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Article

Ecological Stability over the Period: Land-Use Land-Cover Change and Prediction for 2030

by
Mária Tárníková
and
Zlatica Muchová
*
Institute of Landscape Engineering, Faculty of Horticulture and Landscape Engineering, Slovak University of Agriculture in Nitra, Hospodárska 7, 949 76 Nitra, Slovakia
*
Author to whom correspondence should be addressed.
Land 2025, 14(7), 1503; https://doi.org/10.3390/land14071503
Submission received: 30 June 2025 / Revised: 15 July 2025 / Accepted: 17 July 2025 / Published: 21 July 2025

Abstract

This study aimed to investigate land-use and land-cover change and the associated change in the ecological stability of the model area Dobrá–Opatová (district of Trenčín, Slovakia), where increasing landscape transformation has raised concerns about declining ecological resilience. Despite the importance of sustainable land management, few studies in this region have addressed long-term landscape dynamics in relation to ecological stability. This research fills that gap by evaluating historical and recent LULC changes and their ecological consequences. Four time horizons were analysed: 1850, 1949, 2009, and 2024. Although the selected time periods are irregular, they reflect key milestones in the region’s land development, such as pre-industrial land use, post-war collectivisation, and recent land consolidation. These activities significantly altered the structure of the landscape. To assess future trends, we used the MOLUSCE plug-in in QGIS to simulate ecological stability for the future. The greatest structural landscape changes occurred between 1850 and 1949. Significant transformation in agricultural areas was observed between 1949 and 2009, when collectivisation reshaped small plots into large block structures and major water management projects were implemented. The 2009–2024 period was marked by land consolidation, mainly resulting in the construction of gravel roads. These structural changes have contributed to a continuous decrease in ecological stability, calculated using the coefficient of ecological stability derived from LULC categories. To explore future trends, we simulated ecological stability for the year 2030 and the simulation confirmed a continued decline in ecological stability, highlighting the need for sustainable land-use planning in the area.

1. Introduction

Land use is the functional aspect of the land related to human activity, while land cover describes the physical state and natural characteristics of the land [1]. Different types of land cover can be managed and used in different ways. The identification of land-cover types is possible through the analysis of satellite and aerial imagery [2]. Land-cover change typically involves changes in the physical characteristics of land, such as soil properties and vegetation types. In contrast, land-use change refers to changes in the way people use or manage specific areas of land [3]. Research on climate change has demonstrated a strong link between climate change, land-use and land-cover (LULC) changes, and the resulting impact on the ecological stability of landscapes. This relationship is both complex and bidirectional. Changes in land use can influence climate patterns, while climate change can in turn limit or modify how land can be used [4]. Globally, the exploitation of land by humans has increased due to industrialisation and globalisation, posing a significant threat to the environment.
Consequently, analysing land-use land-cover changes has become a critical issue that needs to be addressed worldwide [5]. Just as it is important to assess LULC changes, whether positive or negative, it is equally important to define the impact of these changes on the ecological stability of the area. Ecological stability is described as the capacity of an ecosystem to revert to its original equilibrium state after experiencing a disturbance [6]. The authors of [7,8] state that another concept of ecological stability is also known as resilience. According to [9], ecological stability is a complex concept that aims to encompass a number of dimensions of the dynamics of a system and its resilience to perturbations. In order to determine whether changes in LULC have positive or negative impacts, understanding how to rate and calculate ecological stability is crucial.
The literature review of many research papers identified several coefficients of ecological stability (CES) and, according to [10], the determination of CES was initially considered an academic exercise with little practical impact. However, CES has now become a crucial component of landscape planning. As a result, CES calculation has become a practical tool. The authors of [11] define the calculation of ecological stability as the ratio of relatively stable areas (such as forests, non-forest woody vegetation, meadows, and pastures) to relatively unstable areas (like arable land and built-up areas). The authors of [12] define the calculation for CES as the ratio of a weighted sum of the areas with higher ecological quality levels (forests, water surfaces, riparian vegetation, meadows, and pastures) to a weighted sum of the areas with lower ecological quality levels (arable land and built-up areas). And for example, the authors of [13] define the calculation for CES as the weighted average of the ecological magnitude coefficients of landscape features, where each feature’s weight is its area relative to the total area. These formulas assess the condition of an area, determining whether it is stable or less stable, requires specific interventions for improvement, or needs protection or revitalisation efforts. Therefore, current global land-use change may be one of the most important drivers of landscape stability loss under global change [14,15,16].
The organisation of an area and the positive changes to the landscape through land consolidation are bringing new green and blue infrastructure, as well as other initiatives that support healthy and nature-friendly land management. Land consolidation is an important approach to sustainable development and has been developed from its original goal of increasing agricultural land to an important tool for the comprehensive management and development of urban and rural areas, not only on a European scale. Many authors [17,18,19] define land consolidation as a significant land-use planning process with positive aspects of environmental preservation.
To address the conflicts and challenges between human activities and the environment, society has increasingly turned to the field of land-change science, which includes observation, monitoring, modelling, simulation, and related aspects [20]. Geospatial techniques, including remote sensing (RS) and geographic information systems (GISs), have long been recognised as essential and effective methods for analysing changes in LULC at different spatial scales. Numerous image analysis and change detection methods have been applied to derive information from remotely sensed data [21]. RS provides temporal land-cover data via aerial photography and satellite imagery, while GIS enables the spatial integration, classification, and analysis of these datasets. Common classification approaches include supervised and unsupervised classifiers, object-based image analysis (OBIA), and vegetation indices for vegetation dynamics [22]. Change detection techniques range from pixel-based comparisons to advanced deep learning models for semantic segmentation of multi-temporal imagery. The spatial–temporal modelling of LULC change often employs cellular automata (CA), Markov chains, and artificial neural network (ANN) hybrids [23]. Tools like Modules for Land-Use Change Evaluation (MOLUSCE) in QGIS and TerrSet’s Land Change Modeler (LCM) [24] are widely used to predict future land-cover scenarios. These platforms integrate historical LULC maps with explanatory variables (e.g., DEM, slope, distances to infrastructure and watercourses) to calibrate models and simulate future land-use change while validating predictive accuracy with metrics such as Kappa statistics. Collectively, these methods support robust landscape monitoring and enable scenario-based planning for sustainable land management.
The main objective of this study is to analyse LULC changes in the Dobrá–Opatová locality (Slovakia) over the period from 1850 to 2024 and to simulate potential future developments by predicting land-use changes for the year 2030. The study also aims to evaluate how these changes have affected ecological stability over time and to assess the influence of land consolidation on the local landscape. A further goal is to propose and apply a new, original method for calculating ecological stability tailored specifically for land-consolidation purposes.
The research addresses the following questions:
How has the landscape structure evolved over the past 170 years in the study area? What impact have major historical events (e.g., collectivisation, land consolidation) had on its ecological stability? Can future changes in ecological stability be reliably predicted using LULC simulation tools such as MOLUSCE? How can a tailored ecological stability index support sustainable land management and planning?
The study introduces an original approach to calculating ecological stability tailored to the context of land consolidation and applies a rarely used method (MOLUSCE) for simulating future ecological stability scenarios. The working methodology is also transferable and can be applied in other areas of interest, making it suitable for comparative analyses or broader landscape planning at regional scales.

2. Materials and Methods

2.1. Study Area

Dobrá–Opatová was chosen as the study area for this contribution (48°54′56.25″ N, 18°6′24.17″ E). The area is located in the central part of Slovakia, the region of Stredné Považie, and the district of Trenčín (Figure 1). The area is 1 954 ha and with mean elevation of 325 m.asl and varying between 207 and 670 m.asl. The Váh River (the longest river in Slovakia) flows through the area. The average annual temperature of the area is 9 °C, and the average annual precipitation is 750 mm (Slovak Hydrometeorological Institute, https://www.shmu.sk/). The most prevalent soil type in the area is rendzina, primarily covering forested regions. Agricultural lands are mostly comprised of loamy soil, known as brown earth, while fluvial soil is typically found around watercourses [25].
In May 2006, the District Land Office in Trenčín ordered the implementation of the Dobrá–Opatová land-consolidation project. In addition to the primary objective of the land-consolidation project, which involved the surveying and demarcation of the main boundaries of the new plots, the project also aimed to improve the quality of the area through the design of biocentres, biocorridors and buffer zones in a section called the Local Land System of Ecological Stability. This part of the project introduced new landscape elements to improve ecological stability. The land-consolidation project was completed with the registration of study area in the Land Registry in February 2013.

2.2. Data Sources and LULC Classification

Historical map sources from the Second Military Survey (a military survey was conducted between 1806 and 1869; we will refer to the year 1850 for the purpose of this article), aerial photographs from 1949 (black and white), as well as aerial photographs from 2010, and cadastral maps from 2024 were acquired for four different dates to study landscape changes over more than one hundred and fifty years. The map sources were either vectorised (historical maps from Second Military Survey, aerial photographs from 1949, and aerial photographs from 2009) or taken from the Land Registry of the Slovak Republic (cadastral maps from 2024, Figure 2), then processed and uniformly adjusted according to the land-use classifications. The vectorisation of the data resulted in the transformation of the information into a uniform format, allowing the classification of the vector data. All spatial data were processed in the S-JTSK East North reference coordinate system (EPSG:5514) to ensure spatial consistency across all time horizons.
The landscape structure is divided into eight land types (arable land, garden, orchard, permanent grassland, forest land, water surface, built-up area and courtyard, and other area), with further detailed subdivisions (Table 1) based on land-use elements according to the authors of [26].

2.3. Ecological Stability

Once the data for all four-time horizons had been identified, adjusted, and unified, the land-use elements were classified according to their ecological stability. The elements were classified into six categories of ecological stability (from 0 to 5, with elements in category 0 having the lowest ecological stability and those in category 5 having the highest ecological stability) based on the method for land-consolidation project planning [26]. The classification of the elements according to [27], who generally categorise the elements into natural and near-natural elements based on the originality of the vegetation, as follows:
  • Category 5 (very high significance) includes landscape elements with natural and near-natural vegetation (natural forests, natural grassland communities, wetlands, peatlands, watercourses, areas with natural beds and banks and with characteristic aquatic and riparian communities, etc.).
  • Category 4 (high significance) includes landscape elements with semi-natural and near-natural vegetation (forests and meadows dominated by naturally occurring species, natural water bodies, etc.).
  • Category 3 (moderate significance) includes landscape elements with anthropogenically influenced vegetation with natural elements (e.g., grassed and extensively used orchards, etc.).
  • Category 2 (low significance) includes landscape elements with anthropogenically influenced synanthropic vegetation (e.g., intensively managed orchards, vineyards, reclaimed meadows, etc.).
  • Category 1 (very low significance) includes elements such as intensively used, large-scale blocks of arable land, etc.
  • Category 0 (no significance) includes elements like built-up areas, roads, etc.
After classifying the elements into categories of ecological stability, the data for all time periods were transformed into a raster format with a grid size of 1 m. An innovative calculation formula, modified by the authors of [26], was used to calculate ecological stability. The formula, originally by the authors of [11], was specifically modified to meet the needs of calculating ecological stability in land-consolidation projects, where detailed classification of land-use elements into categories of ecological stability is required. Since we had detailed descriptions of the land-use elements, it was possible to assign them a degree of ecological stability. This allowed us to use the modified calculation for the ecological stability of the given time periods. The calculation is as follows (1):
L C C E S = E 5 + E 4 + E 3 E 2 + E 1 + E 0
where LCCES—the coefficient of ecological stability; E5—land-use elements classified into category of ecological stability 5 (ha); E4—land-use elements classified into category of ecological stability 4 (ha); E3—land-use elements classified into category of ecological stability 3 (ha); E2—land-use elements classified into category of ecological stability 2 (ha); E1—land-use elements classified into category of ecological stability 1 (ha) and E0—land-use elements classified into category of ecological stability 0 (ha).

2.4. LULC Prediction

Predicting future land-use change is often complex and challenging. Researchers are now focusing on developing realistic scenario requirements and building scientific predictions models [28]. The commonly used spatial prediction models are CA, agent-based model (ABM), and Conversion of Land Use and its Effects at Small Region Extent (CLUES). There are also some latest improved models, such as Future Land-Use Simulation (FLUS) and Patch-Generating Land-Use Simulation (PLUS) [29]. Among spatial models, the combination of cellular automata with Markov chain analysis has historically been the most preferred [30]. The authors of [31] say that the cellular automata model features an open structure, allowing it to be combined with other models to predict and simulate land-use patterns. Its widespread use in recent years is attributed to its simplicity, flexibility, and intuitive capability to incorporate the spatio-temporal aspects of processes. The integrated cellular automata artificial neural network (CA-ANN) model in MOLUSCE, with the plugin underneath QGIS, serves as a reliable tool for predicting future LULC, making it valuable for land-use planning and management. MOLUSCE is also employed to examine temporal LULC changes, forecast future land use, predict potential shifts in land cover and forest cover, and detect deforestation in vulnerable areas [32].
Vectorisation, rasterisation, and work with attributes were performed using the GIS software ArcGIS Pro 3.5.0. The prediction of LCLU changes to 2030 was conducted using the GIS software QGIS 2.8.9; MOLUSCE plug-in (https://plugins.qgis.org/plugins/molusce/) (accessed on 23 July 2024). The overview of modules used by MOLUSCE is shown in Figure 3.
To predict changes in ecological stability, raster grids with a 1 m grid size for the time horizons of 1949 (initial raster) and 2009 (final raster) were used as input data (Figure 3). The reasoning behind the selection of the 1949 (initial) and 2009 (final) time thresholds has been clarified in the LULC prediction sub-section. These years were chosen based on the availability and quality of comparable spatial data, as well as their relevance to major historical land-use transformations in the region—specifically post-war collectivisation (around 1949) and the onset of land consolidation (2009), which significantly influenced landscape structure. Although the study spans from 1850 to 2024, these two time points provide the most suitable input layers for model training and simulation. Variables such as elevation (https://geoportal.gov.sk/, accessed on 11 April 2018), slope gradient (derived from elevation), proximity to main roads, distance from urban centres, and distance from streams (https://zbgis.skgeodesy.sk/, accessed on 11 April 2018) were included in the simulation of the ecological stability raster (Figure 4). Several sources [33,34] recommend these variables for analysing and predicting land-use land-cover change, as they provide tangible insights into how human activities and natural influences affect the dynamics of land use and land cover over time.
After conducting an area-change analysis, transition matrices and maps were produced to illustrate these changes over time. The CA-ANN model, integrated into the MOLUSCE plugin, was utilised to simulate changes in ecological stability for the year 2024.
The next step involved validating the model using Kappa statistics, which quantify the agreement between the observed and simulated ecological stability maps. The resulting Kappa value of 0.82 indicates a strong agreement and confirms that the model has a high predictive accuracy. According to standard interpretation thresholds (e.g., Ref. [35]), this value supports the reliability of the simulation. In the final step, the MOLUSCE module was used to generate a cellular automata-based simulation of ecological stability for the year 2030.

3. Results and Discussion

By analysing the landscape structure from the period of the Second Military Survey, we divided all elements of land use into seven categories of land types (Table 1) (the category “garden” is absent). Despite the limited content of these maps, we managed to identify 19 subcategories of land-use elements (Table 1). Forest land covered more than half of the area during the observed period; the hectare and percentage area is displayed in Table 2. The largest forested area was located in the southern part of the territory, where small patches of permanent grassland occasionally appeared. The Opatovský stream meandered through almost the entire forested area, originating within the forest. Part of the forest was also situated on the right side of the Váh River, in the western part of the area of interest. Arable land was the second most represented element in the area (Table 2). The continuity of the arable land was interrupted by roads and permanent grassland elements. The Váh, along with smaller streams that flowed into it as left-bank tributaries, spanned an area. During the period of the Second Military Survey, Opatová had around 600 inhabitants. Permanent grasslands bordered the Váh River on both the right and left sides and appeared as small patches within the forest, created by logging.
The landscape structure of the selected area in 1949 was difficult to distinguish due to black-and-white orthophotos, which are among the first nationwide images of Slovakia. We divided the elements of land use into six categories of land types (Table 2) (the categories “garden” and “orchard” are absent). Despite the low resolution, we managed to identify 22 subcategories of land-use elements (Table 1). In this time period, the group of forest vegetation elements had the largest representation in the observed area. As seen in the aerial photos, the arable land was cultivated as small plots where residents primarily grew cereals, legumes, and root crops. Apple, pear, and plum cultivation was also probably widespread, but not identified in photos. Since the Váh River’s course was not yet regulated, the area of water bodies depended on the time of the black-and-white orthophoto. The relatively wide riverbed contained gravel bars and sand deposits. Built-up areas were located near watercourses and were surrounded by arable land. Close to residential buildings, there were grassed courtyards where residents grew basic types of vegetables for their own consumption. These courtyards also contained fruit trees. Meadows and pastures were spread within the forest vegetation as well as near water bodies, where they protected the arable land from waterlogging and crop destruction in case of rising water levels. The area and percentage of each type of land is shown in Table 2.
The landscape structure elements from 2009 were categorised into eight groups of land types (all categories are included) and, due to the large number of orthophotos available, were further subdivided into 48 subcategories of land-use elements (Table 1). The forest areas extended southeast from the built-up areas and arable land. Within the forests, tourist and forestry paths were already mapped, used not only by foresters but also by tourists visiting the swimming area. Arable lands, located near built-up areas, were managed by agricultural cooperatives as large-scale fields. The Váh River was already regulated. The lower part of the stream (Opatovský stream) also had a modified channel, featuring an artificial reservoir covering nearly 7000 square meters. Table 2 shows the area and percentage of each type during this period.
The landscape structure in 2024 is represented by eight types of land, taken from the Land Registry (all categories are included), which are further divided into 43 subcategories of land-use elements (Table 1). Forests cover almost two-thirds of the total area. Forest areas are located in the highest parts of the area and have very steep slopes. The elevation in this area exceeds 400 m. The second most prevalent type of land is arable land, situated near built-up areas. Due to land consolidation in the area, newly designed field roads can be observed, dividing large blocks of arable land. Water bodies have a stable character, as an artificially created canal, on which a hydroelectric power plant is built, channels a significant amount of water. The Váh River has a regulated channel, surrounded on both sides by non-forest woody vegetation and forested areas. The current population is around 2500 inhabitants in the area of interest. The area and the percentage of the different types of land used during this period are shown in Table 2. Figure 5 shows the graph of the trends for each land-use element from 1850 to 2024.
Forest area has been the dominant land-use category throughout the period, showing a slight increase over time (6% from 1850 to 2024). Arable land experienced a slight increase from 1850 to 1949, followed by a decrease in 2009 and 2024. In general, arable land decreased by 22% from 1850 to 2024. Permanent grassland showed a slight increase from 1850 to 1949, followed by a decrease in subsequent years (decreased by 23% from 1850 to 2024). Other areas have shown a gradual increase over time. Garden was introduced after 1949 and represents a very small percentage of land use. Orchard shows some fluctuation, with a decrease from 1850 to 1949 and a slight increase in later years (orchard decreased significantly by 36% from 1850 to 2024). Water surface decreased by 55% from 1850 to 2024. Built-up area and courtyard increased significantly by 266% from 1850 to 2024, and other areas also increased by 264% from 1850 to 2024. After 1949, the socialist regime in Czechoslovakia implemented land collectivisation, which led to the merging of small plots into large-scale agricultural blocks managed by cooperatives. While this may have improved production efficiency, it often involved the conversion of marginal arable plots into grassland, infrastructure (e.g., roads), or non-agricultural uses. In some areas, land may have even been abandoned due to mechanisation needs or terrain inaccessibility.
From 1949 onward, urbanisation and rural development intensified. The population in the model area increased significantly (from ~600 in 1850 to ~2500 in 2024), resulting in the conversion of arable land into built-up areas, roads, and courtyards. This explains the 266% increase in built-up areas noted in the data.
In the 2009–2024 period, land consolidation further reshaped the arable land into large blocks divided by newly built roads. This may have contributed to a reduction in total arable area due to the footprint of the infrastructure and adjustments in land classification. These trends indicate a shift from agricultural and natural land uses towards more built-up and other areas over the years. In total, 67% of the total area is currently utilised in the same way as it was 200 years ago.
Elements of land use categorised by ecological stability for four different time horizons are displayed in Figure 6.
The ecological stability of the area reached a coefficient value of 3.21 in the surveyed year of 1850, indicating a landscape with high ecological stability. Dominant elements with high ecological stability, marked with the number 5, were identified as natural watercourses and forest areas—these elements occupy 66 percent of the entire area. In the given year, elements categorised with relatively high ecological stability also include riparian grass-herb vegetation and wetlands, classified under category 4, as well as pastures, meadows, and non-forest woody vegetation, classified under category 3 for ecological stability. Elements of low ecological stability were identified as roads, residential buildings, and courtyards (falling into ecological stability category 0), orchards (falling into category 1), and small-block arable land (falling into category 2). Similarly, to the previous time horizon, the analysed data from 1949 also identified natural watercourses and forest areas as the dominant elements with high ecological stability, marked with the number 5 (59 percent of the entire area). Riparian grass-herb vegetation and wetlands were classified under category 4. Abandoned meadows and pastures were classified under category 3. Street vegetation and small-block arable land were placed in category 2. Category 1 was represented by unpaved field roads, while roads, railways, residential buildings, and industrial areas were marked with a 0. The ecological stability coefficient value of 2.76 represented the area in 1949. The third time horizon (the analysed data from 2009) was designated as horizon with the value of CES 1.82. Similar to the time horizon of 1949, elements of land use were classified into categories of ecological stability. However, there was a shift in the category of large-block arable land, which was classified one category lower in ecological stability than small-block arable land. Additionally, there was a shift in the category of watercourses, which became regulated, and therefore, were assigned an ecological stability value of 2. These changes in the landscape caused a relatively sharp decrease in the value of ecological stability coefficient in that time horizon. The latest time horizon (2024) is characterised by a CES value of 1.75. A decline in ecological stability has been noted, though it is not as sharp as the declines identified between 1850 and 1949, and between 1949 and 2009. The values of elements of land use have developed nearly identical characteristics of ecological stability as in the previous time horizon. Since more than half of the area is covered by forest, the region is still considered to have high ecological stability despite the increase in urbanisation and changes in arable land management. The description of ecological stability according to the CES values is as a landscape with high ecological stability.
Figure 7 graphically illustrates changes in ecological stability over time horizons. The table (Table 3) shows the percentage of change in ecological stability between three time periods: 1850–1949, 1949–2009, and 2009–2024. The data is categorised into three types of changes—positive, negative, and without change—with the 2009–2024 period showing only minor changes due to its short duration and the limited ecological impact of the land consolidation carried out in the area.
Following methods of the LCLU prediction, we proceeded with the simulation of changes in ecological stability in year 2030. Figure 8 shows the resulting simulated ecological stability for the model area for the year 2030. The CES has been recalculated and gives a result of 1.63. This means that ecological stability is expected to slightly decline by 0.12 points from 2024 to 2030. Subsequently, a graphical representation of the change in ecological stability between 2024 and the simulated year 2030 was developed (Figure 8).
A positive change in 2030 is projected for 3.35 percent of the area, a negative change is expected for 7.69 percent of the area, and the remaining 88.96 percent of the area is anticipated to experience no change. The observed LULC changes in the Dobrá–Opatová area are consistent with broader trends identified in Central and Eastern Europe. For example, similar studies in post-socialist countries have documented a decline in arable land and an increase in built-up areas and forest cover, largely due to agricultural abandonment and urban expansion (e.g., Refs. [36,37]). These patterns align with the detected decrease in arable land and the relative stability or expansion of forested areas in our study area.
Regarding the CES, the drop from 3.21 in 1850 to 1.75 in 2024 reflects a significant transformation in landscape structure. Comparable studies applying the same index in different regions of Slovakia or Central Europe have reported similar downward trends (e.g., Ref. [38]), especially in areas affected by collectivisation, land consolidation, or infrastructure development. This confirms the CES as a reliable metric for capturing ecological degradation in anthropogenically influenced landscapes.
The simulation for 2030 using the MOLUSCE plugin also corresponds to trends reported in other land-change prediction studies. For instance, Ref. [39] used CA-Markov modelling to predict urban expansion in Central Europe, similarly concluding that built-up areas are likely to increase at the expense of agricultural and semi-natural land. Although spatial and thematic contexts differ, the tendency towards ongoing ecological decline without active intervention is a common conclusion across multiple model-based LULC projections.
While the study provides important insights into long-term changes in land use and ecological stability, several limitations must be acknowledged. First, the accuracy of historical cartographic sources, particularly from 1850 and 1949, may influence the reliability of the land-use classification and the resulting CES values. The interpretation of black-and-white orthophotos and hand-drawn maps involved a degree of subjectivity and generalisation. Second, the spatial resolution of input data (1 m grid) can affect the CES output, especially in areas with fine-scale landscape heterogeneity. Moreover, the simulation of future scenarios using cellular automata models is based on historical trends and assumptions that may not fully capture future socio-economic or environmental changes.
Future research should focus on applying this method to other regions with different landscape dynamics, to test its transferability and robustness. Expanding the simulation beyond 2030, and incorporating various policy or climate change scenarios, would offer a deeper understanding of potential future developments. Additionally, integrating more precise environmental datasets (e.g., soil erosion risk, habitat quality) could further refine the ecological stability assessments. Despite these limitations, this study provides a strong foundation for continued research and planning efforts focused on sustainable landscape management.
In the project area, the resulting value of the ecological stability coefficient decreases over time. In the first time horizon, the ecological stability coefficient reached its highest value. Ecological stability in the studied area was greatest in the year 1850. This was due to the fact that interventions of a technical character in the landscape were kept to a minimum. The environment was largely characterised as natural and minimally altered by human activity. After the abolition of serfdom in 1848, a significant portion of the agricultural land was transferred to local farmers who began cultivating it. In addition to cereals, they also grew fruit trees near their homes, particularly plums, pears, and apples [40]. A relatively large area was covered by water, as the Váh River flowed through the area of interest, and its course was not yet regulated during the studied period. Since the water bodies bordered arable land, there were significant floods in the past, which carried away not only crops but also fertile soil. The subsequent time horizon was characterised by decreasing ecological stability. This was due to the felling of shrubs and the increasing presence of non-forest woody vegetation and permanent grasslands within the forests. Arable land was characterised by small blocks, and its structure resembled strip farming. Settlements began to expand in size. The third time horizon (2009) is defined by large-scale fields, which automatically reduces the ecological stability of the area. During this period, the Váh River formed the largest part of the water bodies, which had already been regulated, with an artificially created channel running parallel to the river. This is another factor that decreases ecological stability. The main traffic route has been diverted away from built-up areas, resulting in reduced noise levels and increased safety for residents. In addition to significant construction of family homes within the built-up area, there was also the beginning of a cottage settlement near the reservoir, which served as a natural swimming area in the summer. Between the second and third mapping periods, the value of the coefficient of ecological stability dropped significantly, caused by changes in the management of arable land. In 2009, a land-consolidation project was conducted in the area with the aim of maintaining the region’s relatively good ecological stability through various landscape interventions. The rapidly declining trend in ecological stability was successfully reversed that year. In the future, it is recommended to design ecological measures in the landscape, such as biocentres, biocorridors, and buffer zones, which would enhance the green-blue infrastructure of the landscape. The ecological stability of areas has become more static in recent years (2009–2024), with a high percentage of areas showing no change. And also, this time period is the shortest. The decrease in both positive and negative changes in the most recent period (2009–2024) could indicate a stabilisation of ecological conditions, but it could also suggest a lack of significant ecological improvements or deteriorations. The sharp decrease in negative changes in the most recent period is a positive sign, indicating fewer areas are experiencing ecological deterioration. For the purpose of understanding how the ecological stability of the area will evolve in the future, we have undertaken simulations of ecological stability up to the year 2030. The method for simulating the development of ecological stability is quite original; as of now, this method has not been used in scientific publications. Most publications focus on predicting changes in LULC based on satellite imagery. Many of these predictions use tools such as MOLUSCE [41], while others predict changes using Land Change Modeler (TerrSet) [42], using CA-Markov [43] and using Google Earth Engine [44]. The study is conducted on a small scale due to the extensive manual processing of data. This is because there is a lack of raster and vector sources stemming from the examination of landscapes in the distant past, when terrain data processing was at a rudimentary level. Despite these factors, the study can be applied at larger scales where it is possible to identify locations that will require increased attention in landscape management. A slight increase in positively classified areas does not contradict the overall long-term degradation. It rather reflects a spatially heterogeneous development, where some patches improve due to reduced human pressure or ecological restoration, even as other parts of the territory undergo degradation.

4. Conclusions

The results of this study introduced us to the identification and assessment of land-type elements across the various time horizons studied. The study identified and assessed changes in land use and ecological stability in the model area across multiple time horizons. The ecological stability coefficient (CES) has decreased steadily from 1850 to the projected year 2030, with the highest stability observed in 1850 due to the minimal human intervention in the landscape. Despite a short-term stabilisation between 2009 and 2024, the overall trend remains negative. The study introduces a novel simulation approach for ecological stability prediction, which has not yet been widely used in existing research. These findings can serve as a warning signal of landscape degradation and underscore the need for targeted landscape planning measures. Future research should expand this method to larger spatial scales and focus on integrating ecological infrastructure such as biocorridors, biocentres, and buffer zones to enhance long-term ecological resilience. This study can also be seen as a warning signal for the continuous decline in ecological stability, which is reaching beyond the boundaries of the study area. Based on the results, we can conclude that ecological stability will continue to decline in the area, even in the projected year 2030, if steps are not implemented in the landscape to reverse this negative trend.

Author Contributions

Z.M. and M.T.: Investigation, data curation, conceptualisation, methodology, formal analysis, visualisation, writing—original draft preparation, writing—reviewing and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the research project 004SPU-4/2025, “Monitoring of Agricultural Soil Erosion with Open Science Participation,” supported by the KEGA, the project APVV-23-0530, “Strip Cropping Rotation in Combination with Agroforestry—An Innovative Soil Management System in the Context of Climate Change,” sponsored by the APVV and the European Commission Horizon Europe project 101081307 “Towards Sustainable Land-Use in the Context of Climate Change and Biodiversity in Europe (Europe-LAND)”.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no competing interests.

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Figure 1. Localisation of the study area (Dobrá–Opatová) in Europe and Slovakia (vector and raster data sources: https://geoportal.gov.sk/, https://zbgis.skgeodesy.sk/).
Figure 1. Localisation of the study area (Dobrá–Opatová) in Europe and Slovakia (vector and raster data sources: https://geoportal.gov.sk/, https://zbgis.skgeodesy.sk/).
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Figure 2. The basis for the land-type identification. (A) Second Military Survey from 1850; (B) aerial photographs from 1949; (C) aerial photographs from 2010; (D) cadastral map from the year of 2024 as a basis for the land-type delineation.
Figure 2. The basis for the land-type identification. (A) Second Military Survey from 1850; (B) aerial photographs from 1949; (C) aerial photographs from 2010; (D) cadastral map from the year of 2024 as a basis for the land-type delineation.
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Figure 3. Flowchart of modules in MOLUSCE.
Figure 3. Flowchart of modules in MOLUSCE.
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Figure 4. Variable inputs in MOLUSCE from the year 2009.
Figure 4. Variable inputs in MOLUSCE from the year 2009.
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Figure 5. The percentage changes in land-use elements over the years 1850–2024.
Figure 5. The percentage changes in land-use elements over the years 1850–2024.
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Figure 6. Element of land types assigned an ecological stability level in four time horizons.
Figure 6. Element of land types assigned an ecological stability level in four time horizons.
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Figure 7. Changes in ecological stability over time horizons.
Figure 7. Changes in ecological stability over time horizons.
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Figure 8. Simulated ecological stability for the model area for the year 2030 (5—very high significance; 4—high significance; 3—moderate significance; 2—low significance; 1—very low significance; 0—no significance) and the change in ecological stability between 2024 and the simulated year 2030.
Figure 8. Simulated ecological stability for the model area for the year 2030 (5—very high significance; 4—high significance; 3—moderate significance; 2—low significance; 1—very low significance; 0—no significance) and the change in ecological stability between 2024 and the simulated year 2030.
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Table 1. The occurrence of the land-use element subdivisions according to the authors of [23] in the years 1850, 1949, 2009, and 2024.
Table 1. The occurrence of the land-use element subdivisions according to the authors of [23] in the years 1850, 1949, 2009, and 2024.
Land-Use Element Subdivisions (Unique Code)Occurrence of the Element in the Given Year Marked with xLand-Use Element Subdivisions (Unique Code)Occurrence of the Element in the Given Year Marked with x
18501949200920241850194920092024
Large-block arable land (0210001) xxGrass-covered yard with planted settlement vegetation (1310403)x xx
Small-block arable land–strip fields (0210002)xxxxNon-residential development (1320001, 1320002) xxx
Garden outside the built-up area (0510001) xxRailway tracks (1330101) xxx
Orchard (0610001)x xxAerial ropeways (1330103) xx
Abandoned orchard (0610003) xxFirst-class road (1332101)xxxx
Abandoned meadows and pastures (weedy) (0710014) xxxLocal road (1332102)xxxx
Intensively used meadows (0710001) xxSidewalk (1332103) xx
Semi-intensively used meadows (0710002)xxxxOther road objects (e.g., guardrails, signs) (1332104) xx
Extensively used meadows (0710004) xxPaved field road (1332105) xx
Semi-intensively used pastures (0710009)xx Wastewater treatment plant (1336104) xx
Abandoned meadows and pastures with shrubs (NDV) (0710018) xxxWater reservoir (1336106) xx
Temporarily removed forest cover–clear-cut area (1020039) x Weir (1336108) x
Forest storage (1023002) xxConstruction site (1370001) xx
Unpaved forest road (1023003) xxMill (1427000)x
Beech and fir–beech forests (1020012)xxxxIndustrial area (1427002) x
Paved forest road (1026002) xxAbandoned quarry (1428002) x
Watercourse with natural channel (1111001)xx Street vegetation (1441003) x
Watercourse with modified channel (1111002) xxNDV—forest ecosystem fragments (1442006)xxxx
Seasonal watercourse (1111004) x Playground (1450003) xx
Canal (1111005) xxCottage settlement area (1450005) xx
Canal with overgrown channel (1111006) xxxCemetery (1460001)xxxx
Water body (1111007) xxGully or ravine (1470007) xx
Riparian grass and herb vegetation (1113008)xx Natural rock formations (1470008) xx
Spring area (1113010) xxGravel banks (1470009)xx
Wetlands with fluctuating water levels (1113014)xx Unpaved field road (1473001) xxx
Family housing (1310002)xxxxUnpaved field manure pit (1473004) x
Isolated residential structures (1310003) xxSacred building (church, monastery) (1480006)xxxx
Concrete-paved yard (1310401)x xxWayside shrine (1480008)x x
Grass-covered yard (1310402) xx
Table 2. The hectare area and the percentage of land type in time horizons of the year 1850, 1949, 2009, and 2024.
Table 2. The hectare area and the percentage of land type in time horizons of the year 1850, 1949, 2009, and 2024.
Land Type1850194920092024
ha%ha%ha%ha%
Arable land416.0321.29438.2222.43324.5416.61321.5916.46
Garden0.000.000.000.001.800.090.510.03
Orchard6.140.310.000.003.650.193.880.20
Permanent grassland172.178.81215.3711.02147.777.56131.596.73
Forest land1097.1956.151040.0053.221152.8659.001164.6459.60
Water surface195.5010.0066.583.4186.554.4386.694.44
Built-up area and courtyard40.332.0659.103.02140.817.21147.827.57
Other area26.681.37134.736.9096.064.9297.284.98
Table 3. The percentage of change in ecological stability between three time periods.
Table 3. The percentage of change in ecological stability between three time periods.
Category1850–19491949–20092009–2024
Positive11.4110.322.13
Negative19.9328.402.49
Without change68.6661.2995.38
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Tárníková, M.; Muchová, Z. Ecological Stability over the Period: Land-Use Land-Cover Change and Prediction for 2030. Land 2025, 14, 1503. https://doi.org/10.3390/land14071503

AMA Style

Tárníková M, Muchová Z. Ecological Stability over the Period: Land-Use Land-Cover Change and Prediction for 2030. Land. 2025; 14(7):1503. https://doi.org/10.3390/land14071503

Chicago/Turabian Style

Tárníková, Mária, and Zlatica Muchová. 2025. "Ecological Stability over the Period: Land-Use Land-Cover Change and Prediction for 2030" Land 14, no. 7: 1503. https://doi.org/10.3390/land14071503

APA Style

Tárníková, M., & Muchová, Z. (2025). Ecological Stability over the Period: Land-Use Land-Cover Change and Prediction for 2030. Land, 14(7), 1503. https://doi.org/10.3390/land14071503

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