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ECOLOGICAL RISK ASSESSMENT BASED ON LAND COVER
CHANGE: A CASE OF ZANZIBAR-TANZANIA, 2003-2027
Hassan Mohammed Oma
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ECOLOGICAL RISK ASSESSMENT BASED ON
LAND COVER CHANGE:
A CASE OF ZANZIBAR-TANZANIA, 2003-2027
Disse a ion supe ised by
Ped o da Cos a B i o Cab al, PHD
P o esso , No a In o ma ion Managemen School
Uni e si y o No a Lisbon, Po ugal
Hanna Meye , PhD
P o esso , Ins i u e o Geoin o ma ics
Uni e si y o Muns e , Ge many
Ca los G anell Canu , PhD
P o esso , Ins i u e o New Imaging Technologies
Uni e si y o Jaume I Cas ellon, Spain
Feb ua y, 2020
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DECLARATION OF ORIGINALITY
I decla e ha he wo k desc ibed in his documen is my own and no om
someone else. All he assis ance I ha e ecei ed om o he people is duly
acknowledged and all he sou ces (published o no published) a e
e e enced.
This wo k has no been p e iously e alua ed o submi ed o NOVA
In o ma ion Managemen School o elsewhe e.
Lisbon, 17 h Feb ua y 2020
Hassan M. Oma
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ACKNOWLEDGEMENTS
Fi s ly, I would like o acknowledge ha , i is only by he G ace o Allah he Almigh y ha I ha e
made his a . The gi o li e, good heal h and sound mind ha has been g an ed has enable me
o accomplish his wo k.
I would like o hank all conso ium o E asmus Mundus Mas e 's p og am in Geospa ial
Technologies o hei inancial and ma e ial suppo du ing all pe iod o my s udy.
Special hanks a e due o my supe iso s P o . Ped o Cab al, P o . Ca los G anell Canu , and
P o . Hanna Meye , who p o ided imme se suppo o ensu e my wo k con o ms o s anda ds.
I would like o hank P o . D . Ma co Painho o con inuous hesis ollow up and encou agemen
and ensu ing ha , his wo k is comple e a he igh ime and in he igh s anda ds.
Finally, bu no leas , I wish o hank my daugh e , Haj a Hassan, my lo ely wi e Asha Sei , and
my whole amily o hei pa ience, inspi a ion, and unde s anding du ing he en i e pe iod o my
s udy. My iends and all ela i es o hei suppo , encou agemen and all hei con ibu ions o
make his s udy success.
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ECOLOGICAL RISK ASSESSMENT BASED ON
LAND COVER CHANGE:
A CASE OF ZANZIBAR-TANZANIA, 2003-2027
ABSTRACT
Land use unde imp ope land managemen is a majo challenge in sub-Saha an A ica, and his
has d as ically a ec ed ecological secu i y. Add essing en i onmen al impac s ela ed o his
majo challenge equi es as e and mo e e icien planning s a egies ha a e based on measu ed
in o ma ion on land-use pa e ns. This s udy was employed o access he ecological isk index o
Zanziba using land co e change. We i s employed Random Fo es classi ie o classi y h ee
Landsa images o Zanziba o he yea 2003, 2009 and 2018. And hen he land change modele
was employed o simula e he land co e o Zanziba Ci y up o 2027 om land-use maps o
2009 and 2018 unde business-as-usual and o he wo al e na i e scena ios (conse a ion and
ex eme scena io). Nex , he ecological isk index o Zanziba o each land co e was assessed
based on he heo ies o landscape ecology and ecological isk model. The esul s show ha he
buil -up a eas and a mland o Zanziba island ha e been inc eased cons an ly, while he na u al
g assland and o es co e we e sh inking. The o es , ag icul u al and g assland ha e been highly
agmen ed in o se e al small pa ches ela i e o he dec ease in hei pa ch a eas. On he o he
hand, he ecological isk index o Zanziba island has appea ed o inc ease a a cons an a e and
i he cu en end con inues his index will inc ease by up o 8.9% in 2027. In compa ing he
h ee u u e scena ios he esul s show ha he ERI o he conse a ion scena io will inc ease by
only 4.6% which is a leas 1.6% less compa ed o 6.2% o he business as usual, while he
ex eme scena io will p o ide a high inc ease o ERI o up o 8.9%. This s udy will help
au ho i ies o unde s and ecological p ocesses and land use dynamics o a ious land co e
classes, along wi h p e en ing unmanaged g ow h and haphaza d de elopmen o in o mal
housing and in as uc u e.

KEYWORDS
Ecological isk assessmen
Ecosys ems se ices
Land co e changes modelling
Landscape ecological s a is ics (LECOS)
Zanziba
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ACRONYMS
ANN A i icial Neu al Ne wo k
BAU Business as Usual
COLA Commission o Land
CONSV Conse a ion Scena io
DEM Digi al Ele a ion Model
ERI Ecological Risk Index
EXTRM Ex eme Scena io
FAO Food and Ag icul u e O ganiza ion
GDP G oss Domes ic P oduc
LCM Land Change Modele
LECOS Landscape ecological s a is ics
LUCC Land Use Land Co e Change
LULC Land Use Land Co e
MCE Mul i C i e ia E alua ion
MLP Mul i-laye Pe cep on
NEMC Na ional En i onmen Managemen Council
NBS Na ional Bu eau o S a is ics
RGZ Re olu ion Go e nmen o Zanziba
USGS Uni ed S a e Geological Su ey
UTM Uni e sal T ans e se Me ca o
WB Wo ld Bank
WWF Wo ld Wide Fund o Na u e
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INDEX OF THE TEXT
ACKNOWLEDGEMENTS ......................................................................................................... iii
ABSTRACT ................................................................................................................................. i
KEYWORDS ................................................................................................................................
ACRONYMS ............................................................................................................................... i
INDEX OF TABLES ................................................................................................................... ix
INDEX OF FIGURES ................................................................................................................... x
1. INTRODUCTION ................................................................................................................. 1
2. LITERATURE REVIEW ...................................................................................................... 4
2.1. Ecosys em se ices and hei bene i s ............................................................................ 4
2.2. The Cu en S a us o Ecosys em Se ices ..................................................................... 5
2.3. The Role o Remo e Sensing on Land use and Land Co e Change ............................. 6
2.4. Fu u e P o isions o Ecological Moni o ing h ough Land Change Modele ................ 8
3. STUDY AREA ...................................................................................................................... 8
4. DATA AND METHODS .................................................................................................... 11
4.1. Desc ip ion o he da a .................................................................................................. 11
4.1.1. Ex e nal a iables .................................................................................................. 12
4.2. Me hods ........................................................................................................................ 14
4.2.1. Image classi ica ion ............................................................................................... 14
4.2.2. Accu acy assessmen ............................................................................................. 15
4.3. Desc ip ions o he chosen scena ios ............................................................................ 16
4.3.1. Business as usual (2027BAU) ............................................................................... 16
4.3.2. Conse a ion scena io (2027CONSV) .................................................................. 16
4.3.3. Ex eme scena io (2027EXTM) ............................................................................ 16
4.4. P epa a ion o ex e nal (independen ) a iables ....................................................... 17
4.4.1. Fac o s ............................................................................................................... 17
4.4.2. Cons ain s ......................................................................................................... 19
4.5. Modelling Fu u e Scena ios wi h LCM ........................................................................ 20
4.5.1. Change Analysis .................................................................................................... 20
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4.5.2. T ansi ion po en ial ............................................................................................... 21
4.5.3. Change p edic ion ................................................................................................. 23
4.5.4. Model alida ion ................................................................................................... 24
4.6. Calcula ion o Ecological isk indices .......................................................................... 25
Ecological Risk index (ERI) ................................................................................................ 25
5. RESULTS ............................................................................................................................ 27
5.1. LULC changes 2003 - 2018 .............................................................................................. 27
5.2. Accu acy assessmen .................................................................................................... 28
5.3. LULC changes 2003 - 2018.......................................................................................... 30
5.4. Land Co e Change o u u e Scena ios ..................................................................... 31
5.4.1. Model alida ion esul s ........................................................................................ 31
5.4.2. Simula ed Maps o 2027 scena ios ...................................................................... 33
5.5. Ecological Risk Assessmen ......................................................................................... 35
5.5.1. Ecological Risk Indices o he yea 2003-2018 ................................................... 35
5.5.2. Ecological Risk Indices o 2027 scena ios .......................................................... 36
5.5.3. Dis ibu ion o ERI pe dis ic .............................................................................. 38
6. DISCUSSION ...................................................................................................................... 40
7. CONCLUSIONS AND RECOMMENDATIONS .............................................................. 42
Bibliog aphic Re e ences ............................................................................................................ 43
Annexes ................................................................................................................................... 49
Change analysis ....................................................................................................................... 49
P obabili y Ma ix.................................................................................................................... 49
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Al e na i ely, he coas al ecosys em like mang o e o es , seag ass beds, and co al ee s, p o ide
s ong suppo o hund eds o species o bo h ha d and so co als and ish as well as sea u les,
c us aceans, and ma ine mammals (A S aeh , 2018).
2.2. The Cu en S a us o Ecosys em Se ices
As (MA, 2005) sugges ed, ecosys em se ice bene i “is he bene i ha humans de i e om
ecosys ems”. Bu in e e se, human ac i i ies ha e been coined as he main agen o a majo
loses o des uc ion o global na u al ecosys ems (Chi, Zhang, Xie, & Wang, 2019). They cause
a majo p essu e in ecosys em species, by ei he imp ope use o na u al esou ces, o due o
inc easing in demands o ecosys em se ices in a pa icula socie y.
In ha sense, he ph ase like “ecosys em se ice loss” is mo e connec ed wi h he cos s ha mus
be paid o elie e he impac o ecosys em des uc ion on human p oduc ion and ea nings (Haden
e al., 2019). Acco ding o Millennium Ecosys em Assessmen epo o 2005, he e is a massi e
decline and h ea ening o ecosys em heal h which a e comp ises wi h he p o ision o ecosys em
se ices upon which indi iduals, communi ies and en i e cul u es depend (O e peck e al., 2013;
Rowland, 2019). This 2005 MA epo had p o ided an es ima ion o mo e han 60% o he
ecosys em se ices which ha e been assessed ac oss he globe a e al eady being deg aded o a e
used unsus ainably wi h he po en ial o become mo e deg aded in he i s hal o his cen u y.
The d i e s like he apid inc ease in human popula ion, mining ac i i ies, subsequen g ow h in
u baniza ion and in o mal se lemen s, oge he wi h o he h ea s, place eno mous s ess on he
ecosys em hea h and hus impac s hei u u e sus ainabili y (Engel, 2012). Zanziba and Tanzania
o example, ha e been epo ed o expe ience an ex ensi e decline in he s a e o i s en i onmen
h ough loss o na u al habi a s and biodi e si y o mo e han h ee decades (A. I. Ali, 2016).
Much o hese changes a e caused by he changes in LULC which a e in luenced by inc ease in
human popula ion, economic ac i i ies and poo managemen o u ban de elopmen s (NEMC,
2006).
Al e na i ely, he 2018 WWF’s li ing Plane epo , p o ide an es ima ion o up o 50% o he
global eshwa e and we land habi a s which ha e al eady been los in he pas 30 yea s since
1970 due o a ious human ac i i ies and clima ic a ia ion (Wo ld Wide Fund Fo Na u e
(WWF), 2018). These eshwa e and we land habi a s a e mo e essen ial in main aining
ecological p ocesses, as well as p o iding an app op ia e wa e lows wi hin an en i e wa e
ca chmen (Paul, 2013). Losing o des uc ing hese habi a s, would a ec he en i onmen al
lows including educing he olume, iming and e en he quali y o wa e lows which a e e y

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essen ial o he su i al o downs eam habi a s and species wi hin hose a eas (Paul, 2013).
In case o o es ecosys ems, which appea o be he mos undamen al sou ces o se ices and
global biodi e si y, hei abili y o main ain hei u u e sus ainabili y is po en ially h ea ened.
The ecen epo o Food and Ag icul u e O ganiza ion, p o ide an es ima ion o abou i e
million hec a e pe yea o global o es which ha e al eady been los be ween 2005 and 2015
(FAO, 2018). This es ima ion is e y wo se o he u u e de elopmen o biodi e si y and
ecosys em sus ainabili y, as well as h ea co esponding o he eedback on clima ic sys em and
ood secu i y (Ga zuglia, 2018). Wi hou p ope plan and managemen , he s a us o ecosys em
se ices will con inue o ace unp eceden ed s ess and p essu e, ha could esul in hei
deg ada ion and con e sion, he eby a ec ing hei sus ainabili y in bo h cu en and u u e
gene a ions.
2.3. The Role o Remo e Sensing on Land use and Land Co e Change
Remo e sensing is he science and o some ex en , he a o acqui ing in o ma ion abou he
Ea h’s su ace wi hou ac ually being in con ac wi h i (Paul, 2013). In combina ion wi h GIS,
Remo e Sensing (RS) p o ide a be e oppo uni y in s udying and analyzing he a ious o m o
he ea h's in o ma ion (Paul, 2013). I p o ides ooms o iew he ea h phenomenon om he
space, which enables he comp ehension o he cumula i e in luence o human ac i i ies on he
ea h's su ace's na u al s a e.
One o he majo applica ions o emo e sensing echniques is he classi ica ion o emo ely
sensed da a o mul i- empo al images. These echniques ha e been conside ed as an ul ima e oo
o a ious analysis and applica ions in he ield o emo e sensing (Mülle o á, Pe gl, & Pyšek,
2013). The undamen al objec i e o emo e sensing image classi ica ion is o di ide he emo ely
sensed image in o a numbe o classes (pixel g oup) in o de o e ec i ely examine and asses
wha changes ha e been occu ed in each o hose g oups (J. Xu, Feng, Zhao, Sun, & Zhu, 2019).
Howe e , he classi ica ion p ocess o emo ely sensed images is some ime conside ed o be he
mos complex ask in luenced by a ious ac o s such as a ailabili y o high-quali y images,
ancilla y da a, p ope classi ica ion p ocedu e, and analy ical abili y o he esea che (Gao & Xu,
2015) . In mos cases, i appea s o be much ha de o iden i y which is he bes classi ie o be
used in a pa icula s udy due o ei he he lack o p ope guidelines in selec ing he algo i hm o
e en he lack o a ailabili y o app op ia e classi ica ion algo i hms o a pa icula band (Gao &
Xu, 2015). In ha sense, many esea che s ha e made a g ea e o in p oposing he mos
e ec i e classi ica ion me hods o imp o e classi ica ion accu acy and as well as hei esul s.
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The mos popula emo e sensing classi ica ion me hods p oposed in di e en s udies a e
supe ised, and unsupe ised classi ica ion. In supe ised classi ica ion, he image pixels a e
classi ied by selec ing ep esen a i e samples o each land co e class (T aining Si es o A eas)
and hen he classi ica ion algo i hm applies hese samples o he en i e image (Bha acha ya,
Ca , & Pal, 2016). This p ocess is done in h ee majo s eps including selec ing aining a eas,
gene a e he signa u e ile and classi y he image. In ano he case, in unsupe ised classi ica ion
he images a e classi ied only by g ouping he pixels in o clus e s based on hei p ope ies i s ,
and hen he classi ica ion o each clus e wi h land co e classes a e done. In gene al, his
classi ica ion me hod is conside ed as he mos basic echnique, since he e is no need o aining
samples o classi y he image. I is a simple way o classi y he image wi h only wo majo s eps
including gene a ing clus e s and assigning classes (Bha acha ya e al., 2016).
All o hese classi ica ion me hods can be u he g ouped in ei he s a is ical me hods, decision
ees, a i icial neu al ne wo ks, and o he a i icial in elligence algo i hms (Xie, Li, Xiao, &
Peng, 2016). S a is ical me hods such as maximum likelihood classi ie and Bayesian classi ie
wo ks in an assump ion ha he membe s o each class in each band ollow a no mal dis ibu ion
in he ea u e space and calcula es he p obabili y ha a gi en pixel belongs o a speci ic class. In
his pa icula me hod, he classi ica ion accu acy dec eases as he dimension o ea u e dec eases
(Xie e al., 2016).
The decision ee me hods like andom o es classi ie make no assump ions conce ning he
dis ibu ion o he inpu ea u es and he e o e hey a e obus and e ec i e me hods in managing
nonlinea ela ionships among he class membe (J. Xu e al., 2019). Howe e , he e ec i eness
o he decision ee me hods is highly in luenced by he size o he ea u e space and hus a e no
app op ia e me hods o da a wi h a high dimension o ea u e space (J. Xu e al., 2019).
In he case o a i icial neu al ne wo ks (ANN) me hods p o e a powe ul pe o mance wi h a
highe dimensional ea u e space compa ed o s a is ical classi ica ion me hods because hey a e
dis ibu ion- ee (J. Xu e al., 2019). Howe e , he mos di icul ness o using ANN models is i s
demands o equi es a signi ican amoun o aining da a and a conside able numbe o i e a i e
aining p ocedu es o ensu e ha he models a e ained success ully. Al e na i ely, he
compu a ion o ANN algo i hms can be ex ao dina ily complex (J. Xu e al., 2019).
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2.4. Fu u e P o isions o Ecological Moni o ing h ough Land Change Modele
Spa ial and dynamic changes in LULC o he ea h’s su ace ha e a majo in luence on he
des uc ion and u u e p o ision o ecosys em sus ainabili y o bo h landscape s uc u e and
agmen a ion o g een space (Tian, Jim, Tao, & Shi, 2011). These dynamic changes in LULC
can esul in an inc ease in he le el o land abandonmen , s a a ion o e en mig a ion ou o he
a ec ed egion (Y. Wang e al., 2018). In ha kind o si ua ion, unde s anding he dynamics and
u u e land co e ends has o be conside ed as a majo componen in app o al s a egies o bo h
he planning and implemen a ion o app op ia e ools in conse ing he deg aded land.
A numbe o compu a ion algo i hms o moni o ing and p edic ing u u e land co e changes
ha e been widely inc eased in ecen yea s. The models like Ma ko chain (MC) (Al-sha i &
P adhan, 2014), a i icial neu al ne wo k (Pijanowski, B own, Shelli o, & Manik, 2001), cellula
au oma a (Cla ke & Gaydos, 1998), cellula Au oma a-Ma ko model (A sanjani, Kainz, &
Mousi and, 2011), bina y logis ic eg ession (A sanjani, Helbich, Kainz, & Boloo ani, 2012),
and simila i y weigh ed ins ance-based machine lea ning algo i hm (Sange mano, Eas man, &
Zhu, 2010) a e among he common used models o he u u e p edic ion and simula ion o
changes in land co e .
Land Change Modele (LCM) o example, was ound o be one among he e ec i e modeling
ool which inco po a es CA-Ma ko chain based on a neu al ne wo k o p edic u u e land-use
change (Eas man, 2006). This CA-Ma ko chain models inco po a ed in LCM a e ai ly simple
and much powe ul in modeling he complex p ocess and changes in land use o planning
pu poses (Eas man, 2006). I models he u u e land co e changes by making an assump ion ha ,
he p obabili y o sys em being in a ce ain ime can only be de e mined i i s p e ious s a e is
known wi h he assump ion ha a es o change obse ed du ing he calib a ion pe iod (T1 o
T2), will emain he same du ing he simula ion pe iod (T2 o T3).
LCM does also p o ides he ools ha can simula e he u u e land-use changes and pa e ns and
allowing es ing o al e na i e planning scena ios. Simula ing u u e land co e change unde
wha called scena ios, can p o ide a be e unde s anding o a ious planning al e na i es,
s imula ing policy discussions be ween en i onmen al and de elopmen goals, and he e o e
helping he esponsible au ho i ies in designing he new policies and p ocedu es ha c ea e
incen i es o ecological conse a ion (McKenzie, E., Rosen hal, A., Be nha d , J., Gi e z, E.,
Ko acs, K., Olwe o, N. and To , 2012).
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3. STUDY AREA
This s udy was conduc ed in Zanziba (Figu e 3.1), he small coas al island o abou 2,461 km²
in o al, loca ed a 39° 05’ E o 39° 55’ E and 4° 45’S o 6° 30’S Wes e n Indian Ocean, in which
ou ism and ag icul u al ac i i ies a e he majo economic sec o s con ibu ing abou 28% and
40% o i s GDP espec i ely (WB, 2019).
Zanziba is a pa o he Uni ed Republic o Tanzania, and consis s o wo main islands which a e
Unguja and Pemba and wi h almos 50 o he small isle s (Mye s, 2010; RGZ, 2008). These wo
islands a e loca ed 40 km o he mainland coas o Eas A ica in he Indian Ocean (Mye s,
2010). The wo main islands a e 50 km apa and a e sepa a ed by he 700 me e deep Pemba
channel. Howe e , he cu en s udy uses he e m Zanziba e e ing o he island o Unguja as
mos o he people li e in his island (Unguja), and because o Zanziba ci y loca ed in i (A
S aeh , 2018).
Acco ding o 2018 Na ional Bu eau o S a is ics (NBS) census epo , he o e all popula ion o
Zanziba island (include Pemba) is abou 1,348,776 people and i s annual g ow h inc ease in a
e y apid a e. In he ecen yea s, he popula ion g ow h a e o Zanziba has been epo ed o
g ow a a a e o 3.4 % annually (NBS, 2018). Na u al popula ion g ow h is conside ed as he
main agen o his high apid a e, bu o he ac o s including g owing o ou ism indus y and
economic in luence has also a ac ed a signi ican numbe o mig an s om Tanzania mainland
and o he pa s o Eas A ica, making his island o be one among he highly popula ed islands
in he wo ld.
Zanziba has a opical clima e wi h ou dis inc seasons. “Kaskazi” ( he ho season, which is
be ween Decembe and Feb ua y and associa ed wi h ei he li le o no ains), “Masika” which
is he long ainy season om Ma ch – May, “Kipupwe” ( he cold season, wi h s ong winds
be ween June and Sep embe ), and “Vuli” which is e y sho ainy season om Oc obe –
Decembe . The annual ain all o island is anging om 1600 mm o Unguja and 1900 mm o
Pemba espec i ely, and he ai empe a u es is anging be ween 29 and 32ºC on a e age.
Zanziba island was o iginally o es ed, bu p essu es like popula ion inc ease, human habi a ion
and clima e change and a iabili y ha e esul ed in widesp ead clea ing o he o es and
ege a ion co e (M. Kukkonen & Käyhkö, 2014).
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Figu e 3. 1: S udy A ea-Zanziba
The land co e pa e ns a ound Zanziba island a e gene ally dis ibu ed unde wo di e en soil
classes, namely he deep soil and he co al ag a eas. The deep soil a eas a e mainly a ibu ed by
pe manen cul i a ion and o es , while he la e co al ag a e cha ac e ized by cul i a ion and
consequen sc ublands (RGZ, 2008). Howe e , all o hese wo soil classes a e no s able in e ms
o i s land co e and bio ope cha ac e is ics. The hilly deep soil a eas a e epo ed o be mo e
ulne able o soil e osion, highly land use demands and simul aneously acing popula ion
p essu e, which all o hese in luences he apid and cons an changes in land co e pa e ns and
na u al esou ce (M. Kukkonen & Käyhkö, 2014; Mye s, 2010). Al e na i ely, shi ing
cul i a ion cha ac e ized in co al ag a eas c ea e a cons an elemen o changes, which unde he
p essu e o diminishing a ea due o comme cial and conse a ion land use may leads owa ds
de e io a ion o ecosys ems and aluable na u al esou ces (Mye s, 2010).

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4. DATA AND METHODS
4.1. Desc ip ion o he da a
Da a om di e en sou ces we e used o achie e he objec i es o his s udy. A e y i s s ages,
he emo e sensed sa elli e images o Zanziba island (Table 4. 1) a di e en ime we e ob ained
om USGS websi e (www.ea hexplo e .usgs.go /), and hen compa ed based on hei di e en
empo al phenomenon. In ha sense, h ee mul ispec al Landsa images o he yea 2003, 2009
and 2018 wi h spa ial esolu ion o 30 m we e downloaded om USGS websi e (Table 4.1).
Ancilla y da a ep esen ing biophysical p ope ies ha we e conside ed o in luence land use
changes we e also applied in his analysis. These ancilla y da a we e ob ained by i s
downloading hei o iginal shape iles including Zanziba oad, p o ec ed o es , buildings and
illages as well as coas al egion om (www.zansdi.en i onmen . i ). By means o digi al
ele a ion model (DEM) which was ob ained om ( h p://openda a. cm d.o g ), we calcula e he
dis ance o each o hese a iables o c ea e he biophysical p ope ies o dis ance o he oad,
dis ance o he buildings, dis ance o he coas , dis ance o he o es and slope (Table 4.2).
P o ec ed a eas including p o ec ed o es , go e nmen al ag icul u al ield, as well as open and
es ic ed zones we e also used o ep esen he cons ain s o land co e changes in he s udy
egion. These p o ec ed a eas da a we e ob ained by eclassi ying he 2012 Zanziba LULC map
which was ob ained om Zanziba commission o land (COLA) o c ea e a Boolean map o 0
o p o ec ed a eas and 1 o all o he classes (Figu e 4.3).
12
Da ase
Desc ip ion
P ojec ion
Resolu ion
Sou ce
Landsa 7,
ETM+
Raw = 064, pa h =
166,
Da e: 14/01/2003
WGS 1984,
UTM Zone
37N
30 m
www.ea hexplo e .usgs.go /
Landsa 5
TM
Raw = 064, pa h =
166, Da e:
1/07/2009
WGS 1984,
UTM Zone
37N
30 m
www.ea hexplo e .usgs.go /
Landsa 7,
ETM+
Raw = 064, pa h =
166, Da e:
18/07/2018
WGS 1984,
UTM Zone
37N
30 m
www.ea hexplo e .usgs.go /
Digi al
Ele a ion Model
(DEM)
SRTM
Clipped om
Tanzania Dem
WGS 1984,
UTM Zone
37N
STRM
30 m
h p://openda a. cm d.o g
Table 4.1: Sa elli e Da a
4.1.1. Ex e nal a iables
In modeling LULC change o a speci ic a ea, he a iables in luencing o con olling he land
co e change on ha a ea should be p ope ly used as independen a iables, and he LU maps
should be used as dependen a iables. The inclusion o independen a iables in land change
modeling is e y essen ial since hese a iables ensu e he u u e land co e maps a e always
p edic ed wi h exis ing pa e ns and d i e s o changes in ha egion (Bulley, & Fü s , 2017).
In his s udy, he selec ion o independen a iables was based on e iews ela ed o simila
models, esea ches conduc ed in o he de eloping coun ies as well as he local ci cums ances o
Zanziba island. In ha sense, he a iables slope, dis ance o he oad, dis ance o he o es ,
dis ance o buildings, dis ance o he coas and p o ec ed a eas we e all selec ed as independen
a iables o simula e he u u e land co e change o Zanziba ci y.
The a iable “slope “was chosen o ep esen he biophysical condi ions o he s udy a ea.
Acco ding o (Liu, 2009; A sanjani e al., 2013), he la opog aphy has much in luence on land
co e changes han ugged opog aphy, and his heo y has al eady been used in many simila
models wi h posi i e esul s (Zhao e al., 2014).
“Dis ance o he oads” and “dis ance o he o es ” we e all connec ed o economic
ci cums ances. The chosen o dis ance o he o es in economic ac o s is connec ed wi h he ac
ha in Zanziba , he cus oms o con e ing o es ed land in o ei he buil -up a eas o a mland is
one o he majo eason o land-use dynamics, and his is highly caused by he economic si ua ion
o Zanziba popula ion (M. Kukkonen & Käyhkö, 2014). This a iable is also ela ed o local
13
spa ial policies o bidding any cons uc ions o de elopmen ac i i ies in p o ec ed o es (M.
Kukkonen & Käyhkö, 2014).
We ega d “dis ance o he coas ” and “dis ance o buildings” o be connec ed in he social ac o s.
In his case, he a iable “dis ance o he coas ” con i ms he global end owa ds highe
quan i ies o land-use changes in coas al egions (Ebada i, 2018). And o he second a iable
“dis ance o he buildings” exp ess he local social and planning condi ions o Zanziba , in which
he houses a e buil ela i ely close o each o he due o subdi isions o small landholdings. In
o he case, his phenomenon is also ela ed o spa ial in e ac ions, p o ided ha he land-use
changes a e highly a ac ed by simila changes in hei neighbo ing a eas (M. O. Kukkonen e
al., 2018; Toble sFi s Lawo Geog aphy, n.d.).
And in he case o he emaining a iable “p o ec ed a eas”, i has been used o ep esen he local
spa ial policies ha ha e ei he obs uc ed o indi ec ly s imula ed land-use dynamics (M. O.
Kukkonen e al., 2018).
Da ase
Desc ip ion
P ojec ion
Resolu ion
Sou ce
Dis - -
oad
Vec o con e ed
o as e ; Euclidian
dis ance o he oad
WGS 1984,
UTM Zone
37N
Re-sampled
o 30m
www.zansdi.en i onmen . i
(Commission o Land
adminis a ion Zanziba )
Dis - -
build
Vec o con e ed
o as e ; Euclidian
dis ance o he
building
WGS 1984,
UTM Zone
37N
Re-sampled
o 30m
www.zansdi.en i onmen . i
Dis - -
o es
Vec o con e ed
o as e ; Euclidian
dis ance o he
building
WGS 1984,
UTM Zone
37N
Re-sampled
o 30m
www.zansdi.en i onmen . i
Dis - -
coas
Vec o con e ed
o as e ; Euclidian
dis ance o he
beaches
WGS 1984,
UTM Zone
37N
Re-sampled
o 30m
www.zansdi.en i onmen . i
slope
Slope calcula ed
om digi al
ele a ion model
WGS 1984,
UTM Zone
37N
Re-sampled
o 30m
h p://openda a. cm d.o g
P o ec ed
a eas/
Cons ain
Vec o con e ed
o as e ;
Reclassi y o ha e
0 o p o ec ed
a eas and 1 o all
o he classes
WGS 1984,
UTM Zone
37N
Re-sampled
o 30m
www.zansdi.en i onmen . i
(Commission o Land
adminis a ion Zanziba
Table 4. 2: Ex e nal Va iables
14
4.2. Me hods
The o e all me hodology o his s udy was ca ied ou in o h ee majo s eps (Figu e 4.1). Fi s ,
he land use land co e classi ica ion was pe o med using andom o es classi ie o gene a e
h ee LULC maps o 2003, 2009 and 2018. Nex he land co e maps o he u u e scena ios
we e modeled using land change modele o he Te Se 18.3 so wa e. And inally, he ecological
isk indices o each map and each dis ic o Zanziba we e calcula ed by i s compu ing he
landscape me ics using LECOS plugin in QGIS 3.8, ollowed by applying he o mulas om
a ious li e a u es o ERI assessmen (Xue e al., 2019; F. Zhang e al., 2018; X. Zhang e al.,
2013).
4.2.1. Image classi ica ion
P e-p ocessing o all h ee sa elli e images (2003, 2009 and 2018), Zanziba adminis a i e
bounda y, oge he wi h all ancilla y da a Shape iles, was pe o med by using A cGIS 10.6. All
da ase s we e p ojec ed o WGS 1984, UTM Zone 37S coo dina e sys em, ollowed by band
combina ion, clipping o he s udy egion, and esampling o each one o hem o appea in he
same spa ial esolu ion o 30 m.
T aining Samples Shape ile o i e classes including buil -up, g assland, o es , we land and
a mland we e gene a ed by digi izing each o he sa elli e images. Wi h hese gene a ed i e land
Figu e 4. 1: Me hodological amewo k
21
4.5.2. T ansi ion po en ial
A e inishing he i s s ep o change analysis, he nex s ep was o es he explana o y powe
o each ex e nal a iable, as well as selec ing ansi ion sub-models ha a e ele an in he
p edic ion o he u u e scena ios.
The e o e, in his s age, i s , he C ame ’s powe o all ex e nal a iables we e es ed o iden i y
all a iables wi h a po en ial explana o y powe . These C ame ’s powe a e anging om 0 – 1,
in which he a iables wi h a leas 0.15 C ame ’s alue a e conside ed o be po en ial in land use
changes, whe eas hose ha ing alue om 0.4 onwa d a e much po en ial (Eas man, 2006).
The esul s o C ame ’s powe o each es ed a iable a e illus a ed in (Table 4.1) below.
Explana o y Va iable
O e all C ame ’s V alue
Dis ance o buildings
0.2852
Dis ance o oad
0.1836
Dis ance o he coas al a ea
0.1653
Dis ance o he o es
0.1354
Slope
0.0725
Res ic ed a eas
0.1932
Table 4. 3: C ame 's powe o ex e nal a iables
Due o he lowe C ame ’s alue, nega i e in luence on he modelling esul s, as well as less
li e a u es in o ma ion ega ding o Slope issues in Zanziba , his a iable was excluded in
analysis.
In he same sense, he a iable dis ance o he o es was also emo ed om he analysis due i s
lowe C ame ’s powe and i s nega i e in luence on he analysis accu acy.
The e o e, only h ee ex e nal ac o s including dis ance o he building, dis ance o he oad and
dis ance o he coas , and one cons ain map o each scena io (excep o ex eme scena io,
whe e cons ain s we e no employed) we e used in his analysis.
The nex s ep was o combine all selec ed a iables ( ac o s and cons ain ) oge he wi h
ansi ions sub-models be ween 2003 and 2009 and employ Mul i-Laye Pe cep on (MLP) o
gene a e ansi ion po en ial o 2018 land co e map p edic ion.
Mul i-Laye pe cep on (MLP) is a eed o wa d a i icial neu al ne wo k (ANN) composed wi h
one o mul iple laye s including inpu , ou pu and hidden laye s be ween inpu and ou pu laye .
The e m eed- o wa d me hod implying ha he da a lows in one di ec ion om inpu o ou pu .

22
The main algo i hm o his model is compu ing he linea ou pu om nonlinea inpu s acco ding
o he weigh s by using a nonlinea ac i a ion unc ion (Ga dne & Do ling, 1998).
MLP employs a supe ised lea ning echnique called backp opaga ion o aining. The back-
p opaga ion algo i hm composed o wo s eps which a e o wa d pass and backwa d pass. In he
o wa d pass, ac i a ion ansmi s om inpu o ou pu laye which means ha he signal low
mo es om he inpu laye h ough he hidden laye s o he ou pu laye . While in backwa d pass
e o s p opaga ed om ou pu o hidden laye (Ga dne & Do ling, 1998).
To model land-use ansi ions in LCM, ei he Logis ic Reg ession, SimWeigh o MLP neu al
ne wo k can be applied. Howe e , in his s udy, he MLP neu al ne wo k was applied because o
i s capabili y o unning mul iple ansi ions o up o 9 pe sub-model as well as explana o y
a iables a once (Eas man, 2006).
To achie e he be e accu acy on he esul s o Mul i-Laye Pe cep on only majo land co e
ansi ions oge he wi h only he d i e ’s a iables wi h highe C ame ’s powe should be
included in he ansi ion sub-model (Eas man, 2006).
Based on his p inciple, oge he wi h he in o ma ion om li e a u es ega ding on s udy egion,
only six majo ansi ions including g assland o buil -up, o es o buil -up, g assland o a mland,
o es o g assland, o es o a mland and a mland o g assland we e selec ed in his s udy.
Toge he wi h ou selec ed a iables, a Mul i-laye pe cep on was employed wi h an accu acy
o 68.4% o gene a e he ansi ion po en ials (Figu e 4.5) ha we e hen used by Ma ko chain
me hod o p edic he Land use map o he yea 2018.
The same p ocedu es we e also applied o model he 2027 maps.
23
Figu e 4. 4: T ansi ion Po en ial 2003 - 2009
4.5.3. Change p edic ion
The hi d s age in modeling u u e scena ios was o pe o m change p edic ion o a u u e
speci ied da e o he alloca ion o land co e changes. In his s age, he de aul p ocedu e in
LCM (Ma ko Chain analysis) was applied o de e mine he amoun o changes a a da e o 2018
based on his o ical land co e maps o 2003-2009.
He e, he algo i hm de e mines exac ly how much land would be expec ed o ansi ion om he
la e da e (2009) o he p edic ion da e (2018) based on a p ojec ion o he ansi ion po en ials
maps ha was gene a ed and hen c ea es a ansi ion p obabili ies ile (Eas man, 2006).
Wi h his ansi ion p obabili ies ile, wo p edic o s maps which a e so p edic ion map and a
ha d p edic ion map we e gene a ed. While a so p edic ion map is a con inuous mapping o
ulne abili y o change which p o ides an indica ion o he deg ee o which he a eas ha e he
igh condi ions o p ecipi a e change in 2018, a ha d p edic ion map is an ac ual p ojec ed map
o 2018, in which each pixel is assigned one land co e class; he class ha i is mos likely o
become (Eas man, 2006).
24
4.5.4. Model alida ion
To ensu e he p edic i e abili y and pe o mance o he model. Model alida ion and calib a ion
mus be pe o med be o e s a ing he p edic ion o LU changes o he speci ied da e. Pe o ming
alida ion o he model is a e y impo an s ep because he e iciency and usabili y o he model
a e always depending on he ou pu o he alida ion ( an Vlie e al., 2016).
Since se e al s udies ha e al eady p oposed he usage o he con usion ma ix (Congal on, 2001)
in assessing he accu acy and pe o mance o he model, we also compu e he con usion ma ix
by pe o ming c oss- abula ion o compa e he ac ual and simula ed maps o 2018. The con usion
ma ix which was gene a ed a e pe o ming c oss- abula ion was hen used o calcula e he
o e all accu acy, use accu acies, p oduce accu acies, and kappa coe icien .
In addi ion o ha , wo o he kappa coe icien s we e also compu ed o gain mo e in o ma ion on
he pe o mance o he model. The o he wo kappa coe icien s a e Khis o and Kloca ion.
Acco ding o Se na, (2011), Khis o is used o measu e he simila i ies in he numbe o cells
be ween simula ed and e e ence maps. In o he wo ds, his kappa is esponsible o measu ing
quan i a i e simila i y be ween wo compa ed maps. In ano he case, Kloca ion is used o measu e
he simila i ies in he spa ial dis ibu ion o classes bu does no di e en ia e be ween classes ha
a e close o dis an and i is independen o he o al numbe o cells pe class (Se na, 2011).
25
4.6. Calcula ion o Ecological isk indices
The inal s ep o ou analysis was o compu e ecological isk indices o each LULC map, as well
as o each dis ic o Zanziba island and hen compa ing hei esul s. This s age was done in
wo majo s eps. Fi s he landscape me ics (pa ch numbe and pa ch a ea) o each LULC maps
was calcula ed using LECOS plugin in QGIS 3.8, and hen he o mulas desc ibed in sec ion 4.6.1
below we e used o calcula e ecological isk indices o each map.
Ecological Risk index (ERI)
P o ide an indica o s o he nega i e en i onmen al impac s ha may occu o a e occu ing
due o one o mo e ex e nal ac o s (USEPA, 1992; Hakanson, 1980; Hunsake e al., 1990, X.
Zhang e al., 2013) .
Ma hema ically:
𝐸𝑅𝐼=∑𝐴𝑘𝑖
𝐴𝑘
𝑛
𝑖=1 𝑅𝑖
Whe e:
ERI: The ecological isk index o he isk a ea
Ak: is he o al a ea o k h egion
Aki = is he i h landscape a ea/class a ea
Ri: is he i h landscape/class loss index which can be calcula ed h ough o mula below.
𝑅𝑖=𝐹𝑖∗𝑆𝑖
Whe e: Fi, is he ecological agili y index which is e e ed as he abili y o a landscape o
esis he human dis u bance.
Acco ding o Zhang e al (2013), and he knowledge o ou s udy egion, he Fi alue o 5 o
buil up, 4 o wa e body, 3 o g ass land, 2 o a m land and 1 o o es we e used in his
s udy. Addi ionally, o an e ec i e esul , hese alues we e no malized be o e di ec ly being
used in calcula ions o ecological isk index.
Si is he landscape dis u bance deg ee index o i h landscape (Jin e al., 2019), which can also be
calcula ed ia a o mula.
𝑆𝑖=𝑎𝐶𝑖+𝑏𝑁𝑖+𝑐𝐷𝑖
26
Whe e:
Ci: Land scape agmen a ion, i e lec s he changes in landscape s uc u e and ecological
p ocess, 𝐶𝑖=𝑛𝑖
𝐴𝑘𝑖
Ni: Land scape isola ion, e e s o he deg ee o sepa a ion o pa ches in a gi en landscape ype,
𝑁𝑖=𝐴𝑘
2𝐴𝑘𝑖√𝑛𝑖
𝐴𝑘
Di: Landscape dominance index, desc ibe he dominance o pa ches in a gi en landscape,
𝐷𝑖=𝑄𝑖+𝑀𝑖+𝐿𝑖
3
ni: Is pa ch numbe o i h land scape, Qi: Ra io o he cell wi h i h pa ch and o al cell, Mi:
Ra io o he numbe o i h pa ches o o al pa ches and Li: Ra io o i h pa ch a ea o he o al
a ea. And a, b and c: ep esen he weigh o Ci, Ni and Di espec i ely whe e a + b + c = 1
(Zhang e al, 2013).

27
5. RESULTS
5.1. LULC changes 2003 - 2018
Figu e 5.1 below illus a e he LULC classi ied maps o he yea 2003-2009. As i can be seen,
he isual in e p e a ion o he classi ied images explo es ex ensi e changes in a ious land co e
classes in he s udy egion especially he apid expansion o buil up a ea as well as dec ease in
o es co e . The high inc ease in buil -up a ea is highly exposed in eas e n zone o he s udy
egion which a e nea o he u ban cen e o Zanziba island.
Figu e 5. 1: LULC classi ied maps o he yea 2003-2018
28
5.2. Accu acy assessmen
Accu acy assessmen o each land use map was pe o med in o de o ind classi ica ion e o s
and make he p oduced land co e maps become eliable and easily in e p e able by use s. Kappa
coe icien , o e all accu acy, use accu acy as well as p oduce accu acy we e all calcula ed and
he assessmen esul s we e summa ized in ( able 5.1, 5.2 and 5.3) below.
The assessmen esul s o each o he p oduced land co e maps p o ide an o e all accu acy
anging om 74% - 78%, and o e all kappa coe icien which is anging om 0.68 – 0.71. On
he o he hand, he use s and p oduce ’s accu acies compu ed o each o he classi ied maps we e
all abo e 65%. These esul s indica ing ha he classi ied images ha e me a good le el o
accu acy and hus hey a e sa is ying o he s udy analysis.
In all o he h ee classi ied images, he we land has shown a high use ’s accu acy (80% - 89%)
compa ed wi h o he land use classes. This implied ha he majo i y o hei pixels we e co ec ly
classi ied in hei espec i e classes. Howe e , in each classi ied image ei he a mland, g assland
o o es co e p o ides a minimum use s o p oduce s accu acy (63%-80%).
The maximum use ’s accu acies p o ided by he we land can be jus i ied by hei dis inc i e
cha ac e is ics in compa isons wi h o he LU classes. This cha ac e is ic makes hem easily being
disc imina ed by classi ica ion algo i hms agains o he land use classes du ing he classi ica ion
p ocess. Howe e , on ano he side, he lowes use s and p oduce ’s accu acies o a mland,
g assland and o es co e can be explained by hei spec al p ope y simila i ies among hem
(Mel ille, Luciee , & A yal, 2018).
o e all accu acy
76%
kappa
0.69
LULC classes
Buil -up
Fo es
g assland
a mland
We land
To al
Use accu acy
Buildup
32
0
5
3
2
42
76%
Fo es
2
37
6
0
1
46
80%
G assland
1
6
36
6
1
50
72%
Fa mland
5
5
3
31
0
44
70%
We land
0
2
0
0
16
18
88%
To al
40
50
50
40
20
200
P oduce s accu acy
80%
74%
72%
77%
80%
Table 5. 1: Con usion ma ix o LULC map o he yea 2003
29
O e all
accu acy
78%
kappa
0.71
LULC classes
Buil -up
Fo es
g assland
a mland
We land
To al
Use accu acy
Buildup
34
2
2
3
3
44
77%
Fo es
0
41
9
0
1
51
80%
G assland
1
3
35
8
0
47
74%
a mland
5
3
3
29
0
40
73%
We land
0
1
1
0
16
18
89%
To al
40
50
50
40
20
200
P oduce s
accu acy
85%
82.00%
70%
72%
80%
Table 5. 2: Con usion ma ix o LULC map o he yea 2009
O e all accu acy
75%
kappa
0.68
LULC classes
Buil -up
Fo es
g assland
a mland
We land
To al
Use accu acy
Buildup
33
1
1
4
2
41
80%
Fo es
1
39
6
3
0
49
79%
G assland
1
6
34
6
1
48
70%
a mland
5
1
9
26
0
41
63%
We land
0
3
0
1
17
21
80%
To al
40
50
50
40
20
200
P oduce s accu acy
82%
78.00%
68%
65%
85%
Table 5. 3: Con usion ma ix o LULC map o he yea 2018
30
5.3. LULC changes 2003 - 2018
The s a is ical esul s co esponding o LULC change in he s udy a eas (Table 5.4) indica ing
ha , he e is an ex ensi e inc ease in he buil -up a ea as equi alen o he dec ease in o es co e .
The buil -up a ea o Zanziba island has been expanding om 4132.44 Hec a e in he yea 2003
o 7201.35 Hec a e in 2018. These a eal changes in he buil -up a ea make up o 42.6% inc ease
in cons uc ion land o only 15 yea s’ (2003-2018) pe iod (2.84% inc ease annually).
The Fa mland/ag icul u al land has also shown a p og essi e expansion wi hin hese 15 yea s o
he s udy, in which 31.7% (2.1% annually) o ag icul u al land we e inc eased.
Fo he o es co e and we land, he ends on hei land co e changes ha e shown a
con inuously dec easing, in which 15.8% o o es co e (1.05% annually) and 24.6% o we land
(1.64% annually) we e disappea ed.
Howe e , in he case o g assland, he end in i s LULC changes does no show a smoo h
a ia ion. This is because, in he i s 6 yea s (2003-2009), he g ass co e o he s udy a ea has
been dec eased by 4.5%, ollowed by inc easing o 7.48% o he nex 9 yea s (2009 – 2018).
LULC
classes
A ea (ha)
(2003)
A ea (ha)
(2009)
A ea ( (2018)
% changes
(2003-2009)
% changes
(2009-2018)
% changes
(2003-2018)
Buil -up
4132.44
5081.94
7201.35
18.68
29.43
42.62
Fo es
73329.39
69460.56
61692.03
-5.28
-11.18
-15.87
Fa mland
22171.77
27669.15
29206.17
24.79
5.26
31.73
G assland
56569.41
54107.73
58484.43
-4.55
7.48
3.27
We land
1925.55
1809.18
1544.58
-6.43
-17.13
-24.66
Table 5. 4: LULC a eal s a is ics om 2003 -2018
This apid LULC changes o se e al land co e classes wi hin he s udy egion could be a ibu ed
by he imp ope way o managing he land including he high a e o in o mal se lemen , casual
a ming me hods as well as he highe le el o de o es a ion ac i i ies in Zanziba (M. O.
Kukkonen e al., 2018).
37
The ex eme scena io shows a e y high inc ease in ERI end compa ed o hose o business as
usual and conse a ion scena io (Table 5.10). Howe e , i has o be no ed he e ha , al hough in
ex eme scena ios he e was no cons ain s ha was employed, i ’s ERI is mo e likely o be
applicable in Zanziba e en mo e han ha o business as usual. This is due o he ac ha in
Zanziba he de elopmen and cons uc ion o buildings in a andom way and wi hou ollowing
es ic ions and go e nmen egula ions a e one among he common and majo p oblem acing
u ban planning and hus hey make no much signi icance in he land change esul s ob ained by
employing cons ain in e e y p o ec ed a ea.
In summa y, he o e all ERI o he s udy a ea shows an inc easing end om 2003 o 2027.
Whe eas, he e is also a signi ican change (mos ly inc easing) in he numbe o pa ches,
agmen a ion and sepa a ion indices as well as a con inuous dec ease in he g ea es pa ch a ea
o he o es , a mland, and g assland.
Yea /Scena io
O e all ERI
% Changes
2003
0.0714
-
2009
0.0867
17.7
2018
0.0957
9.4
BAU
0.1020
6.2
EXTREME
0.1050
8.9
CONSERVATION
0.1002
4.6
Table 5. 10: O e all Ecological isk index changes 2003 – 2027
Figu e 5. 5: T ends o o e all ERI om 2003 - 2027

38
5.5.3. Dis ibu ion o ERI pe dis ic
While compa ing he dis ibu ions o ERI a a dis ic le el (Figu e 5.6 and Table 5.11), i appea s
ha he dis ic s U ban-wes , No h A and No h B expe iences he high and p og essi ely
inc eases in hei ERI alues, while he ERI o he U ban dis ic s seems o be s able.
The ex ensi e inc eases in ERI o hese h ee dis ic s could be highly in luenced by he high
a e o casual ag icul u al ac i i ies, ou ism ac i i ies as well as in o mal housing in hese
egions.
In he case o he h ee u u e scena ios, he esul s show ha he ex eme scena io p o ides he
highe ERI inc eases in almos all dis ic s o Zanziba especially in No h A and No h B whe e
he inc ease in ERI alues co esponding o he ex eme scena io a e 23.5% and 17.92
espec i ely. The conse a ion scena io in o he case shows he ai ERI esul s in almos i e
dis ic s o Zanziba excep o he No h A dis ic which show he highe inc eases in ERI o
all o he 2027 scena ios.
Dis ic
T end o ERI Change (%)
2003-2009
2009-2018
2018-2027
BAU
2018-2027
EXTREM
2018-2027
CONSERVATION
No h A
16.63
8.95
18.51
23.45
17.13
No h B
9.89
14.78
13.51
17.92
2.79
Cen al
10.37
4.39
10.82
8.64
1.82
Sou h
10.37
10.63
10.94
8.35
3.67
Wes
14.57
23.41
5.39
11.61
6.93
U ban
-1.35
5.27
0.24
2.19
0.49
Table 5. 11: ERI Changes by dis ic 2003-2027
39
Figu e 5. 6: T end o ERI pe dis ic 2003 - 2027
40
6. DISCUSSION
This s udy was employed o assess ecological isk condi ions in Zanziba based on land co e
change, wi h he ocus o p o iding a holis ic unde s anding o land co e dynamics and hei
en i onmen al impac s along wi h p e en ing unmanaged g ow h and andom de elopmen o
in o mal housing and in as uc u e. Based on Random Fo es Classi ie , Mul i-Laye Pe cep on
(ANN) and Ma ko Chain, we success ully assessed he end o land-use dynamics om 2003-
2018 i s and hen he land use maps o 2027 was p edic ed. LECOS plugin in QGIS 3.8 was
e ec i ely applied o calcula e landscape me ics in land use maps and hen employed hem o
calcula e ecological isk indices o he landscape. These indices including sepa a ion, agili y,
dominance, loose as well as o e all ecological isk index o he landscape.
Du ing he i s s ages o analyzing land-use dynamics, we ha e ealized ha Zanziba Ci y has
expe ienced a end o apid land-use changes and ex ensi e buil -up expansion mos ly in a eas
nea exis ing buildings, and some o he coas al zones. The esponse dis ance o he buildings and
dis ance o coas a iable suppo s his obse a ion as hey p o ide he highe C amme ’s alues
ensu ing ha hese a iables ha e a g ea in luence on land co e dynamics in Zanziba .
I he cu en end con inues, he buil -up expansion o Zanziba ci y will be expanded o mo e
han 40% by 2027. The La ge pa o ag icul u al and o es ed land has al eady been agmen ed
o housing pa cels. And because o hese, he nega i e en i onmen al e ec s ele an o land
esou ces, such as loss o na u al o es s, open c opland, and we lands ha e been al eady being
highly des oyed in Zanziba (M. H. Ali & Sulaiman, 2006). This is somewha ala ming ha he
go e nmen should en o ce es ic ions and s ong land policies lea ing he emaining land co e
classes o s op being u he des oyed.
The analysis esul s gi e no signs indica ing ha hese land-use changes and ex ensi e buil -up
expansion would a leas decline in he u u e. And his is possibly caused by he poo quali y o
housing and minimum go e nmen e o s o en o ce he op imal usage o land esou ces in
Zanziba island (Ameyibo e al., 2003).
While analyzing he o e all ecological isk indices o he landscape, he esul s sc eened ha , he
o e all ERI o Zanziba Ci y has been cons an ly g own om 2003 onwa d. This end is pa allel
o he inc ease in he numbe o smalle pa ches o some land co e classes, ela i e o he
dec easing in hei la ges pa ch a eas.
41
The obse a ion o he u u e scena ios p o ide he wo s ecological isk indices o he ex eme
whe eas conse a ion scena io p o ides a li le ai esul s. The d as ic ecological isk sco es
p o ided by ex eme scena io, sugges ed ha , he land use es ic ions in Zanziba should be
p ope ly espec ed, a leas by p ope ly u ilizing he al eady exis ing land policies and es ic ions.
Finally, i would be e y impo an o highligh ha , in his s udy we ha e assessed ecological
isk indices o Zanziba island wi h land co e change using ee sa elli e da a. This p ocess
equi ed a consis en and accu a e land co e da a o he pu pose o p o iding eliable and
accu a e in o ma ion. Howe e , i was a bi challenge in acqui ing ee sa elli e images wi h
desi ed s anda d and a desi ed ime. This is due o he geog aphical egion o Zanziba island
making mos o hei sa elli e images being co e ed wi h cloud. In ha sense, i akes a much
ime o sea ch and acqui e he needed sa elli e da a, and e en hough he image o 2018 ha was
ob ained was no in e y good quali y and con ain some scanned lined which somehow a ec ed
he analysis esul s. Howe e , in u u e hese esul s can be imp o ed by using mo e accu a e and
upda e da a, as well as inco po a ing ex ensi e land co e change d i e s.
42
7. CONCLUSIONS AND RECOMMENDATIONS
The indings in his esea ch suppo he e idence o he ecen s udy which ound ha he apid
land-use changes in Zanziba ha e imposed an in ensi e deg ada ion in he ecosys em o Zanziba
(A S aeh , 2018). The main d i e s behind his in ensi e deg ada ion a e inc easing in
uncon olled and poo ly managed in o mal housing, high a e o casual a ming and imp o ised
plan in he ou ism sec o (WB, 2019). E idence has been clea ly exposed by inc easing he
agmen a ion index, sepa a ion index, loss index as well as o e all ERI o he en i e landscape.
In o de o educe such kind o h ea s, mo e sys ema ic app oaches in he ecological assessmen
a e equi ed. Coope a i e esea ch among esea ch g oups, land planne s, and all o he
s akeholde s including ou ism sec o s should be highly en o ced as a ool o moni o and
ecommend a long- e m plan o sus ainable ecosys ems in Zanziba .
In u u e scena ios, he indings ha e p o en ha disobeying land policies and egula ions can
in luence a d as ic shock o he ecological condi ions o Zanziba in he u u e. This has been
p o en by he esul s o an ex eme scena io which was e y wo s . Conse a ion scena io o
somehow had shown li le in luence on di ec ing u u e land co e changes and ecological isk
con ol, bu howe e , he esul s we e only mode a e and he e o e i is eally impo an o he
go e nmen o ake a special e o on se ing up new land-use app oaches such as p omo ing he
densi ica ion o e ical buildings a he han elying in one- loo ho izon al buildings.
In gene al, all o he indings in his s udy can p o ide use ul insigh s o land-use planne s and
policymake s ega ding he cu en ends and u u e p ojec ion o land co e changes in
Zanziba , he ac o s d i ing hese changes as well as he e ec ha hey ha e imposed in hei
ecological heal h. This can help he esponsible au ho i ies on se ing up he p ope ules and
egula ions on land use con ol.
The s udy also assessed he ecological heal h condi ions o he Zanziba islands in h ee di e en
scena ios. In his case, he esul s can se e as he basic no ion in explo ing he sus ainabili y o
he landscape s uc u e o he Zanziba island as well as i s ecological heal h secu i y unde
a ious land-use p ac ices. This can con ibu e o he designa ion o he app op ia e s a egies
owa d he land use managemen so ha a sys ema ic balance be ween economic and sus ainable
ecosys ems can be main ained.

43
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