Presented by Clement Atzberger at the IBLI academic workshop, Nairobi, 10 to 11 June 2015

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Satellite-based drought monitoring
in Kenya in an operational setting
Clement Atzberger
University of Natural Resources and Life Sciences, Vienna (BOKU),
Institute of Surveying, Remote Sensing and Land Information (IVFL)
Luigi Luminari
National Drought Management Authority (NDMA), Kenya
IBLI workshop, 9-11 June 2015, Nairobi

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Traditional reaction to drought
 The traditional reaction to
drought and its effect has
been to adopt a crisis
management approach
 This reactive approach is not good policy and should be replaced
by a risk management approach which is anticipatory and
preventive

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WHY A CONTINGENCY FUND?
 One of the main shortcomings in drought risk management
remains the weak linkage between early warning and early
response;
 Inability of the Government and other relevant stakeholders to
facilitate timely response is caused, to a large extent, by
inadequate set-aside funds (contingency funds)
 The availability of sufficient “set-aside contingency funds” can
ensure timely measures to mitigate the impact of drought,
protecting livelihoods and saving lives.

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 The criteria for the release of contingency funds must be
systematic, evidence-based and transparent
 Drought response activities are specific initiatives triggered by
the stages of the drought cycle as signalled by the EWS
 Multi-sectoral Contingency Plans are prepared and activated for
rapid reaction to the early warning. They cover necessary
interventions at each phase of drought
DISBURSEMENT OF DCF

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EWS & DROUGHT PHASE CLASSIFICATION
The trigger points between warning stages
determined through four categories of drought indicators
ENVIRONMENTAL INDICATORS
(impact on biophysical)
PRODUCTION INDICATORS
(impact on livestock and crop production)
ACCESS INDICATORS
(impact on market and access to food and water)
UTILISATION INDICATORS
(impact on nutrition and coping strategy)

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EN DI WEEE EI ???
Biomass measurements using
reflected light in the visible (red) and
near infrared (nIR) dnIR
dnIR
NDVI
Re
Re






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Problem illustration: Clouds and aerosols
are omni-present

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NDVI time series (MODIS) for Kenya

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Problem description: Anomaly indicators aggravate
data quality issues
Grassland z-score time profile

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Problem description: Avoiding false alarms
Data quality matters:
• Disaster contingency Funds (DCF)
• Index-based insurance (IBLI)

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VCI: Vegetation Condition Index

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Sedano et al. (2014)
Smoothing applies in a post hoc sense, where
there is a need to optimally interpolate past
events in a time series.
Smoothing estimates a state based on data
from both previous and later times.
Filtering is relevant in an online learning
sense, in which current conditions are to be
estimated by the currently available data.
Filtering involves calculating the estimate of
a certain state based on a partial sequence
of inputs.
Definitions
time
NDVI

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Existing filters … used in RS

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Principle of Whittaker smoother (Eilers 2003)
 Only one smoothing parameter
 Interpolates automatically
 No boundary effects
 Inputs (MOD13 from Aqua & Terra):
NDVI
composite day of year
quality & cloud flags
Trade-off between fidelity to observations &
smoothness of output

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• Moving window of 175 days: all available MODIS observations are used
• Weighted filtering and interpolation with Whittaker smoother
• Constrained filtering: using „shape“ from statistics
• Filtered NDVI of last 5 weeks are saved (Mondays): 0 1 2 3 4
• Smoothed NDVI of center week is saved
Constrained filtering using Whittaker smoother

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Output: 1
Filtering: Consolidation periods (zero to fourteen weeks)
last 5 weeks are saved
(Mondays)
Output: 0Output: 2Output: 3Output: 4
Offline
Smoothing
Duration (in weeks) of consolidation period

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“Uncertainty” modeling

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Duration(inweeks)ofconsolidationperiod
Week of Year
4 weeks
2 weeks
0 weeks
Week 27
Uncertainty modelling used smoothed signal (“offline”) as reference &
observation conditions as predictors

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Filtering: Calculation of anomalies (VCI & ZVI)
100
MinMax
MinVI
VCI 



SD
MeanVI
ZVI

(Kogan et al. 2003)

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Downweighting of observations according to “uncertainty”
0
…
123
…
0
1
2
3
„Monday“
Anomaly Uncertainties
monthly
aggregrated
Anomaly
4

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wet
no drought
moderate drought
severe drought
extreme drought
Temporal aggregation to monthly VCI using uncertainties for weighting
Spatial and temporal aggregation of anomalies (e.g. VCI)
incl. uncertainties
Vegetation condition index (VCI)
Spatial aggregation to zones
e.g. counties & national livelihood zones

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Comparison of anomalies with FEWS NET data
 pentadal eMODIS NDVI provided by Famine Early
Warning Systems Network (FEWS NET) of the USGS
 VCI calculated for 2003-2014 from consolidated data
 temporally aggregated for 3 month interval
 spatially aggregated to arid and semi-arid land (ASAL)
counties of Kenya
General good
agreement
RMSE = 6%
R² = 0.89
n = 3312
Intra-annual
variability
Inter-annual variability
Spatial variability

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Achievements
Efficient noise removal and gap-filling

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Achievements
Efficient noise removal and gap-filling
Near real-time data processing &
weekly updating cycle

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Achievements
Efficient noise removal and gap-filling
Near real-time data processing &
weekly updating cycle
Various consolidation phases
Strength of the consolidation
high …………………………..low
01234

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Achievements
Efficient noise removal and gap-filling
Near real-time data processing &
weekly updating cycle
Various consolidation phases
Consistent archive for the various
consolidation phases
Current
Strength of the consolidation
high …………………………..low
01234
Archive
(LTA, σ, min, max)
01234

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Achievements
Efficient noise removal and gap-filling
Near real-time data processing &
weekly updating cycle
Various consolidation phases
Consistent archive for the various
consolidation phases
Modeling of uncertainties at pixel
level & for all products

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Achievements
Efficient noise removal and gap-filling
Near real-time data processing &
weekly updating cycle
Various consolidation phases
Consistent archive for the various
consolidation phases
Modeling of uncertainties at pixel
level & for all products
Integration of uncertainty information
during temporal (& spatial)
aggregration
…
123
…
0
1
2
3
„Monday“
Anomaly Uncertainties
monthly
aggregrated
Anomaly
4

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Conclusions & Outlook
 Data quality is of utmost importance
…… errors propagate
 Perfect filtering (in near-real-time) is unrealistic
…. but uncertainty can be modeled
 Filtering is necessary
…… any filtering is better than none
 User perception matters
…. different products confuse users
 Unified NDVI products for Kenya/HoA would be
an asset for all parties

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THANKS!
30
University of Natural Resources and Life Sciences,
Vienna, Austria (BOKU)
Institute of Surveying, Remote Sensing and Land
Information (IVFL)
Clement ATZBERGER
clement.atzberger@boku.ac.at
http://ivfl-info.boku.ac.at/
National Drought Management Authority
(NDMA), Nairobi, Kenya
Luigi LUMINARI
luigi.luminari@dmikenya.or.ke
http://www.ndma.go.ke/
Automated MODIS data download &
data preparation (projection & mosaicking)
Offline smoothing
of entire time series
Constrained NRT filtering
using „shape“ to constrain
Statistics of NRT
filtered data &
quality indicators
NRT calculation of
anomalies and associated
uncertainties
NRT calculation of temporally and spatially
aggregated anomalies
Uncertainty
modelling