EARLY ASSESSMENT OF FORAGE
AVAILABILITY FOR AN ASSET
PROTECTION INSURANCE SCHEME
Anton Vrielinga, Michele Meronib, Andrew Mudec, Sommarat Chantaratd,
Caroline Ummenhofere, Kees de Biea
aUniversity of Twente, Enschede, The Netherlands
bEuropean Commission, Joint Research Centre, Ispra (VA), Italy
cInternational Livestock Research Institute, Nairobi, Kenya
dThe Australian National University, Canberra, Australia
eWoods Hole Oceanographic Institution, Woods Hole (MA), USA
12 June 2015 – ADRAS workshop – Nairobi, Kenya
11:40 – 12:10

CONTENT
 NDVI time series
 Basics
 Evolving of processing sequence
 Temporal integration period of NDVI
 determine unit-level start- and end- of season from NDVI series
 evaluate earlier predictability of index
 Conclusions
 Challenges

INDICES RELATED TO SEASONAL FORAGE SCARCITY
 Rainfall
 Station-data limited
 Many satellite-derived RFEs, but accuracy for area?
 Vegetation indices
 NDVI (but also others like EVI, fAPAR)
 a real measurement, available from many satellites
 Alternatives products exist:
 soil moisture
 evapotranspiration (from LST)
 temporary water bodies
 Not only what to use, also how to use it!*
* See also: de Leeuw, J., Vrieling, A., Shee, A., Atzberger, C., Hadgu, K., Biradar, C., Keah, H., Turvey, C.,
2014. The potential and uptake of remote sensing in insurance: a review. Remote Sensing 6, 10888-10912.

THE DATA (1): NDVI A SPECTRAL INDEX
NIR
red
indicator of the presence of photosynthetically-active
green vegetation

THE DATA (2): COMPOSITING + SMOOTHING
17-24 May 2015
 Compositing: select ‘best’ pixel
over time period
 Smoothing: reduce remaining
atmospheric effects
 use pixel time series of composites

THE DATA (3): EMODIS 10-DAY SMOOTHED

0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54
averageNDVI(-)
eMODIS dekad number
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
FORAGE SCARCITY INDEX (1): FROM NDVI TO INDEX
 GOAL: indicator of seasonal forage availability within an insurance
unit, relative to ‘normal’ availability
normalization
temporal aggregation
1-10 June 2011 1-10 June
(2001-2014)
1-10 June 2011
Z-score
spatial aggregation

FORAGE SCARCITY INDEX (2): ORIGINAL APPROACH *
1-10 May 2011
NDVI image (10 day) Z-score (compare to 2001-2014) spatial averaging Z-score
Temporal averaging over season Seasonal index
* Chantarat, S., Mude, A.G., Barrett, C.B., Carter, M.R., 2013. Designing index-based livestock
insurance for managing asset risk in northern Kenya. Journal of Risk and Insurance 80, 205-237.

FORAGE SCARCITY INDEX (3): NEW APPROACH *
1-10 May 2011
NDVI image (10 day) NDVI aggregated Temporal averaging
Seasonal average NDVI Z-scoring to get seasonal index
* Vrieling, A., Meroni, M., Shee, A., Mude, A.G., Woodard, J., de Bie, C.A.J.M., Rembold, F., 2014. Historical extension of operational NDVI
products for livestock insurance in Kenya. International Journal of Applied Earth Observation and Geoinformation 28, 238-251.

FORAGE SCARCITY INDEX (3): ADAPTED VS NEW
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54
averageNDVI(-)
eMODIS dekad number
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54
averagezNDVI(-)
eMODIS dekad number
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
20012002200320042005200620072008200920102011201220132014
seasonalz-score
Aggregate NDVI first
Z-score per pixel first

OBJECTIVE
 To better identify the temporal integration period for IBLI’s forage
scarcity index
 phenological analysis
 predictability of end-of-season variability
 Asset replacement vs asset protection
 early assessment  early payout
 Now:
 LRLD: March – September
 SRSD: October – February (payout ideally 1 month later)
Temporal averaging

ARE LRLD/SRSD GOOD SEASONAL DESCRIPTORS?
 Index  overall forage conditions for season
 NDVI describes green vegetation ≠ all forage
 But forage in dry season has been green!
 focus on green biomass build-up only
 Phenological analysis to determine end of season

PHENOLOGICAL ANALYSIS
For more detail, do not hesitate to ask.
Phenological analysis by Michele Meroni.
Adapted version as described in: Meroni M, MM Verstraete, F Rembold, F Urbano, and F Kayitakire. 2014. A phenology-based method to derive
biomass production anomaly for food security monitoring in the Horn of Africa. International Journal of Remote Sensing, 35: 2472-2492.

PIXEL-BASED PHENOLOGY RESULTS (2001-2014 AVERAGE)
seasonality
start long end long
start short end short

PHENOLOGY SUMMARY PER UNIT (AVG ± 0.5 SD)
start long end long
start short end short

CAN WE PREDICT END-OF-SEASON VARIABILITY BEFORE?
 Take as reference identified start/end
Calculate cross-validated R2
Example for Central Wajir (ID=96) but numbers are fictive

WHEN DO WE EXPLAIN 90% OF SEASON VARIABILITY?
end long long: 90%
end short short: 90%

RELATION NEW INTEGRATION TIME VS LRLD / SRSD
long rains short rains R2cv / reduction time

ILLUSTRATION FOR SEVERAL DIVISIONS

CONCLUSIONS
 Phenological analysis provides better seasonal definitions
 Forage scarcity index relates to when forage is developing
 Insurance payments can be made 1-3 month earlier
 considering also season predictability
 accounting for NDVI filtering (rainy season likely more clouds)
 depends on insurance unit
 Earlier payment may allow protection livestock
 purchase of forage, water, medicines

CHALLENGES
 divisions in Turkana with very limited variability
 in-depth understanding of greenness variation on livestock:
 Full assessment of reduction basis risk  reference data
 Plot biomass measurements / time lapse photography / crowdsourcing
 Livestock mortality data / MUAC …
 Drought recalls / weather stations / tree rings …
 effect of previous season on
livestock mortality
 within-season distribution:
importance?
 Is this relationship location-
dependent (or livestock-type
dependent)? 0
0.1
0.2
0.3
0.4
0.5
0.6
1-10Mar
11-20Mar
21-31Mar
1-10Apr
11-20Apr
21-30Apr
1-10May
11-20May
21-31May
1-10Jun
11-20Jun
21-30Jun
1-10Jul
11-20Jul
21-31Jul
1-10Aug
11-20Aug
21-31Aug
1-10Sep
11-20Sep
21-30Sep
averageNDVI(-)
Yabello (Ethiopia)
average (2001-2014)
2011
2014

LEARN MORE ABOUT NDVI SERIES AND ANOMALIES?
 New FAO E-learning course “Remotely Sensed Information for
Crop Monitoring and Food Security – Techniques and methods for
arid and semi-arid areas”
 Lessons 4, 5, 6 by me
http://www.fao.org/elearning/#/elc/en/course/FRS

SPATIAL AGGREGATION
 Use all pixels?
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1
10
19
28
37
46
55
64
73
82
91
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109
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127
136
145
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361
370
379
388
397
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415
424
433
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451
460
469
478
487
496
LRLD SRSD
5-95% dynamic range

14-YEAR VARIABILITY PER DIVISION
 evaluate when R2>0.90
 use R2
cv instead of R2
 fraction explained in prediction
Example for Central Wajir (ID=96)

Phenology retrieval method
 Screening and retrieval of number of GS per year
Time series
> 60 % valid obs
flag missing
n
y
(95th - 5th)
percentile difference
> FAPAR uncertainty?
(0.1 units)
not vegetated
n
y
2 GS per year
Ratio of Lomb normalized
periodogram power spectrum
at 1 and 2 cycles
mono-modal bi-modal
≥6 <6
1 GS per year

Phenology retrieval method
 Retrieval of pixel “climatology”: setting of the temporal breakpoints
that likely separate the periodic climatic cycles in the time-series
Time series
Find minima of
smoothed “median year” *
*iteratively smooth until n. of min = n. of
GS per year
Set the cycle and
sub-cycle breakpoints
Breakpoints are set independently on the actual existence of
a specific season allowing to detect complete season failure

Phenology retrieval method
 Model to be fitted
𝑃𝐷𝐻𝑇 𝑡 = 𝑎0 +
1
2
𝑎1 𝑡𝑎𝑛ℎ 𝑡 − 𝑎2 𝑎3 + 1 +
1
2
𝑎4 𝑡𝑎𝑛ℎ 𝑡 − 𝑎5 𝑎6 + 1 − 𝑎4
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 10 20 30
fAPAR
dekads
grow
dec
PDHT(t)
Parametric Double Hyperbolic Tangent Model: fusion of two ‘S-shaped’
function representative of a typical seasonal signal (7 parameters)

Phenology retrieval method
 Fit the model on the upper envelope of observations
For every expected
cycle, fit the model
to observations if the
season is not failed

Phenology retrieval method
 Extract phenology on the fitted model
GSL
Peak
CFAPAR
Metric Definition
SF Complete season failure if 95th - 5th percentile for that season < 0.05
SOS
Timing of the start of the
growth phase
when modelled season exceeds 20% of local growing amplitude
EOS
Timing of the end of the
decay phase
when modelled season drops below 80% of local decay amplitude
GSL
Length of the growing
season
EOS-SOS
MaxV
Maximum (peak) value of
FAPAR
Max(modelled season)
CFAPAR
Cumulative value of
FAPAR during the period
of plant activity
Integral of the fitted model between SOS and EOS. This
indicator is proportional to the total GPP.
Seasonal GPP proxy, 𝑪𝑭𝑨𝑷𝑨𝑹 =
𝒔𝒐𝒔
𝒆𝒐𝒔
𝑭𝑨𝑷𝑨𝑹 𝒅𝒕