Climate change mitigation related to Tanzanian forests

2. Climate change mitigation related to Tanzanian forests Key factors for analysis and research prioritizing Ole Hofstad

3. Organisation of the presentation • Mitigating climate change through REDD • Monitoring • Carbon accounting • PES mechanisms • Land-use change modelling • Policy measures within the forest sector • Other policies Climate change mitigation and Tanzanian forests

4. Carbon stocks Climate change mitigation and Tanzanian forests

5. GHG emissions Climate change mitigation and Tanzanian forests

6. The importance of degradation Climate change mitigation and Tanzanian forests

7. Monitoring forest ecosystems • area and density • technologies • sampling • accuracy • frequency • costs Climate change mitigation and Tanzanian forests

8. The monitoring problem may be considered as two separate components: • estimating areas of different vegetation types (e.g.: forest, woodland, savannah, cropland, etc.), and • estimating the average biomass density (tons/ha) in each vegetation type. Climate change mitigation and Tanzanian forests Cropland and burned bush in Northern Mozambique (Photo: E. H. Hansen)

9. Area estimates • Areas may be measured on the ground, either by triangulation using surveying equipment, or GPS. These methods are both time consuming and expensive and best suited for small areas with very high precision requirements. • Areas may be measured on aerial photographs. This is expensive if aerial photography is ordered for this particular use alone. • Areas may be measured on satellite images based on reflected sunlight. Classification of vegetation types may be assisted by competent personnel, or be made unassisted by computer. Using satellite images is the preferred method in most modern applications for large areas of low unit value. Climate change mitigation and Tanzanian forests

10. Climate change mitigation and Tanzanian forests

11. Biomass measurements • Biomass density may be measured on temporary or permanent sample plots in the field. Trees (and bushes) are measured in various ways, e.g. stem diameter, height, crown diameter, etc. These measurements are transformed by allometric functions into estimates of volume or weight of individual trees or bushes. • Biomass density may be estimated on the basis of crown cover measured on aerial photos. • Biomassestimates may be based on data collected by the use of light emitted from an airborne or satellite laser, or • from an airborne or satellite radar. Climate change mitigation and Tanzanian forests The three latter methods (photo, laser, radar) require some sample plots on the ground where trees are measured manually. Such data is necessary in order to calibrate the remote sensing data.

12. Combining area estimates with estiamated biomass density Climate change mitigation and Tanzanian forests

13. Air-borne laser Climate change mitigation and Tanzanian forests

14. Remote sensing of biomass density in forests Points of reflection distributed in space Climate change mitigation and Tanzanian forests

15. Sampling • Stratified sampling • Sampling percentage • Permanent plots • Temporary plots Climate change mitigation and Tanzanian forests Stratification: • Forest types [rain forest (flooded or not), montane forest, seasonal green forest, open forest, shrub, savanna, etc.], cropland, grazing land • Agro-ecological zones, regions, districts • Biomass density • The smaller the reporting unit, the larger sampling percentage is required to give precise estimates

16. Proposed laser project • 1. If FRA2010/NFI decides to measure ground plots either from FRA2010 tiles or along the lines formed by FRA2010 tiles (see map), we should consider offering to fly LiDAR along these lines of FRA2010 tiles in all, or parts of, Tanzania. If we fly all over Tanzania, it will imply flying a total distance of ca 9000 stripe-km, which will give a systematic sample of laser data for all of Tanzania. Calibrated with field data from below the flight corridors, one would be able to give a national biomass estimate for the whole of Tanzania in less than one year (given that field data are measured during the same period). We may even be able to break the estimate down into regional partial estimates. • 2. In addition we should select one of the three "ecosystems" as an object for detailed studies, where we either fly wall-to-wall with LiDAR or fly stripes very close (as proposed in Brazil) in an area of 5-10,000 km2. In this area we must establish a set of separate sample plots on the ground. Observations from these plots will be used to calibrate LiDAR measurements of biomass. This set of data will serve two purposes: • 2a: GEO/FCT sites • 2b: detailed studies of design of laser-mapping of biomass through sampling • 2c: “ground” validation of SAR-study. If we choose tropical rain forest as a case, this will be complementary to Brazil since we may find higher biomass density than in Amazonia. Climate change mitigation and Tanzanian forests

17. Precision Relationship between accuracy (Sm) and number of plots (n) according to different patterns of spatial variation Sm = Standard error CV = Coefficient of variation Climate change mitigation and Tanzanian forests For the REDD-activities in Tanzania, where a lot of different inventories will be performed, it will be of crucial importance to gain basic knowledge on patterns of spatial variation for biomass ha-1 (or volume or basal area ha-1) under different forest conditions and plot designs. A research project to approach these challenges could be performed along the following lines; Systematic review of previously performed inventories with respect to spatial variation Undertake inventories in selected study areas covering important vegetation types and inventory designs Perform theoretical inventory simulations in order to select optimal inventory strategies under different conditions and requirements

18. Frequency • How often will new area estimates be presented? • How often shall biomass estimates be updated? • Rotation on permanent sample plots • Repeated flights [airplane or satellite] (with camera, laser, or radar) • Higher frequency, higher costs Climate change mitigation and Tanzanian forests

19. CARBON IN FOREST • IPCC Guidelines: • Three hierarchical tiers of methods that range from: • default data • simple equations • to the use of country-specific data and models to accommodate national circumstances. • It is good practice to use methods that provide the highest levels of certainty, while using available resources as efficiently as possible. • Combination of tiers can be used. • Living biomass • Trees, bushes, herbs and grass • Above ground • Roots • Ded wood • Logging residues • Ded branches, roots and more • Soil Climate change mitigation and Tanzanian forests

20. LIVING BIOMASS • Biomass expansion factor (BEF/BF) • E.g. IPCC default value = 0.44 tons Dry Matter / m3 fresh volume • Biomass equation • Allometric functions for whole trees or fractions like stem, branches and roots. • E.g.: Biomass above ground • B = 0.3623 dbh1.382h0.64 • B = - 4.22412 + 0.56 dbh2 • Field measurements and laboratory measurement of wood density are required. Climate change mitigation and Tanzanian forests

21. Land-use changes to achieve REDD Climate change mitigation and Tanzanian forests

22. Leakage Climate change mitigation and Tanzanian forests

23. Global trade in forest products Climate change mitigation and Tanzanian forests Main trade flows of tropical roundwood 2007. (million m3) Buongiorno, J., D. Tomberlin, J. Turner, D. Zhang, S. Zhu 2003. The Global Forest Products Model: Structure, Estimation, and Applications.

24. Climate change mitigation and Tanzanian forests Source: Jayant Sathaye, Lawrence Berkeley National Laboratory, California

25. Climate change mitigation and Tanzanian forests

26. Climate change mitigation and Tanzanian forests

27. Land-use model Climate change mitigation and Tanzanian forests

28. Land-use models at village or watershed level • Namaalwa, J., P. L. Sankhayan & O. Hofstad 2007. A dynamic bio-economic model for analyzing deforestation and degradation: An application to woodlands in Uganda. Forest Policy and Economics, 9 (5):479-95. • Sankhayan, P. L., M. Gera & O. Hofstad. 2007. Analysis of vegetative degradation at a village level in the Indian Himalayan state of Uttarkhand – a systems approach by using dynamic linear programming bio-economic model. Int. J. Ecology and Environmental Sciences33(2-3): 183-95. • Hofstad, O. 2005. Review of biomass and volume functions for individual trees and shrubs in southeast Africa. J. Tropical Forest Science, 17(1):413-8. • Namaalwa, J., W. Gombya-Ssembajjwe & O. Hofstad 2001. The profitability of deforestation of private forests in Uganda. International Forestry Review3: 299-306. • Sankhayan, P. L. & O. Hofstad 2001. A village-level economic model of land clearing, grazing, and wood harvesting for sub-Saharan Africa: with a case study in southern Senegal. Ecological Economics38: 423-40. • Hofstad, O. & P. L. Sankhayan 1999. Prices of charcoal at various distances from Kampala and Dar es Salaam 1994 - 1999. Southern African Forestry Journal, 186:15-18. • Hofstad, O. 1997. Woodland deforestation by charcoal supply to Dar es Salaam. J.of Environmental Economics and Management, 33:17-32. Climate change mitigation and Tanzanian forests

29. Tanzanian land-use and forest sector trade models • Kaoneka, A.R.S. 1993. Land use Planning and quantitative modelling in Tanzania with particular reference to agriculture and deforestation: some theoretical aspects and a case study from the West Usambara mountains. Dr.Scient. Thesis, Agriculture University of Norway, Aas. • Monela, G. S. 1995. Tropical rainforest deforestation, biodiversity benefits and sustainable land use: Analytical of economic and ecological aspects related to the Nguru Mountains, Tanzania. Dr. Scient. Thesis, Department of Forestry, Agricultural University of Norway. • Ngaga, Y.M. 1998 Analysis of production and trade in forestry products of Tanzania. Dr.Scient. Thesis, Agriculture University of Norway, Aas. • Makundi, W. R. 2001. Potential and Cost of Carbon Sequestration in the Tanzanian Forest Sector.Mitigation and Adaptation Strategies for Global Change, 6(3-4):335-53. • Ngaga, Y. M. & B. Solberg 2007. Assessing the Suitability of Partial Equilibrium Modelling in Analyzing the Forest Sector of Developing Countries: Methodological Aspects with Reference to Tanzania. Tanzania Journal of Forestry and Nature Conservation, 76:11-27. • Monela, G. C. & J. M. Abdallah 2007. External policy impacts on Miombo forest development in Tanzania. In: Dubé, Y. C. & F. Schmithüsen (eds.): Cross-sectoral policy developments in forestry. • Monela, G. C. & B. Solberg 2008. Deforestation and agricultural expansion in Mhonda area, Tanzania. In: Palo, M. & H. Vanhanen (eds.): World forests from deforestation to transition? Climate change mitigation and Tanzanian forests

30. Policy measures • General policies • Good governance (legal system, transparency, corruption) • Energy • Agriculture • Transport • Sector specific measures • PES (monitoring, verification) • Projects (administrative costs, foreign assistance) • Land; ownership and user rights • Cost effectiveness and efficiency (Cost-Benefit) Climate change mitigation and Tanzanian forests

31. International funding: carbon market, global funds, bilateral donors, NGOs, … Internat. level F u n d Information processing (IRA, FBD) Nationallevel Incentives (flow of money) Flow of information Participatory monitoring Mgt. of forest reserves (Govt., FBD ) CBO, villagers VC, NRC DC Local level Village forests P u b l i c f o r e s t s Forest reserves Satellite based inf., plots Schematic view of a REDD PES system Climate change mitigation and Tanzanian forests

32. Climate change mitigation and Tanzanian forests