1. SERVIR Eastern and Southern Africa John Kiema Director Technical Services, RCMRD June 2017 Sunyani Ghana

2. Outline Introduction to RCMRD Objectives of SERVIR-E&SA
Services Areas/Thematic Work
Examples of some Projects

3. Who is RCMRD?

4. RCMRD’s Core Mandate Capacity Development Project Implementation
Advisory Services
Research and Development
Repair, Servicing and Calibration of Mapping Equipment
Data and Information Dissemination

5. SERVIR Network
RCMRD SERVIR Hub since 2008

6. SERVIR E&SA Project Goal
To increase the ability of African institutions to apply geospatial technologies in the societal benefit areas identified by Global Earth Observations (GEO), improve the resilience of the region to the impacts of climate change and ensure land use management reduces greenhouse gas emissions.

7. Key Focus during SERVIR E&SA Phase II
SOCIETY
SCIENCE
SPACE
SERVICE
The next 5 years of SERVIR will be strongly shaped by: 1. Society oriented innovations; 2. Science based innovations; 3. Space science supported innovations; 4. Service to society driven innovations. We can confidently affirm that RCMRD is better placed to drive SERVIR in ESA along those strategic directions.

8. SERVIR-ESA Results Framework

9. Service Area/Thematic Work
Agriculture & Food Security
Ecosystems & Landscapes
Water & Related Disasters
Weather & Climate

10. Frost Monitoring & Mapping
Problem
Massive losses in the tea industry due to frost. The service predicts the likelihood of frost events in vulnerable areas.
Milestones
Development of 72hr forecasts; Daily Frost Monitoring; Development of the frost data collection mobile application and training stakeholders on its use.
Increasing stakeholder network 7 new tea companies.
Collaboration with APA insurance in development of a weather based tea insurance index.
Estimated Frost Occurrence on 30th January 2017
Problem
Massive losses in the tea industry due to frost prompted the development of the service which predicts the likelihood of frost events in vulnerable areas, using remotely sensed MODIS data and WRF Numerical Weather Prediction (NWP) model outputs to create of an algorithm for frost detection and prediction.
Product Users: Kericho County Government; Kenya Tea Research Foundation; James Finlays; Sireet Tea; Eastern Produce of Kenya (EPK)
Saraboit Tea; EMROK Tea; DL Koisagat Tea; Kapsimotwa Tea; Farmers
Product Users: Kericho County Government; KTRF; Large-scale tea farmers; Small-scale farmers

11. Disaster Preparedness: New Hazard & Vulnerability Atlas
“The atlas will show areas that are at risk, in terms of hazards. The idea is to prepare communities so that they should know the vulnerable areas.”
Mr. Bernard Sande, Principal Secretary – Dept. of Disaster Management Affairs (DODMA).
QUOTE
Input data: 1. Exposure (LST –modis; forest fires – modis; rainfall – trmm); 2. Sensitivity (poverty levels, population, infant mortality); 3. Adaptive Capacity
Malawi suffers from serious flooding

12. Flood Damage Mitigation: Nzoia Basin
Nzoia Basin has serious flooding problems
Kenya Government is using SERVIR CREST Tool, to estimate flood height & extent for DYKE CONSTRUCTION – SAVING LIVES!
The crest tool, using EO data is being used by national government in kenya to estimate flood height & extent for dyke construction … tool also being used in Rwanda, Uganda & Namibia; coupled routing & excess storage model: input data: trmm, DEM-SRTM

13. Lake Victoria Basin: Water Quality
Modis for water quality operational system; landsat for landcover changes – effectively being able to link water quality degradation with land use changes …
Linking land use changes (climate & development) to water quality

14. Land Cover Mapping for GHG Inventorying
Land cover maps developed for 9 countries
Personnel trained = over 120
Networking = 50 institutions
Maps being used by countries to report to UNFCCC
Due to the capacity built by SERVIR, Kenya with support of the Clinton Foundation has engaged SERVIR/RCMRD to do the LC MAP for Kenya.
Input data: Landsat series, High resolution imagery …

15. Applying VI Mapping for Ecosystem Analyses
No.
Vulnerability Component
Factor
Possible Indicator 1
Exposure
Rainfall
Annual rainfall variability (CV)
Occurrence (frequency) of drought and floods over the years
Temperature
Monthly min & max temperature
CV for min & max 2
Sensitivity
LULC
LULC dynamic
NDVI
NDVI dynamic
Human population
Population dynamic
Animal population
Level of protection (NP, GCA, NR, GR, FR)
Wild animal & Livestock population dynamics
Topography
Slope (%)
Fire
Fire frequency and intensity 3
Adaptive capacity
Protected areas
Conservation status (conservancies, wildlife management areas, community based conservation development)
LUP
Presence/absence of LUPs

16. User Engagement
L-R (Uganda, Rwanda, Kenya, Tanzania)

17. Thank you!