Potential Applications of GEO-Africa

Transcription

1 Potential Applications of GEO-Africa presentation at the 1 st GEO-Africa Core Team Meeting 22 nd 23 rd February 2010 GEO Secretariat, Geneva Switzerland By Garba Sambo Hassan 1 and Dr Abba Kagu 2 ghsambo@gmail.com kaguabba@yahoo.com 1, Chad 2 Center for Arid Zone Studies, University of Maiduguri - Nigeria

2 Outline Introduction EO Satellite needs in Africa Challenges to Africans Potential Downstream Applications Changes enable by GEO-Africa Conclusion

3 Introduction 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

4 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.

5 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).

6 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

7 Potential downstream applications Agriculture To develop a sustainable & social agriculture Natural Resources Management Preservation of environmental assets Crédit UNEP Coastal & Marine Coastline preservation and sustainable development of offshore activities Risk management To mitigate impacts of natural hazards hitting Africa Land use Land planning, management of land use 30/05/2008 p7

8 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;

9 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Dutse 2007 Dutse Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Desert 2007 Desert 2008 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ngaoundere 2007 Ngaoundere Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mao 2007 Mao Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Bossangoa 2007 Bossangoa 2008 Every feature or land cover has its Spectral Signature that enables its identification/ measurement Basin-wide Monitoring: Lake Chad Watershed NDVI regional values (2007 Decadal)

10 Basin Scale Monitoring of Water related issues Monthly average Vegetation cover of LCB (2007) Decadal Vegetation 2007 Vegetation Peak in Lake Chad Watershed (Oct. 2007)

11 Sand and Dust Storms 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 SeaWifs imagery, 26 Feb, 2000)

12 GeoAfrica for Lake Chad Water Cycle Regime Weekly and Monthly capturing of the Lake with Geo-Africa enable: Minimum, Maximum and Seasonally Flooded Water Extent = Good Monitoring tool. LCWCR Figure 3

13 Nguigmi Rig-Rig Lake Chad Water Cycle Regime 2001 S u rfa c e A re a (K m 2 ) N I G E R Diffa N I G E R I A Kalelawa Bousso Arege Gudunbali GAJIRAM Yebi BAGA Kuwa KUKAWA MONGUNO Liwa C H A D Baga_Sola BOL Djimtilo Mani Hile_Halifa Duiguia MAKARI Isseirom Concerns Fish Yield Prediction High Temporal resolution for flood capture gives more Tourba Ngouri accurate prediction Massakory 0 Legend Djarmaya MARTE Goulfey Legend NGALA Rann Minimum Seasonally Water Minimum Flooded Extent Areas Water Extent Maximum Water Extent Seasonally Flooded Vegetation 1, Legend SqKm Logumane Ala Maximum Water Extent Gajibo DIKWA Kousseri N N Km Km Km Ngelewa Woulki Massaquet DIKWA Class Names Kala C A M E R O O N C A M E R O O N C A M E R O O N Ndjamena ") Kousseri

14 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) on daily/weekly basis effectively as well as to determine accurate areas of recession. Rule: Gradual recession + Wider area coverage = Better for Fishing

15 Fishing Visual Flood Cycle Regime Month to predict fish yield per location/area Frequency of Information Needed (Interval of Capturing) February Decadal (10-Day) March April May June July Decadal (10-Day) Decadal (10-Day) Decadal (10-Day) 5-Day 5-Day Jan Dec Nov Oct Feb Mar Apr May August 5-Day (Egg Laying Period) September 5-Day (Egg Laying Period) October November December 2-Day (Hatching Period) 2-Day (Hatching Period) Daily (Hatching Period) January Daily (Hatching Period) Jun Jul Aug Sep

16 Typical Water Resource Applications : Monitoring Lake Chad Standardized Standardized Annual Annual Discharge Discharge V arition of of standardised Annual Discharge on on Chari River Lake Chad WCR Year Year 2 3 /0 1 / /0 1 / /0 1 / /05/2008 p16

17 2, , , , , , , , , , Monthly Surface Water Area of Lake Chad (September March 2007) 2, , , , , Sep-06 8-Sep Sep Sep Sep-06 6-O ct O ct O ct O ct-06 3-N ov N ov N ov N ov-06 1-D ec-06 8-D ec D ec D ec D ec-06 5-J an J an J an J an-07 2-Feb-07 9-Feb Feb Feb-07 Period Lake CHad (Sept Mar 2007) Surface A rea (SqK m )

18 Typical Land Cover Applications : Vegetation Monitoring Operationalisation Seasonal Changes characteristi detection, cs Dynamics State and potential monitoring growing of Hot Spots vegetation Identification cover Global Changes intensity Périodisme de la végétation Dates d é d émergence mergence. Résilience de la végétation Maximum et l amplitude l de l indice de végétation v Jours Dynamique végétation de 100 Longueur des cycles de 40 végétation NDVI NDVI 120 Date d émergence Décades 120 NDVI max Décades Temps (années) Potentiel de la végétation Productivité primaire et biomasse y = x R 2 = y = x R 2 =

19 Typical Agriculture Applications Mapping Agriculture Extent Main zones of production for food and commercial crops Continent-wide Seasonal need Characterizing Farming System Classes Assessing Land potential (suitability) Determine cropland extent, enumerate crop types and monitor crop growth Continuous observation for all production zones Combined major biophysical constraints : land, climate, water, etc. Region-dependant

20 Agricultural Resources Management Climate modeling for Farmland Operations = Determination of: Onset of rainfall; Retreat of rainfall; Best Planting period; and Duration of rainfall; Onset & Retreat At Stake: Above important farmland operations are presently impossible due to lack of regular spatial and temporal resolution in most situations.

21 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.

22 Determination of Crop Growing, Peak & Harvest in Irrigation Site, Grow ing Peak 21 June NDVI Value st Grow ing Period = 4 months, 10 days 01 Apr to 11 August nd Grow ing Period = 2months, 16 days 17 Sept to 21 Nov st Best Onset Date 21 Feb 2nd Cropping: Best Onset Date 10 September Figure 7 Jan Mar May Jul Sep Nov Irrigation Site Growing Cycle Average Minimum Maximum

23 ESTIMATING AGRICULTURAL STATISTICS IN ETHIOPIA USING THE RAPID LAND COVER MAPPER (RLCM) APPROACH 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

24 Maize and Sorghum Production Areas in Ethiopia

25 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 WARNING SYSTEM to determine uncertainties associated with seasonal flood inundations.

26 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

27 Damages due to Floods

28 Malaria Risk Mapping Risk 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

29 Changes Enabled by GEO-Africa 1/2 Length Vegetation Index Repetitivity (just in time) Date START Date MAX AMOUNT or AVERAGE MAX Spatial Resolution AMPLITUDE Area of Coverage MIN MAI d1 d2 d3 d4 d5 d6 d7../.. d d14 d15 d16 d17 Dekad d18 OCTOBRE 29

30 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.

31 Thank you for your kind attention