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

11. Global change in annual sum NDVI 1981-2006

12. Global change in annual rainfall 1981-2006

13. Global correlation between NDVI and rainfall 1981-2006

14. Global change in rain-use efficiency 1981-2006

15. Global change in annual energy-use efficiency 1981-2006

16. Global negative trend in biomass productivity 1981-2006, climate-adjusted

17. Negative trend in biomass productivity 1981-2006, climate-related

18. Global change in net primary productivity 1981-2006

19. Global loss of NPP 1981-2006, climate-adjusted

20. Global positive trend in biomass productivity 1981-2006, climate-adjusted

21. 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

22. Chen et al., 2008 (Int. J. Remote Sensing, 1-19)

23. 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.

24. Degradation by land cover, %
Broad-leaved forest 24 (32)
Needle-leaved forest 19 (29)
Cropland 19 (22)
Shrub & herbaceous 28 (16)
Other 10

25. Degradation by land use systems (FAO), %
Forest 46 (29)
Grassland 25 (16)
Agricultural land 18 (22)
Wetlands 3 (25)
Other 8

26. 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

27. Aridity index r = -0.12

28. 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

29. 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

30. Simplified GLADA Approach

32. 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.

33. 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!

34. 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

43. Thank you!