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 B-MolBelgiumapplications@vito.beTel: +32 14 336807Fax: +32 14 32 2795