satellite-based drought monitoring in kenya in an operational setting

Institute of Surveying, Remote Sensing and Land Information 1

Satellite-based drought monitoring

in Kenya in an operational setting

Clement AtzbergerUniversity of Natural Resources and Life Sciences, Vienna (BOKU),

Institute of Surveying, Remote Sensing and Land Information (IVFL)

Luigi LuminariNational Drought Management Authority (NDMA), Kenya

IBLI workshop, 9-11 June 2015, Nairobi

Institute of Surveying, Remote Sensing and Land Information

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


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.


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


EWS & DROUGHT PHASE CLASSIFICATION

The trigger points between warning stagesdetermined 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)


EN DI WEEE EI ???

Biomass measurements using reflected light in the visible (red) and near infrared (nIR) dnIR

dnIRNDVIRe

Re


Problem illustration: Clouds and aerosols are omni-present

http://cnt.wetteronline.de/cgi-bin/goto?URL=http://wom.wetteronline.de/?content=marketing&mainmenu=contents&submenu=weathermaps&WERBUNG=click_wom_satbild


NDVI time series (MODIS) for Kenya


Problem description: Anomaly indicators aggravatedata quality issues

Grassland z-score time profile


Problem description: Avoiding false alarms

Data quality matters:• Disaster contingency Funds (DCF)• Index-based insurance (IBLI)


VCI: Vegetation Condition Index


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

ND

VI


Existing filters … used in RS


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


• 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


Output: 1

Filtering: Consolidation periods (zero to fourteen weeks)

last 5 weeks are saved (Mondays)

Output: 0Output: 2Output: 3Output: 4Offline

Smoothing

Duration (in weeks) of consolidation period


“Uncertainty” modeling


Du

rati

on

(in

wee

ks)

of

con

soli

dat

ion

per

iod

Week of Year

4 weeks

2 weeks

0 weeks

Week 27

Uncertainty modelling used smoothed signal (“offline”) as reference &observation conditions as predictors


Filtering: Calculation of anomalies (VCI & ZVI)

100MinMax

MinVIVCI

SD

MeanVIZVI

(Kogan et al. 2003)


Downweighting of observations according to “uncertainty”

0

…

123

…

0

1

2

3

„Monday“Anomaly Uncertainties

monthlyaggregrated

Anomaly

4


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


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 = 0.89n = 3312

Intra-annualvariability

Inter-annual variabilitySpatial variability


Achievements

Efficient noise removal and gap-filling


Achievements


Near real-time data processing & weekly updating cycle


Achievements



Various consolidation phases

Strength of the consolidation

high …………………………..low

01234


Achievements




Consistent archive for the various consolidation phases

Current

Strength of the consolidation

high …………………………..low

01234

Archive (LTA, σ, min, max)

01234


Achievements





Modeling of uncertainties at pixel level & for all products


Achievements





Modeling of uncertainties at pixel level & for all products

Integration of uncertainty informationduring temporal (& spatial) aggregration

…

123

…

0

1

2

3

„Monday“Anomaly Uncertainties

monthlyaggregrated

Anomaly

4


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


THANKS!

University of Natural Resources and Life Sciences, Vienna, Austria (BOKU)

Institute of Surveying, Remote Sensing and Land Information (IVFL)

Clement ATZBERGER

[email protected]://ivfl-info.boku.ac.at/

National Drought Management Authority (NDMA), Nairobi, Kenya

Luigi LUMINARI

[email protected]://www.ndma.go.ke/

Automated MODIS data download & data preparation (projection & mosaicking)

Offline smoothingof entire time series

Constrained NRT filteringusing „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

satellite-based drought monitoring in kenya in an operational setting

Technology

remote sensing

institute of surveying

land information vci

impact of drought

drought cycle

drought risk management

land information en

release of contingency