Mapping Urban Changes Through the Spatio-Temporal Analysis of Vegetation and Built-Up Areas in Iași, Romania

Abstract

Vegetation cover in urban and peri-urban areas is threatened by urban sprawl, through habitat fragmentation, loss of green space, biodiversity reduction, and the urban heat island effect intensifying. The intrusion of cities into natural landscapes reduces vital ecosystem services provided by vegetation. Hence, sustainable and integrated urban planning practices are required. Our study aims to investigate the dynamics of the urban and peri-urban fabric by exploring the relationship between the green fabric distribution and recent trends in urban expansion, focusing specifically on the peri-urban areas of Iași Municipality, Romania. We designed a mixed-method approach combining a multivariate analysis of four critical indicators (vegetation cover, built-up space, land surface temperature, and population density), emerging hot-spots, and space-time cubes in a GIS environment to achieve our research aims. Our results demonstrate that uncontrolled urban expansion has manifested in diverse patterns, impacting territories next to road transport networks and with construction-suitable topography. Concurrently, the development of green spaces prevails in forests and unexpected locations such as brownfields, railway corridors, and old industrial zones, through the growth of urban greenery. This approach provides a comprehensive understanding of how urban sprawl impacts the environment and how different land types are prone to this transformation, creating a path towards sustainability, resilience, and equitable development.

1. Introduction

Urbanization can be considered the most drastic form of land transformation, reducing the ecosystem’s capacity for providing ecosystem services and biodiversity [1]. Urban areas are expected to absorb approximately 68% of the world’s population by 2050 [2], or even more [3], most of them in developing regions. The forces that pull and push people away from the city center vary, and when the centripetal force is greater than the centrifugal force, the urban population will migrate, forming new settlements or suburbs between cities and the countryside [4]. The process of suburbanization in times of economic prosperity was concomitant with the rise of personal automobile ownership, the desire for more privacy and greenery, and with the increasing housing prices in city centers [5]. This comes along with a polycentric and decentralized development, where urban populations have gradually shifted to the suburbs, therefore changing their nature [6].

Urban expansion and urban sprawl are distinct concepts. Urban expansion refers to the physical growth of urban areas as a fundamental urban function [7], while urban sprawl denotes a scattered expansion pattern, characterized by low-density housing and decentralized residential plots [8,9,10]. The lack of a universally accepted definition of urban sprawl [8,11] contributes to confusion. Urban sprawl results from various drivers and leads to complex economic, social, and ecological impacts [12]. It is widely agreed that this human-induced phenomenon accounts for degradation of vegetation and ecosystem services [13], of biodiversity and quality of life [14], pollution, heavy traffic problems [15], and social segregation [11]. Contact with urban green spaces (UGSs) is also reduced, increasing the geographical isolation of people from opportunities to experience nature. This usually happens inside an incorrectly organized strategic framework, that otherwise would be able to balance the correlation between greenery and new urban spaces.

Cities are expanding both in situations where the population is growing and where it is declining [16]. This means that the factors behind urban sprawl are not only demographic. Relocation of households from city centers to peripheries, land prices [11], societal factors, landscape topography [17], technological evolution, increase in transportation systems and public vehicles, and globalization were found to be some of the driving forces leading to urban sprawl [18]. In addition, the physical and social degradation of inner residential areas, the lack and delay of rehabilitation, the functional transformation of the central city after 1990, and gentrification also led to suburban development. The attractiveness of suburban and metropolitan landscapes for expansion can be explained by the availability of open space and the proximity to natural and semi-natural areas or better microclimates [19].

Urban expansion diminishes the availability of green spaces that are vital for enhancing urban liveability [20,21,22]. Green spaces are essential components of a liveable city [5]. The advantages of green spaces are evident in the extensive literature. Creating their own microclimate, plants absorb CO₂ from the air, remove particulate matter, contribute to the water and nitrogen cycles, dampen noise [23], and create shadow [5]. This way, land surface temperature (LST) is strongly alleviated by city green spaces [24,25,26]. They also promote cultural possibilities, social cohesion, and strengthen ties within communities [27]. Finally, green spaces can strengthen resistance to natural disasters and resilience [28], such as floods or pandemics [28,29]. These all contribute to an overall improved quality of life [30] and they are related to increased property values as proximity to green spaces offers the advantages described above.

Globally, urbanization patterns have reached critical thresholds, with urban sprawl manifesting pronounced effects in Eastern Europe, where post-socialist transitions have catalyzed rapid urban landscape transformations. Concurrently, vegetation cover around urban cores, an essential component of urban liveability, faces significant degradation. Hence, uncontrolled urban expansion poses a substantial threat to these ecosystem services. Therefore, understanding the tangled relationship between urban sprawl and the evolution of green spaces in a temporal and spatial manner is essential for fostering a more sustainable and liveable urban environment.

The goal of this research is to investigate the modification of the urban and peri-urban fabric by exploring the complex and imbalanced relationship between the green fabric and urban sprawl. By examining the temporal and spatial evolution of four critical indicators—vegetation cover, built-up space, land surface temperature (LST), and population density—this study aims to reveal the patterns of urban expansion and environmental changes. Using normalized difference vegetation index (NDVI) satellite imagery to assess spatial vegetation changes, dynamics of built-up space, LST, and analyzing population-density patterns, the research will perform a multivariate analysis to provide insights into the interplay between urbanization and its environmental impacts.

Focusing specifically on the peri-urban areas of Iași Municipality, a perfect example of a second-tier city in Eastern Europe, this research will analyze the dynamics of green fabric distribution in relation to recent trends in urban expansion. The city offers a compelling case study of an undergoing significant urban change, particularly due to its distinctive communist-era urban planning and subsequent transition. While other studies have focused on major Eastern European cities, second-tier cities like Iași often face specific challenges and opportunities during urban development. It also possesses a great diversity in the urban structure (forests, agricultural lands, communist heritage, post-socialist residential districts, vulnerable areas), providing an ideal setting to investigate the interplay between urban and peri-urban form and environmental quality, making Iași a valuable representative case study. By correlating the spread of built-up areas with the changes in green spaces and temperature variations, this approach will provide a comprehensive understanding of how urban sprawl impacts the environment in peri-urban areas, creating a path towards sustainability, resilience, and equitable development.

2. Literature Review

As cities continue to expand rapidly and detrimentally to the adjacent natural areas, maintaining and enhancing green spaces within cities becomes imperative. A large body of literature is focusing on different features of green infrastructure, accessibility, perception, usage, and recently there have been considerable advances in techniques adopted via geospatial technologies that collectively enable urban planners and managers to study and monitor urban conditions and growth, hitherto impossible to achieve [31]. Remote sensing technology has become the dominant way, considering its cost-effectiveness and high efficiency. These have been widely implemented in geographical information systems (GIS) in recognizing the land-use and vegetation changes over time [32,33].

Temporal analysis of vegetation is relevant for understanding urban land cover evolution and its relationship to the green fabric and sustainable urban development [34,35]. Remote sensing has become the primary method for quantifying spatio-temporal patterns in vegetation cover due to its large spatial coverage and high temporal resolution. The rise of cloud-based resources has facilitated increased use of satellite imagery for change detection and analysis, employing various techniques including machine learning and deep learning [36]. Also, vegetation indices (VIs) are key metrics for assessing vegetation health, density, and phenology. NDVI is particularly popular due to its long-term data series, simplicity, and relative insensitivity to radiometric attenuation [37]. Various satellite programs offer NDVI datasets at different resolutions, from coarse-scale NOAA-AVHRR to fine-scale Landsat-TM [38]. For comprehensive analysis of urban vegetation in expanding cities, some researchers are moving from pixel classification to object detection using deep learning methods [39,40].

Beyond NDVI, other VIs have been developed to address specific needs in urban vegetation analysis, such as the soil-adjusted vegetation index (SAVI) for sparse vegetation covers, the enhanced vegetation index (EVI) to correct for atmospheric conditions, and the normalized difference water index (NDWI) [41,42]. These indices are particularly relevant in the context of urban expansion, as they can help assess the impact of urbanization on vegetation health and distribution.

The application of these VIs in urban expansion studies has revealed that vegetation plays an important role in mitigating urban heat island (UHI) effects [43], highlighting the importance of green spaces in rapidly growing cities. However, the limitations of VIs, such as sensitivity to atmospheric conditions and sensor capacity, must be considered when analyzing urban vegetation patterns [44,45].

The analysis of VIs and LST provides insights into the complex relationship between urbanization and vegetation coverage. Remote sensing has become the primary method for studying UHI and vegetation patterns due to its large spatial coverage and high temporal resolution. Various studies have employed different VIs alongside LST analysis to understand urban environments. Macarof and Statescu [46] found a linear relationship between NDBI and LST, indicating that urban zones significantly influence LST dynamics. Vegetation has been shown to lower urban temperatures by 1–3 °C in Iași Municipality [47] or even more, highlighting its role in mitigating the UHI effect. The relationship between LST and land cover characteristics is complex and can vary depending on factors such as season, time of day, and geographic location [43]. Xiong et al. [48] observed that surface urban heat island (SUHI) intensity is the highest in built-up areas compared to cropland, while forests and open water exhibit lower intensities. Taking into consideration LST for understanding the relationship between urban expansion and vegetation coverage is relevant as it provides valuable insights into the thermal behavior of different land cover types. This knowledge is essential for developing effective strategies to mitigate UHI effects and promote sustainable urban development. By integrating LST analysis with VI studies, urban planners can make informed decisions about green space allocation and urban design to create more resilient and comfortable urban environments.

Population density significantly impacts vegetation cover and urbanization effects. Studies across Europe and Southeast Asian countries show that higher urban density correlates with reduced per-capita UGS provision [49]. In the OECD countries, a 10% increase in urban density correlates with a 2.9% decline in tree cover, highlighting the trade-offs between density and green space [50]. Similarly, in China, higher urban density has been associated with reduced per-capita urban park and green space, although it has also led to improved air quality and reduced carbon footprints [51]. This inverse relationship between density and green spaces seems paradoxical given growing urban populations’ increased demand for UGSs [52]. High-density cities face challenges like higher housing costs and increased noise pollution. However, density also offers benefits such as improved proximity and enhanced public transport efficiency, balancing socio-economic and environmental factors in urban development.

Scholars have found that urbanization significantly impacts NDVI values in Bucharest [53], highlighting the need for sustainable urban planning. Studies report urban vegetation loss and changes in phenological patterns, affecting land surface albedo and evapotranspiration [54]. Research in Shanghai revealed ecological problems due to urban growth [55]. Following similar objectives, Guastella et al. [18] conducted comprehensive research on European Functional Urban Areas, advocating for compact city development, and Wolff and Haase [56] correlated green supply with city characteristics in over 900 European cities using the Urban Atlas database. While patterns of urban sprawl have been observed extensively across the mentioned regions, local characteristics have influenced the time and pace they evolved. In Western Europe, it gained momentum after the Second World War, while in Central and Eastern Europe, rather, after 1990. The tardive sprawl of the post-socialist cities can be caused by insufficiencies in development strategies, along with an increasing pressure on peri-urban areas. The strategies do not usually include integrated norms and models for a holistic vision of urban expansion. Meanwhile, people with a good economical situation, whose number is generally increasing, are looking for housing outside the city core, leading to the uptake of the available land.

Moreover, of particular interest is the context of urban expansion in the post-socialist states, where the development is marked by the abrupt transition from the state-led planning to the market-led one. The former had more restrictive frameworks (e.g., the difficulty to transform a non-built-up area to a built-up area, being managed differently under Romanian laws), while the latter is more permissive. For the case of Romania, this transition reflected in peri-urban spontaneous development started about ten years after the Revolution of 1989. The land restitution from the state to the previous private owners that followed the fall of communism has paved the way to a patchy development. Most of the land plots had modest surface areas and have been assigned to numerous persons, each of them potentially representing an actor involved in the planning. The loose laws, together with the officers that were unprepared for the new economic reality, have offered each private investor the opportunity to easily change the land use and to develop larger buildings than the ones provided by the planning rules. Authorities acted only to facilitate individual building requests only to obtain incentives from the national government, awarded according to the built-up area and population increase, contributing to an unsustainable urban growth which lacks basic facilities, and contrasting with the planning rules that should dictate future constructions [57,58].

A widely used example of multivariate analysis that can evaluate the relationship between the environment and built-up areas in urban studies, especially in climate research, is the Local Climate Zones (LCZs). It has become a pivotal framework by providing a standardized method to analyze and understand the thermal characteristics and UHI effects in cities. The LCZ classification scheme, initially designed by Stewart and Oke [59], has been widely adopted for LST and SUHI studies, demonstrating its versatility across different regions with similar functions within a city. Mostly based on physical features, it uses remote sensing for data acquisition.

Most studies emphasize the momentary spatial distribution and current spatial changes or relationships between urban features. However, only a limited number of studies incorporate the temporal component and the interconnections between these factors. The present study is significant due to the novelty and relevance of its spatio-temporal analysis and the urban category of the case study. The gap in research is also covered through the data sources that we use, most studies in urban sprawl being based on the Corine Land Cover classification, Copernicus Urban Atlas, image classification, or CLUE models [60,61]. Moreover, significant research studies a large number of cities at once, for comparison purposes, while we believe that an in-depth study is needed for one case study, aiming to find relationships between factors that go further than land use. Given that a substantial portion of the urban population in Eastern Europe resides in second- and third-tier cities, increased research attention is warranted.

3. Materials and Methods

3.1. Study Area

Our study takes into consideration local administrative units (LAU2) of Iași Municipality and the first ring of communes around it, as depicted in Figure 1. Iași is currently the second largest city in Romania according to the National Institute of Statistics, with almost 400,000 legal residents in 2024, and a total population of 500,000 for the metropolitan area [62], being the most important development engine for the whole region of Moldova. The nine adjacent communes are also part of our study area, considering the accelerated development of the peri-urban space. The communes surrounding Iași exhibit some of the most dynamic peri-urban growth patterns in the country. This rapid expansion is driven by a combination of economic opportunities, improved infrastructure, and the availability of land for development. We should not neglect people’s desire to move from a collective housing type of apartment to a different standard, which includes more green space and less pollution. The western and southern edges of the urban core, in particular, have seen significant growth due to investments in industrial platforms and tertiary services. The unique dynamics of these peri-urban areas require special attention in future urban planning. The mosaic-like, low-density built-up fabric, often developed by private investors and residential complexes, lacks coherent or integrated regulations. This spontaneous peri-urban growth leads to environmental degradation, increased traffic congestion, and social segregation. It turns into a space that no longer matches the initial image, being a consequence of weak, unenforced policies that do not comply with urban planning regulations.

The area of interest (AOI) is situated in the Moldavian Plateau, bordered by taller cuestas in the south (over 400 m elevation), where large patches of forest and green areas exist, and an undulated unit in the north, mostly consisting of agricultural terrains, divided by the valley of the Bahlui River. The period before 1989 was marked by the centralized policy-making process by the socialist regime, which was focusing on industrialization, compaction, and systematic organization of the urban fabric. The sudden change in paradigm to a market economy led to privatization, deindustrialization, and a lack of a coherent framework for urban planning strategies. Traces of these phenomena are still observable in the way the city evolves today. The dynamics of Iași and its peri-urban areas post-1989 can be largely attributed to the absence of coherent planning policies following the fall of communism. Under the communist regime, Iași was a prime example of centralized urban planning, with approximately 75% of the population residing in high-density districts. This systematic organization was abruptly disrupted after 1989, leading to several notable phenomena, especially after 2000. The lack of stringent urban planning regulations facilitated the spread of low-density residential areas into the peri-urban zones. This sprawl was driven by the desire for more spacious living conditions and the availability of cheaper land on the outskirts. In the absence of a coherent urban development strategy, many vacant or underutilized plots within the city were developed haphazardly. This infill often lacked the infrastructure and amenities necessary for sustainable urban living. The transition to a market economy saw many industries relocating from the city center to the peripheries or completely shutting down. This shift created large brownfield sites within the urban core, which remain underutilized and pose significant redevelopment challenges.

Overall, the post-communist era in Iași has been characterized by a fragmented and uncoordinated approach to urban development, leading to significant challenges in managing urban growth and ensuring sustainable development.

3.2. Methodological Framework

The methodological framework of this paper integrates valuable data and innovative methods to evaluate the patterns of urban expansion in the city of Iași. By employing a spatio-temporal analysis and multivariate clustering within a GIS environment, the framework explores the interplay between critical indicators such as NDVI, built-up space, population density, and LST. Each indicator provides unique insights into the environmental and socio-economic dynamics of the study area. By analyzing these indicators individually and in relation to one another, patterns and correlations will be revealed, enhancing our understanding of the driving forces of urban expansion and environmental changes, in relation to sustainability and policies.

3.2.1. Data Acquisition

Vegetation index

Proposed for the first time in 1974, NDVI represents a normalized ratio of the near-infrared (NIR) and red bands (RED), as expressed in Equation (1), and is proven to be a reliable monitoring and change-detection tool [63]. NDVI can take values between –1 and 1, where higher values indicate denser vegetation canopy and negative values indicate the absence of vegetation [39].

$$\mathrm{NDVI}={\displaystyle \frac{\mathrm{N}\mathrm{I}\mathrm{R}-\mathrm{R}\mathrm{E}\mathrm{D}}{\mathrm{N}\mathrm{I}\mathrm{R}+\mathrm{R}\mathrm{E}\mathrm{D}}}$$

(1)

Imagery from Landsat 4–5 and Landsat 8 was utilized at four-year intervals from 1987 to 2023, resulting in a total of ten time steps (see

Table 1. Satellite imagery acquisition.

Acquisition Date	Satellite	Bands Used for NDVI Calculation
21 July 1987 14 June 1991 28 August 1995 7 August 1999 19 September 2003 28 July 2007 24 August 2011	Landsat 4–5	RED—band 3, NIR—band 4
3rd August 2015 14 August 2019 17 July 2023	Landsat 8–9	RED—band 4, NIR—band 5

). To ensure optimal results, the selected images for each year had 0% cloud coverage within the AOI and exhibited the highest contrast between vegetation and non-vegetation areas, typically corresponding to the period with the greenest vegetation. These images do not align with the same week or month each year due to varying weather conditions. Klimavičius et al. [64] suggest June, July, or August for the Baltic countries, Pettorelli et al. [65] feature the peak of the growing season in early or mid-summer, especially in temperate climates, and Bégué et al. [66] correlate NDVI in agricultural areas with individual crop cycles. Additionally, only scientifically validated Level 2 Landsat products, that are atmospherically corrected, were employed.

Land Surface Temperature

The LST values for the study area were obtained as a raster using a Google Earth Engine script [67] for 10 September 2023. This script calculates LST based on Landsat imagery, achieving the highest possible spatial resolution. Landsat LST data can complement in situ air temperature measurements, and both can be utilized synergistically in climatic studies and to assess the UHI effect [68].

Built-up space

In order to assess the dynamics of built-up space over the decades, on-site observations provide detailed data, but they are time-consuming and are not efficient in the long run. This is why researchers typically rely on multi-temporal, remote-sensing-derived, gridded built-up surface datasets, such as the Global Human Settlement Layer (GHSL) [69]. We have chosen it for the great advancements it provides: open data, high accuracy, worldwide settlement coverage, extensive time span, high spatial resolution, and the capacity to homogenize territories with both rich and poor data availability. These databases and tools are developed by the European Commission’s Joint Research Centre and map human presence on Earth, sourcing information every 5 years from 1975 to 2020 and up to 2030 (estimations) by delineating built-up areas and population distribution as well as classifying settlement typologies [70]. To create the spatio-temporal analysis of the built-up space in our study area, we used the GHS-BUILT dataset, which maps built-up areas at a spatial resolution of 100 m at a global extent, and after that, we performed a time-series analysis, similar to the case of NDVI described above. It had been previously used in a range of scientific studies of different disciplines, including urban sprawl [71], population density, or evaluation of land-use efficiency [72].

Population density

The GHSL database was utilized to calculate population density, with the GHS-POP dataset combining census data and global built-up area information to generate gridded population-density layers over time [73].

3.2.2. Used Methods

Space-time cube

Several spatio-temporal methods were employed to address the aim of the paper, as it is depicted in the methodological framework (Figure 2). The space-time cube is a three-dimensional visualization method used to represent spatio-temporal data. It combines spatial information (the geographic area of interest on a two-dimensional plane) with temporal information (the vertical axis in a multidimensional feature) to create a 3D cube. This approach allows us to observe how spatial patterns and relationships evolve over time within a single visual framework. This method is particularly useful for identifying spatio-temporal clusters, tracking movement patterns, and analyzing the spread of phenomena over both space and time. We work with multidimensional rasters for NDVI and built-up situations from each of the ten time steps.

Emerging hotspot

Based on the ten time steps, a space-time cube was created, a method developed by ESRI, to analyze spatio-temporal data in the form of a time-series analysis. To statistically assess an evolutionary situation of high and low values of NDVI among different scenes, an emerging hotspot analysis can identify the spatial clusters of greenspace change within cities [74]. Hotspot analyses utilize a series of weighted features and identify statistically significant hot spots and cold spots using the Getis–Ord Gi* statistic, which calculates the GiZScore and GiPValue for the selected parameters [75]. To our knowledge, most of the temporal studies do not use a large number of time steps to evaluate NDVI or land-use-change trends. In some cases, 2–3 time steps are used [40,76], while Xiong et al. [48] and Yao et al. [77] used 7 and 8, respectively. Having more time steps, the hotspot analysis can be put into the temporal context and provide complex results, showing not only linear trends but also details about variation. The resulting hot or cold spots are classified into 15 categories: oscillating, sporadic, diminishing, persistent, intensifying, consecutive, new cold/hotspot, or no pattern.

Time-series clustering

Time-series clustering is a versatile technique used to group similar time-series data into clusters. It aims to identify patterns and similarities among multiple datasets that have time as a dimension, which can be useful for various applications such as trend analysis or the detection of important changes unnoticeable to the bare eye. The process typically involves selecting an appropriate similarity measure to compare time-series data, such as Euclidean distance or correlation-based measures. Then, a clustering algorithm based on machine learning is applied to group the time-series based on their similarities. In the present study, the evolution of the built-up area over the past 50 years was analyzed, utilizing a tool that enables the observation and assessment of various spatial expansion trends. These trends were examined in relation to the decades during which the rate of construction accelerated or decelerated.

Multivariate analysis

Following the individual analysis, we integrate these indicators into a multivariate clustering framework in a GIS environment to identify distinct spatio-temporal patterns and groupings within the data. This method allows us to classify regions based on similar characteristics, facilitating the discovery of clusters that share common traits in terms of greenery, urbanization, and urban climate. The multivariate clustering approach employs statistical techniques such as k-means or hierarchical clustering, which effectively handle the complexity of multidimensional data. By visualizing these clusters with the space-time cube method, we can achieve the main purpose of the paper more profoundly. The software that we use is ArcGIS Pro 3.2.0.

The GIS-based methodological framework integrates satellite imagery and global datasets over varying time spans to analyze spatio-temporal patterns. The analysis employs the aforedescribed techniques like space-time cubes and emerging hotspot detection, focusing on indicators such as NDVI, built-up areas, population, and LST, with results contributing to understanding development patterns through multivariate analysis.

4. Results

The results present a comprehensive analysis of the spatio-temporal variations in vegetation cover and built-up areas, as well as the spatial distribution of LST and population density. By examining these indicators, we aim to uncover patterns and to provide a better understanding of urban expansion and its environmental impacts in the peri-urban areas of Iași Municipality.

4.1. Spatio-Temporal Evaluation of the Indicators

4.1.1. Vegetation Dynamics

The emerging hotspot analysis presented in this study not only identified the default categories outlined in the previous section but also revealed varying intensities of vegetation change over time. These variations were rigorously examined through a statistical time-series analysis covering the period from 1987 to 2023. Although the phenomenon of urban sprawl is easily observable without insightful analyses, a more detailed investigation highlights temporal and specific spatial trends. The map below (Figure 3) presents the persistence of vegetation cover over the last 40 years. The dark green hues represent areas where the vegetation prevailed continuously, while white and light green represent a constant lack of vegetation, be it buildings, concrete, or water. Medium green shows oscillating patterns of vegetation that can be observed, on the one hand, around the peri-urban area, drastically affected by urbanization and by the expansion of the built-up space and, on the other hand, around agricultural terrains.

The resulting patterns of decadal evolution of vegetation cover, their brief explanations, and locational characteristics are described in

Table 2. Vegetation oscillation patterns description.

Number	Name	Attributes	Surface	On-Field Areas
1	Persistent hot spot	Forests, scarcely altered by human influence, partially protected by various legal forms	8.46% 9462.06 ha	Natura 2000 Sites of Community Importance (SCIs) (Bârnova-Repedea, Mârzești, and Uricani Forests), avi-faunistic protection sites (SPAs) (Ciurbești Lake, Bârca hayfield, Jijia Pond and Miletin Pond, Uricani Forest), Cetățuia, Galata, Bucium, Vlădiceni, C. A. Rosetti
2	Intensifying hot spot	Isolated areas inside forests	1.24% 1385.64 ha	Forests around Păun and Breazu
3	Diminishing hot spot	Edge forests at close contact with built-up area	0.04% 40.14 ha	Copou, C. A. Rosetti, Ciric
4	Consecutive hot spot	Isolated areas in forests or green agricultural terrains	1.03% 1151.64 ha	Botanical Garden, Bucium, Rediu
5	Oscillating hot spot	Agricultural fields that were green in most of the new Landsat scene	11.23% 12,562.29 ha
6	New hot spot	Agricultural fields that were green in the most recent Landsat scene and derelict transport areas that used to serve the industrial platforms; ecological restoration (of forests)	2.59% 2897.46 ha	Socola railway, vineyards in Bucium and Copou, CUG, urban designated green spaces, C.A. Rosetti
7	Sporadic hot spot	Forest edges and interstitial green spaces between residential ensembles	13.25% 14,829.12 ha	All forests and peri-urban neighborhoods, including UGSs inside the urban area
8	Sporadic cold spot	Agricultural fields, isolated new residential complexes, small newly built parcels inside the city area, river floodplain	4.67% 5230.35 ha	Iași, Bucium, Vlădiceni, CUG, Valea Adâncă, Miroslava, Bahlui River
9	Diminishing cold spot	Urban green spaces, derelict industrial platforms, railways	0.17% 195.3 ha	Copou neighborhood
10	New cold spot	Very recent residential housing estates built on previously green land parcels	0.15% 165.06 ha	Bucium, Vișani, Miroslava, Lunca Cetățuii, Valea Lupului, Breazu, Tomești, Copou
11	No pattern	Agricultural fields, low-density villages, most UGSs inside the city	43.26% 48,402.81 ha
12	Oscillating cold spot	New residential complexes, the new airport runway, the river floodplain, agricultural fields	7.36% 8235.27 ha	Bucium, Miroslava, Lunca Cetățuii, Hlincea, Vișani, Valea Lupului, Royal Town, airport area, Bahlui River
13	Consecutive cold spot	Low-density neighborhoods inside the city, villages	2.27% 2539.26 ha	Copou, Moara de Vânt, Galata, Nicolina, Bucium, Dancu, Holboca, Rediu, Antibiotice
14	Intensifying cold spot	Former or present-day industrialized and sealed areas, new high-rise and high-density neighborhoods, boulevards, aquatic areas	0.98% 1096.38 ha	Silk District, Palas projects, Himson Residential, hypermarkets, parking areas
15	Persistent cold spot	High-rise neighborhoods, industrial areas, aquatic areas	3.3% 3688.47 ha	Nicolina, CUG, Alexandru, Dacia, the Industrial Area, Tomești, Dancu, Antibiotice

. Areas with persistent vegetation are clearly delimited from the oscillating situations that were severely affected by urbanization or land-use changes related to agriculture. Persistent, consecutive, and intensifying hot spots of the NDVI values comprise up to 12,000 hectares, or 10.53% of the studied area, with almost no detectable change since 1987. They represent forests that grow natively at higher altitudes, such as the Central Moldavian Plateau, in the south of the AOI or around water bodies (Ciric Forest), some of them becoming adjacent to the built-up area and directly threatened by its expansion. Oscillating vegetation areas sum up a much wider surface, with 36.5% of the AOI (40.857 ha), with the addition of 43.26% (48.402 ha) where no pattern could be detected, meaning a very irregular green cover evolution. On the one hand, these are found over agricultural areas that showed different characteristics in the analyzed Landsat images from different years, due to climatic evolutions in a specific year up to the acquisition date, or due to the changing of cultivated crops on a specific land plot. On the other hand, oscillating vegetation patterns are found in peri-urban areas that went through strong urbanization during the last decades, a process that also continues today in neighborhoods like Bucium or Copou or villages like Miroslava, Valea Lupului, or Lunca Cetățuii.

4.1.2. Built-Up Space Dynamics

Employing a time-series analysis, we managed to depict not only the consecutive stages of building in the urban and peri-urban areas, but also specific periods and intensities of built-up space growth. Figure 4 and

Table 3. Built-up area clusters descriptions.

Cluster No.	Name	Explanation	On-Field Areas
1	Light gray—constant built-up surface	Either areas that had been developed prior to the starting year of the analysis (1975), or that were never built	Historical city center, Alexandru cel Bun, Dacia, Păcurari, Nicolina, Poitiers, parts of the Industrial Area, most agricultural terrains and forests in the outskirts
2	Green—omnidirectional expansion of the socialist city	Quick growth rate during the socialist era, starting to slow down after 1990; neighborhoods for the industry workforce, built from scratch and located close to the industrial platforms; besides this, the low-density neighborhoods and the edges of the villages have been developed very recently	Industrial Area, CUG, Copou, Ciurea, Tomești, Bârnova
3	Dark gray—cores of the city and villages	Constant growth of the built-up ground surface, mostly present in high-rise neighborhoods starting with the systematization and the massive rural–urban migration during the socialist era, but also former extents of the villages	Podu Roș, Tătărași, Independenței, Canta, Bucium, Antibiotice, Dancu, Holboca, Tomești, Bârnova, Vânători
4	Orange—urban sprawl	First and widest wave of new buildings at the edge of the city, from 2000 to 2015, with a slower growth rate in the present day as well	Inside Iași Municipality: Bucium, Galata, Moara de Vânt; outside city’s administrative limits: Miroslava, Ciurea, Bârnova, Valea Lupului, Aroneanu
5	Blue—urban sprawl phase II	Significantly developing after 2010 and continuing today in the same rhythm
6	Red—urban sprawl phase III	Most recent residential neighborhoods and tertiary complexes, developed in the last 15 years, with a significant growth since 2015

explain these in correlation to Figure 5, which displays them spatially. The historical center of the city and the first neighborhoods where the communist regime implemented the compact form through systematization before 1975 are the main areas where the built-up space did not record any change. Up to 1990, the cores of the main secondary settlements have been outlined, i.e., villages mostly made of commuters to Iași Municipality that were well connected to it and had high population densities. Around them, the current peri-urban residential neighborhoods arose after 1990. This phenomenon took off around the 2000s, leading to demographic and economic growth in the adjacent communes, such as Miroslava, Ciurea, and Valea Lupului, or in the hilly areas under the administration of Iași, such as Bucium, Copou, or Galata. In fact, it appears that 1990 and 2000 were inflection points for the building patterns of all categories shown below. After the peri-urban lands (mostly agricultural plots, pastures, or meadows) changed to a low-density urban fabric, new housing projects have been launched at the edges after 2010, by private investors.

4.1.3. Land Surface Temperature

Portraying the complete SUHI effect generated by sealed areas, building materials, and intense human activity requires a more comprehensive dataset, with measurements from other seasons and times of the day as well. This is not an objective of the present paper. For the city of Iași, Sfîcă et al. [47] add a great contribution to measuring the intensity of the UHI phenomenon and to its general understanding for medium-sized cities in Eastern Europe. Still, a momentary situation is effective for highlighting the areas where liveability is impaired by high temperatures. During the day, at noon, completely sealed industrial areas and plowed agricultural terrains that are dry and aerated record the highest surface temperatures (over 40 °C). Generally, SUHI intensity is in direct relation to the density of the built-up area. Within the city, differences can be noticed between compact systematized neighborhoods and low-rise housing estates or parks, under the form of organized UGSs. Surrounding forests and lakes help alleviate the UHI effect and establish a spatial boundary to it, on certain edges of the city. Values of 20–25 °C are measured at the canopy-layer level, where the air was kept colder during the morning.

These observations align with the conclusions of other studies of LST in sprawling cities. Iași SUHI presents a single-peak dynamic, with a maximum in summer and a minimum in winter, and great diurnal variability embedded in the seasonal variability manifested throughout the year [47]. Mihăilă et al. [78] find an LST difference of +3.2 °C between the urban and peri-urban areas for the city of Suceava, and of +5.4 °C compared to the rural area. In Bacău, Sfîcă et al. [79] find similar correlations with housing development, where the SUHI effect is extended in peri-urban regions where real estate pressure has led to an expansion of urban space.

4.1.4. Population Density

The spatial distribution of the population density is strongly impacted by the planned urban structure of the communist regime. Most of the population is concentrated in the urban core of Iași, where densely built blocks of flats of Soviet architecture can reach 300–350 people per hectare. Sectors of high population density are also elongated in line with the major transportation corridors and where the topography permitted intensive building. The peripheral areas of the municipality and surrounding communes show much lower population densities of 100–130 inh./ha. in the villages where the socialist collective buildings urban planning was implemented, 60–70 inh./ha. in new high-rise residential complexes, and 10–20 inh./ha. in neighborhoods with individual households. These reflect suburban or rural zones where settlements are more dispersed. Density is also influenced by natural features, such as the slopes of the Moldavian Plateau in the south.

4.2. Spatio-Temporal Patterns of Changes in the Urban and Peri-Urban Fabric

Figure 6 represents the map of the clusters that resulted from the multivariate analysis. It can be interpreted in correlation to the size of each cluster (Figure 7) and to the boxplots showing the statistical distribution of the data (Figure 8).

The first cluster, represented with dark green, is present in areas where the values of the built-up surface, population density, and surface temperatures are the lowest and the vegetation persisted for the whole time span of the analysis. This happens mostly in the forests around the city and further away in the south, representing 10% of the studied geographical area. The forests surrounding Iași represent important nodes in the urban–environment relationship, offering historical, recreational, and ecological value to both city dwellers and nearby villagers. These rich vegetation areas, partially protected under Natura 2000 and national reserve designations, have recently become focal points for civic initiatives aimed at expanding protected forestry surfaces. These community-driven efforts not only aim to preserve the forests’ ecological integrity, but also serve as a foundation for increased public engagement in environmental stewardship, fostering a sense of collective responsibility for the intruding urbanization and its impact on valuable natural habitats. It is one of the classes without noticeable temporal dynamics since 1987, meaning that areas covered by forests have not decreased or increased significantly. It also imposes a great resistance to change and altering due to a strong ecological character, fundamental for a good quality of life of the residents of the nearby areas.

The second cluster represents approximately 22%; showing substantial oscillations in the vegetation cover, high temperatures, and minimum population and built surfaces, it is a space to be harnessed by the agriculture sector. Substantial investments have been made during the communist period in agricultural activities, that were, along with the extraction activities, the foundation of the primary sector in the economy. Agricultural areas around Iași Municipality, characterized by favorable climatic and pedologic conditions, remain active due to rural traditions and post-1990 privatization. However, these fragmented lands are less attractive for peri-urban expansion, being distant from urban centers and transport networks, and often managed by agricultural associations. The Natura 2000 network also owns two Sites of Community Importance corresponding to this cluster, Valea lui David and Dealul lui Dumnezeu, as protected areas. Apparently, they are susceptible to the human-induced change of land use and urbanization, but in practice, the potential real estate value is low and is only appealing to rural and subsistence practicing communities.

The third class highlights agricultural lands with the potential to evolve into vegetation hotspots, classified as areas of opportunity for enhanced vegetation index growth. It has average scores for surface temperatures and vegetation constancy and is not inhabited or built. These characteristics made a subset of these areas designated for afforestation initiatives, exemplified by the former vineyards on Șorogari Hill, which are currently undergoing a reforestation process. This potential transition aligns with studies on the potential of abandoned agricultural lands for carbon sequestration, biodiversity enhancement, and decreasing of average temperatures [80]. The identified areas encompass two primary categories: abandoned agricultural lands experiencing natural vegetation growth, and terrains intentionally populated with vegetation to mitigate soil erosion and stabilize slopes at risk of landslides. These land-use changes correspond to the sustainable land-management practices and contribute to the ecological transition towards sustainability. The transformation of these areas not only enhances biodiversity and carbon sequestration, but also provides ecosystem services for climate change adaptation and mitigation strategies in agricultural landscapes [81]. They have a high potential for being transformed into densely vegetated areas.

The fourth cluster is significantly different from the others, having the largest population density, LST, built-up surface, and a constant absence of vegetation. It coincides with the urban fabric, along with village-extended cores and anthropic areas. Consequently, they face the most problems related to the environment, air pollution, surface sealing, infrastructure, traffic, and commuting times. A polycentricity of the city can only be argued at the population-density level and it is not available for workplaces, firms, and other economic establishments, as they follow the spatial patterns of residential development only partially. This cluster has the potential for a slow and constant change, taking over agricultural or green spaces and converting them into built-up areas. It has the strongest economic potential, but also the most destructive ecological impact unless measures for sustainable peri-urban growth are implemented.

The fifth cluster is also the largest, occupying almost a quarter of the studied geographical area. It is similar to the third cluster but with a lower average temperature and a more intense oscillation in the vegetation cover. To a large extent, this oscillation is explained by the proximity to anthropic activities or settlements. Because they correspond to meadows, pastures, hayfields, and interstitial spaces between households, they are readily available for the change in land use. It is also the case for vineyards, the clay extraction site from Vlădiceni, water reservoirs, and isolated agricultural fields.

The sixth cluster displays low temperatures, population densities, and built-up intensities and moderate vegetation change recorded over the decades. It comprises roughly proportional land uses of arable land, pastures, and forests, having in common the proximity to the peri-urban area. Considering these factors and the vegetation characteristics, it is supposedly the most vulnerable area to the pressure of urban and peri-urban expansion, being the first green spaces that can be replaced by buildings. It occupies an important percentage of the areas that are adjacent to the city, and this should reflect the importance of its protection under administrative and legislative frameworks. The example of Repedea Plateau can be given as a protected area, being a national-interest geological and paleontological reserve, Locul Fosilifer Dealul Repedea.

5. Discussion

The findings highlight a complex relationship between vegetation cover and urban expansion, which aligns with previous studies that emphasize the negative impact of built-up areas, as well as specific patterns and situations [53,82]. Our results stem from a methodological approach that extends beyond temporary indicators like LCZ or similar frameworks. The unique aspects of this study can be better understood through a detailed temporal analysis, high-resolution data, and GIS-based methods.

Time is an essential factor when evaluating the changes in vegetation cover and urbanization, as it plays a significant role in understanding their dynamics and in forecasting future trends and planning strategies. The pattern of urban expansion in Iași Municipality, characterized by fragmented and low-density development, especially in the western and southern outskirts, aligns with findings from other post-socialist cities in Hungary [54], Poland [83,84], or Ukraine [85]. This trend is largely driven by economic growth and the increasing appeal of the metropolitan area, with some influence from investments in new industrial zones. Such uncoordinated sprawl mirrors patterns documented in studies like those of Hirt [86], which highlight similar regulatory challenges and environmental degradation resulting from rapid urban growth. Other research [87] also emphasizes the uneven development within urban areas, noting the stark contrasts between fast development and stagnating regions. Furthermore, the work of Tsenkova [88] illustrates how post-socialist cities experience fragmented urban vegetation due to historical industrialization, reinforcing the need for coordinated urban planning to address emerging social and infrastructural issues.

The spatial evolution of green spaces in Iași, Romania exhibits a heterogeneous pattern characterized by both resilient areas resistant to change (such as protected forests) and regions that have undergone significant transformations over the past four decades. This study reveals that the most intense vegetation loss has occurred in conjunction with major infrastructure projects and along primary transportation axes, particularly in the western part of the city. These findings align with similar trends observed in other urban environments, where rapid urbanization and infrastructure development have led to substantial green space reduction [89]. Conversely, our analysis identifies areas of vegetation growth both within the built environment and along brownfields or abandoned train infrastructure, as a native urban greening.

The differences in green cover and built-up space between the first and the last analyzed time step, 1987 and 2023, respectively, are shown in Figure 9 and Figure 10. In areas that preserved their agricultural usage throughout the whole period, these differences resulted in high values due to inconstant climatic evolutions from year to year and crop shifting, being irrelevant to our study. Based on the most recent Corine Land Cover classification from 2018, we removed the areas belonging to categories 210 (arable land—annual crops), 220 (permanent crops), 230 (pastures), 240 (complex and mixed cultivation patterns), 250 (orchards), and 500 (water). Inside the urban and peri-urban areas, both regions with vegetation growth and loss can be distinguished. Contrary to what one would expect, many of the neighborhoods inside Iași have a denser vegetation cover today than they did forty years ago. This trend can be linked to the maturation of UGSs that were developed among high-rise buildings during the communist era. Furthermore, significant vegetation growth has been observed in previously derelict industrial sites, such as Atelierele CFR Nicolina and the heavy machinery sector at CUG. However, it is important to note that a high density of vegetation does not automatically correlate with improved environmental quality or enhanced quality of life, as discussed by Kuo and Sullivan [90]. Outside the city, forested areas also increased slightly in density, due to canopy maturing, and slope vegetation increased due to stabilization works.

On the other side, vegetation was drastically removed in areas where urbanization was the most intense, along with transportation axes and areas with topographic suitability for development. In the western communes, new industrial areas arise through the process of reindustrialization, after a long collapse of industrial platforms due to privatization and orientation toward other economic sectors after 1989. Strong land sealing and vegetation loss are also observed in the cases of large shopping centers at the edge of the city, the major transportation axis being the driving force in the development.

In the peri-urban region, the areas that should raise attention are the ones with the highest oscillation in the vegetation cover. As noted, western and southern communes follow this pattern. In the case of Iași, the phenomenon of urban sprawl happens through a mosaicked low-density built-up fabric, developed by a large number of private investors, residential complexes, and individually built houses that do not follow coherent or integrated regulations. The eastern communes were greatly developed during the communist era, along with the thermal power station in Holboca and proximity to the industrial area of Iași from Tomești. Today, the region is not as attractive as Miroslava or Valea Lupului, their western counterparts, where vegetation was turned into built-up space by transforming agricultural land and pastures into residential, industrial, or commercial areas.

The persistence and distribution of vegetation in peri-urban areas play an important role in shaping the ecological balance and aesthetic appeal of urban expansion. In the case of Iași Municipality, higher vegetation cover is notably concentrated in the southern regions, where deciduous forests form larger contiguous areas, often at elevated altitudes. Smaller groves and scattered patches of forest, particularly around water bodies and slopes, further contribute to the peri-urban landscape. In certain cases, they even act like a barrier against urban development, that is directed to areas where less human intervention against the environment is required. In peri-urban regions, vegetation not only contributes to environmental sustainability but also serves as a buffer against rapid urbanization. According to Cook [91], UGSs can act as ecological corridors, facilitating wildlife movement and preserving biodiversity amidst urban development. This is particularly relevant in areas like Iași Municipality, where vegetation cover can serve as a natural barrier to urban sprawl, promoting a more sustainable approach to land use.

Up to 1989, most of today’s urban space was already developed, more slowly in the central areas and within the developments from the systematization period, and faster in the heavily industrialized neighborhoods [92]. Built-up spaces around industrial platforms appeared as part of the socialist urban planning paradigm. Urban compaction, high-rise residential projects, and an intense rural–urban migration were planned due to the need for a high workforce in industry. Things changed drastically after 1990 because of the industrial downturn and the transition that urban planning strategies had to go through. After the economy started to stabilize in the 2000s, new residential estates have appeared until today, due to swift population growth and increased quality of life. While in the peri-urban area they are built on formerly green or agricultural spaces, in the urban core of Iași, they are built on the interstitial spaces between dwellings, significantly reducing the liveability and increasing the density. Nowadays, these ensembles are also built on former industrial platforms (e.g., Silk District on the former silk factory) that were decommissioned during the deindustrialization and privatization after 1990 [93], although most of them still confront contamination and other environmental problems. Since the physical space inside the city core was saturated and compacted, people started to prefer and to afford individual homes far from pollution, noise, and crowding, and reachable by automobile, so that the peri-urban area started to develop. Land-use change, infrastructure development, and environmental problems started to have a very quick dynamic. Today, the lack of a legislative framework regarding residential complexes is observable by taking a look at the very different characteristics of such individual projects. Some of them introduce green spaces among their facilities, but they are rather meant to enhance the aesthetic value than address the broader environmental challenges in an integrated manner.

Besides measurement-based results, particular observations were made based on local insights from the territory. Important areas of vegetation cover that are continuously protected belong to historical monuments, nowadays cultural institutions, such as monasteries. Vineyards are also a valuable land use that is preserved in the present day over housing estate projects, especially in the context of a region that is well known for its wineries. Few wineries remain after large surfaces of vineyards have been cut down gradually over the decades. Physical landscapes around the city are an important factor that encourages urban space expansion, through the scenery admired from the hilly areas, that adds value to the land prices. The preservation of vegetation zones belonging to heritage buildings and institutions in the peri-urban area of Iași, like monasteries, is a noteworthy finding. These sites often have long-standing cultural importance, which has motivated their caretakers and local authorities to maintain the surrounding natural landscapes as part of the overall historical ambiance. Such green spaces may be protected by conservation laws or urban planning regulations that recognize their historical and ecological value, preventing their conversion into built-up areas despite urban expansion pressures.

The implementation of various forest protection measures has significantly increased community awareness regarding the importance of urban and peri-urban forests. This heightened awareness has led to the formation of protective associations and grassroots movements, mirroring trends observed in other urban areas globally. For instance, Buijs et al. [94] documented similar community-driven initiatives in European cities, where citizens actively engaged in the governance and protection of urban vegetation. In the context of Iași, Romania, the emergence of NGOs advocating for the protection and inclusion of forests such as Codrii Iașilor, Mârzești Forest, or Ciric Forest within protected-area frameworks aligns with this broader pattern of civic engagement in urban nature conservation. The evolution of these community-based efforts underscores the need for a comprehensive legislative framework specifically addressing urban natural areas as a form of environmental protection.

On a similar note, the methodological approach that we suggest and the obtained results are practical in terms of the process of evidence-based decision making, future policy development, and environmental deterioration mitigation. Green spaces are currently subjected to a great pressure from real estate developers, thanks to their quality, favorable position, and even the fact that these investors are taking advantage of the existing green areas, for promotional purposes. The detailed analysis of vegetation fragmentation patterns provides quantitative evidence that can inform urban planning decisions, completed by the temporal analysis, which helps identify critical areas where green space loss is accelerating, enabling proactive intervention, and which can serve as a guide for the strategic placement of green infrastructure to maximize ecological connectivity. The identified patterns of vegetation change can directly inform the development of a green belt infrastructure that is debated in the city council, particularly in identifying optimal locations for ecological corridors. Our methodology provides a replicable framework for monitoring and assessing urban environmental change which can be integrated into regular urban planning assessments. The results can help prioritize areas for conservation and restoration based on their ecological significance and vulnerability to urbanization. The analysis of green fabric loss enables evidence-based mitigation planning, being able to identify areas where environmental design interventions would have the greatest impact, and able to inform the development of urban growth boundaries and green space preservation strategies.

We highlight the need for integrated frameworks that help balance the need for urban expansion with environmental conservation, ensuring that growth occurs in a controlled and environmentally responsible manner. Growing literature studies support implementing multi-level governance, including regional land-use planning, urban development master planning, regulatory detailed planning, or constructive detailed planning [95]. This approach can mitigate the UHI effect, preserve biodiversity, maintain ecosystem services, and ultimately contribute to more livable and resilient urban environments. Preserving peri-urban green areas while maintaining vegetation and urban development is a real challenge, and the solution requires an interdisciplinary approach and an active management of UGSs [96] from urban residents, communities, and practitioners. Several authors addressed the problem of effective nature conservation outside protected areas in rapidly urbanizing areas [97,98]. Besides preservation, afforestation can be a solution as well, where suitable. Gong and Deng [95] advocate for vegetation to be as much more indigenous as possible, well distributed, and connected. Urban development has to be coordinated with environmental conservation efforts to ensure that land-use changes do not lead to excessive degradation, and to promote zoning laws and regulations that limit the conversion of agricultural and natural lands into urban areas.

6. Conclusions

This study analyzes thoroughly the spatio-temporal relationship between built-up areas and vegetation, highlighting certain geographical territories vulnerable to ecosystem changes while also revealing resilient green cover. By investigating the modification of the urban and peri-urban fabric through the lens of four critical indicators—vegetation cover, built-up space, land surface temperature, and population density—this research provides valuable insights into this complex and often imbalanced discourse. Focusing on Iași Municipality as a representative second-tier city in Eastern Europe, our analysis offers a comprehensive understanding of peri-urban dynamics, specifically the green fabric distribution in relation to recent urbanization trends. The research shows that uncontrolled urban expansion has manifested in diverse patterns, significantly impacting territories most accessible to road transport networks and areas with construction-friendly topography. A general trend shows a decrease in vegetation cover surface, detrimental to the built-up area, under the influence of an irregular pattern.

We can observe a heterogeneous pattern characterized by both resilient areas resistant to human pressure and regions that have undergone significant transformations over the past four decades. The most intense vegetation loss has occurred in the context of major infrastructure projects and along primary transportation axes, particularly in the western part of the city. Strong land sealing and vegetation loss are also observed in the cases of large shopping centers at the edge of the city, the major transportation axis being the driving force in the development. In the peri-urban area, there is a clear contrast between fast-developing and stagnating regions, very distinct to the planned patterns from the communist era.

Regarding vegetation growth over the past four decades, our findings not only demonstrate the persistence of robust green spaces in forests, but also in the urban core, in brownfields, railway corridors, old industrial zones, and through the maturation of urban green areas. Outside the city, forested areas also increased slightly in density, due to canopy maturing and slope stabilization efforts. Forests often serve as natural barriers to urban expansion, directing development towards areas requiring less environmental disruption. In peri-urban zones, vegetation plays a dual role: it enhances environmental sustainability while simultaneously acting as a buffer against rapid urban sprawl.

Today, the lack of a legislative framework regarding housing, residential complexes, and other private investments is accentuating the environmental problems. In certain cases, new green spaces are introduced in the new complexes, without addressing the broader environmental challenges in an integrated manner.

We acknowledge specific limitations of this research, in terms of the far-reaching and complete character of the proposed approach. While it effectively explained physical and measurable empirical changes, a possible integration of socio-economic factors can also be employed in the future. We are currently developing follow-up research addressing these factors through in-depth interviews with residents and stakeholders, analysis of policy documents and implementation processes, examination of changing social structures and community dynamics, and investigation of economic behavior patterns. The present research contributes to a broader agenda combining quantitative and qualitative methods to analyze post-socialist urban contexts in Eastern Europe, focusing on spatial transformations, socio-economic dynamics, and sustainability challenges within these unique urban landscapes.

These findings have significant implications for urban planning and policy interventions, paving the way for more sustainable, resilient, and equitable urban development strategies that balance the needs of expanding cities with the preservation and enhancement of crucial green spaces.