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 NetherlandsbEuropean Commission, Joint Research Centre, Ispra (VA), ItalycInternational Livestock Research Institute, Nairobi, KenyadThe Australian National University, Canberra, AustraliaeWoods Hole Oceanographic Institution, Woods Hole (MA), USA

12 June 2015 – ADRAS workshop – Nairobi, Kenya11:40 – 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 + SMOOTHING17-24 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

14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 540

0.05

0.1

0.15

0.2

0.25

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0.35

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0.45

0.52001

2002

2003

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2011

2012

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eMODIS dekad number

aver

age

ND

VI

(-)

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 2011Z-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

March-S

eptember

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

March-S

eptember

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

14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 540

0.05

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0.35

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0.45

0.52001

2002

2003

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2005

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2008

2009

2010

2011

2012

2013

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eMODIS dekad number

aver

age

ND

VI

(-)

14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54-2

-1.5

-1

-0.5

0

0.5

1

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32001

2002

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eMODIS dekad number

aver

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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

-2

-1.5

-1

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0

0.5

1

1.5

2

Aggregate NDVI first

Z-score per pixel first

seas

on

al z

-sco

re

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 derivebiomass 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)se

ason

ality

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)?

1-1

0Mar

21-

31M

ar

11-

20Apr

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21-

31M

ay

11-

20Ju

n

1-1

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21-

31Ju

l

11-

20Aug

1-1

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21-

30Sep

0

0.1

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0.6Yabello (Ethiopia)

average (2001-2014)20112014av

erag

e N

DV

I (-

)

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?

1 13 25 37 49 61 73 85 97 1091211331451571691811932052172292412532652772893013133253373493613733853974094214334454574694814930

0.1

0.2

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0.7

LRLD SRSD

5-95% dynamic range

14-YEAR VARIABILITY PER DIVISION

evaluate when R2>0.90

use R2cv

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 missingn

y

(95th - 5th) percentile difference > FAPAR uncertainty? (0.1 units)

not vegetatedn

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 andsub-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+12𝑎1{ h𝑡𝑎𝑛 [ (𝑡−𝑎2 )𝑎3 ]+1}+ 12 𝑎4 { h𝑡𝑎𝑛 [ (𝑡−𝑎5 )𝑎6 ]+1}−𝑎4

0

0.1

0.2

0.3

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0.6

0.7

0 10 20 30

fAPA

R

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

Pea

k

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

I ntegral of the fitted model between SOS and EOS. This indicator is proportional to the total GPP.

Seasonal GPP proxy, 𝑪𝑭𝑨𝑷𝑨𝑹=∫𝒔𝒐𝒔

𝒆𝒐𝒔

𝑭𝑨𝑷𝑨𝑹𝒅𝒕