Assessment of land sensitivity to degradation using MEDALUS model - a case study of Grdelica Gorge and Vranjska Valley (southeastern Serbia) ...
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iForest Research Article
doi: 10.3832/ifor3871-015
vol. 15, pp. 163-170
Biogeosciences and Forestry
Assessment of land sensitivity to degradation using MEDALUS model - a
case study of Grdelica Gorge and Vranjska Valley (southeastern Serbia)
Sara Lukić (1), Land degradation is a complex issue caused by diverse drivers, each of which
should be considered in the analysis of land sensitivity to degradation. This
Aleksandar Baumgertel (1), study identifies the areas most sensitive to land degradation in the Grdelica
Snežana Obradović (2), Gorge and Vranjska Valley, which are unique in terms of natural and socioeco -
Ratko Kadović (1), nomic conditions. Land-use changes and inappropriate land management have
Jelena Beloica (1), led to serious degradation in this region. The flexible and multifactorial ap-
proach of the Mediterranean Desertification and Land Use (MEDALUS) model
Damjan Pantić (2), allowed comprehensive land degradation sensitivity analysis in the study area.
Predrag Miljković (1), The main factors driving soil degradation were assessed by estimating climate
Snežana Belanović Simić (1) quality index, soil quality index, and vegetation quality index, and the main
socioeconomic indicators by management quality index and social quality in-
dex. The results showed that forest cover is the main factor to contrast land
degradation, and even minor adverse changes in forest characteristics, such as
structure, canopy cover, health, and quality, could trigger degradation pro-
cesses. The vegetation quality index was defined in terms of the current vege-
tation’s capacity to protect soil from erosion, drought resistance, and fire risk.
Detailed data on forest vegetation cover was obtained from the National For-
est Inventory (NFI). The environmentally sensitive area (ESA) index generated
through the analysis classified 26.11% of the total study area as critical,
69.53% as fragile, and 2.70% as either prone to or unaffected by degradation
processes. According to the ESA index, the areas covered by forests with opti-
mal species composition and high canopy cover were the least susceptible to
degradation. The areas under intensive agricultural production without any ap-
plication of conservation measures were the most susceptible to degradation.
Future strategies for optimal land-use patterns are discussed, such as the in-
tergration of woody species in croplands to protect soil against degradation
and meet human needs in the areas prone to degradation.
Keywords: Land Degradation, Sensitivity, MEDALUS, Vegetation Cover, Spatial
Analysis
Introduction cation (UNCCD 1994). Together with natu- The transition from forest to other land
Land degradation and desertification are ral causes of the land’s sensitivity to degra- uses, such as cropland or settlements, is in-
serious problems causing numerous ad- dation and desertification, which are evitably associated with deforestation, one
verse global and local effects. They impact strongly related to climatic features, soil of the common drivers of degradation, and
all spheres of the environment, leading to properties, and vegetation types, human is a great threat to ecological health in a
irrecoverable ecological losses and affect- activities are usually the direct drivers of wide range of environmental and socioeco-
ing human well-being (Santini et al. 2010). degradation and desertification processes nomic situations (Gharibreza et al. 2020). It
Degradation processes that occur in arid, (Salvati & Zitti 2008). Changes in land use generally results in ecological problems, in-
semi-arid, and dry sub-humid areas where resulting from human activity represent cluding habitat loss, extinction of species
the productivity of the soil depends on wa- the common driving force of degradation and communities, increased CO2 emissions,
ter availability, are referred to as desertifi- and desertification processes nowadays. and many others. Human actions can also
improve the environment through sustain-
(1) Department of Ecological Engineering for Soil and Water Resources Protection, Univer- able land management practices and effec-
sity of Belgrade, Faculty of Forestry (Serbia); (2) Department of Forestry, University of Bel- tive policies (Huang et al. 2020).
grade, Faculty of Forestry (Serbia) A thorough understanding of the land’s
sensitivity to degradation and desertifica-
@ Sara Lukić (sara.lukic@sfb.bg.ac.rs) tion is crucial when considering both im-
provement options and the risk of addi-
Received: May 12, 2021 - Accepted: Feb 21, 2022 tional degradation (Olsson et al. 2019). Esti-
mating land sensitivity to degradation and
Citation: Lukić S, Baumgertel A, Obradović S, Kadović R, Beloica J, Pantić D, Miljković P, desertification requires an integrated ap-
Belanović Simić S (2022). Assessment of land sensitivity to degradation using MEDALUS model proach and detailed analyses of the inter-
- a case study of Grdelica Gorge and Vranjska Valley (southeastern Serbia). iForest 15: 163- play of natural and human factors and their
170. – doi: 10.3832/ifor3871-015 [online 2022-05-07] role in driving land degradation (Mirzabaev
et al. 2015). Accurate estimation is needed
Communicated by: Agostino Ferrara to prevent land degradation and support
the planning and improvement of land
© SISEF https://iforest.sisef.org/ 163 iForest 15: 163-170
Lukić S et al. - iForest 15: 163-170
management at any level (Salvati et al. ponent from management quality index The area is characterized by an erodible
iForest – Biogeosciences and Forestry
2012). and its implementation in MEDALUS algo- parent rock with very disturbed and bro-
Among the available models for assess- rithm as separate layer (social quality in- ken layers weathered to different degrees.
ment of land degradation, the Mediter- dex), it could be possible to assess the The most common soil types in the study
ranean Desertification and Land Use model quality of human interactions with the en- area are leptosols, cambisols, vertisols, lu-
(MEDALUS) is widely used due to its flexi- vironment to some extent. The parameters visols, and fluvisols (Lukić 2013). The associ-
bility in the selection of the main indices of of the ESA indicators, defined in detail be- ation of Hungarian oak and Turkey oak
land degradation, its relatively simple appli- low, enabled a thorough investigation of (Quercetum frainetto-cerris Rud. 1949) is
cation, and its rapid implementation (Kos- the spatial patterns of sensitivity to land the most common forest type at altitudes
mas et al. 1999, Salvati et al. 2015, Prăvălie degradation. Areas with high ESA values up to 600 m a.s.l. in the area of Grdelica
et al. 2020). The assessment of the environ- have been identified as ideal locations for Gorge and Vranjska Valley. Montane beech
mentally sensitive areas (ESAs) identified land management interventions, especially forests (Fagetum moesiacae montanum
by the MEDALUS method was a broader to improve the vegetation cover quality Jov. 1953) are present from 800 to 1300 m
approach guided by several complex and and to protect valuable communities in the asl. In the valley of the South Morava River,
interrelated factors linked to specific local study area. Rapid and comprehensive iden- the most common communities are forests
environmental and human-induced condi- tification of the drivers of degradation and of broom and pedunculate oak (Genisto
tions (Mirzabaev et al. 2015, Olsson et al. alteration of human activities through im- elatae-Quercetum roboris Horv. 1938 s. lat.)
2019). The spatial distribution of areas with proved land management and policies en- and forests of willow and poplar (Salici-
high ESA index values reflects the com- ables control of degradation processes to Populetum albae Drees. 1936 – Tomić
bined impact of environmental conditions reduce or prevent adverse effects on hu- 2004). The area is also characterized by the
and factors such as demographic trends, man well-being and serious disruptions in presence of rare and scattered species and
the effects of land management, and the the environment. communities and the presence of relict and
quality of environmental policy (Ferrara et The aim of this study is to: (i) estimate endemic forest communities (Mišić 1981).
al. 2020). sensitivity to land degradation in an area The area of Grdelica Gorge and Vranjska
The MEDALUS model was originally de- subject to interplay among numerous natu- Valley is socioeconomically underdevel-
veloped for the Mediterranean region but ral and human factors using the MEDALUS oped and demographically depopulated.
has been successfully applied worldwide model; (ii) underline the potentials and
(Ferrara et al. 2020), in Europe (Contador value of data provided in Forest Manage- Methodology
et al. 2009, Salvati et al. 2015, Prăvălie et al. ment plans (special or general plans) and The land degradation sensitivity analysis
2020), Asia (Dindaroğlu 2015), Africa (Bakr National Forest Inventory (NFI) data in en- to identify ESAs was conducted according
et al. 2012, De Pina Tavares et al. 2015), and vironmental modeling; and (iii) identify the to the modified MEDALUS model described
South America (Vieira et al. 2015). In Serbia, areas critically endangered by degradation by Kosmas et al. (1999). This model en-
the MEDALUS model has been applied in using Local Moran’s I analysis. abled analyzing both the dominant envi-
certain specific locations (Momirović et al. ronmental and human-induced degrada-
2019, Perović et al. 2021). Materials and methods tion factors that characterize the study
This study aims at assessing the sensitiv- area. The analysis was based on five indica-
ity to land degradation of an area subject Study area tors of quality related to the driving forces
to interplay among numerous natural and The area of Grdelica Gorge and Vranjska of land degradation in the area of Grdelica
human factors. In Grdelica Gorge and Vran- Valley (42° 22′ to 42° 55′ N; 19° 21′ to 20° 00′ Gorge and Vranjska Valley: climate quality
jska Valley areas, the component of demo- E) covers 1716.94 km2 (Fig. 1). This area has index (CQI), soil quality index (SQI), vege-
graphic pressure significantly affects land a hydrographic network consisting of 137 tation quality index (VQI), management
use patterns (Djorović 2005), as well as the torrential flows, with a total catchment quality index (MQI), and social quality in-
quality of land management. In this study, area of 1700.33 km2. Pronounced altitudinal dex (SoQI).
management and social quality indices differences over short distances, dissected The proposed ESA Index was evaluated
were analyzed separately in MEDALUS al- relief, and steep slopes contribute to an in- by the five quality indicators, as in eqn. 1:
gorithm as proposed by De Pina Tavares et creased potential for degradation pro-
al. (2015) and Vieira et al. (2015), because in cesses, particularly erosion. The continen- ESA =(CQI⋅SQI⋅VQI⋅MQI⋅SoQI )1/ 5 (1)
addition to natural characteristics, analysis tal climate of the study area is tempered by
of population dynamics enables to better the influence of the Mediterranean climate The quality indicators were defined by pa-
understand the degradation process and (Perović et al. 2019). The average annual air rameters relevant to the study area’s sensi-
directions of management actions to con- temperature is 10.9 °C, and the average an- tivity to land degradation processes. The
trol degradation (De Pina Tavares et al. nual precipitation is 672 mm, based on ob- range of parameter values was weighted
2015). By extraction of demographic com- servations from 1949 to 2017 (RHSS 2018). according to the sensitivity to degradation
and standardized from 1 (low sensitivity) to
2 (high sensitivity). For more accurate
weighting of selected parameters, remote
sensing data were combined with data
from available databases. The analysis ex-
cluded the areas under settlements and
bodies of water, which encompass 28.46
km2 and represent 1.66% of the total area of
the Grdelica Gorge and Vranjska Valley,
hereafter referred to as mask areas. The
analyses of CQI, SQI, VQI, MQI, SoQI, and
overall ESA were performed in ArcMap®
ver. 10.5.1 (ESRI, Redlands, CA, USA).
Climate quality index
The Climate Quality Index (CQI) was rep-
resented by four parameters that reflect
Fig. 1 - Study area of Grdelica Gorge and Vranjska Valley (Southeastern Serbia). the impact of climate on degradation pro-
164 iForest 15: 163-170
Land degradation drivers and assessment of land sensitivity to degradation
cesses: aridity index (AI), modified Four- CORINE Land Cover data (CGLS 2012) and terial for the original data).
iForest – Biogeosciences and Forestry
nier’s index (MFI), exposure (E), and rainfall National Forest Inventory data (NFI 2009). The VQI was calculated according to eqn.
(R). The parameters of CQI were obtained General data on land cover were obtained 6:
according to the data for period from 1983 from CORINE and adjusted according to 1/ 4
to 2016 (RHSS 2018). more specific data on forest type (decidu- VQI=(PC⋅EP⋅DR⋅FR ) (6)
The AI was calculated according to eqn. 2: ous, coniferous, or mixed) and stand con-
servation (conserved, thinned, or devas-
AI = P (2) tated stand) obtained from NFI. Management quality index
PET
Stand conservation describes the degree Management quality reflects the human
where P represents the precipitation, and of canopy stocking, the proportion of prin- contribution to land degradation in the en-
PET the potential evapotranspiration, ob- cipal and minor tree species, and the vironment. It was analyzed by assessing
tained according to the Penman-Monteith health, risks, and quality of the stand in the the impact of land-use patterns on the en-
method. The MFI was calculated according inventory unit. vironment, represented as land-use inten-
to eqn. 3: A conserved stand is characterized by a sity (LUI) and policy enforcement (PE),
12
pi2 dense to complete canopy (1.0-0.6), good which characterizes the extent of imple-
F =∑ (3) tree health, good-quality trees, and a favor- mentation of existing environmental regu-
i =1 p able ratio of principal and minor tree lations. The LUI map was obtained by
where pi is the rainfall in each month, and species. A thinned stand is characterized grouping CORINE Land Cover data into
p the annual rainfall, to estimate rainfall ag- by an incomplete canopy (0.4-0.6), good three categories according to Prăvălie et al.
gressiveness (Szilagyi et al. 2016). tree health, and good-quality trees, but a (2017). Policy enforcement was obtained
The environmental conditions relevant less favorable ratio of principal and minor by integrating CORINE data with data on
for vegetation growth are represented by tree species. A degraded stand is charac- the forest primary function derived from
E, which describes solar radiation and tem- terized either by a broken canopy (below the NFI. The primary function is either de-
perature conditions, and by R, the amount 0.4), by poor tree health and poor-quality termined in advance as a legal requirement
of rainfall. Classes and assigned indices for trees or by a completely unfavorable ratio or determined subsequently based on spe-
calculating the CQI are presented in Tab. S1 of principal and minor tree species (favor- cific criteria such as slope steepness, close-
(Supplementary material). The CQI was cal- ing minor species over the principal spe- ness of water source, road network, or in-
culated according to eqn. 4: cies). dustrial area, population density etc.
Weighting indices for stands’ capacity to Classes and assigned indices for calculation
CQI =( AI⋅MFI⋅E⋅R)1 /4 (4) protect soil from erosion are basically de- of the MQI according to Prăvălie et al.
fined according to Salvati & Bajocco (2011) (2017) are presented in Tab. S1 (Supple-
and Contador et al. (2009) who assigned mentary material). The MQI was calculated
Soil quality index weighting indices for vegetation classes according to eqn. 7:
The parameters of the SQI were selected derived from the CORINE Land Cover map.
based on moisture availability and soil re- Erosion protection function of forest cover MQI =(LUI⋅PE)1/ 2 (7)
sistance to erosion processes, being these depends mostly on canopy cover closure
the most important factors for the estab- and species composition (Vatandaşlar et al.
lishment and survival of vegetation as a 2020), species-specific functional traits Social quality index
permanent erosion control measure. The (Seitz et al. 2016), species richness (Salvati Social quality was analyzed by assessing
parameters that define these conditions et al. 2015, Song et al. 2019), etc. the pressure of human actions on the eco-
are: parent material (PM), weighted ac- We used the data on forest type and system that leads to land degradation and
cording to its susceptibility to weathering, stand conservation obtained from NFI to desertification. Two main parameters were
which predicts its resistance to erosion slightly adjust the weighting indices, taking used: population density (PD) and old age
(Schaetzl & Thompson 2015); organic mat- into account that the stand conservation index (OAI). PD represents the total popu-
ter (OM), the main indicator of soil quality parameter defined through the degree of lation per unit area and is closely related to
and stability of soil structure, which pre- canopy stocking, tree health and quality, the level of human pressure on natural re-
dicts both the resistance of soil to erosion and the ratio of principal and minor tree sources (De Pina Tavares et al. 2015). The
and water availability to vegetation (Six et species affects the stand’s ability to pro- OAI represents the ratio of the number of
al. 2000); soil depth (SD); and slope (S). tect the soil from erosion. The weighting inhabitants older than 65 years of age to
The original data used to perform this anal- indices used to perform this analysis are the total number of inhabitants (Kosmas et
ysis are presented in Tab. S1 (Supplemen- given in Tab. S2 (Supplementary material). al. 2014) and emphasizes the number of
tary material). The data on SQI parameters DR was obtained through CORINE Land older people in relation to the total popula-
was obtained according to the 89 soil pro- Cover data by grouping land cover types tion (De Pina Tavares et al. 2015). Older
files (Fig. 1). The SQI was calculated accord- (see Tab. S1 in Supplementary material). FR people puts more pressure on natural re-
ing to eqn. 5: was obtained by combining CORINE Land source by applying tillage techniques and
Cover and NFI data. Data on the stand unit breeding livestock in obsolete manner
SQI =( PM⋅OM⋅SD⋅S)1 /4 (5) in the NFI contain information on the mix- which usually adversely impact the soil and
ture of tree species in the stand. The vegetation condition. Classes and assigned
flammability of the forest depends on the weights of indices for calculation of SoQI,
Vegetation quality index stand structure, the biological and ecologi- according to De Pina Tavares et al. (2015),
Vegetation was analyzed for its capacity cal characteristics of each species in the are presented in Tab. S1 (Supplementary
to protect soil from erosion, drought resis- mixture, and their relationships (Vasić 1992, material).
tance, and fire risk. The VQI was calculated Fernandes 2009, Xanthopoulos et al. 2012). The SoQI was calculated according to
by four standard parameters (Kosmas et al. Stands in the Republic of Serbia are divided eqn. 8:
1999): plant cover (PC), erosion protection into six classes of flammability (Vasić 1992),
(EP), drought resistance (DR), and fire risk from the least endangered class (VI cate- (8)
SoQI =( PD⋅OAI )1/2
(FR). gory, or very low flammability), which in-
PC was obtained from the normalized dif- cludes bare lands, to the most endangered
ference vegetation index (NDVI) common- class (I category, or extremely high flam- Statistical analysis
ly used to assess vegetation cover (Moha- mability), which includes pine and larch Spatial correlation was performed to
med 2013). EP was defined by combining forests (see Tab. S1 in Supplementary ma- identify collinearity among quality indices
iForest 15: 163-170 165
Lukić S et al. - iForest 15: 163-170
Results and discussion
iForest – Biogeosciences and Forestry
Tab. 1 - Quality indices CQI, SQI, VQI, MQI and SoQI distribution by quality classes.
Climate quality index
Class area Class area The CQI shows that most of the studied
Indices Class range Class
(km2) (%) area is high quality (56.51%), while 37.50% is
< 1.3 high 970.25 56.51 medium quality (Tab. 1). The high CQI in the
Climate quality index
1.3-1.5 medium 643.86 37.50 northern and central parts of the study
(CQI) area could be correlated with favorable air
> 1.5 low 74.37 4.33 temperature and precipitation conditions,
< 1.13 high 19.26 1.12 mediated by the Kukavica and Ostrozub
Soil Quality Index mountains (Fig. 2). Such favorable condi-
1.13-1.45 medium 1161.42 67.64
(SQI) tions are reflected in the good health of
> 1.45 low 507.80 29.58
the stands and the presence of preserved
< 1.13 high 98.45 5.73 rare and endemic communities such as
Vegetation Quality Lauroceraso-Fagetum montanum Jov. 1967.
1.13-1. 41 medium 1065.87 62.08
Index (VQI) High to medium climate quality in the
> 1.41 low 524.82 30.57
southern and southeastern parts of the
< 1.25 high 1294.13 75.37 study area results from less steep terrains
Management Quality allowing warmer air masses to break
1.25-1.5 medium 86.87 5.06
Index (MQI)
> 1.5 low 308.22 17.95 through from the Mediterranean (Perović
et al. 2019). This part of the study area is
< 1.3 high 308.04 17.94 also characterized by the presence of en-
Social quality index
(SoQI)
1.3-1.4 medium 212.22 12.36 demic species of the Balkan Peninsula such
> 1.4 low 1168.22 68.04 as Acer intermedium Panč., which forms in
this area the specific communities Fagio-Ac-
Mask areas - - 28.46 1.66
eri intermediae-colurnetum Jov. 1955 and
Total area - - 1716.94 100.00 Querco-Aceri intermediae-colurnetum Miš.
et Din. 1971 (Mišić 1981). The southwestern
and central parts of the study area along
(CQI, SQI, VQI, MQI, SoQI) and correlation fied statistically significant clusters of high the wider portions of the South Morava
of each index with ESA value. The analysis and low ESA values as well as outliers ex- River (4.33% of the studied area) are of low
was performed with the Band Collection hibiting ESA values statistically different climate quality. Significant drivers of degra-
Statistics tool in ArcMap® ver. 10.5.1 (ESRI, from their neighbors. The analysis was per- dation processes in this part of the studied
Redlands, CA, USA). formed in ArcGIS® Pro ver. 2.6 (ESRI, Red- area are temperature extremes and the ir-
The spatial distribution of ESA patterns lands, CA, USA) and tested using 999 per- regular distribution of precipitation (Kosta-
was obtained using Anselin Local Moran’s I mutations with a significance level of 0.05. dinov et al. 2018) with frequent high-inten-
analysis (Anselin 1995). The analysis identi- sity showers.
Fig. 2 - Spatial distribution
of quality indices CQI, SQI,
VQI, MQI and SoQI.
166 iForest 15: 163-170Land degradation drivers and assessment of land sensitivity to degradation
Soil quality index
iForest – Biogeosciences and Forestry
Tab. 2 - Classes of Environmentally Sensitive Areas (ESA) in the area of Grdelica Gorge
The SQI of the majority of the studied
and Vranjska Valley.
area (67.64%) resulted to be medium qual-
ity, 29.58% of the area was low quality, and
only 1.12% of the area was high quality. The Total area Total area
ESA Class Subclass Score range
(km2) (%)
central, northern, and western parts of the
study area showed low SQI values (Fig. 1). Unaffected N ≥ 1.00 ≤ 1.170 1.33 0.08
The central part is characterized by parent Potential P > 1.170 ≤ 1.225 45.10 2.62
rocks prone to weathering, mostly shale, Fragile F1 > 1.225 ≤ 1.275 255.11 14.86
sandstones, and conglomerates (Tanasije-
vić 1956, Schaetzl & Thompson 2015). The F2 > 1.275 ≤ 1.325 542.23 31.58
fragility of the soils formed on parent rock F3 > 1.325 ≤ 1.375 396.43 23.09
prone to weathering is further increased Critical C1 > 1.375 ≤ 1.425 173.76 10.12
by the absence of permanent vegetation
C2 > 1.425 ≤ 1.530 158.08 9.21
cover, especially on steep slopes. In such
conditions, soils can neither maintain pro- C3 > 1.530 116.43 6.78
ductivity nor resist erosion (Gharibreza et Mask area - - 28.46 1.66
al. 2020). The southern and eastern parts Total - - 1716.94 100.00
of the area showed medium SQI values.
The eastern part of the study area is char-
acterized by soils with a higher organic with high population pressure. The areas Social quality index
matter content formed mostly on granites of high vegetation quality include preserv- The SoQI analysis showed that 68.04% of
and granodiorites (Tanasijević 1956). The ed rare and endemic communities in areas the study area has low social quality (Tab.
reduced susceptibility of those parent of low population pressure, mostly at 1). The areas of low social quality are inhab-
rocks to weathering (Schaetzl & Thompson higher altitudes, at small and isolated sites. ited by a population largely over 65 years
2015) and higher organic matter content The dominant vegetation type in the me- of age, which indicates a high proportion
offering increased soil structure stability dium-quality areas is deciduous forests re- of older people relative to the total popula-
(Six et al. 2000) results in higher soil resis- presenting the natural vegetation of the tion (De Pina Tavares et al. 2015, Perović et
tance to degradation. In the southern part study area. Although natural, deciduous al. 2021). In addition, the population den-
of the study area, soils are deep, which is forests have a reduced potential to provide sity in the areas of low social quality is
likely a result of less pronounced relief and sufficient soil protection during the non- greater than 100 inhabitants per square
moderate slopes, among other reasons, growing season (Contador et al. 2009). De- kilometer, which adversely affects the so-
considering that slope is strongly nega- ciduous forests are not greatly endangered cial quality index of the entire area. Anthro-
tively correlated to soil depth (Mehnatkesh in terms of drought resistance and fire risk. pogenic impact, expressed in terms of pop-
et al. 2013). Natural grasslands and pastures are also of ulation density, reflects the level of human
medium vegetation quality. pressure on natural resources (De Pina Tav-
Vegetation quality index ares et al. 2015). Human pressure on crop-
The VQI assessment showed that about Management quality index land, especially land without a clearly de-
one-third (30.57%) of the study area is char- The high management quality estimated fined management policy, can directly ad-
acterized by low vegetation quality, 62.08% for 75.37% of the study area could be re- versely affect the soil condition (Prăvălie et
of the total study area exhibited medium lated to the implementation of sustainable al. 2017). The eastern parts of the study
vegetation quality, and 5.73% had high veg- management policies, mainly in the nature area (covering 17.94% of the total study
etation quality (Tab. 1). Low vegetation protection and forestry sector. The areas area) are high social quality as a conse-
quality mostly corresponds to areas of in- of high management quality coincide with quence of their lower population density
tensive agriculture and high population forests, natural grasslands, and pasture ar- (Fig. 1).
pressure. These areas are situated along eas where agricultural activities are absent
the entire course of the South Morava Riv- (Fig. 1). Sustainable management practices Environmentally sensitive area index
er, especially in the southern parts where are mostly implemented in these areas, According to the synthesis map of the
the river broadens in the valley, creating fa- such as protection regimes, where rare and ESA index (Fig. 3), the majority of the
vorable conditions for agricultural activi- endangered forest communities are pres- Grdelica Gorge and Vranjska Valley (69.53%,
ties. Conventional agricultural production, ent. The low management quality covers or 1,193.78 km2) was classified as fragile (F1,
the dominant land-use pattern in this part 17.95% of the area and is mainly located on F2, and F3). One-third (31.58%) of the total
of the study area, is characterized by agri- agricultural and bare land (Tab. 1), where investigated area (542.23 km2) was in the
cultural activities mostly carried out with- conservation measures are lacking. The ab- subclass F2.
out conservation measures (Fig. 1). Conser- sence of conservation measures is a con- The highest sensitivity classes (C) repre-
vation measures in agriculture include the tradiction of sustainable management sent one-quarter (26.11%) of the total area
cultivation of a cover crop providing suffi- practice (Mehmet Tuğrul 2020). of Grdelica Gorge and Vranjska Valley, or
cient protection against degradation (Lóp-
ez-Vicente et al. 2020). In addition, low veg-
etation quality is a consequence of the ab- Tab. 3 - Spatial correlation matrix of quality indices and ESA.
sence of natural forest vegetation in this
part of the study area. The areas with a
- CQI SQI VQI MQI SoQI ESA
high vegetation quality are small and iso-
lated throughout the study area (Fig. 1). CQI 1.000 -0.023 0.149 0.147 0.065 0.529
Transitional areas of forests and shrubland SQI - 1.000 0.051 -0.120 0.209 0.347
belong to the categories of vegetation
VQI - - 1.000 0.275 0.147 0.675
most resistant to drought (Contador et al.
2009, Prăvălie et al. 2017) and less prone to MQI - - - 1.000 0.023 0.536
fire risk (Contador et al. 2009, Fernandes SoQI - - - - 1.000 0.448
2009, Xanthopoulos et al. 2012). The resis- ESA - - - - - 1.000
tance to fire is especially important in areas
iForest 15: 163-170 167Lukić S et al. - iForest 15: 163-170
maritime climate, these factors suggested
iForest – Biogeosciences and Forestry
Fig. 3 - The synthe- that the southern part of the study area
sis map of ESA would be less susceptible to degradation.
index in the area However, intensive agriculture is the domi-
of Grdelica Gorge nant land use therein, precisely due to the
and Vranjska Val- favorable environmental conditions. Inten-
ley. sive agriculture leads to soil depletion by
overexploitation, loss of soil organic mat-
ter (Gomiero 2016) and acceleration of ero-
sion processes (Gharibreza et al. 2020). In
addition, agricultural management policies
are insufficiently implemented in this area.
As a consequence, the area is character-
ized by high sensitivity to degradation (Fig.
2). The overlooked importance of sustain-
able agricultural measures and insufficient-
ly implemented regulatory measures in
conventional agricultural practices lead to
serious land degradation (Gomiero 2016). A
significant positive correlation between
the MQI and ESA index (r = 0.536) in the
study area indicates the importance of sus-
tainable land management in controlling
land degradation. Statistically significant
clustering of high ESA index values coin-
cides with areas of low management, so-
cial, and vegetation quality (Fig. 3). These
areas are located in the valley along the
South Morava River course in the southern
part of the study area, or are scattered on
pronounced relief and steep slopes in the
448.28 km2. Subclass C1 covers 10.12% of most sensitive to degradation processes northern part of the study area. The lack of
the total area (173.76 km2). Classes N and P are located in the southern part of the forest vegetation due to intensive agricul-
are the least represented in the investi- study area, which is characterized by a ture and high population pressure in the
gated area, covering 0.08% and 2.62% of the wide river valley, milder slopes, and deeper southern part of the study area has also
total area, respectively (Tab. 2). soils compared to the northern part. Com- contributed to higher land susceptibility to
According to the ESA index, the areas bined with the favorable impact of a milder degradation. A positive correlation be-
tween MQI and VQI (r = 0.275 – Tab. 3) indi-
cates the importance of proper land man-
Fig. 4 - Spatial pat-
agement in the maintenance of natural
tern of ESAs to
vegetation cover, especially forest cover
degradation in
which is the most effective vegetation type
Grdelica Gorge and
for controlling land degradation (Salvati et
Vranjska Valley.
al. 2015).
The areas of low sensitivity to land degra-
dation (NP, P, and F1) are located in the
eastern part of the study area and include
high-altitude areas protected by forest veg-
etation. Statistically significant clustering
of low ESA index values also coincides with
eastern areas characterized by medium
vegetation quality, high to medium man-
agement quality, and high social quality
(Fig. 4). The lower sensitivity to degrada-
tion in these areas is probably a conse-
quence of more favorable climatic condi-
tions (lower air temperatures and higher
precipitation). Particularly low sensitivity to
land degradation is shown by small, scat-
tered areas throughout the eastern part of
the study area, which are characterized by
the presence of undisturbed, natural au-
tochthonous forests. The low population
pressure in this area is the main reason of
such forest remains. The sustainable forest
management carried out in this area allows
a higher canopy cover which mitigates
both water and wind erosion and permits a
large accumulation of soil organic matter.
Further, soil organic matter affects the ag-
gregation and stability of the soil structure
168 iForest 15: 163-170Land degradation drivers and assessment of land sensitivity to degradation
and is generally an indicator of soil quality CGLS (2012). Corine land cover 2012. Copernicus selection of indicators for land degradation and
iForest – Biogeosciences and Forestry
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