Next Article in Journal
A Large-Language-Model-Based Dataset of Plant Species for Green Roofs in China
Previous Article in Journal
Optimizing Cross-Regional Mobility Contributes to the Metacoupling Between Urbanization and the Environment for Regional Sustainability
Previous Article in Special Issue
Pinelands: Impacts of Different Long-Term Land Uses on Soil Physical Properties in Red Ferrosols
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Land
Volume 14
Issue 8
10.3390/land14081683
land-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Land Unit Delineation Based on Soil-Forming Factors: A Tool for Soil Survey in Mountainous Protected Areas

by
William Trenti
1,
Mauro De Feudis
1,*,
Massimo Gherardi
2,
Gilmo Vianello
3 and
Livia Vittori Antisari
1
1
Department of Agricultural and Food Sciences, Alma Mater Studiorum-University of Bologna, Via Fanin, 40, 40127 Bologna, Italy
2
Boreal Mapping, Via Casella, 23/a, 40030 Grizzana Morandi, Italy
3
Accademia Nazionale di Agricoltura, Via Castiglione 11, 40124 Bologna, Italy
*
Author to whom correspondence should be addressed.
Land 2025, 14(8), 1683; https://doi.org/10.3390/land14081683
Submission received: 17 July 2025 / Revised: 16 August 2025 / Accepted: 18 August 2025 / Published: 20 August 2025
(This article belongs to the Special Issue Feature Papers for "Land, Soil and Water" Section)
Browse Figures
Versions Notes

Abstract

The present study applied a GIS-based methodology for assessing soil diversity in a protected mountain area of Italy. Using QGIS, morphological (i.e., altitude and slope), lithological, climatic, and land use layers were intersected to delineate 16 land units (LUs), each representing relatively homogeneous conditions for soil formation, according to Jenny’s equation. To obtain the soil map units, a total of 112 soil profiles were analyzed, including 79 from previous studies and 33 that were newly excavated during 2023–2024 to fill gaps in underrepresented LU types. Most soils were classified as Inceptisols/Cambisols, occurring in both Dystric and Eutric variants, mainly in relation to lithology (i.e., arenaceous or pelitic facies). Alfisols, Umbrisols, and hydromorphic soils were also identified. The physicochemical properties showed marked variability among LUs, with sand content ranging from 39 to 798 g kg−1, pH from 4.4 to 7.9, and organic carbon content from 1.6 to 6.1%. This LU-based framework allowed efficient field sampling, if compared to grid-based surveys, while retaining information on fine-scale pedodiversity. No quantitative accuracy assessment (e.g., boundary precision, internal homogeneity metrics) was conducted, even if the spatial coherence of the delineated LUs was supported by the distribution of soil profiles, which provided empirical validation of the LU framework.

1. Introduction

Soils are complex and dynamic ecosystems whose ecological functionality emerges from the interplay among the five classic soil-forming factors—climate, parent material, topography, biota, and time—as conceptualized by Hans Jenny in 1941 [1]. The spatial variability in the intensity and interaction of these factors gives rise to a pronounced vertical and horizontal heterogeneity in soil properties, which is systematically described through hierarchical classification systems such as the USDA Soil Taxonomy and the World Reference Base for Soil Resources [2,3].
This heterogeneity—commonly referred to as soil diversity or pedodiversity—is a foundational concept in soil science [2], as it underpins a wide range of ecosystem functions and services, including nutrient cycling, carbon sequestration, primary productivity, and biodiversity support [4,5]. Despite its central role in regulating ecosystem dynamics, soil is often overlooked in biodiversity assessments and conservation strategies, which tend to focus on aboveground organisms—plants, animals, and microorganisms—while neglecting the integrating and regulating functions of soil systems.
Given the importance of soils and pedodiversity in shaping ecological processes, there is an increasing demand for high-resolution, spatially explicit, and up-to-date soil information to inform natural resource management, conservation strategies, and land use planning [6,7]. Soil maps are essential tools in this regard, as they link observable and measurable soil properties to their environmental context, thereby enabling the spatial assessment of soil functions and degradation risks. These maps are typically derived by integrating field observations, laboratory analyses, and geostatistical modeling of the relationships between soils and their forming factors [8,9,10].
However, detailed and accurate soil maps are still lacking for many parts of the world—particularly in mountainous regions [11,12]. In Italy, as in many other countries, the availability of high-resolution and up-to-date soil maps for mountain areas remains critically limited (e.g., [13,14,15]). Moreover, there is no standardized or widely accepted methodology for delineating land units or producing soil maps at regional and landscape scales in complex terrains.
In the context of land conservation and territorial planning, soil information must be integrated with other environmental and anthropogenic variables—such as climate, geology, geomorphology, vegetation, and land use history—to understand the drivers of ecosystem dynamics [10,16,17]. One promising approach is the subdivision of the landscape into land units (LUs), which are spatial domains characterized by sufficient environmental homogeneity (in terms of relief, climate, parent material, vegetation, etc.) to influence soil development and land use suitability [18,19,20]. Each LU can be regarded as a distinct ecological subsystem and provides a meaningful spatial framework for analyzing interactions among soils, land use, and biodiversity.
This spatial approach is especially relevant in mountain areas, which cover approximately 30% of Europe’s land surface [21] and are globally recognized as biodiversity hotspots [22]. Mountain ecosystems deliver essential services such as water regulation, air purification, and climate moderation [23], but they are also particularly vulnerable to changing global pressures—including land use intensification, warming, and unsustainable development [24,25]. As such, their conservation has become a strategic priority in environmental policy and biodiversity planning [26]. While protected areas are key tools for safeguarding mountain ecosystems [27,28], a critical mismatch persists between the ecological significance of these regions and the availability of adequate planning instruments—particularly in terms of soil-based data and decision-support tools.
In response to this gap, the National Biodiversity Future Centre (NBFC; www.nbfc.it) was established as Italy’s first national research and innovation hub dedicated to biodiversity, funded by the Ministry of University and Research through the European Union’s NextGenerationEU initiative. Within the NBFC’s “Biodiversity 2030” framework, which aims to enhance biodiversity conservation in protected areas, we propose a soil knowledge tool tailored to mountainous landscapes.
This case study represents a novel application of GIS-based delineation of land units in mountain landscapes, demonstrating how integrating multiple soil-forming factors can support spatial planning and ecological monitoring in areas where traditional sampling approaches are limited.
Specifically, we present a GIS-based methodology that operationalizes Jenny’s model of soil formation—expressed as soil = f(climate, organisms, relief, parent material, time)—to support soil surveys and the delineation of pedodiversity in mountain environments. The method relies on intersecting multiple thematic layers to define land units (LUs), which represent pedologically homogeneous areas. These LUs were used to guide targeted field surveys, allowing soil scientists to reasonably expect similar soil types within each unit, distinct from those in adjacent areas. Beyond guiding fieldwork, LUs also provide a reproducible spatial framework to support further pedological, ecological, and land use analyses.

2. Materials and Methods

2.1. Conceptual Model for Land Unit and Soil Map Unit Delineation

Figure 1 illustrates the flowchart of the conceptual framework developed to delineate land units (LUs), which were then used to guide pedological surveys and construct the corresponding soil map units (SMUs).
The model is grounded in Jenny’s soil formation equation, where soil is defined as a function of climate, organisms, relief, parent material, and time. To operationalize this concept, a set of spatial datasets—namely a lithological map, a land use map, and a digital elevation model (DEM)—were clipped to the boundaries of the study area and reclassified. These layers were overlaid to delineate environmentally homogeneous zones based on combinations of elevation class, dominant land use, lithology, and slope gradient.
Georeferenced soil profile data were assigned to the corresponding LUs and used to refine and validate their delineation. These units also served as a basis for targeted additional sampling. Soil temperature and moisture regimes were estimated from long-term climate data and integrated with field descriptions and laboratory analyses for soil classification. The resulting soil classes were used to characterize each LU, thereby defining soil map units (SMUs), i.e., spatial units representing assemblages of soil types with similar genesis and properties within discrete map polygons [29,30].
Geospatial analyses were carried out using QGIS 3.36 and GRASS GIS 8.2. Vector layers were managed in QGIS, while raster operations (e.g., overlays, reclassifications) were performed in GRASS GIS using modules such as r.cross and r.reclass.

2.2. Study Area Description

The study area encompasses the Frignano and Corno alle Scale Regional Parks, two contiguous protected areas located in the Northern Apennines of Emilia-Romagna (Italy), within the UNESCO-designated MAB Biosphere Reserve. Including the Fiumalbo valley, the area spans approximately 21,502 ha (Figure 2), and includes Mount Cimone (2165 m a.s.l.), the highest peak in the Northern Apennines.
Land Use. Land use data were derived from a high-resolution vector map (reference year 2017, published in 2020), scale 1:10,000, with a minimum mapping unit of 0.16 ha. Land cover classification was based on RGB and infrared orthophotos acquired through the TeA program [31] and conformed to the CORINE Land Cover nomenclature [32]. The area is predominantly forested (~80%), with beech forests (~60%) between 900 and 1600 m a.s.l. dominating the landscape. Chestnut and oak woodlands (~9%) and conifer plantations (~5%) follow, while high-elevation shrublands and grasslands (~12%) and agricultural land (<1%) are also present. For modeling purposes, land use categories were reclassified into eight classes based on their pedogenetic influence.
Lithology. The lithological map was obtained from a 1:10,000-scale geological database curated by the regional Geological Service [33]. The substrate comprises sedimentary, metamorphic, and quaternary formations, grouped into eight lithological categories: (i) arenaceous formations (38%), (ii) arenaceous-pelitic (7%), (iii) pelitic-arenaceous (1%), (iv) marly-calcareous (3%), (v) clay-rich formations (6%), (vi) igneous/metamorphic rocks (<1%), (vii) quaternary deposits (43%), and (viii) alluvial terraces (2%).
Morphology. Topographic data were derived from a high-resolution (5 × 5 m) DEM based on 2009 LiDAR surveys (updated in 2014). Elevation ranges from 475 to 2165 m a.s.l. (mean: 1302 m). Steep slopes are predominantly associated with arenaceous formations, while gentler slopes occur on clay-rich substrates. Five elevation classes and three slope classes (<10%, 10–25%, >25%) were defined, with thresholds consistent with FAO slope classes [34] and USDA Soil Taxonomy subgroups.
Climate. Climatic data for the period 1991–2020 (Figure A1) were provided by the ARPAE agency [35]. The area shows a typical altitudinal gradient, with decreasing temperature and increasing precipitation with elevation. For example, Sestola (833 m a.s.l.) records an annual mean temperature of 10.6 °C and 1204 mm of precipitation, while Fiumalbo (1354 m a.s.l.) records 8.6 °C and 1872 mm. At Mount Cimone (2165 m a.s.l.), the average temperature drops to 2.2 °C and precipitation totals 733 mm annually [36]. Based on these data, soil temperature and moisture regimes were classified as follows: cryic/perudic (>2000 m a.s.l.), frigid/perudic (1600–2000 m), frigid/udic (1300–1600 m), and mesic/udic (<1300 m).

2.3. Soil Survey, Sampling, Laboratory Analyses, and Classification

A total of 112 soil profiles were used in this study (Table S1 of the Supplementary Materials). Of these, 79 were obtained from previous surveys conducted between 2010 and 2021 in the present study area [36,37,38,39,40,41,42,43,44,45,46]. Although these earlier surveys were not originally designed according to the Land Unit (LU) framework, they were spatially referenced and described in sufficient detail to allow their integration into the present study. Meanwhile, 33 soil profiles were dug for the aim of this study during field campaigns in 2023 and 2024, as part of the NBFC project. Indeed, these new surveys aimed to fill gaps in spatial coverage and specifically targeted underrepresented combinations of soil-forming factors.
All profiles were excavated to the C or BC horizon and georeferenced. Each genetic horizon was described following the “Field Book for Describing and Sampling Soils” [47], recording properties such as structure, color, root presence, stoniness, horizon boundaries, depth, and thickness. Approximately 500 g per horizon was collected, air-dried, sieved (<2 mm), and analyzed.
Physicochemical analyses included
  • pH: measured potentiometrically in 1:2.5 soil–water suspension.
  • Particle size: determined by pipette method after sodium hexametaphosphate dispersion [48].
  • Organic C and total N: determined via dry combustion (Flash 2000, Thermo Fisher Scientific, Waltham, MA, USA).
  • Cation exchange capacity (CEC) and exchangeable bases (Ca, Mg, Na and K): extracted with 1 M NH4-acetate at pH 7 and analyzed by inductive coupled plasma optical emission spectrometry (ICP-OES, Arcos II, Kleve, Germany, Ameteck Spectro).
  • Exchange acidity: determined by NaOH titration after KCl extraction.
  • Base saturation: calculated as the percentage of Ca, Mg, K, and Na over total CEC [49].
Soils were classified according to the World Reference Base for Soil Resources [50] and USDA Soil Taxonomy [51].

3. Results

3.1. Land Units

The integration of four thematic layers—reclassified elevation range, land use, slope classes, and lithological formations—allowed the delineation of 15 land units (LUs) with soil cover, plus one unit (lithoid outcrops and urban areas) considered not to be soil-bearing. Table 1 and Table 2 show the areas resulting from the intersection of reclassified elevation ranges (five classes) with land use types (eight classes), and of reclassified geological–lithological formations with slope gradient classes, respectively. As expected, most of the study area lies between 900 and 1600 m a.s.l., predominantly covered by beech forests (12,341 ha). Above 1600 m a.s.l., high-elevation heathland areas dominate, covering 2160 ha. Above 1600 m a.s.l., the high elevation heathland areas (2160 ha) dominate. It is worth noting that some beech forests (~500 ha) and conifer reforestations (~100 ha) were identified between 1600 and 1700 m a.s.l. However, these areas were excluded due to their limited extent and to maintain 1600 m a.s.l. as the upper threshold for forested areas. However, such forests were not considered because of their small area and to keep the 1600 m altitude as the cut-off for the forested area. Approximately 82% of the study area features slopes steeper than 25%, mainly occurring on arenaceous formations (7664 ha) and on chaotic, detrital formations and quaternary deposits (6580 ha).
The intersection of reclassified elevation ranges, land use, geological–lithological formations, and slope classes led to the identification of 15 land units (LUs) with soil presence (Table 3 and Figure 3), plus one additional unit expected to lack soil development (lithoid outcrops and urban areas).
The largest LUs were F/F1 (5348 ha), G/G1 (5989 ha), and I (2409 ha). LUs F/F1 and G/G1 correspond to elevations between 900 and 1300 m and 1300 and 1600 m a.s.l., respectively, and are both predominantly covered by beech forests. LUs G and G1 share similar soil-forming factors, differing mainly in lithology: G is underlain by arenaceous and arenaceous-pelitic formations, whereas G1 is predominantly composed of quaternary deposits, often in glacial detrital facies. LUs E and E1 are both located between 500 and 1400 m a.s.l., differing in lithology: LU E overlays arenaceous and arenaceous-pelitic formations, while LU E1 is developed on quaternary deposits, often in detrital and chaotic facies, and both are covered by basal and submontane forest. LU I is characterized by high-elevation (>1600 m a.s.l.) pasture habitats, mainly developed on arenaceous or glacial deposits. Natural and reforested coniferous forests (LU H) cover 1135 ha, mostly occurring between 900 and 1600 m a.s.l.
The defined LUs are also characterized by varying soil temperature regimes, ranging from mesic to cryic, while the soil moisture regime is generally udic or wetter.

3.2. Soil Map Units

According to the USDA-NRCS [51] soil classification system, the investigated soils belong to the orders Entisols, Inceptisols, Alfisols, Mollisols, and Histosols, with Inceptisols being the most represented (e.g., Humudepts, Eutrudepts, Dystrudepts, Dystrocryepts, Humicryepts). According to the IUSS [50] soil classification system, the soils fall within the Reference Soil Groups of Cambisols, Umbrisols, Regosols, Luvisols, Leptosols, Podzols, Stagnosols, and Histosols. Cambisols were the most widespread soil types. Most soils had continuous rock within 50 cm from the surface, corresponding to Lithic subgroups in the USDA-NRCS system and Leptic qualifiers in the IUSS classification. The delineated soil map units (SMUs) are shown in Figure 3 and described in Table 4. The main soil types associated with each SMU are illustrated in Figure 4. Each SMU was associated with one or more land units (LUs), based on observed pedogenetic processes and profile morphology. For instance, LUs F and F1, characterized by beech forests on arenaceous formations between 900 and 1300 m a.s.l., corresponded to SMUs with shallow Dystric Cambisols and Humudepts.
At elevations up to 1300 m a.s.l., the SMUs were mainly composed of Inceptisols, with Alfisols (E1) and Mollisols (F1 and H) occurring to a lesser extent, according to the USDA-NRCS [51] classification. Under the IUSS [50] classification, most SMUs were dominated by Cambisols, followed by Umbrisols (E, F1, and H), Regosols (E), and Luvisols (E1). In both classification systems, most SMUs exhibited dystric features, primarily due to the arenaceous nature of the parent material.
Above 1300 m a.s.l., SMUs were predominantly classified as Inceptisols, with Entisols (G and W), Mollisols (I1 and J), and Histosols and subaqueous soils, as Wassent (W) also represented, according to the USDA-NRCS [47].
According to the IUSS [50] classification, greater pedodiversity in terms of Soil Reference Groups was observed in SMUs above 1300 m a.s.l. compared to those at lower elevations. Overall, SMUs below 1300 m a.s.l. were dominated by moderately developed soils such as Cambisols and Inceptisols, while higher-elevation areas (>1300 m a.s.l.) exhibited greater pedodiversity, with increased occurrence of Leptosols, Podzols, and Histosols, reflecting harsher climatic conditions and lithological constraints.

3.3. Soil Physicochemical Properties Across Soil Map Units and Land Units

The physicochemical characterization of the soils sampled within the soil map units (SMUs) highlights the diversity of pedological conditions across the study area and provides support for the delineation of land units (LUs). The results are summarized in Table 5.
Soil texture varied significantly among SMUs, reflecting differences in parent materials, slope gradients, and geomorphological settings within the corresponding LUs. For example, LU I1 and J, located at higher elevations (>1600 m a.s.l.), are characterized by soils with high sand content (798 and 670 g/kg, respectively) and minimal clay content, indicative of coarse material accumulation and limited pedogenesis. Conversely, LU D and B, situated in lower-elevation areas on finer substrates (e.g., marly or clayey formations), presented higher clay content (325 and 300 g/kg, respectively) consistent with higher weathering intensity or clay illuviation.
Soil reaction (pH) ranged from strongly acidic to slightly alkaline. Acidic conditions (pH < 5.5) dominated in SMUs corresponding to LUs G, H, and I, all located on steep slopes developed on arenaceous or glacial parent materials, often under beech, coniferous forests, and blueberry groves. In contrast, SMUs associated with LU B, C, and E1, developed on marly and clay-rich substrates or colluvial deposits, showed near-neutral to alkaline pH values (up to pH 7.9), suggesting limited leaching and a higher base status.
Exchangeable calcium (Caexch) followed a similar trend, with the highest concentrations recorded in LU C and E1 in addition to the presence of high-altitude Mollisols (J and I1), from 12.3 to 14.1 cmol(+)/kg), while LU H had the lowest values (<1.5 cmol(+)/kg), reflecting the combined influence of lithology and clime.
Base saturation (BS) further confirmed the contrast between eutric units (e.g., LU I1, J, E1), where BS exceeded 90%, and dystric units (e.g., LU G, H, I), with BS values below 15%. This pattern underscores the relevance of LU classification in predicting soil chemical fertility and ecological functioning.
The organic carbon (OC) and total nitrogen (Ntot) content were generally consistent with vegetation cover and climatic conditions. Generally, they decreased along the soil depth. Soils from LU I1 (Lithic Hapludoll) with limited anthropic disturbance and cooler microclimates showed the highest OC values (6.1%), confirming their role as organic matter reservoirs. In contrast, lower OC values were recorded in LU W and H, possibly due to hydromorphism and advanced leaching under coniferous cover.
These findings demonstrate that the land units (LUs), as delineated through the integration of soil-forming factors, also represent ecologically coherent areas in terms of soil physicochemical properties. The spatial correspondence between LUs and SMUs provides a robust framework for interpreting soil variability and pedodiversity and offers a valuable basis for monitoring, conservation planning, and ecosystem service assessment.

4. Discussion

This study made efforts to propose a spatial classification of the Frignano and Corno alle Scale Regional Parks into land units (LUs), based on key soil-forming factors: land use, parent material, morphology, and climate. Soils, as living and non-renewable resources, play a crucial role in the provision of ecosystem services and are essential for biodiversity conservation, particularly in mountain landscapes [52,53,54,55].
Originally developed for land use planning [56,57,58,59], the LU concept has also proven effective for field survey stratification and environmental evaluation [60,61,62]. Since each LU combines specific environmental and anthropogenic features, soils within a given LU are expected to share similar characteristics, thus reducing survey costs while improving representativeness.
In this study, 16 LUs were delineated. Despite attempts to cluster homogeneous zones, many soil map units (SMUs) included multiple soil types, consistent with the high pedodiversity commonly reported in mountain regions [45,63,64,65]. The fine-scale pedological heterogeneity observed in the study area is largely the result of a complex interplay between morphodynamic processes, microscale topographic variability, and the lasting effects of historical land use and glacial history. Specifically, in mountainous regions such as the northern Apennines, soil formation is heavily influenced by local slope instability, landslides, cryoturbation, and colluvial transport. These processes can lead to abrupt variations in soil depth, texture, and horizon development over very short distances [66,67]. Moreover, microrelief features, including convex and concave landforms, depressions, and rocky outcrops, alter the redistribution of water, organic matter, and fine particles. This, in turn, fosters divergent soil development pathways even within the same LU [68,69]. The legacy of anthropogenic land use, such as historical coppicing, charcoal production, and intensive grazing, has also left lasting imprints on soil profiles, affecting their chemical and physical properties for decades or even centuries [70,71]. Additionally, glacial history plays a significant role: soils in previously glaciated areas are typically younger, less developed, and formed from heterogeneous parent materials such as glacial till and debris flows [72,73]. Together, these interacting factors create a patchwork of soil types and developmental stages, even within areas that appear homogeneous at a broader scale. This intrinsic fine-scale heterogeneity underscores the limitations of coarse-resolution soil mapping and highlights the importance of adaptive survey frameworks, such as the LU-based approach used in this study, which can more effectively capture and interpret variability within land units.
Most soils in the study area were characterized by incipient development and belonged to the Inceptisols/Cambisols orders, with Dystrudepts/Dystric Cambisols being the most frequent. At higher elevations, soils with umbric epipedons were classified as Humudepts/Umbrisols, while Eutrudepts/Eutric Cambisols predominated in more base-rich substrates at lower elevations. The widespread presence of Cambisols across all lithologies suggests that pedogenesis may be constrained by steep slopes (>25% in 82% of the area) and by historical land use pressures, such as coppicing, grazing, and deforestation, which have promoted erosion and limited profile development [74,75,76,77,78,79,80,81]. Our findings align with the overarching patterns observed across Italian mountain regions, where Inceptisols/Cambisols dominate, especially over steep, sandy substrates (northern and central Apennines), with Leptosols and Podzols increasingly present at higher elevations [45,82,83,84]. Globally, mountainous landscapes are known for high pedodiversity at fine spatial scales, driven by steep geomorphological gradients and microtopographic variability; such heterogeneity has been documented across the European biogeographical regions [85]. This underlines the relevance of our approach in capturing such diversity through a stratified analysis. In fact, in some low- to mid-elevation sites, more developed soils such as Hapludolls and Luvisols indicated possible pathways of pedogenic evolution under favorable morphological conditions. In contrast, cryic soils (e.g., Dystrocryepts, Haplocryolls) showed variable developmental stages despite uniform vegetation, likely influenced by microtopography or frost action. Hydromorphic areas (LU W), although limited in extent, revealed substantial pedodiversity, including Udifluvents, Histosols, and Fluviwassents, shaped by vegetation gradients and water saturation regimes.
In addition to taxonomic classification, the physicochemical properties of the soils (Table 5) provide valuable insights into pedogenetic processes across different LUs. Soils developed at lower elevations and on more stable surfaces—such as those in LUs B, C, and E1—showed neutral to slightly alkaline pH values (up to 7.9), high exchangeable calcium (up to 14.1 cmol(+)/kg), and base saturation exceeding 50%. These conditions suggest low leaching intensity and a high weathering status of base-rich substrates, consistent with their classification as Eutrudepts or Luvisols [86]. By contrast, mid- and high-elevation soils (e.g., LUs F, G, H) were more acidic (pH 4.4–5.3), with lower Ca availability and base saturation, reflecting leaching-driven acidification and organic matter accumulation under forest cover—typical features of Dystrudepts and Umbrisols [87,88]. In high-elevation units (e.g., I, J, W), coarse textures, shallow profiles, and low nutrient levels were prevalent, consistent with cryic or hydromorphic soils such as Cryepts, Histosols, and Fluviwassents [89,90,91]. These patterns confirm the strong link between soil properties and the soil-forming factors used in LU delineation, supporting the LU approach as a cost-effective method to capture landscape-level soil variability [14,15].
The combination of pre-existing soil data and newly collected data provided a solid foundation for validating the proposed LU-based soil map. While the earlier profiles ensured long-term coverage of the study area, the newly excavated soil profiles filled gaps in environmentally underrepresented contexts. This integrated dataset allowed for a realistic assessment of internal variability within the LUs and improved the overall reliability of the final classification. All 112 soil profiles were used to validate the LU-based soil map, ensuring that each LU was supported by field observations and that the map accurately reflected actual soil variability. This approach maximized the representativeness of the sampling network and enabled the assessment of pedodiversity across contrasting geomorphological and ecological settings.
However, it is important to mention that although this study did not include a quantitative assessment of the geometric accuracy of the LUs boundary or their internal homogeneity, their delineation was carried out at an operational scale of 1:25,000. This scale is appropriate for regional planning and the design of stratified soil surveys in mountainous contexts. The validity of the LU boundaries was supported by field verification using 112 georeferenced soil profiles. Field observations confirmed that most soils within a given LU shared similar morphological, physical, and chemical characteristics, consistent with the factors used in the delineation process. The consistency between expected and observed characteristics supports the reliability of our approach. The internal consistency of the LUs was also indirectly supported by the observed differentiation in key physico-chemical soil properties such as pH, base saturation and texture (Table 5) indicating that the classification effectively captured relevant pedogenetic variability. We acknowledge that a more formal accuracy assessment, combining targeted field validation with high-resolution techniques (e.g., remote sensing) and predictive statistical analyses (e.g., digital soil mapping), would strengthen the method and further enhance its cost-effectiveness and replicability in other protected and mountainous landscapes.
Overall, the identified land units and associated soil patterns offer a robust framework for environmental monitoring, conservation planning, and understanding of soil responses to climatic and anthropogenic drivers. The LU-based approach proves especially suitable for mountain landscapes, where traditional grid or random sampling methods are often impractical or inefficient.

5. Conclusions

GIS-based land units (LUs) model, defined by soil-forming factors, can improve stratified soil surveys and ecological monitoring in complex mountain landscapes.
In our case study, 16 LUs were identified, with LUs F/F1 (5348 ha), G/G1 (5989 ha), and I (2409 ha) as the largest ones. The soil analysis showed diverse conditions across LUs. For example, sandy soil particles dominated LUs I1 and J, with values ranging between 670 and 798 g/kg, while LUs D and B had more clay (from 300 to 325 g/kg) due to marly or clayey substrates. Soil pH varied widely, with acidic conditions in steep, forested areas (LUs G, H, I) and neutral to alkaline pH in LUs on clay-rich or colluvial soils (B, C, E1). Organic carbon content showed highest values in LU I1 (6.1%) due to the dense vegetation and minimal human impact.
Our approach can minimize soil sampling costs compared to grid sampling, considering that, to obtain the soil map, the soil profiles must be dug, and each genetic horizon described and sampled, then classified. In addition to guiding new field investigations, our framework can integrate existing soil data from literature and legacy surveys, thus maximizing the value of prior efforts. Furthermore, beyond its scientific applications, this approach could represent a valuable decision-support tool for park authorities, enabling more informed planning, conservation prioritization, and ecosystem service mapping in protected areas.
The lack of boundary accuracy in areas with complex microtopography, the limited validation in inaccessible zones, and some internal heterogeneity within LUs, indicating great pedodiversity, can be considered the major limitations. Future enhancements can incorporate satellite-derived indicators (e.g., NDVI) and machine learning approaches from digital soil mapping which could improve spatial detail, enhance boundary accuracy, and provide soil properties predictions and ecosystem services across the study area.
Mapping ecosystem services is recognized as vital for conservation planning, managing multifunctional landscapes and increasing protected areas according to Biodiversity 2030 European law. LUs model lays the foundation for the development of derived maps (i.e., carbon storage, water erosion, hydro-pedo-geological risks), providing a spatial structure that can support ecosystem service assessment—enabling efficient identification of hotspots, trade-offs, and conservation priorities.
We conclude that the LU-based method is a practical and replicable tool with strong potential for advancing soil-informed environmental management, biodiversity protection, and ecosystem service evaluation in mountain protected areas.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land14081683/s1, Table S1: Location of soil profiles.

Author Contributions

Conceptualization, G.V. and L.V.A.; methodology, G.V. and L.V.A.; validation, L.V.A.; formal analysis, W.T. and M.G.; investigation, M.D.F. and W.T.; resources, L.V.A.; data curation, W.T., G.V. and L.V.A.; writing—original draft preparation, M.D.F.; writing—review and editing, M.D.F. and L.V.A.; visualization, W.T.; supervision, L.V.A.; funding acquisition, L.V.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4—Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union—NextGenerationEU; Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUPJ33C22001190001, Project title “National Biodiversity Future Center—NBFC”.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

We would like to thank Hugo Ludlam for his thorough English editing of the entire manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Land 14 01683 g0a1
Figure A1. Bagnouls Gaussen climate diagrams referring to the thermopluviometric stations of Fiumalbo (1354 m a.s.l.) and Sestola (833 m a.s.l.).
Figure A1. Bagnouls Gaussen climate diagrams referring to the thermopluviometric stations of Fiumalbo (1354 m a.s.l.) and Sestola (833 m a.s.l.).
Land 14 01683 g0a1

References

  1. Salazar, S.; Ochoa, A.; McCarthy, P.J. How the visit of pedologist Hans Jenny to Colombia (1946–1947) contributed to the theory of soil-forming factors. Geoderma 2023, 437, 116575. [Google Scholar] [CrossRef]
  2. Ibanez, J.J.; De-Alba, S.; Lobo, A.; Zucarello, V.; Yaalon, D.H. Pedodiversity and global soil patterns at coarse scales (with discussion). Geoderma 1998, 83, 171–192. [Google Scholar] [CrossRef]
  3. Yaalon, D.H. The soils we classify Essay review of recent publications on soil taxonomy. Catena 1995, 24, 233–241. [Google Scholar] [CrossRef]
  4. Mikhailova, E.A.; Zurqani, H.A.; Post, C.J.; Schlautman, M.A.; Post, G.C. Soil diversity (Pedodiversity) and ecosystem services. Land 2021, 10, 288. [Google Scholar] [CrossRef]
  5. Adhikari, K.; Hartemink, A.E. Linking soils to ecosystem services—A global review. Geoderma 2016, 262, 101–111. [Google Scholar] [CrossRef]
  6. Songchao, C.; Dominique, A.; Leatitia, M.V.; Laura, P.; Budiman, M.; Pierre, R.; Zamir, L.; Philippe, L.; Zhou, S.; Jacqueline, H.; et al. Digital mapping of GlobalSoilMap soil properties at a broad scale: A review. Geoderma 2021, 409, 115567. [Google Scholar]
  7. Brevik, E.C.; Calzolari, C.; Miller, B.A.; Pereira, P.; Kabala, C.; Baumgarten, A.; Jordán, A. Soil mapping, classification, and pedologic modeling: History and future directions. Geoderma 2016, 264, 256–274. [Google Scholar] [CrossRef]
  8. Stoorvogel, J.J.; Bakkenes, M.; Temme, A.J.A.M.; Batjes, N.H.; ten Brink, B.J.E. S-World: A Global Soil Map for Environmental Modelling. Land Degrad. Dev. 2017, 28, 22–33. [Google Scholar] [CrossRef]
  9. Sermin, T.; Jeff, J. GIS-based automated landform classification and topographic, landcover and geologic attributes of landforms around the Yazoren Polje, Turkey. J. Appl. Sci. 2008, 8, 910–921. [Google Scholar] [CrossRef]
  10. McBratney, A.B.; Odeh, I.O.A.; Bishop, T.F.A.; Dunbar, M.S.; Shatar, T.M. An overview of pedometric techniques for use in soil survey. Geoderma 2000, 97, 293–327. [Google Scholar] [CrossRef]
  11. Rossiter, D.G. Digital Soil Resource Inventories: Status and Prospects in 2015. In Digital Soil Mapping Across Paradigms, Scales and Boundaries; Springer: Singapore, 2016; pp. 275–286. [Google Scholar]
  12. Hartemink, A.E.; Krasilnikov, P.; Bockheim, J.G. Soil maps of the world. Geoderma 2013, 207–208, 256–267. [Google Scholar] [CrossRef]
  13. Costantini, E.A.C.; Lorenzetti, R. Soil degradation processes in Italian agricultural and forest ecosystems. Ital. J. Agron. 2013, 8, e28. [Google Scholar] [CrossRef]
  14. Arrouays, D.; Leenaars, J.G.B.; Richer-de-Forges, A.C.; Adhikari, K.; Ballabio, C.; Greve, M.; Grundy, M.; Guerrero, E.; Hempel, J.; Hengl, T.; et al. Soil legacy data rescue via GlobalSoilMap and other international initiatives. GeoResJ 2017, 14, 1–19. [Google Scholar] [CrossRef]
  15. FAO & ITPS. State of Knowledge of Soil Biodiversity—Status, Challenges and Potentialities; FAO: Rome, Italy, 2020. [Google Scholar]
  16. Smiraglia, D.; Capotorti, G.; Guida, D.; Mollo, B.; Siervo, V.; Blasi, C. Land units map of Italy. J. Maps 2013, 9, 239–244. [Google Scholar] [CrossRef]
  17. Zonneveld, I.S. The land unit—A fundamental concept in landscape ecology, and its applications. Landsc. Ecol. 1989, 3, 67–86. [Google Scholar] [CrossRef]
  18. Fisher, R.A.; Koven, C.D. Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems. J. Adv. Model. Earth Syst. 2020, 12, e2018MS001453. [Google Scholar] [CrossRef]
  19. Sonneveld, M.P.W.; Hack-ten Broeke, M.J.D.; van Diepen, C.A.; Boogaard, H.L. Thirty years of systematic land evaluation in the Netherlands. Geoderma 2010, 156, 84–92. [Google Scholar] [CrossRef]
  20. Kiryushin, V.I. Methodology for Integrated Assessment of Agricultural Land. Eurasian Soil Sci. 2020, 53, 960–967. [Google Scholar] [CrossRef]
  21. Price, M.F. Mountains Move Up the European Agenda. Mt. Res. Dev. 2016, 36, 376–379. [Google Scholar] [CrossRef]
  22. Perrigo, A.; Hoorn, C.; Antonelli, A. Why mountains matter for biodiversity. J. Biogeogr. 2020, 47, 315–325. [Google Scholar] [CrossRef]
  23. Locatelli, B.; Lavorel, S.; Sloan, S.; Tappeiner, U.; Geneletti, D. Characteristic trajectories of ecosystem services in mountains. Front. Ecol. Environ. 2017, 15, 150–159. [Google Scholar] [CrossRef]
  24. Chakraborty, A. Mountains as vulnerable places: A global synthesis of changing mountain systems in the Anthropocene. GeoJournal 2021, 86, 585–604. [Google Scholar] [CrossRef]
  25. Macchi, M. Mountains of the World—Ecosystem Services in a Time of Global and Climate Change; ICIMOD: Kathmandu, Nepal, 2010. [Google Scholar]
  26. D’Amico, M.E.; Pintaldi, E.; Sapino, E.; Colombo, N.; Quaglino, E.; Stanchi, S.; Navillod, E.; Rocco, R.; Freppaz, M. Soil types of Aosta Valley (NW-Italy). J. Maps 2020, 16, 755–765. [Google Scholar] [CrossRef]
  27. Jenkins, C.N.; Joppa, L. Expansion of the global terrestrial protected area system. Biol. Conserv. 2009, 142, 2166–2174. [Google Scholar] [CrossRef]
  28. Chape, S.; Harrison, J.; Spalding, M.; Lysenko, I. Measuring the extent and effectiveness of protected areas as an indicator for meeting global biodiversity targets. Philos. Trans. R. Soc. B Biol. Sci. 2005, 360, 443–455. [Google Scholar] [CrossRef]
  29. Vincent, S.; Lemercier, B.; Berthier, L.; Walter, C. Spatial disaggregation of complex Soil Map Units at the regional scale based on soil-landscape relationships. Geoderma 2018, 311, 130–142. [Google Scholar] [CrossRef]
  30. Odgers, N.P.; Sun, W.; McBratney, A.B.; Minasny, B.; Clifford, D. Disaggregating and harmonising soil map units through resampled classification trees. Geoderma 2014, 214–215, 91–100. [Google Scholar] [CrossRef]
  31. RER. 2017—Coperture Vettoriali Uso del Suolo di Dettaglio—Edizione 2020. 2020. Available online: https://metasfera.regione.emilia-romagna.it/ricerca_metadato?uuid=r_emiro:2020-04-06T135318 (accessed on 13 June 2025).
  32. Copernicus. CORINE Land Cover (from 1990 to 2018). 2021. Available online: https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html/ (accessed on 13 June 2025).
  33. RER. Banca Dati Geologica 1:10,000. 2020. Available online: https://metasfera.regione.emilia-romagna.it/ricerca_metadato?uuid=r_emiro:2017-06-12T113543 (accessed on 13 June 2025).
  34. FAO. Guidelines for Soil Description. Rome: Food and Agriculture Organization of the United Nations; FAO: Rome, Italy, 2006; ISBN 92-5-1055-21-1. [Google Scholar]
  35. ARPAE. Tabelle Climatologiche. 2024. Available online: https://www.arpae.it/it/temi-ambientali/clima/dati-e-indicatori/tabelle-climatiche (accessed on 13 June 2025).
  36. Vittori Antisari, L.; Agnelli, A.; Corti, G.; Falsone, G.; Ferronato, C.; Marinari, S.; Vianello, G. Modern and ancient pedogenesis as revealed by Holocene fire—Northern Apennines, Italy. Quat. Int. 2018, 467, 264–276. [Google Scholar] [CrossRef]
  37. Brombin, V.; Mistri, E.; De Feudis, M.; Forti, C.; Salani, G.M.; Natali, C.; Falsone, G.; Vittori Antisari, L.; Bianchini, G. Soil carbon investigation in three pedoclimatic and agronomic settings of northern Italy. Sustainability 2020, 12, 10539. [Google Scholar] [CrossRef]
  38. Dell’Abate, M.T.; Giovannini, C.; Pontalti, F.; Vianello, G.; Vittori Antisari, L. Caratterizzazione Quali-Quantitativa della Sostanza Organica degli Epipedon di Suoli Bruni Acidi dell’Appennino Emiliano sotto Bosco di Latifoglie e di Conifere; Gessa, C., Lorito, S., Vianello, G., Vittori Antisari, L., Eds.; Atti Convegno Nazionale Società Italiana di Scienze del Suolo “Suolo Ambiente Paesaggio”; Alma Mater Studiorum Universita di Bologna: Bologna, Italy, 2007; Volume 1, pp. 332–342. ISBN 978.88.902831-1-6. (In Italian) [Google Scholar]
  39. Falsone, G.; Marinari, S.; Vittori Antisari, L.; Vianello, G. Soil processes related to organic matter modifications following Douglas-fir mature reforestation. Biol. Fertil. Soils 2015, 51, 277–287. [Google Scholar] [CrossRef]
  40. Marinari, S.; Bonifacio, E.; Moscatelli, M.C.; Falsone, G.; Vittori Antisari, L.; Vianello, G. Soil development and microbial functional diversity: Proposal for a methodological approach. Geoderma 2013, 192, 437–445. [Google Scholar] [CrossRef]
  41. Vittori Antisari, L.; Vianello, G.; Pontalti, F.; Lorito, S.; Gherardi, M. Land use effects on organic matter in brown soils of the emilian apennines. In the Soils Tomorrow: Soils Changing in a Changing World, Proceedings of the 5th International Congress of the European Society for Soil Conservation "Soils Changing in a Changing World: The Soils of Tomorrow", Palermo, Italy, 25–30 June 2007; Catena Verlag: Reiskirchen, Germany, 2008; pp. 311–328. [Google Scholar]
  42. Vittori Antisari, L.; Marinari, S.; Dell’Abate, M.T.; Baffi, C.; Vianello, G. Plant cover and epipedon SOM stability as factors affecting brown soil profile development and microbial activity. Geoderma 2011, 161, 212–224. [Google Scholar] [CrossRef]
  43. Vittori Antisari, L.; Falsone, G.; Carbone, S.; Marinari, S.; Vianello, G. Douglas-fir reforestation in North Apennine (Italy): Performance on soil carbon sequestration, nutrients stock and microbial activity. Appl. Soil Ecol. 2015, 86, 82–90. [Google Scholar] [CrossRef]
  44. Vittori Antisari, L.; Trenti, W.; De Feudis, M.; Bianchini, G.; Falsone, G. Soil quality and organic matter pools in a temperate climate (Northern italy) under different land uses. Agronomy 2021, 11, 1815. [Google Scholar] [CrossRef]
  45. Vittori Antisari, L.; Trenti, W.; Buscaroli, A.; Falsone, G.; Vianello, G.; De Feudis, M. Pedodiversity and Organic Matter Stock of Soils Developed on Sandstone Formations in the Northern Apennines (Italy). Land 2023, 12, 79. [Google Scholar] [CrossRef]
  46. De Feudis, M.; Falsone, G.; Vianello, G.; Agnelli, A.; Vittori Antisari, L. Soil organic carbon stock assessment in forest ecosystems through pedogenic horizons and fixed depth layers sampling: What’s the best one? Land Degrad. Dev. 2022, 33, 1446–1458. [Google Scholar] [CrossRef]
  47. Schoeneberger, P.J.; Wysocki, D.A.; Benham, E.C. Soil Survey Staff Field Book for Describing and Sampling Soils, Version 3.0; Natural Resources Conservation Service, National Soil Survey Center: Lincoln, NE, USA, 2012.
  48. Gee, G.W.; Bauder, J.W. Particle-size analysis. In Methods of Soil Analysis; Soil Science Society of America (SSSA): Madison, WI, USA, 1986; pp. 383–411. [Google Scholar]
  49. Soil Chemistry. 1979. Available online: https://www.taylorfrancis.com/chapters/mono/10.1201/9781315137322-6/soil-chemistry-duane-winegardner (accessed on 13 June 2025).
  50. IUSS. World Reference Base for Soil Resources. International Soil Classification System for Naming Soils and Creating Legends for Soil Maps, 4th ed.; International Union of Soil Sciences (IUSS) Working Group WRB: Vienna, Austria, 2022. [Google Scholar]
  51. USDA-NRCS. Soil Survey Staff Keys to Soil Taxonomy, 13th ed.; USDA-NRCS: Washington, DC, USA, 2022.
  52. Costantini, E.A.C.; Mocali, S. Soil health, soil genetic horizons and biodiversity. J. Plant Nutr. Soil Sci. 2022, 185, 24–34. [Google Scholar] [CrossRef]
  53. Buol, S.W.; Southard, R.J.; Graham, R.C.; McDaniel, P.A. Soil Genesis and Classification; John Wiley and Sons: Hoboken, NJ, USA, 2011. [Google Scholar] [CrossRef]
  54. Chauvier, Y.; Thuiller, W.; Brun, P.; Lavergne, S.; Descombes, P.; Karger, D.N.; Renaud, J.; Zimmermann, N.E. Influence of climate, soil, and land cover on plant species distribution in the European Alps. Ecol. Monogr. 2021, 91, e01433. [Google Scholar] [CrossRef]
  55. Halleux, V. Soil Monitoring and Resilience (Soil Monitoring Directive); European Parliament: Strasbourg, France, 2024.
  56. Vacca, A.; Loddo, S.; Melis, M.T.; Funedda, A.; Puddu, R.; Verona, M.; Fanni, S.; Fantola, F.; Madrau, S.; Marrone, V.A.; et al. A GIS based method for soil mapping in Sardinia, Italy: A geomatic approach. J. Environ. Manag. 2014, 138, 87–96. [Google Scholar] [CrossRef]
  57. Bastian, O. Landscape classification in Saxony (Germany)—A tool for holistic regional planning. Landsc. Urban Plan. 2000, 50, 145–155. [Google Scholar] [CrossRef]
  58. di Lisio, A.; Russo, F. Thematic maps for land-use planning and policy decisionsin the calaggio stream catchment area. J. Maps 2010, 6, 68–83. [Google Scholar] [CrossRef]
  59. Şahin, Ş.; Bekişoğlu, Ü. Landscape planning and management strategies for the Zir Valley, near Ankara, Turkey. Environ. Geol. 2009, 57, 297–305. [Google Scholar] [CrossRef]
  60. Bockheim, J.G.; Gennadiyev, A.N.; Hartemink, A.E.; Brevik, E.C. Soil-forming factors and Soil Taxonomy. Geoderma 2014, 226–227, 231–237. [Google Scholar] [CrossRef]
  61. Adamchuk, V.I.; Viscarra Rossel, R.A.; Marx, D.B.; Samal, A.K. Using targeted sampling to process multivariate soil sensing data. Geoderma 2011, 163, 63–73. [Google Scholar] [CrossRef]
  62. Bouma, J. Contributing pedological expertise towards achieving the United Nations Sustainable Development Goals. Geoderma 2020, 375, 114508. [Google Scholar] [CrossRef]
  63. Vacek, O.; Vašát, R.; Borůvka, L. Quantifying the pedodiversity-elevation relations. Geoderma 2020, 373, 114441. [Google Scholar] [CrossRef]
  64. Ibáñez, J.J.; Feoli, E. Global Relationships of Pedodiversity and Biodiversity. Vadose Zo. J. 2013, 12, 1–5. [Google Scholar] [CrossRef]
  65. Costantini, E.A.C.; L’Abate, G. Beyond the concept of dominant soil: Preserving pedodiversity in upscaling soil maps. Geoderma 2016, 271, 243–253. [Google Scholar] [CrossRef]
  66. Irl, S.D.H.; Harter, D.E.V.; Steinbauer, M.J.; Gallego Puyol, D.; Fernández-Palacios, J.M.; Jentsch, A.; Beierkuhnlein, C. Climate vs. topography—Spatial patterns of plant species diversity and endemism on a high-elevation island. J. Ecol. 2015, 103, 1621–1633. [Google Scholar] [CrossRef]
  67. Garcia-Pausas, J.; Casals, P.; Camarero, L.; Huguet, C.; Sebastià, M.T.; Thompson, R.; Romanyà, J. Soil organic carbon storage in mountain grasslands of the Pyrenees: Effects of climate and topography. Biogeochemistry 2007, 82, 279–289. [Google Scholar] [CrossRef]
  68. Shrestha, D.P.; Suriyaprasit, M.; Prachansri, S. Assessing soil erosion in inaccessible mountainous areas in the tropics: The use of land cover and topographic parameters in a case study in Thailand. Catena 2014, 121, 40–52. [Google Scholar] [CrossRef]
  69. Alexander, C.; Deák, B.; Heilmeier, H. Micro-topography driven vegetation patterns in open mosaic landscapes. Ecol. Indic. 2016, 60, 906–920. [Google Scholar] [CrossRef]
  70. Dufour, A.; Gadallah, F.; Wagner, H.H.; Guisan, A.; Buttler, A. Plant species richness and environmental heterogeneity in a mountain landscape: Effects of variability and spatial configuration. Ecography 2006, 29, 573–584. [Google Scholar] [CrossRef]
  71. Ballabio, C. Spatial prediction of soil properties in temperate mountain regions using support vector regression. Geoderma 2009, 151, 338–350. [Google Scholar] [CrossRef]
  72. Herbst, P.; Gross, J.; Meer, U.W.E.; Mosimann, T. Geomorphographic terrain classification for predicting forest soil properties in Northwestern Switzerland. Z. Fur Geomorphol. 2012, 56, 1–22. [Google Scholar] [CrossRef]
  73. Geitner, C.; Tusch, M.; Meißl, G.; Kringer, K.; Wiegand, C. Effects of topography on the spatial distribution of soils: Basic considerations on interdependencies and data sources with examples from the Eastern Alps. Z. Fur Geomorphol. 2011, 55, 127–146. [Google Scholar] [CrossRef]
  74. Fazlollahi Mohammadi, M.; Jalali, S.G.H.; Kooch, Y.; Said-Pullicino, D. Slope gradient and shape effects on soil profiles in the northern mountainous forests of Iran. Eurasian Soil Sci. 2016, 49, 1366–1374. [Google Scholar] [CrossRef]
  75. Badía, D.; Martí, C.; Aznar, J.M.; León, J. Influence of slope and parent rock on soil genesis and classification in semiarid mountainous environments. Geoderma 2013, 193–194, 13–21. [Google Scholar] [CrossRef]
  76. RER. Foto Aeree del Volo IGMI GAI 1954-1955. 2012. Available online: https://geoportale.regione.emilia-romagna.it/applicazioni-gis/regione-emilia-romagna/cartografia-di-base/cartografia-storica/foto-aeree-del-volo-igmi-gai-1954-1955 (accessed on 13 June 2025).
  77. RER. Ortofoto RER 1976-1978. 2022. Available online: https://geoportale.regione.emilia-romagna.it/catalogo/dati-cartografici/cartografia-di-base/immagini/layer-8 (accessed on 13 June 2025).
  78. Agnoletti, M.; Piras, F.; Venturi, M.; Santoro, A. Cultural values and forest dynamics: The Italian forests in the last 150 years. For. Ecol. Manag. 2022, 503, 119655. [Google Scholar] [CrossRef]
  79. Vacca, A.; Aru, F.; Ollesch, G. Short-term Impact of Coppice Management on Soil in a Quercus ilex l. Stand of Sardinia. Land Degrad. Dev. 2017, 28, 553–565. [Google Scholar] [CrossRef]
  80. Quinton, J.N.; Edwards, G.M.; Morgan, R.P.C. The influence of vegetation species and plant properties on runoff and soil erosion: Results from a rainfall simulation study in south east Spain. Soil Use Manag. 1997, 13, 143–148. [Google Scholar] [CrossRef]
  81. Mohammad, A.G.; Adam, M.A. The impact of vegetative cover type on runoff and soil erosion under different land uses. Catena 2010, 81, 97–103. [Google Scholar] [CrossRef]
  82. De Feudis, M.; Cardelli, V.; Massaccesi, L.; Bol, R.; Willbold, S.; Cocco, S.; Corti, G.; Agnelli, A. Effect of beech (Fagus sylvatica L.) rhizosphere on phosphorous availability in soils at different altitudes (Central Italy). Geoderma 2016, 276, 53–63. [Google Scholar] [CrossRef]
  83. De Feudis, M.; Massaccesi, L.; Poesio, C.; Vittori Antisari, L.; Bol, R.; Agnelli, A. Seasonal effects of altitude and vegetation on litter and organic carbon in deciduous and coniferous forest soils. Geoderma 2025, 459, 117382. [Google Scholar] [CrossRef]
  84. Costantini, E.A.; Barbetti, R.; Fantappiè, M.; L’Abate, G.; Lorenzetti, R.; Magini, S. Pedodiversity. In The Soils of Italy; World Soils Book Series; Costantini, E.A., Dazzi, C., Eds.; Springer: Dordrecht, The Netherlands, 2013; pp. 105–178. [Google Scholar] [CrossRef]
  85. Ibáñez, J.J.; Zinck, J.A.; Dazzi, C. Soil geography and diversity of the European biogeographical regions. Geoderma 2013, 192, 142–153. [Google Scholar] [CrossRef]
  86. Tóth, G.; Jones, A.; Montanarella, L. LUCAS Topsoil Survey. Methodology, Data and Results; Office of the European Union: Luxembourg, 2013; Number 26102. [Google Scholar]
  87. Van Miegroet, H.; Cole, D.W. Acidification sources in red alder and Douglas-fir soils. Soil Sci. Soc. Am. J. 1985, 49, 1274–1279. [Google Scholar] [CrossRef]
  88. Certini, G. Effects of fire on properties of forest soils: A review. Oecologia 2005, 143, 1–10. [Google Scholar] [CrossRef]
  89. Bockheim, J.G. Cryosols as a Three-Part System. In Cryopedology; Progress in Soil Science; Springer: Cham, Switzerland, 2015. [Google Scholar] [CrossRef]
  90. Schaetzl, R.J.; Anderson, S. Soils: Genesis and Geomorphology; Cambridge University Press: Cambridge, UK, 2005. [Google Scholar]
  91. Egli, M.; Mirabella, A.; Sartori, G. The role of climate and vegetation in weathering and clay mineral formation in late Quaternary soils of the Swiss and Italian Alps. Geomorphology 2008, 102, 307–324. [Google Scholar] [CrossRef]
Land 14 01683 g001
Figure 1. Methodological flowchart outlining the steps for identifying land units and soil map units.
Figure 1. Methodological flowchart outlining the steps for identifying land units and soil map units.
Land 14 01683 g001
Land 14 01683 g002
Figure 2. Study area including the Frignano and Corno alle Scale Regional Parks (Emilia-Romagna, Italy), WGS 84-UTM 32.
Figure 2. Study area including the Frignano and Corno alle Scale Regional Parks (Emilia-Romagna, Italy), WGS 84-UTM 32.
Land 14 01683 g002
Land 14 01683 g003
Figure 3. Soil map units in the Frignano Regional Park and Corno alle Scale Regional Park in Emilia-Romagna region (Italy).
Figure 3. Soil map units in the Frignano Regional Park and Corno alle Scale Regional Park in Emilia-Romagna region (Italy).
Land 14 01683 g003
Land 14 01683 g004
Figure 4. Representative pedological profiles of the different soil map units.
Figure 4. Representative pedological profiles of the different soil map units.
Land 14 01683 g004
Table 1. Areas obtained by crossing the considered elevation ranges and reclassified land uses.
Table 1. Areas obtained by crossing the considered elevation ranges and reclassified land uses.
Reclassified Land UsesElevation Ranges (m Above Sea Level)
<900900–13001300–16001600–2000>2000
ha
Urbanized areas64.09249.5231.580.461.24
Agricultural and cultivated areas36.99190.6240.5200
Grassland, pastures and bushes32.82775.65807.382159.6339.72
Forest with prevalent beech44.596112.056229.63554.980
Forest of the sub-mountain plane,
chestnuts		1514.12847.52369.2819.690
Forest with prevalence conifers,
reforestation		27.22468.64527.96110.350
Lithoid outcrops0.4536.2541.03115.0417.96
Water courses and bodies10.669.7913.021.510
Total area1730.948690.048060.42961.6658.92
Table 2. Areas obtained by crossing the reclassified lithology classes and slope gradient.
Table 2. Areas obtained by crossing the reclassified lithology classes and slope gradient.
Reclassified LithologySlope Gradient (%)
0–1010–25>25
ha
Arenaceous formations53.81477.057664.51
Arenaceous-pelitic formations5.1948.551417.97
Pelitic-arenaceous formations1.3213.64281.5
Marly-calcareous formations7.9572.68648.49
Clay formations25.92254.681009.24
Igneous and metamorphic formations0.030.255.81
Chaotic, detrital formations and quaternary deposits351.122228.416580.69
Alluvial formations and river terraces83.32147.26122.57
Total area528.663242.5217,730.78
Table 3. Land units obtained by the cross of land use, lithology, slope gradient and elevation ranges in the Frignano Regional park and Corno alle Scale regional park, Emilia-Romagna region, Italy.
Table 3. Land units obtained by the cross of land use, lithology, slope gradient and elevation ranges in the Frignano Regional park and Corno alle Scale regional park, Emilia-Romagna region, Italy.
Land Unit (Area)Description
A (37.06 ha)Arable land, tree crops, meadows on river terraces and alluvial deposits, mainly adjacent to watercourses, below 1000 m a.s.l., with slopes generally < 10%.
B (157.80 ha)Land mainly cultivated for arable crops or used as pasture, mostly below 1300 m a.s.l., with slopes between 10 and 25%, on marly and clayey formations.
C (932.22 ha)Areas used for arable crops or pasture, mostly below 1500 m a.s.l., with slopes between 10 and 25%, on arenaceous and arenaceous-pelitic formations, also in detrital facies.
D (1813.32 ha)Transitional areas between submontane and montane zones (500–1200 m a.s.l.), with slopes between 10 and 25%, characterized by broadleaf tree and shrub cover, on marly, clayey, igneous-metamorphic formations and alluvial deposits.
E (1579.82 ha)Areas between 500 and 1400 m a.s.l., mainly with slopes > 25%, in a mesic temperature regime, covered by basal and submontane forest, on arenaceous and arenaceous-pelitic formations.
E1 (969.08 ha)Areas between 500 and 1400 m a.s.l., in a mesic temperature regime, with slopes between 15 and 30%, covered by basal and submontane forests, on quaternary deposits often in detrital or chaotic facies.
F (2865.55 ha)Areas between 900 and 1300 m a.s.l., in a mesic temperature regime, covered by broadleaf forests dominated by beech, on arenaceous and arenaceous-pelitic formations.
F1 (2482.62 ha)Areas between 900 and 1300 m a.s.l., in a mesic temperature regime, mainly with slopes >25%, covered by beech forests on quaternary deposits, often in detrital or chaotic facies.
G (3254.50 ha)Areas between 1300 and 1600 m a.s.l., in transition from mesic to frigid temperature regimes, with slopes > 25%, covered by deciduous forests, mostly beech, on arenaceous and arenaceous-pelitic formations.
G1 (2735.38 ha)Areas between 1300 and 1600 m a.s.l., in transition from mesic to frigid temperature regimes, with slopes > 25%, covered by deciduous forests, mostly beech, on quaternary deposits, often in glacial facies.
H (1135.22 ha)Areas mostly between 900 and 1600 m a.s.l., covered by coniferous forests from artificial reforestation, mainly on arenaceous and arenaceous-pelitic formations, also in detrital facies.
I (2409.63 ha)Areas between 1600 and 2000 m a.s.l., with slopes > 25%, characterized by polyphytic meadows and bilberry heaths, in a frigid temperature regime, mainly on arenaceous and glacial deposits.
I1 (504.82 ha)Areas between 1600 and 2000 m a.s.l., with slopes between 20 and 30%, characterized by mountain grasslands, in a frigid temperature regime, mainly on pelitic and glacial deposits, and to a lesser extent on marly, clayey, and chaotic formations.
J (40.31 ha)High-elevation grasslands above 2000 m a.s.l., in a cryic temperature regime, mainly on arenaceous and pelitic-arenaceous formations, also in detrital facies.
L (211.70 ha)Lithoid outcrops on marly, clayey, arenaceous, and pelitic formations, also in detrital facies.
W (22.56 ha)Hydromorphic areas, submerged or intermittently emerged, associated with lake surfaces above 1200 m a.s.l., characterized by peat deposits.
Table 4. Soil map units of the Frignano Regional park and Corno alle Scale regional park, Emilia-Romagna region, Italy. The soil types are classified according to IUSS (2022) and USDA-NRCS (2022) classification systems. In bold type are the most representative soil types within each soil map unit.
Table 4. Soil map units of the Frignano Regional park and Corno alle Scale regional park, Emilia-Romagna region, Italy. The soil types are classified according to IUSS (2022) and USDA-NRCS (2022) classification systems. In bold type are the most representative soil types within each soil map unit.
Soil Map Unit (Area)DescriptionSoil Classification
(IUSS 2022/USDA-NRCS 2022)
A (37.06 ha)Ap-B-C profiles, weakly developed, moderately deep, with anthropogenically influenced epipedon.Hortic or Irragric Fluvic Cambisols/Typic or Oxyaquic Udifluvents
B (157.80 ha)Shallow Ap-Bw-C profiles with vertic features and redoximorphic characteristics due to clay accumulation.Vertic Eutric Cambisols (Oxyaquic), Leptic Eutric Cambisols/Oxyaquic Eutrudepts, Lithic Eutrudepts
C (932.22 ha)Shallow to moderately deep Ap-AB-Bw-C profiles, weakly to moderately developed.Eutric Cambisols, Dystric Cambisols/Typic Eutrudepts, Typic or Lithic Dystrudepts
D (1813.32 ha)Shallow O-A-Bw-C profiles with thick organic horizons and weak cambic features.Leptic Eutric Cambisols (Humic), Eutric Cambisols/Typic Eutrudepts, Typic Dystrudepts
E (1579.82 ha)O-A-AB-C and O-A-C profiles, very shallow, weakly developed, stony.Leptic Skeletic Umbrisols, Leptic Skeletic Regosols (Humic)/Lithic Eutrudepts, Typic Dystrudepts
E1 (969.08 ha)Shallow O-A-AB-Bt-C profiles, developed with argic horizons. Some Bw-C profiles.Leptic Luvisols, Epileptic Dystric Cambisols (Humic)/Typic Hapludalfs, Typic Dystrudepts, Typic Humudepts
F (2865.55 ha)Shallow O-A-AB-Bw-C profiles with umbric features and moderate development.Leptic Skeletic Dystric Cambisols (Humic)/Lithic Humudepts, Typic Dystrudepts, Lithic or Humic Dystrudepts,
F1 (2482.62 ha)Moderately deep O-A-AB-Bw-C profiles with umbric epipedon and cambic horizon.Leptic Cambic Umbrisols, Leptic Eutric Cambisols (Humic)/Typic Humudepts, Typic Hapludolls, Lithic or Typic Eutrudepts
G (3254.50 ha)O-A-AB-Bw-C and O-A-AB-C profiles, shallow, stony, weak to moderate development.Leptic Skeletic Dystric Cambisols (Loamic or Arenic), Dystric Leptosols (Humic)/Typic Dystrudepts, Lithic Udorthents
G1 (2735.38 ha)O-A-AB-Bw-C and O-A-AB-C profiles, shallow, skeletal, moderately to poorly developed.Leptic Skeletic Dystric Cambisols (Loamic, Humic), Umbric Cambic Dystric Leptosols/Typic Dystrudepts, Humic or Lithic Dystrudepts
H (1135.22 ha)O-A-Bw-BC-C and O-AE-Bw-BC-C profiles, skeletal, with umbric/mollic epipedons and cambic horizon.Epileptic Cambic Skeletic Umbrisols, Leptic Skeletic Dystric Cambisols (Loamic, Humic), Skeletic Dystric Cambisols (Protospodic)/Typic or Lithic Humudept, Humic Dystrudepts, Lithic Hapludolls, s
I (2409.63 ha)O-AE-Bw-C profiles, shallow, moderately developed, often umbric.Leptic Dystric Cambisols (Humic, Novic, Protospodic), Haplic Podzols (Humic)/Lithic or Typic Dystrudepts, Lithic Dystrudepts
I1 (504.82 ha)O-A-AE-Bw-CR and O-A-(Bw)-BC-Cr profiles, shallow and skeletal, limited development.Leptic Skeletic Cambisols (Humic), Leptic Colluvic Skeletic Regosols/Lithic Hapludolls, Lithic Humudepts, Lithic or Typic Dystrudepts
J (40.31 ha)A-(Bw)-C profiles, very shallow in cryic climate, minimal development.Epileptic Dystric Regosols (Gelic), Dystric Leptosols (Gelic)/Lithic Haplocryolls, Lithic Dystrocryepts, Lithic Humicryepts,
L (211.70 ha)Lithoid outcrops with minimal soils or initial pedogenesis (A-C).Leptic Regosols, Lithic Skeletic Leptosols/Lithic Entisols
W (22.56 ha)Hydromorphic profiles (OA-AC-BCg or Ag-ACg-Cg), with organic matter accumulation and water saturation.Gleyic (Dystric) Stagnosols (arenic, colluvic, humic), Hemic or Fibric Leptic Histosols (Gleyic)/Typic or Aeric Fluviwassents, Hemic or Terric Haplofibrists, Oxyaquic Udifluvents
Table 5. Physicochemical properties of soils in the soil map units (SMUs) identified within the Frignano and Corno alle Scale Regional Parks. For each SMU, mean values and standard deviations (SD) are reported for particle-size distribution (sand, silt, and clay), soil pH, exchangeable calcium (Caexch), base saturation (BS), total nitrogen (Ntot), and organic carbon (OC). SMUs are spatially associated with land units (LUs) described in Table 3.
Table 5. Physicochemical properties of soils in the soil map units (SMUs) identified within the Frignano and Corno alle Scale Regional Parks. For each SMU, mean values and standard deviations (SD) are reported for particle-size distribution (sand, silt, and clay), soil pH, exchangeable calcium (Caexch), base saturation (BS), total nitrogen (Ntot), and organic carbon (OC). SMUs are spatially associated with land units (LUs) described in Table 3.
SMUSandSiltClaypHCaexchBSNtotOC
g/kg cmol(+)/kg %
B756253007.39.671.00.32.9mean
2974870.72.911.60.33.1SD
C397062567.912.348.50.43.4mean
957650.62.42.20.34.3SD
D1705053255.12.356.70.22.0mean
1991591110.20.720.30.12.2SD
E1887061065.67.850.50.33.3mean
4133130.83.98.10.23.6SD
E1607481927.714.155.30.22.8mean
2169750.653.110.10.13.0SD
F3045731234.99.220.70.34.1mean
4751240.11.18.30.12.4SD
F1544398585.34.540.20.44.5mean
118130.41.03.40.55.7SD
G596352524.64.613.00.13.0mean
4340130.42.33.90.12.2SD
G1615327584.70.725.40.23.3mean
2834110.30.913.20.12.3SD
H5722991294.40.213.60.22.4mean
5159160.30.03.50.11.3SD
I531382874.70.48.70.33.6mean
9966430.30.69.70.32.9SD
I179819586.113.399.10.46.1mean
8530.20.80.20.11.7SD
J670284456.112.393.20.33.4mean
101230.11.10.60.00.4SD
W674310175.01.461.00.11.6mean
8996120.20.35.10.00.7SD
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Trenti, W.; De Feudis, M.; Gherardi, M.; Vianello, G.; Vittori Antisari, L. Land Unit Delineation Based on Soil-Forming Factors: A Tool for Soil Survey in Mountainous Protected Areas. Land 2025, 14, 1683. https://doi.org/10.3390/land14081683

AMA Style

Trenti W, De Feudis M, Gherardi M, Vianello G, Vittori Antisari L. Land Unit Delineation Based on Soil-Forming Factors: A Tool for Soil Survey in Mountainous Protected Areas. Land. 2025; 14(8):1683. https://doi.org/10.3390/land14081683

Chicago/Turabian Style

Trenti, William, Mauro De Feudis, Massimo Gherardi, Gilmo Vianello, and Livia Vittori Antisari. 2025. "Land Unit Delineation Based on Soil-Forming Factors: A Tool for Soil Survey in Mountainous Protected Areas" Land 14, no. 8: 1683. https://doi.org/10.3390/land14081683

APA Style

Trenti, W., De Feudis, M., Gherardi, M., Vianello, G., & Vittori Antisari, L. (2025). Land Unit Delineation Based on Soil-Forming Factors: A Tool for Soil Survey in Mountainous Protected Areas. Land, 14(8), 1683. https://doi.org/10.3390/land14081683

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop