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Satellite-based drought monitoring in Kenya in an operational setting

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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

  1. 1. Institute of Surveying, Remote Sensing and Land Information 1 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
  2. 2. 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
  3. 3. Institute of Surveying, Remote Sensing and Land Information 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.
  4. 4. Institute of Surveying, Remote Sensing and Land Information  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
  5. 5. Institute of Surveying, Remote Sensing and Land Information 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)
  6. 6. Institute of Surveying, Remote Sensing and Land Information EN DI WEEE EI ??? Biomass measurements using reflected light in the visible (red) and near infrared (nIR) dnIR dnIR NDVI Re Re     
  7. 7. Institute of Surveying, Remote Sensing and Land Information Problem illustration: Clouds and aerosols are omni-present
  8. 8. Institute of Surveying, Remote Sensing and Land Information NDVI time series (MODIS) for Kenya
  9. 9. Institute of Surveying, Remote Sensing and Land Information Problem description: Anomaly indicators aggravate data quality issues Grassland z-score time profile
  10. 10. Institute of Surveying, Remote Sensing and Land Information Problem description: Avoiding false alarms Data quality matters: • Disaster contingency Funds (DCF) • Index-based insurance (IBLI)
  11. 11. Institute of Surveying, Remote Sensing and Land Information VCI: Vegetation Condition Index
  12. 12. Institute of Surveying, Remote Sensing and Land Information 12 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
  13. 13. Institute of Surveying, Remote Sensing and Land Information Existing filters … used in RS
  14. 14. Institute of Surveying, Remote Sensing and Land Information 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
  15. 15. Institute of Surveying, Remote Sensing and Land Information • 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
  16. 16. Institute of Surveying, Remote Sensing and Land Information 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
  17. 17. Institute of Surveying, Remote Sensing and Land Information “Uncertainty” modeling
  18. 18. Institute of Surveying, Remote Sensing and Land Information 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
  19. 19. Institute of Surveying, Remote Sensing and Land Information Filtering: Calculation of anomalies (VCI & ZVI) 100 MinMax MinVI VCI     SD MeanVI ZVI  (Kogan et al. 2003)
  20. 20. Institute of Surveying, Remote Sensing and Land Information Downweighting of observations according to “uncertainty” 0 … 123 … 0 1 2 3 „Monday“ Anomaly Uncertainties monthly aggregrated Anomaly 4
  21. 21. Institute of Surveying, Remote Sensing and Land Information 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
  22. 22. Institute of Surveying, Remote Sensing and Land Information 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
  23. 23. Institute of Surveying, Remote Sensing and Land Information Achievements Efficient noise removal and gap-filling
  24. 24. Institute of Surveying, Remote Sensing and Land Information Achievements Efficient noise removal and gap-filling Near real-time data processing & weekly updating cycle
  25. 25. Institute of Surveying, Remote Sensing and Land Information 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
  26. 26. Institute of Surveying, Remote Sensing and Land Information 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
  27. 27. Institute of Surveying, Remote Sensing and Land Information 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
  28. 28. Institute of Surveying, Remote Sensing and Land Information 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
  29. 29. Institute of Surveying, Remote Sensing and Land Information 29 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
  30. 30. Institute of Surveying, Remote Sensing and Land Information 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

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