GLADA Global Land Degradation Assessment

1. This presentation will probably involve audience discussion, which will create action items. Use PowerPoint to keep track of these action items during your presentation • In Slide Show, click on the right mouse button • Select “Meeting Minder” • Select the “Action Items” tab • Type in action items as they come up • Click OK to dismiss this box • This will automatically create an Action Item slide at the end of your presentation with your points entered. GLADAGlobal Land Degradation Assessment Working Session: GIS Techniques and Improved Global Land Degradation Mapping Progress Report Christopher M. Auricht and Freddy Nachtergaele 22 January 2004

2. Agenda • Introduction Background • Progress - • Updating GLASOD Dataset • ASSOD & SOVEUR • Additional improvements • Protected Area, cold areas etc • Techniques for Identification of Hotspots • P/PET • LGP • Other Issues – Clarification and Next Steps • Data & Info Products • Presentation/Visualization • Data Dissemination • Additional processing and analysis – options/requirements

3. Introduction and Background

4. Introduction / Background • Need to appreciate that exercise will remain very much a ‘product in progress’ I.e. On-going activity • Requires a methodology that can accommodate integration of new improved data layers as they become available • Work plan developed around a step-wised approach with outputs from each stage saved as a discrete dataset -> possible to re-run whole process. (Note: Could also develop a spatial model that automates process)

5. ProgressPart 1: ASSOD/SOVEUR • Basic GLASOD Update • Input Datasets • 1:10 million 1990, GLASOD Dataset • ASSOD, 1:5 Million 1995 (South East Asia) • SOVEUR, 1:2.5 Million 2000 (Central & Eastern Europe) • Processing / Merging • Reprojection, recoding and merging of new data • Output • Updated GLASOD dataset

6. GLASOD Update – Major Cause • ASSOD • Original GLASOD

7. GLASOD Update – Degree • ASSOD • Original GLASOD

8. GLASOD Update – Extent • ASSOD • Original GLASOD

9. GLASOD Update – Severity/Impact • ASSOD • Original GLASOD

10. GLASOD Update – Major Cause • SOVEUR • Original GLASOD

11. GLASOD Update – Degree • SOVEUR • Original GLASOD

12. GLASOD Update – Extent • SOVEUR • Original GLASOD

13. GLASOD Update – Degree • SOVEUR • Original GLASOD

14. GLASOD Update – Major Type • SOVEUR • Original GLASOD

15. Summary – ASSOD/SOVEUR • Issues during processing - • Coding not standardised – took time to sort some things out! Would be a lot easier if some standards were introduced and used! • Some polygons without any attributes! • Separating ‘compound’ attributes (eg. Cause/Second Cause Type, severity etc were all in one field in SOVEUR) took additional time -> much easier if each attribute has a separate field • New Dataset produced  • Separate Grids (Datasets) for ASSOD & SOVEUR maintained

16. Improving Rigour based in Thematic Datasets

17. Part 2: Updating of New Master GLASOD Dataset -> Improving Rigour • No of datasets available to improve the rigour – accuracy of GLASOD Dataset • Run a series of iterations on dataset based on specific thematic material and assumptions • Datasets available include • WCMC protected areas • IFPRI’s Agriculture areas dataset • Kassel/FAO Irrigation Dataset • Population • Slope • Livestock • Urban areas etc

18. Part 2: Updating of New Master GLASOD Dataset -> Improving Rigour Cont. • Assumptions • Consider as non-degraded all areas identified as Protected in WCMS dataset regardless of what GLASOD info • Where agriculture is given as cause in GLASOD create new info only retaining that part which is indicated as agriculture in IFPRI’s map or if is irrigated according to Kassel • Where deforestation/loss of vegetation is given as cause only retain where forestry is significant (>25%) • Where overgrazing is problem only retain if livestock density exceeds a certain density • Chemical Pollution – restrict where cause is indicated as ‘salinization’ to areas where there is irrigation • Consider all areas <2% slope free of water erosion • Consider all areas of tropical rain forest as non-degraded except where deforestation is indicated in GLASOD • Consider all areas of population density <2p/km2 as non degraded • Consider all areas with cold or polar climate as non-degraded

19. ProgressPart 2: Results to date • Change Area to NDA if WCMC Protected.

20. Part 2: Results to date. Cont. • Change Area to NDA if Polar Climate

21. Comments • Problems with aggregating 30 arc second datasets to 5 min for Population. Used GAEZ Population Plate 62 – Class One person or less / km2. Surprisingly large area! • Similar problem for WCMC protected areas but overcome by filtering so only cells > 50% protected area retained. • Polar Climate – had minimal impact as low pop areas anyway

22. Comments. Cont. • Tropical Forest & No Forest. Problem summarizing 1 km dataset to 5 min. Used ‘Major Ecosystems’ Plate 55 from GAEZ. Combined Forest Class with Climatic Zones to get ‘Tropical Forest’. Combined Cause Gri, class ‘Deforestation’( Cause-f). -> problem in assiging non-degraded area status to all tropical forest areas no listed as having deforestation as a primary cause of degradation.

23. Comments. Cont. • Sealing – Used ‘Major Ecosystems’ Plate 55 from GAEZ: Class 11: Urban Agglomerates – seemed to work well for Europe and North America, but didn’t pick up some areas in Asia • Water Erosion in Flat Area – Combined Water Erosion (Major_Type, Grid class ‘w’) with slope grid. Worked well.

24. Part 2: Results to date. Cont. • Collective results to date

25. Issues • Datasets kept discrete process can be repeated if/when better info available e.g. population – urban / rural at 5 min • Livestock model not yet available / implemented

26. Techniques for Identification of Hotspots

27. Part 3: Techniques for Identification of Hotspots • 30 Year Datasets for LGP and P/PET obtained from IIASA • Possible to determine hotspot areas • Recently commenced this work

28. ProgressPart 3: Techniques for Identification of Hotspots • Converted all datasets to ESRI Grid format • Explored methods for analysing and visualizing time series data • *.avi movie • Flash movie Click Here to demo *.avi movie Enter Alt+F4 to close the link window Click Here to demo *.flash movie Enter Alt+F4 to close the link window

29. Summary of Outputs to Date

30. Summary of Outputs to date • Updated GLASOD with ASSOD & SOVEUR • Individual GRID datasets for various thematic layers • 30 Year LGP and P/PET Grids • Demo of movie visualization options

31. Next Steps and Issues for Clarification

32. Attention Areas • Other Issues – Clarification and Next Steps • Data & Info Products • Presentation/Visualization –> CD with dynamic viewing tool (Terrastat,Dynamic Atlas, Arc Reader) or pdf etc? • Movies –> Global, regional ? Screen resolution? • Data Dissemination –> Terrastat Integration and/or separate CD?

33. Attention Areas. Cont • Additional processing and analysis for 30 year datasets: options/requirements -> combine grids • Identify hotspots by determining max, min, mean for each cell. • Analyse based on mean class for P/PET and LGP. • Compare results or summarize by FS, AEZ etc?

34. Attention Areas. Cont • Other Datasets – • Use of Remote Sensing products – MODIS, SPOT NDVI, Global Landsat (GeoCover), Africover etc? • Others ?