doi: 10.1111/j.1475-2743.2008.00169.x
Soil Use and Management, September 2008, 24, 223–234
REVIEW ARTICLE
Proxy global assessment of land degradation
Z. G. Bai1, D. L. Dent1, L. Olsson2 & M. E. Schaepman3
1
ISRIC – World Soil Information, Box 353, 6700 AJ Wageningen, The Netherlands, 2Lund University Centre for Sustainability
Studies, Box 170, S-22100 Lund, Sweden, and 3Wageningen University Centre for Geo-Information, Box 47, 6700 AA Wageningen,
The Netherlands
Abstract
Land degradation is always with us but its causes, extent and severity are contested. We define land
degradation as a long-term decline in ecosystem function and productivity, which may be assessed
using long-term, remotely sensed normalized difference vegetation index (NDVI) data. Deviation from
the norm may serve as a proxy assessment of land degradation and improvement – if other factors
that may be responsible are taken into account. These other factors include rainfall effects which may
be assessed by rain-use efficiency, calculated from NDVI and rainfall. Results from the analysis of the
23-year Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data indicate declining
rain-use efficiency-adjusted NDVI on ca. 24% of the global land area with degrading areas mainly in
Africa south of the equator, South-East Asia and south China, north-central Australia, the Pampas
and swaths of the Siberian and north American taiga; 1.5 billion people live in these areas. The results
are very different from previous assessments which compounded what is happening now with historical land degradation. Economic appraisal can be undertaken when land degradation is expressed in
terms of net primary productivity and the resultant data allow statistical comparison with other variables to reveal possible drivers.
Keywords: Land degradation, normalized difference vegetation index, net primary productivity,
rain-use efficiency, global relationships
Introduction
Land degradation is a contentious field. Crucial questions
not yet answered in a scientifically justifiable way include: is
land degradation a global issue or a collection of local problems? Which regions are the hardest hit; how hard are they
hit? Is it mainly a problem of drylands? Is it mainly associated with farming? Is it related to population pressure or
poverty? The present assessment carried out within the FAO
programme Land Degradation Assessment in Drylands
(LADA) addresses these questions using justifiable methods.
The only previous global assessment, the Global Assessment of Human-induced Soil Degradation (GLASOD), distinguished degrees and kinds of degradation, e.g. soil erosion
by water or by wind (Oldeman et al., 1991). It was a map
based on perceptions, not a measure of land degradation; its
qualitative judgments (Table 1) have proven inconsistent and
Correspondence: D. L. Dent. E-mail: [email protected]
Received March 2008; accepted after revision June 2008
Editor: Prof. Donald Davidson
hardly reproducible, relationships between land degradation
and policy-pertinent criteria were unverified (Sonneveld &
Dent, 2007), although, to be fair, its authors were the first to
point out the limitations.
Land degradation may be defined as long-term loss of
ecosystem function and productivity caused by disturbances
from which land cannot recover unaided. It may be measured by change in net primary productivity (NPP) with
deviation from the norm taken as an indicator of land degradation or improvement. As a proxy, we may use the normalized difference vegetation index (NDVI) as derived from
remotely sensed imagery. NDVI has been shown to be
related to biophysical variables that control vegetation productivity and land ⁄ atmosphere fluxes (Hall et al., 2006) such
as the leaf-area index (Myneni et al., 1997), the fraction of
photosynthetically active radiation absorbed by vegetation
(Asrar et al., 1984) and NPP (Alexandrov & Oikawa, 1997;
Rasmussen, 1998). It has also been used to estimate vegetation change, either as an index (Anyamba & Tucker, 2005;
Olsson et al., 2005) or as one input to dynamic vegetation
models (Nemani et al., 2003; Seaquist et al., 2003; Fensholt
ª 2008 The Authors. Journal compilation ª 2008 British Society of Soil Science
223
224 Z. G. Bai et al.
Table 1 GLASOD estimates of human-induced soil degradation (million haa)
Kind of degradation
Water erosion
Wind erosion
Nutrient depletion
Salinity
Contamination
Physical
Other
Sum
World
Asia
West Asia
Africa
Latin America
and Caribbean
North
America
Australia and
Pacific
Europe
1094
548
135
76
22
79
10
1964
440
222
15
53
2
12
3
747
84
145
6
47
+
4
1
287
227
187
45
15
+
18
2
494
169
47
72
4
+
13
1
306
60
35
–
–
–
1
–
96
83
16
+
1
–
2
1
103
115
42
3
4
19
36
2
218
GLASOD, Global Assessment of Human-induced Soil Degradation. aGLASOD indicated that 15% of land is degraded. The highest proportions
were reported for Europe (25%), Asia (18%) and Africa (16%); the least for North America (5%). By the same measure, as a proportion of
the degraded area, soil erosion affects 83% of the global degraded area (ranging from 99% in North America to 61% in Europe); nutrient
depletion affects 4% globally but 28% in South America; salinity less than 4% worldwide but 16% in West Asia; chemical contamination about
1% globally but 8% in Europe; soil physical problems 4% globally but 16% in Europe.
et al., 2006). Consistent time-series data at spatial resolutions
from 20 m to 8 km (Brown et al., 2006) enable analysis and
generalization.
A negative trend in NPP does not necessarily indicate land
degradation, nor does a positive trend necessarily indicate land
improvement. Biomass depends on several factors including
climate – especially fluctuations in rainfall, sunshine and
length of growing season; land use; large-scale ecosystem disturbances such as fires; and the global increase in nitrate deposition and atmospheric carbon dioxide. To interpret NDVI
trends in terms of land degradation or improvement, we have
to eliminate false alarms arising from climatic variability and
land-use change. Globally, this is possible for climatic variables for which consistent data are available but for land-use
change this issue has to be addressed on a case-by-case basis as
global time-series data on land use are not available.
Where productivity is limited by rainfall, rain-use efficiency (the ratio of NPP to precipitation) accounts for variability of rainfall and, to some extent, local soil
characteristics (Le Houérou, 1984). The combination of
satellite-based estimation of NPP and station-observed rainfall has been used successfully to assess land degradation at
various scales (Holm et al., 2003; Prince et al., 2007). However, there are caveats when applying these data globally:
1. NDVI is a better indicator of NPP for sparse to moderate
vegetation cover than for a closed vegetation canopy (Ripple, 1985). In other words, it is better for cropland and
rangeland than for forest although it is still useful for forest.
2. In Global Inventory Modeling and Mapping Studies
(GIMMS), cloud screening was performed and maximum
NDVI was determined for a composite of 15 days, but
the results may still be an underestimate for cloudy areas.
3. The great spatial variability of rainfall in dry lands makes
interpolation of point measurements problematic, and
observation stations are sparse in many of these areas.
The final caveat is that NDVI cannot be other than a
proxy; it does not tell us anything about the kind of degradation or improvement. What is happening in degrading
areas as identified, say, in South-East Asia is different from
what is happening in the Pampas both in terms of the driving changes in soil use and management, and the symptoms
of land degradation. However, because the index is mapped
as a continuous surface, the drivers may be revealed by correlation with other geo-located biophysical and socio-economic data. Within the LADA programme, we are using this
indicator to identify, delineate and rank hot spots of land
degradation, and their counterpoint bright spots of land
improvement, for subsequent assessment of the actual field
situation.
Data and methods
The GIMMS radiometer (AVHRR) data are collected by
National Oceanic and Atmospheric Administration (NOAA)
satellites. These data are corrected for calibration, variations
in solar and view zenith angle, El Chichon and Mt Pinatubo
stratospheric aerosols and other effects not related to vegetation change, and generalized to 15-day, 8-km grids. Data are
currently available for the period July 1981 to December
2003 (Tucker et al., 2004). The annual sum of NDVI for each
pixel is taken to represent annual accumulated greenness.
Rain-use efficiency (RUE) was estimated from the ratio of
the annual sum of NDVI to annual rainfall and was calculated from the VASClimO station-observed monthly rainfall
data (Beck et al., 2005), gridded to 0.5 lat ⁄ long.
Energy-use efficiency, represented by the ratio of NPP and
accumulated temperature, was calculated from the CRU 2.1
data set (Mitchell & Jones, 2005). This proved to be more
of an issue in defining areas of land improvement than in
defining degrading areas.
ª 2008 The Authors. Journal compilation ª 2008 British Society of Soil Science, Soil Use and Management, 24, 223–234
Proxy global assessment of land degradation 225
Urban areas were masked. These were taken from the
Urban ⁄ Rural Extents dataset of the Global Rural–Urban
Mapping Project (CIESIN, 2004) at a resolution of 30 arcseconds.
NDVI-NPP correlation. NPP was estimated by correlation
with MODIS 8-day NPP values (Running et al., 2004) for
the overlapping years of the GIMMS and MODIS data sets
(2000–2003) and re-sampling the annual mean MODIS NPP
at 1-km resolution to GIMMS 8-km resolution using the
nearest neighbour assignment. The empirical relationship is:
NPPMOD17 ðkg C ha1 year1 Þ ¼ 1106:37 sum NDVI 564:55;
ðr ¼ 0:83; n ¼ 3128207Þ
where NPPMOD17 is the annual mean NPP derived from
MODIS MOD17 Collection 4 data and sum NDVI is the
4-year (2000–2003) mean annual sum NDVI derived from
GIMMS. Uncertainty for the slope is ±3.818 and for the
intercept ±16.364.
Trend analysis. Trends were determined by linear regression with absolute change (D) as the slope of the regression.
The data were tested for temporal and spatial independence
following Livezy & Chen (1983). When the absolute values
of the autocorrelation coefficients of lag-1 to lag-3 calculated
for a time series of n observations were not larger than the
pffiffiffi
typical critical value, i.e. 1:96= n corresponding to 5% significance level, then the observations in the time series were
accepted as independent of each other. The t-test was used
to arrange the slope values in classes showing strong or weak
positive or negative trends:
t ¼ b=seðbÞ
where b is the estimated slope of the regression line between
the observation values and time and se(b) represents the
standard error of b. The class boundaries were defined for
99, 95 and 90% confidence levels.
Aridity index was calculated as P ⁄ PET
where P isffi annual
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
precipitation in mm and PET ¼ P= ð0:9 þ ðP=LÞ2 Þ where
L = 300 + 25T + 0.05T 3 and T is the mean annual temperature (Jones, 1997). Precipitation was taken from the
gridded VASClimO data set and mean annual temperature
from the CRU TS 2.1 data set (Mitchell & Jones, 2005).
Comparisons between land degradation and other indices.
Maps of the RUE-adjusted NDVI index were overlaid on
the other global maps. Corresponding comparative values
were calculated, and the correlation calculated for all pixels.
Sequence of analysis
Degrading areas were first identified by a negative trend in
sum of NDVI. To distinguish between productivity decline
caused by land degradation and declining productivity due
to other factors, it was necessary to eliminate false alarms.
Urban areas were masked. To take account for rainfall
variability, areas with a positive correlation between rainfall
and NDVI and, also, a positive RUE were masked and, for
these areas, declining productivity was attributed to drought.
The areas remaining were mapped as RUE-adjusted NDVI.
The following sequence of analyses was adopted:
1. map the linear trends of NDVI;
2. map the correlation between NDVI and rainfall;
3. mask the pixels with a positive correlation between NDVI
and rainfall and, also, positive RUE trend;
4. NDVI trends were calculated for the remaining areas, i.e.
pixels where there was a negative relationship between
NDVI and rainfall and, also, pixels with a positive relationship but declining RUE.
Results and discussion
Figure 1 depicts global change in NDVI, scaled in terms of
NPP, over the period 1981–2003; ice and extreme desert with
NPP less than 1 g C per m2 are designated as no change. The
global sum NDVI (and NPP) increased by 3.8% (P < 0.05)
over the period; the increase was 3% in Africa and North
America, 4.4% in Latin America, 4.5% in Australia, 5.4% in
Europe and 6% in Asia (Figure 2). Figure 3 shows RUEadjusted NDVI; 24% of the land area suffered from a declining RUE-adjusted NDVI.
Figure 4 shows the confidence levels in these negative
trends in NDVI. Two per cent of the land area exhibits a negative trend at the 99% confidence level, 5% at the 95% confidence level and 7.5% at the 90% confidence level. The
smallness of these areas may be explained by the coarse
(8-km) resolution of the data; an area of land degradation
much smaller than 8 km across must be severe to significantly
change the signal from a much larger surrounding area.
These results have been validated by field observations in
north China (Bai et al., 2005) and independently by Chen &
Rao (2008); Kenya (Bai & Dent, 2006); and Bangladesh (Bai,
2006). They are very different from the previous global assessment of land degradation and challenge long-held assumptions. To address the questions posed at the outset, comparisons
were made with global data for land cover, aridity, population
density and as proxies for poverty, infant mortality rates and
proportion of underweight children under the age of 5 years.
Which regions are the hardest hit?
Areas severely affected (Table 2) include:
• Africa south of the equator (13% of the global degrading
area and 18% of lost global NPP);
• Indo-China, Myanmar, Malaysia and Indonesia (6% of the
degrading area and 14% of lost NPP);
• south China (5% of the degrading area and 5% of lost
NPP);
• north-central Australia and the western slopes of the Great
Dividing Range (5% of the degrading area and 4% of lost
NPP);
ª 2008 The Authors. Journal compilation ª 2008 British Society of Soil Science, Soil Use and Management, 24, 223–234
226 Z. G. Bai et al.
80°
80°
60°
60°
40°
40°
20°
20°
0°
0°
–20°
–20°
–40°
–40°
–60°
–60°
–80°
Δnet primary productivity (kgC/ha/year)
-5 <0 5
-4 – - 0
0 4
-3 – - 0
0 3
-2 – - 0
0 20
–
-1 -1
0 0
N - – -5
o 5
ch –
an 0
g
0 e
5 –5
–
10 1
0
20 – 2
0
30 – 3
0
–
40 4
50 – 0
5
– 0
15
>1 0
50
–140°–80°–40° 0° 40° 80° 120° 180°
–80°
Mollweide Projection
Central Meridian: 0.00
1.12
1.11
1.10
1.09
1.08
1.07
1.06
1.05
1.04
1.03
1.02
Globe, x107
1.86
1.84
1.82
1.80
1.78
1.76
1.74
1.72
1.70
1.68
1.66
Africa, x106
2.70
2.65
2.60
2.55
2.50
2.45
2.40
2.35
Europe, x106
2.02
2.00
1.98
1.96
1.94
1.92
1.90
1.88
1.86
1.84
1.82
Asia, x106
2.15
Latin America, x106
2.05
2.00
1.95
1.90
1.85
Australia, x106
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
0.66
0.64
0.62
0.60
0.58
0.56
0.54
0.52
2.10
1.84
1.82
1.80
1.78
1.76
1.74
1.72
1.70
1.68
1.66
1.64
1.62
North America, x106
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Spatially aggregated sum NDVI
Figure 1 Global change in net primary productivity, 1981–2003.
Figure 2 Spatially aggregated sum normalized difference vegetation index, 1981–2003.
ª 2008 The Authors. Journal compilation ª 2008 British Society of Soil Science, Soil Use and Management, 24, 223–234
Proxy global assessment of land degradation 227
80°
80°
60°
60°
40°
40°
20°
20°
0°
0°
–20°
–20°
–40°
–40°
–60°
–60°
–80°
–80°
Slope of linear regression of sum NDVI
-0 <
.0 -0
-0 4 – .04
.0 -0
-0 3 – .03
.0 -0
2 .0
– 2
N -0 -0.0
ot .0 1
ve 1
ge – 0
ta
te
d
Mollweide Projection
Central Meridian: 0.00
Figure 3 Global negative trend in rain-use efficiency-adjusted normalized difference vegetation index, 1981–2003.
80°
80°
60°
60°
40°
40°
20°
20°
0°
0°
–20°
–20°
–40°
–40°
–60°
–60°
Significant (t-test)
99%
95%
90%
NS
–80°
–80°
Mollweide Projection
Central Meridian: 0.00
Figure 4 Confidence levels of negative trend in rain-use efficiency-adjusted annual sum normalized difference vegetation index, 1981–2003.
ª 2008 The Authors. Journal compilation ª 2008 British Society of Soil Science, Soil Use and Management, 24, 223–234
228 Z. G. Bai et al.
Table 2 Statistics of degrading areas by country 1981–2003a
Country
Afghanistan
Albania
Algeria
Andorra
Angola
Argentina
Armenia
Australia
Austria
Azerbaijan
The Bahamas
Bangladesh
Belgium
Belize
Benin
Bhutan
Bolivia
Bosnia and Herzegovina
Botswana
Brazil
Brunei
Bulgaria
Burkina Faso
Burundi
Belarus
Cambodia
Cameroon
Canada
Cape Verde
Central African Republic
Chad
Chile
China
Colombia
Comoros
Congo
Costa Rica
Croatia
Cuba
Cyprus
Czech Republic
Demark
Djibouti
Dominica
Dominican Republic
Ecuador
Egypt
El Salvador
Equatorial Guinea
Eritrea
Estonia
Ethiopia
Falkland Islands (Islas Malvinas)
Degrading
area (km2)
1
1
1
2
7658
2334
63 475
281
828 029
902 438
743
994 268
28 291
2633
4130
68 422
5404
3026
14 155
27 011
60 339
7737
97 831
881 702
2663
9139
9255
13 516
4053
77 958
151 605
985 085
375
126 927
52 735
77 230
193 697
291 295
181
201 614
14 691
2822
32 430
266
11 218
91
6107
126
18 507
40 136
36 514
5585
15 376
15 573
423
296 812
1635
% Territory
% Global
degrading area
Total NPP loss
(tonne C ⁄ 23 years)
% Total
population
1.17
8.12
2.67
60.00
66.42
32.62
2.49
25.94
33.74
3.04
29.63
47.52
17.71
13.18
12.57
57.47
5.49
15.13
16.30
22.11
46.15
8.24
3.38
48.56
1.95
43.06
31.89
19.90
9.30
20.37
4.11
10.20
22.86
25.58
8
58.95
28.75
4.99
29.25
2.87
14.22
0.21
27.76
16.67
37.98
14.15
3.65
26.54
54.81
12.84
0.93
26.33
13.43
0.025
0.009
0.196
0.001
2.370
3.130
0.003
6.182
0.117
0.009
0.009
0.199
0.024
0.008
0.041
0.073
0.175
0.030
0.284
5.381
0.008
0.035
0.026
0.037
0.019
0.225
0.417
11.575
0.001
0.356
0.152
0.265
7.627
0.818
0.001
0.569
0.042
0.011
0.095
0.001
0.048
0.001
0.017
0.000
0.054
0.101
0.112
0.016
0.037
0.045
0.003
0.843
0.009
62 859.1
47 250.4
1 977 970.5
2603.7
37 602 596.6
23 556 380.4
13 886.6
46 905 278.7
1834.8
123 082.9
195 146.3
2 851 384.3
69 560.0
65 978.5
373 746.7
1 705 766.5
1 656 318.9
157 646.3
4 111 881.5
63 346 318.0
127 917.7
178 002.7
123 794.9
972 686.3
82 415.8
2 524 941.7
9 657 119.7
93 963 813.5
12 087.3
3 701 987.8
627 041.5
1 950 751.6
58 840 236.6
17 999 691.4
17 515.6
20 091 044.3
529 400.5
28 610.0
75 5492.5
9142.6
304 242.8
290.1
19 272.3
8975.8
560 540.6
2 401 058.1
16 638.9
234 649.5
1 434 524.4
33 256.1
4083.4
1 427 6064.5
50 944.4
2.56
4.29
22.45
20.53
60.74
36.95
1.99
11.31
21.51
2.98
32.01
49.12
13.48
16.94
12.84
54.99
16.39
16.77
30.74
26.67
85.02
11.72
8.26
52.09
2.56
24.03
26.30
17.69
24.76
23.27
10.82
10.42
34.71
36.02
21.50
54.93
13.41
7.95
28.31
0.74
13.24
0.24
59.3
7.57
43.43
16.13
13.92
16.76
45.39
5.27
0.75
29.10
23.18
ª 2008 The Authors. Journal compilation ª 2008 British Society of Soil Science, Soil Use and Management, 24, 223–234
Affected
people
7
9
14
2
1
72
1
1
1
46
1
3
3
4
5
1
457
16
1
3
1
3
2
10
1
20
671 770
137 861
168 600
20 865
263 348
455 278
75 632
187 493
730 745
238 076
19 029
728 775
396 093
39 513
932 170
332 662
518 038
704 321
476 893
595 573
264 401
881 122
101 414
881 071
254 841
583 464
326 977
509 584
72 997
894 315
995 721
645 825
202 031
309 420
135 144
895 981
592 632
338 952
050 838
5164
358 728
10 824
282 700
4532
843 087
199 904
100 710
139 730
171 542
235 381
9180
650 316
365
Proxy global assessment of land degradation 229
Table 2 Continued
Country
Degrading
area (km2)
% Territory
% Global
degrading area
Finland
France
French Guiana
Gabon
The Gambia
Georgia
Germany
Ghana
Greece
Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Ivory Coast
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Peoples Republic of Korea
Republic of Korea
Kyrgyzstan
Laos
Latvia
Lebanon
Lesotho
Liberia
Libya
Lithuania
Macedonia
Madagascar
Malawi
Malaysia
Mali
Mauritania
Mexico
Moldova
Mongolia
Morocco
Mozambique
Myanmar (Burma)
Namibia
27 779
46 691
24 947
172 865
1396
5647
32 479
50 365
6914
55 884
91 415
18 851
93 448
11 821
30 145
31 398
34 483
592 498
1 028 942
29 190
28 000
6416
3085
28 693
117 595
3372
130 563
13 574
487 083
104 994
60 959
54 091
23 189
133 395
4416
704
10 344
50 500
12 672
2664
1757
163 843
30 869
175 817
35 637
6301
487 804
1751
66 559
67 399
226 567
358 887
288 945
8.24
8.54
27.41
64.58
12.35
8.10
9.10
21.11
5.24
51.32
37.18
52.19
43.47
42.60
26.89
33.75
33.48
18.02
53.61
1.77
6.41
9.13
14.85
9.53
36.47
30.68
34.56
15.21
17.93
18.02
50.57
54.93
11.68
56.33
6.84
6.77
34.08
45.34
0.72
4.09
6.94
27.91
26.05
53.32
2.87
0.61
24.73
5.17
4.25
15.09
28.26
52.89
35.01
0.178
0.190
0.064
0.471
0.004
0.021
0.144
0.143
0.024
0.163
0.262
0.048
0.257
0.034
0.084
0.128
0.225
1.751
2.703
0.095
0.092
0.035
0.010
0.109
0.331
0.010
0.451
0.048
2.041
0.294
0.226
0.182
0.087
0.382
0.022
0.002
0.033
0.123
0.037
0.016
0.007
0.492
0.089
0.475
0.106
0.019
1.474
0.007
0.271
0.201
0.651
1.053
0.875
Total NPP loss
(tonne C ⁄ 23 years)
1
2
2
2
1
2
22
67
1
1
6
4
5
6
2
1
7
2
6
1
9
23
2
8
23
6
327 719.0
605 160.1
033 318.1
23 880.9
26 354.9
141 370.2
730 979.9
520 818.8
116 914.8
866 596.1
008 341.6
452 425.3
230 119.3
383 261.7
450 818.0
765 915.3
693 153.9
484 085.7
679 850.0
282 438.0
030 763.2
363 385.0
49 570.7
696 408.7
221 305.3
106 751.2
268 668.1
100 581.9
308 145.4
612 571.4
206 450.2
570 729.4
282 173.2
232 762.0
136 363.4
1894.0
485 250.6
097 992.3
86 082.8
55 189.8
32 910.1
678 188.7
370 894.6
257 510.4
357 823.5
17 918.0
871 309.5
32 362.0
623 761.6
807 952.0
398 072.7
625 067.9
388 446.8
% Total
population
3.46
10.48
14.36
35.85
1.93
11.76
6.97
20.95
6.76
30.46
46.51
43.43
26.49
34.56
23.38
28.90
23.51
16.50
40.52
3.42
6.58
11.95
30.07
7.80
36.33
28.98
24.20
19.13
13.31
35.59
45.08
31.81
12.71
55.13
9.49
3.37
44.49
38.12
6.92
2.91
1.42
21.56
19.89
46.39
6.60
2.18
34.30
3.17
2.51
35.71
26.36
47.86
35.87
Affected
people
171
6 159
25
468
25
591
5 676
4 466
662
3 936
4 108
536
198
2 823
1 673
2 810
58
177 437
86 656
2 572
1 718
653
2 035
4 306
6 252
741
29 666
1 574
2 131
11 803
10 124
14 364
682
3 304
213
123
941
1 441
402
132
30
3 901
2 486
10 401
870
67
36 234
133
66
11 278
5 155
23 608
670
458
286
745
972
821
918
882
773
921
416
349
156
445
765
952
672
021
809
550
958
397
134
012
062
711
313
795
810
386
311
149
205
075
253
414
717
131
085
408
351
073
784
085
113
031
349
761
140
138
600
480
512
983
ª 2008 The Authors. Journal compilation ª 2008 British Society of Soil Science, Soil Use and Management, 24, 223–234
230 Z. G. Bai et al.
Table 2 Continued
Country
Degrading
area (km2)
% Territory
% Global
degrading area
Total NPP loss
(tonne C ⁄ 23 years)
% Total
population
Affected
people
Nepal
The Netherlands
New Caledonia
New Zealand
Nicaragua
Niger
Nigeria
Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
The Philippines
Poland
Portugal
Puerto Rico
Reunion
Romania
Russia
Rwanda
Sao Tome and Principe
Saudi Arabia
Senegal
Sierra Leone
Singapore
Slovakia
Slovenia
Solomon Islands
Somalia
South Africa
Spain
Sri Lanka
Sudan
Suriname
Swaziland
Sweden
Switzerland
Syria
Tajikistan
United Republic of Tanzania
Thailand
Togo
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Turks and Caicos Islands
Uganda
Ukraine
UK
USA
54 704
7051
6902
147 014
47 223
22 563
91 443
57 109
419
20 644
8735
205 500
66 704
197 211
132 275
41 514
11 536
436
175
16 902
2 802 060
11 404
125
8327
34 655
35 902
243
5066
2492
9065
52 520
351 555
63 266
21 057
166 031
50 503
16 533
78 964
4982
11 327
8412
386 256
309 245
11 064
675
12 476
30 851
1273
92
41 506
47 414
23 506
1 983 886
38.85
16.98
36.21
54.72
36.47
1.78
9.90
17.61
0.20
2.57
11.17
44.40
16.40
15.34
44.09
13.28
12.49
4.79
6.98
7.12
16.41
43.30
12.50
0.42
17.66
50.04
37.50
10.37
12.30
31.86
8.24
28.82
12.53
32.09
6.63
30.93
95.22
17.55
12.07
6.12
5.88
40.87
60.16
19.48
13.16
7.63
3.95
0.26
21.43
17.58
7.85
9.60
20.60
0.182
0.028
0.020
0.545
0.134
0.062
0.256
0.352
0.002
0.073
0.023
0.564
0.200
0.565
0.362
0.188
0.041
0.001
0.001
0.067
16.519
0.031
0.000
0.025
0.101
0.102
0.001
0.021
0.010
0.030
0.149
1.124
0.231
0.060
0.480
0.125
0.051
0.475
0.020
0.039
0.030
1.081
0.895
0.032
0.002
0.040
0.111
0.005
0.001
0.120
0.200
0.103
7.935
2 375 267.1
92 199.1
1 008 271.2
6 992 962.9
2 060 424.0
141 698.7
3 066 734.7
1 212 968.8
3302.1
235 711.3
513 508.6
16 275 368.4
1 659 008.0
11 414 777.0
4 100 145.0
890 969.3
233 458.4
19 230.6
6294.3
364 407.2
56 663 083.1
1 053 147.5
30 359.7
4335.1
408 832.5
1 507 871.3
5833.5
110 642.2
38 131.6
628 541.4
1 834 048.4
23 123 363.6
1 712 505.7
634 812.6
3 627 514.5
2 102 420.5
1 226 857.4
1 594 303.0
106 619.3
224 232.9
104 020.6
22 603 896.1
15 990 860.1
299 272.9
113 407.3
398 422.7
453 231.2
8416.9
15 960.7
1 513 211.6
1 048 460.3
262 089.7
39 672 698.1
48.93
17.25
31.44
30.97
29.28
6.61
13.33
9.23
0.06
3.58
7.78
40.58
66.97
10.89
42.75
14.37
4.58
2.91
5.24
4.47
6.20
39.11
21.82
2.00
20.49
39.33
55.95
6.86
17.99
33.82
14.77
38.14
6.41
25.62
9.43
10.13
98.77
10.37
6.81
6.71
2.39
39.48
56.66
12.79
5.51
15.47
5.08
0.33
21.49
15.04
5.25
5.95
10.79
13 332 932
2 779 551
48 235
1 015 925
1 684 227
844 506
17 035 650
361 786
1848
5 838 072
232 958
2 019 646
4 071 629
3 001 345
33 064 628
5 505 161
440 851
111 458
38 724
980 580
8 588 604
3 299 059
28 128
471 248
2 078 643
2 103 046
2 017 090
370 606
396 448
206 290
1 544 921
17 041 101
2 417 996
4 788 637
3 280 414
38 529
947 510
841 284
484 619
1 243 265
151 676
15 300 003
36 991 080
654 476
65 120
1 512 817
3 571 290
17 554
166
4 112 702
2 466 172
3 324 064
31 144 568
ª 2008 The Authors. Journal compilation ª 2008 British Society of Soil Science, Soil Use and Management, 24, 223–234
Proxy global assessment of land degradation 231
Table 2 Continued
Degrading
area (km2)
Country
Uruguay
Uzbekistan
Vanuatu
Venezuela
Vietnam
Yemen
Yugoslavia (Macedonia, Serbia,
Montenegro)
Zaire (Democratic Republic of the
Congo)
Zambia
Zimbabwe
The World (land, excluding inland
water bodies)
a
% Territory
% Global
degrading area
Total NPP loss
(tonne C ⁄ 23 years)
% Total
population
Affected
people
87 566
5974
2210
207 916
134 026
14 422
10 507
49.69
1.34
14.97
22.80
40.67
2.73
8.23
0.294
0.022
0.005
0.587
0.387
0.032
0.032
1 874 536.8
123 701.0
4588.8
520 023.0
342 631.6
7569.5
27 197.0
33.03
2.22
9.61
8.28
35.27
2.30
6.37
1 058
585
16
2 156
28 085
507
678
877
887
965
456
074
751
700
1 346 914
57.43
3.760
3 403 930.4
53.49
32 081 359
454 630
180 125
35 058 104
60.41
46.12
23.54
1.312
0.531
100.000
19 900 480.6
8 861 748.2
955 221 418.5
50.07
39.51
23.89
5 789 865
5 424 488
1 537 679 148
Countries or regions with no degradation are not listed. Area data refer to pixels showing any declining trend, irrespective of degrees of confidence.
• the Pampas (3.5% of the degrading area and 3% of lost
NPP);
• Swaths of the high-latitude forest belt in North America
and Siberia.
The usual suspects around the Mediterranean, the Middle
East, south and central Asia are represented by only relatively small areas of degradation in southern Spain, the
Maghreb, Nile delta, Iraqi marshes and the Turgay steppe.
Differences from the previous assessment arise because
GLASOD compounded current land degradation with the
legacy from past centuries. These are two different things.
Both are important but most areas of historical land degradation have become stable landscapes with a stubbornly
low level of productivity. The present assessment deals only
with 1981–2003 and we have no comparable data for earlier
periods.
Is land degradation a global issue or just a collection of
local problems?
Twenty-four per cent of the land area has been degrading over
the last 25 years, directly affecting the livelihoods of 1.5 billion people; this is on top of the legacy of thousands of years
of mismanagement in some long-settled areas. GLASOD
estimated that 15% of the land was degraded (Table 2), much
of which does not overlap with the areas highlighted by the
new analysis; land degradation is cumulative – this is the
global issue.
In terms of C fixation, degrading areas represent a loss of
NPP of 9.56 · 108 tonne C relative to the mean NPP over
the period 1981–2003, that is 9.56 · 108 tonne C not removed
from the atmosphere, equivalent to 20% of the global CO2
emissions for the year 1980. At the shadow price for carbon
used by the British Treasury in February 2008 ($50 per tonne
C), this amounts to $48bn in terms of lost C fixation. The
cost of land degradation is at least an order of magnitude
greater in terms of C emissions from loss of soil organic carbon. Estimates can also be made in respect of the effects of
land degradation on food and water security, drought, flood
and sedimentation.
Is land degradation mainly associated with farming?
Comparison of degrading areas with global land cover (JRC,
2003) reveals that 19% of degrading land is cropland, 24%
is broad-leaved forest and 19% needle-leaved forests
(Table 3).
Cropland occupies only 12% of the land area and a further 4% in mosaics with woodland and grassland; so, degradation is over-represented in cropland at the global scale. In
Kenya over the period 1981–2003, NPP increased in woodland and grassland, but hardly at all in cropland; across
40% of cropland it decreased, a critical situation in the context of a doubling of human population over the same period
(Bai & Dent, 2006). In South Africa, NPP decreased overall;
29% of the country suffered land degradation, including
41% of all cropland (Bai & Dent, 2007); about 17 million
people, 38% of the South African population, depend on
these degrading areas (Figure 5). Forest is even more overrepresented: broadleaved and needle-leaved forest occupy
28% of the land but 43% of degrading land!
Land-use change may itself generate false alarms. Conversion of forest or grassland to arable will usually result in an
immediate reduction in NPP and NDVI. This may or may
not be accompanied by land degradation, as usually understood, and may well be profitable and sustainable, depending
on management. Lack of consistent time-series data for land
use or land cover precludes a generalized analysis of land-use
ª 2008 The Authors. Journal compilation ª 2008 British Society of Soil Science, Soil Use and Management, 24, 223–234
232 Z. G. Bai et al.
Table 3 Global degrading areas by land cover type
Total pixels
(TP) (0.54¢ · 0.54¢)
Code
Land cover
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
22
23
Total
Tree cover, broad-leaved, evergreen
Tree cover, broad-leaved, deciduous, closed
Tree cover, broad-leaved, deciduous, open
Tree cover, needle-leaved, evergreen
Tree cover, needle-leaved, deciduous
Tree cover, mixed leaf type
Tree cover, regularly flooded, fresh water
Tree cover, regularly flooded, saline water
Mosaic: tree cover ⁄ other natural vegetation
Tree cover, burnt
Shrub cover, closed-open, evergreen
Shrub cover, closed-open, deciduous
Herbaceous cover, closed-open
Sparse herbaceous or sparse shrub cover
Regularly flooded shrub and ⁄ or herbaceous cover
Cultivated and managed areas
Mosaic: cropland ⁄ tree cover ⁄ other natural vegetation
Mosaic: cropland ⁄ shrub and ⁄ or grass cover
Bare areas
Artificial surfaces and associated areas
No data
a
12
8
4
15
8
5
4
3
15
17
23
3
21
4
3
24
177
875
688
099
080
054
606
579
115
269
587
195
605
560
573
089
692
025
921
629
378
29
658
Degrading pixels
(DP) (0.54¢ · 0.54¢)
179
097
003
165
159
446
763
705
938
270
387
651
702
022
962
769
653
904
888
999
056
718
4
2
1
4
2
1
1
2
2
2
4
1
35
222
441
616
633
043
993
228
26
097
225
093
953
824
567
689
522
293
692
931
35
561
119
582
961
323
934
306
157
533
758
184
414
775
417
713
988
550
613
207
442
120
133 657
DP ⁄ TP
(%)
DP ⁄ TDPa
(%)
32.8
28.1
39.4
30.7
25.4
17.7
39.4
22.6
25.7
38.4
34.2
18.9
16.1
10.9
22.3
20.9
32.1
17.7
3.8
9.4
0.4
19.8
12.0
6.9
4.6
13.2
5.8
2.8
0.6
0.1
3.1
0.6
3.1
8.4
8.0
7.3
2.0
12.9
3.7
2.0
2.7
0.1
0.0
100.0
TDP, total degrading pixels; water, snow and ice are excluded.
change but this can be undertaken manually for the hot spots
identified in this analysis.
regions, 8% in the dry sub-humid, 9% in the semi-arid and
5% in arid and hyper-arid regions.
Is land degradation a dry land issue?
Is it related to population pressure?
Drylands do not feature strongly in ongoing land degradation apart from in Australia. Indeed, the recovery of the
Sahel from the droughts of the 1980s is a notable feature
(Figure 1 and Olsson et al., 2005). Globally, there is
little correlation (r = )0.12) between land degradation and
the aridity index; 78% of degradation by area is in humid
Comparison of rural population density (CEISIN, 2007) with
land degradation shows no simple pattern. Globally, the correlation coefficient is negative ()0.3); in general, the more
the people, the less the degradation. However, in some contexts, population pressure is positively related to land degradation; for South Africa, the correlation between land
N
N
Persons per square kilometer
0–20
21–40
41–60
61–80
81–100
101–120
121–140
141–160
>160
Degradation
Slight
Severe
0 100 200
400
600
0 100 200
800
km
Figure 5 South Africa, land degradation and persons affected, 1981–2003.
ª 2008 The Authors. Journal compilation ª 2008 British Society of Soil Science, Soil Use and Management, 24, 223–234
400
600
800
km
Proxy global assessment of land degradation 233
degradation and loge population density is positive
(r = 0.25), but the coincidence of degrading areas with some
of the former apartheid homelands indicates that something
more than simple rural population density is at work.
Is it related to poverty?
Taking infant mortality rate and the percentage of children
under 5 years who are underweight (CEISIN, 2007) as proxies,
there is some global relationship between land degradation
and poverty: correlation coefficients are 0.20 for both infant
mortality and for underweight children. However, a much
more rigorous analysis is needed, especially to tease out the
underlying biophysical and social and economic variables.
This might be carried out using more specific geo-located data.
Conclusions
1. Biomass trends depend on several factors other than land
degradation and improvement. We have taken account of
rainfall variability by screening NDVI trends for rainuse efficiency in those areas where productivity is limited
by rainfall. Globally, data are not available to take
account of land-use change over the period.
2. All changes measured by the RUE-adjusted NDVI ⁄ NPP
index are not land degradation as usually understood.
Land-use changes which reduce NDVI (e.g. from forest
to cropland of lesser biological productivity, or an
increase in grazing pressure) may or may not be accompanied by soil erosion, salinity or other symptoms of
land degradation of concern to soil scientists.
3. Long-term trends of NDVI derivatives are unsophisticated indicators of land degradation. However, as a
proxy, the NDVI ⁄ NPP trend does provide a globally consistent yardstick, and it does highlight places where biologically significant change is happening. This is its
purpose and, in the LADA programme, this global scan
will be used to direct attention to areas that demand
investigation and action on the ground.
Acknowledgements
This work is part of the FAO project Land Degradation
Assessment in Drylands. We thank C. J. Tucker, J. E. Pinzon
and M. E. Brown for access to the GIMMS data, J. Grieser
for VASClimO data, M. Salmon for CRU data, P. Tempel
for assistance in data handling and A. Anyamba, R. Biancalini, G.W.J. van Lynden, F. Nachtergaele, S. Prince,
A. Tengberg and P. Vlek for critical review.
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