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Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai
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Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai

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  1. Global Assessment of Land Degradation and Improvement (GLADA) Zhanguo Bai, Godert van Lynden For DESIRE Plenary Meeting 12 Oct 2010
  2. Definition of Land Degradation FAO (1979): Land degradation is a process which lowers the … capability of soils to produce Millennium Ecosystem Assessment (2005): The reduction in the capacity of land to perform ecosystem goods, functions and services that support society and development UNEP (2007): A long-term loss of ecosystem function and productivity caused by disturbances from which land cannot recover unaided LADA (2008): The reduction in the capacity of the land to provide ecosystem goods and services and assure its functions over a period of time for its beneficiaries
  3. In GLADA,we adopt the UNEP (2007) definition: ‘a long-term loss of ecosystem function and productivity caused by disturbances from which land cannot recover unaided’ then land degradation may be measured by long- term change in net primary productivity (NPP) if other factors that may be responsible (climate, soil, terrain and land use) are accounted for
  4. Biomass is an integrated measure of productivity; deviance from the norm may be a proxy measure of land degradation or improvement Biomass can be assessed by the Normalized Difference Vegetation Index (NDVI), a proxy for NPP Deviations from the norm – Negative trend, hot spots + Positive trend, bright spots Rationale
  5. 1. Identify hot/bright spots using indicators NDVI/NPP trend 2. Eliminate false alarms using RUE, EUE… in addtion with RESTREND 3. Stratify/explain: using phenology, land cover/use change, soil, terrain, population, poverty …. 4. Validate and characterize in field Procedure:
  6. Data: NDVI (NIR-red)/(NIR+red): NASA GIMMS: 8km resolution, fortnightly since 1981, HANTS-ed Climate: Monthly precipitation, GPCC/DWD CRU TS 3.0 : 0.5 degree resolution, monthly NPP: MODIS 8-day NPP, 2000-2006 Land cover/use: JRC GLC2000, FAO Land Use System Soil and terrain: SOTER scale 1:1M incorporating 90m- resolution SRTM digital elevation model Socio-economic: Population, urban areas and poverty indices: The CIESIN Global Rural-Urban Mapping Project : population and urban extent, gridded at 30 arc-second resolution Rate of infant mortality and child underweight status and the gridded population for 2005 at 2.5 arc-minutes resolution
  7. Interpolated Smoothed Harmonic Analysis of NDVI Time-Series (HANTS) algorithm
  8.  Calculate NDVI anomalies (correct for seasonal component) Harmonic Analysis of NDVI Anomalies
  9. Global change in annual sum NDVI 1981-2006
  10. Global change in annual rainfall 1981-2006
  11. Global correlation between NDVI and rainfall 1981-2006
  12. Global change in rain-use efficiency 1981-2006
  13. Global change in annual energy-use efficiency 1981-2006
  14. Global negative trend in biomass productivity 1981-2006, climate-adjusted
  15. Negative trend in biomass productivity 1981-2006, climate-related
  16. Global change in net primary productivity 1981-2006
  17. Global loss of NPP 1981-2006, climate-adjusted
  18. Global positive trend in biomass productivity 1981-2006, climate-adjusted
  19. Hotspots A quarter of land is degrading by area by NPP Africa south of the Equator 13 18 SE Asia 6 14 S China 5 5 N-Central Australia 5 4 The Pampas 3.5 3 Siberian & N American taiga
  20. Chen et al., 2008 (Int. J. Remote Sensing, 1-19)
  21. Chikhaoui et al., (2005) A spectral index for land degradation mapping using ASTER data: Application to a semi-arid Mediterranean catchment. International Journal of Applied Earth Observation and Geoinformation 7, 140–153 (A) Highly degraded soils (B) moderately degraded soils (C) slightly degraded soils.
  22. Degradation by land cover, % Broad-leaved forest 24 (32) Needle-leaved forest 19 (29) Cropland 19 (22) Shrub & herbaceous 28 (16) Other 10
  23. Degradation by land use systems (FAO), % Forest 46 (29) Grassland 25 (16) Agricultural land 18 (22) Wetlands 3 (25) Other 8
  24. 1.5 billion people live on degrading area No simple relationship between rural population density and degradation Global: r= ­0.3 Argentina: 0.2 S Africa: 0.25 Senegal: 0.33 China: 0.04 Tunisia: 0.22 Cuba: 0.23
  25. Aridity index r = -0.12
  26. Soil & Terrain 62% in plain 26% in medium-gradient hill & mountain TOTC (g/kg) Pixels in class % Pixels in degrading land % 0 - 5 28 511 43.8 6 471 32.4 5 - 10 23 605 36.2 9 239 46.3 10 - 30 12 816 19.7 4 134 20.7 > 30 192 0.3 98 0.5 total 65 124 100 19 942 100
  27. SOTER - China a 0 10 20 30 40 50 60 70 80 90 0-5 5-15 15-20 >20 ORMA (%) % Degrading area Degrading area at 95% confidence Whole country c 0 5 10 15 20 25 30 35 40 45 50 <15 15-25 25-45 45-65 >65 CLPC (%) % Degrading area Degrading area at 95% confidence Whole country b 0 5 10 15 20 25 30 35 40 45 50 <=5.0 5.0-6.5 6.5-7.5 7.5-8.5 >8.5 pH % Degrading area Degrading area at 95% confidence Whole countrty
  28. Simplified GLADA Approach
  29. What GLADA can and cannot show  The proxy does not equal land degradation phenomena (or improvement) – such as soil erosion, salinity, or nutrient depletion but gives an indication where this may be found;  Land use change from forest to cropland of lesser biological productivity (<NPP) may well be sustainable and profitable, depending on management;  Vice versa, an increasing biological production (>NPP) may reflect bush encroachment in rangeland or cropland, considered as degradation.
  30. What GLADA can and cannot show  8km resolution; too coarse for simple field checking;  Similarly, a 26-year trend cannot be checked by a single “snapshot”  The lack of consistent time series land use data prohibits consideration of land use change in the global assessment  Method has some inherent limitations, e.g. NDVI has saturation problem for dense forest; and scant rainfall observations in many parts of the world  More local verification remain to be done!
  31. And finally: ­ Other assessments coumpound what happening now with historical legacy ­ Usual suspects (Mediterranean basin, W Asia) cannot get much worse and are hard to recover ­ Areas degrading now can recover ­ Spatial and trend analysis, matching degradation indicators with geo-located socio-economic data, may reveal the drivers
  32. Thank you!

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