a e gene a ed om a ying decomposi ions o he same
da a o he same sou ce, se e al o hese me ics a e expec ed o be highly co ela ed
(Appendix B). High co ela ion amongs he p edic o s causes o model o e i ing
and biased R² alue. The e o e, wi h he h eshold o a iance in la ion ac o
maximum o 5 was se up. Adjus ed R² is calcula ed. Fu he , Lea e-One-Ou c oss-
alida ion is ca ied ou and R² as well as RMSE a e calcula ed. As he RMSE
ep esen s he ela i e e o o 0,09 ha a ea (MMU), la e he absolu e RMSE was
calcula ed o he hec a e and is p o ided in he Table 9.
Linea Model 1 consis ing o S1 C-band and Palsa L-band SAR da a and he GLCM
ex u e analysis o hose da a yielded low p edic ion esul s: Adjus ed R² equal o
0.46, LOO alida ion es ga e R² equal o 0.38 and RMSE equal o 70 pe hec a e.
The bes p edic o a iables we e Palsa HH and HV pola iza ion o m he ini ial
image y, and he es o he a iables we e di e en ex u e measu es: Sum o
A e age (SAVG) GLCM o S1 s ack o Oc obe , SAVG o Palsa HV. I is wo h o
men ion, ha none o he Sen inel ini ial images appea ed in any o he models as
impo an p edic o s.
Model 2 combined all he inpu s om Model 1 and he 3 ege a ion indices (NDVI,
NDMI, RVI) om S2. De e mina ion coe icien o LOO R² dec eases insigni ican ly
o 3.3 and he RMSE inc eases o 72.8 ha¯¹. The s epwise p ocess disca ded many o
he a iables ha we e impo an o he Model 1 and educes he dimension o he
p edic o s. Palsa HH and HV s ay as he mos impo an p edic o s oge he wi h
En opy measu e o Oc obe s ack om S1 da a. NDVI a iable was be e sui ed o
47
AGB p edic ion amongs he ege a ion indices wi h he less signi icance o he
model.
Model
(sou ces)
P edic o s
VIF (<5)
p- alue<0.05
(signi icance)
Adj.
R²
LOO
R²
ela i e
RMSE
( /0,09 ha)
RMSE
( /ha)
Model 1
Palsa HH
2.26764
1.68E-05
***
0.46
0.38
6.3
70
Palsa HV
2.211967
1.18E-05
***
S1 VH 10 sa g
1.913548
6.34E-05
***
SAR
S1 VH 12 co
1.744905
0.03294
*
S1 VH 10 en
2.79781
0.03224
*
Palsa HH en
1.703185
0.00836
**
Palsa HV sa g
2.345768
2.16E-05
***
Palsa HH co
1.483653
0.04045
*
Model 2
Palsa HH
2.703908
3.24E-06
***
0.41
0.33
65.6
72.8
Palsa HV
2.21506
0.00045
***
SAR+
Op ical
NDVI
1.104523
0.022439
*
S1 VH 10 en
1.134397
0.000457
***
Palsa HH sa g
2.323204
0.020287
*
Model 3
Aspec (4,6,7)
1.21354
5.00E-07
***
0.65
0.6
50
57.7
Slope
1.283596
7.15E-07
***
SAR+
Ancilla y
Palsa HV
2.930844
9.66E-06
***
Palsa HH
2.438455
0.002232
***
Palsa HV sa g
1.721521
3.65E-10
***
S1 VH 10 en
2.126821
9.23E-05
***
Model 4
Aspec (4,6,7)
1.216558
0.000842
***
0.68
0.62
4.9
56.6
Slope
1.290634
1.90E-07
***
SAR+
Op ical+
Ancilla y
Palsa HV
2.933416
4.20E-06
***
Palsa HH
2.459693
0.000854
***
Palsa HV sa g
1.122131
0.000503
***
S1 VH 10 en
1.736985
4.22E-10
***
NDVI
1.122131
0.019642
*
Signi icance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05
Table 9. E alua ion o s a is ics o eg ession analysis o each model
The p edic ion accu acy changes eno mously when he o es ancilla y da a (namely,
aspec , slope and he o es ype) we e added o he p e ious da a. Model 3 in able 9
shows he o e iew o he s a is ics o eg ession analysis wi h all he inpu s wi hou
da a om op ical senso , and he Model 4 combines also he op ical da a. The LOO-
alida ion R² o he Model 3 inc eases up o 0.6 and RMSE dec eases o 57.7 ha¯¹.
Fo he Model 4 he p edic ion imp o es e y sligh ly wi h he implemen a ion o
54
combined in he same model o eg ession analysis. Simila ly, Huang uses 5 di e en
measu es o m he same sou ce o aining he bes model (Huang e al., 2018).
The co ela ion o op ical da a o o es AGB was e y weak and only NDVI amongs
he es ed ege a ion indices was selec ed by SWR wi h low impo ance. The
insigni ican con ibu ion o op ical da a in he AGB p edic ion can be explained wi h
he ac , ha he a e age biomass o he o es s o his s udy a ea was measu ed 148
ha¯¹ while he obse ed sa u a ion le el o op ical da a in AGB p edic ion in he
li e a u e al e s a ound 50 – 70 ha¯¹ biomass (Zhang e al., 2018). Al hough some
o he s udies depic highe co ela ion be ween NDMI index and he biomass
(li e a u e e iew by Joshi e al., 2016), his analysis showed no simila esul s. I is
possible ha o NDMI calcula ion mo e humid season is p e e ed, while he e was
no cloudless image o he s udy a ea om Ma ch and June.
The use o ancilla y da a oge he wi h all he emo ely sensed da a b ough a g ea
impac on he eg ession model accu acy imp o ing R² up o 0.62. This is he bes
esul achie ed wi hin his esea ch and is e y impo an , as one o he hypo heses
and he main inno a ion o e ed by his pape was he assump ion ha he gain o bias
o he SAR backsca e ing alue in moun ainous a eas canno be co ec ed by he
adiome ic e ain co ec ion me hods, i migh be mi iga ed by he p edic ion model
when he o es y ancilla y da a on he sampling si e is gi en. Amongs he ancilla y
da a he slope and aspec in o ma ion we e always in a highe co ela ion wi h he
biomass while combined wi h SAR da a. Fo es ype in o ma ion did no imp o e he
biomass p edic ion in con as o o he s udies (Lau in e al., 2018), which ound i an
impo an componen o he p edic ion model. E en i he implemen a ion o
ancilla y da a g ea ly imp o es he p edic ion model pe o mance as well as he isual
obse a ion o he p oduc maps depic imp o emen in biomass dis ibu ion, mo e
ield da a is needed o check he eliabili y o hose models o implemen ing he ada
emo ely sensed da a in ca bon s ock moni o ing ac i i ies in moun ainous o es s.
Backwa d s epwise mul iple linea eg ession echnique conside ably au oma ed he
bes a iable selec ion as well as isola ed he mul icollinea i y e ec . Fo ins ance,
VIF es applied a e SWR es did no de ec mul icollinea i y in he a iables.
Ne e heless, no all he s udies use he same me hods and measu es o model
diagnos ics, which makes he esul e alua ion and compa ison qui e p oblema ic. Fo

55
ins ance, many pape s use only adjus ed coe icien o de e mina ion (R²) o
eg ession models o he e alua ion (Chen e al., 2018; Ecke , 2012; Huang e al.,
2018), which is a esul s wi hou model c oss- alida ion, so we canno di ec ly
compa e ou esul wi h he ones om hose wo ks.
The sa u a ion le el o SAR backsca e ing as a gene al limi a ion, was ha d o de ine
as he backsca e ing alue is su e ing by he opog aphy and canno se e o such
calib a ion. Howe e , om he model 1 which uses SAR da a only, we can obse e
le el o 150-160 ha¯¹, a e which he p edic ion line indica es mos ly
unde p edic ion. This esul is simila o he one om Be ninge , 2018, e en hough
he claims sa u a ion le el o L-band wi h ex u e measu es a e possible o imp o e up
o 250 ha¯¹. The sa u a ion le el esea ch can be explo ed in a mo e accu a e way
once mo e sampling da a is a ailable, which can make possible o sepa a e only la
a eas no a ec ed by shadowing o o esho ening and do analysis wi hou applying
opog aphic in o ma ion, e en hough diag am (Fig. 18) shows highe unce ain y o
he low biomass p edic ion. This is likely because he measu ed ees a e only he
ones la ge han 8 cm DBHOB, he e o e smalle ees a ailable in he sampling plo s
can ha e signi ican impac on he Sen inel 1 C-band (3.75–7.5 cm wa eleng h)
backsca e ing causing o AGB o e es ima ion which makes his ange (<100 ha¯¹)
he mos in luen ial. This end o o e es ima ion o samples wi h smalle biomass
alue is obse ed in all he simila esea ches (Be engue e al., 2018; Be ninge , e
al., 2018; Huang e al., 2018; Lau in e al., 2018).
E en i e y ew o he e iewed pape s pe o m new delinea ion o o es bounda y,
he impo ance o FNF classi ica ion pe o med wi hin he ames o his pape wo k is
p o en wi h wo main poin s:
• FNF upda e shows signi ican (13%) non- o es ed a eas wi hin he o es
en e p ise, which should be aken in o conside a ion while es ima ing he ca bon
s ock o mo e ealis ic ca bon budge e alua ion.
• As he Hansen map is a global model, he FNF delinea ion based on ha map
was no adjus able o he s udy a ea. The e o e, he ield sampling plo s design
gene a ed based on he Hansen map can mislead he e iciency o he ield wo k.
As he esul showed, 26 sampling plo s ou o 115 we e loca ed ou side o he
newly upda ed o es bounda ies.
56
Thus, we conclude ha he biomass da a calcula ed by s a is ical me hods using he
allome ic equa ions can be signi ican ly a ec ed by he easons men ioned abo e. As
his pape was aiming o imi a e he o es ca bon moni o ing ac i i ies, and
pa icula ly, o es AGB measu emen s, we a e planning o compa e ou esul s wi h
he one om adi ional NFI once i becomes a ailable.
The inal biomass map has 30 m spa ial esolu ion, which is he i s map ha ing
synop ic iew on he spa ial dis ibu ion o he biomass in NE o es o A menia, ye
wi h a compa able accu acy. The de eloped model is applicable o simila o es s
whe e in si u da a is a ailable. Ano he e y impo an achie emen is ha his ine
esolu ion map con aining he p opo ional dis ibu ion o biomass can lead o mo e
co ec ca bon es ima ion when used as sou ce o ca bon- ela ed models. The e o e
his s udy can acili a e he GEF/UNDP-REDD+ ac i i ies in NE o es s in A menia
o achie e hei objec i es in be e accu acy, as well as help o make o es ca bon
s ock measu emen s and moni o ing cos -e ec i e. In o de o calcula e he ca bon
s ock o he s udy a ea, he modeled o es s and biomass was educed by a ac o o
0.47 (UNDP-GEF 00091048, 2015). Fo he model 1 measu ed ca bon s ock in he
s udy a ea was 4.1 G , and o he model 4 i was 3.5 G (Fig. 16). This da a is ye o
be compa ed wi h NFI esul s once i is a ailable.
5.4 Limi a ions and Recommenda ions o Fu u e Resea ch
One o he majo limi a ions o his s udy was he lack o la a eas ha could be
analysed sepa a ely om hose a eas wi h complex opog aphy in o de o be able o
calib a e he ue alue o SAR backsca e ing o he o es s in he s udy a ea. The
nex obs acle was he lack o sampling poin s. As seen in numbe o s udies, he
ou comes o such esea ch a e s ongly dependan on he sampling da a quan i y and
quali y․ As o he u u e, i is planned o collec o al o 315 samples o his s udy
a ea wi hin he GEF-UNDP p ojec . This amoun o da a i sel is signi ican ly bigge
o model aining and can make i possible o de elop mo e sus ainable p edic ion
models and ca y ou esea ch only on he ela i ely la a eas.
This s udy, on he o he hand, made i ob ious ha complex opog aphy pu s high
unce ain y on he s udy in he way, ha o es s uc u e and dis ibu ion changes
quickly on e y sho dis ances, which is no desi able o SAR da a. This is because i
57
is no e y clea i he pixel on he SAR scene is i ing wi h he ield sampling plo
e y well. Thus, he backsca e ing alue can be p esen ing no he eal biomass
in o ma ion o he sampling plo .
GLCM ex u e measu es can be s onge o weake co ela ed o o es biomass
depending on he selec ed window size o he ope a ion (Ecke , 2012). As o his
esea ch only one window size was adop ed (4x4 ke nel size), hus, we assume
GLCM analysis o be no exhaus i e and we conside po en ial possibili y o imp o e
p edic ion accu acy by adop ing di e en window sizes o ex u e measu es. On he
o he hand, ex u e analysis should be es ed on he aw L-band ada da a in o de o
keep he ue speckle dis ibu ion and backsca e ing alue s a is ics.
When ield da a becomes a ailable, his me hodology o biomass p edic ion can be
applied pe speci ic g oup o biomasses. We assume o ha e be e co ela ion
be ween SAR backsca e ing alue and he biomass o unde 150-160 ha¯¹ whe e he
sa u a ion le el is obse ed.
As he ele ance o he SAR da a and i s combina ion wi h o he sou ce da a a e well
explo ed and he bes inpu a iables o o es AGB p edic ion a e desc ibed, we
p opose applica ion o non-pa ame ic eg esso s o gene a ing mo e accu a e
biomass map, as he e iew o he la es s udies e eal hei p io i y o e pa ame ic
eg esso s (Chap e 2.3).
58
CONCLUSION
O e all, his s udy shows he use ulness o SAR backsca e ing da a on o es biomass
mapping, which allows o ha e spa ially explici AGB dis ibu ion wi hin he o es
si e. In his esea ch, we p opose a me hod based on he combina ion o mul isou ce
emo ely sensed da a, image ex u e measu es and o es ancilla y in o ma ion o
o es AGB es ima ion on he moun ainous a eas. Fo his pu pose, only eely
a ailable da a sou ces we e used. The esul s in e he impo ance o mul i empo al C-
band da a o a oid om backsca e ing sa u a ion om mois u e, also o
mul i empo al speckle il e ing. Howe e , he e is a need o in eg a ion C-band wi h
L-band da a o educe he unce ain y in he p edic ion. This s udy also showed ha
he GLCM ex u e cha ac e is ics o SAR da a we e he mos ele an p edic o s o
explaining he obse ed a iabili y o AGB in he s udy a ea. The ela ionships
be ween SAR da a and s and cha ac e is ics, howe e , can be nega i ely in luenced in
a eas o high opog aphic a ia ion and make he AGB modelling uly p oblema ic.
The aspec and slope ancilla y da a was able o mi iga e he opog aphy e ec on SAR
da a and ensu ed imp o ed eg ession analysis wi h accu acy o R² = 0.62 and RMSE
= 56.6 ha¯¹.
The ou pu o his esea ch will be p o ided o GEF/UNDP p ojec in NE o es s o
Republic o A menia and i can ha e an inpu in achie ing hei objec i es, as well as
suppo ing o es moni o ing and ca bon s ock egula ions. The achie ed esul s need
o be alida ed wi h mo e sophis ica ed g ound- u h da a in o de o be implemen ed
as cos -e ec i e su oga e o NFI ac i i ies. This AGB es ima ion app oach is
adap able and allows modeling o biomass in o he moun ainous o es s wi h simila
condi ions.
Fu he , i is wo h no ing ha wo king wi h SAR da a demands p io knowledge and
deep unde s anding o p e-p ocessing s eps and he physics behind hem. I should
also be said, ha his s udy was ca ied ou wi h eely a ailable emo ely sensed da a,
and mo e p e e ed comme cial da a a ailabili y can signi ican ly inc ease he model
pe o mance accu acy.
59
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