An adequate choice of independent variables – geographical factors – represents a fundamental step in creating a successful model of Land Use/Cover Change (LUCC). Spatial predictors derived from the digital elevation model (DEM) encompass a significant group of variables that are relatively easy to obtain. However, a detailed review of more than 100 scientific articles has revealed that predominantly only some basic morphometric characteristics are used, specifically elevation, slope, aspect, and solar radiation characteristics. In this study, geomorphometric variables and their importance in land cover models are analysed. The modelled response consists of five distinct changes in land cover in the territory of Slovakia from 1990 to 2018: urbanisation, agricultural intensification, agricultural extensification, afforestation, and deforestation; derived from the 3rd level of CORINE Land Cover. Simple logistic regression models were used to determine the predictive power of factors on land cover changes. The hierarchical partitioning method was used to identify independent contributions in multiple regression models. For ruggedness characteristics, the calculation scale was crucial: The quantities computed in the window with radius of 100 m performed best, regardless of their type. Topographic Wetness Index confirmed its significant role in other studies. The duration of solar radiation achieved significantly better results than global radiation. Casorati curvature achieved the best result among curvatures, pointed out that distinguishing between positive and negative curvature is undesirable, and recorded a relatively significant share of independent contributions compared to other factors.
Álvarez MartínezJ-MSuárez-SeoaneSDe Luis CalabuigE (2011) Modelling the risk of land cover change from environmental and socio-economic drivers in heterogeneous and changing landscapes: the role of uncertainty. Landscape and Urban Planning101(2): 108–119.
2.
BanduraP (2016) Vertical and Horizontal Terrain Dissection of the Western Carpathians. Bratislava: Comenius University, 70.
3.
BanduraPMinárJBielikM (2021) Physically based morphostructural land surface segmentation: case of the Alps and Western Carpathians. Transactions in GIS25(5): 2394–2418.
4.
BansalSSrivastavSKRoyPS, et al. (2016) An analysis of land use and land cover dynamics and causative drivers in a thickly populated Yamuna river basin of India. Applied Ecology and Environmental Research14(3): 773–792.
5.
BaumannMKuemmerleTElbakidzeM, et al. (2011) Patterns and drivers of post-socialist farmland abandonment in Western Ukraine. Land Use Policy28(3): 552–562.
6.
BevenKJKirkbyMJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin24(1): 43–69.
7.
BičíkIJelečekLŠtěpánekV (2001) Land-use changes and their social driving forces in Czechia in the 19th and 20th centuries. Land Use Policy18(1): 65–73.
8.
BirhaneEAshfareHFentaAA, et al. (2019) Land use land cover changes along topographic gradients in Hugumburda national forest priority area, Northern Ethiopia. Remote Sensing Applications: Society and Environment13: 61–68.
9.
BolligerJSchmatzDPazúrR, et al. (2017) Reconstructing forest-cover change in the Swiss Alps between 1880 and 2010 using ensemble modelling. Regional Environmental Change17(8): 2265–2277.
10.
BraimohAKOnishiT (2007) Spatial determinants of urban land use change in Lagos, Nigeria. Land Use Policy24(2): 502–515.
11.
BugáňováE (2023) Detection of Land Cover Changes after 1989 and Their Geographical Factors. Bratislava: Comenius University, 68.
12.
BürgiMHerspergerAMSchneebergerN (2004) Driving forces of landscape change – current and new directions. Landscape Ecology19: 857–868.
13.
BürgiMCelioEDiogoV, et al. (2022) Advancing the study of driving forces of landscape change. Journal of Land Use Science17: 1–16.
14.
CapelloR (2014) Classical contributions: von thünen, weber, christaller, lösch. In: Handbook of Regional Science. Berlin, Heidelberg: Springer, 507–526.
15.
CebecauerTHofierkaJ (2008) The consequences of land-cover changes on soil erosion distribution in Slovakia. Geomorphology98(3–4): 187–198.
16.
ChevanASutherlandM (1991) Hierarchical partitioning. The American Statistician45(2): 90–96.
17.
Climate atlas of Slovakia (2015). 1st edition. Slovak hydrometeorological institute. https://klimat.shmu.sk/kas/
18.
Del BarrioGAlveraBPuigdefabregasJ, et al. (1997) Response of high mountain landscape to topographic variables: central pyrenees. Landscape Ecology12(2): 95–115.
DrugaMMinárJ (2023) Cost distance and potential accessibility as alternative spatial approximators of human influence in LUCC modelling. Land Use Policy132: 106840.
21.
DrugaMRusinkoA (2023) Comparison of DEM-derived determinants for modelling of long-term land cover change in a large scale : case studies from Slovak Western Carpathians. Geographia Cassoviensis17(1): 21–36.
EvansIS (1972) General geomorphometry, derivatives of altitude and descriptive statistics. In: ChorleyRJ (ed) Spatial Analysis in Geomorphology. London: Methuen, 17–90.
FawcettT (2006) An introduction to ROC analysis. Pattern Recognition Letters27(8): 861–874.
26.
FeciskaninRMinárJ (2021) Polygonal simplification and its use in DEM generalization for land surface segmentation. Transactions in GIS25(5): 2361–2375.
27.
FeranecJOťaheľJ (1999) Mapovanie krajinnej pokrývky metódou CORINE v mierke 1 : 50 000: návrh legendy pre krajiny programu PHARE. Geograficky Casopis51(1): 19–35.
28.
FeranecJJaffrainGSoukupT, et al. (2010) Determining changes and flows in European landscapes 1990-2000 using CORINE land cover data. Applied Geography30(1): 19–35.
29.
FlorinskyIVKuryakovaGA (1996) Influence of topography on some vegetation cover properties. Catena27(2): 123–141.
30.
GallayMHochmuthZKaňukJ, et al. (2016) Geomorphometric analysis of cave ceiling channels mapped with 3-D terrestrial laser scanning. Hydrology and Earth System Sciences20(5): 1827–1849.
31.
GellrichMBaurPKochB, et al. (2007) Agricultural land abandonment and natural forest re-growth in the Swiss mountains: a spatially explicit economic analysis. Agriculture, Ecosystems & Environment118(1–4): 93–108.
32.
GibsonLMünchZPalmerA, et al. (2018) Future land cover change scenarios in South African grasslands – implications of altered biophysical drivers on land management. Heliyon4(7): e00693.
33.
GounaridisDChorianopoulosISymeonakisE, et al. (2019) A Random Forest-Cellular Automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales. The Science of the total environment646: 320–335.
34.
GriffithsPMüllerDKuemmerleT, et al. (2013) Agricultural land change in the Carpathian ecoregion after the breakdown of socialism and expansion of the European Union. Environmental Research Letters8(4): 045024.
HofierkaJGallayMOnačillováK, et al. (2020) Physically-based land surface temperature modeling in urban areas using a 3-D city model and multispectral satellite data. Urban Climate31: 100566.
37.
How Jin AikDIsmailMHMuharamFM, et al. (2021) Evaluating the impacts of land use/land cover changes across topography against land surface temperature in Cameron Highlands. PLoS One16: 1–26.
38.
HoyosLCabidoMRCingolaniAM (2018) A multivariate approach to study drivers of land-cover changes through remote sensing in the dry chaco of Argentina. ISPRS International Journal of Geo-Information7(5): 170.
39.
JennessJBrostBBeierP (2013) Land facet corridor designer produced with the generous support of land facet corridor designer. July. https://www.corridordesign.org/
40.
KhalyaniAHFalkowskiMJMayerAL (2012) Classification of Landsat images based on spectral and topographic variables for land-cover change detection in Zagros forests. International Journal of Remote Sensing33(21): 6956–6974.
41.
KimYNewmanGGüneralpB (2020) Land A review of driving factors, scenarios, and topics in urban land change models. Land9(246): 246.
42.
KrchoJ (1973) Morphometric analysis of relief on the basis of gemetric aspect of field theory. Acta Geographica Universitas Comenianae, Geographico-Physica1: 7–233.
43.
KuemmerleTHostertPRadeloffVC, et al. (2008) Cross-border comparison of post-socialist farmland abandonment in the Carpathians. Ecosystems11(4): 614–628.
44.
LetaMKDemissieTATräncknerJ (2021) Modeling and prediction of land use land cover change dynamics based on land change modeler (Lcm) in nashe watershed, upper blue nile basin, Ethiopia. Sustainability13(7): 3740.
45.
LeversCSchneiderMPrishchepovAV, et al. (2018) Spatial variation in determinants of agricultural land abandonment in Europe. The Science of the total environment644: 95–111.
46.
LiGSunSFangC (2018) The varying driving forces of urban expansion in China: insights from a spatial-temporal analysis. Landscape and Urban Planning174: 63–77.
47.
LieskovskýJBezákPŠpulerováJ, et al. (2015) The abandonment of traditional agricultural landscape in Slovakia - analysis of extent and driving forces. Journal of Rural Studies37: 75–84.
48.
LiuXLiangXLiX, et al. (2017) Landscape and Urban Planning A future land use simulation model ( FLUS ) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning168: 94–116.
49.
Mac NallyR (2002) Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables. Biodiversity & Conservation11: 1397–1401.
50.
MarchiLDalla FontanaG (2005) GIS morphometric indicators for the analysis of sediment dynamics in mountain basins. Environmental Geology48(2): 218–228.
51.
MasnýMWeisKBoltižiarM (2017) Agricultural abandonment in chosen terrain attributes context - case study from the polana unesco biosphere reserve (Central Slovakia). Ekologia Bratislava36(4): 339–351.
52.
MegahedYCabralPSilvaJ, et al. (2015) Land cover mapping analysis and urban growth modelling using remote sensing techniques in greater Cairo region-Egypt. ISPRS International Journal of Geo-Information4(3): 1750–1769.
53.
MeltonMA (1965) The geomorphic and paleoclimatic significance of alluvial deposits in southern Arizona author(s). The Journal of Geology73(1): 1–38. https://www.jstor.org/stable/30066379
54.
MillingtonJDAPerryGLWRomero-CalcerradaR (2007) Regression techniques for examining land use/cover change: a case study of a mediterranean landscape. Ecosystems10(4): 562–578.
MinárJEvansISKrchoJ (2013) Geomorphometry: quantitative land-surface analysis. In: ShroderJSwitzerADKennedyDM (eds) Treatise on Geomorphology. San Diego, CA: Academic Press, Vol. 14, 22–34.
57.
MinárJEvansISJenčoM (2020) A comprehensive system of definitions of land surface (topographic) curvatures, with implications for their application in geoscience modelling and prediction. Earth-Science Reviews211: 103414.
58.
MinárJDrăguţLEvansIS, et al. (2024) Physical geomorphometry for elementary land surface segmentation and digital geomorphological mapping. Earth-Science Reviews248: 104631.
59.
MitasovaHHofierkaJZlochaM, et al. (1996) International journal of geographical information science comment on ’ modelling topographic potential for erosion and deposition using. International Journal of Geographical Information Systems10(2): 629–641.
60.
MitsudaYItoS (2011) A review of spatial-explicit factors determining spatial distribution of land use/land-use change. Landscape and Ecological Engineering7(1): 117–125.
61.
MonteiroATFavaFHiltbrunnerE, et al. (2011) Assessment of land cover changes and spatial drivers behind loss of permanent meadows in the lowlands of Italian Alps. Landscape and Urban Planning100(3): 287–294.
62.
MooreIDGraysonRBLadsonAR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological Processes5(1): 3–30.
63.
MottetALadetSCoquéN, et al. (2006) Agricultural land-use change and its drivers in mountain landscapes: a case study in the Pyrenees. Agriculture, Ecosystems & Environment114(2–4): 296–310.
64.
MüllerDLeitãoPJSikorT (2013) Comparing the determinants of cropland abandonment in Albania and Romania using boosted regression trees. Agricultural Systems117: 66–77.
65.
OťaheľJFeranecJKopeckáM, et al. (2017) Modifikácia metódy corine land cover a legenda pre identifikáciu a zaznamenávanie tried krajinnej pokrývky v mierke 1:10 000 na báze príkladových štúdií z územia slovenska. Geograficky Casopis69(3): 189–224.
66.
PazúrRBolligerJ (2017) Land changes in Slovakia: past processes and future directions. Applied Geography85: 163–175.
67.
PazúrRLieskovskýJFeranecJ, et al. (2014) Spatial determinants of abandonment of large-scale arable lands and managed grasslands in Slovakia during the periods of post-socialist transition and European Union accession. Applied Geography54: 118–128.
68.
PazúrRLieskovskýJBürgiM, et al. (2020) Abandonment and recultivation of agricultural lands in Slovakia-patterns and determinants from the past to the future. Land9(9): 316.
69.
PiedalluCGégoutJclaude. (2008) Efficient assessment of topographic solar radiation to improve plant distribution models. Agricultural and Forest Meteorology148(11): 1696–1706.
70.
PlieningerTDrauxHFagerholmN, et al. (2016) The driving forces of landscape change in Europe: a systematic review of the evidence. Land Use Policy57: 204–214.
71.
PopovABMinárJDrǎguţL (2025) Physically-based digital geomorphological mapping: case study of glacial and karst topography. Geomorphology470: 109539.
72.
PrishchepovAVMüllerDDubininM, et al. (2013) Determinants of agricultural land abandonment in post-Soviet European Russia. Land Use Policy30(1): 873–884.
73.
ProkopováMCudlínOVčelákováR, et al. (2018) Latent drivers of landscape transformation in eastern Europe: past, present and future. Sustainability10(8): 1–17.
74.
RileySJDeGloriaSDElliotR (1999) A terrain ruggedness index that qauntifies topographic heterogeneity. Intermountain Journal of Sciences5(1–4): 23–27.
75.
RusinkoADrugaM (2022) Barrier and corridor effects in cost-distance-based accessibility approximation for LUCC modelling: a case study of Slovakia from 2000 to 2018. Landscape Research47(3): 316–332.
76.
RusinkoAMinárJ (2023) The role of geomorphometric predictors in LUCC modeling. Geomorphometry2023: 1–4.
77.
RutherfordGNBebiPEdwardsPJ, et al. (2008) Assessing land-use statistics to model land cover change in a mountainous landscape in the European Alps. Ecological Modelling212(3–4): 460–471.
78.
SchirpkeULeitingerGTappeinerU, et al. (2012) SPA-LUCC: developing land-use/cover scenarios in mountain landscapes. Ecological Informatics12: 68–76.
SerraPPonsXSauríD (2008) Land-cover and land-use change in a Mediterranean landscape: a spatial analysis of driving forces integrating biophysical and human factors. Applied Geography28(3): 189–209.
81.
SiedentopSFinaS (2012) Who sprawls most? Exploring the patterns of urban growth across 26 European countries. Environment and Planning A: Economy and Space44(11): 2765–2784.
82.
SkokanováHFalťanVHavlíčekM (2016) Driving forces of main landscape change processes from past 200 years in Central Europe - differences between old democratic and post-socialist countries. Ekologia Bratislava35(1): 50–65.
83.
SmaliychukAMüllerDPrishchepovAV, et al. (2016) Recultivation of abandoned agricultural lands in Ukraine: patterns and drivers. Global Environmental Change38: 70–81.
84.
ŠúriMHofierkaJ (2004) A new GIS-based solar radiation model and its application to photovoltaic assessments. Transactions in GIS8(2): 175–190.
85.
TappeinerUTasserELeitingerG, et al. (2008) Effects of historical and likely future scenarios of land use on above- and belowground vegetation carbon stocks of an Alpine valley. Ecosystems11(8): 1383–1400.
86.
TarbotonDG (1997) A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resources Research33(2): 309–319.
87.
TasserEWaldeJTappeinerU, et al. (2007) Land-use changes and natural reforestation in the eastern central alps. Agriculture, Ecosystems & Environment118(1–4): 115–129.
88.
TomášikováV (2010) Demo(geo)grafický a Metadátový Informačný Systém So Zreteľom Na Dynamiku Obyvateľstva Slovenska - Source Data. Bratislava: Comenius University.
89.
TrevisaniSTezaGGuthPL (2023) Hacking the topographic ruggedness index. Geomorphology439: 108838.
VerburgPHOvermarsKP (2009) Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecology24: 1167–1181.
92.
VerburgPHKokKPontiusRG, et al. (2006a) Modeling land-use and land-cover change. In: LambinEFGeistHJ (eds) Land-Use and Land-Cover Change. Heidelberg: Springer Berlin Heidelberg, 117–135.
93.
VerburgPHSchulpCJEWitteE, et al. (2006b) Downscaling of land use change scenarios to assess the dynamics of European landscapes. Agriculture, Ecosystems & Environment114: 39–56.
94.
VitovičLMinárJPánekT (2021) Morphotectonic configuration of the podtatranská kotlina basin and its relationship to the origin of the western Carpathians. Geomorphology394: 107963.
XiangQKanAYuX, et al. (2023) Assessment of topographic effect on habitat quality in mountainous area using InVEST model. Land12(1): 186.
98.
YuBSongWLangY (2017) Spatial patterns and driving forces of greenhouse land change in Shouguang City, China. Sustainability9(3): 359.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
