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Article

Long-Term Landscape Changes in the Ojców National Park (Poland) and Its Surroundings: Implications for the Effectiveness of Buffer Zones

by
Michał Jakiel
*,
Dominik Kaim
and
Krzysztof Ostafin
Institute of Geography and Spatial Management, Faculty of Geography and Geology, Jagiellonian University, Gronostajowa 7, 30-387 Kraków, Poland
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(15), 6649; https://doi.org/10.3390/su16156649 (registering DOI)
Submission received: 9 June 2024 / Revised: 8 July 2024 / Accepted: 26 July 2024 / Published: 3 August 2024
(This article belongs to the Special Issue Sustainable Urban Planning: Biodiversity, Greening, and Forestry)
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Versions Notes

Abstract

:
Protected areas (PAs) serve as crucial elements in biodiversity conservation but are in danger of becoming isolated islands in human-dominated landscapes. It is related to landscape changes, especially changes in land use and land cover (LULC). Over the past decades, most research on the effectiveness of nature conservation has focused mainly on PAs, while the areas surrounding PAs are of key importance for maintaining ecological connectivity and biodiversity. Therefore, the main objective of this study was to determine the long-term changes in LULC within the selected national park in Poland and its surroundings and to assess the effectiveness of the BZ based on these changes. We hypothesized that, despite restrictions within the buffer zone, land development has intensified and increased, in the nearest surroundings of the analyzed national park. For the analysis, we selected Ojców National Park (southern Poland), one of the oldest national parks in Poland. We analyzed landscape changes before (since the 1930s) and after establishing the park and its BZ. We conducted a comprehensive quantitative analysis of the landscape structure and LULC. We used historical maps and the contemporary national LULC database. Our results showed that almost 40% of the study area consisted of lands with non-persistent LULC. The main changes include a three-fold increase in built-up areas and an increase in forest cover, mainly on abandoned agricultural land. We also found that land development around the national park is at a level similar to the general rate for the area outside the BZ. It suggests the ineffectiveness of the buffer zone in preventing land development. The identified long-term landscape changes the basis for sustainable development land management from the nature conservation perspective.

1. Introduction

According to the IUCN report [1], over the past 30 years, the surface area and number of protected areas (PAs) worldwide have increased by more than 50%. Nevertheless, despite these efforts, many studies show that global biodiversity has declined in recent decades [2,3,4]. This is mainly associated with landscape changes, especially the increasing landscape fragmentation, and land use and land cover changes (LULC) [5,6].
Furthermore, the basic needs of many species are often significantly greater than the surface area of national parks (NPs), nature reserves, or other protected areas [7]. National parks and reserves themselves are protected by law, and human activities within them are controlled and often limited. Therefore, most changes in land use within their boundaries are often managed and can be foreseen to a large extent. However, LULCs in their surroundings are often not subject to such control [8,9]. Furthermore, many PAs, when designated, were not considered in association with their surroundings, often resulting in uncontrolled development in these areas [10]. In addition, the areas surrounding PAs are often attractive for tourism and housing growth, leading to increased settlement development [11,12]. Hence, there is an increasing scientific emphasis on the surroundings of PAs, which are crucial to maintaining ecological connectivity and biodiversity protection, including the protected areas themselves [13,14]. These studies often have a broad scope, covering various aspects on local [15,16], regional, or global scales [17,18,19,20], primarily addressing changes in forest cover [21], landscape structure [22,23], and habitat fragmentation [24], as well as urbanization pressure on PAs [12,25,26].
In recent years, scientists have become increasingly interested in environmental changes and PAs [27], and the number of studies on the effectiveness of PAs is also increasing [28]. Most studies on the evaluation of the effectiveness of PAs (i.e., in the context of deforestation or LULC changes) involve comparisons of the areas inside and outside PAs, creating buffers (concentric buffers/rings) around the PAs [29,30]. However, these basic comparative studies do not account for the non-random distribution of PAs or the environmental diversity of surrounding areas, nor do they address the potential leakage effects [31,32]. Leakage effects are understood as a negative type of unanticipated displacement or spillover land use change that is restricted within a protected area, causing it to spread into nearby uncontrolled areas beyond the boundaries, where it would not otherwise have occurred [33].
Most of the research is focused on areas within the PAs themselves or their immediate surroundings, without considering the existence of buffer zones (BZs). BZs are designated to reduce external hazards and negative edge effects on PAs [34]. The idea of buffer zones became more widely addressed in the 1970s when the UNESCO Man and the Biosphere Programme (MAB) was developed, and Biosphere Reserves (BRs) were established. Buffer zones can be located inside (around the core zone) or outside of PAs. Buffer zones have a different status depending on the country’s nature conservation system and the types of PAs. For example, in Poland, buffer zones are mandatory by national law for national parks and optional for nature reserves and landscape parks. It is important to add that the land use planning in Poland within the buffer zones is controlled by the authorities responsible for the PAs to which the buffer zones pertain [35]. The International Union for Conservation of Nature (IUCN) indicates that buffer zones may or may not be part of a protected area category [36]. However, often their complicated legal status hinders research, and until now, there are very few studies that evaluate the effectiveness of buffer zones [37,38].
LULC in Poland concerning PAs has been studied in many different contexts. Most of this research is based on individual case studies, mainly focusing on the areas within the PAs themselves and covering short periods. Some studies analyzed only parts of the PAs [39,40]. The pressure of land development in selected PAs has also been analyzed [41,42,43,44], as well as the landscape structure [45] also for a larger number of PAs at the national and regional level [46,47,48]. However, these national- and regional-scale analyses were conducted for short periods (mainly the last 20–30 years), and their analyses were based on highly generalized databases, such as Corine Land Cover (CLC) data.
Therefore, the main objective of this study is to determine the long-term changes in LULC within the selected national park in Poland and its surroundings and to assess the effectiveness of the buffer zone based on these changes. Ojców National Park (ONP) was selected for analysis because it is one of Poland’s oldest national parks, and also because of its location, close to a large city, with an area exposed to particularly dynamic changes. The results of this study help us answer questions about the effectiveness of buffer zones and protected areas themselves in the context of ongoing changes in land use and land development. The novelty of this research lies not only in the consideration of the national park area and its buffer zone but also in the area surrounding the buffer zone in a long period (1930s–2022), including the time before and after the establishment of the national park and its buffer zone. This study contributes to the worldwide discussion of the effectiveness of PAs and their buffer zones in nature conservation.

2. Materials and Methods

2.1. Study Area

The analyzed study area is located in southern Poland within three macroregions: the Kraków-Częstochowa Upland, the Kraków Gate, and the Nida Basin (Figure 1A) [49]. The study area is characterized by an upland landscape with karst topography associated with limestones. Its characteristic features include extensive plateau surfaces and deep incised valleys with the main river called Prądnik. The total study area is 29,282 ha (Figure 1). The Ojców NP was established in 1956, and over the years, it has been enlarged several times, and currently, it covers 2152 ha. It is one of the oldest national parks in Poland, which allows us to conduct analyses of long-term changes in LULC in the area. In 2008, the park was designated also as a Natura 2000 site (a Site of Community Importance, PLH 120004). The OPN is located near Kraków (15 km), within an area considered the Kraków Metropolitan Area. The buffer zone was officially designated in 1997 (6777 ha), but in similar boundaries, it was described earlier in spatial plans [50]. The research was carried out in five municipalities (Jerzmanowice-Przeginia, Skała, Sułoszowa, Wielka Wieś, and Zielonki), within which the ONP or its buffer zone is located. We chose the municipalities as the determinant of the study area because they are the primary units responsible for land use management and planning. Therefore, they seem most suitable for LULC analysis. Almost 30% of the ONP area belongs to private users. In the case of the ONP buffer zone, the detailed ownership structure is not known. It is estimated that more than 90% of the land is privately owned [50].
The diversity of the landscape results in various microclimatic conditions and vegetation cover. Although forests are the natural vegetation formations for this area, they currently occupy just over 13% of the study area and are mainly located in areas that are difficult for agricultural use. Until the 12th century, the forests covered almost the entire area of Kraków-Częstochowa Upland. The fragmentation of forest complexes today is a result of intense deforestation, which began in the 17th and 18th centuries and was associated with the development of agriculture and industry. Subsequently, for many centuries, the landscape was dominated by agriculture [50,51]. Currently, arable land covers more than 60% of the study area (Figure 1B). Semi-natural plant communities are characteristic of the area, such as xerothermic grasslands that occur mainly in the vicinity of limestone monadnocks, meadows, and pastures. Many species and habitats that occur here are protected under the Natura 2000 network [52].

2.2. Data Sources

Various types of cartographic materials (topographic maps) and imagery data (aerial photographs) were used for the analysis of past and current land use in the study area. Analyses were performed over a long period (1930s–2022) to capture the most significant changes in landscape structure and land use, including trends before the establishment of the national park and its buffer zones. The analysis was carried out in six time periods (1930s, 1950s, 1980s, 2003, 2015, and 2022) (Table 1), with a time interval from 7 to 25 years between cross sections. The selection of time periods and the length of the intervals were determined by the availability of cartographic materials. In this study, the main source materials were detailed topographic maps (1:25,000), including topographic maps published by the General Staff of the Polish Army in the 1950s, which were rarely used in research because they were kept secret for many years. They have significant potential, especially the maps from the 1950s, which cover the entire area of Poland [53].

2.3. Data Processing

The topographic maps were calibrated and then vectorized (using ArcGIS 10.7 software) and combined with newer orthophotomaps and the National Database of Topographic Objects (BDOT10k) to create the spatial databases of land use. In recent years, numerous studies [54,55,56,57] have addressed the methodological aspects and methods of the proper calibration and transformation of historical maps [58]. The oldest maps from the 1930s and 1950s required calibration. Calibration was mainly based on ground control points (GCPs). We used 20 to 25 GCPs distributed on the maps in characteristic locations, with special attention to the oldest map sheets, which required adjustment to reliable measurements. The number of GCPs used was high enough to achieve a low value of Root Mean Square Error (RMSE), which was in the range between 2 and 8 m and differed slightly between the map sheets.
In the second stage, the LULC classes were delineated, and the calibrated maps and orthophotomaps were manually vectorized. Based on the analysis of signatures and symbols, and the interpretative possibilities of the collected maps, 9 land use classes were identified: arable lands (A), built-up areas (BU), forests (F), grasslands (G), orchards (O), other artificial areas (OA), road and rail networks (RN), transitional woodland/shrubs (TW), and water body (WB) (Figure 2).
To correctly conduct analyses based on land use data from materials of different scales, and therefore also different levels of accuracy and generalization, standardization was necessary along with the determination of a common Minimum Mapping Unit (MMU) [59,60]. The surface area of the smallest object (polygon) for the delineated classes (except for forests) was established to be 0.05 hectares. For forests, an MMU of 0.1 ha was adopted because according to the Forestry Law in Poland from 1991, land covered with trees on a compact area of at least 0.1 ha is considered a forest. Therefore, all smaller patches, over 0.05 ha, were considered transitional woodland/shrubs areas. All polygons with smaller areas (the so-called residual vectors) were eliminated by incorporating them into adjacent polygons with the largest area.
The main method of data acquisition in this study was visual and manual classification and vectorization, which is commonly used in historical GIS research on local and regional scales [55,60,61,62]. To speed up the data acquisition process, dependent vectorization was applied, which means that existing databases were used as reference data. This is a retrospective method, and after the vectorization and preparation of input data, the data for subsequent, older timeframes were progressively vectorized. This significantly speeds up the work, facilitates visual interpretation of land use classes, and reduces the number of potential topological errors [59]. In this study, as the main reference layer in dependent vectorization for contemporary land use, data on LULC from BDOT10k were used. The BDOT database was reclassified, that is, each of the 35 detailed BDOT classes was assigned and aggregated to one of the 9 previously defined and selected land use classes for analysis (Figure 2). In the analyses, we focused on the main classes of use, based on which the efficiency of the buffer zone can be assessed.
To appropriately identify changes in LULC and landscape structure, the data were analyzed in concentric buffers (zones) of different ranges (0.5 km; 1 km; 1.5 km; 2 km; 5 km, and >5 km) and in areas with different protection statuses: I) the ONP area, II) the ONP buffer zone, III) the concentric buffer around the ONP boundaries, and additionally IV) the remaining area of the municipality outside the buffer zone (Figure 2). In developing the buffer analysis, it was based on other studies [63,64]. This enabled the analysis of changes within the national park and in the buffer zone compared to the area not covered by any legal protection and in individual concentric buffers. The primary unit responsible for land use management and planning are municipalities, hence it is important to examine all LULCs considering municipalities within the study area.

2.4. Analyses of LULC Changes

Based on the prepared LULC data, the spatio-temporal analysis of the changes was performed in ArcGIS Pro 3.1 using the overlay and intersect geoprocessing tools. We analyzed absolute and relative change, class persistence, gains, losses, and net change. We compared the general spatial changes between six periods and transitions between selected main land use classes. We considered only LULC changes that affected at least 0.1% of the area. Hence, some use classes were not included in the analyses. Subsequently, we analyzed where changes in land use occurred, divided into different zones (Figure 2). Additionally, to explain a better rate of changes, we computed the Dynamics Index (DI), which was developed by modifying earlier similar formulas [65,66,67]:
DI = ( A b i , t 2 − A e i , t 1 ) A × 1 t 2 − t 1 × 100 %
where
Ab and Ae—the proportion of the built-up area, respectively, at the beginning and at the end of the time interval, t1—the starting year of the time interval, t2—the end year of the time interval, and A—the total area of the buffer (zones) for which the index is calculated.
The index can be calculated for different classes of land use; in this study, it was used for built-up areas, as their changes associated with land development are the most relevant to evaluate the effectiveness of the buffer zone. The DI takes into account both the variation in the area and the different lengths of the analyzed time intervals, showing the degree of the annual rate of changes in LULC.

2.5. Analyses of Landscape Structure

Previously processed LULC data were used as input for the analyses, which are one of the most commonly used variables to describe changes in landscape structure. When selecting metrics, based on previous research [45], we selected sets of metrics for different groups that provide the most comprehensive descriptions of landscape structure changes [68]. We chose six indicators: Mean Patch Size (MPS), patch density (PD), largest patch index (LPI), Mean Fractal Dimension Index (FRAC), Shannon’s Diversity Index (SHDI), and Interspersion and Juxtaposition Index (IJI). All selected metrics are from different groups, surface and edge, shape, diversity, and division [68], and are presented in Table 2. The selected metrics were calculated using the FRAGSTATS 4.2 software [68] on the landscape and class levels [68]. Considering that FRAGSTATS has some limitations, in calculating metrics at the level of the zones, e.g., patches being cut by the borders of zones, artificial small patches occur when larger patches are cut. For the calculation of zone-level metrics, we used the ZonalMetrics Tools, a specialized functionality for calculating landscape metrics on the zone level [69].

3. Results

3.1. LULC Changes by Study Area

Land use in the ONP and its surroundings during the study period (1930s–2022) was characterized by significant and dynamic changes. Land use conversion was highly variable throughout the study period, and we can observe different types of change trajectories, such as cyclical (A→G→A: rotation between two land use classes) and stepped (A→G→TW→F: change between classes, related to abandonment land). The main changes in LULC within the study area were the loss of open areas, mostly due to the land abandonment and development processes (residential urbanization) at the expense of arable land. The analysis showed that during the study period, the arable land area decreased significantly, from 80.5% in the first period (1930s) to 57% in 2022 (Table 3). The largest decrease in the proportion of arable land (by 8.5%) occurred between 2003 and 2015 (Figure 3). Overall, throughout the study period, arable land was transformed mainly into grasslands (9.5%), built areas (6.9%), and forests (4.8%) (Table 3, Figure 3).
During the study period, the forests increased their area by more than half. Only after WW II, there was a slight decrease in forest cover to 9.5%. However, in the following years, the percentage of forest area progressively increased to 15.7% in 2022. The built-up areas increased more than three times, from 3% to 10.6% in the study period, at the expense of arable land, grassland, orchards, and plantations. The grassland area increased more than three times, from 3.7% in the 1930s to 11.5% in 2022, and appeared mainly on arable land. Orchards and plantations, which were most often transformed into arable land, grasslands, and built-up areas, were characterized by great variability (Table 3, Figure 3). The transitional woodland/shrubs area did not change significantly (from 0.8% to 1.2%) in the study period. But these areas mainly include abandoned arable lands or grasslands undergoing secondary succession or afforestation. Hence, this is a temporary and transitional form of land use subject to rapid transformations, such as the conversion back to agricultural land or the return to forest again after several years (Table 3, Figure 3).

3.2. LULC Changes by Buffers

We found a spatial and temporal variation of the change in LULC between the zones analyzed. Approximately 37% of the study area consisted of lands under unstable (non-persistent) land use. These changes included both the development of new built-up areas and the abandonment of arable land that was subsequently subject to secondary succession. The largest areas of changes were observed in the buffers >5 km (42%) and up to 0.5 km (41.4%). Most strikingly, there were more areas under unstable use in the buffer zone (39.7%) than in areas outside the buffer zone (36.8%) (Figure 4A and Figure 5). The fewest changes in land use occurred in the northern (Sułoszowa municipality) and northeastern (the Skała municipality) parts of the analyzed area, where agriculture has been preserved (Figure 4A,C). Meanwhile, in the remaining areas, a large amount of land was transformed into grasslands and then reforested due to secondary succession (Figure 4C,D), mainly on abandoned arable lands (Table 3). A relatively high proportion of areas with non-persistent land use were observed within the ONP, mainly associated with the cessation of agriculture use. The area of arable land within the park decreased from 21.4% to 6.4%. These areas were transformed into forests as a result of natural secondary succession on abandoned land or intentional afforestation (Figure 4D).
The largest share of the built-up areas was in the buffer of 1 to 1.5 km (13.8%), >5 km (13.3%), and 0.5 to 1 km (13.1%). The largest increase in the proportion of built-up areas occurred in the last three time periods. In 2022, a slightly higher percentage of built-up areas was observed outside of the buffer zone (11.4%) compared to the buffer zone (11.1%). However, in earlier periods, a little higher proportion was observed within the buffer zone. It is noteworthy that the highest proportion of transitional woodlands/shrubs was observed within the buffer up to 0.5 km from the ONP boundary, indicating intensive abandonment of the arable land in this area. Figure 5 shows the proportion of change in LULC by buffers.

3.3. The Growth of Built-Up Areas

The built-up areas were characterized by high change dynamics and showed an increasing trend. The value of the DI gradually increased, with significantly higher growth observed in the last two time intervals (2003–2015 and 2015–2022). This trend applies to almost all zones, except for the area within the ONP, where the changes in dynamic degree have varied. The largest increase in the DI occurred in the buffer >5 km, between 2015 and 2022, and when the annual rate of change in the built-up areas was 0.26% (Figure 6). It should be noted that the high dynamics of the buffer >5 km was particularly visible in the southern part of the study area. Similarly, a relatively high increase in the DI was observed in the buffer up to 1 km and up to 1.5 km, with a slight decrease observed in the last period. The dynamics within the buffer zone were slightly lower than the area outside the buffer zone. However, despite establishing the buffer zone in the 1990s, there is no significant slowdown in the growth rate of built-up areas. Within the ONP, the lowest dynamics were recorded, with negative dynamics observed in two time intervals (1950s–1980s and 2014–2022), which can be attributed to the demolition of buildings, which in some cases occurred when the ONP became the owner.
The rate of change in the built-up areas varied between and within municipalities. In the municipalities located in the southern part of the study area (Wielka Wieś and Zielonki), there was a much higher rate of growth of the built-up areas. This is confirmed by the very significant expansion of the built-up areas in the buffer >5 km in the Wielka Wieś municipality in the last two time intervals (2003–2015 and 2015–2022). In three municipalities (Sułoszowa, Skała, and Jerzmanowice-Przeginia), where residential development dominates, the value of the DI was slightly higher in the buffer zone than outside the buffer zone (Figure 7).

3.4. The Landscape Structure Changes at a Landscape Level

The main changes in landscape structure were associated with an increase in PD and a decrease in MPS. Additionally, the shapes of the patches became more irregular and the length of the boundaries of the patches increased (Figure 8A). During the study period (1930s to 2022), PD increased significantly from 8 to 35 patches per 1 km2 (Figure 8). At the same time, with the increase in PD, their MPS decreased from 12.5 ha to less than 3 ha. The LPI also decreased, from more than 30% to 9.5%, indicating that the main patches have a decreasing proportion and are subject to fragmentation. The FRAC_MN significantly increased its values, suggesting that the shapes of the patches became more irregular. The IJI increased from 64 to 68 during the study period, indicating a higher degree of adjacency and interweaving of the patches representing different land use classes. However, in two time intervals (1980s and 2022), a decrease in this index was observed. In the first case, it resulted from a decrease in the area or the temporary removal of the orchards, which were most commonly neighboring several different land use classes. In the second case, it was associated with a decrease in the number of patches of transitional woodland/shrubs. In the first period (1930s), the SHDI had a relatively low value, indicating the dominance of one class (i.e., arable land). However, over time, the value of this index increased, indicating a decreasing dominance of arable land and an increase in the diversity of the landscape mosaic.

3.5. The Landscape Structure Changes at a Class Level

All land use classes showed an increasing trend in PD. This indicates a trend of increasing landscape fragmentation, also at the level of individual classes. The highest PD and the most significant increase occurred in built-up areas, from 4 in the 1930s to 11 patches per 1 km2 in 2022. The highest PD of the built-up areas was observed in a buffer of up to 1.5 km, while the lowest was within a buffer of >5 km, indicating that the built-up areas are more compact in this buffer. Similarly, when we compared the buffer zone with areas outside the buffer zone, a higher level of PD was observed in the BZ, indicating a greater dispersion of built-up areas within the buffer zone and larger and more compacted patches outside the buffer zone (Figure 8B). It is worth noting that for some buffers, there was a slight decrease in the PD of built-up areas in the last time period, and this is related to the merging of smaller patches into larger ones. For grasslands and arable land, in the last two periods, there was a significant increase in PD in almost all buffers (Figure 8B). In the case of forests, a gradual increase in the number of patches was observed, from 0.6 patches per km2 in the 1930s to almost 3 patches in 2022. The increase in the number and PD is mainly due to the appearance of new forest patches in areas previously used for agriculture, mainly due to secondary succession and less frequently through the fragmentation of existing patches. The highest PD of forests is observed in the buffer up to 0.5 km (Figure 8A). The value of the LPI for the forest gradually increased, indicating that the area of the largest forest complex, represented by forests within the ONP, increased and, most likely, it also merged with adjacent forest patches (Figure 8A).

4. Discussion

4.1. LULC Changes: Abandonment of Arable Land and Land Development in the Context of the Effectiveness of Buffer Zones

Our most significant finding was that a substantial increase in the proportion of built-up areas (more than doubled in the last 40 years) remains an important threat to Ojców National Park, even though the buffer zone was established around the park at this time. We also found that land development around the national park is at a similar level to the general rate for the area outside the buffer zone. Our results also show significant dynamics in LULC around the ONP, where nearly 40% of study areas have non-persistent LULC. The identified changes are directly or indirectly related to the abandonment of arable lands and land development. Such changes in land use are characteristic of Central Europe [70,71]. The results indicate that arable land decreased by one-fourth during the study period, while the area of built-up areas increased more than three times, and abandoned land occurred in all buffers (zones), but most of such land was located in the surroundings of the national park and within the buffer zone. It also resulted in increased landscape fragmentation expressed in higher PDs. The abandonment of arable lands and the accompanying secondary succession is observed contemporarily both in Poland [72] and in most European countries [73].
Our results suggest the ineffectiveness of the buffer zone in preventing land development, especially since, according to the legislation, spatial planning is under the control of national park authorities. The overall dynamic degree of built-up areas for the study area was slightly higher outside the buffer zone; however, the dynamic degree for three of the five municipalities was higher in the buffer zone than outside of it. Our analysis was not designed to identify the detailed causes and mechanisms of increased land development. However, it is probably associated with intense suburbanization processes, as these are municipalities that directly neighbor the city of Krakow. There have been several commercial areas with storage facilities, as well as large areas with residential buildings. But when dividing the area into municipalities, the result was different, and in some municipalities, the dynamics were higher or on a level similar to areas neighboring the Park. These results align with other studies [12,25] that have reported the increasing isolation of PAs caused by land development. PAs can significantly attract new residential developments, such as second homes or leisure facilities, to be built in close proximity to benefit from the ecosystem services that protected areas conserve and provide, such as recreation, landscape values, or environmental quality [11,25,64]. This is confirmed by studies from Poland [74], which have shown that most municipalities in Poland with national parks are characterized by a higher level of tourism development than their neighboring areas. The high human pressure and density of built-up areas in the vicinity of PAs may have a significant effect on ecological processes and biodiversity within PAs [13,24,25]. The process mentioned above, in which land abandonment can lead to faster urbanization, can have many negative consequences. This increases the possibility of the development of a wildland–urban interface (WUI) because many built-up areas appear near forests or forests appear close to built-up areas, resulting in human–wildlife conflicts, habitat fragmentation, and other human–nature interactions [75].
The results indicated the higher rates of LULC changes close to the ONP boundaries, which mainly relate to land abandonment. It should be noted that agricultural abandonment can have positive and negative effects on environmental processes [70]. On the one hand, it can lead to an increase in forest areas that can improve some ecosystem services, such as soil recovery, and water regulation, and also increase biodiversity, especially concerning the potential increase in ecological connectivity of PAs for large mammals [70,72]. On the other hand, agricultural abandonment can also lead to the loss of cultural landscapes and semi-natural habitats such as grasslands [73]. One of the characteristic semi-natural habitats of the analyzed area is biodiverse xerothermic grasslands [51,52]. As a recent study indicates [18], the surroundings of many PAs in Europe are subject to naturalization processes, which may be the result of land abandonment. However, land abandonment can also have negative effects in terms of land management and planning. As previous studies have shown by [71], land abandonment has the potential to be considered a precursor of built-up development. In the study area, most of the new built-up areas appeared on land previously used as arable land, which was progressively abandoned as a result of economic transformations.
Our findings emphasize the need for more strategic spatial planning in the future. This is particularly crucial given that, until now, there have been no studies that evaluate the effectiveness of buffer zones for PAs in terms of preventing land development. Furthermore, comparing the effectiveness of buffer zones across countries is challenging due to differences in their objectives and legal status [38]. Furthermore, there is a lack of developed methodologies for evaluating buffer zone effectiveness. Most of the buffer zones aim to mitigate the negative human impact on PAs and maintain ecological connectivity. Therefore, an increase in the built-up area, including the length and density of roads leading to landscape fragmentation, can serve as an indicator of negative impact. For example, in tropical zones, avoiding deforestation often serves as a common measure or indicator of the effectiveness of the buffer zone [17,18].

4.2. Methodological Limitations

We suggest that the buffer zone in Poland may be ineffective in preventing land development in the vicinity of national parks. However, we urge readers to interpret our results with caution due to potential limitations in our analysis. A common way to measure the effectiveness of PAs involves comparisons of the areas inside and outside PAs, intending to estimate how protection prevented the change in LULC (deforestation or land development). Concentric buffers are often used for this type of analysis, but as many researchers have pointed out, they have many limitations [26,31]. They do not consider the fact that PAs are not distributed randomly in space, so concentric buffers may not take into account many biophysical or legal characteristics of the area [32]. Many PAs are effective in reducing many of the negative impacts of LULC within their boundaries. However, they frequently become isolated islands amid neighborhoods experiencing intensified changes in land use [8]. This phenomenon, known as negative spillover, often occurs when people or their activities are displaced from national parks, and this has been called ‘leakage’. If the leakage effect occurs, then assessments of PA effectiveness based on buffer comparisons over- or underestimate the apparent effect of protection [32]. However, not many studies have taken into account the division of the study area into municipalities, which is crucial in land use planning. As our results show, municipalities can vary widely when it comes to the rate of growth of built-up areas.
Another important methodological aspect of this type of research is the quality and accuracy of the source data, which are of crucial importance in studies based on spatial analysis methods that use historical maps. This becomes particularly significant in the analysis of landscape structure, where the results depend largely on the scale and detail of the input data [76]. As indicated by [60], errors in calibration or differences in scale, quality, and the level of generalization of the content of the materials used can lead to false results for various landscape metrics. The scale and associated size of the MMU can also affect the results. The authors are aware that the results obtained for some landscape metrics related to PD and MPS may have been overestimated. Hence, the enumeration of more sophisticated landscape metrics was abandoned. However, it should be noted that most studies analyzing historical LULC and landscape structure over such long time periods often rely on much less detailed maps.
Studies involving the use of contemporary databases on LULC also may be subject to errors. Recently, there have been an increasing number of studies on national and regional scales for PAs, which often use the CLC data [19,20,23]. However, they have disadvantages, one of which is the short period during which analyses can be performed. The other is the inaccuracy of the CLC base, especially in the case of the analysis for built-up areas, where there is low accuracy (100 ha). A comparison of the surface of the built-up areas from the CLC database 2018 with BDOT10k and other detailed databases showed discrepancies that reached 35%, showing that CLC-based analyses can be subject to large errors [77]. These studies also show that the quality of data is important, and that case study research can be important and helpful in designing research on a larger scale.

5. Conclusions

Historical data on LULC are very useful in capturing environmental changes over long-term periods, helping to evaluate the effectiveness of protected areas and buffer zones. It may provide key information for the effective management and monitoring of PAs and their surroundings. Our results indicate that in the last 90 years, LULC has been very intense both within the ONP and in its surroundings. They involved three major changes: land abandonment, increased forest cover, and built-up areas. These changes may have both positive and negative effects on environmental processes. However, within the national park, they were concerned only with the abandonment of agricultural land and reforestation and were rather positive. We observed that urbanization in the surroundings of the ONP has more than tripled in the last 90 years, and the highest growth occurred in the last 40 years. Furthermore, our study aligns with others that have pointed to the increasing landscape fragmentation in the surrounding area of the national park.
This study also reveals that the buffer zone in Poland does not significantly limit development areas around national parks. It was observed that in some municipalities the rate of growth of built-up areas was higher in the buffer zone than outside it; in addition, a high percentage of built-up areas was recorded there. Buffer zones should mitigate the expansion of built-up areas, according to national regulations, because land use planning is controlled within. Therefore, our results show a low effectiveness of the buffer zone in preventing land development which seems to be very important for sustainability in these areas. Although focused on a single region, this research contributes to enriching the current debate on nature conservation by tackling an up-to-date problem effectiveness of the buffer zones. It is necessary to design and implement relevant policies to improve more sustainable land management in the surrounding areas of the protected areas and buffer zones.

Author Contributions

Conceptualization, M.J., D.K. and K.O.; methodology, M.J.; software, M.J.; validation, M.J., D.K. and K.O.; formal analysis, M.J.; investigation, M.J.; resources, M.J.; data curation, M.J.; writing—original draft preparation, M.J.; writing—review and editing, M.J., D.K. and K.O.; visualization, M.J.; supervision, D.K. and K.O.; project administration, M.J.; funding acquisition, M.J. All authors have read and agreed to the published version of the manuscript.

Funding

The study was supported by the National Science Centre, Poland project contract no. (UMO-2018/31/N/HS4/00634) and (UMO-2019/32/T/HS4/00517).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The location of the study area: (A) within the physical–geographical regions and (B) the contemporary LULC of the study area according to CLC.
Figure 1. The location of the study area: (A) within the physical–geographical regions and (B) the contemporary LULC of the study area according to CLC.
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Figure 2. The workflow used in the study: (I.) data collection, (II.) data processing, (III.) data analysis.
Figure 2. The workflow used in the study: (I.) data collection, (II.) data processing, (III.) data analysis.
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Figure 3. LULC transitions for selected classes for different time periods.
Figure 3. LULC transitions for selected classes for different time periods.
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Figure 4. Land use changes between 1930s and 2022: (A,B)—unstable long-term land use, (C)—changes in grasslands, (D)—changes in forest cover, and (E)—repeated photograph showing grassland abandonment (source: 1980s—fotopolska.eu, 2021—M. Jakiel).
Figure 4. Land use changes between 1930s and 2022: (A,B)—unstable long-term land use, (C)—changes in grasslands, (D)—changes in forest cover, and (E)—repeated photograph showing grassland abandonment (source: 1980s—fotopolska.eu, 2021—M. Jakiel).
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Figure 5. Changes in land use from the 1930s to 2022 divided into analyzed zones.
Figure 5. Changes in land use from the 1930s to 2022 divided into analyzed zones.
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Figure 6. Dynamics Index (DI) for built-up areas divided into analyzed zones.
Figure 6. Dynamics Index (DI) for built-up areas divided into analyzed zones.
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Figure 7. Dynamics Index (DI) for built-up areas divided by municipalities.
Figure 7. Dynamics Index (DI) for built-up areas divided by municipalities.
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Figure 8. Characteristics of landscape structures: (A)—landscape metrics at landscape level; (B)—selected landscape metrics at class level divided into analyzed zones.
Figure 8. Characteristics of landscape structures: (A)—landscape metrics at landscape level; (B)—selected landscape metrics at class level divided into analyzed zones.
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Table 1. The time periods of the analyses along with a list of cartographic data used for the analyses.
Table 1. The time periods of the analyses along with a list of cartographic data used for the analyses.
Time Periods Data
(Map Series and Aerial Photos, Scale)		Supplementary DataSource
I.1930sPolish Military Map (WIG) 1932–1934
(1:25,000)		Polish tactical map—Military Geographic Institute 1928–1936
(1:100,000)		Map archive of WIG: www.mapywig.org (accessed on 15 April 2024);
National Library of Poland
II.1950sMilitary Topographic Maps of Poland 1956–1958 (1:25,000)Aerial photographs 1956–1957 (1:23,000)National Library of Poland
III.1980sTopographic Maps of Poland 1979–1984 (1:25,000)Aerial photographs 1977–1985 (1:16,000)Head Office of Geodesy and Cartography (GUGiK)
IV.2003Orthophotos (1:13,000)Topographic maps 1999–2006 (1:10,000);
Aerial photographs 1996–1998 (1:26,000)		Head Office of Geodesy and Cartography (GUGiK)
V.2015National Database of Topographic Objects (BDOT 10k) (1:10,000)Orthophotos
(1:10,000)		Head Office of Geodesy and Cartography (GUGiK)
VI.2022BDOT 10k (1:10,000) Orthophotos
(1:10,000)		Head Office of Geodesy and Cartography (GUGiK)
Table 2. Description of selected landscape metrics for analysis.
Table 2. Description of selected landscape metrics for analysis.
AbbreviationsLandscape MetricDescription
MPSMean Patch SizeThe average area of patches in a landscape, in the aggregate, or for a specific land use class (landscape type).
PDPatch DensityThe number of patches per unit of area, in this case, per 1 km2. Used to evaluate the degree and dynamics of landscape fragmentation
LPILargest Patch IndexThe percentage of the area of the largest patch in a given area or the entire landscape. It is a simple measure of dominance. It takes a value from 0 to 100. Can also show fragmentation of the landscape, relative to the largest patch.
FRACMean Fractal Dimension IndexReflects the complexity of the shape of the patches, based on the perimeter-area relationship, quantifies the complexity of the patches. An index with a value close to 1 indicates regular patch shapes and uncomplicated perimeters, i.e., square, circle, value of 2 or above indicates patches with complex and irregular shapes. Can show if the landscape has anthropogenic patches, such as built-up areas, that have regular shapes.
SHDIShannon’s Diversity IndexThe indicator is used to compare different landscapes or landscape changes over time. It takes into account both the number of use classes and the proportion of their area, increasing in value as one and/or the other element increases.
IJIInterspersion and Juxtaposition IndexThe indicator takes into account the adjacency relations of patches of different use classes, determining the degree and manner of their interlacing. Each class is analyzed in terms of its proximity to other classes, and the indicator determines the extent to which the different classes are adjacent (contiguous) and interwoven in the structure. Lower values are characteristic of landscapes in which a particular use class is adjacent to only a few other classes.
Table 3. The general result of the LULC matrix [%] from the 1930s to 2022.
Table 3. The general result of the LULC matrix [%] from the 1930s to 2022.
2022AFBUGTWOWBRNOASum 1930s
1930s
A55.1%4.8%6.9%9.5%1.2%2.8%-0.2%0.1%80.5%
F0.5%9.3%0.1%0.3%0.1%----10.3%
BU-0.0%2.9%------3.0%
G0.9%1.0%0.4%1.3%0.1%0.1%---3.7%
TW0.2%0.5%-0.1%-----0.9%
O0.3%0.1%0.4%0.3%-0.2%---1.3%
WB---------0.0%
RN---------0.4%
OA---------0.0%
Sum 202257.0%15.7%10.6%11.5%1.4%3.1%0.1%0.6%0.1%-
Explanation: A—arable lands, F—forests, BU—built-up areas, G—grasslands, TW—transitional woodland/shrubs, O—orchards, WB—water body, RN—road and rail networks, OA—other artificial areas.
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Jakiel, M.; Kaim, D.; Ostafin, K. Long-Term Landscape Changes in the Ojców National Park (Poland) and Its Surroundings: Implications for the Effectiveness of Buffer Zones. Sustainability 2024, 16, 6649. https://doi.org/10.3390/su16156649

AMA Style

Jakiel M, Kaim D, Ostafin K. Long-Term Landscape Changes in the Ojców National Park (Poland) and Its Surroundings: Implications for the Effectiveness of Buffer Zones. Sustainability. 2024; 16(15):6649. https://doi.org/10.3390/su16156649

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Jakiel, Michał, Dominik Kaim, and Krzysztof Ostafin. 2024. "Long-Term Landscape Changes in the Ojców National Park (Poland) and Its Surroundings: Implications for the Effectiveness of Buffer Zones" Sustainability 16, no. 15: 6649. https://doi.org/10.3390/su16156649

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