2.1. Study area
The study area was Lublin, a medium-sized Central-European city, and the most populous in eastern Poland. The city occupies 147 km2, with a population of 321,324 inhabitants. The city is situated at 163–238 m above sea level, within the Lublin Upland (Statistics Poland 2018). The Bystrzyca River and its three tributaries – the Czechówka, the Czerniejówka and the Nędznica – and two periodic watercourses flow through the city. Two distinct parts of Lublin can be distinguished. The western part is characterised by a thick loess cover, intersected by numerous dry valleys and gorges, which creates a diverse landform. The majority of residential areas, usually on hilltops, are located in this part. In land depressions there are green areas, as well as communication routes. The eastern part, on the other hand, is devoid of a loess cover and has a less diversified, rolling terrain. In this part industrial development and smaller clusters of residential development predominate. The highly diverse landform features and watercourses have an impact on remarkable flora and landscape diversity. Research on this topic was carried out from 2016. Detailed field inventory was made between 2016 and 2018 in different seasons and weather conditions. Of all urban green spaces (UGS), we focused on the “unmanaged informal green spaces” after Sikorska et al. (2020). They include IGS (Rupprecht and Byrne 2014), sites of remnant vegetation (Florgård 2007) and protected areas.
2.2. GIS and remote sensing data
In our study, we used satellite imagery from the Operational Land Imager (OLI) sensor on the Landsat 8 satellite. Data was obtained from the Earth Explorer service of the USGS (U.S. Geological Survey) (EarthExplorer 2021). The image was recorded on 14 August 2018, 09:25 am, in 11 spectral channels from the visible and near-infrared (VNIR) and short-wave infrared (SWIR) (430–2290 nm) and thermal infrared (TIR) (1060–1251 nm) ranges. The spatial resolution of the image was 30 m and the radiometric resolution was 12 bits (USGS 2021). For the study, we used VNIR (channels 2, 3, 4, 5) and SWIR (channels 6, 7) channels and channel 10 of the TIR range. A calibration procedure was carried out for the image: transformation of Radiance values to Surface Reflectance (VNIR, SWIR) and conversion of channel 10's DN (Digital Number) row value to the Land Surface Temperature (LST) value on the day the image was taken (Figs. 1 and 2). For this purpose we used the ATCOR module with Catalyst Professional software (Catalyst.Earth 2021). We also used cartographic data, orthophotos and the Digital Elevation Model (DEM), which were acquired free of charge from the Polish Central Office of Geodesy and Cartography (www.geoportal.gov.pl). DEMs were acquired in the ARC/INFO ASCII GRID format, containing the height value of points in a regular 1-metre grid. The data was processed into an image and then the slope was calculated from it (Zevenbergen and Thorne 1987). The Slope for the assessment of the land surface topography was used. The Urban Atlas 2018 database (UA2018), Copernicus programme was used too. The UA2018 database contains pan-European land cover and land use data for functional urban areas (Copernicus 2021). For the purpose of our analysis, we combined the following categories: Construction sites; Continuous urban fabric; Discontinuous dense urban fabric; Discontinuous low density urban fabric; Discontinuous medium density urban fabric; Discontinuous very low density urban fabric; Industrial, commercial, public, military and private units; Fast transit roads and associated land; Other roads and associated land; Isolated structures; Mineral extraction and dump sites. The table with full data: expert assessment, spectral indexes and digital data for all sites is added in supplementary materials.
2.3. Course of the research
Based on cartographic analyses and available databases (geoportal.gov.pl), we identified 91 unmanaged IGS with expected visual attractive landscape (Carver et al. 2012; Müller et al. 2018; Ode et al. 2008; Tveit et al. 2006). We also analysed the surrounding areas. For this purpose, we set a buffer of 300 m for each area as a zone of closest impact between the study area and its surroundings (Shah et al. 2021; Ossola et al. 2021; Sun et al. 2020; Sikorska et al. 2020). In addition, we have distinguished built-up areas in these buffers based on the UA2018 database. These included buildings, roads and infrastructure. These areas are poor in biologically active surface and are largely impermeable to rainwater. We did so with the knowledge that each database gives slightly different results – such situation is highlighted by Feltynowski, Kronenberg, Bergier, Kabisch, Łaszkiewicz and Strohbach (2018). To obtain results for the entire surroundings of each site, including those close to or on the border of Lublin, we added a 300-metre buffer around the administrative border of the city to the study area.
2.3.1. Expert assessment
After a study of publications and authors team discussions, we developed criteria for the expert assessment of the site’s landscapes. We did not determine a minimum size, the smallest area under investigation was 0,23 ha and the largest 86,14 ha. The expert assessment was carried out in the field from 2016 to 2018 by members of authors team in different seasons and weather conditions. Some indicators were quantitative, on a 5 − 1 scale, similar to the methods used by Carver et al. (2012)d ller et al. (2015). We adopted the following categories for expert assessment:
landscape contrast, understood as a visual contrast with the surrounding area (Ode et al. 2008; Kerebel et al. 2019; Rink and Emmrich 2005). This feature can increase the attractiveness of an enchanted natural place – for example, when we are surprised by spontaneous greenery in close proximity to buildings: 5 – very clear difference, contrast with anthropogenic elements: e.g. buildings, roads, very clear differences in landform; 4 – clear difference with surrounding natural elements e.g. open area/wooded area, clear contrast in landform; 3 – noticeable differences in land cover – for example, height of vegetation, noticeable differences in landform; 2 – minor differences in nature of cover e.g. different forest type, no terrain differences; 1 – no apparent difference between the place and its surroundings;
naturalness, understood as the similarity of the growing vegetation to the potential vegetation (Carver et al. 2012; Ode et al. 2008; Ode et al. 2009; Tveit et al. 2006; Kowarik 2018) found in Lublin (Matuszkiewicz 2008): 5 – very similar vegetation to natural communities, multi-layered forest communities; 4 – tall vegetation, trees, self-sown plants, many of which are species typical for the habitat; 3 – mixed areas, shrubland, low trees, bushes, grassland vegetation, meadows; 2 – area covered mostly with grassland vegetation, extensively used, rarely mown; 1 – intensively used area, mown;
uniqueness, understood as the frequency of the occurrence of a given landscape type in the studied area (Ode et al. 2008; Tveit et al. 2006; Pietrzyk-Kaszyńska et al. 2017); the indicator was verified after experts assessed all the sites: 5 – unique sites (found once in the city); 4 – very rare sites (found 2–3 times); 3 – rare sites (found 4–6 times); 2 – common sites (7–10 times); 1 – very common sites (occurring more than 10 times).
In addition, we added an indicator expressed quantitatively: usage: (5) none, (4) single path, (3) single road, (2) several paths, (1) dense path network and an indicators expressed descriptively: main type of landform: flat, slope, gorge, valley, valley bottom, escarpment, plateau, hill, type of landscape composition: enclosure, massif, exposed, unclear, and for enclosure also the degree of view obstruction – expressed in % of the enclosure (Ashihara 1970; Bogdanowski, Łuczyńska-Bruzda and Novák 1979; Tveit et al. 2006; Ode et al. 2008) and main type of landcover: trees, shrubs, grass-herbaceous, mixed. We also added features that are not assigned a scale but only +/- values, following the guidance of Kerebel et al. (2019) – instead of talking about beauty in general, it is easier to break it down into understandable elements: low number of human artefacts in sight (Müller et al. 2018); interesting landform (Carver et al. 2012; Kerebel et al. 2019; Ode et al. 2008); presence of water (Ode et al. 2008); diversity of plant forms (Kerebel et al. 2019; Ode et al. 2008; Tveit et al. 2006); diversity of plant height (Kerebel et al. 2019; Ode et al. 2009); presence of old trees (Kerebel et al. 2019); screened-off, hidden place (Rink and Emmrich 2005); “visual access” to the area from the outside (Kowarik 2018) and distant view from the site, added after authors team discussion. We also added the sites’ names, which point to the type of the area and define its uniqueness – for example, shrubs, thicket, oxbow lake, etc. On the basis of the completed table (Table 1), the expert also provided a subjective final evaluation general expert assessment, on a scale of 1–5. The expert awarded a score based on the average of the naturalness, landscape contrast, uniqueness scores; however, it could be lower or higher by 2. When indicating the overall assessment, the expert took into account other indicators. If a site received an general assessment of 5, 4 and 3, it was referred to as an ENP – an enchanted natural place. For the purposes of the study, we formulated a definition of ENP as a place within an urban area, where the landscape takes on particularly attractive forms approximating naturalness, with minimum human interference, where we can experience a sense of naturalness and which is clearly distinguishable from the urban surroundings.
We adopted the following rating scale of the general expert assessment for the study sites:
5 – an outstanding site, very attractive for a number of reasons and characteristics, with a high degree of naturalness – enchanted natural place (ENP);
4 – very attractive, very rare, highly natural area with several important features - ENP;
3 – valuable area, occurs rarely or often in the city, with high or medium naturalness, typical ENP for the city;
2 – spontaneous green area, IGS, has some features of an enchanted natural place but does not qualify as a full ENP.
1 – semi-natural green area, IGS, has no features of an ENP.
The contents of the evaluation sheet are shown in Table 1.
Tab.1 Contents of the evaluation sheet
![](data:image/png;base64,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)
2.3.2. Spectral indexes
Based on the Landsat image channels, we calculated spectral indexes to determine the status and biological activity of the studied area. We adopted the following spectral indexes: LST – Land Surface Temperature – to assess temperature differences (Cheng et al. 2019; Shah et al. 2021; Ossola et al. 2021); NDVI – Normalized Difference Vegetation Index – to assess biomass quantity, plant health and vigour (Rouse, Haas, Schell and Deering 1973; Yang et al. 2017; Shah et al. 2021; Kimm and Ryu 2015; Wang et al. 2005; Sikorska et al. 2020); NDMI – Normalized Difference Moisture Index – to assess the level of water stress in plants (Wilson and Sader 2002; Zheng et al. 2019; Zhu et al. 2019; Wang et al. 2005); LAI – Leaf Area Index – to determine the extent to which plants make use of light (Hauser et al. 2021; Sun et al. 2020; Kimm and Ryu 2015; Opik, Rolfe and Willis 2005).
In the next step, we calculated the mean values of each spectral indexes (NDVI, NDMI, LAI, LST) and the slope inside the polygons representing the boundaries of the ENPs and the polygons forming buffers, without ENPs. Then we compared the resulting indexes with the general expert assessment and the scores for landscape contrast, naturalness, and uniqueness.