349Balogh, Sz. and Novák, T.J. Hungarian Geographical Bulletin 69 (2020) (4) 349–361.DOI: 10.15201/hungeobull.69.4.2 Hungarian Geographical Bulletin 69 2020 (4) 349–361. Introduction Soils traditionally are considered a four-di- mensional product of five natural soil-form- ing factors, namely: substrate, climate, to- pography, vegetation, and time (Forman, R.T.T. 1995). Only in the last decades, human society was considered to add as the sixth effective soil-forming factor (Dudal, R. 2005; Ellis, E.C. and Ramankutty, N. 2008; Ellis, E.C. et al. 2010). Even though, the soil-form- ing function of anthropogenic activities can- not be disregarded since the first spread of agricultural land cultivation (Kertész, Á. 2009; Ball, B.C. et al. 2017; Baude, M. et al. 2019). The extent of area affected by the hu- man processes, the intensity, and the diversi- ty of ways how the society modified natural soil bodies is continuously increasing. The contribution of the humankind to soil devel- opment seems to be more and more relevant, which is indicated by occurrence and spread of anthropogenic, technogenic soils (Bouma, J. et al. 1998; Antrop, M. 2004). Parallelly, always were regions, where human activity decreased or disappeared for a while, which allowed regeneration, renewal of former disturbed or devastated soils (Ceauşu, S. 1 University of Debrecen, Faculty of Technology and Sciences, Department of Landscape Protection and Envi- ronmental Geography, H-4002, Debrecen, Egyetem tér 1, POB 400, Hungary. Corresponding author’s e-mail: novak.tibor@science.unideb.hu Trends and hotspots in landscape transformation based on anthropogenic impacts on soil in Hungary, 1990–2018 Szabolcs B A L O G H 1 and Tibor József N O V ÁK 1 Abstract The transformation of the landscapes due to the anthropogenic activities is increasing worldwide. These changes are also manifested in the change of soil-forming processes. The land cover (LC) changes evaluated according to their influence on anthropogenic features of soils allows to distinguish between LC changes result- ing increased and decreased human impact (HI). In our study, we assess the changes of HI on landscapes and its spatial distribution across Hungary. The changes were evaluated by using LC data of four periods between 1990 and 2018 reclassified based on the related anthropogenic soil features. To identify the hotspots of the changes 1×1 grids were applied in which the direction (increasing, neutral or decreasing HI) and frequency (number of landscape patches with LC changes) of changes were evaluated. In our research, the hotspots were identified over the studied four periods. We point out that the spatial distribution of hotspots is very differ- ent. The hotspots of the increased human impact are 2,449 cells (643.0 km2) between 1990 and 2018, and the most of it localized in the Pest Plain (67), Csepel Plain (64) and Nagykálló-Nyírség (60). Most of the multiple hotspots are in the outskirts of Budapest to Kiskunlacháza, Bugyi, Délegyháza. As we examine the decreasing hotspot data we found 1,679 cells (1,524.9 km2) between 1990 and 2018. In largest number, they occur on the Kiskunság Sand Ridge (38), Majsa–Szabadka Sand Ridge (37) and Nagykállói-Nyírség (36). Multiple hotspots are located in settlements Izsák, Ásotthalom, Vatta and Nyírmihályi. Regions with numerous hotspots require special management to moderate its negative consequences on soils to consider both increased anthropisation, but also extensification of land use and their consequences. Keywords: landscape degradation, landscape rewilding, land cover change, soil naturalness changes Received February 2020; Accepted October 2020. Balogh, Sz. and Novák, T.J. Hungarian Geographical Bulletin 69 (2020) (4) 349–361.350 et al. 2015; Navarro, L. and Pereira, H. 2015). Globally, less and less part of the surface of the Earth remains, where human society should not be considered as a signif- icant factor of soil-forming. (Bouma, J. et al. 1998; Hill, M. et al. 2002; Antrop, M. 2004; Klijn, J.A. 2004; Csorba, P. and Szabó, Sz. 2009; Dale, V.H. and Kline, K.L. 2013; Balogh, Sz. et al. 2018). Spatial pattern and its changes of these opposite processes (like soil anthropization and renewal by natural succession) could be considered as the indicator of the actual land needed by the society. Otherwise, it is a phenomenon which should be compensated, regulated, managed, and planned intention- ally by the society (Lambin, E.F. et al. 2006; Novák, T.J. et al. 2013; Lundberg, A. 2018). The study aimed to point on the changes of the spatial extent of areas, which are affected by changes in intensity of the human impacts between 1990 and 2018. In our consideration, the “Land Cover” classes (LC) and its chang- es are related to the grade of the transforma- tion of soils by society. Therefore, analysis of LC and the LC changes allows an estima- tion of the grade of human transformation of landscapes (Incze, J. and Novák, T.J. 2016; Szilassi, P. et al. 2017; Novák, T.J. et al. 2019). In our research, we supposed that besides of well-known processes of intensification (e.g., soil sealing, construction, degradation of soils), on extent areas LC changes were concluded in the decreased grade of “human impacts” (HI). At these sites, the changes al- low a restart of soil development towards a less degraded state (i.e., organic carbon se- questration, recharge of soil nutrients, regen- eration of aggregate structure and renewed horizon development). Further, we intended to identify the locations with the highest pos- sible spatial accuracy, where these changes appear, since we consider both increasing and decreasing HI-s on landscapes requires compensatory management by local society. Therefore, a hotspot analysis was carried out, for all four study periods, where we identi- fied single and multiple hotspots – for de- creased and increased HI-s. Data sources and methods As the main data source to our analysis, the CORINE Land Cover (CLC) database and the CORINE Land Cover Change (CHA) datasets were applied. In our study, the status datasets of CLC (1990, 2000, 2006, 2012 and 2018) were ap- plied to calculate the share of re-grouped CLC classes according to the grade of their anthro- pogenic transformation. The methodology of surveys is standardized as it is described at Mari, L. and Mattányi, Zs. (2002), and Mari, L. 2010. The datasets at a scale of 1:100,000 were applied, on the study area (Hungary) it consists of 32 CLC classes (FÖMI, 2002). The minimum mapping unit for areal objects is >25 ha, and the minimum width of the linear objects is >100 m. Although only the area ele- ments are identified in the CLC, there are some “linear elements” in it. Due to the differences between two time and layer, we use the CHA datasets. In these, they have mapped the less than 5 ha changes, included the motorway and some road changes. So, the GIS database, which we are created, is extended to the digital road changes. Furthermore, we only have these “linear layers” because of the mapping scale of the CORINE Land Cover Change dataset. The important road impact and the fragmentation are sometimes appeared in the CHA polygons. Afterwards, the status data time series was complemented by change layers (CHA 1990– 2000, 2000–2006, 2006–2012 and 2012–2018). It allows higher accuracy to identify changes as a comparison of the consecutive status lay- ers (Mari, L. and Mattányi, Zs. 2002; Mari, L. 2010). As in our earlier works it was described (Novák, T.J. et al. 2013; Novák, T.J. and Tóth, Cs.A. 2016; Novák, T.J. and Incze, J. 2018; Balogh, Sz. et al. 2019) to the CLC classes, the expectable influence on the soil cover was assigned, and the CLC classes were reclas- sified based on the grade of anthropogenic transformation of the soils, into four groups (Figure 1). One of these groups consists of CLC classes which have not been considered to have soils according to the definition of soil by WRB (World Reference Base). 351Balogh, Sz. and Novák, T.J. Hungarian Geographical Bulletin 69 (2020) (4) 349–361. Due to repeated surveys of the CLC data, change layers (CHA) from five different years the spatial extent and share of the groups based on the grade of anthropogenic trans- formation could be calculated, and therefore the changes in the last three decades over the country can be tracked. Using this classification, the CHA poly- gons were also evaluated. Therefore, the an- thropogenic transformation grades (accord- ing to the Figure 1) of the cover class before and after the LC conversion was considered. In furthermore, we ignored the polygons in which the anthropogenic transformation grade before and after the conversion was falling into the same group. Here the inten- sity of human impact (HI) remained approxi- mately at the same level in spite of the altered LC class, and the change was evaluated as neutral concerning the anthropogenic trans- formation grade. Polygons, in which the LC conversion also resulted in an altered grade of HI, were divided into two groups: LC conversions with a) increasing, and b) de- creasing HI (Figure 2). This reclassification of CHA polygons was done for all four in- tervals provided by CHA dataset (1990–2000, 2000–2006, 2006–2012, 2012–2018). The extent and number of polygons showing increas- ing or decreasing HI were calculated for each above-mentioned time intervals. From each interval, the five most extent LC conversion types resulting increased or decreased HI were listed for further analysis concerning their consequences to soil resources. To identify hotspots of increasing and de- creasing HI a grid with 1×1 km cell size was overlain on the LC change maps, the same grid for each type and period. In every 1×1 km grid number of polygons with LC con- versions resulting changed intensity of HI were counted (Figure 3). As we have seen in Figure 3 the number of land cover changes (LCC) polygons are demonstrated. The hot- spot methodology was assorted only those cells, which LCC number are in the upper quartile of it (e.g. like in Figure 4. only those which are included 2–6 polygons in the cells). The polygons of LC conversions with un- changed intensity (see Figure 2) of HI were eliminated from later analysis. Separately for the type of change (increasing and decreas- ing HI) and each study period, the basic sta- tistic data of the grid cells were calculated. The upper quartiles of the number of LC change polygons per grid cell were consid- ered to be the lower limit to evaluate a grid cell as a hotspot, i.e., hotspots were consid- Fig. 1. Reclassification of CORINE LC classes based on the expectable anthropogenic transformation of soils Balogh, Sz. and Novák, T.J. Hungarian Geographical Bulletin 69 (2020) (4) 349–361.352 Fig. 2. Evaluations of LC changes based on the expectable change in the intensity of the HI Fig. 3. The method of assigning the LCC polygons to grid cells, for later identification of hotspots 353Balogh, Sz. and Novák, T.J. Hungarian Geographical Bulletin 69 (2020) (4) 349–361. Fi g. 4 . I d en ti fi ca ti on o f h ot sp ot s in s tu d ie d p er io d s of L C c ha ng es , a nd h ig hl ig ht in g ho ts po t m ul ti pl ic it y w he n a gr id c el l i s a ho ts po t i n m or e th an o ne p er io d Balogh, Sz. and Novák, T.J. Hungarian Geographical Bulletin 69 (2020) (4) 349–361.354 ered those grids, which had higher number of LC change polygons as the upper quartile of the grids for the same period and type of change (Figure 4). If the hotspot lies in the bordel, we attached to that region which is occupied the largest part of the cell. Hotspots of increasing and decreasing HI were calculated separately. Further, numer- ous grids proved to be hotspots not only for one period but in more studied periods. These multiple hotspots for increased and decreased HI were represented with deeper colour intensity on maps. Results Changes of HI Distribution of LC classes among the groups according to the grade of HI showed slight changes between 1990 and 2018. Most LC classes belong to the group, in which the anthropogenic properties in the soil (dis- turbance of soil horizons, modification of soil structure, and occurrence of artificial or transported materials) can be recognized. Still, the solum itself is not predominantly a natural product of pedogenic processes. They covered 68.3 per cent of the country in 1990, and their share decreased by 3.2 per cent to 64.9 per cent for 2018 (Table 1). LC classes, which are related to soils pro- duced completely by the anthropogenic accumulation of transported, relocated or industrially produced materials (‘anthropo- genic soils’ – Table 1) occupied 5.5 per cent in 1990 and their share increased by 0.8 per cent up to 6.3 per cent until 2018. LC classes, in which soil development and horizona- tion are driven by meanly natural processes (succession of vegetation, topography, and climate) was 24.3 per cent and increased by 2.5 per cent in 2018 up to 26.8 per cent. These changes indicate that share of moderately an- thropized soils is only decreasing, and both extreme: anthropization and renaturation of soils affect an increased area. Anyway, an- thropization (LC changes with increased HI) affected smaller part of the surface between 1990 and 2018, than LC changes with a de- crease of HI. We also considered the total extent (area, km2) and the number of polygons with changed LC class related to increased and decreased human influence separated for the four study periods (Table 2). Generally, LC change polygons with in- creased HI had a smaller total area and a higher number in every period. The averaged area of these polygons varied between 0.24 km2 (2012–2018) and 0.29 km2 (2000–2006). Table 1. Distribution of CORINE land cover classes among anthropogenic transformation groups (%) between 1990 and 2018 in Hungary based on anthropogenic WRB diagnostics of soils Year No soils (0) Natural or close to natural soils (1) Soils with anthropogenic features (2) Anthropogenic soils (3) 1990 2000 2006 2012 2018 1.9 2.0 2.1 2.1 2.0 24.3 24.7 25.5 26.0 26.8 68.3 67.6 66.5 65.9 64.9 5.5 5.7 5.9 6.0 6.3 Table 2. Extent and number of land cover change polygons with decreased and increased human influence between 1990 and 2018 in Hungary Period Land cover changes with decreased human influence increased human influence Total area, km2 number area, km2 number 1990–2000 2000–2006 2006–2012 2012–2018 401.1 483.7 532.7 107.4 593 447 398 238 185.9 189.6 136.4 131.2 677 651 570 551 355Balogh, Sz. and Novák, T.J. Hungarian Geographical Bulletin 69 (2020) (4) 349–361. LC polygons with decreased HI had in op- posite larger area, but a smaller number. Therefore, averaged extent of polygons with these types of conversion varied between 0.45 km2 (2012–2018) and 1.34 km2 (2006–2012). To have an overview about the substantial LC changes of the conversions, we ranked the conversion types (coded by their CORINE code before and after the LC change) accord- ing to their frequency based on their area and number we found, separately for each period. LC conversions with decreased HI proved to be less diverse than with increased HI. Namely, 77 per cent per cent (2012–2018) to 93 per cent (2006–2012) of the area affected by LC changes with decreased HI belonged into the five most frequent types (Table 3), and the polygons in most frequent five types are 63–66 per cent respectively of the total number of polygons with LC changes with decreased HI. In case of LC conversions with decreased HI the initial LC was in most of the cases arable land, mineral extraction sites, complex cultivation patterns and vineyards. The conversions resulted transitional wood- lands, shrubs, pastures and broad-leaved forests (see Table 3). It does not mean that at the moment of the conversion, the quality of landscape units suddenly changes, but this type of changes results inherently reduced level of disturbance and gives chances for the regeneration of the ecosystems and soils. LC conversions with increased HI, there- fore, show higher diversity, since the most frequent five types cover only 41 per cent (1990–2000), and 61 per cent (2000–2006) of the total area of them, and they mean only 25–37 per cent respectively of the total num- ber of polygons with increased HI. When LC changes resulted increase in HI mostly arable lands, grasslands and pastures were turned into construction sites, mineral extraction sites, railways, and roads, industrial or com- mercial units (see Table 3). Also, when inland marshes were converted into water bodies, soils were devastated submerging under con- stant and higher water cover, even cancelled, according to definition of WRB, if submerged in more than 2 metre deep water. For each period, both types of LC conver- sions (increased and decreased HI) the most frequent five conversion types are listed in Table 3. Also, the summarized data show, that the number of polygons with LC changes af- fecting decreased HI are smaller (1,676 and 2,449), but the extent larger (1,524.9 km2 and 643 km2) as that of LC changes with in- creased HI. Hotspots of changed HI As we mentioned in the data source and methods section, 1×1 km2 grids were creat- ed. Using the grids, we prepared the hotspot analysis, based on the mentioned methodol- ogy earlier. Hotspots were identified over the studied four periods. As before, we also highlighted the areas that contained the largest number in both the four periods and the total. To fa- cilitate the research, these were summarized within the micro-regional boundaries and then we created the sequent table. The spatial distribution of hotspots with increased HI is shown on Figure 5. Numerous hotspots proved to be hotspots not only in one studied period, but for more. Totally 2,449 between 1990 and 2018, in highest num- ber on the Pest Plain (67), Csepel Plain (64) and Nagykállói-Nyírség (60) (Table 4). Most likely, the non-irrigated arable lands (211) are converted into construction sites (133) and industrial or commercial units (121), or road and railway network and associated lands (122). Furthermore, many pastures (231) are converted into construction sites (133) to. In the case of 1,012 hotspots num- ber of LC change polygons with increased HI was in two periods higher than the respective threshold value of the hotspots. These are twofold hotspots, illustrated on Figure 5 with more intense colour. Triple hotspots were found in 223 grids and fourfold hotspot in 45. These multiple hotspots are shown with darker colour intensity in Figure 5. On the map, several aggregations of triple or four- fold hotsopts can be identified. The top 5 are Balogh, Sz. and Novák, T.J. Hungarian Geographical Bulletin 69 (2020) (4) 349–361.356 Ta bl e 3. L C c on ve rs io ns w it h al te re d gr ad e of H I p ro ve d to b e am on g th e m os t e xt en t fi ve ty pe s in a ny o f t he fo ur s tu di ed p er io ds N at ur al ne ss c la ss ba se d o n so ils C O R IN E c od e be fo re -a ft er co nv er si on L an d c ov er T ot al a re a, km 2 N um be r of po ly go ns be fo re af te r be fo re af te r co nv er si on co nv er si on A ) D ec re as ed H I t ot al ly 1 99 0– 20 18 1, 52 4. 9 1, 67 6 C on ve rs io n ty pe s am on g th e m os t e xt en t fi ve ty pe s w it h in a ny o f t he fo ur s tu d ie d p er io d s w it h d ec re as ed H I 2 1 21 1– 32 4 N on -i rr ig at ed a ra bl e la nd T ra ns it io na l w oo d la nd / sh ru b 87 4. 5 45 0 23 1– 32 4 Pa st ur es B ro ad -l ea ve d fo re st 26 9. 2 35 7 21 1– 31 1 N on -i rr ig at ed a ra bl e la nd T ra ns it io na l w oo d la nd / sh ru b 96 .9 81 24 3– 32 4 L an d p ri nc ip al ly o cc up ie d b y ag ri cu lt ur e, w it h si gn ifi ca nt a re as o f n at ur al v eg et at io n 23 .3 54 22 1– 32 4 V in ey ar d s 21 .7 16 24 2– 32 4 C om pl ex c ul ti va ti on p att er ns 12 .2 25 21 3– 32 4 R ic e fi el d s 8. 3 2 22 2– 32 4 Fr ui t t re es a nd b er ry p la nt at io ns 7. 9 23 0 2 13 1– 23 1 M in er al e xt ra ct io n si te s Pa st ur es 6. 4 17 0 3 13 1– 13 2 D um p si te s 5. 8 2 3 2 13 2– 23 1 D um p si te s Pa st ur es 3. 8 10 B ) I nc re as ed H I t ot al ly 1 99 0– 20 18 64 3. 0 2, 44 9 C on ve rs io n ty pe s am on g th e m os t e xt en t fi ve ty pe s w it h in a ny o f t he fo ur s tu d ie d p er io d s w it h in cr ea se d H I 2 3 21 1– 13 3 N on -i rr ig at ed a ra bl e la nd C on st ru ct io n si te s 89 .8 18 6 21 1– 13 1 M in er al e xt ra ct io n si te s 63 .6 14 3 21 1– 12 1 In d us tr ia l o r co m m er ci al u ni ts 49 .0 18 7 2 0 21 1– 51 2 W at er b od ie s 41 .1 96 1 2 32 1– 21 1 N at ur al g ra ss la nd s N on -i rr ig at ed a ra bl e la nd 31 .5 65 2 3 23 1– 13 3 Pa st ur es C on st ru ct io n si te s 20 .2 75 1 0 41 1– 51 2 In la nd m ar sh es W at er b od ie s 15 .0 31 2 3 21 1– 12 2 N on -i rr ig at ed a ra bl e la nd R oa d a nd r ai lw ay n et w or k an d as so ci at ed la nd 11 .6 18 357Balogh, Sz. and Novák, T.J. Hungarian Geographical Bulletin 69 (2020) (4) 349–361. in the outskirts of Budapest highlighted by Kiskunlacháza, Bugyi, Délegyháza, besides in Győr and Bükkábrány. In case of the LC changes with decreased HI the upper quartile of the number of poly- gons was 1 in all periods. This means, that all grids having at least two polygons with LC change showing decreased HI are regarded as a hotspot. The number of them is 593, 447, 401, and 238 in the above order. Totally 1,679 cells are regarded as a hotspot in any of the four-study periods between 1990 and 2018 (see Table 4). In largest number they occur on the Kiskunság Sand Ridge (38), Majsa– Szabadka Sand Ridge (37) and Nagykállói- Nyírség (36). Figure 6 shows the location of further hotspots of decreased HI. Number of one-fold hotspots was found 2,379, two-fold hotspots were found in 135 grid cells and tri- ple hotspots in 15, and no fourfold hotspot was found. The colour intensity also shows hotspot multiplicity in Figure 6. The most af- fected areas are in Izsák, Ásotthalom, Vatta and Nyírmihálydi. Usually, the non-irrigated arable lands (211) and pastures (231) and sometimes the agricultural types (213, 221, 222, 242, 243) are converted into transitional woodlands or shrubs (324). Some of the hot- spots are the mineral extraction sites (131) conversation into pastures (231). Discussion – Considerable effects of land cover conversions to soils Numerous studies analysed the CORINE data- sets based on the changes in LC (Feranec, J. et al. 2007; Verburg, P.H. and Overmars, K.P. 2009; Stürck, J. et al. 2015; Kuemmerle, T. et al. 2016; Plieninger, T. et al. 2016; Foški, M. and Zavodnik Lamovšek, A. 2019), but the high number, small individual extent and high vari- Fig. 5. Hotspots of increased human impact in Hungary based on LC changes 1990–2018 Balogh, Sz. and Novák, T.J. Hungarian Geographical Bulletin 69 (2020) (4) 349–361.358 ability of conversion types make the overview difficult and the evaluation of them compli- cated. We classified the conversions into three types based on the change of degree in HI, and evaluated their spatial extent considering their spatial frequency, using an auxiliary 1×1 km grid to identify the hotspots of changes. Also, in our earlier studies (Novák, T.J. and Incze, J. 2018; Novák, T.J. et al. 2019) we pointed on to the regional differences of landscape changes in Hungary on micro-and meso-region level. Considering these spatial units, quite frequently changes with in- creased and decreased HI were possible to observe within the same micro- or meso-re- gions, on the other hand, the exact identi- fication of the changes was not possible. Therefore, this different approach: applying a grid and identification of hotspots proved to be more useful. It allowed the more pre- cise delineation of areas with increased and decreased HI, and hotspots diversity among the four study periods highlighted the loca- tion of ongoing LC changes. Our finding, that on the expense of the ex- tent of LC classes with moderate HI both: the extent of LC classes with increased and the decreased HI impact was growing proved useful to point on increasing differentiation of LC. Regions with numerous hotspots of increased HI requires special management to moderate its negative consequences on soils, such as accelerated erosion, soil compaction, loss of organic carbon, mixing of soil hori- zons, soil sealing, soil loss appear concentrat- ed, and affect urbanized areas. Highway con- structions, industrial areas, and areas with intense peri-urban residential sprawl are typ- ical places, where the LC conversions affects intense soil anthropization (Falkenberg, J.A. et al. 2003; Ebels, L.J. et al. 2004; Csorba, P. 2005; Almajmaie, A. et al. 2017), so far that Fig. 6. Hotspots of decreased human impact in Hungary based on LC changes 1990–2018 359Balogh, Sz. and Novák, T.J. Hungarian Geographical Bulletin 69 (2020) (4) 349–361. natural soils are no more possible to identify and even the substrate of soils are either ar- tificial (construction waste composited from brick, slag, concrete, asphalt) or transported by humans (gravel, sand, rock). Besides their environmental risks, it makes these regions vulnerable against climate change, limiting the potential for resilient responses. Multiple hotspots of LC changes with increased HI warns to the ongoing degradation of soils in these areas. In contrast, regions with hotspots of decreased HI can count with organic ma- terial accumulation, carbon sequestration, regeneration of structure, increase of thick- ness of fertile soil layer (Van Etvelde, V. and Antrop, M. 2009; Sándor, G. et al. 2013; Horváth, A. et al. 2015). That is true that these soil processes can compensate the unfavourable ones of hotspots of increased HI, but in many cases decreasing human impact on landscapes also requires special management and control, that must be considered. In case of decreasing HI on former industrial, mining, military and constructed areas a rewilding or renaturation can be related to the long-lasting release of pollutants into the groundwater or to the at- mosphere, and therefore mean environmental risks besides of their regeneration processes. 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