INTEGRATING NATIONAL SOIL DATABASE AND OTHER ENVIRONMENTAL
DATABASES TO PRODUCE A DESERTIFICATION RISK ATLAS OF ITALY AT
1:250,000 SCALE
Ferdinando Urbano, Edoardo A.C. Costantini, Giovanni L’Abate, Roberto Barbetti
Istituto Sperimentale per lo Studio e la Difesa del Suolo - P.za D’Azeglio 30, Firenze, 50121, Italy
Website: www.soilmaps.it - E-mail: [email protected]
Abstract
There is a general consensus that the main factors causing desertification are soil, climate,
vegetation and land management. The first problem that arises in assessing desertification
risk is the integration of different data sets in the same model. At the moment, in Italy it is
possible to find or to derive databases covering continuously all the national territory for
land use, morphology, lithology, climatic data and vegetation indexes, although at different
reference scales. On the contrary, the relevant soil information for the evaluation of
desertification processes (i.e. soil depth, available water capacity, salinity, moisture
regimes …) is referred to punctual observations. Our project focuses on a spatialization
methodology applied to about 15,000 soil observations of the Italian National Soil
Database, in order to integrate soil data with the other geographic databases. We
generalized punctual soil data through the identification of the relationship between soils
and their environmental pedo-genetic factors (morphological pattern and processes,
lithology, land uses) in the different pedo-geographic contexts. The Land components of
Land subsystems (1:250,000) are the geographic units used as base for the spatialization.
The methodology estimates desertified, sensitive and vulnerable lands integrating soil
characteristics to Landsat and Aster images, NDVI index, orthophotos, an original land
cover database at 1:100,000 scale, DEM with 20m resolution, socio-economic databases,
digital geological map at 1:500,000 scale, Soil regions, Land systems, Land subsystems and
field surveys of local experts.
Key words: desertification risk, desertification indicators, soil mapping, GIS
1. Introduction
The aim of this work is to present the methodology used to produce a desertification risk atlas
of Italy at 1:250,000 scale, with a special focus on the spatialization of soil data and its
integration with the other environmental databases. It is useful to introduce the framework of
the whole project before entering the core problem of soil mapping.
1.1 Desertification: a definition
The most widely accepted definition of desertification is the one given by the United Nations
Convention to Combat Desertification (http://www.unccd.int). It defines desertification as
“land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors,
including climatic variations and human activities”. Land is defined as the terrestrial bioproductive system which includes soil, vegetation, other biota and the ecological and
hydrological processes that operate within the system. Land degradation means the reduction
or loss of the biological and economic productivity and complexity of irrigated and nonirrigated agricultural land, pastures, rangeland, forest and woodland. Arid, semi-arid and dry
sub- humid areas are the territories, other than polar and sub-polar regions, in which the aridity
index, that is the ratio of annual precipitation to potential evapo-transpiration, falls within the
range from 0.05 to 0.65.
1.2 Desertification risk maps in Italy: state of the art
Studies regarding mapping the desertification risk in Italy have already been carried out at
global (Eswaran and Reich, 1998), continental (DISMED, 2003) and national scale (National
Technical Services, 1998). More recently, using the MEDALUS approach (MEDALUS,
Mediterranean Desertification And Land Use, Kosmas et al., 1999) the European
Environment Agency (DISMED, 2003) worked out a new map of the desertification risk in
Italy at 1:250,000 scale. Other maps of desertification risk have been drawn up/compiled at
regional level, using modified MEDALUS approaches (UNCCD-CRIC, 2002). These projects
define a unique index of desertification calculated assembling together many indicators. They
use a model that assigns a score to the classes in which each indicator is subdivided and
weights all the scores for the final evaluation. This approach is not able to emphasize the
dynamics of the different desertification processes. Moreover, the soil variable is considered
only using soil maps at 1:1,000,000 or 1:250,000 scale and assigning a score to each
taxonomic class of the cartographic units. Our methodology tries to overcome this pure
modellistic approach, by building a geographic information system on desertification risk that
is not only a synthesis map. We provided significant evidence to the primary role of soil
characteristics in desertification processes through the use of about 15,000 soil observations
of the National Soil Database.
1.3 Desertification risk: the evaluation model
The specific objective of the project is the construction of a national atlas of desertification
risk where the indicators are built up according to the PSR framework (Pressure, State,
Response) (Table 1).
PRESSURE
Soil characteristics
SURFACE
DENUDATION
BECAUSE OF
WATER EROSION
Vegetation cover
Human activities
DEPOSITION
URBANIZATION
SALINIZATION
DROUGHT
STATE
Slope
Rooting depth
Presence of rills and gullies
Normalized Difference Vegetation
Index (NDVI)
Number of cattle and sheep
Protected areas
Burnt areas
Recent volcanic effusion
Urban areas and main infrastructures
Distance from the sea
Elevation
Saline lithotypes
Irrigated vulnerable lands
Climatic region
Aridity index
Soil moisture and temperature regimes (following Soil
Taxonomy)
RESPONSE
Vulnerable lands
Sensitive lands
Desertified and
sensitive lands
Risk increase
Risk mitigation
Risk increase
Desertified lands
Desertified lands
Vulnerable lands
Sensitive lands
Potentially
vulnerable lands
(national scale)
Vulnerable lands
(regional scale)
Table 1. Indicators considered in assessing the areas sensitive and vulnerable to the risk of desertification
and drought in Italy.
Mean annual number of days when the soil is dry
Desertified lands are the ecosystemic responses to the pressures on the environment. State
factors of land desertification are the environmental characteristics (measured by indicators)
which regulate the occurrence of desertification processes. Pressure factors are the processes
of desertification, measured by indices that are aggregations of the state indicators, combined
in different ways for each desertification process. The processes considered are: erosion,
deposition, drought, salinization (Fig. 1) and urbanization. The desertified, sensitive and
vulnerable lands are the answer indicators.
Å
LEGEND
Water bodies
Not affected
Vulnerable
Sensible
0
25
50
100
150
200
Kilometers
Figure 1. Lands sensitive and vulnerable to salinization in Sardinia. State indicators: “distance from the
sea”, “elevation”, “saline lithotypes” and “irrigated lands”.
A desertified area is a tract of land occurring in dry climates and showing “functional
sterility”, that is an area where agriculture and forestry are currently no longer economically
or ecologically sustainable. A sensitive land is a surface where the process leading to
desertification is active, although the land does not yet have functional sterility. A land is
called vulnerable when environmental characteristics are close to that of a desertified area, but
some factors successfully mitigate the process of desertification. The choice of the indicators
was made taking into account the parameters suggested by previous experiences (Enne and
Zucca, 2000), as well as the actual possibility of having specific databases at national scale.
The project is organized on a regional base because many databases have a regional origin
and because of the relevance of this administrative unit in the definition of policy to fight
against desertification. We used the following databases: Soil regions, Soil systems, Soil
subsystem, an original land cover database at 1:50,000 scale, DEM with 20m resolution,
socio-economic databases (census, agro-environmental measures, breeding), meteorological
database, digital geological map at 1:500,000 (in some regions at 1:250,000), Landsat images,
Aster images, NDVI index, orthophotos, the National Soil Database and field surveys of local
experts.
1.4 Study area
According to the UNCCD definition reported above, all the lands in arid, semi-arid and dry
sub- humid areas must be considered as potentially at risk of desertification. We elaborated
data from 656 meteorological stations to characterize the aridity index and data from 140
stations to evaluate the pedoclimatic regimes of the whole country. Soil moisture regime was
obtained using EPIC daily outputs (Costantini et al., 2002), soil temperature regime from the
algorithm proposed by Costantini et al. (2001), based upon annual mean air temperature and
soil water field capacity. The aridity index was obtained applying the Hargreaves-Samani
methodology (1982) on the long term mean monthly temperature and rainfall values and it
was spatialized with the Inverse Distance Weighing method. The aridity index map and
pedoclimatic regimes were matched with the climatic regions of Italy (Finke et al., 1998;
Righini et al., 2001). Potential desertification risk resulted in all climatic regions with a
Mediterranean type of climate (Fig. 2).
¤
Legend
Study area
Soil regions
0
100
200
Kilometers
400
600
800
Figure 2. Areas potentially at risk of desertification (study area).
2. Spatialization of soil data
The focal problem about soil data spatialization is that the data are collected punctually. Their
variation in the space depends on many factors and it is very difficult to define a predictive
model.
The National Center for Soil Mapping (CNCP) manages the Italian National Soil
Database, with about 25,000 soil observation distributed in the whole country (about 15,000
in the study area). The soil attributes that we have considered are rooting depth, presence of
rill and gullies and the mean annual number of days when the soil is dry.
The geographic units used for the spatialization of soil data are the Land subsystem
polygons. A “Land subsystem” is a cartographic unit at a reference scale of 1:250,000 that is
the set of polygons with same geographic attributes (morphology, lithology, land use)
according to legends which reflects the perception of the pedogenetic factors at the reference
scale. The combination of principal and secondary morphology, principal and secondary
lithology and principal and secondary land use are the ID code for the Land subsystem. Two
polygons belo ng to the same Land subsystem if they have the same ID and they are referred
to the same Land system and Soil region. The key that links the geographic elements to the
soil observations is the Land components. They are the list of combinations of morphology,
lithology and land use present in each subsystem polygon. The reference scale of Land
components is 1:100,000. We defined the geography of the Land components intersecting the
thematic data layers (Fig. 3).
[
3
7
7
7
7
3
5
3
6
3
3
7
4
7
8
4
7
5
5
3
3
8
1
4
2
2
2
Legend
Land components
Land subsystems
2
0
1
2
4
Kilometers
Land components: (morphology-lithology-land use)
Soil region:
1 = Slope with small incisions - Rhyolite - Sown rotation
2 = Slope with small incisions - Rhyolite - Mixed agriculture
3 = Slope with small incisions - Rhyolite - Grazing land
4 = Slope with small incisions - Rhyolite - Natural areas
5 = Slope with small incisions - Rhyolite - Broadleaf
6 = Slope with small incisions - Rhyolite - Coniferous
7 = Slope with small incisions - Rhyolite - Mixed forest
8 = Slope with small incisions - Rhyolite - Sclerophyllous
Land system:
59.8
67 EV 616360
Land subsystem:
EV MA2101
Subsystem poly ID:
1168
Figure 3. Example of a subsystem polygon with its land components. The backdrop is the near infrared
band of an Aster image (15 m resolution).
The delineation of Land subsystems is coordinated with the project “Soil map of Italy at
1:250,000 scale ” (Costantini, 1999).
We added the combination of morphology, lithology and land use for each soil
observation of the National Database recoding the data collected by the surveyors in the field,
according to the legends used for the Land subsystems. This was the land component of the
soil observation.
The physical link between soil data and geography is built by matching the land
component of the soil observations with the land components of the Land subsystem
polygons. We defined hierarchic levels of attribution, with a decreasing reliability measured
by a weight. The first level is the attribution of the soil observation to the matching land
component of the polygon where the observation was surveyed. The second level is the
attribution of the soil observation to the matching land component of all the polygons of the
same Land subsystem. The third level is the attribution of the soil observation to the matching
land component of all the Land subsystem polygons pertaining to the same Land system of
the observation. We introduced a fourth level that is the attribution of the soil observation to
the matching land component of all the Land subsystem polygons pertaining to the same Soil
region of the observation. Other mechanisms of attribution derive from the use of existing soil
maps.
Table 2 shows the associations between Land subsystem polygons and soil observations,
through the common field “Land component”. We computed a single value of each soil
characteristics weighing the value of all the soil observations attributed to each polygon.
No evaluation is possible for polygons without soil data associations. This case is more
frequent in natural areas then in agricultural lands, but there it is possible to use the NDVI and
land cover databases as an indirect measure of the erosion effects (denudation).
Polygon
Land component
Soil Observation code
Weight
250
EV-SE2001-20
pSARP98
2
250
EV-SE2001-65
hBASP10
4
250
EV-SE2001-65
hBASP12
4
250
EV-SE2001-65
IAOsP3
4
250
EV-SE2001-65
IAOsP4
4
251
EV-ME1003-20
hBASP26
1
251
EV-ME1003-20
hBASP29
1
251
EV-ME1003-20
pSARP80
4
251
EV-ME1003-20
pSARP81
4
251
EV-ME1003-20
pSARP93
1
251
EV-ME1003-20
pSARP96
1
251
EV-ME1003-50
hBASP6
1
251
EV-ME1003-50
hBASP7
1
251
EV-ME1003-50
hBASP8
1
251
EV-ME1003-50
RPSP7
4
Table 2. The table shows the link between soil observations and the geographic elements (Land subsystem
polygons) through the key field “Land component”. The weight depends on the method used to establish
the connection. Through the field “Soil observation code” it is possible to recall all the soil characteristics
from the National Soil Database.
3. Results
Works are still in progress. Two examples of desertification indexes are given in Figures 4
and 5.
Figure 4 shows the desertification risk because of water erosion in Sardinia. Vulnerable
lands are the areas with rooting depth lower than 50 cm and slope greater than 15 degrees.
Vulnerable lands with increasing risk factors (presence of rills and gullies, overgrazing, fires)
become sensitive lands. These data were integrated with an NDVI analysis in natural and
semi- natural areas. The NDVI calculated form Landsat images was classified according to
about sixty sample points. It was used for soil data analysis where no soil data is available and
identifies desertified lands.
Figure 5 presents the result of the analysis of drought sensitivity in Sardinia. The
vulnerable lands are those where the number of days when the soil is dry is greater then 150.
This data was calculated for 230 benchmark soil observations coupled with their reference
meteorological stations using the EPIC model (Sharpley et al., 1990). We extended this data
to all the soil observations of the National Database, using a multilinear regression based on
available water capacity of the control section and mean annual precipitation and air
temperature. The punctual values were then spatialized with the described methodology.
Å
LEGEND
No data
No risk
Vulnerability
Sensibility
Desertified
0
25
50
100
150
200
Kilometers
Figure 4. Desertification risk index because water erosion in Sardinia. State indicators: “rooting depth”,
“slope”, “ presence of rills and gullies”, “vegetation cover (NDVI)”, “cattle and sheep”, “protected areas”.
Å
LEGEND
No data
Not affected
Vulnerable
0
25
50
100
150
200
Kilometers
Figure 5. Index of sensitivity to drought in Sardinia. State indicators: “annual number of days when the
soil is dry”.
4. Conclusions
The desertification risk atlas is an information system realized in a GIS framework. It is a
decision support system to fight desertification. We considered soil data as a key factor to
identify desertification processes. The data layers obtained spatializing soil characteristics of
the National Soil Database observations on the base of Land subsystem polygons allowing the
integration of pedology with other environmental data. The use of Land subsystems is in
agreement with other current soil mapping projects at national scale.
When all the soil observations of the database will be correlated, it will be possible to
obtain better results using soil typologies instead of single observations, giving stronger
statistical meaning to the links between soils and geography.
This methodology can be also applied to Land units at a reference scale of 1:50,000 for a
more detailed geographic attribution.
We are planning to use data mining techniques to define a soil spatial prediction function
with a pixel approach for some pilot Soil regions. The final goal is to compare the efficiency
and efficacy of the two approaches.
Acknowledgments
We acknowledge the Italian Ministry for Agricultural and Forestry Policies and the Italian
Ministry for the Environment for their financial support.
References
Costantini, E.A.C. (1999) Preparing the soil survey of Italy at scale 1:250,000. Boll. Soc. It Sc. Suolo, 48, 655665.
Costantini E.A.C., Bocci M., L’Abate G., Fais A., Leoni G., Loj G., S. Magini, Napoli R., Nino P., Paolanti M.,
Salvestrini L., Tascone F., Urbano F. (in press) Mapping the State and Risk of Desertification in Italy by
means of Remote Sensing, Soil GIS and the EPIC Model. Methodology Validation on the Island of Sardinia,
Italy. International Symposium: Evaluation and Monitoring of Desertification. Synthetic Activities for the
Contribution to UNCCD, Tsukuba, Ibaraki, Japan, February 2 2004, NIES publication.
Costantini E.A.C., Castelli F., Iori M., Magini S., Lorenzoni P., and Raimondi S. (2001) Regime termico del
suolo in alcuni campi sperimentali del nord, centro e sud Italia. Proceedings of SISS conference, Gressoney
Saint Jean, 1999, (ISNP, Roma.
Costantini E.A.C., Castelli F., Lorenzoni P., Raimondi S. (2002) Assessing soil moisture regimes with traditional
and new methods. Soil Sci. Soc. Am. J. Soil Sci. Soc. Am. J., 66, 6, 1889-1896.
Costantini E.A.C., Magini S., and Napoli R. (2003). A Land system database of Italy. 4th European Congress on
regional Geoscientific Cartography and Information systems. Proceedings vol.1, 124-126
DISMED (2003) Map of Sensitivity to Desertification and Drought in the Mediterranean Basin – Italy [online]
www.ibimet.cnr.it/programmi/Pcase/dismed_products (verified on March 2004).
Enne G., and Zucca C. (2000) Desertification indicators for the European Mediterranean region. State of the art
and possible methodological approaches, ANPA, Rome, Italy, pp. 261.
Eswaran H. and Reich P. (1998): Desertification: A global assessment and risk to sustainability. In: Proc. Of
16th Int. Congr. Soil Science, Montpellier, France. CD ROM.
Finke, P., Hartwich R., Dudal R., Ibanez J., Jamagne M., King D., Montanarella L., and Yassoglu N. (1998)
Georeferenced soil database for Europe. EUR 18092, Ispra, Italy.
Hargreaves, G.H., and Samani, Z.A. (1982) Estimating potential evapotranspiration. Tech. Note, J. Irrig. and
Drain. Eng., ASCE, 108(3):225-230
Kosmas C., Kirkby M., Geeson N. (1999) The MEDALUS project. Mediterranean Desertification and land use.
Manual on key indicators of Desertification and mapping environmentally sensitive areas to desertification.
EUR 18882, Bruxelles, Belgium.
McBratneya A.B., Mendonça Santos M.L., Minasnya B. (2003) On digital soil mapping. Geoderma 117, 3-52.
Priestley, C.H.B., and Taylor R.J. (1972) On the assessment of surface heat flux and evaporation using large
scale parameters. Mon. Weath. Rev. 100:81-82.
Righini G., Costantini E.A.C., and Sulli L. (2001) La banca dati delle regioni pedologiche italiane. Boll. Soc. It.
Scienza del Suolo, 50, Suppl., 261-271.
Sharpley, A.N., and Williams J.R. (1990) EPIC-Erosion/Productivity Impact Calculator. USDA Tech. Bull.
1768.
Soil Survey Staff (1999) Soil Taxonomy: A basic system of soil classification for making and interpreting soil
surveys. 2nd ed. USDA-NRCS Agric. Handb. 436. U.S. Gov. Print. Office, Washington, DC.
UNCCD-CRIC (2002) Italy national report. Report of the Ministry of Environment and Territorial Protection.
Rome, Italy, 43 pp.