Project OverviewIsabelle Piccard (VITO)Presented by, Lieven Bydekerke (VITO)
CODIST-ii, UNECA, 5 May 2011
Content
Introduction to ISAC
Main objectives
Remote Sensing methods for monitoring Agriculture
Questions for discussion
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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
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
Analysis of the Growing Season
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< 25%
25 - 50%
50 - 75%
75 - 100%
100 - 125%
125 - 150%
150 - 175%
> 175%
no data or LTA = 0 mm15/11 15/12 30/03
Sowing Harvesting
Maize crop calendar (FAO 2011)
Analysis of Cumulative rainfall Zimbabwe: October 2010 / February 2011 Comparison to long term Average
October November December
FebruaryJanuary
Cumulative
Analysis of the Growing Season
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15/11 15/12 30/03
Sowing Harvesting
Maize crop calendar (FAO 2011)
Analysis of Vegetation Condition Zimbabwe: October 2010 / February 2011 Comparison to long term Average
October November December
FebruaryJanuary
March
Anomaly maps
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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
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fAPAR return frequency, end of June – mid August 2006, per municipality, unmixed for grassland
Anomaly maps (return frequency)
fAPAR = Remote Sensing based Vegetation Indicator
Damage assessment
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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
Risk mapping
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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
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? ….
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Questions
Thank you!
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Flemish Institute for Technological ResearchRemote sensing department - Applications unit Boeretang 2002400 [email protected]: +32 14 336807Fax: +32 14 32 2795
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