The Impact of Land Use/Land Cover Changes on Land Degradation Dynamics: A Mediterranean Case Study

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The Impact of Land Use/Land Cover Changes on LandDegradation Dynamics: A Mediterranean Case StudyS. Bajocco A. De Angelis L. Perini A. Ferrara L. SalvatiReceived: 10 August 2010 / Accepted: 25 January 2012 / Published online: 15 March 2012 Springer Science+Business Media, LLC 2012Abstract In the last decades, due to climate changes, soildeterioration, and Land Use/Land Cover Changes (LUL-CCs), land degradation risk has become one of the mostimportant ecological issues at the global level. Land deg-radation involves two interlocking systems: the naturalecosystem and the socio-economic system. The complexityof land degradation processes should be addressed using amultidisciplinary approach. Therefore, the aim of this workis to assess diachronically land degradation dynamicsunder changing land covers. This paper analyzes LULCCsand the parallel increase in the level of land sensitivity todegradation along the coastal belt of Sardinia (Italy), atypical Mediterranean region where human pressure affectsthe landscape characteristics through fires, intensive agri-cultural practices, land abandonment, urban sprawl, andtourism concentration. Results reveal that two factorsmainly affect the level of land sensitivity to degradation inthe study area: (i) land abandonment and (ii) unsustainableuse of rural and peri-urban areas. Taken together, thesefactors represent the primary cause of the LULCCsobserved in coastal Sardinia. By linking the structuralfeatures of the Mediterranean landscape with its functionalland degradation dynamics over time, these results con-tribute to orienting policies for sustainable land manage-ment in Mediterranean coastal areas.Keywords Land sensitivity Land management Coastalarea Multi-temporal land cover maps Sardinia ItalyIntroductionLand degradation is becoming one of the major environ-mental issues all over the world and affects also developedregions like North America, Australia and Southern Europe(Romm 2011). Land degradation is hence an interactiveprocess involving multiple causal factors, among whichclimate variability, soil quality, and land management playa significant role (Lambin and others 2001; Reynolds andStafford Smith 2002; Geist and Lambin 2004). In theMediterranean region, both biophysical variables andsocioeconomic conditions affect land quality, and theirinteraction may become extremely complex through spaceand time, resulting in typical land degradation patterns(UNEP 1994; MEA 2005a, b).In the present study, land degradation is considered as aprocess occurring not only in semi-natural areas, but also inagricultural and peri-urban lands (Bajocco and others2011). For instance, besides soil erosion, the major landdegradation processes in the Mediterranean basin are soilsealing, compaction mainly due to agricultural intensifi-cation, salinization, and contamination due to industrialactivities (Montanarella 2007). Agropastoralism is con-sidered one of the most important land degradation drivingforces as it acts both directly (e.g., overgrazing) and indi-rectly generating land cover changes to create new pastures(Harrington 1981; Valentine 1990; Margaris 1992) andS. Bajocco (&) A. De Angelis L. PeriniUnit for Climatology and Meteorology in Agriculture(CRA-CMA), Italian National Agricultural Research Council,Via del Caravita 7a, 00186 Rome, RM, Italye-mail: sofia.bajocco@entecra.itA. FerraraDepartment of Crop Science, University of Basilicata,Via dellAteneo Lucano, Potenza, PZ, ItalyL. SalvatiCentre for Soil-Plant Relationships (CRA-RPS), Italian NationalAgricultural Research Council, Via della Navicella 2-4,00184 Rome, RM, Italy123Environmental Management (2012) 49:980989DOI 10.1007/s00267-012-9831-8promoting mechanical tillage to improve forage production(Perez-Trejo 1994). This is particularly true in specifiedMediterranean areas, such as southern Spain, Sardinia,Crete, and the Aegean islands, which suffer from long-termhuman pressure (e.g., Marathianou and others 2000).Ancillary land degradation drivers include drought, thenatural or human-induced reduction in vegetation cover,poor agricultural practices determining soil organic matterlosses, as well as irrational irrigation practices leading tosalinization, which are all factors contributing to increasingthe level of land degradation sensitivity of a region(Symeonakis and Drake 2004).In the past, the impact of human activities on the Medi-terranean landscapes has increased considerably causing,among others, biodiversity loss, deforestation, and soil ero-sion (Giordano and Marini 2008). Land Use/Land CoverChanges (LULCCs) represent one of the most importantconsequences of the increasing human pressure (e.g., Con-acher and Sala 1998), since reflect changes in both the ruralsystem (e.g., crop intensification vs extensification) and set-tlement patterns (e.g., littoralisation vs inland depopulationwith the consequent abandonment of cultivated land).It has been widely studied and demonstrated that duringthe last decades the Mediterranean region underwent majorLULCCs as a result of the relocation of people to thecoastal border, forest fires, the abandonment of farms andgrazing land, the rapid expansion of tourism-relatedactivities, urbanization, deforestation, as well as theintensification of agriculture (Balabanis and others 2000;Burke and Thornes 2004; Bonet 2004; Sluiter and de Jong2007). Many studies demonstrated that LULCCs affect thelevel of land sensitivity to degradation often acceleratingland degradation processes (Burke and Thornes 2004;Drake and Vafeidis 2003; Symeonakis and others 2007).Examples of the existing nexus between LULCCs and landdegradation sensitivity include(i) crop intensification determining soil pollution due toan increase in fertilizer use;(ii) land abandonment leading to unmanaged vegetationprone to fire and favoring soil erosion;(iii) urbanization favoring soil sealing and the consequentalteration of the hydrogeological cycle; and(iv) deforestation causing biodiversity loss and habitatfragmentation.The European Environmental Agency (2006) publisheda well-known report focusing on LULCCs in the Medi-terranean region. According to this study, population den-sity on coastal areas is on average 10% higher than inland,reaching a peak of 50% in some countries. Even moreworrying is the conversion rate of natural coastal areas intoartificial ones, being faster than population increase (Alvesand others 2007). In Italy, especially in southern Italy, thisprocess impacts on dry landscapes determining loss ofnatural areas and biodiversity, soil deterioration, and theconsequent decrease in land productivity and quality(Salvati and Zitti 2008).The aim of this study is therefore to analyze main landdegradation processes through the evaluation of the rela-tionship between LULCCs and land degradation over aMediterranean coastal area using multitemporal land cover(LACOAST (LC) and CORINE Land Cover map (CLC)) anddiachronic land sensitivity maps (according to Environ-mental Sensitive Areas (ESA) procedure (e.g., Brandt 2005))within a Geographic Information System (GIS) framework.In particular, this paper focuses on the coastal area of Sardinia(Italy), a typical Mediterranean region characterized bynatural and human pressures, including planning deregula-tion, coastal erosion, tourism concentration, and pressingurbanization. The methodology illustrated in this paper,based on modest data requirements and a readily imple-mented procedure, is a useful tool supporting regional plan-ning and land management in Mediterranean drylands.Methodology of the StudyOutline of the MethodologyAs clearly stated by Thornes (2004), Otto and others (2007),and Symeonakis and others (2007), the study of LULCCstrajectories may provide a meaningful contribution to theland degradation assessment. This is particularly true in theMediterranean region where land degradation is not onlydriven by climate change (like in several other regions inthe world, see Sivakumar 2007 for a review), but it dependsspecifically on anthropogenic processes (see Wilson andJuntti 2005). Although a number of EU-funded projectsconcentrated on the interrelation between LULCCs andland degradation (Burke and Thornes 2004), they rarelyreferred to land degradation as a dynamic process (Hawkes2004). Assessing LULCCs trends and determining theirpossible impact on the changing level of land sensitivity todegradation is a relatively new research topic with crucialpolicy implications (Geist and Lambin 2004; Otto andothers 2007; Symeonakis and others 2007). As far as weknow, there is no methodology of how to use LULCCsanalysis to assess changes in land degradation sensitivity.The present study contributes to this deserving need.Study Area and Data SetsThe Study AreaSardinia is the second largest island in the MediterraneanSea, with an area of roughly 24,000 km2. It is situatedEnvironmental Management (2012) 49:980989 981123between 38510 and 41150 latitude north and 880 and9500 east longitude and is one of the twenty Italianadministrative regions (Fig. 1). The island shows a com-plex geography with 1,840 km of coasts and a prevalentlyhilly topography (Bajocco and others 2010). Meadows andpastures cover nearly 40% of the Island, the Mediterraneanmaquis occupies over 20% of the surface area, whereaswoodlands cover almost 10% of the total area (Santini andothers 2010). Sardinias climate is predominantly Medi-terranean. The mean annual temperature ranges from 11 to17C, while rainfall varies from 400 to 1,100 mmdepending on the elevation. Dry periods are often followedby heavy rainfall episodes, triggering soil erosion andflooding events (Santini and others 2010). The mainhuman-related activity causing land degradation in Sardi-nia is mainly represented by grazing (DAngelo and others2000; Enne and others 2002), drought (Fiori and others2004), mismanagement and salinisation of groundwaterresources (Salvati and Zitti 2008), intense wildfire activity(Bajocco and Ricotta 2008), and decrease in vegetationcover as a result of deforestation and land abandonment(Giordano and Marini 2008).Land Cover MapsAs determined by the combined effect of biophysical andsocioeconomic factors, LULCC is a fundamental indicatorfor integrated coastal zone management and land qualityassessment over time (Freire and others 2009). Land coverreflects the structural state of the landscape. This is thereason why land cover data are increasingly used forderiving landscape attributes, such as its composition,diversity, and changes, as well as for modeling its differentproperties (Feranec and others 2010). In this study, we usedcomparable land cover maps from 1975 to 2000. The19751990 change map was derived from the LACOAST(LAnd cover changes in COASTal zones) project, whilethe map dated 1990 and 2000 from the CORINE (Coor-dinated Information on the European Environment) LandCover project.LACOAST (LC) is a research project aimed at quanti-fying LULCCs in a 10 km land strip from the coastlineduring 19751990. LC is based on Landsat satellite imagesfrom 1970s (Perdigao and Christensen 2000) and usesCORINE Land Cover (CLC) 1990 as its reference dataset.It uses CLC hierarchical classification (three-level hierar-chical nomenclature with 44 categories at the third classi-fication level) at 1:100,000 scale (see Bossard and others2000). This project was carried out in the mid 1990s by theAgricultural Information Systems Unit (AIS) of the SpaceApplications Institute (SAI) based at the Joint ResearchCentre (JRC), Ispra (Italy).The CLC project was aimed at providing land covermaps at various times for the whole of Europe and wascoordinated by the European Environment Agency (EEA).It contributes to the knowledge of the land cover and itschanges in 26 European countries between 1990 and 2000(Feranec and others 2010), providing two CLC databases(CLC1990 and CLC2000) with comparable technical fea-tures (Buttner and others 2002).The CLC inventory is based on Landsat satellite imagesas primary information source. The choice of scale(1:100.000), minimum mapping unit (MMU) (25 ha), andminimum width of the polygons (100 m) represents atrade-off between production costs and land cover infor-mation details (EEA 2007). The geometrical accuracy is100 m, that means that there are no shifts higher that100 m between the Landsat images and the CLC polygonFig. 1 Location of the studyarea (left) and the coastal area ofSardinia with the changedpolygons of both LACOASTand CORINE Land Cover maps(right)982 Environmental Management (2012) 49:980989123edges. These basic variables are the same for CLC1990 andCLC2000. The standard CLC nomenclature includes 44land cover classes and it is standardized for all of Europewhich makes comparison and aggregation at the Europeanlevel easier. The classes are grouped in a three-level hier-archy (Table 1). The five main (level-one) categories are:(i) artificial surfaces, (ii) agricultural areas, (iii) forests andsemi-natural areas, (iv) wetlands, and (v) water bodies.The approach of computer assisted visual interpretationof satellite images was chosen as the CLC mappingmethodology. Raw satellite images were pre-processed andenhanced to yield a geometrically correct document innational projection. Ortho-corrected Landsat satellite ima-ges were provided with an RMSE error below 25 m.Detailed topographic maps and in some cases orthophotoswere used to achieve this accuracy. Geospatial informationwere validated in the field according to sampling proce-dures. The main CLC technical characteristics are sum-marized in Table 2. As for the CLC change product(CLC19902000), the technical features are the same as forthe CLC basic products (i.e., CLC1990 and CLC2000),except for the MMU that is 5 ha (Bossard and others 2000).Environmentally Sensitive Areas MapsWe used the ESA (Environmentally Sensitive Area)framework to quantify land degradation sensitivity over theinvestigated area (Basso and others 2000). This frameworkwas applied at both regional and local scale in Portugal,Spain, Italy, and Greece. The procedure is capable ofintegrating variables from different data sources. It wasextensively validated in the field in several target sites byanalyzing the correlation between the ESAI and someindicators of soil quality and physical degradation (Kosmasand others 1999; Basso and others 2000; Lavado Contadorand others 2009). The methodology is based on more thanten variables covering different themes, including thegeological, topographical, and climatic conditions, humanpressure, and land mismanagement. A set of sensitivityscores was assigned to each variable. Scores were derivedfrom statistical analyses and additional information gath-ered from the available literature (e.g., Kosmas and others2000a, b; Lavado Contador and others 2009). For eachtheme, a quality indicator was calculated by averaging thesensitivity scores of the selected variables. A compositeindex was then calculated by averaging the values of thequality indicators.Climate characteristics were described in the ESAframework by mean annual rainfall rate, aridity index(defined as the ratio between annual average rainfall rateand reference evapotranspiration), and aspect (Sivakumar2007). The average annual reference evapotranspirationwas calculated using PenmanMonteith formula (Incertiand others 2007). Climate analysis was carried out for theperiods 19611990 and 19712000 (Salvati and Zitti2008). Soil information was obtained from variablesTable 1 CORINE Land Cover map legendCode Land cover type111 Continuous urban fabric112 Discontinuous urban fabric121 Industrial or commercial units122 Road and rail networks and associated land123 Port areas124 Airports131 Mineral extraction sites132 Dump sites133 Construction sites141 Green urban areas142 Sport and leisure facilities211 Non-irrigated arable land212 Permanently irrigated land213 Rice fields221 Vineyards222 Fruit trees and berry plantations223 Olive groves231 Pastures241 Annual crops associated with permanent crops242 Complex cultivation patterns243 Land principally occupied by agriculture, with significantareas of natural vegetation244 Agro-forestry areas311 Broad-leaved forest312 Coniferous forest313 Mixed forest321 Natural grasslands322 Moors and heathland323 Sclerophyllous vegetation324 Transitional woodland-shrub331 Beaches, dunes, sands332 Bare rocks333 Sparsely vegetated areas334 Burnt areas335 Glaciers and perpetual snow411 Inland marshes412 Peat bogs421 Salt marshes422 Salines423 Intertidal flats511 Water courses512 Water bodies521 Coastal lagoons522 Estuaries523 Sea and oceanEnvironmental Management (2012) 49:980989 983123including soil texture, depth, slope, and drainage.According to Basso and others (2000) some variables canbe considered as static in the ESA model as they changeslowly or rarely and by their nature are infrequently mea-sured or mapped. This is the case for soil quality, whichwas regarded as constant in the following analyses (Salvatiand Zitti 2008).Vegetation quality in the ESA model was assessed byconsidering four variables: fire risk, vegetation protectionagainst soil erosion, vegetation resistance to drought, andvegetation cover (Basso and others 2000). Such indicatorswere obtained from the elaboration of land cover maps dated1990 and 2000. A weight was attributed to each third-levelCLC category based on its different level of sensitivityrelated to vegetation and landscape characteristics (seeSalvati and Bajocco 2011). Finally, human-derived landdegradation was assessed as a result of processes such as therelocation of people along the coastal areas, increasingpopulation density around the major cities, and the intensi-fication of agriculture. Population density measured at themunicipality level in 1991 and 2001 by the National Censusof Households was used as a proxy for human pressure(Salvati and Zitti 2008). Moreover, a demographic variationindex calculated for a time horizon of 10 years was defined atthe same geographical scale (Salvati and Zitti 2005). Anindex of agricultural intensification was further obtainedfrom land cover maps in 1990 and 2000; a weight wasattributed to each cover class in order to obtain a land clas-sification based on crop intensity (Salvati and others 2007).Four partial indicators quantifying the environmentalquality in terms of climate (Climate Quality Index, CQI),soil (Soil Quality Index, SQI), vegetation (VegetationQuality Index, VQI), and land management (Land Man-agement Quality Index, MQI) were then estimated as thegeometric mean of the different scores attributed to eachselected variable. The scores of each thematic indicatorranges from 1 (the lowest contribution to land sensitivity todegradation) to 2 (the highest contribution to sensitivity todegradation). The final index of land sensitivity (ESAI)was subsequently estimated in each i-th spatial unit and j-thyear as the geometric mean of the four partial indicators(Basso and others 2000) as follows:ESAIij SQIij CQIij VQIij MQIij 1=4The ESAI score ranges from 1 (the lowest land sensitivityto land degradation) to 2 (the highest sensitivity to landdegradation). Based on the ESAI values, it is possible toidentify four classes of land sensitivity which refer to themost used classification thresholds (Basso and others 2000;Brandt 2005): (i) non-affected areas (ESAI \ 1.17), (ii)potentially affected areas (1.17 \ ESAI \ 1.225), (iii)fragile areas (1.225 \ ESAI \ 1.375), and (iv) criticalareas (ESAI [ 1.375). ESAI maps of 1990 and 2000 wereproduced after the various elementary layers were registeredand referenced to an elementary pixel of 1 km2 (scale1:250,000) (Basso and others 2000; Salvati and others 2007;Lavado Contador and others 2009).GIS and Statistical AnalysisA preliminary analysis was undertaken considering the firstCLC level to identify which LULCCs occurred along theSardinian coastal belt during the two reference periods(19751990 and 19902000). This allowed us to reduce thepossible errors, caused by the use of different data sources,and their implications. Working at such coarse nomen-clature level ensures that only real processes are identified,and avoids misinterpretation of the same land cover class(possible when working at the second or third CLC level).In order to assess the land degradation sensitivity trendassociated with the LULCCs observed from 1975 to 2000,we matched the ESAI maps of 1990 and 2000, respectively,with the LULCC maps of LACOAST (LC75-90) andCORINE Land Cover (CLC90-00). Since the spatial scalesTable 2 Summary of the main technical features of the CORINE Land Cover (CLC) products used in this paperCLC1990 CLC2000Satellite data Landsat-4/5 TM Landsat-7 ETMTime consistency 19861998 2000 1 YearGeometric accuracy satellite images B50 m B25 mCLC mapping MMU 25 ha 25 haGeometric accuracy CLC data 100 m Better than 100 mThematic accuracy C85% (Probably not achieved) C85% (Achieved, see Buttner-Maucha 2006)Change mapping Change area for existing polygons C5 ha; isolated changes C25 haProduction time 10 Years 4 YearsDocumentation Incomplete metadata Standard metadataAccess to the data Unclear dissemination policy Dissemination policy agreed from the startNumber of European countries involved 26 28984 Environmental Management (2012) 49:980989123of LULCCs and ESAI maps are different (1:100,000 and1:250,000 respectively), we re-sampled the ESAI mapsusing ArcGIS Resample tool (ESRI Inc., Redwoods,USA) in order to have a reliable comparison between them.We thus obtained a minimum pixel size (100 m 9 100 m)comparable with the minimum LULCC polygons width(100 m).In order to analyze the relationship between LULCCsand land degradation sensitivity dynamics, we focused onthe multi-temporal trend of the ESAI values (ESAI90 andESAI00) referred only to the LC75-90 polygons. Thisanalysis enabled us to monitor how the land quality statusvaried (improving or worsening) over time in a given areawhere LULCCs occurred in the past (Symeonakis andothers 2007). We hence carried out a change detectionanalysis of the ESAI trend during 19902000 and matchedthe results with the LC75-90 map in order to quantify theESAI increase (or decrease) associated to each changedpolygon during the investigated time period.Finally, through the zonal statistics ArcGIS tool, wederived the ESAI90 and ESAI00 average value associated,respectively, to each LC75-90 and CLC90-00 polygon inorder to identify which LULCCs have led over time to adifferent level of land sensitivity to degradation. Weexcluded the polygons associated to null ESAI pixels andto the land cover categories indicating wetlands and waterbodies. We elaborated the LULCC classes at the first andsecond CLC levels in order to interpret the results byreducing the number of records, but still keeping theinformation on the landscape variability (Feranec andothers 2010).Results and DiscussionA preliminary overview of LULCCs in coastal Sardiniashows that during 19751990 (Table 3) a larger surface areaunderwent land cover change compared to 19902000(61.9 km2 in 19751990 vs 32.3 km2 in 19902000 on atotal surface area covering 7,780 km2). The greatest modi-fications occurred in coastal Sardinia during 19751990(45% of the area undergoing changes in land cover) involvedLULCCs within the semi-natural class 3 areas (change class3 ? 3). However, a relatively high percentage of this con-version type is recorded also in 19902000 (21%), possiblyindicating the impact of repeated burning on land cover.Important land cover conversions of agricultural into semi-natural areas (change class 2 ? 3), mainly shrublands,burnt areas, or sparsely vegetated areas were also observed.These transitions increased from 19751990 (10%) to19902000 (45%) possibly indicating a progressive aban-donment of cultivated land. By contrast, the conversion ofnatural and semi-natural areas into urban (change class3 ? 1) and agricultural areas (change class 3 ? 2) has beenreduced over time. In particular, the latter conversion pathdecreased from 19751990 (20%) to 19902000 (1%). Therate of land conversion into artificial areas (change class2 ? 1) was relatively stable all over the investigated period.This type of conversion may be considered a honest indi-cator of urban sprawl and littoralization with consequenceson the soil sealing status of coastal areas. LULCCs withintwo different agricultural land cover classes (change class2 ? 2) were mainly observed from non-irrigated fieldstowards permanent crop, rice fields, and heterogeneouscultivations in both time periods and may indicate cropintensification (10% in 19751990 and 15% in 19902000).Based on the previous results and according to theapproach proposed by Feranec and others (2010), the majorLULCCs occurred in coastal Sardinia from 1975 to 2000were: (i) urbanization (land conversion to CLC class 1), (ii)agricultural intensification (conversion to CLC class 2 andmodifications within classes 2 to classes 21 and 22), and(iii) deforestation (conversion from 31 second-level CLCclass to 1 and 2 first-level CLC classes). Feranec and others(2010) did not consider two additional LULCCs that wereobserved in our study area: wildfires and land abandonment(land conversion to CLC classes 33 or 24).The assessment of LULCCs in coastal Sardinia wassupplemented by the analysis of land sensitivity to degra-dation according to the average ESAI calculated at eachchanging land cover class. The most critical ESAI values in1990 (Table 4) were associated to the LULCCs observed in19751990 and related to:(i) urbanization, from forested to urban areas (31 ? 11)and from cropland to industrial areas (21 ? 12) ormines, pits, and dumps (21 ? 13);(ii) crop intensification, from heterogeneous agriculturallands to arable land (24 ? 21) or permanentTable 3 Total and percent surface of each change class (firstCORINE level) by periodChangeclassesLC75-90 CLC90-00Surface(ha)Surface(%)Surface(ha)Surface(%)1-1 248.7 0.4 42.5 0.11-2 392.2 0.6 0.0 0.01-3 647.3 1.1 188.7 0.62-1 5988.4 9.7 4158.0 12.92-2 5106.8 8.3 5395.0 16.72-3 6551.4 10.6 14698.4 45.53-1 2990.8 4.8 687.0 2.13-2 12265.2 19.8 314.1 1.03-3 27731.8 44.8 6855.0 21.2Total 61922.5 100.0 32338.6 100.0Environmental Management (2012) 49:980989 985123cultivations (24 ? 22), and from bare or sparselyvegetated areas to arable land (33 ? 21);(iii) deforestation, from forested to urban areas (31 ?11) or permanent crops (31 ? 22);(iv) land abandonment, from mine, dumps, constructionsites (13) and arable land (21) to heterogeneousagricultural areas (24); and finally(v) wildfires, from cropland to bare or sparsely vegetatedareas (21 ? 33) due to stubble burning and pasturerenewal.Interestingly, also those polygons that did not changeland cover class during 19751990 (i.e., CLC classes 21and 31) are characterized by a nearly critical status of landdegradation sensitivity in 1990; this aspect is particularlyserious for those classes that should highly represent nat-ural areas (e.g., CLC class 31), which means that even if nostructural changes occurred, the landscape functionality isthreatened by high environmental fragility.The results of the diachronic analysis of the ESAI(19902000) associated to the LC75-90 polygons areshown in Table 5. The change classes associated to adecreasing land degradation sensitivity were related to:(vi) afforestation (24 ? 31), i.e. natural or human-induced forest regeneration of cultivated areas; and(vii) vegetation recovery (24 ? 33 and 21 ? 33), pos-sibly indicating a process of natural or human-induced land requalification after land abandonmentor fire, and (33 ? 32) related to natural recoveryafter burning phenomena.On the contrary, an increasing land degradation sensi-tivity was associated to the following land cover transi-tions: (i) urbanization (24 ? 11 and 24 ? 12), (ii) cropintensification (24 ? 22), and (vi) afforestation (33 ? 31)related to post-fire forestation. The last result demonstratesthat, on the one hand, forested areas that have sufferedstructural damages can hardly be reclaimed from a landdegradation perspective, and that, on the other hand, themanagement of the new forest resources has not beenadequately carried out.Finally, Table 6 compares the ESAI dynamics in dif-ferent years and LULCCs. Polygons undergoing urbani-zation (conversion to CLC class 1) in 19751990 showed astable level of land sensitivity in 2000, while those poly-gons that underwent edification in 19902000 showed anincreasing ESAI level. On the contrary, polygons that wereconverted into agricultural areas (CLC class 2) revealedworsened sensitivity levels in the following years, thusindicating crop intensification that could have had a strongimpact on land quality especially in ecologically-fragilesites. Polygons undergoing modifications within the semi-natural lands (CLC class 3) in 19751990 showed a weakincrease in the level of land sensitivity to degradation in2000, while those polygons that underwent re-naturaliza-tion processes in 19902000 showed lower ESAI values in2000 compared to those observed in the previous timeperiod. This suggests that in the past the ecologicalrecovery of burnt and abandoned areas (as also demon-strated by the results presented in Table 5) was relativelydifficult to manage compared with that observed in recentyears probably due to the more effective land practices.ConclusionsLand degradation is not a static process and needs approa-ches capable of addressing its spatial and temporal dynamics(Salvati and Zitti 2009). Land degradation cannot be facedas a single problem since it impacts on water and soil quality,public health, and biodiversity. A better knowledge of theprocesses driving LULCCs is a key issue to promote a sus-tainable land management system. In this context, moni-toring LULCCs at regional scale represents a major concernTable 4 Average ESAI90 values for each combination of LULC change classes of LC75-90 at the second CORINE levelLand cover class 1990 (Final state)11 12 13 21 22 24 31 32 331975 (Initial state) 13 1.457 1.332 1.330 21 1.466 1.565 1.532 1.449 1.494 1.364 1.376 1.50622 1.400 1.386 1.32524 1.425 1.434 1.471 1.529 1.508 1.395 1.415 1.399 1.32931 1.545 1.419 1.503 1.359 1.439 1.336 1.40732 1.435 1.431 1.457 1.409 1.387 1.375 1.365 1.40633 1.526 1.414 1.384 1.414 1.329The mean was weighted on the surface area of each category. Absent values correspond to LULCCs that do not occur in the reference period orthat are not enough representative (\5 ESAI pixels)Values indicating high sensitivity to land degradation were marked in bold986 Environmental Management (2012) 49:980989123for the identification of areas threatened by land degradationwhere mitigation actions should be carried out (DAngeloand others 2000). LULCCs are traditionally interpreted bydistinguishing two transformation types: conversion andmodification. Land use/land cover conversion refers to thecomplete replacement of one land cover type with another,while land use/land cover modification refers to the moresubtle changes that affect the character of the land coverwithout changing its attribute classification (Leemans andZuidema 1995).Identifying the causes of land cover changes (LULCCs)requires understanding how people make land-use deci-sions (decision-making processes) and how specific envi-ronmental and social factors interact to influence thesedecisions (decision-making context) (Geist and Lambin2004). Hence, assessing the decision-making context rep-resents a major concern when analyzing the mutual rela-tionship between land management and land quality status.Through the analysis of such changes, negative effectsstrongly linked to land degradation could be highlighted,and the spatial pattern of the degradation processes couldbe evaluated (Maitima and others 2009).This contribution strongly endorses the importance ofhigh to medium-resolution time-series land cover data.Detailed land cover information is required in manyaspects dealing with sustainable land management, as aprerequisite for monitoring environmental change andmodeling land use, and as a basis for land statistics at alllevels (Jansen and Di Gregorio 2004). A permanentassessment of LULCCs and human-related causes andresponses is essential in land degradation studies. Thecombined use of land cover and land degradation data, onthe one hand, allows to detect where certain changes occur,what type of change, as well as how the land quality statusis changing. On the other hand, these data support decision-makers to develop short- and medium-term plans for theconservation and sustainable use of natural resources(Jansen and Di Gregorio 2004). The evidence emerged inthis paper, linking the structural feature of theTable5Differenceandchangerate(%)betweenmeanESAI90andmeanESAI00foreachcombinationofLULCCclassesofLC75-90atthesecondCORINElevelLandcoverclass1990(Finalstate)11(%)12(%)13(%)21(%)22(%)24(%)31(%)32(%)33(%)1975(Initialstate)130.009(0.6)0.012(0.9)0.016(1.2)210.012(0.8)0.001(0.1)0.018(1.2)0.006(0.4)0.007(0.5)0.000(0.0)0.016(1.2)-0.079(-5.2)220.016(1.1)0.016(1.2)0.008(0.6)240.022(1.5)0.087(6.1)1.253(0.9)0.009(0.6)0.046(3.1)0.009(0.6)-0.022(-1.6)0.005(0.4)-0.033(-2.5)310.004(0.3)0.009(0.6)0.018(1.2)0.010(0.7)0.017(1.2)0.009(0.7)0.016(1.1)320.003(0.3)1.398(1.0)0.004(0.3)0.017(1.2)-0.002(-0.1)0.011(0.8)0.007(0.5)-0.003(-0.2)330.018(1.2)0.01(1.0)0.019(1.5)-0.023(-1.6)0.016(1.2)Normalcharacterreferstoaworseninginlanddegradationstatus.BoldcharacterreferstoanESAIincreaseratehigherthan1.5%(arbitrarythreshold).ItalicsrefertoanESAIdecreaseratehigherthan1.5%(arbitrarythreshold).AbsentvaluescorrespondtoLULCCsthatdonotoccurinthereferenceperiodorthatarenotenoughrepresentative(\4ESAIpixelsof1km2)Table 6 Weighted mean of the ESAI90 and ESAI00 values related toeach change class (final state) of LC75-90 (both ESAI90 and ESAI00)and CLC90-00 (only ESAI00), at the first CORINE levelPeriods Land cover classes (final state)1 2 3LC75-90 versus ESAI90 1.483 1.459 1.385LC75-90 versus ESAI00 1.477 1.474 1.386% Change between -0.4 1.0 0.07CLC90-00 versus ESAI00 1.482 1.480 1.367The mean was weighted on the relative surface of each category.Normal character indicates an increase of the weighted mean. Italicsindicate a reduction of the ESAI weighted meanEnvironmental Management (2012) 49:980989 987123Mediterranean landscape with its functional dynamics overtime, provide a simple framework to foresee the landsensitivity response to changing LULCCs scenarios, andcan effectively contribute to land management policiestargeted to preserve the environmental quality of Medi-terranean coastal areas. Finally, land cover mapping anddocumentation may not provide the ultimate explanationfor all problems related to land degradation and cannot bean end in itself. Nevertheless, it serves as a stepping stonefor understanding trends and possible causes of LULCCsand their implications.Acknowledgments Thanks are due to T. 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Academic Press, SanDiegoWilson G, Juntti M (2005) Unravelling desertification: policies andactor networks in southern Europe. Wageningen AcademicPublishers, WageningenEnvironmental Management (2012) 49:980989 989123The Impact of Land Use/Land Cover Changes on Land Degradation Dynamics: A Mediterranean Case StudyAbstractIntroductionMethodology of the StudyOutline of the MethodologyStudy Area and Data SetsThe Study AreaLand Cover MapsEnvironmentally Sensitive Areas MapsGIS and Statistical AnalysisResults and DiscussionConclusionsAcknowledgmentsReferences

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