Challenges for land use and land cover data in integrated global change assessments

1. Challenges for land use and land cover data in integrated global change assessments Peter Verburg

2. Use of land use and land cover data • Emission reporting of LULUCF sector • Ex-ante assessments of policies (modelling, e.g. GTAP, land use models etc., esp. biofuels) • Socio-economic scenarios • Vulnerability and impact assessment • Adaptation options • Integrated assessment and climate change models

3. Land use effectson cloud formationPielke et al., 1997

4. Land cover change effects on climate assessment • Lack of consistency due to different land cover implementation in climate models • Land cover change effects are significant • Past land cover change are often model-based reconstructions with large uncertainties

5. Impact assessment Land use change Climate change Hydrology Flood risk Hurkmans et al., 2009 Ward et al., 2011

6. Inundation depth Impact assessment Land use change De Moel and Aerts, 2009 Climate change Hydrology Potential damage / Vulnerability Flood risk Land use X Inundation = Damage

7. Role of land use in climate adaptation Land use change Climate change Adaptation measures Hydrology Flood risk Potential damage / Vulnerability

8. remote sensing aerial photographs census / statistics questionnaires narratives reconstructions back casting land transactions (cadastral information) Different sources of land use and land cover data contain different types of information

9. Characteristics High resolution, pixels Available for last 25 years High temporal resolution possible Classification based on user, area and spectral information Land cover!!! Not land use! Remote sensing

10. Different sensors provide different information Temporal consistency problems

11. Land use focus on agriculture resolution: administrative units classification fixed by survey more land use types within one spatial unit spatial units have different spatial extent temporal resolution often 10 years Some land management data available Census data - characteristics

12. Hybrid products Ramankutty et al., 2008

13. Global distribution of irrigation in farmland Irrigated farmland Rainfed farmland Portmann et al., 2010

14. Uncertainty/Inconsistencies in data Observation problems Classification problems (what’s a forest) Temporal dynamics Politics Monitoring land use change

15. Agricultural surveys/census Agricultural surveys • Explanations for inconsistency: • Classification problems ERS-SAR image • (irrigation canals, dykes, swamps, etc.) • Inconsistency of statistical data sources ERS-SAR multi-temporal image interpretation (van de Woerd et al. 2000) Central Luzon, Philippines

16. Statistical sources of rice area

17. EUROPE Remote sensing Statistics

18. EUROPE Remote sensing Statistics

19. Inconsistency in defenitions

20. Inconsistencies due to different definitions Forest Grassland Lund 2004

21. Mosaics and landscapes

25. Representations

26. Polder Zegveld

27. N2O emission of Dutch Western fen meadow system

28. Errors in land cover data are systematic and do not average out in space

29. Aggregation procedures / data processing

32. Differences in landscape structure change are larger due to method than due to scenario (Dendoncker et al., 2008)

34. Land cover vs land use

35. From land cover to land use to land function “Land cover can be a cause, constraint or consequence of land use” (Cihlar and Jansen, 2001) -intended -unintended Verburg et al., 2009 Journal of Environmental Management Land functions: the capacity of the land to provide goods and services

36. Intensity of agriculture in 160.000 LUCAS points (N/ha) LUCAS 2003, 2006 CAPRI 2000

37. Methodology

39. Result: agricultural intensity Europe

40. Drivers of agricultural intensity – Global scale Actual yield Crop specific yields, 5 arc-min [Monfreda et al., 2008] Frontier yield/ yield gap Stochastic frontier production function Reasons for inefficiency Inefficiency factors / Multiple Regressions Neumann et al., 2010 Agricultural Systems

41. Explaining global distributions of yield gab Determinants for the frontier yield: Temperature, PAR, precipitation, soil fertility constraints Determinants for deviation from the frontier yield (=inefficiency effects): Irrigation, market accessibility, market influence, agricultural population, slope Neumann et al., 2010 Agricultural Systems

42. Results Central- USA Efficiency = 1 Germany, France, UK Efficiency = 1 China Efficiency = 1 USA Nile Delta, Europe, E-USA China, Japan, South Korea E-China, E-USA Argentina, NE-China, SE-Europe Mexico, Africa, India Afghanistan, Kazakhstan Bulgaria, Argentina West Africa, NE-India, Thailand Neumann et al., 2010 Agricultural Systems

43. Accessibility Labor Accessibility Irrigation Market influence Accessibility Slope Irrigation Neumann et al., 2010 Agricultural Systems

44. Market influence Irrigation Irrigation Market influence Accessibility Market influence Market influence Accessibility Neumann et al., 2010 Agricultural Systems

45. Irrigation Labor Irrigation Market strength Accessibility Labor Neumann et al., 2010 Agricultural Systems

46. Cropping intensity Siebert et al., 2010

47. Inconsistencies between land use / land cover / land function in integrated assessment models

48. Multi-scale, multi-model approaches for land use analysis Verburg et al., 2006. Agriculture, Ecosystems and Environment Banse et al., 2008. European Review of Ag. Economics Verburg et al., 2008. Annals of Regional Science Verburg and Overmars, 2009. Landscape Ecology Hellmann and Verburg, 2009. Biomass and Bioenergy Eggers et al., 2009. Global Change Biology Bioenergy http://www.cluemodel.nl

49. Impact of Biofuel Directives on Agricultural Land Use, in million ha, 2030 relative to 2007 Banse et al., 2009; Results of GTAP model DG-ENV project