1. Project Overview Isabelle Piccard (VITO) Presented by, Lieven Bydekerke (VITO) CODIST-ii, UNECA, 5 May 2011

2. Content  Introduction to ISAC  Main objectives  Remote Sensing methods for monitoring Agriculture  Questions for discussion 14/01/2011 2

3. Introduction  Information Service for Agricultural Change (EC FP7)  Agriculture is diverse, and changes rapidly  Agricultural production is not constant due to climatic conditions  Policies steer towards agricultural insurance to safeguard farmers  => need for transparent & reliable information on agriculture  Agricultural monitoring methods rely on: Meteorological data Agrometeorological models Remote sensing (mainly satellite images)  Remote Sensing component is based on low spatial data  => ISAC: Improve current montoring methods 3

4. ISAC objectives  Development of 3 prototype services:  Mapping Service Biophysical Parameters  Information Service on Drought stress  Information Service on Agricultural Change  Service demonstration in Belgium, Spain and Ethiopia Existing services based on satellite data with low spatial data, increase level of detail by using better / more recent satellite data Needs assessment

5. Analysis of the Growing Season 14/01/2011 5  Analysis of Cumulative rainfall Zimbabwe: October 2010 / February 2011  Comparison to long term Average OctoberNovemberDecember February January Cumulative

6. Analysis of the Growing Season 14/01/2011 6  Analysis of Vegetation Condition Zimbabwe: October 2010 / February 2011  Comparison to long term Average OctoberNovemberDecember February January March

7. Anomaly maps 14/01/2011 7 Deviations: exceptional or not? From Z-scores (SDVI) to probabilities and return frequencies… Assumptions:  fAPAR: normal distribution  z-scores: standardized normal distribution (mean = 0, stdev = 1)  Associated probabilities (1-sided) and return frequencies: e.g. z-score of -1.64 → probability of obtaining this z-score is 95% or 5% chance of getting a lower score: “once in 20 years” Z = -1.64 95% fAPAR = Remote Sensing based Vegetation Indicator

8. 14/01/2011 8 fAPAR return frequency, end of June – mid August 2006, per municipality, unmixed for grassland Anomaly maps (return frequency) fAPAR = Remote Sensing based Vegetation Indicator

9. Damage assessment 14/01/2011 9 Potential damage map: number of dekads in June-July 2006 (at a total of 6 dekads) with SDVI fAPAR value below -1.64 threshold (return frequency of >20 years), per municipality, for grassland Dark areas: potentially damaged fAPAR = Remote Sensing based Vegetation Indicator

10. Risk mapping 14/01/2011 10 Risk map based on deviations of fAPAR MUNI with fAPAR REG in June-July over a period of 11 years: frequency of deviations > -1.64 (return frequency of >20 years), per municipality, for grassland Dark areas: higher risk fAPAR = Remote Sensing based Vegetation Indicator

11.  Needs assessment:  Is remote sensing actively being used for monitoring the growing season?  What index-based insuracne products currently exist?  What are the experiences, positive and negative?  What is the way forward?  …. 14/01/2011 11 Questions

12. Thank you! 14/01/2011 12 Flemish Institute for Technological Research Remote sensing department - Applications unit Boeretang 200 2400 B-Mol Belgium applications@vito.be Tel: +32 14 336807 Fax: +32 14 32 2795