This document summarizes a study that assessed desertification risk in the Apulia region of southern Italy. The researchers modified an existing model called the ESAs (Environmental Sensitive Areas) model by adding new indicators to better account for the region's environmental and socioeconomic factors. These included rainfall erosivity indices, a fire risk probability index, and indices related to crop land use, pasture land use, and forest land use. By integrating these new indices into the ESAs model in a GIS, the researchers were able to more accurately evaluate desertification risk across different administrative units in Apulia. The results demonstrated that the modified approach improved the assessment of vulnerability to land degradation compared to using the original ESAs model alone

1. Published on: Zdruli P., Kapur S. and Faz Cano A. (Eds.). 2010 - Land
Degradation and Desertification: Mitigation and Remediation. Springer, New
York, ISBN 978-90-481-8656-3
Assessment of Desertification in Semi-Arid Mediterranean
Environments:
the Case Study of Apulia region (southern Italy)
G. Ladisa1
, M. Todorovic and G. Trisorio Liuzzi
Abstract
This work focuses on the assessment of the areas threatened by desertification
in the semi-arid Mediterranean environments. The presented approach
represents a modification of the ESAs model (Environmental Sensitive Areas to
Desertification; Kosmas et al. 1999) through a set of new indicators established
to account for the regional-specific environmental characteristics as well as
identifiable parameters relevant for land use planning and control measures.
These supplementary indicators, comprehending socio-economic and
environmental factors, were integrated in the ESAs model and, by using a GIS,
were applied to Apulia region (Southern Italy), a typical representative of many
Mediterranean areas affected by land degradation. The analyses include the
elaboration of a whole set of indices on both regional and the administrative
scales that constitute the principal territorial units for the management of natural
resources. The results have demonstrated that the introduction of the new
indices has improved substantially the overall evaluation of the desertification
risk in the Apulia region. The proposed approach permits not only the
identification and refinement of different degrees of vulnerability of an area to
land degradation, but allows also the analyses of specific factors affecting
desertification as well as their evaluation in terms of spatial and temporal
distribution. Furthermore, the presented method is conceptually very simple and
easy to implement from local to regional and national scale, and could be
proposed as a standard methodology for the definition of priorities in
implementation of strategies to mitigate desertification in the semi-arid
Mediterranean environments.
Key Words: desertification risk, sensitivity areas, Apulia region, Italy,
Mediterranean environments
1. Introduction
The assessment of the state of land degradation and desertification represents
the key phase in the studies aiming the identification of areas threatened by
desertification and a consequent planning of mitigation and remediation
1
Corresponding author ladisa@iamb.it

2. 2
measures. In these studies, the preliminary step is a careful selection of key-
variables and indicators that should describe the actual state of the system and
highlight the degradation changes and related effects in both time and spatial
scales.
The explicit definition of the minimum threshold values signaling the
conversion towards an irreversible state of land degradation represents a
particularly complex issue. This is particularly the case in many semi-arid
Mediterranean environments where the numerousness and variability of the
factors influencing the process makes difficult to integrate and synthesize all of
them in a unique value representing the threshold of degradation. Hence, it is
necessary to assign to each triggering factor its own threshold value and to
attribute to each one a different weight relevant to the role played in the
desertification process in a space-specific and time-specific case (Hunsaker and
Carpenter, 1990; Kosmas et al. 1999; Trisorio Liuzzi et al. 2004). Therefore, the
solution must consist in a balanced merger of different aspects of environmental
stress ensuring a straightforward link among indicators themselves and the state
and the tendency of the system they are representing.
A minimum set of indicators (Minimum Data Set) is proposed for the Apulia
region (Southern Italy), with the aim to identify of areas sensitive to
desertification and subsequently the development of action plans to combat land
degradation. The region represents a typical case of many Mediterranean areas,
being seriously affected by land degradation due to both unfavourable climatic
conditions and adverse human activities triggering the desertification processes.
Such factors include arid and semiarid climatic conditions, seasonal droughts,
high rainfall variation and intensity, poor and erodible soils, diversified
landscapes, extensive man-induced deforestation and forest losses due to
frequent wildfires, land abandonment and deterioration, unsustainable
exploitation of water resources leading to environmental impairment,
salinisation and exhaustion of aquifers, uncontrolled urbanization, concentration
of economic activities in coastal areas, tourism pressure, etc (Fig. 1).

3. 3
Fig. 1. Problem-tree of the main causes of soil degradation and desertification in
Apulia region (Ladisa, 2007)
2. Reference framework and applied methodology
The approach used for the identification of areas vulnerable to desertification
represents a modification of the Environmental Sensitive Areas to
Desertification (ESAs) model (Kosmas et al. 1999), formerly developed for the
watershed scale, within the frame of the MEDALUS (Mediterranean
Desertification and Land Use) project (Basso et al. 1999).
The ESAs model was based on four broad systems of indicators, within which
the minimum data set selection has to be assessed, representative of the soil
quality (texture, rock fragments, drainage, parent material, soil depth), the
climate (rainfall, aridity, aspects), vegetation (plant cover, fire risk, erosion
protection, resistance to aridity) and of the management practices (intensity of
land use in rural zones, pastures and forest areas, managerial policies).
The indicators are grouped and combined into 4 quality layers, independently
on the structure of the input layers (number of classes, etc.), allowing that the

4. 4
corresponding indices (the Soil Quality Index SQI, the Climate Quality Index
CQI, the Vegetation Quality Index VQI and the Management Quality Index
MQI) can be consistently compared among each other, no matter what could be
the format of the input data/indicator (qualitative or quantitative; measured or
estimated, etc.). In particular, the values of the Quality Indices for each
elementary unit within a layer, are obtained as geometric average of the scores
assigned (following the factorial scaling technique) to the single indicators
according to the following formula:
( ) ( ) ( ) n
ij
ij
ij
ij n
layer
layer
layer
x
Quality
/
1
_
...
2
_
1
_
_ ⋅
⋅
⋅
=
where i,j represent the “coordinates” (rows and columns) of a single elementary
unit of a layer and n is the number of layers (equal to the number of indicators)
used for determination of each quality layer.
The scores range from 1 (good conditions) to 2 (deteriorate conditions),
whereas the "zero" value is assigned to the areas where the measure is not
appropriate and/or those, which are not classified (e.g. water bodies, urban
areas, etc.). It is possible in some cases, a non-linear variation of the function
representing the variation of the indicators (scores) between the extreme values
(Hunsaker and Carpenter, 1990; Kosmas et al. 1999).
The integration of four quality layers represents the synthetic Environmental
Sensitive Areas Index (ESAI):
4
/
1
)
*
*
*
( MQI
VQI
CQI
SQI
ESAI =
Such index identify three main classes of areas threatened by land degradation
("critic", "fragile" and "potentially affected"), that could be further
differentiated in three subclasses (from low to medium and high sensitivity).
Applying the original ESAs model to the ‘regional (Apulia region) scale’
(which is wider if compared to the ones of the pilot areas successfully tested
previously (Kosmas et al., 1999; Basso et al., 1999), produced questionable
results (Regione Puglia, 2000), as witnessed by clear evidences of the real
conditions on the ground . The main reasons for this were in the simplifications
caused by the lack of some input information, as well as the availability and
increasing relevance of some other data non considered in the model.
Accordingly, the original approach was modified and applied on one of the
Apulia provinces – the province of Bari (Ladisa, 2001; Ladisa and Trisorio
Liuzzi, 2001; Ladisa et al. 2002; Trisorio Liuzzi et al. 2004; Trisorio Liuzzi et
al. 2005) - introducing a new set of indicators, derived from the specific
environmental context of the Apulia region, where the geographic,
hydrological, geo-morphological, climatic and anthropic characteristics co-act

5. 5
in determining the complex of predisposing and triggering conditions to
desertification. These supplementary indicators, introduced in the original ESAs
approach are presented in Fig. 2.
Fig. 2. Indicators and Quality Indices used in the modified ESAs approach (in
italic are written new and/or modified indicators and quality indices)
Regarding the climatic factors, two additional new indicators were proposed for
inclusion in the model. They both depend on the data availability and are related
to the rainfall erosivity and have the scope to establish the erosion impact (that
previously was simply derived either by a slope indicator included in the soil
quality index (Kosmas et al., 1999) or by means of a Erosion Quality Index
estimated from the Erosion Risk Map and based on the USLE equation).
Our first new climatic factor is the Rainfall Erosivity Index (R), expressed
through the factor R of the USLE-Universal Soil Loss Equation, and calculated
according to the proposal of D'Asaro and Santoro (D’Asaro & Santoro, 1983)
as:

6. 6
2
3
.
2
096
.
0
21
.
0 −
−
= NRD
P
q
R
where q represents the altitude of meteorological stations (m), P is the average
annual precipitation (mm) and NRD is the average number of rainy days during
the year.
The second one is the Modified Fournier's index (MFI), that, highly correlated
to the R factor of USLE within the homogeneous climatic zones (Fournier,
1960; Ferro et al. 1999; Porto, 1994; Gabriels, 2000), is calculated according to
Arnoldus (1980) as:
∑
=
P
p
MFI i
2
where pi is the average rainfall of the month i (i=1 to 12) and P is average
annual precipitation.
These indices are used alternatively in the determination of Climate Quality
Index as:
4
/
1
)
*
*
*
( R
P
Aspect
BGI
CQI =
or
4
/
1
)
*
*
*
( MFI
P
Aspect
BGI
CQI =
where:
BGI is the Bagnouls-Gaussen Aridity Index (Bagnouls and Gaussen, 1952)
calculated as:
i
i
i
i k
p
t
BGI )
2
(
12
1
−
= ∑
=
where t is the average monthly air temperature, k is a coefficient indicating the
number of months in which 2t>p and pi is the average rainfall of the month i
(i=1 to 12).
The CQI was computed using alternatively both abovementioned indices. Since
there is no significant difference between the two methods, due to the fact that
they are highly correlated, in this study we show only the results of data
elaboration with the Rainfall Erosivity Index. The similarity of results (Trisorio
Liuzzi et al., 2005) demonstrates that Modified Fournier's Index and Rainfall
Intensity Index may be applied alternatively for the definition of the Climate
Quality Index, where the choice depends on the trustworthiness and availability
of necessary input information. Slightly greater intensity of CQI, observed
when the Rainfall Intensity Index was used, may be explained by the fact that
this method takes into consideration the altitude of meteorological stations
resulting in the higher impact to desertification of the high elevation areas.

7. 7
The new Vegetation Quality Index takes into account, besides the vulnerability
to burning (i.e. flammability and capacity of vegetation to recover after burning)
of various species, already presented in the original approach, a new
"probability of fire" index, developed by means of cluster analysis using the
data available at municipality scale. Their multiplication gives a new aggregated
index for fire risk, which, together with vegetation cover, drought resistance and
capacity of protection from erosion, describes the Vegetation Quality Index.
The Land Use Index concept is explicated through the Crop Land Use Index
(CLUI), the Pasture Land Use Index (PLUI) and the Forest Land Use Index
(FLUI), in turn combined with management policies in order to obtain
integrated land-use management quality index.
In particular, the Crop Land Use Quality Index (CLUI) is estimated as a
geometric average of five new statistical indices on both provincial and
municipal scale:
5
/
1
5
4
3
2
1 )
*
*
*
*
( CLUI
CLUI
CLUI
CLUI
CLUI
CLUI =
whose meaning is the following:
1) The Intensity of land cultivation index (CLUI1) is defined as the ratio
between the Utilized Agricultural Area (including arable land, permanent
grassland, pastures, vegetables and orchards) and the total surface area. The
lowest impact to desertification (score = 1) is assigned to the areas with the
index ranging between 40 and 60% and the greatest impact (score = 2) to the
areas where the index was either below 20% or greater than 80% (because a
high percentage of cultivated area may indicate degradation risk due to
intensification of agricultural practices, increased use of mechanization,
fertilizers and pesticides etc., whereas a low percentage could display land
abandonment). From the CLUI1 it is possible to deduce the impact of farming
activities on the environment, independently of the farm sizes and structures,
applied agricultural practices, abandonment of marginal land and other
phenomena correlated to either negative or positive effects on soil quality.
2) The Intensity of irrigation index (CLUI2), that represents the ratio between
the Irrigated Area and the Agricultural Utilized one, can also highlight the effect
of water withdrawal on land degradation in terms of seawater intrusion and
salinization (two-thirds of irrigation water needs in the Apulia region comes
from groundwater sources).
3) The Use of mechanization index (CLUI3) links the compaction of superficial
soil layers and the human induced activities (overgrazing, traffic of agricultural
mechanization, etc.). The "proxy" indicator (unit of pressure: q/ha), based on
the number of vehicles (tractors and hill-side combine harvester) present in the
area, gives indications about their density, power and weight (directly

8. 8
proportional to the degree of modification of soil structure) and the number of
travel-passes (related to the type of crop) (Fig. 3). In the following formula, “0.5
q/kW” represents the average weight of vehicles per kW, "5 travelling"
corresponds to the principal agricultural activities where mechanization is used,
and “AS” is agricultural surface area (in hectares):
AS
traveling
kW
q
kW
vehicles
N
ha
q
avg 5
*
)
/
5
.
0
(
*
*
/
°
=
Fig. 3. Compaction Risk related to number and power of tractors and hillside
combined harvester in Apulia region (Blonda et al., 2007)
4) The Use of nitrogen fertilizers index (CLUI4), or the ratio between the
applied nitrogen fertilizers (in q of N) and the agricultural surface (ha), is only
referred to the areas where fertilizers could be effectively applied (grassland and
pastures are excluded). Just mineral fertilizers have been considered for the
purpose of the application, representing the main cause of groundwater
pollution.
5) The Use of plant protection products index (CLUI5) defined as a ratio
between the total quantity (in kg) of plant protection products (herbicides,
fungicides, insecticides, etc.) sold for agricultural use and the surface area (ha)
of agricultural land at which they could be applied (agricultural land excluding

9. 9
grassland and pastures).
6) The Pasture Land Use Index (PLUI), globally describing the overall pressure
of stocking rate which includes both the surface area available for grazing and
the number of animals existing in the total area, is obtained by the integration
(by means of geometric average) of two "proxy" indices:
2
/
1
2
1 )
*
( PLUI
PLUI
PLUI =
where:
1) the Permanent Grassland and Pasture Index PLUI1 represents the
percentage of permanent grassland and pasture land in respect to the potentially
utilized agricultural land,
2) the Intensity of grazing index PLUI2 provides information about the
number of Adult Cattle Units (ACU) existing in one hectare of the potentially
utilized agricultural land. It is computed by multiplying the number of cattle
heads and the number of sheep and goat heads with the conversion coefficients,
which are 0.85 and 0.1 respectively.
The Forest Land Use Index (FLUI) is computed integrating two new statistical
indices:
2
/
1
2
1 )
*
( FLUI
FLUI
FLUI =
where:
1) the Forest Index (FLUI1) represents the degree of forestry cover through the
ratio between the surface area covered by forest and total surface area,
2) the Wood Harvesting Index (FLUI2), defined as the ratio between harvested
woody material in one administrative unit (in m3
) and the total wood harvesting
over the whole territory under consideration, is calculated from the statistical
data (available for each of the five Apulia provinces).
The Management Quality Index (MQI) represents the aggregation of four
indices as:
4
/
1
3
2
1
1 )
*
*
*
( REG
REG
REG
DIR
MQI =
representing the following:
a) DIR1 regards to the Directive CEE 43/92 ("Habitat"), describing the
percentage of protected land in respect to the total surface area and applied on
the ‘municipality scale’;
b) REG1 is related to the Regulation CEE 2078/92, regarding the low impact
agro-environmental measures and applied on the ‘province – scale’ as the ratio
between the area considered by regulation and the utilized agricultural land;
c) REG2 regards to the Regulation CEE 2080/92, concerning the improvement
of forestation and forestry practices and described at the ‘province- scale’
through the percentage of agricultural land where the regulation is in use;

10. 10
d) REG4 regards to the Regulation CEE 2092/91, concerning the development
of organic agriculture and measured, at the ‘province – scale’, by comparing the
agricultural area converted to organic farming and total utilized agricultural
land.
The effect of the management practices is globally estimated for each of three
main agricultural land use types (cropland, pasture and forestry) as:
2
/
1
)
*
(
_ MQI
CLUI
MQI
CLU =
2
/
1
)
*
(
_ MQI
PLUI
MQI
PLU =
2
/
1
)
*
(
_ MQI
FLUI
MQI
FLU =
where CLU_MQI is management quality index for cropland use, PLU_MQI is
management quality index for pasture land use and FLU_MQI represents the
management quality index for forest land use.
A new Human Pressure Index (HPI) has been proposed taking into account the
population density, the resident population at the end of year, the employment
in agricultural sector and the tourism pressure.
Very high and very low population density values, as well as excessive
increases and decreases of resident population, are considered to have a
negative influence on desertification processes.
The values of resident population at the end of year (measuring the population
and migratory rates within an administrative unit) have been considered for
three periods: 1980-1990, 1990-2000 and 2000-2005.
The employment in agricultural sector has been analysed because, if considered
for a period of years, it could provide information about the shifting of workers
from agriculture to other sectors as well as about the abandonment of
agricultural land.

11. 11
Fig. 4. Inhabitant density (a) and total density (b), considering tourist presence,
of Apulia region in 2005
The tourism pressure represents a very important indicator for the coastal areas
of the Apulia region, characterized with a significant increase of residents
during the summer months (Fig. 4). This map shows that the tourist presence
increases the population density by more than two times in the coastal areas,
representing about 13% of the regional territory.
The Tourism Pressure Index (TPI) is estimated from the series of statistical data
and referred to both the tourist presence and the tourism vocation of the area. A
complex algorithm has been elaborated in order to make comparable the
information related to both parameters. The algorithm attributes numerical
values weighted for discrete classes of the presence of tourists in addition to a
value assigned to the area particularly affected by tourism pressure and used as
multiplication coefficient. In such a way is obtained a predictive indicator,
which multiplies a percentage value determined from the vocation of the
territory (and therefore its vulnerability) and a value of tourism pressure gained
from the presence of tourists. The TPI is calculated as:
V
L
TPI a
*
=
where L is the ratio between the presence of tourists and the number of
residents, a is an exponent fixed to 3 as proposed by the literature (ANPA,
2000; Ladisa, 2001), and V represents the vocation to tourism estimated through
the number of available beds at receptions over the surface area under
consideration.

12. 12
3. Description of the study area
The above-described approach was applied in Apulia Region that covers a
surface area of approximately 19,500 km2
, subdivided in six Provinces
(recently, a sixth one was established ) on which the elaborations were based.
Most of the region territory is flat to slightly sloping lowland except for the
Gargano area, situated in the North-East, and Sub-Apennine part, located
mainly in the North-West of the region.
A semi-arid Mediterranean type of climate with hot and dry summer and mild
and rainy winter season characterise the region. The spatial interpolation of
historical rainfall data shows that annual precipitation varies between 450 and
550 mm in the greatest part of the region (Fig. 5). The lowest values, around
400 mm, are observed in the area of Tavoliere, in the province of Foggia,
whereas the highest values of more than 900 mm/year referred to the Gargano
area, in the North of the same province. The hydrological regimes are irregular,
of torrential type with high flow rates during the rainy season and practically no
water flow during summer.
Fig. 5. Average annual temperature (a) and average annual precipitation (b) in
Apulia region
The land use distribution in Apulia region is elaborated according to the
CORINE Land Cover data (CORINE, 2000) as illustrated in Fig. 6. The greatest
part of territory (81.4%) is allocated for agricultural use while forestry and
semi-natural areas occupy about 13.3% of the region. Water bodies cover about

13. 13
1.2% of territory including both natural lakes and artificial storage dams. Water
availability is one of the main factors limiting agricultural productivity in the
region.
Fig. 6. Apulia region: main land use classes according the CORINE Land Cover
data-catalogue (CORINE, 2000)
Five physiographic units may be distinguished in the Apulia region (Fig. 7).
Three of them (Sub-Appennino Dauno, Tavoliere delle Puglie and Gargano) are
situated in the North from NW to NE respectively, the Murge covers
predominantly the Central part of the region, and the peninsula of Salento is
located in the South. The dominant soils are Cambisols, Luvisols and Vertisols,
characterised by cretaceous limestone, marl and clayey to sandy deposits.

14. 14
Fig. 7. Apulia region: main physiographic units and soil regions (Zdruli 2000)
Irrigation management is run by 6 reclamation consortia, which cover an
administrative area of 1,743,591 hectares, or about 90% of territory. The area
equipped with the consortia water distribution networks, and therefore,
potentially irrigated extends over 236,012 hectares. However, in general, only
one-third of it is effectively irrigated due to huge irrigation requirements and
chronic shortage of water for irrigation and other uses.
The protected areas in Apulia region occupy about 238,535 ha, which represents
12.33% of territory (Table 1). These areas are mainly located in Foggia
province (Foresta Umbra and Gargano National Park), in Bari province (Alta
Murgia National Park) and in Taranto province (Gravine Joniche Park).
Table 1. Protected areas in Apulia Region - 2003 (Source: ISMEA elaboration
on data from 5th
updating of Natural Protected Areas Official List 2003 and
from Apulia Region, Parks and Natural Reserves Office)
Protected Area typology Surface (ha)
National Park 185,833.00
State Natural Reserve 9,906.33
Regional Natural Park 39,014.55

15. 15
Oriented Regional Natural Park 5,989.00
Municipality Park 590.00
Marine Natural Protected Area 20,347.00
Total regional (inland surface) 238,534.88
Protected Areas surface /Region surface 12.33%
4. Results and Discussion
The data input, derived from statistical databases, already existing at municipal
and provincial scales, as well as from specific regional projects (e.g. ACLA
project on Agro-ecological characterization of Apulia region by means of
potential productivity, (Steduto and Todorovic, 2001), have been georeferenced
and elaborated through a GIS (Todorovic and Steduto, 2003), and assembled in
several thematic layers, each one representing one of five quality indices (the
Soil Quality Index - SQI, the Climate Quality Index - CQI, the Vegetation
Quality Index - VQI, the Land Use and Management Quality Index - LU_MQI
and the Human Pressure Index - HPI) and their effects to desertification. These
quality indices have been elaborated for the whole region characterizing the
impact of each factor by means of several quality classes (low, medium, high
and very high) as illustrated in Figures 8, 9, 10, 11 and 12 for SQI, CQI, VQI,
LU_MQI and HPI, respectively.
The data related to the Soil Quality Index (Fig. 8) indicate that the most of
Apulia’s territory is of medium soil quality (69.1%), almost one-fourth is of low
quality and only 6.5% of territory is of high soil quality (Fig. 8b). The areas of
high soil quality, corresponding to deep soils, are located mainly in the province
of Foggia, close to the principal water courses (Ofanto, Celone, etc.)
characterized by alluvial deposits. The low quality land, corresponding to
shallow soils, is distributed primarily in the provinces of Bari and Brindisi
characterized by the calcareous terraces well-known as “Le Murge”.

16. 16
Fig. 8. Spatial distribution of SQI – Soil Quality Index (a), distribution of
regional (b) and provincial (c) surface in SQI classes.
Furthermore, it is important to underline that the SQI has been elaborated
according to the original classification of parent material (developed for the
Island of Lesvos by Kosmas et al. 1999), which results in a significant degree of
homogeneity when applied to the Apulia region. Consequently, the future
activities should be focused on the reclassification of parent material classes
according to the erodibility as considered in USLE method.

17. 17
Fig. 9. Spatial distribution of CQI – Climate Quality Index with R – Rainfall
Erosivity Index (a), distribution of regional (b) and provincial (c) surface in CQI
classes.
The CQI characterizes almost half of the Apulia territory (48.8%) of medium
quality, 42.4% of low quality and 8.7% of good quality (Fig. 9b). The good
quality land by means of CQI is located almost exclusively in the province of
Foggia due to favourable ratio between the precipitation amount and number of
rainy days (rainfall intensity), low aridity index of Bagnouls-Gaussen and
exposure (aspect). In particular, the CQI produces strong negative impact in the
province of Taranto (77.2% is low quality land), and then, in the provinces of
Lecce and Brindisi (Fig. 9a and 9c) where the low quality land occupies more
than 50% of territory (57.1% and 51.5%, respectively).

18. 18
Fig. 10. Spatial distribution of VQI – Vegetation Quality Index (a), distribution
of regional (b) and provincial (c) surface in VQI classes.
The Vegetation Quality Index shows that almost half of Apulia region is of low
quality (46.1%), one-third (31.2%) is of medium quality and about 17.4% is of
high quality (Fig. 10b). The low quality land by means of VQI is distributed
predominantly in the province of Foggia (57.7%) and Taranto (50.2%) whereas
in other provinces it occupies less than 50% of territory. Probably, such results
originate from a large extension of annual crops (e.g. cereals) with low
resistance to drought and presence of high fire risk areas especially in the
Gargano relief and along the Gulf of Taranto.

19. 19
Fig. 11. Spatial distribution of LU_MQI – Land use & Management Quality
Index (a), distribution of regional (b) and provincial (c) surface in LU_MQI
classes.
The results of elaborations related to the impact of LU_MQI to desertification
show that the greatest part of region (44.3%) can be classified as high quality
land, about one-fourth (25.9%) as medium quality land, one-fifth (19.9%) as
low quality land and about 4.6% as very high quality land. The impact of land
use and management practices to desertification is particularly negative in the
province of Foggia where more than half of territory (53%) is of low quality
(Fig. 11a and 11c).

20. 20
Fig. 12. Spatial distribution of HPI – Human Pressure Index (a), distribution of
regional (b) and provincial (c) surface in HPI classes.
The Human Pressure Index depicts 9.4% of Apulia region as low-pressure
territory, 29.8% as medium pressure land, 24% as high pressure and 36.8% as
very high pressure areas. The results show that HPI has particularly great
negative impact to desertification processes in the province of Foggia (Fig. 12a
and 12.c) where 64.5% of territory is classified as high-pressure area. This is
mainly due to the important vocation of this province to tourism where many
areas may be characterized with strong presence of non-resident population
during the summer months and very limited resident population.

21. 21
The proposed system of evaluation assigns the equal weight to each basic layer
in the calculation of the quality indices (e.g. the resistance of vegetation to
aridity has the same relevance as the vulnerability to fire, within the Vegetation
Quality Index), and, simultaneously, each of the five quality indices has the
same weight in the determination of the final Environmental Sensitive Areas
Index (ESAI) independently of the number of layers contributing to its
definition. Consequently, the ESAI is not influenced by the number of the basic
indicators (each of them represents one layer) which means that none of the
main quality indices (soil, climate, vegetation, management and human
pressure) is neither penalized nor advantaged by the fact to be constituted of
different number of layers in respect to the other indices.
For the whole region, the integration of various indices necessary for the zoning
of territory into several “sensitivity to desertification” classes (non-affected,
potential, fragile, critical) has been done considering two scenarios:
the first, including only physical aspects of territory by means of
soil, climate and vegetation, and
the second, embracing also the socio-economic factors, i.e. land
use and management practices and human pressure.
Accordingly, the environmental areas sensitive to desertification have been
obtained considering:
firstly, the three primary indices, related to the soil, climate and vegetation
databases, that is SQI, CQI, VQI, for deriving the Environmental Sensitive
Areas Index (ESAI1) as:
3
/
1
1 )
*
*
( VQI
CQI
SQI
ESAI =
secondly, including also the supplementary indices, related to the human-
induced activities, that are LU_MQI and HPI, for deriving the final
Environmental Sensitive Areas Index (ESAI2) as:
5
/
1
2 )
*
_
*
*
*
( HPI
MQI
LU
VQI
CQI
SQI
ESAI =
The mapping of these elaborations is given in Fig. 13, for the hypothesis that
includes only physical aspects, and in Fig. 14 for the hypothesis with both
physical and socio-economic indicators.
Furthermore, the overall results are elaborated for each province and the
repartition into the different sensitivity classes is highlighted in Figures 12c and
13c for both scenarios.

22. 22
The results of desertification risk by means of physical factors (soil, climate and
vegetation), presented in Fig. 13, show that more than half of the territory
(51.7%) could be characterized as critical, 27.7% as fragile, 8% as potentially
affected by desertification processes, 7% as non-affected land while 5.6%
represents urban areas and water bodies. A particularly serious situation is
observed in the province of Taranto, where 80% of territory is characterized as
critical, followed by the provinces of Lecce, Brindisi and Bari, where the land
areas critical to desertification occupy 63%, 59% and 55% respectively. A
relatively good situation is notified for the province of Foggia, where the critical
to desertification land covers 33% of territory.
Fig. 13. Spatial distribution of ESAI1 – Environmentally Sensitive Areas Index
(without LU_MQI and HPI) (a), distribution of regional (b) and provincial (c)
surface in ESAI1 classes.

23. 23
Fig. 14. Spatial distribution of ESAI2 – Environmentally Sensitive Areas Index
(with LU_MQI and HPI) (a), distribution of regional (b) and provincial (c)
surface in ESAI2 classes.
The inclusion of human-induced factors changes significantly the situation
about desertification risk in the Apulia region by describing 80.1% of territory
as critical, 12.9% as fragile, 1.2% as potentially affected and 0.2% as non-
affected land (Fig. 14b). In particular, the results of elaboration per each
province are totally different. In fact, the worst situation is indicated for the
provinces of Foggia and Brindisi, both with 90% of land classified as critical to
desertification, and then, for the provinces of Taranto and Lecce where the
percentage of areas critical to desertification is 87% and 77% respectively (Fig.

24. 24
14a and 14c). A more favourable situation is observed for the province of Bari
where 60% of territory is classified as critical to desertification processes.
These results are mainly due to lower human pressure impact in the province of
Bari than in the other Apulian provinces (most of urban areas are regularly
populated during the whole year), and, probably, due to better implementation
of the EU regulations and directives related to the agronomical and forestry
practices, protected areas and introduction of organic farming.
The introduction of the HPI and LU_MQI into the original approach has
improved the overall evaluation of the desertification risk. Especially, it helps to
distinguish between the areas with different management practices (poor and
adequately applied) and those where the human pressure plays an important
role. As an example, the difference between the ESAI indices, with and without
consideration of HPI and LU&MQI, is illustrated in Figure 15.
Fig. 15. Spatial distribution of variation between ESAI1 and ESAI2 for the
whole region.
A clear dissimilarity is observed between the provinces of Foggia and Brindisi
and the other areas of Apulia (Fig. 15). While the provinces of Foggia and
Brindisi are exposed to high human pressure and have medium-to-low level of

25. 25
quality of management and land use (which increases the overall desertification
risk), such a risk is attenuated in the other areas, where the application of better
(medium-to-high) management practices are supposed to be better effective,
although the human pressure still remains high.
5. Conclusions
This study confirms high flexibility of the original ESAs approach that could be
modified offering large opportunities for the evaluation of complex
environmental conditions. The method is conceptually very simple, easy to be
implemented from local to regional or national scale since much of the input
information is already available in geo-referenced format (e.g. Soil Map of
Europe, CORINE - Land Use and Land Cover database (CORINE, 2000),
different climatic database, etc.). Therefore, the presented methodology could
be used as one of the guiding criteria for the definition of priorities in adoption
of strategies to mitigate desertification not only in the Apulia region but also in
other semi-arid areas of the Mediterranean region and beyond.
The introduction of the new indices, especially those with a notable degree of
details (e.g. Land Use Intensity and Fire Risk) has improved the analysis of the
range of the causes of the desertification process. In addition, the application of
prevalently quantitative indicators, instead of the qualitative ones, as used in the
original MEDALUS approach, has enhanced the definition of the areas
vulnerable to desertification. The proposed modifications permit not only the
identification and refinement of different degrees of sensitivity of an area
subject to land degradation, but also allow the analyses of the factors affecting
desertification and their evaluation in terms of spatial and temporal distribution.
It is worthwhile mentioning that the selection of indicators and the scale of
application are “open” processes: some indicators may be excluded and some
other may be added into the framework, in order either to adapt the model to the
specific environmental conditions, or to improve the knowledge about some
particular aspects of desertification.
Furthermore, the presented approach allows for the crossed analyses and
elaborations focusing on the specific aspects of land degradation process. Such
aspects could be particularly useful for the development of the strategies to
combat desertification processes. They should be based on the local knowledge
and specific features characterising each part of the territory. This would permit
the identification of hot-spot areas and type of intervention, the allocation of
financial resources, the commitment of local authorities, institutions and
communities in the implementation process. In fact, in 2008, the regional
authorities have applied the methodology presented in this study in a project
dealing with the estimation of desertification risk at the regional scale following

26. 26
the guidelines of the EU Strategy for Soil Protection (COM(2006) 232 of 22
September 2006).
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