Landslide risk assessment using digital photogrammetry and Gis multi criteria evaluation IN Matmata region (SE Tunisia)

Landslide risk assessment using digital photogrammetry and Gis multi criteria evaluation IN Matmata region (SE Tunisia) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Landslide risk assessment using digital photogrammetry and Gis multi criteria evaluation IN Matmata region (SE Tunisia) Hassen Bensalem, Houda Besser, Soulef Amamria, Mohamed Sadok Bensalem, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4659295/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Identifying the prone sites and recognizing the influencing factors of rock failure remains a major challenge, especially for the regions lacking a historical database of the chronological evolution of the different potential factors influencing the frequency and the amplitude of this hazard in the mountain zones. In this context, the present study aims to delineate the movement of the rocky masses after the frequent torrential rainfall and to assess the main driving factors of the landslide hazards in the Matmata region (SE Tunisia). The used approach relies on field observations, remotely sensed data, digital photogrammetry, and GIS-multi criteria assessment. The analysis of the kinematics of the rock cliffs triggering in the region between 2016 and 2023 highlights a relative movement of about 39 m of the carbonate rock masses related to the impacts of geological factors, weathering, land use changes, hydrogeology, and human activities on slope stability and rockfall occurrences. The hierarchical influence of these factors illustrates relevant spatio-temporal variability of susceptibility indices. The southern part of the region is characterized by the highest degree of vulnerability due to many factors such as slope, rainfall and lithology. The spatial distribution of the final susceptibility index indicates varying degrees of susceptibility across the study area amplified during the last years given the frequency of the extreme events. The susceptibility map is validated by landslide inventory. The findings highlight the relevance of the rockfall hazard and the relative amplitude in the region explained by a high index of urban expansion and infrastructure development in hilly areas. The obtained results present a valuable tool for decision-making for land use management and landslide mitigation measures. landslides digital photogrammetry modeling susceptibility SE Tunisia. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 I. Introduction The intensity and the frequency of rockfalls define a huge challenge for the mountain region causing serious damage even with small dimensions and non-periodic movement. These hazards are characterized by high speed, unpredictable triggering and under-estimated amplitude inhibiting, constraining the wariness of the target population in the appropriate time (Baillifard et al. 2003 ). Thus, different national and international strategies are sought to mitigate the impacts of these rockfalls and to assess triggering and invasion susceptibility based on scientific works, field observations, and geospatial management tools. Indeed, the significant financial losses and the serious threat to human safety explain the increasing attention given to rockfall assessment and the prevention measures for sustainable safe management. Owing to the inaccessibility of the most rockfall-prone sites, and the difficulty of accurate field inventories of the rock cliffs, the assessment of these hazards requires the use of geospatial platforms and the remote sensed data and the digital photogrammetry to delineate the run out (Rockfall trajectories) and to highlight the influence of the different landslides conditioning and triggering variables (geo-lithology, rainfall intensity, rainfall spatial distribution, elevation faults, seismic, slope, aspect, hydrological network density, land cover, rods and infrastructure, …) (Lin & Jeng 2000 ; Chen et al. 2018 ; Peng et al. 2020 ;Xu et al. 2020;Guo et al. 2021 ;He et al. 2021 ; Wen et al. 2022 ). The literature review indicates that the assessment of the landslide’s frequency defines a large spectrum of physical methods (field observation, direct measures), mathematical models (estimation, future evaluation, statistical analysis), and geospatial platform and cartographic (Modeling and radiometric analysis, automatic mapping) (Liu et al. 2021; Guo et al. 2021 ; He et al. 2022; Zhou et al. 2023 ). Different works have recently developed the active methodology for accurate evaluation of the relative risks and to predict the evolution of these vulnerable terrains. The developed methods can be synthesized according to (Baillifard et al. 2003 ), on three different types: (i) the statistical analysis relying on the field observations and spatial-distribution of physical factors involving directly or indirectly on the rockfall hazards; (ii) hierarchical classification of multi-criteria influence on the amplitude of rockfalls and the frequency of these hazards involving the estimation of the susceptibility of the regions and the assessment of triggering or invasion based on experts' opinion, and or mathematical evolution of the causality links; (iii) physically based techniques relying on an assessment based on the physical laws of the region stability and the intensity of rockfall hazard (Carrara 1983 , 1988 ; Loye te al. 2009; Vishal et al. 2015; Schulz et al. 2016 ; Pokharel & Thapa 2019 ). The Quantitative assessment methods have been commonly used for the evaluation of rock failure-prone sites namely the use of numerical simulation, statistical treatment, 2D/3D modeling based on accurate terrain data (topography, slope, roughness, water content, …) for the assessment of the dynamic and kinematic of the geologic hazard and the distribution of the moment concerning specific site condition. These newly developed techniques permit a rapid reliable assessment for a comprehensive monitoring of rock fall susceptibility that should be assessed to field inventories and systematic monitoring to provide accurate information for sustainable rehabilitation measures (Pokharel & Thapa 2019 ; Guo et al. 2020; He et al. 2022; Alvioli et al. 2023 ). Thus, a thorough understanding of the occurrence of these hazards and the spatial distribution of the amplitude of these risks requires modeling trials with validation data sets based on field surveys (Collison et al. 2000 ; Crescenzo & Santo 2005 ; Chen 2009 ). The cartography of the landslides via the geospatial platform and remotely sensed data coupled with quantitative assessment permitting the elaboration of vulnerability map to rockfall susceptibility map with a relative accuracy that may be used for preventing potential land losses in the decision-making process (Pradhan et al. 2010 ; Liu et al. 2016 ; He et al. 2021 ; Wen et al. 2022 ). This complementary approach is highly required for rationalizing hazard prevention for human safety. The scarce available data related to rockfall in the Mediterranean region and nationwide, and the lack of a local detailed assessment of this geo-hazard in southern Tunisia (Matmata region ) makes this work very challenging for developing an initial baseline study and, gives, at the same time, a great importance for this work, via a preliminary assessment of rock fall susceptibility and sustainable land use and social development (Lin & Jeng 2000 ; ,Chen & Lee 2004; Yoshimatsu & Abe 2006; Xu et al. 2017 ; Guo et al. 2021 ;He et al. 2022; Alvioli et al. 2023 ). In this context, the present work aims to estimate the susceptibility of the region of Matmata (SE Tunisia) for the rock cliffs and the intensity of the relative impacts based on terrestrial photogrammetry (drone mission) completed by geo-thematic susceptibility assessment prioritizing the influence of the different factors based on scientific evidence explained by the literature review and field surveys. II. Site description The study area, Matmata region, is located in SE Tunisia, between the latitudes 3702678 mN to 3715260 mN and longitudes 597328 mE to 609596 mE (Fig. 1 ). The region is characterized by extensive infrastructures, including a network of roads spanning over 150 km and several cities and villages situated in both the hilly areas and the lowlands. It represents one of the most prone areas to landslides and rockfall hazards (Bensalem et al.,2024) with increasing frequency during the last decades. The most spectacular landslides are related to the extreme rainfall events of November 2017. Given the unpredictable hazards and the uncontrolled intensity of these rock masses, triggering, upgrading, and bordering the road are essential for ensuring local population safety. However, the importance of the region as a tourist zone, an experimental pilot region for different multi-sectoral projects, and a specific site for different geological structures, the sustainable development of the region raises the concern about the required management of these prone sites. The region is characterized by semi-arid climate conditions, with annual rainfall of about 147 mm shifting towards less than 200 mm during the last five years. The spatiotemporal distribution of the annual rainfall illustrates the impacts of climate variability with the intensification of extreme events of floods (102 mm in 11 November 2017, December, April 2023) and droughts of less than 170 mm during 2020–2022 (INM 2023 ). The average annual temperature is about 24°C exceeding 33,4°C During July-August coupled with a high evapotranspiration amount of 262 mm (CRDA 2023 ). The studied site has a catchment area of 91,088 km² and a perimeter of 45,815 km trending generally 2° to the east. The maximum high sites are 580 m with a difference of about 415 m concerning the lowlands. Geologically, the region encompasses large sedimentary sequences dating from Permian to Eocene Within an active tectonic and structural context. The outcrops formations in the hilly areas of Matmata are mostly limestones and dolomite, with frequent intrusions of clays and marls (Bouaziz,1995). The main geological units are represented by the carbonates of Guettar formation. The area features limestone beds with occasional marly layers, characterized by high bioclast content. Locally, these beds are para-reefal , containing Inoceramus, sea urchins, and gastropods , and are exposed in the southwest and northwest regions beneath the dunes of the Grand Erg Oriental. The thickness of this unit is challenging to determine precisely due to its formation as a gently inclined structural plateau covering a large area. However, it is estimated to exceed 40 meters (Fig. 2 ). The potential rock blocks exposed to triggering are made of the Guettar formation. Field inventories indicated that the weathering degree of these units ranged from moderate to highly weathered, with important joints and karst systems. The intersection of these permeable and vulnerable frameworks enhances the conditions of rock mass failure from the steep slopes (36 degree) in the hilly areas (400 m). Despite the importance of the rainfall influence in enhancing the trigger process of rock masses, the lack of representative stations across the study area restraints the accurate quantitative evaluation of this mechanism on the sedimentary unit discontinuities or failure process. III. Methodology The overview of the susceptibility of the region to landslides depicted in Fig. 3 , defines five main steps related to (i) landslide inventory; (ii) development of a database relating to spatial measurements and conditioning factors; (i) prioritizing the different classes and subclasses for SI modeling, (iv) kinetic assessment via digital photogrammetry; (v) model evaluation and safety index assessment Landslide inventories four field campaigns were carried out between 2022 and 2024 for a full description of the observed sites of landslides, located essentially in the Northeast and the Southeast of the study area. The collected field data are related mainly to the geotechnical properties of the soil, the structural characteristics of the rock formations, the extent and morphology of the landslides, and the environmental conditions contributing to the instability (Figs. 2 & 4 ) Digital photogrammetry Digital photogrammetry and remotely sensed data represent a significant advance in landslide risk assessment. The approach relies on the photogrammetry workstations and sentinel-2 imagery (Table 1 ) to highlight accurate interpretation of surface ground changes over the past years. The cartography defines a complementary approach of terrestrial photogrammetry (drone mission) carried out during May 2023. The main objective of this mission is the primary objective of the drone mission is to identify and monitor rock falls within the region to track their movement over time. The obtained data is coupled with the satellite imagery (Sentinel 2) to outline the preferential pathways of the rocky mass movement, especially during the last extreme rainfall events of 2017 and 2023, 2024. Table 1 Metadata of the acquired Sentinel imagery Sentinel_2 Product ID Acquisition date Spatial resolution Cloud cover 2A_MSIL1C_20161112T100242_N0204 2016-11-12 10 m 3% S2A_MSIL1C_20171117T100301_N0206 2017-11-17 10 m 4% S2A_MSIL1C_20181122T100321_N0207 2018-11-22 10 m 3% S2A_MSIL2A_20191127T100341_N0500 2019-11-27 10 m 3% S2A_MSIL2A_20201121T100331_N0500 2020-11-21 10 m 4% S2B_MSIL1C_20211111T100149_N0301 2021-11-11 10 m 3% S2A_MSIL2A_20221121T100321_N0400 2022-11-21 10 m 3% S2A_MSIL1C_20231126T100331_N0509 2023-11-26 10 m 3% S2A_MSIL1C_20240504T100031_N0510 2024-05-04 10 m 3% Susceptibility modeling The assessment of the susceptibility to landslides relies on the agglomeration of different geo-thematic maps describing the major factors influencing this vulnerability based on the geostructural, geological, and geomorphological features of the study area to outline, via The association of the hierarchical influence of the direct or indirectly involved factors. The resulting map can be used for subsequent studies aiming the planning of the required interventions to mitigate rockfall hazards and (or) to inhibit the potential impacts. A classified susceptibility index will be helpful to delineate the appropriate section of infrastructure management and to prioritize the required safety measures and the detailed assessment of the rockfalls. The selected parameters and criteria used in this study are related to geomorphological features (slope, elevation, aspect, lithology), hydrologic characteristics (Network density, ….), and climate conditions, and they are synthesized by Table 2 . Table 2 the used indices Factor Source Formula/ expression Classification Assigned ratio Altitude DEM (30m) Direct extraction 165–263 m 264–339 m 340–443 m 444–580 m Different terrain elevations and associated risks Slope DEM (30m) Direct extraction < 5 ° 6–11 ° 12–18 ° 19–36 ° Steeper slopes increase rockfall risk Aspect DEM (30m) Direct extraction Plate (-1); North (0°-22.5°; 337.5°-360°); Northeast (22.5°-67.5°); East (67.5°-112.5°); Southeast (112.5°157.5°; South (157.5°202.5°); Southwest (202.5°-247.5°); West (247.5°292.5°); Northwest (292.5°-337.5°) Influence of direction on rockfall occurrence Plan Curvature DEM (30m) Direct extraction -1.23 to -0.27 -0.26 to -0.06 -0.05 to 0.1 0.11 to 0.35 0.36 to 1.38 Influence of terrain shape on rockfalls Profil Curvature DEM (30m) Direct extraction -1.3 to -0.4 -0.39 to -0.11 -0.1 to 0.09 0.091 to 0.34 0.35 to 1.2 Influence on debris deposition and rockfalls Lithology Matmata map (1/100000) Direct extraction Clays and Marls Limestones and Dolomites Sandy clays Influence of rock types on rockfall occurrence Hydrographic network DEM (30m) Direct extraction Four classes of stream Influence of watercourses on rockfalls Hydrographic density DEM (30m Direct extraction 200 Influence of rainfall on rockfall occurrence The Analytical Hierarchy Process (AHP) is a semi-qualitative method that involves matrix-based pairwise comparisons to assess the contribution of various factors to landslides. As a multi-objective, multi-criteria decision-making approach, AHP enables users to derive a scale of preference from a set of alternatives (Pourghamesi et al. 2012). This method aids decision-makers in identifying the best solution aligned with their goals and understanding of the problem (Table 3 ). The equation used for landslide susceptibility mapping with AHP is as follows (Eq. 1): LSI = Σ n i=1 (R i x W i ) (Eq. 1) where: R i represents the rating classes for each layer, and W i denotes the weights for each landslide conditioning factor. To determine the landslide susceptibility map, the effects of each parameter relative to each other are assessed through pairwise comparisons. The final map is constructed using the following equation (Eq. 2): LS AHP = (elevation x W AHP ) + (slope x W AHP ) + (slope x W AHP ) + (curvature plan x W AHP ) + (curvature profile x W AHP ) + (rainfall x W AHP ) + (hydrologic network x W AHP ) + (stream density x W AHP ) + (lithology x W AHP ) (Eq. 2) Here, W AHP represents the weight for each landslide conditioning factor. The pixel values obtained are then classified into four classes (low, moderate, high, and very high) based on natural breaks to determine the class intervals in the landslide susceptibility index map. In the AHP method, the consistency ratio (CR) serves as an index of inconsistency, indicating the likelihood that the matrix judgments were randomly generated (Saaty 1980, 1994). The CR is calculated using the formula (Eq. 3): CR = CI/RI (Eq. 3) where (RI) is the average consistency index for a given order of the matrix, as provided by Saaty (1980), and (CI) is the consistency index, which can be expressed as (Eq. 4): CI= (λ max -n)/(n-1) (Eq. 4 ) Here, λ max is the largest eigenvalue of the matrix and n is the order of the matrix. The CR, ranging from 0 to 1, reflects the matrix's consistency. A (CR) of 0.1 or less indicates a reasonable level of consistency (Vargas 2001), while a(CR) above 0.1 suggests the need for revising the judgments in the matrix due to inconsistencies. Using the AHP method, spatial factor weights were determined and applied in a weighted linear sum procedure (Voogd 1983) to calculate landslide susceptibility. In this study, a CR of 0.099 indicates a reasonable level of consistency in the pairwise comparisons, sufficient for recognizing factor weights. Consequently, rainfall received the highest weight, whereas the stream density had the lowest (Table 4 ). Data treatment the collected field data and the information obtained from geospatial platforms and open remote sensing sources are treated with different software namely Agisoft, Metashape Professional, and Cloud Compare. IV. Results & discussion IV.1. Kinematic In this study, high-resolution images from a photogrammetry mission in May 2023 and sentinel-2 images free of clouds in addition to Google Earth, were used to delineate the areas with intense movement of landslides in the region of Matmata from 2017 to 2023 (Fig. 5 ). The mosaic of these images is illustrated by the model in Fig. 6 , highlighting the direction and the magnitude of the displacement, especially within extreme weather events (floods of 2017 and torrential rainfall of 2023). IV.2. Susceptibility for landslides IV.2.1. thematic maps -Altitude : The spatial distribution of the altitude in the prone sites to rockfall indicates relevant information delineating the areas of varying terrain steepness and elevation, which are important factors influencing the likelihood and severity of rockfall occurrences. In the study area, the hilliest zones are located in the southern part referring to the presence of rugged terrain and steep slopes, while the lowlands are primarily found in the region, indicating relatively flat landscapes and lower elevation gradients, which may pose flood risks. The analyzed sites are mostly located in the transition zone between elevations 340 m and 443 m, while the two sites are characterized by relatively low altitudes of about 165 m and 264 m (Fig. 7 ). -Slope : For assessing the risk of rockfall events, slope steepness defines a key factor governing the triggering of rock cliffs. Steeper slopes are generally associated with a higher risk of rockfalls, as they increase the likelihood of detachment and movement of rocky materials. By subdividing the slope into classes, it becomes possible to identify areas with an increased risk of rockfalls, which is essential for urban planning, infrastructure safety, and the protection of populations living or working in these high-risk areas. The distribution of the slope across the study area illustrates a heterogeneous distribution with increasing accentuation along the mountain's structures while the southeastern part illustrates the lowest values of less than five degrees. The analyzed sites of landslides are located along the structures with high elevated steepness of values ranging between 20° and 30° (Fig. 7 ). -Slope Aspect : The slope aspect provides valuable insights into the orientation of terrain features within the study area. This map underwent reclassification into nine distinct classes, ranging from flat to various cardinal directions. Analysis reveals that the Southwest (SW) class exhibits the highest susceptibility, followed by the East (E) class across all four models. Additionally, the South (S) and Southeast (SE) classes also demonstrate elevated weights, albeit lower than those of the SW and E classes (Fig. 7 ). - Plan curvature : The distribution of the obtained values illustrates that rockfall areas are primarily situated between 0.36 to 1.38, indicating regions with high and convex curvature. This observation suggests a correlation between terrain curvature and the occurrence of rockfall events, with convex terrain features potentially serving as predisposing factors for rockfall initiation (Fig. 7 ). - Profil curvature : It is evident that rockfall areas are predominantly situated between 0.091 to 0.34 and 0.35 to 1.2, indicating regions with elevated and concave profile curvature that facilitate the deposition of debris. This observation underscores the significance of profile curvature in influencing the occurrence and distribution of rockfall events. Concave terrain features, characterized by high profile curvature, are prone to accumulating loose material and promoting rockfall initiation and propagation. Therefore, understanding the relationship between profile curvature and rockfall susceptibility is crucial for effective hazard assessment and mitigation strategies, particularly in areas where concave terrain features predominate (Fig. 7 ). -Lithology : The lithological map was constructed through the digitization of the 1/100000 geological map of Matmata (ONM). It underwent a reclassification process into three main groups: clayey-to-early formations, clayey-sandy formations, and limestone and dolomitic formations (Fig. 7 ). -Hydrographic Network : The hydrographic network map presents four classes of watercourses, with one primary class and the others being secondary. The primary class serves as a focal point for water flow and the secondary classes complement the main watercourse (Fig. 7 ). -Hydrographic Network Density : The hydrographic network density within the study area is categorized into five distinct classes, each providing valuable insights into the distribution and concentration of watercourses. These classes range from areas with minimal hydrographic network density, indicated by values less than 1.98, to regions with significantly denser networks, represented by values between 7.94 and 9.92 (Fig. 7 ). -Rainfall distribution : The rainfall map was generated by kriging the average annual precipitation data from the meteorological stations of Matmata Nouvelles, Matmata, El Hamma, and Mareth, using ArcMap with a spatial resolution of 30m x 30m. Statistical analysis of the class weights by the four bivariate models indicates that susceptibility to rockfall increases with higher precipitation. The most susceptible class is between 200mm/year (Fig. 7 ). IV.2.3. Validation of the landslide susceptibility map In this study area, the landslide susceptibility map was created using the multicriteria analytical hierarchy process. This map was then categorized into three susceptibility levels: low, moderate, and high (Fig. 8 ). Following a common approach in related research (Yanli et al. 2016), it was found that the low susceptibility zone constitutes 20% of the study area, while the moderate and high susceptibility zones comprise 70% and 10% of the area, respectively. Validating a landslide susceptibility model is crucial to assess the predictive accuracy of the generated susceptibility map. To evaluate the model's predictive capability, both physical and statistical methods were used. The physical validation involves verifying if the majority of significant landslides are located within the high-susceptibility zones. For statistical validation, methods such as calculating landslide frequency and generating a success rate curve were employed. The landslide susceptibility map presented in Fig. 8 indicates that most major landslides, including loess landslides, debris flows, unstable slopes, and rock falls, are predominantly located in regions identified as having high susceptibility. The results presented by Table 4 , reveal that the landslide frequency is highest in the high susceptibility zone at 5, followed by the moderate susceptibility zone at 0.57, and the low susceptibility zone at 0.5. These findings suggest that the landslide susceptibility zones identified by the SI model are accurate and align well with the actual occurrences of landslides. Table 4 Comparison of Forecasted Landslide Hazard Zones with Actual Landslide Occurrences Landslides susceptibility zones Area (%) Landslides (%) Landslides frequency Low 20 10 0.5 Moderate 70 40 0,57 High 10 50 5 It was found that 50% of all observed landslides occurred in areas classified as highly susceptible to landslides, 40% were in moderately susceptible zones, and 10% were in zones with low susceptibility. (Table 4 ) The success-rate curve was derived by comparing existing landslide occurrences with the landslide susceptibility map (Fig. 8 ). In this study, we utilized the area under the curve (AUC) method to qualitatively evaluate the prediction accuracy (Fig. 9 ). A high AUC value indicates that the model is effective and can reliably produce landslide susceptibility maps. The verification results demonstrated an AUC value of 0.94 (Fig. 9 ), indicating a 94% success rate. The SI model applied in this study area exhibited high prediction accuracy, suggesting its suitability for generating landslide susceptibility maps for the region. IV. 3. Discussion The delineation of the landslide precursors for remote mountain environments reveals a complex task that requires accurate field validation of the known and failed landslides (Qi et al. 2021 ). This evaluation should take into account the uncertainties related to data treatment and pixel spatial clustering. The assessment of these hazards relies on, however, a time-series analysis of surface deformation for about 30 years to highlight the influence of anthropogenic activities and urban management on the one hand and to evaluate the impacts of climate change namely the intense rainfall events in enhancing the frequency of these issues. It is challenging however, to highlight short-term interval-related deformations may be hard as (i) the scale of the acquired data and the related treatment may not enhance the delineation of small-scale movement and (ii) the major influencing factors governing the movement of these landslides have occasional impactful effects that are related to extreme climate events or intense anthropogenic perturbation. In fact, given the hierarchical classification obtained from the susceptibility model, the evaluation of the different integrated factors highlighted that the main influence is related to slope gradient, lithology, and precipitation respectively. The cumulative actions of these factors are hard to detect by one-year changes. Thus, the time-series analysis may reveal more relevant information, especially for the evolution of the influence of the analyzed parameters within the changing context (Aksay,2023; Bokharel et al.,2023). The classification of the relevance of the impacts of the different analyzed factors on the frequency and amplitude of these geo-hazards defines the slope gradient as the main factor. The spatial distribution of the susceptibility index reveals a concerning situation as more than 10% of the area falls into the high vulnerability class with a high risk of landslide occurrence and 70% of the area is into a moderate risk of landslide occurrence, while only 20% are classified as safe areas. This distribution explains the importance of hydroclimatic conditions, in concordance with previous works that indicate that intense rainfall and soil saturation are critical triggers for landslides in this region (Bensalem et al.,2024). Based on the distribution of the vulnerability index across the study area, the areas with moderate to high susceptibility are located in the eastern and southeastern parts. Suppose the region was overlapped by carbonate lithology and active faults. In that case, these areas with low distances to the stream network and to roads illustrate, furthermore, intense vulnerability, especially in the central part of the region. These areas exhibit multifactorial overlaps that define increasing susceptibility to landslide occurrence. These results are following field observation and field analytical work. The verification of the obtained results revealed that the hierarchical multifactorial analysis based on field observation and remote sensed data revealed high accordance with the selected sites of landslides occurrence and the distribution of the areas with the highest indices for susceptibility highlighting the effective assessment via the combination of landslides relative data and the expert’s opinion of the impactful factors especially with lack of reliable historical records for reference baseline evaluation. The relatively small-scale difference between the simulated and the observed or measured data indicates the influence of different secondary factors that may not be considered; however, their impacts seem to be relevant at some localities, namely the activation of small-scale faults. This, the geophysical investigation and the profile of subsurface anomaly distribution should be taken into consideration for a more relevant and accurate assessment of landslide susceptibility (Wu et al.,2016). Furthermore, given the limited number of sites for field observation and analytical work, the assessment of landslide occurrence based on the multifactorial hierarchical analyses is of crucial importance as the lack of accessibility for some points across the study area and the representativeness of the quantitative evaluation of the measured sites. The susceptibility index presents an appropriate method for highlighting the hierarchical influence of the different rehabilitation and management measures for short- and long-term intervention (Myronidis et al.,2015). However, despite the relevance of the obtained results, the lack of historical data, and the tile-series assessment of landslide occurrence, frequency, and amplitude, the validation of the findings of the study is hard. It requires further investigation with detailed analytical work. It represents, nonetheless, a baseline study for the quantitative assessment of the vulnerability index of the region to landslides, for prioritizing the feasible actions, and for the integrated approach of aerial photogrammetry, and remote sensed data analysis with relative uncertainties (Erener.A et al.,2011). Since no similar initiatives have been adopted in this region, this work offers a new perspective on preliminary rockfall susceptibility assessment. This serves as a foundation for detailed rockfall hazard and risk assessments, as well as sustainable land use planning. Utilizing a high-resolution DEM in specific parts of the study area, along with a detailed lithological map, would enhance the identification of rockfall source areas. Knowledge of block sizes, detailed geomorphological settings, and the potential inclusion of specific triggers for rockfalls would aid in the quantitative estimation of rockfall hazards. V. Conclusions and perspectives This paper conducts a baseline characterization of the prone sites to rockfall in the Matmata region (SE Tunisia) and the relative kinematic analysis. The occurrence of the first landslides seems to be related to active tectonics, discontinuities, and the geo-structural context. Intense rainfall represents an important triggering factor. The kinematic analysis indicates that the locations of the most prone sites are principally related to steep slopes and the presence of fractures. The evaluation of the dispersion and distribution of the energy and velocity require, undoubtedly, proposing engineered management actions. The study evaluates the influence of geological, geomorphological, hydrological, geostructural, and climatic factors on the triggering of rock masses and the hierarchical importance of initiating the landslide mechanisms. The kinematics of the unstable units are dominated by toppling and sliding movements. The main results indicate that the combination of steep slopes, intense rainfall, and weak lithology significantly contributes to the occurrence and severity of rockfalls in the region. The used approach in this study presents a mixing of typical field observation studies, statistical and geospatial treatment, and geo-mechanical approach defining a multi-dimensional assessment of the susceptibility of rock falls in the Matmata region and of the kinetic of these movements. The photogrammetric survey coupled with a geostructural survey allows a detailed description of the region that is difficult to access for terrain data collection. The high-precision maps, permit, consequently, a quantitative estimation of the instability of rocky units and a delineation of the preferential trajectories of the failure masses. Further detailed surveys are necessary for an infield intervention based on detailed quantitative assessing (surface, volume, losses, …). Depending on the specific conditions of the investigated sites, the rock-fall hazards involve multiple parameters and factors of which the influence is hierarchically evaluated based on the local context. The causality links between these factors define a complex failure process that is difficult to predict in occurrence or estimate in intensity by the simplified physical, statistical or mathematical modeling tools. The uncertainties related to this study are represented mainly by these multi-data sources of different degree of detailing that may constrain the representativeness and (or) the reliability of the findings of this research. The uncertainties related to the different estimation methods of rockfall hazards and the performance of the adopted protection structures reveal the necessity of further detailed studies relying on the field inventories for modeling outputs validation. These estimations allow an optimal set of measures that reduces the damaging impacts and ensures compliance with relevant multi-criteria risk assessment. The consistency of these processes used for hazard mitigation requires, furthermore, a long time series of treated data using thorough evaluation of this phenomenon allowing a reconstitution of the paleo-evolution and consequently a prediction of the further occurrence. Modeling of these geo-hazards via physical-based models relying on terrain data collection is required. Declarations Author Contribution Hassen.Bensalem and Houda.Besser wrote the main manuscript text , Soulef.Amamria and Mohamed.Sadok.Bensalem and Claudia.Meisina prepared figures and Tables and Noureddine.Hamdi prepared conclusions.All authors reviewed manuscript References Aksay, B. (2023). Recent advances in landslide prediction models. Landslides , 20 (1), 45-60. https://doi.org/10.1007/s10346-023-01800-4 Alvioli M, Falcone G, Mendicelli A et al (2023) Seismically induced rockfall hazard from a physically based model and ground motion scenarios in Italy. Geomorphology 429:108652. https://doi.org/10. 1016/j.geomorph.2023.108652 Alvioli M, Guzzetti F, Marchesini I (2020) Parameter-free delineation of slope units and terrain subdivision of Italy. Geomorphology 358:107124. https://doi.org/10.1016/j.geomorph.2020.107124 Baillifard, F. & Jaboyedoff, Michel & Sartori, Mario. (2003). Rockfall hazard mapping along a mountainous road in Switzerland using a GIS-based parameter rating approach. Natural Hazards and Earth System Science. 3. 10.5194/nhess-3-435-2003. Bensalem H; Amamria S; Bensalem MS; Meisina C; Hamdi N. 2024. Effect of lithological and geotechnical characteristics on the generation of debris flows in Matmata, Southeastern Tunisia. Studia Quaternaria, vol. 41, no. 1 : 23–35. DOI : 10.24425/sq.2024.149970 Bokharel, R., Sharma, P., & Dahal, R. K. (2023). Innovative techniques in landslide hazard assessment. Landslides , 20 (3), 789 -805. https://doi.org/10.1007/s10346-023-01900-2 Bouaziz, S., 1995. Etude de la tectonique cassante dans la plate-forme et l’Atlas Sahariens (Tunisie méridionale) : Evolution des paléochamps de contraintes et implications géodynamiques. Unpublished thesis ès-Sciences, Université Tunis II, 484. Carrara A (1983) Multivariate methods for landslide hazard evaluation. Math Geol 15:403–426 Carrara A (1988) Landslide hazard mapping by statistical methods: a black box model approach. In: Proceedings of the Workshop on Natural Disaster in European Mediterranean Countries, Consiglio Nazionale delle Ricerche, Perugia Carrara, A., Cardinali, M., Guzzetti, F. and Reichenbach, P. (1995) GIS Technology in Mapping Landslide Hazard. In: Carrara, A. and Guzzetti, F., Eds., Geographical Information Systems in Assessing Natural Hazards, Kluwer Academic Publisher, Dordrecht, 135-176. https://doi.org/10.1007/978-94-015-8404-3_8 Chen, Chien-Yuan. (2009). Sedimentary impacts from landslides in the Tachia River Basin, Taiwan. Geomorphology. 105. 355-365. 10.1016/j.geomorph.2008.10.009. Chen, L., Guo, Z., Yin, K., Pikha Shrestha, D., Jin, S., 2019. The influence of land use and land cover change on landslide susceptibility: A case study in Zhushan Town, Xuan’en County (Hubei, China). Nat. Hazards Earth Syst. Sci. 19, 2207– 2228. https://doi.org/10.5194/nhess-19-2207-2019 Chen, W., Zhang, S., Li, R. W., and Shahabi, H. (2018). Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naive Bayes tree for landslide susceptibility modeling. Sci. Total Environ. 644, 1006–1018. doi: 10.1016/j.scitotenv.2018.06.389 Chung CF, Fabbri AG (1995) Multivariate regression analysis for landslide hazard zonation. In: Carrara A, Guzzetti F (eds) Geographical Information Systems in assessing natural hazards. Kluwer, Dordrecht, pp 107–142 Collison, A., Wade, S., Griffiths, J., Dehn, M., 2000. Modelling the impact of predicted climate change on landslide frequency and magnitude in SE England. Eng. Geol. 55, 205–218. https://doi.org/10.1016/S0013-7952(99)00121-0. CRDA. 2023. Internal report Crescenzo G, Santo A (2005) Debris slides-rapid earth flows in the carbonate massifs of the Campania region (southern Italy): morphological and morphmetric data for evaluating triggering susceptibility. Geomorphology 66:255–276 Erener, A. (2011). Remote sensing of vegetation health for reclaimed areas of Seyitömer open cast coal mine. International Journal of Coal Geology, 86(1), 20-26. Guo, C., Xu, Q., Dong, X., Li, W., Zhao, K., Lu, H., Ju, Y., 2021. Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas. J. Earth Sci. 32, 1079–1091. https://doi.org/10.1007/s12583-021-1467-2 He, Q., Yang, J., Chen, H., Liu, J., Ji, Q., Wang, Y., Tang, F., 2021. Evaluation of extreme precipitation based on three long-term gridded products over the qinghai-tibet plateau. Remote Sens. 13, 3010.https://doi.org/10.3390/rs13153010. He, Q., Yang, J., Chen, H., Liu, J., Ji, Q., Wang, Y., Tang, F., 2021. Evaluation of extreme precipitation based on three long-term gridded products over the qinghai-tibet plateau. Remote Sens. 13, 3010. https://doi.org/10.3390/rs13153010. Herrera, G.; Notti, D.; García-Davalillo, J.C.; Mora, O.; Cooksley, G.; Sánchez, M.; Arnaud, A.; Crosetto, M. Analysis with C- and X-band satellite SAR data of the Portalet landslide area. Landslides 2011, 8 , 195–206 INM. 2023. Open climate data Lin ML, Jeng FS (2000) Characteristics of hazards induced by extremely heavy rainfall in Central Taiwan – typhoon herb. Eng Geol 58:191–207. https://doi.org/10.1016/S0013-7952(00)00058-2 Liu, Y.; Li, H.; Zheng, H.; Tan, F. Research progress and prospect of the relationship among active tectonics, earthquakes and geological disasters in China. J. Nat. Disasters 2022, 31, 1–14 Liu, Y.i., Yin, K., Chen, L., Wang, W., Liu, Y., 2016. A community-based disaster risk reduction system in Wanzhou, China. Int. J. Disaster Risk Reduct. 19, 379–389. https://doi.org/10.1016/j.ijdrr.2016.09.009. Loye A, Jaboyedoff M, Pedrazzini A (2009) Identification of potential rockfall source areas at a regional scale using a DEM-based geomorphometric analysis. Natural Hazards and Earth System Science 9:1643–1653. https://doi.org/10.5194/nhess-9-1643-2009 Meisina, C.; Zucca, F.; Notti, D.; Colombo, A.; Cucchi, G.; Giannico, C.; Bianchi, M. Geological Interpretation of PSInSAR Data at Regional Scale. Sensors 2008, 8 , 7469–7492. Myronidis, D., & Fotakis, D. G. (2015). Utilising 3D solid modelling tools for simplified designing of a small concrete gravity dam. International Journal of Sustainable Agricultural Management and Informatics, 1(4), 351-357. Peng, J. B., Cui, P., and Zhuang, J. Q. (2020). Challenges to engineering geology of Sichuan-Tibet railway. Chin. J. Rock Mech. Eng. 39, 2377–2389. (In Chinese with English abstract). doi:10.13722/j.cnki.jrme.2020.0446 Pokharel B, Thapa PB (2019) Landslide susceptibility in Rasuwa District of central Nepal after the 2015 Gorkha Earthquake. Journal of Nepal Geological Society 59:79–88. https://doi.org/10.3126/jngs.v59i0.24992 Pokharel, B., Lim, S., Bhattarai, T.N. et al. Rockfall susceptibility along Pasang Lhamu and Galchhi-Rasuwagadhi highways, Rasuwa, Central Nepal. Bull Eng Geol Environ 82, 183 (2023). https://doi.org/10.1007/s10064-023-03174-8; Pradhan, Biswajeet & Oh, Hyun-Joo & Buchroithner, Manfred. (2010). Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomatics, Natural Hazards and Risk. 1. 199-223. 10.1080/19475705.2010.498151. Qi, L., Chen, Y., Wang, S., & Zhang, H. (2021). Monitoring and early warning systems for landslides: Advances and challenges . Landslides , 18 (2), 123 -136. https://doi.org/10.1007/s10346-021-01600-4 Roering, J.J., Stimely, L.L., Mackey, B.H., Schmidt, D.A., 2009. Using DInSAR, airborne LiDAR, and archival air photos to quantify landsliding and sediment transport. Geophys. Res. Lett. 36, L19402. http://dx.doi.org/10.1029/2009GL040374. Schulz, W.H., Coe, J.A., Ricci, P.P., Smoczyk, G.M., Shurtleff, B.L., Panosky, J., Jones, E.S., 2016. Data Related to a Ground-based InSAR Survey of the Slumgullion Landslide, Hinsdale County, Colorado, 26 June 2010–1 July 2010. http://dx.doi.org/10.5066/ F7TX3CFW Tarchi, D., Casagli, N., Fanti, R., Leva, D., Luzi, G., Pasuto, A., Pieraccini, M., Silvano, S., 2003. Landslide monitoring by using ground-based SAR interferometry: an example of application to the Tessina landslide in Italy. Eng. Geol. 68, 15–30. Mantovani, F.; Soeters, R.; Van Western, C.J. Remote Sensing techniques for landslide studies and hazard zonation in Europe. Geomorphology 1996, 15 , 213–225. Vishal V, Siddique T, Purohit R et al (2017) Hazard assessment in rockfall-prone Himalayan slopes along National Highway-58, India: rating and simulation. NatHazards 85:487–503. https://doi.org/10. 1007/s11069-016-2563-y Wen, H., Wu, X. Y., Ling, S. X., Sun, C. W., Liu, Q., and Zhou, G. Y. (2022). Characteristics and susceptibility assessment of the earthquake-triggered landslides in moderate-minor earthquake prone areas at southern margin of Sichuan Basin, China. Bull. Eng.Geol. Environ. 81, 346. doi:10.1007/s10064-022-02821-w Werner, C., Wegmuller, U., Strozzi, T., Wiesmann, A., 2003. Interferometric Point Target Analysis for deformation mapping. Proceedings of IGARSS 2003, Toulouse (Francia) 7, pp. 4362–4364. Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., ... & Dean, J. (2016). Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144. Xu C, Tian Y, Zhou B et al (2017) Landslide damage along Araniko highway and Pasang Lhamu highway and regional assessment of landslide hazard related to the Gorkha, Nepal earthquake of 25 April 2015. Geoenvironmental Disasters 4:14. https://doi.org/10. 1186/s40677-017-0078-9 Xu, Y., Gao, X., Giorgi, F., Zhou, B., Shi, Y., Wu, J., Zhang, Y., 2018. Projected Changes in Temperature and Precipitation Extremes over China as Measured by 50-yr Return Values and Periods Based on a CMIP5 Ensemble. Adv. Atmos. Sci. https:// doi.org/10.1007/s00376-017-6269-1. Zhou, Jinxuan & Tan, Shucheng & Li, Jun & Xu, Jian & Wang, Chao & Ye, Hui. (2023). Landslide Susceptibility Assessment Using the Analytic Hierarchy Process (AHP): A Case Study of a Construction Site for Photovoltaic Power Generation in Yunxian County, Southwest China. Sustainability. 15. 5281. 10.3390/su15065281. Additional Declarations No competing interests reported. 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Pavia","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Meisina","suffix":""},{"id":323731801,"identity":"bbe4ad5f-10a1-47ab-8182-de5e70e4d3e8","order_by":5,"name":"Noureddine Hamdi","email":"","orcid":"","institution":"University of Gabès","correspondingAuthor":false,"prefix":"","firstName":"Noureddine","middleName":"","lastName":"Hamdi","suffix":""}],"badges":[],"createdAt":"2024-06-29 12:22:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4659295/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4659295/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61312422,"identity":"c2c3e20f-c502-46d0-899c-c241d12e864a","added_by":"auto","created_at":"2024-07-29 11:20:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":179275,"visible":true,"origin":"","legend":"\u003cp\u003eLocalization of the study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4659295/v1/f9805c68a31dbc1be37c2e1e.png"},{"id":61313007,"identity":"defd0387-7f9c-4076-a671-a81e9b6246c6","added_by":"auto","created_at":"2024-07-29 11:28:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":502612,"visible":true,"origin":"","legend":"\u003cp\u003eGeological map of the study area (Bensalem et al.,2024)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4659295/v1/71d60da4f7d3a73412e24239.png"},{"id":61312426,"identity":"dfd1f49e-a793-4e8d-9100-9b5b1ccbf8ec","added_by":"auto","created_at":"2024-07-29 11:20:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":164200,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the used methodology\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4659295/v1/06088db95d758749d4fbbbdd.png"},{"id":61311781,"identity":"82bf7e0d-7eb2-46d1-815e-c582f24d1a73","added_by":"auto","created_at":"2024-07-29 11:12:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":955114,"visible":true,"origin":"","legend":"\u003cp\u003eRisks of rockfalls in the study area\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4659295/v1/0ef32eb213fb57b7995bfc49.png"},{"id":61312425,"identity":"85a932e5-0e2a-40a6-9fc5-c6f865fde3fd","added_by":"auto","created_at":"2024-07-29 11:20:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1160021,"visible":true,"origin":"","legend":"\u003cp\u003edelineation of rockfall via Aerial photogrammetry image: \u003cstrong\u003eA. \u003c/strong\u003ein 2016. \u003cstrong\u003eB.\u003c/strong\u003e in 2023\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4659295/v1/8c497e42f653410280867d33.png"},{"id":61312424,"identity":"d60c2a30-dbfa-4932-8dad-029cefa8e93b","added_by":"auto","created_at":"2024-07-29 11:20:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":779924,"visible":true,"origin":"","legend":"\u003cp\u003eRisk distribution according to\u003cstrong\u003e: A.\u003c/strong\u003eElevation; \u003cstrong\u003eB. \u003c/strong\u003eSlope; \u003cstrong\u003eC.\u003c/strong\u003e Curvature plan; \u003cstrong\u003eD.\u003c/strong\u003e Curvature profil; \u003cstrong\u003eE. \u003c/strong\u003eHydrologic Network; \u003cstrong\u003eF. \u003c/strong\u003eNetwork density; \u003cstrong\u003eG. \u003c/strong\u003eRainfall distribution; H.\u003cstrong\u003e \u003c/strong\u003eLithology;\u003cstrong\u003e I.\u003c/strong\u003e Slope Aspect\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4659295/v1/5b7aec27618a981ed7fb960b.png"},{"id":61311787,"identity":"4dbce39d-600a-4c43-b9c2-5c7672468a90","added_by":"auto","created_at":"2024-07-29 11:12:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":159591,"visible":true,"origin":"","legend":"\u003cp\u003eRockfall Susceptibility Index map based on analytical hierarchy process (AHP) model\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4659295/v1/651dbf0d8c8283f3b128ae78.png"},{"id":61311779,"identity":"1c5a4146-fea2-47ff-90e0-20588893b575","added_by":"auto","created_at":"2024-07-29 11:12:04","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":45691,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction Rate Curves for Susceptibility Maps Generated in This Study area Using AHP (AUC=0.94)\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4659295/v1/3db1d399eac25954458459fb.png"},{"id":62436097,"identity":"2908317d-acbc-4be1-af6d-d1b78b8baea5","added_by":"auto","created_at":"2024-08-14 07:50:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4842992,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4659295/v1/192a9198-1d5a-472e-be3d-8711d981f97a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eLandslide risk assessment using digital photogrammetry and Gis multi criteria evaluation IN Matmata region (SE Tunisia)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"I. Introduction","content":"\u003cp\u003eThe intensity and the frequency of rockfalls define a huge challenge for the mountain region causing serious damage even with small dimensions and non-periodic movement. These hazards are characterized by high speed, unpredictable triggering and under-estimated amplitude inhibiting, constraining the wariness of the target population in the appropriate time (Baillifard et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Thus, different national and international strategies are sought to mitigate the impacts of these rockfalls and to assess triggering and invasion susceptibility based on scientific works, field observations, and geospatial management tools. Indeed, the significant financial losses and the serious threat to human safety explain the increasing attention given to rockfall assessment and the prevention measures for sustainable safe management. Owing to the inaccessibility of the most rockfall-prone sites, and the difficulty of accurate field inventories of the rock cliffs, the assessment of these hazards requires the use of geospatial platforms and the remote sensed data and the digital photogrammetry to delineate the run out (Rockfall trajectories) and to highlight the influence of the different landslides conditioning and triggering variables (geo-lithology, rainfall intensity, rainfall spatial distribution, elevation faults, seismic, slope, aspect, hydrological network density, land cover, rods and infrastructure, \u0026hellip;) (Lin \u0026amp; Jeng \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Peng et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e;Xu et al. 2020;Guo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e;He et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wen et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe literature review indicates that the assessment of the landslide\u0026rsquo;s frequency defines a large spectrum of physical methods (field observation, direct measures), mathematical models (estimation, future evaluation, statistical analysis), and geospatial platform and cartographic (Modeling and radiometric analysis, automatic mapping) (Liu et al. 2021; Guo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; He et al. 2022; Zhou et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Different works have recently developed the active methodology for accurate evaluation of the relative risks and to predict the evolution of these vulnerable terrains. The developed methods can be synthesized according to (Baillifard et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), on three different types: (i) the statistical analysis relying on the field observations and spatial-distribution of physical factors involving directly or indirectly on the rockfall hazards; (ii) hierarchical classification of multi-criteria influence on the amplitude of rockfalls and the frequency of these hazards involving the estimation of the susceptibility of the regions and the assessment of triggering or invasion based on experts' opinion, and or mathematical evolution of the causality links; (iii) physically based techniques relying on an assessment based on the physical laws of the region stability and the intensity of rockfall hazard (Carrara \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1983\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Loye te al. 2009; Vishal et al. 2015; Schulz et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pokharel \u0026amp; Thapa \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Quantitative assessment methods have been commonly used for the evaluation of rock failure-prone sites namely the use of numerical simulation, statistical treatment, 2D/3D modeling based on accurate terrain data (topography, slope, roughness, water content, \u0026hellip;) for the assessment of the dynamic and kinematic of the geologic hazard and the distribution of the moment concerning specific site condition. These newly developed techniques permit a rapid reliable assessment for a comprehensive monitoring of rock fall susceptibility that should be assessed to field inventories and systematic monitoring to provide accurate information for sustainable rehabilitation measures (Pokharel \u0026amp; Thapa \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Guo et al. 2020; He et al. 2022; Alvioli et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, a thorough understanding of the occurrence of these hazards and the spatial distribution of the amplitude of these risks requires modeling trials with validation data sets based on field surveys (Collison et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Crescenzo \u0026amp; Santo \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Chen \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The cartography of the landslides via the geospatial platform and remotely sensed data coupled with quantitative assessment permitting the elaboration of vulnerability map to rockfall susceptibility map with a relative accuracy that may be used for preventing potential land losses in the decision-making process (Pradhan et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; He et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wen et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This complementary approach is highly required for rationalizing hazard prevention for human safety.\u003c/p\u003e \u003cp\u003eThe scarce available data related to rockfall in the Mediterranean region and nationwide, and the lack of a local detailed assessment of this geo-hazard in southern Tunisia (Matmata region ) makes this work very challenging for developing an initial baseline study and, gives, at the same time, a great importance for this work, via a preliminary assessment of rock fall susceptibility and sustainable land use and social development (Lin \u0026amp; Jeng \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; ,Chen \u0026amp; Lee 2004; Yoshimatsu \u0026amp; Abe 2006; Xu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e;He et al. 2022; Alvioli et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this context, the present work aims to estimate the susceptibility of the region of Matmata (SE Tunisia) for the rock cliffs and the intensity of the relative impacts based on terrestrial photogrammetry (drone mission) completed by geo-thematic susceptibility assessment prioritizing the influence of the different factors based on scientific evidence explained by the literature review and field surveys.\u003c/p\u003e"},{"header":"II. Site description","content":"\u003cp\u003eThe study area, Matmata region, is located in SE Tunisia, between the latitudes 3702678 mN to 3715260 mN and longitudes 597328 mE to 609596 mE (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The region is characterized by extensive infrastructures, including a network of roads spanning over 150 km and several cities and villages situated in both the hilly areas and the lowlands. It represents one of the most prone areas to landslides and rockfall hazards (Bensalem et al.,2024) with increasing frequency during the last decades. The most spectacular landslides are related to the extreme rainfall events of November 2017. Given the unpredictable hazards and the uncontrolled intensity of these rock masses, triggering, upgrading, and bordering the road are essential for ensuring local population safety. However, the importance of the region as a tourist zone, an experimental pilot region for different multi-sectoral projects, and a specific site for different geological structures, the sustainable development of the region raises the concern about the required management of these prone sites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe region is characterized by semi-arid climate conditions, with annual rainfall of about 147 mm shifting towards less than 200 mm during the last five years. The spatiotemporal distribution of the annual rainfall illustrates the impacts of climate variability with the intensification of extreme events of floods (102 mm in 11 November 2017, December, April 2023) and droughts of less than 170 mm during 2020\u0026ndash;2022 (INM \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The average annual temperature is about 24\u0026deg;C exceeding 33,4\u0026deg;C During July-August coupled with a high evapotranspiration amount of 262 mm (CRDA \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The studied site has a catchment area of 91,088 km\u0026sup2; and a perimeter of 45,815 km trending generally 2\u0026deg; to the east. The maximum high sites are 580 m with a difference of about 415 m concerning the lowlands.\u003c/p\u003e \u003cp\u003eGeologically, the region encompasses large sedimentary sequences dating from Permian to Eocene Within an active tectonic and structural context. The outcrops formations in the hilly areas of Matmata are mostly limestones and dolomite, with frequent intrusions of clays and marls (Bouaziz,1995). The main geological units are represented by the carbonates of Guettar formation. The area features limestone beds with occasional marly layers, characterized by high bioclast content. Locally, these beds are \u003cem\u003epara-reefal\u003c/em\u003e, containing \u003cem\u003eInoceramus, sea urchins, and gastropods\u003c/em\u003e, and are exposed in the southwest and northwest regions beneath the dunes of the Grand Erg Oriental. The thickness of this unit is challenging to determine precisely due to its formation as a gently inclined structural plateau covering a large area. However, it is estimated to exceed 40 meters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe potential rock blocks exposed to triggering are made of the Guettar formation. Field inventories indicated that the weathering degree of these units ranged from moderate to highly weathered, with important joints and karst systems. The intersection of these permeable and vulnerable frameworks enhances the conditions of rock mass failure from the steep slopes (36 degree) in the hilly areas (400 m). Despite the importance of the rainfall influence in enhancing the trigger process of rock masses, the lack of representative stations across the study area restraints the accurate quantitative evaluation of this mechanism on the sedimentary unit discontinuities or failure process.\u003c/p\u003e"},{"header":"III. Methodology","content":"\u003cp\u003eThe overview of the susceptibility of the region to landslides depicted in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, defines five main steps related to (i) landslide inventory; (ii) development of a database relating to spatial measurements and conditioning factors; (i) prioritizing the different classes and subclasses for SI modeling, (iv) kinetic assessment via digital photogrammetry; (v) model evaluation and safety index assessment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLandslide inventories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003efour field campaigns were carried out between 2022 and 2024 for a full description of the observed sites of landslides, located essentially in the Northeast and the Southeast of the study area. The collected field data are related mainly to the geotechnical properties of the soil, the structural characteristics of the rock formations, the extent and morphology of the landslides, and the environmental conditions contributing to the instability (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDigital photogrammetry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital photogrammetry and remotely sensed data represent a significant advance in landslide risk assessment. The approach relies on the photogrammetry workstations and sentinel-2 imagery (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) to highlight accurate interpretation of surface ground changes over the past years. The cartography defines a complementary approach of terrestrial photogrammetry (drone mission) carried out during May 2023. The main objective of this mission is the primary objective of the drone mission is to identify and monitor rock falls within the region to track their movement over time. The obtained data is coupled with the satellite imagery (Sentinel 2) to outline the preferential pathways of the rocky mass movement, especially during the last extreme rainfall events of 2017 and 2023, 2024.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMetadata of the acquired Sentinel imagery\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSentinel_2 Product ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAcquisition date\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpatial resolution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCloud cover\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_MSIL1C_20161112T100242_N0204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2016-11-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS2A_MSIL1C_20171117T100301_N0206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2017-11-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS2A_MSIL1C_20181122T100321_N0207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2018-11-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS2A_MSIL2A_20191127T100341_N0500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019-11-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS2A_MSIL2A_20201121T100331_N0500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2020-11-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS2B_MSIL1C_20211111T100149_N0301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021-11-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS2A_MSIL2A_20221121T100321_N0400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022-11-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS2A_MSIL1C_20231126T100331_N0509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023-11-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS2A_MSIL1C_20240504T100031_N0510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024-05-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSusceptibility modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe assessment of the susceptibility to landslides relies on the agglomeration of different geo-thematic maps describing the major factors influencing this vulnerability based on the geostructural, geological, and geomorphological features of the study area to outline, via The association of the hierarchical influence of the direct or indirectly involved factors. The resulting map can be used for subsequent studies aiming the planning of the required interventions to mitigate rockfall hazards and (or) to inhibit the potential impacts. A classified susceptibility index will be helpful to delineate the appropriate section of infrastructure management and to prioritize the required safety measures and the detailed assessment of the rockfalls. The selected parameters and criteria used in this study are related to geomorphological features (slope, elevation, aspect, lithology), hydrologic characteristics (Network density, \u0026hellip;.), and climate conditions, and they are synthesized by Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ethe used indices\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFormula/\u003c/p\u003e\n \u003cp\u003eexpression\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClassification\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAssigned ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEM (30m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect extraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165\u0026ndash;263 m\u003c/p\u003e\n \u003cp\u003e264\u0026ndash;339 m\u003c/p\u003e\n \u003cp\u003e340\u0026ndash;443 m\u003c/p\u003e\n \u003cp\u003e444\u0026ndash;580 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifferent terrain elevations and associated risks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEM (30m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect extraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5 \u0026deg;\u003c/p\u003e\n \u003cp\u003e6\u0026ndash;11 \u0026deg;\u003c/p\u003e\n \u003cp\u003e12\u0026ndash;18 \u0026deg;\u003c/p\u003e\n \u003cp\u003e19\u0026ndash;36 \u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSteeper slopes increase rockfall risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAspect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEM (30m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect extraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlate (-1);\u003c/p\u003e\n \u003cp\u003eNorth (0\u0026deg;-22.5\u0026deg;; 337.5\u0026deg;-360\u0026deg;);\u003c/p\u003e\n \u003cp\u003eNortheast (22.5\u0026deg;-67.5\u0026deg;);\u003c/p\u003e\n \u003cp\u003eEast (67.5\u0026deg;-112.5\u0026deg;);\u003c/p\u003e\n \u003cp\u003eSoutheast (112.5\u0026deg;157.5\u0026deg;;\u003c/p\u003e\n \u003cp\u003eSouth (157.5\u0026deg;202.5\u0026deg;);\u003c/p\u003e\n \u003cp\u003eSouthwest (202.5\u0026deg;-247.5\u0026deg;); West (247.5\u0026deg;292.5\u0026deg;);\u003c/p\u003e\n \u003cp\u003eNorthwest (292.5\u0026deg;-337.5\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfluence of direction on rockfall occurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlan Curvature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEM (30m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect extraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.23 to -0.27\u003c/p\u003e\n \u003cp\u003e-0.26 to -0.06\u003c/p\u003e\n \u003cp\u003e-0.05 to 0.1\u003c/p\u003e\n \u003cp\u003e0.11 to 0.35\u003c/p\u003e\n \u003cp\u003e0.36 to 1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfluence of terrain shape on rockfalls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfil Curvature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEM (30m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect extraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.3 to -0.4\u003c/p\u003e\n \u003cp\u003e-0.39 to -0.11\u003c/p\u003e\n \u003cp\u003e-0.1 to 0.09\u003c/p\u003e\n \u003cp\u003e0.091 to 0.34\u003c/p\u003e\n \u003cp\u003e0.35 to 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfluence on debris deposition and rockfalls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLithology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMatmata map (1/100000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect extraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClays and Marls\u003c/p\u003e\n \u003cp\u003eLimestones and Dolomites\u003c/p\u003e\n \u003cp\u003eSandy clays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfluence of rock types on rockfall occurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydrographic network\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEM (30m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect extraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFour classes of stream\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfluence of watercourses on rockfalls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydrographic density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEM (30m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect extraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1,98\u003c/p\u003e\n \u003cp\u003e1,99\u0026thinsp;\u0026minus;\u0026thinsp;3,97\u003c/p\u003e\n \u003cp\u003e3,98\u0026thinsp;\u0026minus;\u0026thinsp;5,95\u003c/p\u003e\n \u003cp\u003e5,96\u0026thinsp;\u0026minus;\u0026thinsp;7,93\u003c/p\u003e\n \u003cp\u003e7, 94\u0026thinsp;\u0026minus;\u0026thinsp;9,92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfluence of stream density on rockfalls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRainfall distribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual\u003c/p\u003e\n \u003cp\u003eprecipitation\u003c/p\u003e\n \u003cp\u003e(CRDA Gab\u0026egrave;s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeasured Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;80\u003c/p\u003e\n \u003cp\u003e81\u0026ndash;110\u003c/p\u003e\n \u003cp\u003e111\u0026ndash;140\u003c/p\u003e\n \u003cp\u003e141\u0026ndash;200\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfluence of rainfall on rockfall occurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe Analytical Hierarchy Process (AHP) is a semi-qualitative method that involves matrix-based pairwise comparisons to assess the contribution of various factors to landslides. As a multi-objective, multi-criteria decision-making approach, AHP enables users to derive a scale of preference from a set of alternatives (Pourghamesi et al. 2012). This method aids decision-makers in identifying the best solution aligned with their goals and understanding of the problem (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The equation used for landslide susceptibility mapping with AHP is as follows (Eq.\u0026nbsp;1):\u003c/p\u003e\n\u003cp\u003eLSI\u0026thinsp;=\u0026thinsp;\u0026Sigma;\u003csup\u003en\u003c/sup\u003e\u003csub\u003ei=1\u003c/sub\u003e (R\u003csub\u003ei\u003c/sub\u003e x W\u003csub\u003ei\u003c/sub\u003e) (Eq.\u0026nbsp;1)\u003c/p\u003e\n\u003cp\u003ewhere: R\u003csub\u003ei\u003c/sub\u003e represents the rating classes for each layer, and W\u003csub\u003ei\u003c/sub\u003e denotes the weights for each landslide conditioning factor.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"602\" height=\"368\"\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the landslide susceptibility map, the effects of each parameter relative to each other are assessed through pairwise comparisons. The final map is constructed using the following equation (Eq.\u0026nbsp;2):\u003c/p\u003e\n\u003cp\u003eLS\u003csub\u003eAHP\u003c/sub\u003e = (elevation x W\u003csub\u003eAHP\u003c/sub\u003e) + (slope x W\u003csub\u003eAHP\u003c/sub\u003e) + (slope x W\u003csub\u003eAHP\u003c/sub\u003e) + (curvature plan x W\u003csub\u003eAHP\u003c/sub\u003e) + (curvature profile x W\u003csub\u003eAHP\u003c/sub\u003e) + (rainfall x W\u003csub\u003eAHP\u003c/sub\u003e) + (hydrologic network x W\u003csub\u003eAHP\u003c/sub\u003e) + (stream density x W\u003csub\u003eAHP\u003c/sub\u003e) + (lithology x W\u003csub\u003eAHP\u003c/sub\u003e) (Eq.\u0026nbsp;2)\u003c/p\u003e\n\u003cp\u003eHere, W\u003csub\u003eAHP\u003c/sub\u003e represents the weight for each landslide conditioning factor. The pixel values obtained are then classified into four classes (low, moderate, high, and very high) based on natural breaks to determine the class intervals in the landslide susceptibility index map.\u003c/p\u003e\n\u003cp\u003eIn the AHP method, the consistency ratio (CR) serves as an index of inconsistency, indicating the likelihood that the matrix judgments were randomly generated (Saaty 1980, 1994). The CR is calculated using the formula (Eq.\u0026nbsp;3):\u003c/p\u003e\n\u003cp\u003eCR\u0026thinsp;=\u0026thinsp;CI/RI (Eq.\u0026nbsp;3)\u003c/p\u003e\n\u003cp\u003ewhere (RI) is the average consistency index for a given order of the matrix, as provided by Saaty (1980), and (CI) is the consistency index, which can be expressed as (Eq.\u0026nbsp;4):\u003c/p\u003e\n\u003cp\u003eCI= (\u0026lambda;\u003csub\u003emax\u003c/sub\u003e -n)/(n-1) (Eq. 4 )\u003c/p\u003e\n\u003cp\u003eHere, \u0026lambda;\u003csub\u003emax\u003c/sub\u003e is the largest eigenvalue of the matrix and n is the order of the matrix. The CR, ranging from 0 to 1, reflects the matrix\u0026apos;s consistency. A (CR) of 0.1 or less indicates a reasonable level of consistency (Vargas 2001), while a(CR) above 0.1 suggests the need for revising the judgments in the matrix due to inconsistencies. Using the AHP method, spatial factor weights were determined and applied in a weighted linear sum procedure (Voogd 1983) to calculate landslide susceptibility. In this study, a CR of 0.099 indicates a reasonable level of consistency in the pairwise comparisons, sufficient for recognizing factor weights. Consequently, rainfall received the highest weight, whereas the stream density had the lowest (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ethe collected field data and the information obtained from geospatial platforms and open remote sensing sources are treated with different software namely Agisoft, Metashape Professional, and Cloud Compare.\u003c/p\u003e"},{"header":"IV. Results \u0026 discussion","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eIV.1. Kinematic\u003c/h2\u003e\n \u003cp\u003eIn this study, high-resolution images from a photogrammetry mission in May 2023 and sentinel-2 images free of clouds in addition to Google Earth, were used to delineate the areas with intense movement of landslides in the region of Matmata from 2017 to 2023 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The mosaic of these images is illustrated by the model in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, highlighting the direction and the magnitude of the displacement, especially within extreme weather events (floods of 2017 and torrential rainfall of 2023).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eIV.2. Susceptibility for landslides\u003c/h2\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003eIV.2.1. thematic maps\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003e-Altitude\u003c/strong\u003e: The spatial distribution of the altitude in the prone sites to rockfall indicates relevant information delineating the areas of varying terrain steepness and elevation, which are important factors influencing the likelihood and severity of rockfall occurrences. In the study area, the hilliest zones are located in the southern part referring to the presence of rugged terrain and steep slopes, while the lowlands are primarily found in the region, indicating relatively flat landscapes and lower elevation gradients, which may pose flood risks. The analyzed sites are mostly located in the transition zone between elevations 340 m and 443 m, while the two sites are characterized by relatively low altitudes of about 165 m and 264 m (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-Slope\u003c/strong\u003e: For assessing the risk of rockfall events, slope steepness defines a key factor governing the triggering of rock cliffs. Steeper slopes are generally associated with a higher risk of rockfalls, as they increase the likelihood of detachment and movement of rocky materials. By subdividing the slope into classes, it becomes possible to identify areas with an increased risk of rockfalls, which is essential for urban planning, infrastructure safety, and the protection of populations living or working in these high-risk areas. The distribution of the slope across the study area illustrates a heterogeneous distribution with increasing accentuation along the mountain\u0026apos;s structures while the southeastern part illustrates the lowest values of less than five degrees. The analyzed sites of landslides are located along the structures with high elevated steepness of values ranging between 20\u0026deg; and 30\u0026deg; (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-Slope Aspect\u003c/strong\u003e: The slope aspect provides valuable insights into the orientation of terrain features within the study area. This map underwent reclassification into nine distinct classes, ranging from flat to various cardinal directions. Analysis reveals that the Southwest (SW) class exhibits the highest susceptibility, followed by the East (E) class across all four models. Additionally, the South (S) and Southeast (SE) classes also demonstrate elevated weights, albeit lower than those of the SW and E classes (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- Plan curvature\u003c/strong\u003e: The distribution of the obtained values illustrates that rockfall areas are primarily situated between 0.36 to 1.38, indicating regions with high and convex curvature. This observation suggests a correlation between terrain curvature and the occurrence of rockfall events, with convex terrain features potentially serving as predisposing factors for rockfall initiation (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- Profil curvature\u003c/strong\u003e: It is evident that rockfall areas are predominantly situated between 0.091 to 0.34 and 0.35 to 1.2, indicating regions with elevated and concave profile curvature that facilitate the deposition of debris. This observation underscores the significance of profile curvature in influencing the occurrence and distribution of rockfall events. Concave terrain features, characterized by high profile curvature, are prone to accumulating loose material and promoting rockfall initiation and propagation. Therefore, understanding the relationship between profile curvature and rockfall susceptibility is crucial for effective hazard assessment and mitigation strategies, particularly in areas where concave terrain features predominate (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-Lithology\u003c/strong\u003e: The lithological map was constructed through the digitization of the 1/100000 geological map of Matmata (ONM). It underwent a reclassification process into three main groups: clayey-to-early formations, clayey-sandy formations, and limestone and dolomitic formations (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-Hydrographic Network\u003c/strong\u003e: The hydrographic network map presents four classes of watercourses, with one primary class and the others being secondary. The primary class serves as a focal point for water flow and the secondary classes complement the main watercourse (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-Hydrographic Network Density\u003c/strong\u003e: The hydrographic network density within the study area is categorized into five distinct classes, each providing valuable insights into the distribution and concentration of watercourses. These classes range from areas with minimal hydrographic network density, indicated by values less than 1.98, to regions with significantly denser networks, represented by values between 7.94 and 9.92 (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-Rainfall distribution\u003c/strong\u003e: The rainfall map was generated by kriging the average annual precipitation data from the meteorological stations of Matmata Nouvelles, Matmata, El Hamma, and Mareth, using ArcMap with a spatial resolution of 30m x 30m. Statistical analysis of the class weights by the four bivariate models indicates that susceptibility to rockfall increases with higher precipitation. The most susceptible class is between 200mm/year (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003eIV.2.3. Validation of the landslide susceptibility map\u003c/h2\u003e\n \u003cp\u003eIn this study area, the landslide susceptibility map was created using the multicriteria analytical hierarchy process. This map was then categorized into three susceptibility levels: low, moderate, and high (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Following a common approach in related research (Yanli et al. 2016), it was found that the low susceptibility zone constitutes 20% of the study area, while the moderate and high susceptibility zones comprise 70% and 10% of the area, respectively.\u003c/p\u003e\n \u003cp\u003eValidating a landslide susceptibility model is crucial to assess the predictive accuracy of the generated susceptibility map. To evaluate the model\u0026apos;s predictive capability, both physical and statistical methods were used. The physical validation involves verifying if the majority of significant landslides are located within the high-susceptibility zones. For statistical validation, methods such as calculating landslide frequency and generating a success rate curve were employed. The landslide susceptibility map presented in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e indicates that most major landslides, including loess landslides, debris flows, unstable slopes, and rock falls, are predominantly located in regions identified as having high susceptibility. The results presented by Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, reveal that the landslide frequency is highest in the high susceptibility zone at 5, followed by the moderate susceptibility zone at 0.57, and the low susceptibility zone at 0.5. These findings suggest that the landslide susceptibility zones identified by the SI model are accurate and align well with the actual occurrences of landslides.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of Forecasted Landslide Hazard Zones with Actual Landslide Occurrences\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLandslides susceptibility zones\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLandslides (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLandslides frequency\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIt was found that 50% of all observed landslides occurred in areas classified as highly susceptible to landslides, 40% were in moderately susceptible zones, and 10% were in zones with low susceptibility. (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eThe success-rate curve was derived by comparing existing landslide occurrences with the landslide susceptibility map (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). In this study, we utilized the area under the curve (AUC) method to qualitatively evaluate the prediction accuracy (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). A high AUC value indicates that the model is effective and can reliably produce landslide susceptibility maps. The verification results demonstrated an AUC value of 0.94 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e), indicating a 94% success rate. The SI model applied in this study area exhibited high prediction accuracy, suggesting its suitability for generating landslide susceptibility maps for the region.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eIV. 3. Discussion\u003c/h2\u003e\n \u003cp\u003eThe delineation of the landslide precursors for remote mountain environments reveals a complex task that requires accurate field validation of the known and failed landslides (Qi et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This evaluation should take into account the uncertainties related to data treatment and pixel spatial clustering. The assessment of these hazards relies on, however, a time-series analysis of surface deformation for about 30 years to highlight the influence of anthropogenic activities and urban management on the one hand and to evaluate the impacts of climate change namely the intense rainfall events in enhancing the frequency of these issues.\u003c/p\u003e\n \u003cp\u003eIt is challenging however, to highlight short-term interval-related deformations may be hard as (i) the scale of the acquired data and the related treatment may not enhance the delineation of small-scale movement and (ii) the major influencing factors governing the movement of these landslides have occasional impactful effects that are related to extreme climate events or intense anthropogenic perturbation. In fact, given the hierarchical classification obtained from the susceptibility model, the evaluation of the different integrated factors highlighted that the main influence is related to slope gradient, lithology, and precipitation respectively. The cumulative actions of these factors are hard to detect by one-year changes. Thus, the time-series analysis may reveal more relevant information, especially for the evolution of the influence of the analyzed parameters within the changing context (Aksay,2023; Bokharel et al.,2023).\u003c/p\u003e\n \u003cp\u003eThe classification of the relevance of the impacts of the different analyzed factors on the frequency and amplitude of these geo-hazards defines the slope gradient as the main factor. The spatial distribution of the susceptibility index reveals a concerning situation as more than 10% of the area falls into the high vulnerability class with a high risk of landslide occurrence and 70% of the area is into a moderate risk of landslide occurrence, while only 20% are classified as safe areas. This distribution explains the importance of hydroclimatic conditions, in concordance with previous works that indicate that intense rainfall and soil saturation are critical triggers for landslides in this region (Bensalem et al.,2024). Based on the distribution of the vulnerability index across the study area, the areas with moderate to high susceptibility are located in the eastern and southeastern parts. Suppose the region was overlapped by carbonate lithology and active faults. In that case, these areas with low distances to the stream network and to roads illustrate, furthermore, intense vulnerability, especially in the central part of the region. These areas exhibit multifactorial overlaps that define increasing susceptibility to landslide occurrence. These results are following field observation and field analytical work.\u003c/p\u003e\n \u003cp\u003eThe verification of the obtained results revealed that the hierarchical multifactorial analysis based on field observation and remote sensed data revealed high accordance with the selected sites of landslides occurrence and the distribution of the areas with the highest indices for susceptibility highlighting the effective assessment via the combination of landslides relative data and the expert\u0026rsquo;s opinion of the impactful factors especially with lack of reliable historical records for reference baseline evaluation. The relatively small-scale difference between the simulated and the observed or measured data indicates the influence of different secondary factors that may not be considered; however, their impacts seem to be relevant at some localities, namely the activation of small-scale faults. This, the geophysical investigation and the profile of subsurface anomaly distribution should be taken into consideration for a more relevant and accurate assessment of landslide susceptibility (Wu et al.,2016).\u003c/p\u003e\n \u003cp\u003eFurthermore, given the limited number of sites for field observation and analytical work, the assessment of landslide occurrence based on the multifactorial hierarchical analyses is of crucial importance as the lack of accessibility for some points across the study area and the representativeness of the quantitative evaluation of the measured sites. The susceptibility index presents an appropriate method for highlighting the hierarchical influence of the different rehabilitation and management measures for short- and long-term intervention (Myronidis et al.,2015). However, despite the relevance of the obtained results, the lack of historical data, and the tile-series assessment of landslide occurrence, frequency, and amplitude, the validation of the findings of the study is hard. It requires further investigation with detailed analytical work. It represents, nonetheless, a baseline study for the quantitative assessment of the vulnerability index of the region to landslides, for prioritizing the feasible actions, and for the integrated approach of aerial photogrammetry, and remote sensed data analysis with relative uncertainties (Erener.A et al.,2011).\u003c/p\u003e\n \u003cp\u003eSince no similar initiatives have been adopted in this region, this work offers a new perspective on preliminary rockfall susceptibility assessment. This serves as a foundation for detailed rockfall hazard and risk assessments, as well as sustainable land use planning. Utilizing a high-resolution DEM in specific parts of the study area, along with a detailed lithological map, would enhance the identification of rockfall source areas. Knowledge of block sizes, detailed geomorphological settings, and the potential inclusion of specific triggers for rockfalls would aid in the quantitative estimation of rockfall hazards.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"V. Conclusions and perspectives","content":"\u003cp\u003eThis paper conducts a baseline characterization of the prone sites to rockfall in the Matmata region (SE Tunisia) and the relative kinematic analysis. The occurrence of the first landslides seems to be related to active tectonics, discontinuities, and the geo-structural context. Intense rainfall represents an important triggering factor. The kinematic analysis indicates that the locations of the most prone sites are principally related to steep slopes and the presence of fractures. The evaluation of the dispersion and distribution of the energy and velocity require, undoubtedly, proposing engineered management actions.\u003c/p\u003e \u003cp\u003eThe study evaluates the influence of geological, geomorphological, hydrological, geostructural, and climatic factors on the triggering of rock masses and the hierarchical importance of initiating the landslide mechanisms. The kinematics of the unstable units are dominated by toppling and sliding movements. The main results indicate that the combination of steep slopes, intense rainfall, and weak lithology significantly contributes to the occurrence and severity of rockfalls in the region. The used approach in this study presents a mixing of typical field observation studies, statistical and geospatial treatment, and geo-mechanical approach defining a multi-dimensional assessment of the susceptibility of rock falls in the Matmata region and of the kinetic of these movements. The photogrammetric survey coupled with a geostructural survey allows a detailed description of the region that is difficult to access for terrain data collection. The high-precision maps, permit, consequently, a quantitative estimation of the instability of rocky units and a delineation of the preferential trajectories of the failure masses. Further detailed surveys are necessary for an infield intervention based on detailed quantitative assessing (surface, volume, losses, \u0026hellip;). Depending on the specific conditions of the investigated sites, the rock-fall hazards involve multiple parameters and factors of which the influence is hierarchically evaluated based on the local context. The causality links between these factors define a complex failure process that is difficult to predict in occurrence or estimate in intensity by the simplified physical, statistical or mathematical modeling tools.\u003c/p\u003e \u003cp\u003eThe uncertainties related to this study are represented mainly by these multi-data sources of different degree of detailing that may constrain the representativeness and (or) the reliability of the findings of this research. The uncertainties related to the different estimation methods of rockfall hazards and the performance of the adopted protection structures reveal the necessity of further detailed studies relying on the field inventories for modeling outputs validation. These estimations allow an optimal set of measures that reduces the damaging impacts and ensures compliance with relevant multi-criteria risk assessment. The consistency of these processes used for hazard mitigation requires, furthermore, a long time series of treated data using thorough evaluation of this phenomenon allowing a reconstitution of the paleo-evolution and consequently a prediction of the further occurrence. Modeling of these geo-hazards via physical-based models relying on terrain data collection is required.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHassen.Bensalem and Houda.Besser wrote the main manuscript text , Soulef.Amamria and Mohamed.Sadok.Bensalem and Claudia.Meisina prepared figures and Tables and Noureddine.Hamdi prepared conclusions.All authors reviewed manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAksay, B. (2023). 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Sustainability. 15. 5281. 10.3390/su15065281.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"landslides, digital photogrammetry, modeling, susceptibility, SE Tunisia.","lastPublishedDoi":"10.21203/rs.3.rs-4659295/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4659295/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIdentifying the prone sites and recognizing the influencing factors of rock failure remains a major challenge, especially for the regions lacking a historical database of the chronological evolution of the different potential factors influencing the frequency and the amplitude of this hazard in the mountain zones. In this context, the present study aims to delineate the movement of the rocky masses after the frequent torrential rainfall and to assess the main driving factors of the landslide hazards in the Matmata region (SE Tunisia). The used approach relies on field observations, remotely sensed data, digital photogrammetry, and GIS-multi criteria assessment.\u003c/p\u003e \u003cp\u003eThe analysis of the kinematics of the rock cliffs triggering in the region between 2016 and 2023 highlights a relative movement of about 39 m of the carbonate rock masses related to the impacts of geological factors, weathering, land use changes, hydrogeology, and human activities on slope stability and rockfall occurrences. The hierarchical influence of these factors illustrates relevant spatio-temporal variability of susceptibility indices. The southern part of the region is characterized by the highest degree of vulnerability due to many factors such as slope, rainfall and lithology. The spatial distribution of the final susceptibility index indicates varying degrees of susceptibility across the study area amplified during the last years given the frequency of the extreme events. The susceptibility map is validated by landslide inventory.\u003c/p\u003e \u003cp\u003eThe findings highlight the relevance of the rockfall hazard and the relative amplitude in the region explained by a high index of urban expansion and infrastructure development in hilly areas. The obtained results present a valuable tool for decision-making for land use management and landslide mitigation measures.\u003c/p\u003e","manuscriptTitle":"Landslide risk assessment using digital photogrammetry and Gis multi criteria evaluation IN Matmata region (SE Tunisia)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 11:11:59","doi":"10.21203/rs.3.rs-4659295/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"110597c0-f33f-40d6-928b-272c6dce70c2","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-23T05:15:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-29 11:11:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4659295","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4659295","identity":"rs-4659295","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}