Upload
others
View
2
Download
0
Embed Size (px)
Citation preview
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/