i
SPATIOTEMPORAL FLOOD HAZARD AND FLOOD
RISK ASSESSMENT USING REMOTE SENSING
TECHNIQUES
Case s udy: Kha oum S a e, Sudan
Abee Awad Abdulmagied Khai y
ii
SPATIOTEMPORAL FLOOD HAZARD AND FLOOD RISK
ASSESSMENT USING REMOTE SENSING TECHNIQUES IN
KHARTOUM STATE, SUDAN
Disse a ion supe ised by
PhD, Ana C is ina Ma inho da Cos a
PhD, Filibe o Pla Banon
PhD, Ma co Oc á io T indade Painho
Feb ua y 2020
iii
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, Feb ua y 14 h, 2020
Abee Awad Abdulmagied Khai y
The signed o iginal has been a chi ed by he NOVA IMS se ices
i
ACKNOWLEDGMENTS
O Allah, ou Lo d, o hee be he p aise in all he hea ens, and in all he ea h,
and in all ha i pleases Thee o c ea e a e wa ds. O hou Who a wo hy o p aise
and glo y, mos wo hy o wha a se an says, and we a e all hy se an s, no one
can wi hhold wha Thou gi es o gi e wha Thou wi hholdes . I is only h ough
he guidance o Allah ha his wo k has been accomplished, and o Him all he
p aise is due.
The ea e , I would like o exp ess my since e app ecia ion o my supe iso s,
whom, wi hou hei con inuous suppo and encou agemen I wouldn’ ha e been
able o accomplish his wo k. I am g a e ul o and hono ed by all he ene gy and
ime you ha e pu h ough his wo k, o you insigh ul obse a ions and
commen s ha helped me imp o e on my wo k. I would like o hank he Eu opean
Commission o acili a ing he E asmus Mundus Join Mas e P og am and hus
allowing me o bene i om his unique oppo uni y. I also wan o ex end my
g a i ude o he s a and acul y membe s o NOVA In o ma ion Managemen
School IMS o p o iding me wi h endless suppo h oughou he cou se o he
hesis wo k.
I would like o hank my iend B endan O'Han ahan, o his encou agemen
and help in ge ing ex a in o ma ion, and Abdallah Almokash i and Ghazi El ayah
o hei suppo .
I am humbled by he amoun o emo ional and physical suppo ha my small
and ex ended amily ha e gi en me o ca y h ough and comple e his wo k.
Fo e e I will be in g a i ude o my sis e s, my husband Ahmed, and my in-laws,
who, h ough hei wo ds o ad ice and suppo ga e me he s eng h o push
o wa d.
Finally, I dedica e his wo k o my daugh e , my mo he , and my la e a he , all
ha I am and all ha I do is due o you and o you, you a e my o e.
Spa io empo al lood haza d and lood isk assessmen using
emo e sensing echniques
Case s udy: Kha oum S a e, Sudan
ABSTRACT
The s a e o Kha oum being he mos popula ed s a e in Sudan, aces he
consequences o loods ecu ence almos annually du ing ainy season. Policy
make s and on g ound NGOs need o ackle he haza d o loods in an e ec i e and
e icien manne . Recen esea ch demons a ed he capabili ies and po en ials o
emo e sensing in lood haza d and isk mapping. This s udy aims o map lood
haza d and assess he isk o loods in s a e o Kha oum, Sudan. In o de o iden i y
he lood haza d in s a e coun ies, an inunda ion indica o is used, namely he
ela i e equency o inunda ion (RFI).
Flood e en s ha occu ed om 1988 o 2018 we e mapped using Landsa sa elli e
images, and maximum lood ex en was hen delinea ed. RFI was ob ained using
maximum lood ex en maps and se ed as he lood haza d map. We de eloped a
Land Co e Land Use (LCLU) map using Landsa 8 o iden i y a ec ed u ban and
c oplands a eas in he s a e o Kha oum. RFI alues was used along wi h LCLU
map o assess s a e coun ies, and o assess he ulne abili y o public acili ies
(heal h and educa ional acili ies) using zonal s a is ics. I was demons a ed ha , in
e ms o a e age RFI alues o LCLU classes pe coun y, c oplands had he
highes lood haza d, and U ban a eas ca ied a ela i ely mode a e lood haza d.
The esul s o his s udy indica e ha c oplands on he i e banks a e he mos
inunda ed a eas in he s a e o Kha oum, and he mos u banized coun ies ha e he
highes lood haza d.

i
KEYWORDS
Flood Haza d Mapping
Flood Risk Assessmen
Remo e Sensing
Land Co e Land Use
ii
ACRONYMS
AHP – Analy ical Hie a chy P ocess
CCD – Cold Cloud Du a ion
IDW – In e se Dis ance Weigh ing
LCLU – Land Co e Land Use
MCA – Mul i-C i e ia Analysis me hods
NDBI – No malized Di e ence Buil -up Index
NDVI – No malized Di e ence Vege a ion Index
NDWI – No malized Di e ence Wa e Index
OSM – Open S ee Maps
RFI – Rela i e F equency o Inunda ion
SAR – Syn he ic Ape u e Rada
UNDRR – Uni ed Na ion O ice o Disas e Risk Reduc ion
UNITAR – Uni ed Na ion Ins i u e o T aining and Resea ch
UNOSAT – UNITAR’s Ope a ional Sa elli e Applica ion P og amme
UNOCHA – Uni ed Na ion’s O ice o Coo dina ion o Humani a ian A ai s
USGS - Uni ed S a es Geological Su ey
VHR – Ve y High Resolu ion
WHO – Wo ld Heal h O ganiza ion
iii
INDEX OF THE TEXT
ACKNOWLEDGMENTS .................................................................................................................. IV
ABSTRACT ........................................................................................................................................ V
KEYWORDS ..................................................................................................................................... VI
ACRONYMS ................................................................................................................................... VII
1. INTRODUCTION ...................................................................................................................... 1
1.1. MOTIVATION ..................................................................................................................... 1
1.2. AIM AND OBJECTIVES ........................................................................................................ 1
1.3. THESIS OUTLINE ................................................................................................................ 2
2. LITERATURE REVIEW ........................................................................................................... 3
2.1. FLOOD RISK ASSESSMENT USING REMOTELY SENSED DATA ............................................... 3
2.2. FLOOD RISK ASSESSMENT IN SUDAN ................................................................................. 5
3. METHODS ................................................................................................................................. 6
3.1. STUDY AREA ..................................................................................................................... 8
3.2. DATA ................................................................................................................................. 9
3.2.1. Remo ely Sensed Da a .................................................................................................. 9
3.2.1. Auxilia y Da a ............................................................................................................ 11
3.3. MAPPING FLOOD EXTENT ................................................................................................ 11
3.4. MAPPING MAXIMUM FLOOD EXTENT .............................................................................. 12
3.5. RELATIVE FREQUENCY OF INUNDATION .......................................................................... 13
3.6. FLOOD RISK ASSESSMENT ............................................................................................... 14
3.6.1. LCLU Map.................................................................................................................. 14
3.6.2. Zonal S a is ics ........................................................................................................... 15
4. RESULTS AND DISCUSSION ............................................................................................... 17
4.1. FLOOD EXTENT MAPS ...................................................................................................... 17
4.1.1. Scan-Line Co ec o (SLC) p oblem .......................................................................... 17
4.2. MAXIMUM FLOOD EXTENT MAPS .................................................................................... 18
4.3. RELATIVE FREQUENCY OF INUNDATION MAP .................................................................. 20
4.4. FLOOD RISK ASSESSMENT ............................................................................................... 21
4.4.1. LCLU Map.................................................................................................................. 21
4.4.2. Zonal S a is ics ........................................................................................................... 24
4.5. HUMANITARIAN MAPPING RESPONSE FOR AUGUST 2013 AND JULY 2016 FLOOD EVENTS
25
5. CONCLUSION ........................................................................................................................ 30
5.1. LIMITATIONS.................................................................................................................... 31
5.2. RECOMMENDATIONS ........................................................................................................ 31
REFERENCES ................................................................................................................................... 32
ANNEX 1: SPECTRAL SPECIFICATIONS FOR LANDSAT IMAGES ........................................ 36
ANNEX 2: MAXIMUM FLOOD EXTENT MAP FOR 1991 .......................................................... 38
ANNEX 3: MAXIMUM FLOOD EXTENT FOR 1992 .................................................................... 39
ix
ANNEX 4: MAXIMUM FLOOD EXTENT FOR 1993 .................................................................... 40
ANNEX 5: MAXIMUM FLOOD EXTENT FOR 1994 .................................................................... 41
ANNEX 6: MAXIMUM FLOOD EXTENT FOR 1996 .................................................................... 42
ANNEX 7: MAXIMUM FLOOD EXTENT FOR 1997 .................................................................... 43
ANNEX 8: MAXIMUM FLOOD EXTENT FOR 1998 .................................................................... 44
ANNEX 9: MAXIMUM FLOOD EXTENT FOR 1999 .................................................................... 45
ANNEX 10: MAXIMUM FLOOD EXTENT FOR 2001 .................................................................. 46
ANNEX 11: MAXIMUM FLOOD EXTENT FOR 2003 .................................................................. 47
ANNEX 12: MAXIMUM FLOOD EXTENT FOR 2005 .................................................................. 48
ANNEX 13: MAXIMUM FLOOD EXTENT FOR 2006 .................................................................. 49
ANNEX 14: MAXIMUM FLOOD EXTENT FOR 2007 .................................................................. 50
ANNEX 15: MAXIMUM FLOOD EXTENT FOR 2009 .................................................................. 51
ANNEX 16: MAXIMUM FLOOD EXTENT FOR 2013 .................................................................. 52
ANNEX 17: MAXIMUM FLOOD EXTENT FOR 2016 .................................................................. 53
ANNEX 18: MAXIMUM FLOOD EXTENT FOR 2017 .................................................................. 54
ANNEX 19: MAXIMUM FLOOD EXTENT FOR JUNE 2018 ....................................................... 55
ANNEX 20: MAXIMUM FLOOD EXTENT FOR AUGUST - SEPTEMBER 2018 ...................... 56
ANNEX 21: MAXIMUM FLOOD EXTENT FOR NOVEMBER 2018 .......................................... 57
ANNEX 22: ERROR MATRIX AND UNCERTAINTY AND CONFIDENCE ANALYSIS FOR
LCLU MAP ........................................................................................................................................ 58
ANNEX 23: NDWI FOR 15/09/2016 LANDSAT IMAGE .............................................................. 59
ANNEX 24: RFI ZONAL STATISTICS FOR KHARTOUM STATE COUNTIES ........................ 60
ANNEX 25: RFI DESCRIPTIVE STATISTICS FOR HEALTH AND EDUCATIONAL
FACILITIES IN KHARTOUM STATE ............................................................................................ 62
5
independen ly o cloud co e (Ma cus & Fons ad, 2008; Pul i en i e al., 2011;
Sca pino e al., 2018; Schumann e al., 2009), and he a ailabili y o SAR images
hanks o Sen inel mission (Pou sanidis & Ch ysoulakis, 2017).
2.2. Flood Risk Assessmen in Sudan
Wi hin he A ican con inen , Sudan is conside ed o be a lood-p one coun y
(Li e al., 2016). As a coun y associa ed wi h loods, i also exhibi s a pa e n o
ex eme lash loods, ollowing a ecu ing pa e n since he ea ly nine een hs and
wen ie h cen u ies (Da ies & Walsh, 1997). In hei s udy, (Su cli e e al., 1989;
Walsh e al., 1994) had also indica ed ha ex eme lood damage was no only
caused by he Nile lood in G ea e Kha oum a ea ( he main h ee ci ies in
Kha oum S a e; Kha oum, Kha oum No h – also e e ed o as Bah i, and
Omdu man), bu also due o he hea y ain s o ms ha las ed o 4-5 consecu i e
days causing a uno , and he epheme al wa e cou ses h oughou he a ea. One o
he ea lies analysis o he loods impac o e Sudan was ca ied ou by (Su cli e e
al., 1989) o he 1988 lood e en , whe e Me eosa Cold Cloud Du a ion (CCD)
measu emen s we e used along wi h ae ial pho og aphy. Recen s udies conduc ed
on lood haza d mapping in Sudan included geo-s a is ical da a analysis using
In e se Dis ance Weigh ing (IDW) me hod o ain all in si u-s a ions, as well as
quan i a i e in e iews o calcula e a social ulne abili y index o Kha oum S a e
o 2013 – 2014 loods (Mahmood e al., 2017), also he opinions o expe s on he
same lood e en s h ough a quali a i e app oach (Ho n & Elagib, 2018) which
sugges ed a lood managemen amewo k, ha included lood isk mapping as a
pa o da a esou ce enhancemen . The sho age in lood haza d mapping and lood
isk assessmen in Sudan, is he mo i e behind his esea ch.
E en hough apid lood mapping has been ca ied o e he las decade by he
Uni ed Na ion Ins i u e o T aining and Resea ch (UNITAR) p og am, UNITAR’s
Ope a ional Sa elli e Applica ions P og am (UNOSAT) in a semi eal- ime analysis
(Sudan maps | UNITAR, n.d.), Kha oum s a e s ills su e s om he consequences
o lash loods.

6
3. Me hods
Figu e 1 illus a es he me hodology ha was ollowed du ing his s udy. In he
low cha lood e en s a e mapped using Landsa 5, Landsa 7, and Landsa 8
images. Wi hin he lood e en , pe each Landsa image, a lood ex en map was
p oduced using image classi ica ion, and inally all lood ex en maps a e used o
calcula e he maximum lood ex en map pe lood e en . In his s udy he imeline
o mapping loods s a s om 1988 un il 2018, spanning 30 yea s. The maximum
lood ex en maps a e hen used o p oduce he lood haza d map using Rela i e
F equency o Inunda ion RFI Index. Fo lood isk assessmen a Land Co e Land
Use (LCLU) map is p oduced, he lood haza d map is analyzed using he LCLU
map as well as heal h acili ies and educa ional acili ies da a om Open S ee
Maps (OSM). The ollowing sec ions de ails all he s eps o he me hodology.
7
Figu e 1: Me hodology Flow Cha
8
3.1. S udy A ea
Sudan is a coun y loca ed in he no heas e n pa o he A ican con inen . The
coun y is bo de ed by Egyp , Libya, Cen al A ican Republic, Chad, Sou h Sudan,
E hiopia, E i ea, and he Red Sea.
Kha oum s a e is he mos popula ed s a e o he 18 s a es o Sudan, al hough
he smalles in e m o a ea. I s capi al is Kha oum ci y, which is also he na ional
capi al o Sudan. Kha oum is si ua ed be ween 31˚E and 35˚E longi ude and 15˚N
and 17˚N la i ude. Kha oum s a e coun s o 17% o he o al popula ion o Sudan,
Figu e 2 shows he bounda y o he s a e in addi ion o he s a e coun ies. The s a e
consis s o 7 coun ies:
• Kha oum
• Jebel Awliya
• Umdu man
• Oumbada
• Ka a i
• Bah i
• Sha g Alneel
Figu e 2: S udy A ea
9
The Nile i e , he wo ld’s longes i e is o med in he ci y o Kha oum, by
he joining o he Blue Nile ha o igina e om Tana Lake in E hiopia, and he
Whi e Nile ha o igina e om Vic o ia lake in Uganda. The s a e has an a id
clima e, wi h a ainy season om July o Sep embe . Kha oum has a his o y o
ecu en looding, plu ial and lu ial (Da ies & Walsh, 1997), and ex emely
ulne able due o i s high popula ion.
3.2. Da a
3.2.1. Remo ely Sensed Da a
Fo his esea ch 70 emo ely sensed images acqui ed Landsa di e en
missions we e eely ob ained o he pe iod 1988 – 2018 and downloaded om he
Uni ed S a es Geological Su ey (USGS) Ea hExplo e websi e
h ps://www.ea hexplo e .usgs.go , de ails abou spec al band speci ica ions o
each Landsa senso used a e a ailable in Annex 1. The images we e selec ed based
on he Da mou h Flood Obse a o y’s Ac i e A chi e o La ge Floods
(B aken idge, n.d.). A o al o 21 lood e en s we e eco ded. When acqui ing he,
images o he lood e en on Oc obe 1997 we e no a ailable on he Ea hExplo e
a chi e. Table 1 shows mo e de ails abou acqui ed images.
Table 1: Remo ely Sensed Images speci ica ions
Acquisi ion
da e
Pa h/Raw
Senso
Spa ial
esolu ion
Landsa
Numbe
o bands
25/08/1988
173/049
TM
30 m
Landsa 5
7
02/09/1988
173/049
TM
30 m
Landsa 4
7
10/09/1988
173/049
TM
30 m
Landsa 5
7
18/09/1988
173/049
TM
30 m
Landsa 4
7
18/08/1991
173/049
TM
30 m
Landsa 5
7
05/09/1992
173/049
TM
30 m
Landsa 5
7
23/08/1993
173/049
TM
30 m
Landsa 5
7
10/08/1994
173/049
TM
30 m
Landsa 5
7
26/08/1994
173/049
TM
30 m
Landsa 5
7
16/09/1996
173/049
TM
30 m
Landsa 5
7
02/08/1997
173/049
TM
30 m
Landsa 5
7
18/08/1997
173/049
TM
30 m
Landsa 5
7
03/09/1997
173/049
TM
30 m
Landsa 5
7
10
19/09/1997
173/049
TM
30 m
Landsa 5
7
05/10/1997
173/049
TM
30 m
Landsa 5
7
21/10/1997
173/049
TM
30 m
Landsa 5
7
06/09/1998
173/049
TM
30 m
Landsa 5
7
22/09/1998
173/049
TM
30 m
Landsa 5
7
08/08/1999
173/049
TM
30 m
Landsa 5
7
24/08/1999
173/049
TM
30 m
Landsa 5
7
09/09/1999
173/049
TM
30 m
Landsa 5
7
13/08/2001
173/049
TM
30 m
Landsa 5
7
29/08/2001
173/049
TM
30 m
Landsa 5
7
14/09/2001
173/049
TM
30 m
Landsa 5
7
11/08/2003
173/049
ETM+
30 m
Landsa 7
8
17/09/2005
173/049
ETM+
30 m
Landsa 7
8
19/08/2006
173/049
ETM+
30 m
Landsa 7
8
04/09/2006
173/049
ETM+
30 m
Landsa 7
8
20/09/2006
173/049
ETM+
30 m
Landsa 7
8
06/10/2006
173/049
ETM+
30 m
Landsa 7
8
07/09/2007
173/049
ETM+
30 m
Landsa 7
8
23/09/2007
173/049
ETM+
30 m
Landsa 7
8
09/10/2007
173/049
ETM+
30 m
Landsa 7
8
27/08/2009
173/049
ETM+
30 m
Landsa 7
8
12/09/2009
173/049
ETM+
30 m
Landsa 7
8
06/08/2013
173/049
ETM+
30 m
Landsa 7
8
07/09/2013
173/049
ETM+
30 m
Landsa 7
8
05/07/2016
173/049
OLI_TIRS
30 m
Landsa 8
11
13/07/2016
173/049
ETM+
30 m
Landsa 7
8
21/07/2016
173/049
OLI_TIRS
30 m
Landsa 8
11
29/07/2016
173/049
ETM+
30 m
Landsa 7
8
06/08/2016
173/049
OLI_TIRS
30 m
Landsa 8
11
14/08/2016
173/049
ETM+
30 m
Landsa 7
8
22/08/2016
173/049
OLI_TIRS
30 m
Landsa 8
11
30/08/2016
173/049
ETM+
30 m
Landsa 7
8
07/09/2016
173/049
OLI_TIRS
30 m
Landsa 8
11
15/09/2016
173/049
ETM+
30 m
Landsa 7
8
23/09/2016
173/049
OLI_TIRS
30 m
Landsa 8
11
25/08/2017
173/049
OLI_TIRS
30 m
Landsa 8
11
02/09/2017
173/049
ETM+
30 m
Landsa 7
8
10/09/2017
173/049
OLI_TIRS
30 m
Landsa 8
11
18/09/2017
173/049
ETM+
30 m
Landsa 7
8
26/09/2017
173/049
OLI_TIRS
30 m
Landsa 8
11
17/06/2018
173/049
ETM+
30 m
Landsa 7
8
25/06/2018
173/049
OLI_TIRS
30 m
Landsa 8
11

11
03/07/2018
173/049
ETM+
30 m
Landsa 7
8
11/07/2018
173/049
OLI_TIRS
30 m
Landsa 8
11
19/07/2018
173/049
ETM+
30 m
Landsa 7
8
27/07/2018
173/049
OLI_TIRS
30 m
Landsa 8
11
04/08/2018
173/049
ETM+
30 m
Landsa 7
8
20/08/2018
173/049
ETM+
30 m
Landsa 7
8
12/08/2018
173/049
OLI_TIRS
30 m
Landsa 8
11
28/08/2018
173/049
OLI_TIRS
30 m
Landsa 8
11
05/09/2018
173/049
ETM+
30 m
Landsa 7
8
13/09/2018
173/049
OLI_TIRS
30 m
Landsa 8
11
21/09/2018
173/049
ETM+
30 m
Landsa 7
8
29/09/2018
173/049
OLI_TIRS
30 m
Landsa 8
11
08/11/2018
173/049
ETM+
30 m
Landsa 7
8
16/11/2018
173/049
OLI_TIRS
30 m
Landsa 8
11
24/11/2018
173/049
ETM+
30 m
Landsa 7
8
3.2.1. Auxilia y Da a
Heal h acili ies and educa ional acili ies ac oss he coun y we e a ailable by
OSM h ough Uni ed Na ions O ice o he Coo dina ion o Humani a ian A ai s
(UNOCHA)’s humani a ian esponse online po al
h ps://www.humani a ian esponse.in o.
OSM p o ides eely a ailable c owdsou ced spa ial da a, hese da a hough may
no be comp ehensi e, ye a e egula ly upda ed.
3.3. Mapping Flood Ex en
In o de o p oduce lood ex en maps Landsa images we e classi ied o he
h ee ca ego ies sugges ed by (Skakun e al., 2014); wa e o he h ee Nile i e s
and looded a eas, no wa e o all d y lands, and no da a o a eas co e ed wi h
clouds, cloud shadows o missing da a. The alues co esponding o each class is
illus a ed in Table 2, his in o ma ion is impe a i e o unde s anding u he wo k.
Since images we e no cloud ee, aining samples had o be collec ed pe image
o he h ee classes.
A e wa ds, he T ain Suppo Vec o Machine Classi ie ool was ained
using Landsa image and aining samples shape ile. The ou pu is a Classi ie
De ini ion ile. A e ha he classi ie de ini ion ile is used as an inpu in addi ion
12
o he Landsa image o he Classi y Ras e ool, which ou pu is a classi ied
image, ha is he lood ex en map. The ools a e wi hin he Segmen a ion and
Classi ica ion oolbox in he Spa ial Analys Tools in A cMap 10.6.
Table 2: Classes and he co esponding alues
Class Name
Value
Wa e
0
No Wa e
1
No Da a
2
3.4. Mapping Maximum Flood Ex en
A e ob aining a map o lood ex en o all he acqui ed images, a maximum
lood ex en map is p oduced o e e y lood e en , in which lood ex en maps
wi hin he lood e en a e used. E e y pixel on he map is assigned one o he h ee
classes men ioned abo e. A pixel is assigned wa e i a leas he same pixel was
assigned wa e in one o he lood ex en maps, i is assigned no wa e i he same
pixel was assigned as no wa e in all he lood ex en maps, and inally i is assigned
no da a i i was in all he lood ex en maps we e assigned no wa e o no da a.
In o de o calcula e he maximum lood ex en o each lood e en , Ras e
Calcula o ool in he Map Algeb a oolbox was used. A simple mul iplica ion o all
lood ex en maps was pe o med o ob ain an in e media e map wi h wa e class,
no wa e class and o he classes o alues mul iples o he numbe 2. The maximum
lood ex en map is inally p oduced a e assigning all o he classes o he no da a
class alue 2, by using Reclassi y ool in he Reclass oolbox. Bo h he Map
Algeb a oolbox and Reclass oolbox a e in he Spa ial Analys Tools in A cMap
10.6.
13
3.5. Rela i e F equency o Inunda ion
In s a is ics, he equency o an e en is he numbe o imes his e en has
occu ed in a da ase , and ela i e equency is he a io o he equency o an e en
occu ing in a da ase o he numbe o all e en s occu ing in he same da ase .
F om his we can induce ha he equency o inunda ion is o be calcula ed pe
pixel om he maximum lood ex en maps p oduced o all lood e en s. The
equency o inunda ion equals he numbe o imes a pixel was classi ied as wa e .
In o de o calcula e he ela i e equency o inunda ion, he equency o
inunda ion pe pixel is he numbe o imes i was classi ied as wa e di ided by he
o al numbe o imes he pixel was classi ied as wa e and no wa e . To calcula e
RFI alue i s a cons an as e named Wa e cons an as e wi h 0 alue was
c ea ed, ano he cons an as e was c ea ed, named No Wa e cons an as e and i s
alue was 1, hese wo as e iles we e c ea ed using C ea e Cons an Ras e ool in
he Ras e C ea ion Toolbox. A e ha he ool Equal To F equency in he Local
oolbox was used o calcula e he Wa e F equency Ras e and No Wa e F equency
Ras e , in each u n one o he cons an as e iles was used in addi ion o all
maximum lood ex en maps. A o al equency as e was c ea ed using he Ras e
Calcula o ool by adding he wa e and no wa e equency as e iles o each
o he . In o de o calcula e he RFI alue any pixel in he wa e equency ile equal
o 0 was se o null using he Se Null ool in he Condi ional Toolbox. Las ly, in
o de o ob ain he RFI map he Ras e Calcula o ool was used one mo e ime o
di ide he wa e equency as e by he o al equency as e , all he ools used
eside in he Spa ial Analys Tools in A cMap 10.6. The p ocess is illus a ed in
Figu e 3.
14
Figu e 3: RFI Mapping P ocess
3.6. Flood Risk Assessmen
3.6.1. LCLU Map
Since he e we e no LULC maps o he s udy egion eadily a ailable,
we p oduced one as desc ibed below. The LULC map o A ica disclosed
by FAO
(h p://www. ao.o g/geone wo k/s /en/main.sea ch? i le=land%20co e ;
e ie ed 10 Janua y 2020) is e y coa se. (Salman e al., 2008) published a
LULC map o g ea e Kha oum-Sudan, bu i s digi al e sion is no
a ailable, and he au ho s did no answe o he eques o sha e i in due
ime.
The Kha oum s a e LCLU map was p oduced o iden i y lood p one a eas, o
be used la e along wi h he lood haza d map p oduced om he RFI index. The
LCLU classes we e inspi ed om (B ox on e al., 2014)’s LCLU Map o A ica.
The h ee main classes we e Wa e Bodies, U ban, C opland and Vege a ion, in
addi ion o hese classes, he emaining un-u banized a eas ha a e a om he
i e s’ banks we e classi ied as Ba en Land.
In his s ep a Landsa 8 image om 28/12/2016 was acqui ed and clipped in o
he s udy a ea ex en . In o de o ob ain high accu acy o classi ica ion he
21
Figu e 6: Kha oum S a e Flood Haza d Map using he ela i e equency o inunda ion (RFI) de i ed
om Landsa images acqui ed om 1988 o 2018
This map, along wi h he LCLU map o he s a e, and poin ea u es o heal h
acili ies and educa ional acili ies, a e used o assess he deg ee o haza d in a eas
o Kha oum s a e in sec ions below. The signi icance o his map elies on i s use
as a base o u he in ensi e lood isk assessmen o he s a e.
4.4. Flood Risk Assessmen
4.4.1. LCLU Map
In his s ep he LCLU map and he lood haza d map a e used o assess he isk
o loods on Kha oum S a e. The LCLU map is shown in Figu e 7. To assess he
accu acy o he map, Google Ea h® was used as he g ound u h. The o e all
accu acy o he map is 84.2%.

22
Figu e 7: Kha oum S a e LCLU Map
De ailed accu acy assessmen esul s a e shown in Annex 22. The na u e o he
s a e’s a id clima e and u ban s uc u e ha can be seen in Figu e 8, as well as he
mode a e esolu ion (30 m) o Landsa images, led o con usion be ween U ban,
C opland/Vege a ion, and Ba en Land classes. Bu when compa ed o Open S ee
Maps OSM da a as shown in Figu e 9 and Figu e 10, he bounda ies o u ban a eas
o s a e coun ies in ela ion o he oad ne wo k da a we e sa is ac o y.
23
Figu e 8: U ban S uc u e in Aljazee a Eslang, Ka a i Coun y (sou ce: Google Ea h®)
Figu e 9: Kha oum Coun y oad ne wo k (Open S ee Maps OSM) combined wi h LCLU map
24
Figu e 10: Sha g Alneel and Jebel Awlyia coun ies oad ne wo k (Open S ee Maps OSM) combined
wi h LCLU map
4.4.2. Zonal S a is ics
The esul s o he zonal s a is ics o each coun y is shown in Annex 24, in
which he LCLU map was used o zoning. Wi h ega ds o RFI alues o U ban
a eas, Umdu man a e aged 0.23 ± 0.24 as he highes a e age be ween all se en
coun ies, Kha oum and Jebel a e aged 0.21 ± 0.16 and 0.21 ± 0.15 espec i ely.
Ombadah had he lowes a e age o 0.12 ± 0.06. In a eas o c opland and
ege a ions, Umdu man a e aged he highes be ween coun ies wi h 0.66 ± 0.32,
coming a e ha , Ka a i and Kha oum wi h 0.64 ± 0.27 and 0.52 ± 0.35
espec i ely. The lowes RFI a e age o c oplands and ege a ions a ea was also in
Sha g Alneel wi h 0.23 ± 0.17. Ombadah coun y anked a lowe RFI o c oplands
and ege a ions a ea bu was no conside ed because c oplands and ege a ion
ep esen only 1% o coun y a ea.
Since he esul s o zonal s a is ics o heal h and educa ional acili ies we e pe
poin , he a e age RFI alue o each acili y was he used o de i e desc ip i e
s a is ics. Ou o 715 heal h acili ies in he s a e o Kha oum, a o al o 511 heal h
25
acili ies had RFI alue associa ed wi h hem, a e aging 0.19 ± 0.09. As o he
educa ional acili ies, ou o 216 acili ies, a o al o 165 acili ies wi h RFI a e age
alue 0.18 ± 0.09 we e analyzed. De ailed esul s a e shown in Annex 25.
4.5. Humani a ian Mapping Response o Augus 2013 and July
2016 Flood E en s
In Augus 2013, hea y ains ook on se e al s a es o Sudan leading o lash
loods, along wi h Nile i e looding. The c isis led o he dea h o 45 people and
o e 70 inju ed. The se e e damages in in as uc u e has a ec ed a ound 150,000
people ac oss he coun y. Kha oum s a e, being he mos popula ed s a e in Sudan,
was he mos a ec ed, wi h mo e han 84,000 people a ec ed by he loods,
acco ding o epo s by he Wo ld Heal h O ganiza ion (WHO)
(h ps://www.who.in /hac/c ises/sdn/sudan loods2013si ep2.pd ; e ie ed 24
Janua y 2020) and UNOCHA
(h ps://disas e scha e .o g/documen s/10180/13939/OCHASudanFlashUpda eFloo
ds4.pd /a d50da8-cdac-4d22-a906-073e87e47876? e sion=1.0; e ie ed 24
Janua y 2020).
In esponse o a eques by UNITAR/UNOSAT on behal o UNDP o ice in
Sudan, he In e na ional Cha e Space and Majo Disas e s was ac i a ed o
p o ide sa elli e image y, in o de o assess in elie wo k. The cha e is a
collabo a ion be ween 17 space agencies and space esea ch ins i u es. In addi ion
o he cha e membe s, 19 o he en i ies con ibu e o he cha e in e ms o
disas e moni o ing, sa elli e image p o ision, and image analysis and maps
p oduc ion.
Along wi h he cha e , he Da mou h Flood Obse a o y also con ibu ed in
delinea ing he lood e en h ough he analysis o MODIS Te a sa elli e images.
Figu e 11 illus a es wo k done by UNOSAT h ough he cha e and he
obse a o y. I can be clea ha when compa ed o he maximum lood ex en map
p oduced using Landsa 7/Landsa 8 o he same lood e en in Annex 16, he
lood ex en in u ban and c oplands and ege a ions a eas is exceedingly la ge han
wha was cap u ed h ough he Landsa images. This is due o he na u e o he
26
di e en sa elli es used in he wo maps, since Fo mosa 2 and Rada sa 2 bo h
p oduce SAR images ha a e cloud ee. In addi ion, he images we e wi hin days
o he lood e en . Landsa images used in his s udy we e o e 30 days apa , hence
hey we e no able o p ope ly de ec he maximum lood ex en o his e en .
Figu e 11: UNOSAT Flood Ex en o 2013 lood e en using images om Fo mosa 2, Rada sa 2, and
MODIS Te a
As can be seen in he igu e abo e, al hough Sha g Alneel and Bah i coun ies
a e mos inunda ed coun ies, ye mos o hese a eas a e ba en. When o e laying
ba en a ea on op o he lood ex en , he a ec wi h ega ds u ban and c opland
and ege a ion a eas, which is illus a ed in Figu e 12 below. . The maximum lood
ex en map ob ained by Landsa images a e o e laid he cha e and Da mou h
Flood obse a o y lood ex en in Figu e 13 below.

27
Figu e 12: UNOSAT Flood Ex en o 2013 lood e en wi h o e laying ba en a ea
Figu e 13: UNOSAT Flood Ex en o 2013 lood and MODIS Te a wi h he maximum lood ex en o
augus 2013 by Landsa 7, Landsa 8.
28
In 2016 an ea ly ain season s a ed in June wi h hea y ains a ec ing 80,000
people na ionwide, causing loods in 13 ou o 18 s a es. Du ing hese loods
Kha oum s a e was no he mos a ec ed a ea, hence he e was no ex ensi e
mapping o loods o he s a e. The cha e was no ac i a ed o he s a e un il he
9 h o Augus . Images we e analyzed by UNOSAT ocused on he lood ex en on
he banks o he Whi e Nile and Blue Nile i e s. Sa elli e images used o mapping
we e Te aSAR-X o 3 m esolu ion, and SPOT-7 o 1.5 m esolu ion. Bo h maps
a e illus a ed in Figu e 14 and Figu e 15 below. The maximum lood ex en map
o 2016 is based on 11 images collec ed be ween July 5 h and Sep embe 23 d.
Hence, i cap u es almos he whole e en , al hough i s spa ial esolu ion is coa se .
Because o he ex en o he images, a s a e wise compa ison wi h he maximum
lood ex en map o 2016 (Annex 17) is no possible.
Figu e 14: UNOSAT Flood Ex en map on 11 Augus 2016 using Te aSAR-X sa elli e image
29
Figu e 15: UNOSAT Flood Ex en map on 15 Augus 2016 using SPOT-7 sa elli e image
UNITAR has an online po al o hei lood analysis p oduc s unde he
ollowing URL h p:// loods.unosa .o g/geopo al/. The po al is a aluable sou ce
o geospa ial in o ma ion, wi h one downside o he exclusion o o iginal images,
due o copy igh s, which does no allow o analysis g ow h o esea ch pu poses.
30
5. Conclusion
This s udy aimed a mapping lood haza d and assessing lood isk using mul i-
empo al sa elli e image y. Using images om Landsa 4,5,7 and 8, and h ough
speci ic objec i es his s udy was able o iden i y a eas - on coun y le el - ha a e
exposed o loods in Kha oum s a e, Sudan. A eas we e analyzed based on hei
use, and we e and classi ied o u ban, c opland and ege a ion, ba en, and wa e
bodies using a LCLU map ha was p oduced as a pa o he s udy.
Flood haza d was iden i ied using he ela i e equency o inunda ion RFI. By
mapping he RFI indica o , his s udy wen a s ep o wa d in lood isk assessmen
o he s a e o Kha oum, Sudan. A s ep ha was sugges ed by (Ho n & Elagib,
2018) in hei lood managemen amewo k o he capi al ci y Kha oum. This
map can also be used as he haza d ac o when calcula ing lood isk.
Using zonal s a is ics, i was ound ha he g ea es haza d was associa ed wi h
c oplands, due o hei p oximi y o i e banks, anking Umdu man and Ka a i
wi h he highes RFI alues. As o u ban a eas, Jebel Awlyia and Kha oum
coun ies, he mos u banized coun ies had he highes RFI alues. When haza d
deg ee was in es iga ed o public acili ies such as heal h and educa ional
acili ies, hese acili ies we e ound o be wi hin he u ban a eas haza d le el.
Heal h and educa ional acili ies lood haza d a e aged 0.19 and 0.18
consecu i ely, which was calcula ed o 511 heal h acili ies and 216 educa ional
acili ies wi h no RFI ze o alue.
The absence o a Land Co e Land Use LCLU map wi h a mode a e esolu ion
o Kha oum s a e has led o addi ional con ibu ion o his s udy. A LCLU map
was p oduced wi h an o e all accu acy o 84%, whe e he s a e was classi ied o as
u ban, c oplands and ege a ion, ba en, and wa e bodies. This map was an
essen ial componen in de ec ing u ban and c opland a eas wi h high exposu e o
loods.
The a ailabili y o sa elli e images wi h mode a e spa ial esolu ion o delinea e
maximum lood ex en was an essen ial componen o he s udy. The quali y o
acqui ed images, be i he ime o acquisi ion wi h ega ds o he lood e en o he
37
Table 3: Landsa 8 (OLI - TIRS) bands speci ica ions (Sou ce: U.S. Geological Su ey)
Bands
Wa eleng h (µm)
Resolu ion (m)
Band 1 - Coas al ae osol
0.43 – 0.45
30
Band 2 – Blue
0.45 – 0.51
30
Band 3 – G een
0.53 – 0.59
30
Band 4 – Red
0.64 – 0.67
30
Band 5 – Nea In a ed (NIR)
0.85 – 0.88
30
Band 6 – SWIR 1
1.57 – 1.65
30
Band 7 – SWIR 2
Band 8 - Panch oma ic
Band 9 - Ci us
Band 10 – The mal In a ed (TIRS) 1
Band 11 – The mal In a ed (TIRS) 2
2.11 – 2.29
0.50 – 0.68
1.36 – 1.38
10.6 – 11.19
11.50 – 12.51
30
15
30
100
100

38
Annex 2: Maximum Flood Ex en Map o 1991
Table 1: Change ma ix o 1991 lood e en
1991
Wa e
No Wa e
LCLU
U ban
0.96%
99.04%
Wa e Bodies
95.05%
4.95%
C opland/Vege a ion
10.00%
90.00%
Ba en
0.35%
99.65%
39
Annex 3: Maximum Flood Ex en o 1992
Table 1: Change ma ix o 1992 lood e en
1992
Wa e
No Wa e
No Da a
LCLU
U ban
1.60%
67.31%
31.09%
Wa e Bodies
82.15%
5.77%
12.1%
C opland/Vege a ion
12.02%
71.61%
16.38%
Ba en
0.54%
63.19%
36.27%
40
Annex 4: Maximum Flood Ex en o 1993
Table 1: Change ma ix o 1993 lood e en
1993
Wa e
No Wa e
No Da a
LCLU
U ban
1.24%
54.36%
44.40%
Wa e Bodies
94.71%
1.40%
3.9%
C opland/Vege a ion
9.29%
34.74%
55.98%
Ba en
0.86%
83.95%
15.19%
41
Annex 5: Maximum Flood Ex en o 1994
Table 1: Change ma ix o 1994 lood e en
1994
Wa e
No Wa e
No Da a
LCLU
U ban
4.01%
17.84%
78.15%
Wa e Bodies
72.26%
0.19%
27.6%
C opland/Vege a ion
12.75%
3.66%
83.59%
Ba en
1.05%
19.65%
79.30%
42
Annex 6: Maximum Flood Ex en o 1996
Table 1: Change ma ix o 1996 lood e en
1996
Wa e
No Wa e
No Da a
LCLU
U ban
2.29%
94.17%
3.55%
Wa e Bodies
95.50%
4.36%
0.1%
C opland/Vege a ion
9.08%
90.49%
0.44%
Ba en
0.13%
99.26%
0.61%

43
Annex 7: Maximum Flood Ex en o 1997
Table 1: Change ma ix o 1997 lood e en
1997
Wa e
No Wa e
No Da a
LCLU
U ban
1.23%
7.95%
90.83%
Wa e Bodies
95.13%
0.26%
4.6%
C opland/Vege a ion
7.49%
5.01%
87.50%
Ba en
3.88%
17.58%
78.55%
44
Annex 8: Maximum Flood Ex en o 1998
Table 1: Change ma ix o 1998 lood e en
1998
Wa e
No Wa e
No Da a
LCLU
U ban
7.07%
34.03%
58.89%
Wa e Bodies
98.78%
0.57%
0.7%
C opland/Vege a ion
36.83%
35.17%
28.00%
Ba en
2.05%
58.54%
39.42%
45
Annex 9: Maximum Flood Ex en o 1999
Table 1: Change ma ix o 1999 lood e en
1999
Wa e
No Wa e
No Da a
LCLU
U ban
13.83%
6.13%
80.05%
Wa e Bodies
90.46%
0.16%
9.4%
C opland/Vege a ion
32.25%
7.61%
60.14%
Ba en
10.40%
16.89%
72.71%
46
Annex 10: Maximum Flood Ex en o 2001
Table 1: Change ma ix o 2001 lood e en
2001
Wa e
No Wa e
No Da a
LCLU
U ban
1.40%
96.11%
2.49%
Wa e Bodies
97.60%
1.25%
1.1%
C opland/Vege a ion
10.44%
69.13%
20.43%
Ba en
0.03%
92.73%
7.23%
53
Annex 17: Maximum Flood Ex en o 2016
Table 1: Change ma ix o 2016 lood e en
2016
Wa e
No Wa e
No Da a
LCLU
U ban
8.83%
0.50%
90.67%
Wa e Bodies
99.93%
0.00%
0.1%
C opland/Vege a ion
24.92%
0.12%
74.96%
Ba en
4.25%
3.39%
92.36%

54
Annex 18: Maximum Flood Ex en o 2017
Table 1: Change ma ix o 2017 lood e en
2017
Wa e
No Wa e
No Da a
LCLU
U ban
6.93%
30.41%
62.67%
Wa e Bodies
99.86%
0.01%
0.1%
C opland/Vege a ion
26.11%
22.06%
51.83%
Ba en
3.42%
46.99%
49.59%
55
Annex 19: Maximum Flood Ex en o June 2018
Table 1: Change ma ix o June 2018 lood e en
June 2018
Wa e
No Wa e
No Da a
LCLU
U ban
5.91%
11.99%
82.10%
Wa e Bodies
98.57%
0.11%
1.3%
C opland/Vege a ion
13.87%
16.32%
69.81%
Ba en
4.30%
30.91%
64.79%
56
Annex 20: Maximum Flood Ex en o Augus -
Sep embe 2018
Table 1: Change ma ix o Augus 2018 lood e en
Augus 2018
Wa e
No Wa e
No Da a
LCLU
U ban
10.35%
16.76%
72.89%
Wa e Bodies
99.91%
0.01%
0.1%
C opland/Vege a ion
35.17%
8.32%
56.51%
Ba en
7.01%
23.38%
69.61%
57
Annex 21: Maximum Flood Ex en o No embe
2018
Table 1: Change ma ix o No embe 2018 lood e en
No embe 2018
Wa e
No Wa e
No Da a
LCLU
U ban
4.91%
59.54%
35.54%
Wa e Bodies
99.54%
0.29%
0.2%
C opland/Vege a ion
11.83%
59.51%
28.65%
Ba en
2.49%
67.94%
29.57%
58
Annex 22: E o Ma ix and Unce ain y and
Con idence Analysis o LCLU Map
Table 1: Accu acy assessmen esul s o LCLU Map
Table 2: Con idence analysis o accu acy assessmen o LCLU Map
Use ’s
P oduce ’s
U ban
0.7±0.16
0.91±0
Wa e Bodies
1±0
1±0
C opland/Vege a ion
0.9±0.11
0.77±0.14
Ba en Land
0.77±0.15
0.72±0.16
O e all Con idence
0.84 ± 0.07
Use ’s
P oduce ’s
U ban
70.0%
91.0%
Wa e Bodies
100.0%
100.0%
C opland/Vege a ion
90.0%
77.0%
Ba en Land
77.0%
72.0%
O e all Accu acy
84.2%

59
Annex 23: NDWI o 15/09/2016 Landsa image
60
Annex 24: RFI Zonal S a is ics o Kha oum S a e
coun ies
Bah i
MINIMUM
MAXIMUM
MEAN
STANDARD DEVIATION
U ban
0.06
1
0.15
0.10
Wa e Bodies
0.08
1
0.95
0.11
C opland/Vege a ed Land
0.06
1
0.40
0.28
Ba en Land
0.05
1
0.11
0.06
Sha g Alneel
MINIMUM
MAXIMUM
MEAN
STANDARD
DEVIATION
U ban
0.06
1
0.18
0.14
Wa e Bodies
0.07
1
0.94
0.14
C opland/Vege a ed Land
0.06
1
0.23
0.17
Ba en Land
0.05
1
0.12
0.09
Jebel Awlyia
MINIMUM
MAXIMUM
MEAN
STANDARD DEVIATION
U ban
0.06
1
0.21
0.15
Wa e Bodies
0.08
1
1.00
0.05
C opland/Vege a ed Land
0.06
1
0.29
0.21
Ba en Land
0.07
1
0.35
0.25
Kha oum
MINIMUM
MAXIMUM
MEAN
STANDARD DEVIATION
U ban
0.07
1
0.21
0.16
Wa e Bodies
0.23
1
0.99
0.05
C opland/Vege a ed Land
0.06
1
0.52
0.35
Ba en Land
0.08
1
0.40
0.32
Umdu man
MINIMUM
MAXIMUM
MEAN
STANDARD DEVIATION
U ban
0.06
1
0.23
0.24
Wa e Bodies
0.15
1
0.99
0.05
C opland/Vege a ed Land
0.06
1
0.66
0.32
Ba en Land
0.06
1
0.13
0.08
61
Ombadah
MINIMUM
MAXIMUM
MEAN
STANDARD DEVIATION
U ban
0.06
1
0.12
0.06
Wa e Bodies
0.10
0.94
0.65
0.27
C opland/Vege a ed Land
0.06
0.85
0.13
0.07
Ba en Land
0.06
1
0.18
0.13
Ka a i
MINIMUM
MAXIMUM
MEAN
STANDARD DEVIATION
U ban
0.06
1
0.15
0.13
Wa e Bodies
0.12
1
0.98
0.06
C opland/Vege a ed Land
0.06
1
0.64
0.27
Ba en Land
0.06
1
0.15
0.11
62
Annex 25: RFI Desc ip i e S a is ics o Heal h and
Educa ional Facili ies in Kha oum S a e
Heal h Facili ies
Mean
0.19
S anda d E o
0.00
Median
0.18
Mode
0.11
S anda d De ia ion
0.09
Sample Va iance
0.01
Ku osis
0.53
Skewness
0.93
Range
0.47
Minimum
0.08
Maximum
0.55
Sum
97.48
Coun
511.00
Educa ional Facili ies
Mean
0.18
S anda d E o
0.01
Median
0.17
Mode
0.18
S anda d De ia ion
0.09
Sample Va iance
0.01
Ku osis
2.54
Skewness
1.33
Range
0.52
Minimum
0.08
Maximum
0.60
Sum
29.83
Coun
165.00