The European Commission’s

science and knowledge service

Joint Research Centre

Remote sensing for food

security early warning and

restoration project monitoring

Felix Rembold, Michele Meroni

Directorate D - Sustainable Resources

Food Security Unit - D5

Satellite Remote Sensing (RS) is a cost-effective tool to monitor the seasonal

development of crops and rangelands globally

RS analysis focuses on the availability pillar of Food Security

Outline of the presentation

JRC remote sensing applications potentially useful to food security analysis:

• Early warning decision support system ASAP (Anomaly hot Spots of Agricultural

Production)

• Land restoration/rural development interventions monitoring with RS

Goal

• Provide (to non-remote sensing experts) timely warnings and short narrative in case of ongoing agricultural problems

ASAP 2-steps workflow

1. Automatic warning classification. Global, every 10 days at first sub-national

admin level

2. Analysts assessment of triggered warnings to validate and describe hotspots

with short narratives. 80 countries of interest, every month at national level

The final selection of hot spot countries depends on expert judgement,

supported by the warning classification system and auxiliary information.

Customers

• EC and EU delegations

• GEOGLAM CM4EW users

• Agricultural experts in general

• Multi-agency food security assessments (IPC, Cadre Harmonisé)?

ASAP early warning system

Globally, Gaul 1 level (province), 10-day

Targeting cropland and rangelands (where and when they grow)

Step 1. Warning classification scheme

4

Detect crop season

based on satellite

imagery

Flag areas where

precipitation or biomass

deficit is observed

Determine area affected

at sub-national admin.

level

Classify type of warning

Poor precipitation1

Poor biomass2

Poor biomass & prec. 3

Poor biomass @ end of seas.4

Indicators concernedLevel

Critical

GAUL 1

Cropland

Active crops

-3 -2 -1 1 2 3

16%

Warning classification, WHAT

Pixel level

Keep track of three possible anomalies (all standardised):

Cumulative from

start of season

1 month 3 months

MODIS (1 km)ECMWF (25 km)

Vegetation conditions

(NDVI)

Rainfall

(SPI)

Indicator:

Time scale:

Source:

Flag as “critical” all the pixels with severe negative standardized anomalies (<-1

standard deviation σ)

SOS25%

SEN75%

EOS35%

MAX

Warning classification, HOW

Admin level

Look at the Area Fraction classified as “Critical”

𝐶𝐴𝐹𝑥 =𝑎𝑟𝑒𝑎 𝑙𝑎𝑏𝑒𝑙𝑙𝑒𝑑 𝑎𝑠 "𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙"

𝑡𝑜𝑡𝑎𝑙 𝑎𝑟𝑒𝑎 𝑜𝑓 𝑎𝑐𝑡𝑖𝑣𝑒 𝑝𝑖𝑥𝑒𝑙𝑠x = SPI1, SPI3, zNDVIc

Any CAF > 25 % will trigger a warning for that admin level

Two conditions to trigger a warning: the

area is subject to a severe negative anomaly

& the area concerned is relevant

Retrieve the admin median progress of

the season (% and phase)

“Expansion” “Maturation “

“Senescence”

Critical

ASAP unit

Cropland

Active crops

zNDVIc • 2 • 4

Phenological phase

Expansion,SenescenceIndicator with CAF>25% maturation

SPI3 • 1+ -

SP1 • 1 -

SPI1 SPI3 • 1++ -

SPI1 zNDVIc • 3 • 4

SPI3 zNDVIc • 3+ • 4

SPI1 SPI3 zNDVIc • 3++ • 4

Ranking of warning levels

Admin level

The final warning level builds on:

Phenological-stage & type of indicator with CAF>25%

RFE-based - Rainfall deficit

possibly evolving into poor growth

NDVI-based - Evidence of poor growth

Both - Poor growth & negative

prospects

Classification output

The Warning Explorer webGIS page 3 November 2017

NDVI of October difference with previous year

500 m

Better

Worse

Step 2. Hotspot analyst assessment

Country level, monthly

Analyse warnings and

auxiliary information

Publish hotspots with

short narrative

Maps and graphs

Analysis of high resolution satellite imagery

Warning Explorer

Overview of past warning levels

Local news by JRC Media Monitor tailored queries

Assign hotspot status at

national level

Analyst assessment output

End of October ‘17

Global overview

map and

narrative

The main ASAP web page

Analyst assessment output

The country reports

End of October ‘17

Country

overview

narrative

Example, Country report

Gaul 1 warning levels and their recent history

The country reports

Example, Country report

Anomaly maps and statistics of areas affected

The country reports

Example, Country report

Land cover and phenology

The country reports

Tabular stats on

agricultural area affected

Outline of the presentation

JRC remote sensing applications potentially useful to food security analysis:

• Early warning decision support system ASAP (Anomaly hot Spots of Agricultural

Production)

• Land restoration/rural development interventions monitoring with RS

• Restoration/rural development interventions can improve FS by

> stopping desertification/land degradation

> improving land productivity (e.g. soil & water conservation, irrigation,

management)

• Studying the effect on FS requires knowledge about correct

implementation / success

• Evaluating the success of an intervention is challenging (lack of

standardized and affordable methodologies)

> success is rarely checked

Objective

Provide a first, standardized and objective assessment of the biophysical

impact of a intervention, in terms of vegetation cover

Land restoration monitoring with RS

• NDVI from satellite imagery as a proxy of vegetation cover

• Rely on comparative methods using a state-of-art statistical techniques:

BACI (Before/After-Control/Impact) design

Method

Time

Controls

ND

VI

Before After

Impact

• to account for natural

changes, the “impact” site is

compared other areas, the

control sites

• it is the differential change

(after vs. before) with respect

to control that matters

t1 t2 t3 t6t5t4

Restoration monitoring

Automatic selection of control areas using RS data

ControlsImpact

Time

Controls

ND

VI

Before After

Impact

B.A.C.I. design

Statistical test on relative NDVIdifferences to assess success (yes/no) and magnitude

Applications: Senegal, Matam region

Great Green Wall for the Sahara and the Sahel Initiative (GGWSSI), a pan-

African project to combat desertification creating a green belt south of the

Sahara

• Mainly tree plantations and rangeland restoration (fencing)

• Cash for work activities indirectly funded by EU through local government

Results

• Good agreement with qualitative

information on the ground

• No biophysical impact detected for 10 out

of 15 projects!

• Success of intervention detected (BACI effect p<0.0001)

Test in 5 countries so far, upscale

planned to systematically monitor

WFP projects

Example: Reforestation in Gamberi desert, Afghanistan

Applications: WFP

Conclusions

ASAP

• Focus on availability pillar of FS

• Continuous and updated evidence-based assessment of ongoing agricultural

season as direct support to FS analysis

• Automatic warning classification (mars.jrc.ec.europa.eu/asap/hsds) followed

by analysts’ assessment(mars.jrc.ec.europa.eu/asap)

• Downloadable summary reports produced for all countries

• Operational and public since May 2017

Rural intervention monitoring with RS

• Objective and cost-effective method to provide a first screening of restoration/rural

development interventions

Thank you!ASAP https://mars.jrc.ec.europa.eu/asap/

Warning Explorer https://mars.jrc.ec.europa.eu/asap/hsds/