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Article

A Small Landslide as a Big Lesson: Drones and GIS for Monitoring and Teaching Slope Instability

by
Benito Zaragozí
1,*,
Pablo Giménez-Font
2,
Joan Cano-Aladid
3 and
Juan Antonio Marco-Molina
2
1
Departament de Geografia, Universitat Rovira i Virgili, C/Joanot Martorell, 43480 Vilaseca, Spain
2
Instituto Interuniversitario de Geografía, Universidad de Alicante, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig, 03690 Alicante, Spain
3
Liberam Technologies S.L., Calle Rincón del Cielo, 5, 50022 Zaragoza, Spain
*
Author to whom correspondence should be addressed.
Geosciences 2025, 15(10), 375; https://doi.org/10.3390/geosciences15100375
Submission received: 5 June 2025 / Revised: 1 September 2025 / Accepted: 16 September 2025 / Published: 30 September 2025
(This article belongs to the Special Issue Remote Sensing Monitoring of Geomorphological Hazards)
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Abstract

Small landslides, though frequent, are often overlooked despite their significant potential impact on human-affected areas. This study presents an analysis of the Bella Orxeta landslide in Alicante, Spain, a rotational landslide event that occurred in March 2017 following intense and continued rainfall. Utilizing multitemporal datasets, including LiDAR from 2009 and 2016 and drone-based photogrammetry from 2021 and 2023, we generated high-resolution digital terrain models (DTMs) to assess morphological changes, estimate displaced volumes of approximately 3500 cubic meters, and monitor slope activity. Our analysis revealed substantial mass movement between 2016 and 2021, followed by relatively minor changes between 2021 and 2023, primarily related to fluvial erosion. This study demonstrates the effectiveness of UAV and DTM differencing techniques for landslide detection, volumetric analysis, and long-term monitoring in urbanized settings. Beyond its scientific contributions, the Bella Orxeta case offers pedagogical value across academic disciplines, supporting practical training in geomorphology, geotechnical assessment, GIS, and risk planning. It also highlights policy gaps in existing territorial risk plans, particularly regarding the integration of modern monitoring tools for small-scale but recurrent geohazards. Given climate change projections indicating more frequent high-intensity rainfall events in Mediterranean areas, the paper advocates for the systematic documentation of local landslide cases to improve hazard preparedness, urban resilience, and geoscience education.

1. Introduction

1.1. Small Landslides and Regional Context

Landslides are a widespread geomorphic process involving the downslope movement of rock, soil, or debris due to gravity. These movements can range from slow soil creep to rapid mass failures and are commonly classified into types such as falls, topples, slides, spreads, and flows [1]. Although gravity is the main driver, landslides are frequently triggered or intensified by factors such as intense rainfall, seismic activity, deforestation, excavation, and changes in slope hydrology [2,3].
These events can have severe consequences for human populations and infrastructure, including fatalities, property loss, and disruption of essential services [4]. Regions with steep terrain, weak geological formations, and high precipitation levels—such as those found throughout the Mediterranean—are particularly susceptible [5].
While catastrophic landslides often dominate public attention, small-scale landslides occur much more frequently and can cumulatively have substantial environmental and socioeconomic impacts [6,7]. Despite this, small landslides remain under-represented in the scientific literature and are often excluded from formal risk management strategies, yet they account for a significant proportion of annual losses in infrastructure, road networks, and hillside communities, especially in mountainous and urbanizing areas [8]. Addressing their impact requires tailored monitoring, cost-effective mitigation, and improved documentation to support informed decision-making at the local scale.
The study and monitoring of small landslides remains challenging, as traditional inventories often prioritize larger events and may overlook these frequent but subtle hazards. Recent advances in high-resolution remote sensing, particularly drone photogrammetry and LiDAR, now enable precise detection and monitoring of small landslides, improving understanding of their spatial distribution, recurrence, and dynamics [9,10]. These tools are especially valuable for refining hazard assessments and informing mitigation in vulnerable, often urbanized, settings [11,12].
In Mediterranean regions such as the Valencian Community (eastern Spain), a combination of steep slopes, weathered sedimentary rocks, and intense seasonal or continued rainfall contributes to a heightened landslide risk. Rainfall plays a key role by increasing pore water pressure and decreasing soil shear strength, often triggering failures on already weakened slopes [13,14]. Human activities—such as road construction, urban sprawl linked to tourism, and vegetation removal—further exacerbate this risk by altering natural slope stability and drainage patterns. Urban expansion and land-use changes have been widespread along the Spanish Mediterranean coast since the 1970s. Urban development was often carried out without prior planning or geotechnical studies, which has led to serious medium-term problems of exposure to natural disasters, such as floods and landslides [15].
To address these issues, national and regional planning tools like BDMoves (Spanish Land Movements Database–IGME, 2016; https://info.igme.es/BDMoves/ (accessed on 21 September 2025), COPUT database landslides risk [16], and the basic geomorphological cartography collection [17] have incorporated landslide mapping into their frameworks. However, the cartographic resolution, infrequent updates, and static nature of these maps limit their utility for detailed, site-specific interventions. Research indicates that while regional assessments are essential for broad-scale planning, they must be complemented by local-scale studies using modern, affordable, high-resolution monitoring methods [18,19]. In this context, the Bella Orxeta case offers an opportunity to demonstrate how targeted studies can supplement existing planning frameworks and improve community resilience.

1.2. Pedagogical and Scientific Relevance of Case-Based Landslide Studies

The integration of real-world case studies into geoscience education has become increasingly important for developing students’ analytical, technical, and decision-making skills. Particularly in fields such as geomorphology, natural hazard management, and spatial analysis, localized case studies that combine empirical data, technological tools, and interdisciplinary perspectives offer a powerful platform for experiential learning. In this context, a pedagogically oriented case study of the Bella Orxeta landslide (Alicante, Spain) was developed and documented in this study. Section 1.3 presents and details this case study that supports both teaching and research by offering high-resolution spatial data and a well-documented example of a rainfall-triggered slope failure in a peri-urban Mediterranean environment.
The Bella Orxeta landslide case study was developed as a real-world, multitemporal example to support teaching and learning in geoscience, environmental risk management, and spatial analysis. It provides a dynamic, data-rich context for students in undergraduate geography and land planning programs, as well as graduate-level courses in natural risk management at the University of Alicante. By integrating diverse data sources and technologies—including UAV-based photogrammetry, LiDAR, GIS, and historical imagery—the study enables an experiential approach to hazard education grounded in local reality. This reflects broader trends in geoscience education where real-world applications of spatial technologies foster critical thinking, interdisciplinary learning, and problem-solving skills [20].
Beyond theory, the Bella Orxeta case challenges students to engage with the complexities of a dynamic landslide scenario and the problems of land-use planning at the local level. In geomorphology, it serves as a practical framework for understanding slope instability, mass movement processes, and the role of lithology, hydrology, and land-use changes in shaping hazardous landscapes. Students can classify the landslide, trace its evolution, and assess contributing factors, mirroring field-based pedagogical approaches used in landslide education across Europe [21,22].
In hazard assessment, the case encourages students to integrate temporal data, susceptibility indicators, and urban exposure into risk evaluations. This mirrors current models used in regions such as Eastern Europe and South Asia, where educational programs promote systemic, multi-variable risk assessments [23].
The use of modern remote sensing technologies—drone photogrammetry and LiDAR—adds significant value as a training platform for spatial data acquisition and analysis. Students gain practical experience in generating and interpreting digital surface models (DSMs), understanding the limits of each method, and critically evaluating workflow decisions. Studies have shown that incorporating UAV and LiDAR into geoscience instruction enhances student engagement and spatial reasoning through applied, technology-driven learning environments [24].
GIS plays a central role in the study, enabling students to map and visualize the landslide’s spatial impact, model future scenarios, and communicate complex risk information through thematic cartography. These competencies are essential for professionals working at the intersection of spatial science and hazard management [25].
Finally, the Bella Orxeta case offers rich material for civil and geotechnical engineering education. Students can explore mitigation options such as slope reinforcement and drainage design, assess potential environmental trade-offs, and simulate cost-effective intervention strategies using deterministic models integrated within GIS platforms [26]. It is also a good case study for implementing research-based learning strategies.
As climate change increases the likelihood of intense rainfall events, and urban development expands into hazard-prone areas [27], pedagogical resources like this case study are essential for preparing students to address complex, real-world challenges with analytical rigor and interdisciplinary insight.

1.3. The Bella Orxeta Landslide: Local Context and Hazard History

The Bella Orxeta urbanization, located in the municipality of Orxeta (Alicante, Spain), offers a representative example of localized landslide activity within the broader context of the Valencian Community’s geohazard susceptibility (see Figure 1). The study area is located on the edge of a trough [28,29]. The Sella River flows through the center of the trough, feeding the Amadorio Reservoir (1957), located near the studied landslide. Marl predominates in the valley, but it is covered by alluvial and colluvial deposits on distinct fluvial terraces composed of gravel, sand, and clay. These terraces are crossed by small channels (ravines) that have incised the Quaternary deposits, producing steep slopes. Marl outcrops below the alluvium and colluvial materials, visible in the lower part of the slope under study.
This south-facing slope has shown persistent geological instability over several decades, particularly intensified during three episodes of intense rainfall. The successive rainfall episodes, between December 2016 and March 2017, resulting in almost 300 mm of rain in the study area. The most significant event occurred between 13 and 14 March 2017, when a rotational landslide damaged three houses and affected two others. Prior indicators included numerous ground cracks and infrastructure damage—especially near Reino Unido street— suggesting ongoing deformation and hydrological stress. Testimonies and municipal actions confirm that slope instability forced the demolition of a residence following heavy rainfall earlier that year.
The development of Bella Orxeta began in the 1970s with poorly planned, scattered construction. Roads and residential plots were gradually built into the unstable terrain through the 1980s and 2000s. These anthropogenic interventions, combined with natural susceptibility factors, created the conditions for progressive destabilization. The landslide dynamics observed include subsidence, uplift, and lateral displacement, with surface activity documented until at least May 2018. The urban planning was approved in 1994 and has not yet been renewed, necessitating a costly legalization process for the Bella Orxeta development, without sufficient attention to the dangers posed by landslides.
Comparable cases underscore the relevance of Bella Orxeta case in the Valencian Community [30] as part of a broader Mediterranean trend. In Azazga, Algeria, rainfall-triggered rotational slides linked to slope excavation led to severe structural impacts [31]. In Lisbon and Eposende, Portugal, complex slope movements were driven by prolonged rainfall and urban pressure [32,33]. Likewise, Catanzaro, Italy, has seen damaging landslide reactivations due to cumulative rainfall and hillside construction [34], while hazard mapping in Crete, Greece, illustrates the interaction between terrain, climate, and built environments [35].
This case therefore highlights the importance of localized, high-resolution monitoring strategies. With traditional regional tools such as BDMoves or COPUT database landslides offering only coarse-scale risk mapping, detailed analysis of slope behavior is essential. The ongoing instability at Bella Orxeta justifies a dedicated monitoring framework to
  • Accurately assess the current condition of the slope;
  • Quantify displacement magnitude and extent over time;
  • Guide effective mitigation and planning decisions for community safety.
To support these objectives, this study incorporates UAV-based photogrammetry datasets acquired in 2021 and 2023. UAVs provide centimeter-scale resolution, temporal flexibility, and cost-effective alternatives to traditional survey methods [36]. Their integration enables the generation of multitemporal DTMs and the detection of fine-scale deformation in active and potentially unstable slopes [10,37].

1.4. Research Objectives and Structure of the Paper

This study of the Bella Orxeta landslide was developed with a dual aim: to advance the scientific understanding of small-scale landslide processes using modern geospatial methods and to serve as a pedagogical case study for training in risk analysis, geomorphology, and geotechnical monitoring. By leveraging a multitemporal dataset and integrating drone photogrammetry with LiDAR, the study also contributes to evaluating best practices in monitoring small but persistent slope failures in semi-urbanized Mediterranean settings.
The main objectives of the study are to
  • Characterize the spatial and volumetric evolution of the Bella Orxeta landslide between 2016 and 2023 using high-resolution digital terrain models (DTMs) derived from LiDAR and UAV photogrammetry.
  • Assess the strengths and limitations of combining drone-based photogrammetry with LiDAR data for landslide monitoring, particularly in terms of precision, resolution, and change detection capacity.
  • Develop a replicable methodological workflow for data acquisition, processing, and visualization aimed at both scientific audiences and educational contexts.
The structure of the paper is as follows: Section 1 introduces the research context, outlines the relevance of small landslides in Mediterranean environments, and presents the specific case of Bella Orxeta. It also explains the pedagogical rationale and states the study’s objectives and research questions. Section 2 (Materials and Methods) describes the data sources—LiDAR surveys, UAV photogrammetry, and ancillary datasets—and details the workflow used for processing, terrain modeling, and landslide change detection. Section 3 (Results) presents the findings, including a temporal reconstruction of the landslide, volumetric and morphometric changes, and evidence of ongoing instability. Section 4 (Discussion) interprets the results considering regional and international case studies, evaluates the performance of the applied methodologies, and discusses the pedagogical implications for geoscience education. Section 5 (Conclusions) summarizes the main contributions of the study and highlights opportunities for future research, monitoring, and curriculum development.

2. Materials and Methods

The methodological design of this study responds to both scientific and pedagogical goals. As a teaching-oriented case study, it aims to provide students with direct exposure to terrain analysis, landslide monitoring, and geospatial workflows using real-world data. The full process—from data acquisition to digital terrain modeling and volumetric analysis—has been structured to support experiential learning in geomorphology, GIS, and natural hazard assessment. Students engage with core concepts in slope instability while gaining technical skills in UAV photogrammetry, LiDAR interpretation, and multitemporal analysis.
To support this pedagogical framework, a replicable monitoring methodology was developed in this study based on high-resolution spatial data. A preliminary assessment of the Bella Orxeta landslide area had already identified signs of surface deformation and instability through LiDAR, orthophotos, and field observations. Building upon this foundation, the present analysis focuses on the period from 2018 to 2023, integrating publicly available LiDAR datasets and new UAV-acquired photogrammetry to quantify terrain changes and assess ongoing slope processes.
Digital terrain models (DTMs) were generated to derive morphometric indicators, volumetric displacements, and patterns of erosion and deposition. DTM differencing techniques were applied to detect surface elevation changes and compute landslide volumes with high precision [38]. Multitemporal airborne LiDAR has proven particularly effective in capturing landslide kinematics and subtle terrain deformations, even in vegetated or morphologically complex areas [39,40].
This remote sensing-based approach forms the methodological foundation of the study and enables a detailed reconstruction of the landslide’s behavior over a five-year monitoring window.

2.1. Data Acquisition

This study integrates multiple sources of spatial data to document and analyze the evolution of the Bella Orxeta landslide. These include airborne LiDAR datasets from national mapping initiatives, high-resolution UAV-acquired imagery, and additional geospatial references such as orthophotos, cadastral data, and base maps.
Airborne LiDAR data were sourced from the Spanish National Geographic Institute (IGN) through the National Center for Geographic Information Download Center. The data originates from the Plan Nacional de Ortofotografía Aérea (PNOA) LiDAR program. Two periods of coverage were utilized: the first acquired in 2009 and the second between 2016 and 2017. Both point clouds offer a mean density of 0.5 points per square meter. The 2009 dataset provides planimetric and altimetric accuracies of 0.30 m and 0.40 m, respectively, while the 2016–2017 data improve vertical accuracy to approximately 0.20 m. A third coverage period is planned for 2025 and may support future monitoring initiatives.
The LiDAR point clouds were processed to generate digital surface models (DSMs), which represent pre-landslide terrain conditions. This methodology aligns with best practices in hazard mapping, where national-scale LiDAR programs are leveraged for the accurate derivation of terrain models used in geomorphic and risk analyses [41].
To assess post-2016 slope changes, UAV-based photogrammetric surveys were conducted in 2021 and 2023. Drones were selected for their high-resolution data capture, cost-effectiveness, and flexibility in challenging terrain, supporting their growing role in landslide monitoring applications [42,43].
The first flight, conducted in November 2021 by an individual operator, lacked ground control points (GCPs). The operator was the owner of the most severely affected property and carried out the survey on his own initiative. Georeferencing was later refined by identifying common, time-invariant features visible in the 2023 survey (Figure 2). The second flight, performed in March 2023 by Liberam Technologies, S.L., used RTK-enabled UAV equipment, enabling centimeter-level accuracy without the need for external GCPs [44]. Imagery from both campaigns was processed using Pix4D Matic software (version 1.24.0). Georeferencing accuracy was evaluated by calculating the root mean square error (RMSE) of the control point residuals (Table 1).
To provide the temporal context and enhance spatial analysis, additional datasets were employed. Historical orthophotos from PNOA enabled interpretation of land-use changes and pre-event terrain configuration. Cadastral maps and official base layers were used to ensure accurate georeferencing and overlay consistency. This integrative approach supports advanced spatial correlation and visualization and reflects current best practices in combining remote sensing and cadastral data for hazard risk analysis.

2.2. Photogrammetric Processing

The UAV images acquired during the 2021 and 2023 drone surveys were processed using Pix4D Matic software to generate dense point clouds and digital terrain models (DTMs). Image alignment and 3D reconstruction were conducted using Structure-from-Motion (SfM) techniques. The 2023 dataset was processed in the ETRS89 UTM 30N (EPSG:25830) coordinate system with the EGM08-REDNAP vertical reference, ensuring consistency with national geodetic standards.
Following point cloud generation, classification was performed to isolate ground points necessary for accurate terrain modeling (Figure 3). Point cloud filtering was conducted using Pix4D Survey, employing a combination of manual editing and semi-automatic methods. This included the application of terrain classification algorithms such as cloth simulation filtering and multiscale curvature-based classification, widely recognized for producing reliable bare-earth DTMs in complex or vegetated terrain [45].
This process parallels LiDAR-derived DTM workflows and ensures compatibility between datasets for subsequent change detection and volume calculations.

2.3. DSM Comparison and Landslide Analysis

To analyze terrain changes associated with the Bella Orxeta landslide, all point clouds from 2009, 2016, 2021, and 2023 were filtered, projected into a common coordinate system, and converted into Triangulated Irregular Networks (TINs) using Pix4D Survey. These were used to generate digital terrain models (DTMs) suitable for morphological analysis and change detection.
The landslide’s spatial extent, surface deformation, and volumetric changes were quantified by comparing DTMs from different time periods. DTM differencing enabled the calculation of elevation gains and losses, identification of erosion and accumulation zones, and visualization of surface evolution over time. This method reflects best practices in high-resolution geomorphological change detection using LiDAR and UAV photogrammetry [46].
The geometric characterization of the landslide followed the classification criteria developed by the UNESCO Working Group on Landslide Inventory (1993) and formalized by Cruden and Varnes (1996) [47]. According to these standards, several metrics allowed for the classification of the landslide’s size, style, and geometry. Specific values are reported in Figure 6 (see Section 3.3). Similar approaches have been successfully applied to more complex landslides, such as the La Clapière landslide in France [48].
Volumetric analysis was conducted by differencing DTMs from sequential time steps (e.g., 2016 vs. 2023). For consistency, all point clouds were interpolated into Digital Elevation Models (DEMs) using a Triangulated Irregular Network (TIN) method. The photogrammetric data from the 2021 and 2023 UAV flights were processed at a resolution of 3 cm/pixel, reflecting the native resolution of the imagery. Although the LiDAR-derived point cloud did not support such high spatial density, it was interpolated at the same resolution to match the photogrammetric datasets and allow direct comparison. Slope and aspect metrics were subsequently derived from a generalized 5-m resolution DEM to reduce local noise and facilitate broader terrain analysis. This alignment ensured pixel-level correspondence across datasets, which is critical for cell-by-cell elevation change analysis [49,50].
Volume change was computed by subtracting the pre-landslide DEM (2016) from the post-event DEM (2023). Positive elevation changes indicated deposition; negative values reflected erosion or subsidence. The landslide boundary, defined through visual analysis of orthophotos and elevation models, was used to constrain volume calculations via the Zonal Statistics tool. The net volume of displaced material was calculated in cubic meters, offering a quantitative assessment of landslide magnitude and its spatial footprint.
To evaluate terrain modification and slope behavior, additional terrain derivatives—including slope and aspect—were extracted from each DTM using QGIS and Pix4D Survey. These raster layers enabled the assessment of changes in slope inclination and orientation between pre- and post-failure conditions. This derivative analysis helps interpret how geomorphic and hydrologic processes evolved across the landslide body, supporting both hazard assessment and geomorphological interpretation.
The use of multitemporal, high-resolution terrain models and standardized landslide parameters aligns with international guidelines for landslide inventory, hazard analysis, and magnitude estimation [51,52,53]. This integrative approach increases the reliability of landslide classification and impact assessment and enhances comparability across studies.

3. Results

3.1. Historical Analysis Using Aerial Photographs, LiDAR, and Photogrammetry

A temporal analysis of the Bella Orxeta study area, based on aerial photographs and orthoimages dating back to 1957, reveals progressive landscape changes leading up to and following the 2017 landslide (see Figure 4). Early imagery (pre-2005) shows a terraced southwest-facing slope with scattered vegetation dominated by low-profile, autochthonous shrubs. The lower terrace, located beneath the house that would be most severely affected, appeared stable at that time.
Between 2014 and 2017, significant structural changes become evident. The house later damaged by the 13–14 March 2017 landslide disappears from the imagery, and visible damage to nearby structures can also be discerned. These visual changes coincide with reports of ground deformation and increased instability in the area during this period.
From the orthoimages, the initial area affected by the landslide is estimated at approximately 3666 m2. Post-2017 images, extending through 2023, show continued surface modification, particularly due to fluvial activity. Sediment redistribution—likely driven by surface run-off—has been observed both in the plot where the damaged house once stood and in lower terrain. Notably, some sediment appears to have accumulated near a house constructed between 2014 and 2015, with the rest transported into the adjacent ravine.
LiDAR datasets from 2009 and 2016 offer additional temporal reference points. However, changes in slope dynamics between these years are not clearly detectable, likely due to limitations in resolution. The average point density of 0.5 points/m2 and decimeter-level vertical and horizontal accuracy reduce confidence in detecting subtle terrain deformations. Thus, while these datasets contribute to a broad understanding of pre-failure conditions, they cannot be considered a definitive baseline for small-scale displacement analysis.
The 2016 LiDAR dataset is used as the primary pre-failure baseline in this study. Despite its limitations, the point cloud was processed through filtering and converted to a Triangulated Irregular Network (TIN), maximizing its utility by focusing on terrain-representative features. This provides a reasonable foundation for the subsequent multitemporal comparison with UAV-derived DTMs from 2021 and 2023.

3.2. Morphological Changes Post-2016

The analysis of multitemporal digital terrain models (DTMs) reveals substantial morphological changes in the Bella Orxeta slope between 2016 and 2021, corresponding to the main landslide event, followed by more localized surface reshaping between 2021 and 2023. These changes were assessed through shaded relief models, orthomosaics, and DTM differencing, all referenced to the 2016 LiDAR-based DTM.
Between 2016 and 2021, the slope underwent pronounced deformation consistent with a rotational landslide (Figure 5). The most affected area showed a maximum vertical subsidence of approximately −1.7 m in the central body and an accumulation zone with uplift up to +0.9 m at the toe. These metrics correspond with bulging terrain due to material displacement and are indicative of a rotational failure mechanism. The crown of the landslide exhibited measurable retreat and surface lowering, while lateral margins showed minor tension cracks and scarps.

3.3. Geometric Classification of the Landslide

To characterize the geometry and extent of the Bella Orxeta landslide, the criteria established by the UNESCO Working Group on the World Inventory of Landslides (1993) and standardized by Cruden and Varnes (1996) were applied to the 2016–2021 digital terrain models. These frameworks define key parameters for classifying landslide types and evaluating their spatial and volumetric significance based on observable surface changes.
The geometric analysis was conducted using the filtered DTMs from 2016 and 2021, which were processed to extract morphological boundaries and key dimensions. Elevation profiles and cross-sections were used to support visual interpretation of the landslide margins and confirm the rotational nature of the failure.
The following parameters were measured and are displayed in Figure 6:
  • Width of displaced mass (Wd): Maximum width of the displaced material, measured perpendicular to the landslide’s length axis.
  • Width of failure surface (Wr): Lateral extent between the flanks of the rupture surface.
  • Length of displaced mass (Ld): Distance from the toe to the top of the displaced mass.
  • Length of failure surface (Lr): Distance from the toe to the crown of the rupture surface.
  • Total landslide length (L): Overall downslope distance from the toe to the uppermost crown.
  • Depth of displaced mass (Dd): Maximum depth of displaced material, normal to the slope plane.
  • Depth of failure surface (Dr): Estimated maximum depth of the rupture surface below the original ground surface.
These values were digitized and verified within Pix4D Survey and QGIS using topographic cross-sections, elevation contours, and hillshade maps. The landslide shows a geometry typical of small, rainfall-induced rotational slides, with an estimated length of 73 m, a displaced mass width of 62 m, and a total surface area of 3666 m2.
These parameters confirm that the Bella Orxeta event fits within the lower size categories defined by Cruden and Varnes but with clear deformation features typical of rotational mass movements. This quantitative framework allows for meaningful comparison with other regional or international case studies and supports further classification and hazard assessment work.

3.4. Volumetric Change over Time

To quantify the magnitude of material displacement associated with the Bella Orxeta landslide, volumetric analysis was performed using the DTM differencing method across three-time intervals: 2009–2016, 2016–2021, and 2021–2023. This approach enabled the detection of both erosion (negative elevation change) and deposition (positive elevation change) within the delineated landslide boundary.
All Digital Elevation Models (DEMs) were resampled to a uniform spatial resolution of 1 m, ensuring pixel alignment across all datasets. The volume of displaced material was then computed by subtracting the earlier DTM from the later one using the Raster Calculator, producing a raster layer of elevation change. Positive values indicate zones of material gain (accumulation), while negative values reflect material loss (subsidence or erosion).
Within the mapped landslide area (3666 m2), the volumetric change for each interval is summarized below:
  • 2009–2016: Minimal change was observed; total displacement remained within the LiDAR margin of error (±0.2–0.3 m), suggesting no significant pre-failure deformation.
  • 2016–2021: This interval captured the main landslide event, with a net volumetric loss of approximately 3500 m3. The greatest subsidence occurred near the central scarp, while material accumulation was concentrated near the toe.
  • 2021–2023: A smaller volume change was recorded, with a net volume loss of approximately 150 m3, primarily due to surface erosion and sediment redistribution by run-off into the nearby ravine.
These values are visualized in Figure 7, which shows the distribution of elevation change across the slope from 2016 to 2023.
This volumetric assessment confirms that the 2016–2021 period accounted for most of the mass movement and validates the interpretation of the event as a single major rotational landslide, followed by minor fluvial reshaping. These results are consistent with those reported in small to medium-sized landslides triggered by high rainfall events and localized slope disturbances [49,50].

3.5. Terrain Derivatives: Slope and Aspect Analysis

In addition to volumetric and spatial analyses, topographic derivatives such as slope and aspect were extracted from each digital terrain model (DTM) to better understand how the landslide altered local terrain configuration and potentially influenced subsequent hydrological and geomorphological processes.
Using QGIS and Pix4D Survey, slope and aspect rasters were generated for the years 2016, 2021, and 2023. These rasters allowed for the identification of areas where terrain inclination and orientation shifted significantly due to ground movement, especially along the landslide’s flanks and central axis (see Figure 8 and Figure 9).
The 2016 slope map shows moderately steep slopes in the crown and mid-slope areas, with values ranging from 18° to 28°, typical of unstable, weathered sedimentary terrain. Following the main landslide event, the 2021 map shows areas of reduced slope near the crown, where subsidence flattened the terrain, and increased slope values (up to 32°) at the toe due to bulging and material accumulation.
Between 2021 and 2023, slope changes were minor, with localized reductions likely due to sediment erosion. The highest slope gradients (>30°) remained concentrated near the lower slope adjacent to the drainage channel, suggesting ongoing susceptibility to run-off-induced erosion.
Aspect maps illustrate the directional exposure of slope surfaces. Prior to the event, the overall slope predominantly faced southwest (SW). After 2021, aspect diversity increased due to scarps, minor rotational lobes, and surface irregularities created by ground deformation.
Changes in aspect were most evident in the upper and lateral margins, where detachment and rotational movement caused terrain to reorient, particularly near the crown. These micro-orientation shifts may influence future infiltration patterns and surface run-off, potentially increasing localized instability.
The analysis of these derivatives reinforces the interpretation of the Bella Orxeta event as a rotational landslide with slope steepening at the toe and surface reconfiguration in the upper and lateral sections. These patterns also highlight areas where slope instability may persist or reinitiate, particularly in response to future rainfall or human disturbance.

4. Discussion

4.1. Interpretation of Results

The Bella Orxeta landslide provides a valuable case study illustrating the long-term impact of small, rainfall-triggered slope failures in semi-urban Mediterranean terrain. While modest in volume (3500 m3), its consequences—structural damage, slope instability, and persistent reactivation—highlight the disproportionate risks such events pose in developed settings. According to standardized classification frameworks [47], these dimensions fall within the lower range of magnitude, yet the event exemplifies how even small landslides can evolve into chronic hazards when terrain modification, urbanization, and climatic triggers converge [8].
One of the key contributions of this case is its detailed, multitemporal characterization using both pre-event LiDAR and post-event UAV photogrammetry. This allowed not only for geometric classification but also for the identification of progressive deformation and sediment redistribution well after the initial failure. These processes—subtle yet persistent—are often overlooked in hazard assessments focused solely on peak failure events. Their detection here supports the growing emphasis on post-failure geomorphic evolution in landslide science [48,49].
The findings also reinforce the critical role of rainfall as a primary trigger, particularly in Mediterranean climates where short-duration, high-intensity precipitation is becoming more frequent due to climate change [54,55]. In Bella Orxeta, intense antecedent rainfall in early 2017 and the concentration of run-off from Reino Unido Street likely raised pore water pressure and reduced shear strength, leading to rotational failure. This mechanism is well-documented in the literature and aligns with other studies in similarly susceptible terrain [56,57].
Furthermore, the evidence of prior terrain modification—visible in historical imagery from 1957 onward—underscores the cumulative effects of anthropogenic alteration on slope stability. Excavations for roads and housing, combined with poor drainage management, likely amplified pre-existing instability. These observations are consistent with findings from other Mediterranean landslide cases, where urban expansion has intensified geomorphic vulnerability [34,35].
Finally, the reduced net volume change observed between 2021 and 2023—compared to the earlier period—suggests a temporary stabilization of the main landslide body. However, ongoing fluvial erosion and sediment redistribution, particularly near the ravine, continue to shape the landscape and signal potential reactivation. This transition from mass movement to sediment dynamics is frequently observed in landslide recovery phases and has been documented in post-failure terrain evolution studies [58,59].

4.2. Methodological Considerations

The methodological approach adopted in this study underscores the effectiveness of drone photogrammetry and multitemporal DTM analysis for monitoring localized landslide activity. By integrating pre-event LiDAR data with high-resolution UAV surveys from 2021 and 2023, the study demonstrates how terrain changes can be precisely measured over time—even in complex semi-urban terrain. This approach aligns with growing evidence that UAV photogrammetry offers a reliable and flexible alternative to traditional ground-based monitoring, particularly in areas where access is limited or rapid response is needed [42,43].
A key contribution of this case lies in the scalability and operational adaptability of the UAV workflow. The 2023 survey, executed using RTK-enabled positioning, delivered centimeter-level spatial accuracy, while the 2021 survey—despite lacking ground control points (GCPs)—was successfully georeferenced by aligning persistent features from the later dataset. This reflects established practices in UAV-based terrain modeling, where post hoc georeferencing and RTK systems have been shown to effectively compensate for the absence of GCPs when combined with accurate terrain feature matching [60].
The study also exemplifies a replicable DTM generation and terrain analysis workflow. Using Pix4D Survey and QGIS, dense point clouds were filtered, meshed into TINs, and converted into DTMs to enable slope, aspect, and volumetric change detection—approaches consistent with terrain modeling best practices [45]. The inclusion of Zonal Statistics and Raster Calculator tools further enabled cell-by-cell volumetric estimations, illustrating how open-source GIS software can be effectively harnessed for landslide risk studies [49,50].
Nevertheless, the analysis also highlights several data limitations and methodological lessons. The lower point density and decimetric vertical error of the 2016 LiDAR data—nominally 0.5 pts/m2 with ±0.20–0.40 m accuracy—reduced the sensitivity of early-stage change detection, particularly for small-scale terrain displacements. However, by applying point cloud filtering and DTM resampling, this dataset was still used effectively as a baseline, reinforcing findings from prior work that legacy LiDAR remains viable when pre-processed rigorously [38,39,40].
One of the most important methodological takeaways is the critical role of timing. The lack of UAV data immediately after the 2017 landslide limited the ability to accurately reconstruct the initial event dynamics. This underscores a core recommendation for future monitoring: deploy UAVs as soon as possible after failure events, ideally with GCPs or RTK support, to create a precise and temporally relevant baseline [41].
This case illustrates the broader need for standardized technical protocols for landslide monitoring, especially at the local scale. While flood risk mapping frameworks in Spain and the Valencian Community specify cartographic and modeling standards, landslide monitoring currently lacks such regionally endorsed guidelines. Echoing prior recommendations, this study supports the development of formalized UAV-based protocols for landslide-prone areas—including specifications for spatial resolution, acquisition intervals, and data processing accuracy.

4.3. Comparative Case Studies and Mediterranean Context

Recent case studies across the Mediterranean—including examples from Spain, Italy, and Greece—underscore the growing importance of documenting localized landslide events such as the Bella Orxeta case. These efforts are not only vital for advancing scientific understanding and improving hazard monitoring but also hold significant value for pedagogical purposes and urban planning strategies.
In southeastern Spain, the Cármenes del Mar landslide in Granada stands out as a compelling parallel. There, unplanned development over geologically unstable terrain led to long-term structural damage and increasing slope deformation. Researchers employed UAV photogrammetry and PSInSAR to track years of progressive failure, ultimately advocating for the integration of geohazard assessments into planning procedures [61]. Similarly, in Andalusia, the applicability of drone photogrammetry for monitoring landslides in agricultural landscapes was demonstrated—emphasizing not only its scientific utility but also its effectiveness as a teaching and training tool in environmental studies [46,62]. Comparable cases in other Mediterranean countries further reinforce these insights. In Italy, Carri et al. [63] and Notti et al. [64] utilized UAV-based terrain modeling to assess slope deformation and inform both emergency response and long-term mitigation strategies. These studies exemplify the advantages of systematic, well-documented local landslide investigations—particularly when data are shared openly to support research, education, and planning.
The urgency of such documentation is amplified by climate change projections. In the Mediterranean basin, short-duration but high-intensity rainfall events—widely recognized as primary triggers of shallow landslides—are expected to become more frequent due to global warming. Roccati et al. [55] found that climate-driven increases in rainfall intensity are already lowering the thresholds required for slope failure in many parts of the region. Similarly, Gariano et al. [57] projected a 21–46% increase in landslide occurrence and a substantial rise in population exposure in southern Italy by mid-century, depending on emission scenarios.
Taken together, these case studies and projections reinforce the relevance of the Bella Orxeta landslide as both a scientific and societal reference point. It exemplifies the type of event that—while small in scale—can have significant localized impacts and offer critical lessons for land-use planning, climate adaptation, and geoscience education. Institutionalizing local-scale monitoring programs, such as the one implemented in this study, is essential to building more resilient communities. Integrating these case studies into educational curricula and regional planning instruments can help ensure that research outcomes actively inform proactive risk governance and sustainable territorial development across the Mediterranean.

4.4. Pedagogical Implications

The Bella Orxeta landslide case study offers substantial educational value across multiple levels—from secondary and university curricula to professional training in geosciences, civil engineering, and urban planning. It also offers an opportunity to use learning by research strategies, as a teaching and learning approach where students actively engage in the research process to understand and acquire knowledge [65].
At the secondary level, the case serves as a relatable and visually rich example to introduce key Earth science concepts such as mass movement, erosion, weathering, and the environmental impacts of human activities. The combination of the narrative context, aerial imagery, and measurable impacts enhances student engagement by making abstract concepts tangible. This approach reflects international efforts to embed local case studies into science education, improving hazard awareness and student understanding of environmental processes.
In higher education, the Bella Orxeta dataset enables in-depth, interdisciplinary exploration. Students in geology and Earth science can classify the landslide using Cruden and Varnes typologies, analyze its geomorphology, and apply slope stability principles. Civil engineering programs can assess structural damage, evaluate mitigation measures such as retaining walls, and examine the role of geotechnical studies in urban development. Geography and environmental science students can explore interactions between land use and hazard dynamics, using tools like GIS and remote sensing for mapping and monitoring. Urban planning curricula benefit from case-based learning that highlights the consequences of inadequate geological risk assessment in development policy. This multidisciplinary integration aligns with best practices in hazard education, where geospatial analysis, field-based studies, and risk modeling are increasingly embedded in academic training [66].
For professional training, the Bella Orxeta case offers a real-world scenario to sharpen decision-making in hazard-prone urban areas. It showcases the need for interdisciplinary collaboration, the application of advanced tools such as LiDAR and UAV photogrammetry, and the importance of multitemporal monitoring for long-term risk assessment. These competencies are central to modern geotechnical and urban risk management, as emphasized in forensic case histories and the engineering geology literature [67].
The case also enables the design of practical learning activities. Undergraduate exercises can include GIS-based change detection using DEMs to quantify displacement or landslide classification based on morphometry [68,69]. Graduate-level assignments can focus on developing monitoring protocols, integrating remote sensing with field observations, and conducting risk assessments that combine hazard and vulnerability layers [70]. These activities foster critical thinking, technical skills, and applied problem-solving, bridging the gap between theoretical learning and professional practice.
Finally, the use of a well-documented, real-world case promotes experiential learning. The availability of multitemporal datasets—including orthophotos, LiDAR, and drone imagery—provides students with authentic tools to analyze geomorphic processes, model hazard scenarios, and evaluate mitigation strategies. The narrative component, detailing both physical and human impacts, enhances retention and promotes a more holistic understanding of geohazard risk. This hands-on, case-driven approach aligns with evidence-based strategies in geoscience education, which demonstrate the effectiveness of GIS-supported, scenario-based learning for building both competence and confidence in students [71].

4.5. Implications for Landslide Management

The Bella Orxeta case study reveals important gaps in existing territorial planning instruments, particularly in the integration of high-resolution monitoring technologies for localized landslide hazards. Available databases lack the technical specificity and spatial resolution needed to address smaller-scale but high-impact landslide events in urban and peri-urban areas. The findings from Bella Orxeta demonstrate the operational feasibility and effectiveness of using drone photogrammetry, high-resolution LiDAR, and GIS-based terrain analysis to monitor slope instability and assess both immediate and long-term risk. These insights echo international calls for incorporating remote sensing and geotechnical datasets into standardized hazard monitoring protocols for landslide-prone regions [9,72].
From a practical perspective, this study provides several takeaways for landslide risk mitigation. The multitemporal mapping of terrain deformation, volume displacement, and fluvial erosion zones provides essential information for guiding interventions. The proximity of at-risk residential structures underscores the urgency of detailed geotechnical investigations and the design of stabilization measures. The high-resolution baseline established via UAV surveys allows for frequent, cost-efficient monitoring, essential for implementing early warning systems and supporting urban development control. Integrating this type of monitoring into municipal planning practices—and potentially informing updates to regional instruments—would mark a strategic shift from reactive risk response to proactive hazard management [10]. This mirrors findings from other case studies where UAV-based assessments enhanced inspection efficiency, reduced safety risks, and improved decision-making in local hazard mitigation planning [73,74,75].
Looking ahead, a comprehensive landslide risk management strategy for Bella Orxeta should include regular UAV monitoring campaigns with robust georeferencing for detecting ground movement trends. Fluvial erosion assessments and hydrological controls in the ravine system are necessary to limit sediment destabilization and slope base erosion. Subsurface investigations to inform stabilization works, including borehole sampling and lab-based geotechnical testing, are also crucial. Exploring a multi-sensor monitoring network, combining inclinometers, UAV-mounted sensors, and field-based instrumentation, can enable real-time alerts and informed response planning [76].
Given the documented history of instability and the risk of reactivation, revising local urban planning regulations to formally integrate landslide risk assessments is essential for safeguarding the community. This aligns with international best practices, which emphasize the adoption of risk-informed, terrain-sensitive land-use planning—particularly in areas undergoing rapid or unregulated growth [77]. Any such regulatory reforms should ideally be synchronized with regional initiatives dedicated to flood risk, incorporating state-of-the-art technologies and flexible risk management tools into the policy framework.
Finally, community engagement and nature-based solutions represent vital, complementary pillars of resilience. Efforts to communicate landslide risk clearly and transparently can build public awareness and support for intervention measures. At the same time, ecological stabilization strategies, such as reforestation, bioengineering, and green slope design, offer sustainable alternatives that address both environmental and safety objectives. By embracing these recommendations, Bella Orxeta and similar communities can move toward a more adaptive, forward-looking model of risk governance, grounded in technology, informed by science, and responsive to both human and environmental needs.

5. Conclusions

This study documents the evolution of the Bella Orxeta landslide (Alicante, Spain) between 2016 and 2023 using UAV photogrammetry, airborne LiDAR, and GIS-based analysis. Despite its small size, the landslide presents complex terrain dynamics and continued movement influenced by rainfall, slope morphology, and nearby urban development.
By combining multitemporal remote sensing datasets, we reconstructed the geometry and volume of terrain changes over time, highlighting the value of integrating UAV and LiDAR data for localized hazard assessment. The study also developed a replicable methodological workflow applicable to both scientific research and geoscience education.
As a pedagogical tool, the Bella Orxeta case enables hands-on learning in slope instability monitoring, terrain modeling, and spatial analysis. It offers a realistic scenario for students to work with real-world data, improving their technical skills and critical understanding of geomorphological processes and natural hazards.
The proposed methodology and case study can be transferred to similar contexts where small landslides occur under urban or peri-urban conditions and where accessible spatial data can be used to enhance both hazard knowledge and training programs.

Author Contributions

Conceptualization, B.Z. and J.C.-A.; methodology, B.Z. and J.C.-A.; software, J.C.-A.; investigation, P.G.-F. and J.A.M.-M.; resources, P.G.-F. and J.A.M.-M.; data curation, B.Z. and J.C.-A.; writing—original draft preparation, B.Z. and J.C.-A.; writing—review and editing, B.Z., J.C.-A., P.G.-F., and J.A.M.-M.; visualization, B.Z. and J.C.-A.; supervision, P.G.-F. and J.A.M.-M.; project administration, P.G.-F. and J.A.M.-M.; funding acquisition, P.G.-F. and J.A.M.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The digital surface models (DSMs) and orthophotos generated from UAV-based photogrammetric surveys and used in this study are openly available in the Zenodo repository: Liberam Technologies, S.L. (2025). UAV-Based photogrammetric monitoring of a minor landslide: a case study in Bella Orxeta, Alicante, Spain. Zenodo. https://doi.org/10.5281/zenodo.15528618 (accessed on 21 September 2025).

Acknowledgments

During the preparation of this manuscript, the authors used ChatGPT 4o and Overleaf AI to assist with grammar checking and language editing. The authors have carefully reviewed and revised the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

DTMDigital Terrain Model
DSMDigital Surface Model
DEMDigital Elevation Model
UAVUnmanned Aerial Vehicle
LiDARLight Detection and Ranging
GCPGround Control Point
RTKReal-Time Kinematic
SfMStructure from Motion
TINTriangulated Irregular Network
RMSERoot Mean Square Error
GISGeographic Information System
PNOAPlan Nacional de Ortofotografía Aérea
IGNInstituto Geográfico Nacional
EGM08Earth Gravitational Model 2008
REDNAPRed de Estaciones de Referencia GNSS de Alta Precisión

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Figure 1. Study area location.
Figure 1. Study area location.
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Figure 2. Orthomosaic of the 2021 photogrammetric flight with ground control points (GCPs) used in 2023.
Figure 2. Orthomosaic of the 2021 photogrammetric flight with ground control points (GCPs) used in 2023.
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Figure 3. Top: oblique aerial photograph of the 2023 UAV survey showing image alignment and point cloud coverage. Bottom: semi-automatic classification of the 2021 point cloud into ground points (orange) and non-ground points (blue).
Figure 3. Top: oblique aerial photograph of the 2023 UAV survey showing image alignment and point cloud coverage. Bottom: semi-automatic classification of the 2021 point cloud into ground points (orange) and non-ground points (blue).
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Figure 4. Aerial photographs illustrating the evolution of the study area since 1957.
Figure 4. Aerial photographs illustrating the evolution of the study area since 1957.
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Figure 5. Perspective view of the profile derived from the 2016 DTM (red) and 2021 DTM (green) using the polyline of parameter L [47].
Figure 5. Perspective view of the profile derived from the 2016 DTM (red) and 2021 DTM (green) using the polyline of parameter L [47].
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Figure 6. Analysis using parameters described first for the Global Landslide Inventory [1].
Figure 6. Analysis using parameters described first for the Global Landslide Inventory [1].
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Figure 7. Elevation differences between 2016 and 2021. Areas with red tones and negative values indicate a loss of altitude, and blue areas with positive values indicate an increase in altitude.
Figure 7. Elevation differences between 2016 and 2021. Areas with red tones and negative values indicate a loss of altitude, and blue areas with positive values indicate an increase in altitude.
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Figure 8. Slope maps for the Bella Orxeta landslide area from 2016 to 2023. Notable terrain steepening near the toe and reorientation of surface elements are visible, especially post-2017.
Figure 8. Slope maps for the Bella Orxeta landslide area from 2016 to 2023. Notable terrain steepening near the toe and reorientation of surface elements are visible, especially post-2017.
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Figure 9. Analysis of ground movements (slope and aspect) between 2016 and 2021.
Figure 9. Analysis of ground movements (slope and aspect) between 2016 and 2021.
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Table 1. Drone and camera specifications for photogrammetric surveys.
Table 1. Drone and camera specifications for photogrammetric surveys.
Feature2021 Survey2023 Survey
Drone ModelDJI MiniMavic 3E
CompanyIndividualLiberam Technologies, S.L.
Image Sensor1/2.3″ CMOS4/3 CMOS
Effective Pixels12 MP20 MP
Lens FOV83°84°
Lens Focal Length (35 mm equiv.)24 mm24 mm
Aperturef/2.8f/2.8
ISO Range100–3200 (auto)100–6400
Shutter Speed1/8000 s (electronic)8–1/8000 s (electronic), 8–1/2000 s (mechanical)
Image Size4000 × 30005280 × 3956
Image FormatJPEGJPEG/DNG (RAW)
GNSSGPS + GLONASSRTK
RTK AccuracyN/AHorizontal: 1 cm + 1 ppm; Vertical: 1.5 cm + 1 ppm
Number of Images596533
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MDPI and ACS Style

Zaragozí, B.; Giménez-Font, P.; Cano-Aladid, J.; Marco-Molina, J.A. A Small Landslide as a Big Lesson: Drones and GIS for Monitoring and Teaching Slope Instability. Geosciences 2025, 15, 375. https://doi.org/10.3390/geosciences15100375

AMA Style

Zaragozí B, Giménez-Font P, Cano-Aladid J, Marco-Molina JA. A Small Landslide as a Big Lesson: Drones and GIS for Monitoring and Teaching Slope Instability. Geosciences. 2025; 15(10):375. https://doi.org/10.3390/geosciences15100375

Chicago/Turabian Style

Zaragozí, Benito, Pablo Giménez-Font, Joan Cano-Aladid, and Juan Antonio Marco-Molina. 2025. "A Small Landslide as a Big Lesson: Drones and GIS for Monitoring and Teaching Slope Instability" Geosciences 15, no. 10: 375. https://doi.org/10.3390/geosciences15100375

APA Style

Zaragozí, B., Giménez-Font, P., Cano-Aladid, J., & Marco-Molina, J. A. (2025). A Small Landslide as a Big Lesson: Drones and GIS for Monitoring and Teaching Slope Instability. Geosciences, 15(10), 375. https://doi.org/10.3390/geosciences15100375

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