1. (GLADA) Global Assessment of Land Degradation and Improvement (?) Godert van Lynden Zhanguo Bai David Dent

2. Method: NDVI analysis (1) Land degradation is defined as a long-term decline in ecosystem function and measured in terms of net primary productivity; NDVI-based indicators are used as proxy indicator of land degradation (“hot spots”) or improvement (“bright spots)” for the period 1981 – 2003; “False alarms” must be eliminated, i.e. other possible causes of biomass change: –Rainfall variability and irrigation have been accounted for by identifying the relationship between productivity and rainfall; –Rain Use Efficiency (RUE): where productivity declined but RUE increased, the cause may be declining rainfall; those areas are masked; –Energy-use efficiency (EUE) is also considered to take account of climate change, esp. at high latitudes; –Urban areas are also masked. Figure 4. Correlation between annual sum NDVI and annual rainfall, 1981-2003

3. Method: NDVI analysis (2) Land degradation is indicated by a negative trend of RUE- adjusted NDVI and is quantified as RUE-adjusted Net Primary Productivity (NPP); As an alternative indicator, the residual trend of sum NDVI (RESTREND) is calculated for all pixels; Land improvement is indicated by a positive trend in both RUE-adjusted NPP and EUE, and is quantified as climate- adjusted NPP; Comparisons are made with other parameters like land cover; soil and terrain; rural population density; and indices of aridity and poverty. Figure 5.Global negative trend in RUE-adjusted NDVI, 1981-2003

4. Scope and limitations of GLADA (1) GLADA is an interpretation of NDVI data –taken as a proxy for NPP; 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.

5. Scope and limitations of GLADA (2) 8km resolution does not capture certain (serious) land degradation phenomena, e.g. gullying and is too coarse for simple field checking; Similarly, a 23-year trend cannot be checked by a single “snapshot “; The lack of consistent time series land use data prevents a general accounting of land use change in the global assessment; Method has some inherent limitations, e.g. saturation of the NDVI signal by dense vegetation; interference by perennial cloudiness; and scant rainfall observations in many parts of the world; Findings remain provisional until validated in the field!

6. GLADA Status and Progress since Pretoria meeting Meeting with LADA partners China National Desertification Monitoring Centre, Dept of Desertification Monitoring, Academy of Forest Inventory and Planning (CNDMC), Beijing, January 2009 Delivery of GLADA preliminary data and metadata of the to FAO 2 papers published in Soil & Use Management, 1 paper in press in Ambio, 1 abstract and full paper being sent to 33rd ISRSE Conference for oral presentation, 4-8 May 2009 Stresa, Italy On-going mapping land use directly from NASA GIMMS NDVI & MODIS data to make allowance for land use change in global land degradation assessment. Start incorporating SOTER data into GLADA index – this is very difficult.

7. New Developments We have devised a procedure that incorporates soil and terrain data and, so fulfils our contract (although it involves a lot of work); The new product shows relative departure from the NDVI trend of the SOTER unit, not an absolute value. It gives a different perspective on hot spots and bright spots but does not supplant the absolute measure given in the climate-adjusted NPP; Adding this information to what we have done already, we can see that the 25-year trend in NPP is explained to some extent by rainfall patterns (a quarter), less by global warming, but hardly at all by soil, terrain, or land cover. The finger points to management in the shape of land use change - the analytical nut that we have yet to crack.

8. A refined RESTREND analysis by SOTER units Taking into account climate terrain and soils.

9. GLADA future GLADA is not a stand-alone exercise but part of the entire LADA project How does GLADA relate to the national assessment – or how could this be improved?

10. Questions for field validation What can we measure in relation to GLADA? Is the biomass trend indicated by GLADA real? Does it correspond with land degradation or improvement measurable on the ground? If so, what is the type of land degradation responsible for the biomass decline? If neither, what has caused the observed NPP trend? For instance, is it a question of timing of observations - where the situation on the ground has recovered?

11. Method: Field validation (for discussion) Check GLADA country reports (and indicate how checked) Further refinement and localization of the NDVI analysis for hot and bright spots; Characterisation of processes at work in the hot and bright spots, using manual or semi-automatic interpretation high- resolution satellite imagery or aerial photos; Examination of other non-continuous spatial data – e.g. soil attributes, socio- economic data, that may reflect the drivers of land degradation; Field examination of hot and bright spots within LADA national program, using the WOCAT/LADA mapping method;