Landcover Mapping for GHG Inventories in the Eastern and Southern Africa RCMRD Experiences Phoebe Oduor AfriGeoss Symposium 27-29th April 2016, Victoria Falls

Vision and Mission

RCMRD Interventions Supporting Earth Observation & Spatial Information for Societal Benefit Land Policy, Governance, Land Management, Agriculture, Biodiversity, Climate, Disasters, Ecosystem, Energy, Health, DRR & Water Education & Policy Advice Capacity Building

Project Countries

Objective To collect ancillary and ground reference data for validating land cover maps derived from satellite imagery for each countryTo collect ancillary and ground reference data for validating land cover maps derived from satellite imagery for each country To develop Land Cover maps from Landsat satellite images using remote sensing techniques.To develop Land Cover maps from Landsat satellite images using remote sensing techniques. To build capacity of the countries through training on Land Cover mapping for GHG Inventory Development in the ESA RegionTo build capacity of the countries through training on Land Cover mapping for GHG Inventory Development in the ESA Region DisseminationDissemination

Project Partners Ministries/Departments of: Forestry Land Management Surveys and Mapping Environment Land Use and/or Planning Land Use Natural Resources Wildlife Agriculture Universities International Organizations Climate and Meteorology Water and Wetlands Private Sector

Image Classification Classification Schema 1. FORESTLAND i.Dense Forest ii.Moderate Dense Forest iii.Sparse Forest iv.Woodland v.Planted Forest 2. SHRUBLAND i.Closed Shrubland ii.Open Shrubland iii.Closed Grassland iv.Open Grassland 3.SETTLEMENT i.Urban Settlement ii.Rural Settlement 4.CROPLAND i.Annual Cropland ii.Perennial Cropland iii.Subsistence iv.Commercial 5.WETLAND i.Water Body ii.Vegetated Wetland 6. OTHERLAND i.Barren Land ii.Beaches iii.Rock Outcrop iv.Snow/Ice Caps v.Salt Crusts vi.Lava Flows vii.Desert Dunes\Sand

Ancillary Data Collection Classification Scheme Development Workshops : Zambia Classification Scheme Development Workshops : Botswana Classification Scheme Development Workshops : Ethiopia Ancillary Data Collection and Classification Scheme Development Workshops: Lesotho

Fieldwork

Capacity Building Namibia and Botswana Capacity Building

Review of Output Based on Fieldwork data Based Ancillary data In country peer review Technical Advisory Board- NASA, USEPA, RCMRD, ICFI, Gazetted Community Forest in Areas marked 1 and 2 mostly Bare ground and Barren Rock  Accuracy Assessment  Google Earth

Dissemination Data catalogue includes: –Land Cover Maps –Technical Reports –Training Manual –Accuracy Reports –Web portal: All this is freely available and can be accessed by a number of open source applications

Dissemination: Interactive DVD

Dissemination: Online Platform

Driver’s of Land Cover Change –Rapid population growth –Urbanisation and mushrooming of informal settlements –Industrialization –Poverty –Farming Practices –Climate Change –Unsustainable use of natural resources: overexploitation Leading to threats on the existing natural resources hence –Deforestation –Increase in Agricultural lands –Charcoal burning for fuel –Conversion of wetlands to agricultural areas and other vegetation.

Achievements of the Project 40 Land Cover maps for the nine countries (Malawi, Rwanda, Zambia, Botswana, Tanzania, Namibia, Ethiopia, Uganda and Lesotho) have been developed. Over 120 people trained in the duration of the project Online platform for accessing maps developed Network with over 50 institutions in the region established. Baseline data to support GHG emission reporting created. Countries have been able to develop their own Land Cover maps from training given: Zambia, Ethiopia

The System for Land-based Emissions Estimation in KENYA The System for Land-based Emissions Estimation in Kenya (SLEEK) is a Government of Kenya program to develop a robust and credible system for estimating land- based emissions in Kenya. SLEEK aims to build a comprehensive account of the land sector, it will draw together data from a range of sources, including:  Forest cover and growth rates.  Soil data and measurements of carbon emissions resulting from various land ‐ use regimes.  Climate and meteorological data.  Remote sensing data that will be used to delineate the country into various land cover types.  Measurements of carbon emissions associated with various crop and plant types.

Land Cover Change Mapping Program Develop (17) Land cover maps that can be used in a Conditional probability Network to monitor Land Cover change over time. Develop data that can not only be used by the SLEEK program but data that is owned by the Government for use in different sectors to inform decision making at the same time used by the National Climate Change secretariat for reporting on Kenya’s emissions.

Multi-temporal Classification- Time Series Multi-temporal processing resolves the uncertain spectral region and more accurately detects genuine land cover change by using the temporal trends in the probabilities of forest cover. Conditional probability models are used to combine probabilities from a number of years to give an overall assessment of the likelihood of land cover change.

CPN processing handles uncertainty AND missing data (e.g. cloud) using temporal CPN output forest classifications using temporal rules 2005 image cloudy2004 image good 2006 image good 2002 image good 2002 input probs from classification

Change and Attribution This is a process for verification of Land Cover Change. It considers changes could be based on:  Fire  Urbanization  Land Degradation  Encroachment  Policy Change e.g., Nyayo tea zones, Excision  Governance e.g.. Degazettement of forests  Climatic Parameters- rainfall, temperature  Economic Drivers- Poverty, Reliance on Charcoal fuel  Technology- Effective planting and Harvesting  Cultural Practices e.g.. Clearing of Forests for Agriculture and Settlements  Change in Crop types

Uses of Land Cover maps The initial objective was to develop reliable, consistent maps with a methodology that is replicable for use in GHG inventories. This would inform the Agriculture Forestry and Land Use sector (AFOLU) Activity data from other sectors was also being collected in the country:  Agriculture  Forestry  Water and Waste treatment  Industries… All this would be integrated using the Agriculture Land use tool (ALU) to give the national emissions level for the selected epochs.

Uses of Land Cover maps Land cover maps are baseline data required for a number of applications: –Baseline data for quantification of GHG emissions –Land degradation –REDD (Reduced Emissions from Degradation and Deforestation) and REDD+ initiatives –Informed decision making on policy issues affecting climate change and environmental protection –Environmental monitoring –Land Use Planning –Agriculture: Crop yield prediction –Vulnerability Assessment Studies

Lessons Learnt Importance of bringing all the stakeholders together to cater for their user needs as well as manage their expectations at the same time understand the gaps and areas of collaboration. Lack of LC mapping standards in many countries. Different map scales suit different needs, it is important for the stakeholders to understand this. Small countries require better resolution data. Importance of continuous capacity building for effective sustainability. Greater impact is achieved when the products are used for the intended and/or unintended purpose. Maps need to be continuously updated for effective monitoring.

30 Thank you!