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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 Cn tu12 6_isric_glada_mapping_of_desire_study_sites_bai Presentation Transcript

      • Global Assessment of Land Degradation
      • and Improvement (GLADA)
      Zhanguo Bai, Godert van Lynden For DESIRE Plenary Meeting 12 Oct 2010
      • 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
    • 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
      • 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
    • Procedure: 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
    • 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 R ate of infant mortality and child underweight status and the gridded population for 2005 at 2.5 arc-minutes resolution
    • Interpolated Smoothed Harmonic Analysis of NDVI Time-Series (HANTS) algorithm
      • Calculate NDVI anomalies (correct for seasonal component)
      Harmonic Analysis of NDVI Anomalies
    • Global change in annual sum NDVI 1981-2006
    • Global change in annual rainfall 1981-2006
    • Global correlation between NDVI and rainfall 1981-2006
    • Global change in rain-use efficiency 1981-2006
    • Global change in annual energy-use efficiency 1981-2006
    • Global negative trend in biomass productivity 1981-2006, climate-adjusted
    • Negative trend in biomass productivity 1981-2006, climate-related
    • Global change in net primary productivity 1981-2006
    • Global loss of NPP 1981-2006, climate-adjusted
    • Global positive trend in biomass productivity 1981-2006, climate-adjusted
    • 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
    • Chen et al ., 2008 (Int. J. Remote Sensing, 1-19)
    • 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.
    • Degradation by land cover, % Broad-leaved forest 24 (32) Needle-leaved forest 19 (29) Cropland 19 (22) Shrub & herbaceous 28 (16) Other 10
    • Degradation by land use systems (FAO), % Forest 46 (29) Grassland 25 (16) Agricultural land 18 (22) Wetlands 3 (25) Other 8
    • 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
    • Aridity index r = -0.12
    • 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
    • SOTER - China
    • Simplified GLADA Approach
      • 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.
      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!
      What GLADA can and cannot show
    • 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
    • Thank you!