This paper was peer-reviewed for scientific content.

Pages 1082-1089. In: D.E. Stott, R.H. Mohtar and G.C. Steinhardt (eds). 2001. Sustaining the Global Farm. Selected papers from the 10th International Soil
Conservation Organization Meeting held May 24-29, 1999 at Purdue University and the USDA-ARS National Soil Erosion Research Laboratory.

A GIS-based Methodology for Soil Degradation Evaluation

Rami Zurayk*, Faraj el Awar Christine Sayegh, Shady Hamadeh and Abdel Ghani Chehab
ABSTRACT number of similar endeavors related to natural resource
This paper reports on an attempt to delineate soil management (Theocharopoulos et al., 1995; Davidson, 1992).
degradation areas in Aarsal, a dryland zone in However, the accuracy and relevance of the information
Lebanon. The study area covers 360 km2, including produced by GIS is only as good as the data sets available. In
highlands and steppes. With limited data which developing countries, especially in remote, marginal, and poor
included soil map, contours map, and land cover map, areas, data is often inexistent.
we have created soil degradation assessment maps This paper reports on an attempt to develop a GIS-based
based on three approaches: drainage density, drainage methodology for the evaluation of the intensity of the soil
texture, and factorial scoring of the main soil degradation on land resources in an arid, marginal environment,
degradation agents: slope, grazing, and land use. The in the locale of Aarsal, situated in the Eastern mountain range of
three methods classified the project area Lebanon.
predominantly in the low and very low soil degradation The specific objectives of this study are to:
categories. There was little overlap between the three 1. Develop two generalized erosion hazard assessment maps
maps due to the use of different data sets, indicating based on drainage density and drainage texture.
different soil degradation mechanisms. A combined soil 2. Develop a soil degradation risk map based on the factorial
degradation assessment map, joining the data from the scoring of the dominant risk factors.
three assessments, was produced and successfully field 3. Integrate the maps to produce a generalized soil degradation
checked. The approach adopted in this paper, which assessment map.
consists in combining different data sets, requires
limited field measurements, and can provide reliable MATERIALS AND METHODS
indicators of soil degradation risk. It appears, Study location
therefore, to be appropriate for regions of similar Aarsal is a large highland village (pop. 36,000) on the
environmental and economic characteristics, especially western slopes of the Anti-Lebanon mountains (mean annual
that it may be adapted to include site-specific rainfall 250 mm). The total village land area covers 36,000 ha,
parameters. and is divided into four agroecological zones:
1) The High Jurd lands which used to be marginally cultivated
INTRODUCTION with cereals and pulses and summer grazed, are being
Poor agricultural practices, overgrazing and deforestation massively converted to stone fruit orchards with an
over the past three millennia have resulted in widespread estimated 2 million trees planted in the past 20 years.
degradation of the land resources of the Middle East 2) The Low Jurd lands that used to be cultivated with cereals
(Dregne, 1992, Lowdermilk, 1953). Current global and and grazed by flocks of small ruminants (predominantly
regional economic changes are inducing further pressure on sheep).
the land. There is a pressing need for action to mitigate land 3) The Valleys, mostly planted with grape vines. The rainfall
degradation. and snowmelt from the highlands, especially the Low Jurd,
Land degradation results from the interaction of human feed into the Valleys as seasonal streams.
activity, such as agriculture, with the biophysical and The Sahl Lands (plain) surrounding the village are the
socio-economic characters of a specific ecosystem. When wintering site for flocks of small ruminants (predominantly
studying large areas, it is necessary to identify zones where sheep) maintained on crop residues and feed concentrates.
urgent intervention is required from those which are stable Soil variability in Aarsal is limited due to the relatively
under the current land use. Achieving this complex task homogeneous parent rock formation (Cenomano-Turonian hard
requires 1) the selection of land quality indicators limestone). The soils of the highlands are predominantly xeralfs,
appropriate to the natural and socio-economic while those of the steppe are predominantly haplocambids.
environment, 2) the use of a flexible methodology that
easily allows a number of permutations and “what-if” Geographic Information System
scenarios, and 3) replicability and moderate cost. The GIS used in this study is the PC Arc Info platform (ESRI),
Geographic Information Systems (GIS) are ideal for this running on a Pentium II, 200 MHz, 32 Mb RAM computer,
endeavor, as they offer the speed, flexibility and the power to with support from the Arc Info platform running on a SUN
integrate large quantities of data. They have been used in a microstation for complex data treatment.

*Rami Zurayk, Faraj el Awar Christine Sayegh, Shady Hamadeh, and Abdel Ghani Chehab, Faculty of Agricultural and Food Sciences,
American University of Beirut, P.O. Box: 11-236, Beirut, Lebanon. *Corresponding author: rzurayk@aub.edu.lb
 Thematic coverages project area. Rainfall and soil characteristics were not
The following coverages were used: considered as rainfall data was only available for one
The contour Map and slope classes Map location over the whole project area, and the level of detail
The contour map (50 m intervals) was manually digitized in the available reconnaissance soil map did not show
from the 1:100,000 maps developed by the Department of significant variation in soil types. Thus, the coverages that
Geographic Affairs of the Lebanese Army (DGA) in 1962. were used in the production of the factorial soil degradation
The slope map was derived from the contour map of Aarsal analysis map were the slope, the grazing pattern and
using ArcTIN and DTM, by generating a TIN from which a intensity, and the land use. These represent the dominant
grid was derived. The grid was then converted to slope biophysical soil degradation factors in Aarsal.
polygon coverages with the desired slope classes in Arc This approach of classification of areas at risk with 3
View format. parameters representing the resource base, cropping and
The Land Cover Map livestock systems appears to have been successfully used in
The land cover map was derived from Spot Multispectral degradation assessment in drylands (Mati et al., 1998).
Satellite image (20 m x 20 m resolution) that was taken in
August, 1992. This image was then processed using the Development of the rating scheme
ERDAS Imagine software. Ground truthing was carried out Sub-rating schemes were developed for land use types,
in all accessible areas. Five land cover classes were grazing patterns and slope classes. Ratings ranged from the
identified based on the purpose of the project. These are: lowest hazard (0) to the highest (10) and were allocated
Annuals, Grazing/Old Fallow, Fruit Trees, Grapes, & Not based on measurable parameters (see Table 1), literature
Agriculture. reports and expert knowledge.
The Grazing Map Rating soil degradation intensity associated with specific
The grazing map of Aarsal was derived by monitoring land use classes
and surveying the grazing patterns of sheep and goat flocks. Farmer practices were surveyed in order to produce a
The map includes information about the stocking rate and comparative assessment of the soil degradation level they
the grazing season. will pose on the land resources. The approach we adopted is
The Rivers and Streams map similar to that of Mellerowicz et al. (1994) who used
This map was manually digitized from the 1: 100,000 information about cropping support practices to determine
scale maps of the Lebanese Army and includes two the CP (crop/practices) factors in the USLE (Universal Soil
waterways classifications: Main and Temporary. These Loss Equation).
streams were reclassified into three hydrologic classes The most critical determinants of soil degradation in the
(secondaries, tertiaries, and mains) and were coded different LUTs of Aarsal were found to be associated with
accordingly. agricultural practices related to land preparation. Tillage
practices are most intensive in fruit tree orchards, followed
Procedure application by grape vine fields, then annual field cropping and finally
Factorial soil degradation risk map fallow. Details can be found in Table 1.
The approach is adapted from the procedure for land These inferences were confirmed by erosion
evaluation in arid grazing ecosystems of the Food and measurements in 100 m2 plots using the pin method, in
Agriculture Organization of the United Nation (FAO) which 300 mm iron pins are driven into the soil so that the
(Breimer et al., 1986). It is a stepwise approach in which 1) top of the pins can give a datum from which changes in the
the relevant land use types (LUTs) are determined, 2) the soil surface levels can be measured (Hudson, 1993). The
land characters of relevance to the LUTs are defined, 3) a pins were installed to a 2 m2 grid and replicated twice in
subrating system is developed for each land character, 4) a each land use type in 3 ecozones. Results from one year do
final evaluation is obtained by the summation of the not constitute conclusive evidence (Table 1), but showed
different subratings. that our ranking was adequate.
The approach was adapted to the data sets available in
this study and to the specific environmental conditions of the

Table 1. Degradation risk associated with different land uses in Aarsal.

Land use Risk factors Absolute Risk Rating
level
Fruit trees 2-3 tillage operations per year, 20-25 cm deep, mostly mechanized, Moderate 5
and up and down the slopes. No weeds or other surface cover. Trees
are deciduous. Estimated erosion rate: 2.6 t/ha/yr.
Grape vines 2 tillage operations per year, shallow to protect roots. Very little Moderate-low 4
protection provided by the vines due to local pruning method.
Estimated erosion rate: 1.8 t/ha/yr.
Annual field crops 2 tillage operations per year, mostly animal driven. Good soil cover Low 3
during winter. Estimated erosion rate: 0.9 t/ha/yr.
Old fallow No agricultural activity. Estimated erosion rate: 0.7 t/ha/yr. Very low 1
 Table 2. Subrating grazing pressure and slope density except that statistics are performed to calculate the
characteristics total number rather than the length-sum of the secondaries
Stocking rate per unit area. An arbitrary value of 10 was taken to separate
(head of small Degree slope Rating areas of “High” and “Moderate” erosion risk.
ruminant/ha)
<0.5 0-1 1 Matching the three assessment methodologies
1-2 2 The factorial-scoring map (FS) was matched with the
0.5 - 1.5 2-4 3 drainage density map (DD) and the drainage texture map
4-6 4 (DT) in order to determine whether the two approaches will
6-10 5 identify the same high hazard/soil degradation areas. Fraser
10-16 6
16-21 7
et al. (1995) use a similar method for comparing land cover
1.5 -<5 21-25 8 classifications from different remote sensing sources.
≥5 >25 10 The GIS procedures adopted were based on class
selective matching. The proportion of each soil degradation
class in a selected map that is matched by the same class in
another map calculated by selecting each soil degradation
Rating the grazing activity class from the two maps, intersecting the resulting coverages
Recent research has shown that the maximal stocking and calculating the area of the overlap.
rate that can be allowed in Aarsal for sustainable
management is around 1 head of goat of sheep per ha Combined soil degradation evaluation map
(Hamadeh, 1999). Subratings were developed accordingly The factorial soil degradation assessment map, the
(Table 2). drainage density map and the drainage texture maps were
combined in order to provide a complete picture of soil
Rating the slope characteristics degradation in the project area. In the combination
The slope intervals were selected after reviewing a procedure, the highest soil degradation rating of overlapping
number of sources summarized in Morgan (1986). The polygons was considered to be the actual soil degradation
subratings appear in Table 2. rating of the resulting land polygon (precautionary
Factorial analysis principle).
The three themes were overlaid and their scores were
added up to form a final score map coverage. The resulting Validation by ground truthing
map classified the polygons created by the overlay into five The outcome of the combined soil degradation
categories of soil degradation: Very High, High, Moderate, assessment map was validated by ground truthing in 5
Low, and Very Low. quadrants. The 1 km2 quadrants were randomly selected
from a grid applied onto the map. Their location in the field
Drainage density was identified using a Global Positioning System. They were
Drainage density is the length of primary streams per unit then field surveyed and land degradation was described in
area and is a commonly used index of erosion intensity in the various landforms and LUTs of the quadrants areas using
generalized erosion hazard assessment (Morgan, 1986). A the FAO soil degradation-mapping framework (Breimer et
field survey showed that the number of first order streams al., 1986).
could be estimated by multiplying the number of secondary
streams by a factor of 2.5, which is the mean bifurcation RESULTS AND DISCUSSION
ratio. This value is the mean of 31 direct measurements in The summary of the results of the assessment using the
locations throughout Aarsal’s ecozones. three methods appears in Table 3 and in Map 1. The factorial
The following procedure was used to generate the drainage soil degradation assessment resulted in the classification of
density map: over 90% of the area in the low and very
A grid coverage (1 km2 grids) was generated and clipped
based on the base map. The waterway map was overlaid on
the grid coverage. Finally, statistics were performed to Table 3. Summary of land degradation risk assessment
calculate the sum of secondary streams per unit area (m/m2). using 3 methodologies and their combination, in percent of
the total project area (29,000 ha)
Based on Mikhailov (1972) and Iana (1972), arbitrary
values of 0.001mm-2 and 0.006 mm-2 (based on secondaries) Stress Factorial Drainage Drainage
Combined
class scoring density texture
were selected to represent “Low” and “Very High” erosion
Very
risk. The grid cells were classified into the same risk high
0 0 1 1
categories as the factorial map. High 0 0 5 6
Drainage texture Moderate 6 7 15 25
Drainage texture, the number of first order streams per Low 47 33 24 50
unit area, is another commonly used index of erosion Very low 47 60 50 18
intensity in generalized erosion hazard assessment (Morgan, Total 100 100 100
1986). The procedure is essentially the same as for drainage
Map 1. Soil degradation analysis using three approaches (drainage density, drainage texture, and factorial scoring) in the
locality of Aarsal, Lebanon.
low soil degradation categories. Although this may seem specific area in indicated by the marks they leave on the
counter intuitive, considering the desert-like aspect of the land. The study of drainage density (the total length of
land, this is explained by the fact that due to the “low-input” streams per unit area), and of drainage texture (the density of
agricultural practices, overgrazing is the main soil streams per unit area) allows us to obtain an indication of
degradation agent on the land. this effect.
The main soil erosion risk in the mountainous areas of The map of drainage density classified over 90 % of the
Aarsal would, in theory, be high rainfall intensity on steep project area in the low and very low risk categories. The
slopes. However, annual rainfall is very limited, and, drainage texture assessment resulted in 80% of the area
although data from various ecozones is unavailable, local classified in the low and very low risk categories, while a
knowledge indicates that it is similar over the whole area, significant proportion (15%) was classified in the moderate
except in the high elevation where precipitation is mostly as risk class. The match in risk zoning between the different
snow. This would have little additional effect on soil approaches was limited (Table 4). Lack of correlation
erosion, thereby limiting the effect of the slope. This between the drainage texture and the drainage density was
assumption is probably correct except in two situations: reported by Morgan (1976) in generalized assessment
- In the case of severe, short duration storm events, which studies in Peninsular Malaysia. Drainage density indicates
produce severe rill and gully erosion. There is no data transport of runoff from moderate, regular rainfall, while
available on the intensity and duration of these storms, drainage texture indicates the response to seasonal rainfall
which appear to have a recurrence period of 10 years. regime with rainfall of greater intensity (gully density). The
From local reports and observed erosion pattern, it latter is closer to the rainfall regimes in Aarsal. Moreover,
appears that these events can be very damaging, which the factorial scoring map delineates areas which are
explain the severe gullying observed in the mountains, currently under soil degradation, mostly from anthropic
and the size of the streams that cut across the otherwise origin, while the drainage maps indicate the combined effect
desert-like Eastern Zone. of slope, rainfall regime and soil.
- Where mechanical disturbance, such as keeping herds The combination of all three assessments, each
for prolonged periods on a limited area. This is, representing a different soil degradation mechanism, is
however, accounted for in the grazing patterns coverage therefore expected to produce the most “realistic” results
analysis. (see map 2 and Table 3), with nearly half the area in the
These remarks are confirmed by the findings that the “moderate” to “very high” classifications. The combination
moderate and high soil degradation level areas are those of the three maps allows, therefore, a holistic perspective on
where the stocking rate is highest, independently of the slope the soil degradation on Aarsal. The drainage maps offer an
and the land use insight on what has happened (past effects) while the
Our survey revealed, however, that grazing has become factorial analysis map addresses the current status of the
geographically very limited, as herd movement is declining land. Moreover, as the different maps use different data sets,
due to the availability of hand feeding. Moreover, as herd it is possible to combine them without producing any
size has been declining (from 90,000 to 60,000 over the past redundancy.
40 years) (Hamadeh, 1999), this would results in further Validation by ground truthing in 5 randomly selected
alleviation of the impact of overgrazing. It is to be noted, locations indicated that the delineation was close to the
however, that this situation may be only temporary. Indeed, actual field situation. Table 5 shows the results of the field
stone fruit production in the orchards is starting to shift investigation. The heaviest soil degradation was observed to
towards a higher-input system. This will lead to the change take place on the rocky summits, where grazing is the
in the land degradation risks imposed by this land use. The dominant form of land use, and in the poor pastures of the
methodology used in this study can, however, readily Eastern ecozones (Tahoun el Hawa and Khirbet Daoud
accommodate this change and a new factorial map may be quadrants). The other land uses show some signs of soil
produced. degradation, but this is generally moderate of low on most of
In order to account for the effects of storms, an indirect low soil the quadrant’s area. Although the number of
approach was selected. The impact of storm events on a quadrants is small and its statistical representability may be

Table 4. Matching factorial scoring map (FS) with drainage density map (DD) and drainage texture map (DT)
Total area Total area Total area FS/DD FS/DT DD/DT
Stress class
FS DD DT Match (%) match (%) match (%)
Very Low 13578.08 16855.95 14493.18 55.73 49.21 67.91
Low 13538.28 9666.36 8501.96 32.56 31.51 36.13
Moderate 1864.08 2123.36 4426.90 10.13 8.78 13.76
High 16.01 346.54 1318.29 3.75 0.00 0.00
Very High 0.00 4.21 256.06 0.00 0.00 0.00
Total 28996.45 28996.42 28996.40 41.95 38.32 52.53
 Table 5. Results of the field investigation of soil degradation levels in 5 quadrants in Aarsal
Landform Quadrant area (%) Land use/land cover Soil degradation*
Quadrant 1: Bdeirieh
Rocky hill top 20 Small shrubs, grazing Moderate Wt, f, g
Terraced fields 40 Stone fruits SH
Catchment floor 30 Stone fruits SA
Quadrant 2: Wadi el Hosn
Rocky hill top 25 Sparse vegetation Wt-Wd, f, g
Hillside field 45 Old fallow Moderate Wt, f, a
Cuvette (wadi floor) 20 Grapes SA
Quadrant 3: Tahoun el Hawa
Alluvial plain 30 Barley, vines, grapes fields SA, Cn
Alluvial plain 50 Steppe-pastures Pc, Et, f, g
Hillside 10 Grazing Wt, f, g
Flood route 10 Road Extreme Wd
Quadrant 4: Khirbet Daoud
Alluvial plain 70 Grazing Mild Wt, Pc
Hillside 30 Grazing Wt + gullies, f, g
Quadrant 5: Rahweh
Plain/large cuvette 60 Stone fruits SA
Hillside 25 Stone fruits Mild Wt, a
Rocky hilltop 15 Grazing Wt, g, f
*Water erosion: Wt= loss of topsoil, Wd= terrain deformation. Wind erosion: Et=loss of topsoil. Stable terrain: SA: under
agriculture, SN: under natural conditions, SH: stabilized by human intervention. Chemical deterioration: Cn=loss of nutrients
and organic matter. Physical deterioration: Pc=compaction/crusting. Causative factors: f=deforestation and removal of natural
vegetation, g=overgrazing, a=agricultural activities.

argued, it conveys, however, sufficient information to were required for developing data entry and analysis skills.
confirm the output of the analytical procedure adopted for Similar limitations were also reported by Theocharopoulos
mapping soil degradation on the whole territory of Aarsal. et al. (1995) for soil surveys using GIS, and by Harris et al.
(1997) who compared manual and GIS-based systems for
CONCLUSION riparian restoration projects. However, the digital data that
It may be deduced, therefore, that the Northern part of was produced is now available for other usages and is
the Eastern Ecozone is the “problem zone” of Aarsal, as both currently being used to develop the agroecological zoning of
drainage density and soil degradation analysis tend to the area in collaboration with a number of international
demonstrate. In addition, the drainage maps show the donors and research agencies. Thus, even though the initial
location of the areas where flash storms will be most cost is relatively large, we believe it is a sound investment as
damaging, and where structural interventions may be the digital data may have multiple users, which increases the
needed. Orchard expansion must be avoided in these areas. return on the initial investment.
The current soil degradation levels/erosion risks appear to be
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