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Digital soil map of Cyprus (1:25,000)
AGWATER
Options for sustainable agricultural production and water use in Cyprus under global change
Scientific Report 6
Deliverable D15, D16
Zomenia Zomeni1, Corrado Camera2, Adriana Bruggeman2,
Andreas Zissimos1, Irene Christoforou1, Jay Noller3
1 Geological Survey Department of Cyprus
2 Energy, Environment and Water Research Center, The Cyprus Institute
3 Department of Crop and Soil Science, Oregon State University
Nicosia, 15 November 2014
i
Table of Contents
ABSTRACT ...................................................................................................................................................................... 2
INTRODUCTION ............................................................................................................................................................ 3
METHODS ........................................................................................................................................................................ 3
DATA ................................................................................................................................................................................. 4
TRAINING DATA ............................................................................................................................................................. 4
PREDICTORS ................................................................................................................................................................... 5
DERIVED SOIL SERIES MAPS ........................................................................................................................................... 7
DERIVED SOIL PROPERTIES MAPS ................................................................................................................................... 7
RESULTS .......................................................................................................................................................................... 9
SOIL SERIES MAP ............................................................................................................................................................ 9
SOIL PROPERTY MAPS ................................................................................................................................................... 11
REFERENCES ............................................................................................................................................................... 14
2
ABSTRACT
Considering the increasing threats soil are experiencing especially in semi-arid,
Mediterranean environments like Cyprus (erosion, contamination, sealing and salinisation),
producing a high resolution, reliable soil map is essential for further soil conservation studies.
This study aims to create a 1:25.000 soil map covering the area under the direct control of the
Republic of Cyprus (5.760 km2).
The study consists of two major steps. The first is the creation of a raster database of
predictive variables selected according to the scorpan formula. It is of particular interest the
possibility of using, as soil properties, data coming from three older island-wide soil maps
and the recently published geochemical atlas of Cyprus. Electric conductivity, pH, total
carbon and the Mafic Index of Alteration (MIA-R) were selected to represent soil properties;
maximum and minimum temperature for climate; organic carbon for organic matter; the
DEM and related relief derivatives (slope, aspect, curvature, landscape units); and bedrock
and surficial geology for parent material and age.
In the second step, the digital soil map including soil series and soil properties (depth and
texture) is created using the Random Forests package in R. Random Forests is a decision tree
classification technique where many trees, instead of a single one, are developed and
compared to increase the stability and the reliability of the prediction. The model is trained
and verified on areas where a 1:25.000 published soil maps obtained from field work is
available and then it is applied for predictive mapping to the other areas.
Results shown that the average error of the model, both for soil series and soil properties, is
around 10%, demonstrating the robustness of the methods proposed.
3
INTRODUCTION
The soils of Cyprus are unique due to the geological complexity, the Mediterranean island
climate and the long presence of man on the landscape. The geology of Cyprus is dominated
by the Troodos Ophiolite, which is a fragment of a fully developed oceanic crust, consisting
of plutonic, intrusive and volcanic rocks and chemical sediments. Sedimentary formations
cover the coastal plains in the south and the intermountain plain in the north. The soils on
Cyprus vary between lithosols, leptosols, regosols, gypsisols, solonchaks, solonetz, vertisols,
and cambisols based on the WRB (World Reference Base) of FAO (Food and Agriculture
Organization of the United Nations) soil classification system (FAO, 1989). They are
generally poor in organic matter (Koudounas, and Makin, 1981; Grivas, 1988) and closely
associated to parent material and landscape position. An incomplete series of soil surveys and
maps at a scale of 1:25,000 have been prepared by the Soil Section of the Department of
Agricultural from 1967-1985, using traditional field survey methods The soils are mapped
and classified based on their development stage, origin and parent material. These maps
formed the basis of the development of a digital soil map of Cyprus at a scale of 1:250,000.
The aim of the study was to create, using digital soil mapping techniques, a soil map of
Cyprus (including soil series and soil properties) at 1:25,000 scale. This involved the creation
of digital soil data to be used as training data and the creation of other data of physical
parameters involved in the soil forming process to be used as predictors. The analysis was run
for areas under the effective control of the government of the Republic of Cyprus were data
were available.
METHODS
The soil series and soil property maps are calculated using Random Forest. Random Forest is
a multiple tree classification and regression method developed by Leo Breiman (2001). A
clear overview of the method’s functioning is presented by Boulesteix et al. (2012) and
summarized in Figure 1. Each tree is a standard classification tree. At each node the code
randomly samples N (mtry) predictors and it picks the predictor that ensures the best split,
evaluated by the decrease of Gini impurity (DGI). A bootstrap sample from the original data
set is used to build a tree. Each target point is then classified aggregating the trees and
picking the class that received the major number of votes. A very relevant feature of Random
Forest is the out-of-bag (OOB) error. As stated, trees are calculated using a bootstrap sample
from the original data set, this means that some values are not actually used to construct the
trees. Therefore these data can be used for validation purpose. The OOB error is the average
error, calculated for each target class, coming from the comparison of the observations that
have been left out and the model output. An additional feature of Random Forest is the
capacity to rank the relative importance of the variables in the prediction.
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Figure 1: flow chart explaining the functioning of the Random Forest algorithm (from Boulesteix et al.,
2012)
DATA
Training Data
The sample data for deriving the train and out-of-bag data were based on published soil maps.
These detailed soil maps have a 1:25,000 scale with only ten out of the forty-some possible
sheets having been published to date (Figure 2). By far the most detailed soil reference on the
island are these ten 1:25.000 scale soil sheets (Soteriades and Georgiades, 1967, Soteriades
and Grivas, 1968, Soteriades, Koudounas and Markides 1968, Soteriades.and Markides,
1969, Grivas and Georgiades, 1972, Markides, 1975, Koumis, 1980a, Koumis, 1980b,
Koumis, 1980c, Markides, 1985) which are always accompanied by a land suitability for
agriculture map. Two of them, the Pafos sheet (Soteriades and Koudounas, 1968) and the
Polemi sheet (Markides, 1973) are also accompanied by extensive soil memoirs. These ten
soil sheets form the basis for the most detailed and thorough digital soil information on the
island.
5
Figure 2: Soil map of Cyprus and availability of soil maps at the 1:25,000 scale (from the Soil Map of
Cyprus (1999), by the Soil and Water Use Section, Cyprus Department of Agriculture).
The maps were scanned, georeferenced and digitized in a GIS environment. The resulting
dataset is a merge of the 10 soil maps and consists of with 11.000 polygons classified in 52
soil series with further 4-8 subseries classification for each series. The dataset was converted
to raster format with cell size of 25 x 25 m2 and used as training data for the training areas in
building the multiple tree classification.
Predictors
Predictors have been selected according to the scorpan formula (McBratney et al., 2003) and
include physical variables like relief, climate, geology, geomorphology, and geochemistry
(Table 1). Some graphical examples of these data are shown in Figure 3.
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Table 1: predictors used in the model.
Short
name Description
Source Source
scale
pH pH measured from a surface soil sample and ranging between 0-9 Cohen et al., 2011 1 km grid
EC Electrical conductivity measured from a surface soil sample and ranging between
0-20 ms/cm Cohen et al., 2011 1 km grid
TotC Total Carbon measured from a surface soil sample and expressed in % Cohen et al., 2011 1 km grid
OrgC Organic Carbon measured from a surface soil sample and expressed in % Cohen et al., 2011 1 km grid
Miar Mafic Index of Alteration derived from various geochemical parameters Cohen et al., 2011 1 km grid
DEM Digital Elevation Model with a 25m grid created from 24 digitized topographical
maps of Cyprus (contours and trigonometrical points) Series K717 1:50.000
Aspect Aspect, derived from Digital Elevation Model using ArcGIS 3-D Analyst Derivative of DEM 1:50.000
Curv Curvature, derived from Digital Elevation Model using ArcGIS 3-D Analyst Derivative of DEM 1:50.000
Slope Slope [deg] derived from Digital Elevation Model using ArcGIS 3-D Analyst Derivative of DEM 1:50.000
Quat Quaternary Geology (surficial geology), with 5.900 polygons in many categories
according to depositional environment and relative age Noller, 2009 1:50.000
Bedrock
Geological Map of Cyprus based on the digitisation and merge of numerous
published and unpublished geological maps of the Cyprus Geological Survey,
contains 14.000 polygons in hundreds of classes pertaining to age and formation
Digital data of Cyprus
Geological Survey 1:50.000
LU Landuse (CORINE, 2006) Büttner and Kosztra, 2007 1:250.000
GLU Geomorphological Landscape Units, with 29.300 polygons in 9 categories
according to landscape position, derived from Digital Elevation Model Noller, 2009 1:50.000
Tmax Mean maximum temperature (July) Camera et al., 2013 1 km grid
Tmin Mean minimum temperature (January) Camera et al., 2013 1 km grid
7
Figure 3: Input rasters of some selected variables are shown here in western Lemesos district at a scale of
1:300,000. Quaternary geology (top left), slope (top right), Landscape units (bottom left) and pH (bottom right).
Derived Soil Series maps
In detail, 52 soil series were recognized across the island and all of them are present in the training
areas. To train the model, a random selection from 50% of the area covered by the existing 1:25,000
soil maps (approximately one million points) and a total of 350 trees have been created. The
number of selected training points and trees is the maximum that the available computing facilities
allowed. The model has been run at the High Performance Computing (HPC) facility of the Cyprus
Institute (Cy-Tera) in Lefkosia.
Derived Soil Properties maps
From the existing ten 1:25,000 sheets (Soteriades and Georgiades, 1967, Soteriades and Grivas,
1968, Soteriades, Koudounas and Markides 1968, Soteriades.and Markides, 1969, Grivas and
Georgiades, 1972, Markides, 1975, Koumis, 1980a, Koumis, 1980b, Koumis, 1980c, Markides,
1985) 8 soil depth classes have been identified from the legend descriptions of all the 52 soil series
and many subseries on the maps. Each class is characterized by a depth interval. To end up with a
single value of soil depth for use in the modeling applications, the average of the interval has been
calculated (Table 2).
In the same fashion, 17 soil texture classes have been recognized. However, among the 17 classes
different nomenclature and classification systems were used. Therefore, data was harmonized and
reclassified in 9 consistent classes (Table 3). Available water capacities (AWC) were assigned for
all standard textures according to Saxton and Rawls (2006), except for clay, which was taken from
Allen et al. (1998).
8
Soil property maps over the whole study area were created running RandomForest with exactly the
same configuration used for soil series, with the only exception that the previously created soil
series map was used during this run as a predictor.
Table 2: soil depth classes.
Depth class
(from 1:25.000 scale soil maps)
Depth [cm]
(from 1:25.000 scale soil maps)
Depth [cm]
(single value)
Zero 0-10 5
very shallow 10-25 17
very shallow to
shallow 10-50 30
Shallow 25-50 37
shallow to moderate 25-75 50
moderately deep 50-75 57
Deep 75-100 cm 87
very deep > 100 cm 120
Table 3: texture classes and corresponding available water capacities (AWC).
Texture 17 classes Texture 9 classes AWC [%]
rock bedrock 1
gravel gravel 2
sandy loam gravelly gravelly sand 3
coarse
light sand 5
sand
light to medium loamy sand 7
coarse to medium
sandy loam sandy loam 10
medium loam 14
clay loam
medium to fine clay loam 14
moderately heavy
clayee
clay 16 fine
heavy
medium heavy
9
RESULTS
Soil Series map
The calculated 1:25,000 soil series map of Cyprus is presented in Figure 4. The mean OOB error in
classifying the 52 soil series over the training area is 0.1, meaning that 10% of the soils are wrongly
classified. In detail, only four soil series show errors higher than 0.2 (Figure 5), demonstrating the
value of the applied method. The four soil series that are worst classified are: Troodos (class error
0.28), Quarries (error 0.23), Argaki (error 0.22), and Rivers (error 0.20). However, while the errors
are low for the training areas, this does not mean that all the soils of the mapped areas outside the
training areas are correctly predicted. The methodology implicitly assumes that the 10 soil maps
(training areas) are representative for the full mapped area. An obvious problem is the lack of soil
maps for the Troodos massif. Thus, the soils for the Troodos may be not perfectly predicted.
The importance of the different predictors is shown in Figure 6. It is interesting to notice how the
first three predictors, in terms of relative importance, are all geochemistry variables (pH, OrgC,
EC). Therefore, these variables are crucial in the classification process. In Figure 7 the correlations
between the geochemistry variables are shown. These figures demonstrate that the four predictors
are independent of each other.
Figure 4: 1:25,000 soil map of Cyprus obtain with digital soil mapping techniques.
10
Figure 5: errors in predicting the 52 soil series
with increasing number of trees in the forest.
The three worst predicted and noisy soils are:.
Figure 6: relative importance of the environmental co-variables used in the classification.
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Figure 7: scatter plots showing how geochemistry variables are correlated to each other.
Soil Property maps
The predicted soil depth and soil texture maps are presented in Figure 8 and Figure 9, respectively.
Similar to the soil series map, the results are very good with the mean error equal to 0.10 (range
0.05 – 0.14) and 0.11 (range 0.05-0.25) for soil depth and texture, respectively. For both maps the
previously predicted, the soil series data, pH and Aspect are the three predictors with the highest
importance in the classification process. It is also worth pointing out that the results in the Troodos
area could not be as good as in the training areas, for the reasons explained in the previous section
(Soil map Series).
In Table 4 a summary of the areal percentages for each class of soil depth and texture is presented.
This gives a quick overview of the amount of land suitable for agriculture (soil depth > 30 cm and
no stoniness).
12
Figure 8: 1:25,000 soil depth map of Cyprus obtained through digital soil mapping techniques.
Figure 9: 1:25,000 soil texture map of Cyprus obtained through digital soil mapping techniques.
13
Table 4: summary of percentage of the whole study attributed to each soil depth and texture class.
Depth class Depth [cm]
Cell count
%area Texture Cell
count %area
zero 10 509559
5 53.3 bedrock
502694
8 52.5
very shallow 17 521457 5.5 gravel 222 0.0
very shallow to
shallow 30 5865 0.1 gravelly sand 5534 0.1
shallow 37 191014
7 20.0 sand 194037 2.0
shallow to moderate 50 57321 0.6 loamy sand 10090 0.1
moderately deep 57 560850 5.9 sandy loam 93555 1.0
deep 87 137920 1.4 loam 273210
1 28.6
very deep 120 127699
0 13.3 clay loam 160916 1.7
clay 134274
2 14.0
14
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gridded datasets for meteorological variables: Cyprus, 1980-2010 and 2020-2050, AGWATER
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Sydney: UNSW Press, 2011.
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du monde, No. 60, FAO, Rome
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Agriculture, Ministry of Agriculture and Natural Resources, Cyprus
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15
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