GFOI Space Data Needs and Issues Kenya SDCG-9 Frascati, Italy February 2016

Space-based Data Needs What space-based data is being used for forest management purposes in Kenya? Landsat Imagery Preference for Landsat data due to availability on time series and its moderate resolution that allows categorization of our features of interest. Landsat is used for land cover mapping and change detection. Spot Imagery (2.5m resolution) Aerial photos for specific hotspots SDCG-9 Frascati, Italy February 2016

Space-based Data Needs How is the data Acquired and Managed: Following the Sydney’s GFOI meeting between CCI, GFOI, NASA, SEO and Government of Australia, the Kenya Government has received from GFOI through NASA (Brian Killough’s team): LandSat Imagery for 2014, 2012, 2013, 2010, 2008 covering the entire country. A workflow is in place for the delivery of additional Landsat images from NASA for 2006 to 1990. Few Spot Images were obtained through the MESA (Monitoring of the Environment for Security in Africa) project for the IGAD region Aerial photos were provided by the Government of Kenya (The Department of Surveys) SDCG-9 Frascati, Italy February 2016

Scope of data use in Kenya What is the scope of data use in Kenya? Nation-wide, wall to wall mapping from 1990 to 2015. 3 Landcover maps completed so far based on the LandSat data provided - Landcover map for 2014 Landcover map for 2010 Draft landcover map for 2012. Completed landcover maps shows areas covered by forest land, annual croplands, wooded grasslands, open grasslands, vegetated wetlands, water areas and ‘other classes’ category. What new space-based data is desired? Sentinel 2. High resolution imagery to support validation process ( Spot Imagery, etc) SDCG-9 Frascati, Italy February 2016

Challenges What are the challenges and issues with acquiring and making use of space-based data where GFOI and the CEOS Space Data Coordination Group (SDCG) can help? Most of the challenges have been addressed by GFOI through NASA (Brian Killough): Time required to complete pre-processing of the LandSat scenes (now delivered as LT1 pre-processed by Brian which has tremendously reduced the efforts to process the imagery) Downloading of the data (1990 to 2015) due to internet limitations – also resolved by data delivery by Brian in batches. ‘Strips’ in Landsat 7 (SLC-off problem) – any potential recommendations for gap filling? Cloud free imagery for Kenya is a big problem – any potential recommendations? Seasonality – not enough coverage for the entire country for the dry seasons Data storage – due to the large volume of data, storage will become an issue for the Government. SDCG-9 Frascati, Italy February 2016