Evaluating the Degradation of Natural Resources in the Mediterranean Environment Using the Water and Land Resources Degradation Index, the Case of Crete Island

Abstract

Natural resources degradation poses multiple challenges, particularly to environmental
and economic processes. It is usually difficult to identify the degree of degradation and the critical vulnerability values in the affected systems. Thus, among other tools, indices (composite indicators) may also describe these complex systems or phenomena. In this approach, the Water and Land Resources Degradation Index was applied to the fifth largest Mediterranean island, Crete, for the 1999–2014 period. The Water and Land Resources Degradation Index uses 11 water and soil resources related indicators: Aridity Index, Water Demand, Drought Impacts, Drought Resistance Water Resources Infrastructure, Land Use Intensity, Soil Parent Material, Plant Cover, Rainfall, Slope,
and Soil Texture. The aim is to identify the sensitive areas to degradation due to anthropogenic interventions and natural processes, as well as their vulnerability status. The results for Crete Island indicate that prolonged water resources shortages due to low average precipitation values or high water demand (especially in the agricultural sector), may significantly affect Water and Land degradation processes. Hence, Water and Land Resources Degradation Index could serve as an extra tool to assist policymakers to improve their decisions to combat Natural Resources degradation.

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Citation: Tsesmelis, D.E.; Karavitis,
C.A.; Kalogeropoulos, K.; Zervas, E.;
Vasilakou, C.G.; Skondras, N.A.;
Oikonomou, P.D.; Stathopoulos, N.;
Alexandris, S.G.; Tsatsaris, A.; et al.
Evaluating the Degradation of
Natural Resources in the
Mediterranean Environment Using
the Water and Land Resources
Degradation Index, the Case of Crete
Island. Atmosphere 2022,13, 135.
https://doi.org/10.3390/
atmos13010135
Academic Editor: Junhu Dai
Received: 20 December 2021
Accepted: 10 January 2022
Published: 14 January 2022
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Copyright: © 2022 by the authors.
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Attribution (CC BY) license (https://
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4.0/).
atmosphere
Article
Evaluating the Degradation of Natural Resources in the
Mediterranean Environment Using the Water and Land
Resources Degradation Index, the Case of Crete Island
Demetrios E. Tsesmelis 1,2, * , Christos A. Karavitis 3, Kleomenis Kalogeropoulos 4, Efthimios Zervas 2,
Constantina G. Vasilakou 3, Nikolaos A. Skondras 3, Panagiotis D. Oikonomou 5,6 , Nikolaos Stathopoulos 7,
Stavros G. Alexandris 3, Andreas Tsatsaris 8and Constantinos Kosmas 3
1Department of Science & Data Analysis, NEUROPUBLIC S.A., Methonis 6, 18545 Piraeus, Greece
2Laboratory of Technology and Policy of Energy and Environment, School of Applied Arts and Sustainable
Design, Hellenic Open University, 26335 Patra, Greece; zervas@eap.gr
3Department of Natural Resources Development and Agricultural Engineering, Agricultural University of
Athens, 75 Iera Odos, 11855 Athens, Greece; ckaravitis@aua.gr (C.A.K.); vasilakou@aua.gr (C.G.V.);
nskondras@aua.gr (N.A.S.); stalex@aua.gr (S.G.A.); ckosm@aua.gr (C.K.)
4Department of Geography, Harokopio University of Athens, El. Venizelou St., 70, Kallithea,
17671 Athens, Greece; kalogeropoulos@hua.gr
5Vermont EPSCoR, University of Vermont, Burlington, VT 05405, USA; panagiotis.oikonomou@uvm.edu
6Gund Institute for Environment, University of Vermont, Burlington, VT 05405, USA
7
Institute for Space Applications and Remote Sensing, National Observatory of Athens, BEYOND Centre of EO
Research & Satellite Remote Sensing, 15236 Athens, Greece; n.stathopoulos@noa.gr
8Department of Surveying and Geoinformatics Engineering, University of West Attica, Ag. Spyridonos Str.,
12243 Athens, Greece; atsats@uniwa.gr
*Correspondence: d_tsesmelis@neuropublic.gr
Abstract:
Natural resources degradation poses multiple challenges particularly to environmental
and economic processes. It is usually difficult to identify the degree of degradation and the critical
vulnerability values in the affected systems. Thus, among other tools, indices (composite indicators)
may also describe these complex systems or phenomena. In this approach, the Water and Land
Resources Degradation Index was applied to the fifth largest Mediterranean island, Crete, for the
1999–2014 period. The Water and Land Resources Degradation Index uses 11 water and soil resources
related indicators: Aridity Index, Water Demand, Drought Impacts, Drought Resistance Water
Resources Infrastructure, Land Use Intensity, Soil Parent Material, Plant Cover, Rainfall, Slope,
and Soil Texture. The aim is to identify the sensitive areas to degradation due to anthropogenic
interventions and natural processes, as well as their vulnerability status. The results for Crete
Island indicate that prolonged water resources shortages due to low average precipitation values or
high water demand (especially in the agricultural sector), may significantly affect Water and Land
degradation processes. Hence, Water and Land Resources Degradation Index could serve as an extra
tool to assist policymakers to improve their decisions to combat Natural Resources degradation.
Keywords: natural resources; contingency planning; spatial analysis; risk assessment; geographical
information systems
1. Introduction
Numerous challenges are linked to natural resources degradation, such as pollution,
water scarcity and stress, overexploitation, extreme hydrological events (droughts and
floods) soil erosion and desertification [
1
–
8
]. In addition, the compound effect of anthro-
pogenic interventions and inappropriate and/or uncoordinated management actions to
use and/or protect natural resources could cause significant environmental degradation
that may even be irreversible in vulnerable ecosystems [
9
,
10
]. A degraded ecosystem
Atmosphere 2022,13, 135. https://doi.org/10.3390/atmos13010135 https://www.mdpi.com/journal/atmosphere

Atmosphere 2022,13, 135 2 of 17
may not rehabilitated beyond a critical point. Contingency planning incorporating an
appropriate environmental design may play a major role in vulnerable cases, so as to
prevent irreversible conditions and mitigate potential risks [
10
–
15
]. The combination of
population growth accompanied by increasing demands, unsustainable natural resources
use, and climate variabilities and changes have accelerated natural resources’ degradation.
Agricultural production practices, especially in developing countries may have dramatic
socioeconomic impacts in such fragile states, including spurring potential conflicts and
instability that may lead to higher internal and external migration fluxes [
16
,
17
]. It has
been noted that the majority of the previously reported environmental or natural resources
scarcity–induced conflicts have been proved to be processual [
18
]. It is essential for the
policymakers to follow the guidelines, methods and strategies set by the experts for the
integrated management of such ecosystems [19–21].
Water, the most widely used component of the Earth, covers about 70% of the planet’s
surface and is vital for any form of life [
22
,
23
]. It is also the most crucial solvent and
transporter of ingredients in plants, animals, humans, and all-natural processes performed
on Earth [
24
,
25
]. Water is in constant motion and usually represented through the hy-
drological cycle, and then water budgets. Thus, the increasingly intensive development
of water resources projects on a global scale, simultaneously to continuously increasing
water demands, has made imperative the implementation of integrated methods for water
management [
26
,
27
]. The growth of the world’s population, the intensity of urbanization,
and the changes in land use affect water availability to the extent that its reserves would
be significantly unable to fulfil the increasing demands, or become non-exploitable due to
pollution and contamination [28]. The above reasons may cause conflicts among different
users (urban use, irrigation, industry, and other uses) for accessing the needed water. For
example, the total area of wetlands in the USA decreased from 890,300 km
2
before 1998 to
435,600 km2after 2004 [29].
Soil is one of the valued natural resources; the preservation of terrestrial life on the
planet and its economy depends on the soil conditions at both local and global
level [30–32]
.
Soil is the highest layer of the Earth’s surface and divides the atmosphere from the litho-
sphere [
33
]. The soil is generally a mixture of decomposed geological material, mineral
nutrients, voids, moisture, air, oxygen, and decomposed organic matter [
34
–
36
]. Soil is
considered to be a renewable natural resource and it is formed at a prolonged low rate [
37
].
In brief, soil is the basis of agricultural and forestry production, and is the living space
of organisms, the natural filter or contaminant, the first layer of groundwater reserves,
and contributing to a large enough degree as the medium for the feeding of the popula-
tion [31,38–40].
Urbanization through land-use changes leads usually to the fragmentation of ecosys-
tems and poses a significant threat to wildlife [
19
]. At the same time, urbanization increases
non-food species dominance in specific areas and alters the existing balances developed
over time and through adaptation between edible species [
41
]. In the USA alone, more than
10% of the edible species are considered endangered [
42
]. The fragmentation of ecosystems
and the expansion of non-food species may be the main factors such an endangerment of
the edible species as they affect respectively the 85% and 49% of these species [43–45].
Overall, Indicators and indices (composite indicators) are either complex or simple
ones. They provide information or describe a phenomenon and are recognized as valuable
and powerful tools to characterize the situation of a process or a system [
46
,
47
]. The indica-
tors are mostly derived from the appropriate selection of processes and of corresponding
appropriate data. They are used to simplify, quantify and express information on com-
plex phenomena, thus facilitating communication and decision-making [
12
,
13
,
47
–
50
]. The
value of such tools increases when, due mostly to lack of direct measurable information,
indicators may be the only means to provide the required relevant information [
51
]. The
assessment of ecosystem services is an example of such use [
44
,
48
,
49
]. In other words, the
indicators play the role of a communication channel between the parts of a complex reality
and the policymakers [
44
,
49
,
50
]. There are two main categories of indicators, (i) simple

Atmosphere 2022,13, 135 3 of 17
ones, using individual variables that describe specific dimensions of the phenomenon
or system under study and provide limited information and (ii) composite indicators,
created by groups of simple indicators so as to synergistically describe complex systems or
phenomena [2,49,52–54].
The significance of such tools in policymaking and decision-making is usually reflected
in the large and continuously-increasing number of composite indices synthesized from
various usually more simple indicators, both to describe a multitude of phenomena and to
assess the performance of different regions and countries in terms of specific objectives (i.e.,
environmental sustainability). However, complex indices have provoked many reactions
related to their objectivity and reliability [
55
,
56
]. To some extent, these reactions may
be considered justified, as the process of developing complex indices involves certain
stages, at which the degree of subjectivity may be relatively high [
53
]. At such stages,
the developers of an indicator usually select or create the various tools to carry out the
indicator’s aim. Thus, it may be considered appropriate that these stages would “bear
the signature” of the developer concerned and they are characterized by the composite
indicators reliability [
9
,
10
,
46
,
52
]. In this context, the central objective of the present work is
to find common drivers for the pressures inflicted by drought and desertification as they
portrayed by the application of WLDI (Water and Land Degradation Index) in the area.
Furthermore, according to the well-known DPSIR (drivers, pressures, state, impact and
responses) framework, driving forces are applying pressure on a system [
12
,
57
–
59
]. Thus,
the main scope of the current study is to identify the soil and water resources degradation
status through the application of the already developed composite index WLDI for the
period 1999–2014 [
59
]. As described in detail by the index development process [
59
], it
uses 11 indicators, namely Aridity Index (AI), Water Demand (WD), Vegetation Drought
Impacts (VDI), Vegetation Drought Resilience (VDR), Water Resources Infrastructure (WRI),
Land Use Intensity (LUI), Soil Parent Material (SPM), Plant Cover (PC), Rainfall (R), Slope
(S), and Soil Texture (ST).
2. Materials and Methods
2.1. Study Areas and Data
Greece, as a Mediterranean country, faces frequently extreme hydrological events,
especially droughts, and is considered as an ideal case for the development and application
of the WLDI. In this work, WLDI applied in the Greek island of Crete (Figure 1) for the
period 1999 to 2014.
Crete is the largest Greek island and the fifth largest one in the Mediterranean Sea.
Administratively, it constitutes along with a number of small peripheral islands and islets,
the Region of Crete, with Heraklion being the capital and the largest city of the island.
According to the Hellenic Statistical Service, the population of Crete was 623,065 inhab-
itants in 2011. Crete has a Mediterranean climate, with mild, rainy winters and dry hot,
sunny summers. According to the European Centre for Medium-Range Weather Forecasts
(ECMWF), relevant climate parameters of Crete are in Table 1.
The climate data were taken from the ECMWF and were transformed in the referred
time step [
60
]. Climate data used are Temperature (maximum, minimum and average),
Relative Humidity (maximum, minimum and average), Precipitation, Wind Speed (max-
imum and average) and Solar Radiation from 22 grid cells (0.25
◦×
0.25
◦
) on a daily
basis (Table 1). These parameters were used to estimate the various indicators (Potential
Evapotranspiration (ETp), Aridity, and Rainfall).
The Water Demand indicator was calculated utilizing Evapotranspiration (ETp), the
crop coefficients for each irrigated cultivation and the population water consumption
including tourism [
60
–
64
]. Based on Landsat 7, sixteen annual Enhanced Vegetation Index
(EVI) maps for drought events impacts have been collected [
65
–
67
]. Finally, the soil and
vegetation parameters calculated based on soil mapping and databases according to the
Corine Land Cover 2012 [68,69].

Atmosphere 2022,13, 135 4 of 17
The current methodology for the WLDI development has followed the “XERASIA”
framework as already described in [
58
,
59
,
70
]. It is important to note again that aridity,
which occurs in areas with continuous low rainfall, and as a permanent climatic feature
is quite different from temporary water shortages. The latter show a deviation from the
average state, but they are still within the natural variability of the system. In addition, the
induced changes such as desertification caused by human activities mostly by misuse of
soil and water resources and unstainable cultivation practices must be distinguished from
drought which has natural causes [
58
,
59
,
70
]. All such conditions signify water deficits. It is
very difficult to find a definition of drought universally accepted by all societal activities,
and at the same time by all the specializations of the scientific community [
58
]. Whatever
the definition and the general context in which drought is portrayed, it should always be
related to its impacts, taking into account the existing environmental, social, economic and
technological characteristics [49,58,59,71].
Atmosphere 2022, 13, x FOR PEER REVIEW 4 of 17
Figure 1. Crete, Greece. The study area and the polygons (1–20) used for downscaling (Hellenic
Geodetic Reference System 1987—HGRS87).
Crete is the largest Greek island and the fifth largest one in the Mediterranean Sea.
Administratively, it constitutes along with a number of small peripheral islands and islets,
the Region of Crete, with Heraklion being the capital and the largest city of the island.
According to the Hellenic Statistical Service, the population of Crete was 623,065 inhabit-
ants in 2011. Crete has a Mediterranean climate, with mild, rainy winters and dry hot,
sunny summers. According to the European Centre for Medium-Range Weather Forecasts
(ECMWF), relevant climate parameters of Crete are in Table 1.
Table 1. Monthly values for Temperature (minimum, maximum and average) Relative Humidity
(maximum, minimum and average), Wind Speed (average) and Solar Radiation (1999–2014).
ID Tmin
(°C)
Tmax
(°C)
Tavg
(°C) Rs (W/m2)
RHmin
(%) RHmax (%)
RHavg
(%)
WSavg
(m/s)
1 16.0 19.4 17.5 217.5 66.8 82.2 74.8 5.3
2 14.3 19.6 16.8 214.5 65.8 82.0 74.2 4.4
3 14.1 18.0 15.8 217.1 66.8 82.5 75.0 4.5
4 11.4 18.6 14.8 214.5 65.9 82.1 74.3 3.4
5 13.8 20.1 16.7 217.2 66.4 82.5 74.8 3.4
6 15.8 19.4 17.5 214.9 66.1 81.9 74.4 4.1
7 13.1 20.0 16.2 218.2 66.4 82.5 74.8 3.5
8 17.6 19.7 18.6 215.4 66.4 82.0 74.6 5.0
9 17.3 20.0 18.5 221.1 66.8 81.5 74.5 5.0
10 12.1 18.3 14.9 218.4 66.3 82.3 74.7 3.3
11 16.2 18.7 17.4 216.6 66.5 81.9 74.6 4.5
12 17.2 20.1 18.5 221.0 66.6 81.2 74.2 5.2
Figure 1.
Crete, Greece. The study area and the polygons (1–20) used for downscaling (Hellenic
Geodetic Reference System 1987—HGRS87).

Atmosphere 2022,13, 135 5 of 17
Table 1.
Monthly values for Temperature (minimum, maximum and average) Relative Humidity
(maximum, minimum and average), Wind Speed (average) and Solar Radiation (1999–2014).
ID Tmin (◦C) Tmax (◦C) Tavg (◦C) Rs
(W/m2)
RHmin
(%)
RHmax
(%)
RHavg
(%)
WSavg
(m/s)
1 16.0 19.4 17.5 217.5 66.8 82.2 74.8 5.3
2 14.3 19.6 16.8 214.5 65.8 82.0 74.2 4.4
3 14.1 18.0 15.8 217.1 66.8 82.5 75.0 4.5
4 11.4 18.6 14.8 214.5 65.9 82.1 74.3 3.4
5 13.8 20.1 16.7 217.2 66.4 82.5 74.8 3.4
6 15.8 19.4 17.5 214.9 66.1 81.9 74.4 4.1
7 13.1 20.0 16.2 218.2 66.4 82.5 74.8 3.5
8 17.6 19.7 18.6 215.4 66.4 82.0 74.6 5.0
9 17.3 20.0 18.5 221.1 66.8 81.5 74.5 5.0
10 12.1 18.3 14.9 218.4 66.3 82.3 74.7 3.3
11 16.2 18.7 17.4 216.6 66.5 81.9 74.6 4.5
12 17.2 20.1 18.5 221.0 66.6 81.2 74.2 5.2
13 12.3 19.0 15.3 218.5 66.2 81.9 74.5 3.5
14 17.0 20.0 18.3 221.1 66.5 81.0 74.1 5.5
15 12.3 19.4 15.5 218.5 66.2 81.8 74.4 3.6
16 16.8 19.6 18.0 221.4 66.5 80.9 74.1 5.1
17 12.0 19.0 15.1 218.7 66.2 81.6 74.3 3.6
18 17.3 19.8 18.5 221.5 66.3 80.5 73.7 5.3
19 16.7 19.3 17.9 219.8 66.4 80.9 74.0 5.5
20 17.5 20.2 18.7 222.0 66.2 80.4 73.7 5.5
21 16.4 20.0 18.0 220.7 66.3 80.8 73.9 5.4
22 16.8 20.4 18.4 221.5 65.9 80.4 73.5 5.8
2.2. Methodology for the Indicators Calculation in WLDI
The spatial results of WLDI computed based on remote sensing information, demo-
graphics, and soil data described in the previous session as described in detail [
59
]. It is
just pointed out that the WLDI calculated on a multiannual time step (1999–2014) as the
degradation of natural resources usually has a slow onset and development. Briefly the
steps for the WLDI implementation highlighted in the following [59].
The Bagnouls-Gaussen aridity index (BGI) is calculated using Equation (1), where T
i
is
the monthly average temperature, P
i
the monthly precipitation and k the factor symbolizing
the cases of the month where 2Ti−Piis positive. Then:
BGI =
n
∑
I=1
(2Ti−Pi)×k (1)
Based on the Enhanced Vegetation Index (EVI) annual values, the Vegetation Drought
Impacts indicator was derived for the referred period 1999–2014 [
67
]. The values of EVI
transformed drought impacts based on Jenks Natural breaks and presented in the following
Table 2.
Table 2. Transformation from EVI spatial values to Drought Impacts Indicator.
EVI Classes Vegetation
Drought Impacts Description
−1.00–0.14 3.0 >50% Losses
0.14–0.27 2.0 16–50% Losses
0.27–0.62 1.0 15% Losses
0.62–1.00 0.0 None

Atmosphere 2022,13, 135 6 of 17
The indicators participating in the Vegetation Drought Resilience, Land Use Intensity,
and Plant Cover (Vegetation parameters) produced from Corine Land Cover 2012, the Soil
Parent Material and the Soil Texture are reclassified according to the
procedure [59,69,72]
.
Then, ECMWF climate data are used to calculate monthly and annual values of the pertinent
parameters [
59
,
60
]. The precipitation downscaling transformation from polygons to spatial
distribution used the simple co Kriging based on semi-variograms and covariances (Figure 1
Map 6). Copais ET method was employed due to its better estimations comparing to other
models, including Penman-Monteith formula, standardized by the Food and Agriculture
Organization (FAO-56PM) [
59
,
73
–
75
].The Water Demand indicator calculated according
to the ET Copais method. Crop coefficients were used for each irrigated cultivation water
consumption introduced as noted [
59
]. The water demand used includes domestic and
touristic consumptions during the summer season, as
suggested [60–64,76,77]
. Finally,
slope indicators are extracted from the Shuttle Radar Topography Mission (SRTM) data [
78
].
The Principal Components Analysis (PCA) statistical method Equation (2) applied and
produced Equation (2) using all the above-mentioned data, according to the WLDI devel-
opment as described [59].
The classification of the WLDI outputs is depicted in Table 3accordingly [49,59].
WLDI = 18.2 ×AI + 7.2 ×VDI + 6.8 ×VDR + 9.4 ×WRI + 8.0 ×LUI + 10.6 ×PC
+ 7.6 ×R + 9.4 ×S + 7.7 ×SPM + 4.1 ×ST + 11.0 ×WD (2)
According to the stated the methodology, the first step of the whole effort includes the
calculation and visualization of the indicators within the area of interest [
59
]. Then, all the
parameters transformed into their respective scaled values and the WLDI was calculated
and visualized. Using the data from the above-mentioned indicators, the composite index
calculated to identify the study area’s soil and water resources degradation for the specific
period in accordance to the produced map. The result of this composite index scaled values
presented in Table 4.
The most vulnerable areas to degradation, for the 1999–2014 period, determined
by calculating WLDI and then deriving the common drivers’ characteristics. The used
time scale corresponds to the available data for climate, soil, vegetation and economic
indicators [59].

Atmosphere 2022,13, 135 7 of 17
Table 3. WLDI components and degradation scales [45,68,69,76,77].
Aridity Index
(BGI Range)
Water
Demand
Vegetation
Drought
Impacts
Vegetation Drought
Resilience
Water
Resources
Infrastructure
Land Use
Intensity
Soil Parent
Material
Plant
Cover Rainfall Slope Soil
Texture
<50
1.0
No
Deficits
0.0
None
0.0
Very high
1.0
No
Deficits
0.0
Low
1.0
Good
1.0
High
1.0
>650
1.0
<6
1.0
Good
1.0
50–75
1.1
15%
Deficits
1.0
15%
Losses
1.0
High
1.2
15%
Deficits
1.0
Medium
1.5
Moderate
1.7
Low
1.8
280–650
2.0
6–18
1.2
Moderate
1.2
75–100
1.2
16–50%
Deficits
2.0
16–50%
Losses
2.0
Medium
1.3
16–50%
Deficits
2.0
High
2.0
Poor
2.0
Very Low
2.0
<280
4.0
18–35
1.5
Poor
1.6
100–125
1.4
>50%
Deficits
3.0
>50%
Losses
3.0
Moderate
1.4
>50%
Deficits
3.0
- - - - - - - - >35
2.0
Very Poor
2.0
125–150
1.8
- - - - Low
1.7
- - - - - - - - - - - - - -
>150
2.0
- - - Very Low
2.0
- - - - - - - - - - - - - -

Atmosphere 2022,13, 135 8 of 17
Table 4. WLDI scaled values of degradation degree [49,59].
Classes Values Description
1 <94 No degradation
2 94–118 Very Low Degradation
3 118–142 Low Degradation
4 142–167 Mild Degradation
5 167–191 Moderate Degradation
6 191–215 High Degradation
7 >215 Extreme Degradation
3. Results
3.1. Results of WLDI Used Indicators
The spatial distribution of the values of the indicators used for the calculation of WLDI
in the study area presented in Figure 2. For example, in terms of the Aridity Index, high
values presented in southern and south-eastern Crete. In terms of Rainfall, high values are
in western Crete, and, in terms of Slope, high values presented mainly in the mountainous
areas. According to the weights and the maximum value of indicators, the more significant
factors are the Aridity index, Rainfall and Water Demand [59].
In addition, vegetation factors play a significant role. Especially, Vegetation Drought
Resistance, Vegetation Drought Impacts and Pant cover indicators represent the 24.6% of
WLDI values in the equation. However, there are annual agricultural crops and annual
grasslands (Figure 2, Map 3) which have very low drought resilience.
Specifically, BGI values (Figure 2, Map 1) evaluated in Crete Island are found between
112 to 227, corresponding to Dry and Very Dry Climate Type; higher values are depicted
in the South and East with the mountainous areas having lower values. The EVI ranges
from
−
0.57 to 0.67 (Figure 2, Map 2), and the negative values present the lowest vegetation
drought impacts for the referred period. The indicator Vegetation Drought Resilience
represents the plant type’s ability to “survive” in dry periods (either seasonality or drought
events), and the results appear in Map 3). The percentage of the Very High class (1.00) is
38.9%, the second class is 3.4% (1.20), the third class (1.30) is 24.0%, with Low vegetation,
drought resilience is 22.5%, and the Very Low class (2.0) is 0.8%. However, there are not
high values of land use intensity (Figure 2, Map 4). Similar conditions prevail for plant
cover indicator (82.2%, 13.1% and 4.7% respectively 1.00, 1.80 and 2.00). The annual rainfall
map based on daily data (1991–2014) and the co-kriging (rainfall and Digital Elevation
Model) interpolation method transformed the point data to spatial values. However, the
rainfall map may vary greatly, even more than 1400 mm annually (orographic rain as there
are peaks above 2400 m) between west and east Crete. There are not significant issues from
Slope and Soil Texture indicators as depict in Figure 2(Maps 7 and 9). Water Demand
Indicator has attributed high-water demand during the summer seasons (agricultural and
touristic needs), and the lower water consumption during the winter seasons (agricultural
and touristic needs are minimal). The Reservoirs on the Crete Island shows in Table 5.
3.2. Results of the Degradation Degree on Water and Land Resources
Figure 3summarizes the results of the WLDI with high values in the mountains and
coastlines and low in the Heraklion County and the urban areas. Figure 3shows that the
average degradation of the examined region is 119.5—portraying Low Scale Degradation.
Only 1% out of the f the study area displays Moderate Degradation (
≥
119). The locations
with Moderate Degradation are mostly located in the coastline and the mountain tops.
Moreover, Figure 3shows that the maximum and minimum WLDI values correspond to
the moderate degradation of water and land resources class on a small scale (Figure 2,
Map 2).

Atmosphere 2022,13, 135 9 of 17
Atmosphere 2022, 13, x FOR PEER REVIEW 8 of 17
Figure 2. The inputs and indicators for the calculation of WLDI. (a) BGI Aridity Index, (b) EVI—
Vegetation Drought Impacts, (c) Vegetation Drought Resilience, (d) Land Use Intensity, (e) Plant
Cover, (f) Rainfall, (g) Slope, (h) Soil Parent Material, (i) Soil texture and (j) ETp and Water Re-
sources Infrastructure.
Figure 2.
The inputs and indicators for the calculation of WLDI. (
a
) BGI Aridity Index, (
b
) EVI—
Vegetation Drought Impacts, (
c
) Vegetation Drought Resilience, (
d
) Land Use Intensity, (
e
) Plant
Cover, (
f
) Rainfall, (
g
) Slope, (
h
) Soil Parent Material, (
i
) Soil texture and (
j
) ETp and Water
Resources Infrastructure.

Atmosphere 2022,13, 135 10 of 17
Table 5. Reservoirs on the Crete Island.
Name Prefecture Volume (106m3)Purpose Construction Year
Mpramianon Lasithi 16.00 Irrigation 1986
Partiron Heraklion 1.50 Irrigation 2000
Iniou Heraklion 1.75 Irrigation 2002
Damanion Heraklion 1.50 Irrigation 2003
Amourgeles Heraklion 1.56 Irrigation 2004
Armanogeion Heraklion 1.50 Irrigation 2004
Faneromeni Heraklion 19.67 Irrigation 2005
Potamon Rethymno 22.50 Domestic-Irrigation 2008
Aposelemi Heraklion 27.30 Irrigation 2012
Valsamioti Chania 5.50 Irrigation 2014
3.3. Validation of the Degradation Degree on Water and Land Resources
For the evaluation process of WLDI two indices were used, namely, Soil Organic
Carbon (SOC) using the Map from the Food and Agriculture Organization of the United
Nations (FAO) and the Normalized Difference Water Index (NDWI) for Land and Water
Resources, respectively [
79
,
80
]. SOC received values from the FAO database and adapted
them in the case study. However, NDWI calculated based on annual values from the
Google Earth Engine for the investigated period 1999–2014 [
67
]. Figure 3presents the
referred indices with the various spots for quantitative validation. In addition, it was done
a statistical analysis through ArcMap 10.8 (Toolbox: Band Collection Statistics) between
individual layers. Specifically, WLDI and NDWI had a negative correlation (
−
0.286). This is
a logical result since the high values in the first corresponds to low values in the second. In
addition, a positive correlation computed between SOC and WLDI (0.223). The correlation
values seem low, but they are valid because WLDI consists of climatic, soil, geological,
infrastructure, and agricultural data. Additionally, the above results may be validated from
Figure 4with the selected spots. Particularly, the high values of WLDI (d2) portray similar
conditions with SOC (a2). Different situations depicted in the NDWI spots (b2 and c2),
where the mountain tops restrain snow cover. Similar behavior shows with the low values
of the WLDI (d1) with the other maps (a1, d1 and c1). Finally, the case study produced
a polynomial equation dependent on the WLDI and the independent variables SOC and
NDWI. The produced equation is:
WLDI = 103.675 −2.12652 ×NDWI −0.121078 ×SOC (3)
The R-Squared statistic indicates that the model as fitted explains 0.583049% of the
variability in WLDI. The adjusted R-squared statistic, which, is more suitable for comparing
models with different independent variables values, is 0.565%. The standard error of the
estimate shows a standard deviation of the residuals of 9.040. The mean absolute error
(MAE) of 7.452 is the average value of the residuals. The Durbin-Watson (DW) statistic
tests the residuals to determine if there is any significant correlation based on the order in
which they occur in your data file. Since the p-value is less than 0.05, there is an indication
of possible serial correlation at the 95.0% confidence level. Plotting the residuals versus
row order to see if there is any pattern appears. In determining whether the model can
be simplified, it is noted that the highest p-value on the independent variables is 0.024,
belonging to NDWI. Since the p-value is less than 0.05, that term is statistically significant
at the 95.0% confidence level.

Atmosphere 2022,13, 135 11 of 17
Atmosphere 2022, 13, x FOR PEER REVIEW 10 of 17
Figure 3. SOC, NDWI, WLDI and Landsat maps in Crete Island. (a1) Spots with low values of Soil
Organic, (a2) spots with high values of Soil Organic (b1), spots with low values of Normalized Dif-
ference Water Index, (b2) spots with high values of Normalized Difference Water Index, (c1,c2)
spots with low values of Landsat current state, (d1) spots with low values of Water and Land Deg-
radation Index and (d2) spots with high values of Water and Land Degradation Index.
Figure 3.
SOC, NDWI, WLDI and Landsat maps in Crete Island. (
a1
) Spots with low values of
Soil Organic, (
a2
) spots with high values of Soil Organic (
b1
), spots with low values of Normalized
Difference Water Index, (
b2
) spots with high values of Normalized Difference Water Index, (
c1
,
c2
)
spots with low values of Landsat current state, (
d1
) spots with low values of Water and Land
Degradation Index and (d2) spots with high values of Water and Land Degradation Index.

Atmosphere 2022,13, 135 12 of 17
Atmosphere 2022, 13, x FOR PEER REVIEW 11 of 17
3.3. Validation of the Degradation Degree on Water and Land Resources
For the evaluation process of WLDI two indices were used, namely, Soil Organic Car-
bon (SOC) using the Map from the Food and Agriculture Organization of the United Na-
tions (FAO) and the Normalized Difference Water Index (NDWI) for Land and Water Re-
sources, respectively [79,80]. SOC received values from the FAO database and adapted
them in the case study. However, NDWI calculated based on annual values from the
Google Earth Engine for the investigated period 1999–2014 [67]. Figure 3 presents the re-
ferred indices with the various spots for quantitative validation. In addition, it was done
a statistical analysis through ArcMap 10.8 (Toolbox: Band Collection Statistics) between
individual layers. Specifically, WLDI and NDWI had a negative correlation (−0.286). This
is a logical result since the high values in the first corresponds to low values in the second.
In addition, a positive correlation computed between SOC and WLDI (0.223). The corre-
lation values seem low, but they are valid because WLDI consists of climatic, soil, geolog-
ical, infrastructure, and agricultural data. Additionally, the above results may be vali-
dated from Figure 4 with the selected spots. Particularly, the high values of WLDI (d2)
portray similar conditions with SOC (a2). Different situations depicted in the NDWI spots
(b2 and c2), where the mountain tops restrain snow cover. Similar behavior shows with
the low values of the WLDI (d1) with the other maps (a1, d1 and c1). Finally, the case
study produced a polynomial equation dependent on the WLDI and the independent var-
iables SOC and NDWI. The produced equation is:
WLDI = 103.675 − 2.12652 × NDWI − 0.121078 × SOC (3)
Figure 4. Histogram (Index classes) of WLDI and application in Crete islands (1999–2014).
The R-Squared statistic indicates that the model as fitted explains 0.583049% of the
variability in WLDI. The adjusted R-squared statistic, which, is more suitable for compar-
ing models with different independent variables values, is 0.565%. The standard error of
the estimate shows a standard deviation of the residuals of 9.040. The mean absolute error
(MAE) of 7.452 is the average value of the residuals. The Durbin-Watson (DW) statistic
tests the residuals to determine if there is any significant correlation based on the order in
which they occur in your data file. Since the p-value is less than 0.05, there is an indication
of possible serial correlation at the 95.0% confidence level. Plotting the residuals versus
row order to see if there is any pattern appears. In determining whether the model can be
simplified, it is noted that the highest p-value on the independent variables is 0.024, be-
longing to NDWI. Since the p-value is less than 0.05, that term is statistically significant at
the 95.0% confidence level.
Figure 4. Histogram (Index classes) of WLDI and application in Crete islands (1999–2014).
4. Discussion
According to the weights and the maximum value of the indicators, the more signifi-
cant factors are the Aridity index, Rainfall, and Water Demand. The degradation of water
and land resources based on WLDI from the crops occurs mainly in the areas with the
highest agricultural activity, due to the intensive use of water resources. This exacerbates
the degradation according to the Water Demand indicator. These indicators (Water Demand
indicator, Drought Vegetation Resistance and Land Use Intensity) coincide mostly with
areas depending on rain fed agriculture, which exhibited impacts on the vegetation (EVI)
due to drought events. In other words, they exhibit high water demands accompanied by
serious supply deficits and thus, having significant environmental impacts. Generally, the
southwest-center side of the island displays higher values of degradation. Overall, under
the occurred conditions, the island of Crete displayed Very Low and Low Degradation for
the referred period (Figures 2–4).
Based on climatic zones (Tables 1and 6, Figure 1), zonation similarities occur in the
index application. The zones refer to the natural ecosystems and with the application of
the specific indicators; they related to anthropogenic actions and interventions directly
associated to the corresponding ecosystems. There are a variety of factors (climate, geo-
morphology, land use, etc.) such as air temperature, precipitation, parent material, relative
humidity, soil texture, solar radiation, altitude and land use that are related to the spec-
ifications and categories of the zones [
81
–
84
]. The zones with the minimum, maximum,
average, and standard deviation values of WLDI portrayed in the following Table 6. Natu-
ral Resources Degradation are increased in alti-Mediterranean snow zone due to the high
values of soil erosion, since there are significant impacts in the first zone (0–300 m) from
the human activities [58].
Table 6. Zonal Statistics for WLDI according to the various zones [82].
Elevation Zones Min Max Mean Standard Deviation
0–350 Thermo-Mediterranean 70.70 159.10 94.33 9.36
350–600 Mesomediterranean 70.70 127.19 96.71 7.67
600–1200 Super-Mediterranean 75.40 127.19 98.07 6.00
1200–1700 Montane 88.79 157.19 100.08 6.21
1700–2600 Alti-Mediterranean 97.49 168.10 101.34 5.23
It is clear that high values of WLDI occur mainly in the mountainous areas, such as
the “White Mountains” in Chania Region, the “Psiloritis” mountain between Rethimnon
and Heraklion Region and the “Dikti” mountain between Heraklion and Lasithion Region.

Atmosphere 2022,13, 135 13 of 17
In addition, high values of WLDI occur at the Lasithion Region, and especially the coastal
areas (Figures 1and 3).
As stated, complex indicators have provoked many reactions related to their objectivity
and reliability [
55
,
56
]. In some cases, those factors may be relatively high, mainly due to
the uncertainty that lies in certain development stages [53].
However, in the present case, the developed index is composed of individual indi-
cators, which incorporated in the structure of two well-established indices in the field of
environmental sciences. Namely, the Standardized Drought Vulnerability Index (SDVI)
and the Environmentally Sensitive Areas Index (ESAI) [
30
,
33
]. Finally, the results of the
present effort maybe highly correlated with the findings of similar research efforts on the
field of drought impact assessment and the environmental sensitivity assessment in the
island of Crete [85–89].
Thus, the aforementioned features increase the reliability of the proposed index and
they allow its application to be expanded to other regions with similar or related climates.
However, and despite the first positive results regarding the application of the index,
additional analysis is required.
5. Conclusions
Based on the inputs and indicators used for the calculation of Water and Land Re-
sources Degradation Index in Crete from 1999 to 2014 showed that water resources’ degra-
dation is greater than that of land resources. This deterioration occurs mainly in the areas
with the highest agricultural activity. The increased use of water resources due to irrigation
exacerbates the degradation, according to the Demand Indicator. A master plan in areas
with increased demand due to irrigation is imperative. It is proposed to change crops or to
choose varieties demanding less water. Infrastructure shortages such as storage, transport,
distribution, water and wastewater treatment processes are posing additional obstacles
and lead in degrading available water resources.
According to the weights and the maximum value of indicators, the more significant
factors are the Aridity index, Rainfall, and Water Demand. The results of the applied
methodology of the Water and Land Resources Degradation Index can be expanded to other
regions with similar or different climates, since the index is developed using indicators
from two tested composite indices, namely Standardized Drought Vulnerability Index
(SDVI) and the Environmentally Sensitive Areas Index (ESAI) [
30
,
33
,
85
]. Thus, there seems
to be high reliability on the use of this index, because many applications are based on these
indices, and may be found on global scale [29,30,85–87].
However, decision-support tools must be implementable not only in the short but also
in a longer time horizon. It is necessary to assess the progress in assimilating and using
such systems in a short period of time and simultaneously to sustain the interest, effort,
and participatory conviction of decision-makers throughout the whole process for a few
future decades.
Even if the sustainable development concept takes into account awareness, there are
not general acceptant guidelines established about it. The use of this index emphasizes
the need to organize and control the dynamics and complex interactions between anthro-
pogenic activities and natural resources to promote their coexistence and general growth.
This effort exhibits and develops qualitative and quantitative data along with formal links
between decision-making and precautionary techniques in natural resources management.
Author Contributions:
D.E.T., C.A.K. and C.K. conceived and designed the experiments; D.E.T.,
C.G.V., N.A.S. and N.S. performed the experiments, analyzed the data, D.E.T. wrote the paper; C.A.K.,
D.E.T., E.Z., K.K., P.D.O., N.S., S.G.A., A.T. and C.K. reviewed the paper. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.

Atmosphere 2022,13, 135 14 of 17
Informed Consent Statement: Not applicable.
Data Availability Statement:
Data used in this paper can be provided by Demetrios E. Tsesmelis
(d_tsesmelis@neuropublic.gr) upon request.
Conflicts of Interest: The authors declare no conflict of interest.
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