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

Rationale

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:

Data:NDVI (NIR-red)/(NIR+red): NASA GIMMS: 8km resolution, fortnightly since 1981, HANTS-edClimate: Monthly precipitation, GPCC/DWD

CRU TS 3.0 : 0.5 degree resolution, monthlyNPP: MODIS 8-day NPP, 2000-2006Land 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

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

a

0

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0-5 5-15 15-20 >20ORMA (%)

%

Degrading areaDegrading area at 95% confidenceWhole country

c

0

5

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45

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<15 15-25 25-45 45-65 >65CLPC (%)

%Degrading area

Degrading area at 95% confidence

Whole country

b

0

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45

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<=5.0 5.0-6.5 6.5-7.5 7.5-8.5 >8.5pH

%

Degrading areaDegrading area at 95% confidenceWhole countrty

Simplified GLADA Approach

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.

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!

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!