International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
  
which is the rainfall-runoff relation used in the SCS method 
(SCS, 1972). A transformation of S, the runoff curve number or 
hydrologic soil-cover complex number CN, was developed by 
SCS to facilitate with the calculations: 
Sz ss (0m — i0 Jum) (2) 
CN 
Substituting Equation 2 into Equation 1 gives the basic SCS 
relationship for estimating Q from P and CN, which has the 
advantage of having only one parameter since CN can readily 
be extracted from published tables, such as the following 
extract (Table 1), which contains only information relevant to 
this study: 
  
  
  
  
  
  
Landuse/Landcover slope Soil group 
A-IB ic |D 
Urban 23 26. 96 96 . 96 
<3 93 93 93 93 
Orchards >3 68 78 84 88 
<3 64.5 73 * 78 8 
Irrigated 23 56... 70 . 80 84 
«3 82- 67: -76 79 
Bare 23 94 94 94 94 
<3 9p 919A 9 
Matorral, (Xaló) 46 68 18 8 
Matorral (Lesbos) $6 .735., 86. Ol 
Maquis, sparse 46 08 78 83 
Maquis, dense 40< 60-75-69: 76 
Forest, decidious 36 ‘52 62 69 
Forest (Xaló) 40 00 69 76 
Forest (Lesbos) 20 44,1 34 60 
  
  
Table 1. SCS Curve numbers (after Ferrer et al., 1995) 
As can be seen from the above sample table, the value of CN 
depends on factors such as landuse/cover, slope and soils which 
the SCS has divided into four groups according to their 
infiltration, retention and evaporation capacity. These factors 
control not only the amount of water that becomes runoff, but 
also the initial abstraction I,, since they are closely related to 
the amount of interception, initial infiltration, surface 
depression storage, evaporation and transpiration. 
In order to estimate runoff, the LULC maps were combined 
with the data of Table 1 to extract the necessary SCS CN. For 
the Xalo area, soil textural data, namely percentages of sand, 
silt and clay, from 16 point locations in the wider catchment 
area, were combined with lithological maps to extrapolate the 
point measurements over the area and produce three separate 
maps of percentage sand, silt and clay. These were then 
combined with the United States Department of Agriculture 
(USDA) soil textural triangle (Miller, 1996) to create a map of 
the four SCS soil groups. In the case of Lesbos, the previously 
mentioned 1:200000 textural soils data were employed. The 
resulting CN maps were then used along with Equations 1 and 2 
556 
and rainfall data to estimate runoff Q. As for the precipitation 
data, a uniform 200mm rainfall map, representing an event with 
a 10-year return period (Gisbert and Ibáüez, 2003), was used 
over Xaló. Due to the lack of such information for the island of 
Lesbos, rainfall values of a minimum of 100mm and a 
maximum of 200mm were distributed over the island according 
to the altitude of each pixel and the climatic zone to which it 
belonged, using the following equation: 
P, = 100 + 100/ £x 2 (3) 
max 
where — P, - precipitation at point X (mm) 
Z, 7 altitude at point X (m) 
7 = maximum altitude over the island (m) 
max 
w, = linear weight according to climatic zone 
3.4 Erosion modelling 
Thornes (1985, 1989) established a physical-based soil erosion 
model by combining sediment transport and vegetation 
protection in the following equation: 
E-4AQ" s" e" (4) 
where  E = erosion (mm) 
k = soil erodibility coefficient 
Q = overland flow (mm) 
s = slope (% 
VC = vegetation cover (%) 
The coefficients m and n have been described by a number of 
researchers. They vary according to different measurements: m 
changes between 0.91 to 2.07 and n from 0.24 and 1.67. 
Thornes (1976) suggested values of 2.0 for m and 1.67 for n. 
When modelling the competitive behaviour of vegetation and 
erosion, Thornes (1990) indicated that erosion is reduced 
exponentially in relation to the bare soil value by increased 
vegetation cover. The value b=-0.07 was used which is in 
accordance with the results of other researchers for a variety of 
environments (Drake et al., 2004, Symeonakis and Drake, 2004; 
Thornes, 1990). 
Vegetation cover was then estimated using the NDVI estimate 
and the scaled NDVI or N*, which is equal to (Choudhury et 
al., 1994, Carlson et al., 1995): 
yo NDVI - NDVI, (5) 
NDVI , - NDVI , 
where NDVI, = the value of NDVI at 100% cover 
NDVI ; = the value of NDVI for bare soil 
According to Carlson et al., (1995), the index N* is useful 
because it is relatively insensitive to viewing angle, sensor drift, ' 
and uncertainties in atmospheric corrections. 
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