potential applications of geo-africa - earth observations 1st core... · potential applications of...

Potential Applications of

GEO-Africa

presentation at the 1st GEO-Africa Core Team Meeting22nd – 23rd February 2010

GEO Secretariat, Geneva – Switzerland

ByGarba Sambo Hassan1 and Dr Abba Kagu2

[email protected]@yahoo.com

1Lake Chad Basin Commission, N’Djamena, Chad2Center for Arid Zone Studies, University of Maiduguri - Nigeria

Lake Chad Basin Commission, Lake Chad Basin Commission, NN’’DjamenaDjamena

Member States: Nigeria, Niger, Libya, Chad, CAR & Cameroon; Member States: Nigeria, Niger, Libya, Chad, CAR & Cameroon; [email protected]

Outline

�Introduction

�EO Satellite needs in Africa

�Challenges to Africans

�Potential Downstream Applications

�Changes enable by GEO-Africa

�Conclusion



� Human security and poverty alleviation are inconceivable without sustainable development, SD. ECONOMIC GROWTH is nowadays substituted with SD.

� Access to reliable and adequate geospatial information (GI) stimulate growth and sustainable development of any nation. Over 80% of socio-economic benefits are based on quality and accurate information on natural resources and other geospatial data.

� Accessibility to satellites images when needed in Africa is still difficult (huge challenges: archive/current, cloud free, data policy, regular continental wide coverage at medium resolution, etc).

� This presentation dwells on potentials of EO satellites applications in Africa

Introduction



EO Satellite needs in Africa� For an effective management of any SD, monitoring of

our environmental status and its changes in both time and space is most essential. Earth observation (EO) satellites play a vital role in the collection and dissemination of geospatial information, in a very timely manner to provide crucial inputs required for addressing a variety of societal problems of the developing countries, particularly in the areas of natural resources management, disaster monitoring and management and agricultural production among others (as identified by GEO-Africa technical specification’s white paper).

� Hence, steady continental wide coverage at medium and high temporal resolution is needed for attainment of MDG.



Challenges to Africans

� In most African countries, spatial data are often missing,

where exit (gaps) – or out dated and become less useful.

Attainment of SD obviously becomes difficult.

� Most African regions lack reliable monitoring systems that

would enable good understanding of the societal problems.

� There are serious challenges in the use of existing EO

system (available archived and current EOS, difficulties in

acquiring free cloud areas and lack of rational ground

receiving stations for medium resolution optical EO, poor

data policy. Others are poor continental wide coverage, lack

of operational applications, low manpower and

infrastructure base (ICT, power, etc).

Potential Downstream Applications

In terms of Socio-economic:

1. Natural Resources Management;

2. Agricultural resources management;

3. Land use/cover

4. Risk management;

5. Health; and

6. Infrastructure development


Member States: Nigeria, Niger, Libya, Chad, CAR & Cameroon; Member States: Nigeria, Niger, Libya, Chad, CAR & Cameroon; [email protected]/05/2008 p7

Crédit UNEP

AgricultureTo develop a sustainable &

social agriculture

Natural Resources

ManagementPreservation of environmental

assets

Coastal & MarineCoastline preservation and sustainable development of

offshore activities

Land useLand planning, management

of land use

Risk management

To mitigate impacts of natural hazards hitting Africa

Potential downstream applications



Natural Resources Management

In the economic world, What you do not measure, you cannot manage. This is an everlasting dictum. In the natural resources, various development projects indicated the use of EO systems to measure and monitor the resources for their effective management: e.g.

� Forest/Vegetation/Fishing management –preservation of environmental assets;

� Water resources management through better understanding of the Water Cycle;



Every feature or

land cover has

its Spectral

Signature that

enables its

identification/

measurement

-0.2

0

0.2

0.4

0.6

0.8

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Desert 2007 Desert 2008

-0.2

0

0.2

0.4

0.6

0.8


Mao 2007 Mao 2008

-0.2

0

0.2

0.4

0.6

0.8


Dutse 2007 Dutse 2008

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8


Ngaoundere 2007 Ngaoundere 2008

-0.2

0

0.2

0.4

0.6

0.8


Bossangoa 2007 Bossangoa 2008

Basin-wide Monitoring:

Lake Chad Watershed

NDVI regional values

(2007 Decadal)



Basin Scale Monitoring of Water related issues

Monthly average Vegetation cover of LCB (2007)

Vegetation Peak in

Lake Chad Watershed

(Oct. 2007)

Decadal

Vegetation 2007



African dust affecting air quality in Europe (30 October 2001) (after Scientific American: Global Transport of Dust, 2002)

Dust particles carry micro-organisms to Central America, possibly triggering disease outbreaks (after SeaWifsimagery, 26 Feb, 2000)

Sand and Dust Storms



GeoAfrica for Lake Chad Water Cycle Regime

Figure 3

�Weekly and Monthly capturing of the Lake with Geo-Africa enable: Minimum, Maximum and Seasonally Flooded Water Extent = Good Monitoring tool.

LCWCR



C A M E R O O N

N I G E R I A

N I G E RC H A D

")

BOL

Ala

Liwa

Mani

Kala

Rann

Yebi

BAGA

Kuwa

NGALA

DIKWA

Arege

MARTE

Diffa

Ngouri

Tourba

Woulki

MAKARI

Gajibo

KUKAWA

Bousso

Rig-Rig

Duiguia

Goulfey

Ngelewa

MONGUNOGAJIRAM

Nguigmi

Isseirom

Djimtilo

Djarmaya

Kousseri

Logumane

Kalelawa

Baga_Sola

Massakory

Massaquet

Gudunbali

Hile_Halifa

Ndjamena

Legend

Minimum Water Extent

0 20 40 60 80 10010Km

C A M E R O O N

N I G E R I A

N I G E RC H A D

")

BOL

Ala

Liwa

Mani

Kala

Rann

Yebi

BAGA

Kuwa

NGALA

DIKWA

Arege

MARTE

Diffa

Ngouri

Tourba

Woulki

MAKARI

Gajibo

KUKAWA

Bousso

Rig-Rig

Duiguia

Goulfey

Ngelewa

MONGUNOGAJIRAM

Nguigmi

Isseirom

Djimtilo

Djarmaya

Kousseri

Logumane

Kalelawa

Baga_Sola

Massakory

Massaquet

Gudunbali

Hile_Halifa

Ndjamena

0 20 40 60 80 10010Km

Legend

Maximum Water Extent

N

C A M E R O O N

N I G E R I A

N I G E RC H A D

LegendSeasonally Flooded Areas1,189.89 SqKm

")

BOL

Ala

Liwa

Mani

Kala

Rann

Yebi

BAGA

Kuwa

NGALA

DIKWA

Arege

MARTE

Diffa

Ngouri

Tourba

Woulki

MAKARI

Gajibo

KUKAWA

Bousso

Rig-Rig

Duiguia

Goulfey

Ngelewa

MONGUNOGAJIRAM

Nguigmi

Isseirom

Djimtilo

Djarmaya

Kousseri

Logumane

Kalelawa

Baga_Sola

Massakory

Massaquet

Gudunbali

Hile_Halifa

Ndjamena

0 20 40 60 80 10010KmN

Lake Chad Water Cycle Regime 2001

1969.98

3159.17

1189.98

0

500

1000

1500

2000

2500

3000

3500

Minimum Water Extent Maximum Water Extent Seasonally Flooded Vegetation

Class Names

Sur

face

Are

a (K

m2)

Concerns Fish Yield

Prediction

High Temporal

resolution for flood

capture gives more

accurate prediction



Fishing Cycle Regime (FVCR)

� Fishing Visual Cycle Regime (FVCR) = Ability to use EO satellite images to predict Fish Yield within wetlands or Floodplains.

� At Stake: Lack of regular spatial and temporal resolution images, hence, difficult, inaccurate prediction and impossible to observe/realize the FVCR.

� With GEO-Africa, higher temporal and medium resolution can monitor fishing activity per location (Lakes/Floodplains) ondaily/weekly basis effectively as well as to determine accurate areas of recession.

Rule: Gradual recession + Wider area coverage = Better for Fishing



Fishing Visual Flood Cycle RegimeFishing Visual Flood Cycle Regimeto predict fish yield per location/areato predict fish yield per location/area

FebMarAprMay

JunJulAugSep

JanDecNovOct

Daily (Hatching Period)January

Daily (Hatching Period)December

2-Day (Hatching Period)November

2-Day (Hatching Period)October

5-Day (Egg Laying Period)September

5-Day (Egg Laying Period)August

5-DayJuly

5-Day June

Decadal (10-Day)May

Decadal (10-Day)April

Decadal (10-Day)March

Decadal (10-Day)February

Frequency of Information

Needed (Interval of Capturing)Month


Member States: Nigeria, Niger, Libya, Chad, CAR & Cameroon; Member States: Nigeria, Niger, Libya, Chad, CAR & Cameroon; [email protected]/05/2008 p16

Typical Water Resource Applications : Monitoring Lake Chad

2 3 /0 1 /1 9 9 92 3 /0 1 /1 9 9 9 2 5 /0 1 /1 9 9 82 5 /0 1 /1 9 9 8 2 1 /0 1 /1 9 9 72 1 /0 1 /1 9 9 7

Varition o f standardised Annual Discharge on Chari Varition o f standardised Annual Discharge on Chari Varition o f standardised Annual Discharge on Chari Varition o f standardised Annual Discharge on Chari

River River River River

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

1955 1965 1975 1985 1995

Year

Stand

ardize

d Ann

ual D

isch

arge

Varition o f standardised Annual Discharge on Chari Varition o f standardised Annual Discharge on Chari Varition o f standardised Annual Discharge on Chari Varition o f standardised Annual Discharge on Chari

River River River River

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

1955 1965 1975 1985 1995

Year

Stand

ardize

d Ann

ual D

isch

arge

Lake Chad WCR



Monthly Surface Water Area of Lake Chad (September 2006 - March 2007)

2380.16

1,874.48

2,457.91

2,630.782,673.602,673.602,673.602,673.60

2,503.87

1,000.00

1,200.00

1,400.00

1,600.00

1,800.00

2,000.00

2,200.00

2,400.00

2,600.00

2,800.00

1-S

ep-0

6

8-S

ep-0

6

15-S

ep-0

6

22-S

ep-0

6

29-S

ep-0

6

6-O

ct-0

6

13-O

ct-0

6

20-O

ct-0

6

27-O

ct-0

6

3-N

ov-0

6

10-N

ov-0

6

17-N

ov-0

6

24-N

ov-0

6

1-D

ec-0

6

8-D

ec-0

6

15-D

ec-0

6

22-D

ec-0

6

29-D

ec-0

6

5-Ja

n-07

12-J

an-0

7

19-J

an-0

7

26-J

an-0

7

2-F

eb-0

7

9-F

eb-0

7

16-F

eb-0

7

23-F

eb-0

7

Period

Sur

face

Are

a (S

qKm

)

Lake CHad (Sept 2006 - Mar 2007)



OperationalisationOperationalisationOperationalisationOperationalisationOperationalisationOperationalisationOperationalisationOperationalisation

Seasonal Seasonal Seasonal Seasonal Seasonal Seasonal Seasonal Seasonal

characteristicharacteristicharacteristicharacteristicharacteristicharacteristicharacteristicharacteristi

cscscscscscscscs

State and State and State and State and State and State and State and State and

potential potential potential potential potential potential potential potential

growing of growing of growing of growing of growing of growing of growing of growing of

vegetation vegetation vegetation vegetation vegetation vegetation vegetation vegetation

cover cover cover cover cover cover cover cover

Global Global Global Global Global Global Global Global

Changes Changes Changes Changes Changes Changes Changes Changes

intensityintensityintensityintensityintensityintensityintensityintensity

Changes Changes Changes Changes Changes Changes Changes Changes

detection, detection, detection, detection, detection, detection, detection, detection,

Dynamics Dynamics Dynamics Dynamics Dynamics Dynamics Dynamics Dynamics

monitoring monitoring monitoring monitoring monitoring monitoring monitoring monitoring

Hot Spots Hot Spots Hot Spots Hot Spots Hot Spots Hot Spots Hot Spots Hot Spots

IdentificationIdentificationIdentificationIdentificationIdentificationIdentificationIdentificationIdentification

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Décades

NDVI

NDVI max

20

40

60

80

100

120

140

160

Temps (années)

Jours

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Décades

NDVI

Date d’émergence

y = 0.0002x + 0.1676

R2 = 0.8112

y = 0.0002x + 0.1638

R2 = 0.7232

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0 500 1000 1500 2000

PPéériodismeriodisme de la de la

vvééggéétationtationDates dDates dDates dDates d’é’é’é’émergencemergencemergencemergence.

RRéésilience de la silience de la

vvééggéétationtationMaximum et lMaximum et lMaximum et lMaximum et lMaximum et lMaximum et lMaximum et lMaximum et l’’’’’’’’amplitude de amplitude de amplitude de amplitude de amplitude de amplitude de amplitude de amplitude de

llllllll’’’’’’’’indice de vindice de vindice de vindice de vindice de vindice de vindice de vindice de vééééééééggggggggéééééééétationtationtationtationtationtationtationtation

Dynamique de Dynamique de

vvééggéétationtationLongueur des cycles de Longueur des cycles de Longueur des cycles de Longueur des cycles de Longueur des cycles de Longueur des cycles de Longueur des cycles de Longueur des cycles de

vvvvvvvvééééééééggggggggéééééééétationtationtationtationtationtationtationtation

Potentiel de la Potentiel de la

vvééggéétationtationProductivitProductivitProductivitProductivitProductivitProductivitProductivitProductivitéééééééé primaire et primaire et primaire et primaire et primaire et primaire et primaire et primaire et

biomassebiomassebiomassebiomassebiomassebiomassebiomassebiomasse

Typical Land Cover Applications : Vegetation Monitoring



Typical Agriculture Applications

Mapping Agriculture Extent

Main zones of production for food and commercial crops

⇒ Continent-wide

⇒ Seasonal need

Characterizing Farming System Classes

Determine cropland extent, enumerate crop types and monitor crop growth

⇒ Continuous observation for all production zones

Assessing Land potential (suitability)

Combined major biophysical constraints : land, climate, water, etc.

⇒ Region-dependant



Agricultural Resources Management

� Climate modeling for Farmland Operations = Determination of:

�Onset of rainfall;

�Retreat of rainfall;

�Best Planting period; and

�Duration of rainfall;

� At Stake: Above important farmland operations are presently impossible due to lack of regular spatial and temporal resolution in most situations.

Onset & Retreat



Modelling for Farmland

Operations/Food Security Early Warning

� With GeoAfrica, specified locations within Lake Chad Basin can be captured at least 5-day interval, continuously as the satellite is right above the Basin. Thus, Onset and Retreat of Rainfall can be determined from images.

�Also the Satellite helps to choose:

�Best Planting days (from NDVI measurements)

�Monitors Crop growths and Actual Cultivable extent per location.



Determination of Crop Growing, Peak & Harvest in Ir rigation Site, 2007

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Jan

Mar

May Ju

l

Sep Nov

ND

VI V

alue

Irrigation Site Growing Cycle Average Minimum Maximum

1st Best Onset Date 21 Feb

2nd Cropping:Best Onset Date10 September

1st Grow ing Period = 4 months, 10 days01 Apr to 11 August 2007

2nd Grow ing Period = 2months, 16 days17 Sept to 21 Nov 2007

Grow ing Peak 21 June

Figure 7



� Objective:� To undertake rapid mapping for improved derivation of agricultural statistics (area under cultivation, crop estimation, etc) using remote sensing technology and help clear up the discrepancies in published statistics

� Current Methodology:� High resolution images (QuickBird, Ikonos, etc) are interpreted based on a vector dot-grid matrix.

� Raster land use / land cover maps are generated from the dot-grid matrix

� The interpretation is used to monitor and extract agricultural statistics from low resolution images (e.g MODIS, MERIS) that have higher repeat

ESTIMATING AGRICULTURAL STATISTICS IN ETHIOPIAUSING THE RAPID LAND COVER MAPPER (RLCM) APPROACH



Maize and Sorghum Production Areas in Ethiopia



Risk management

� Variability in seasonal inundation levels → loss of crops; destruction of infrastructure.

� Use GEO-Africa images (areal extent of water) in conjunction with radar altimeter data (flood level height) to relate with precipitating cloud (MSG) for EARLY EARLY WARNING SYSTEMWARNING SYSTEM to determine uncertainties associated with seasonal flood inundations.



Africa Vegetation Cover April 2007

For flood prediction and monitoring;

For development and improvement of Operational

Agricultural Monitoring;

•Agricultural Production Monitoring

•Famine Early Warning

•Food Security



Damages due to Floods



Malaria Risk MappingRisk Maps based on Vegetation Health (VHI), Moisture Conditions (VCI) and Thermal Conditions (TCI). All estimations from AVHRR. Correlation of Annual Malaria Incidence (AMI) with Moisture Index (VCI) is possible



� Repetitivity (just in time)

� Spatial Resolution

� Area of Coverage

Changes Enabled by GEOChanges Enabled by GEO--AfricaAfrica

OCTOBRE

d1d2

d3d4

d5d6

d7../..

2000d14

d15d16

d17

d13

d18

Dekad

Vegetation Index

MAI

MIN

MAX

AMPLITUDE

DateSTART

DateMAX

1/2 Length

AMOUNT or AVERAGE



Conclusion

� SD is achieved through access to reliable, timely and adequate geospatial information. African countries are faced with numerous challenges in terms of accessibility, regular spatial and temporal resolutions in most disaster situations. Hence, the need for prediction and early warning system.

Thank you for your kind attention

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