• Research article
  • Open access
  • Published: 28 February 2015

Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung Valley, Sikkim

  • Rathinam Anbalagan 1 ,
  • Rohan Kumar 1 ,
  • Kalamegam Lakshmanan 1 ,
  • Sujata Parida 1 &
  • Sasidharan Neethu 1  

Geoenvironmental Disasters volume  2 , Article number:  6 ( 2015 ) Cite this article

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Sikkim Himalaya is under consistent distress due to landslides. Abrupt thrust on infrastructure development in the valley regions of Sikkim Himalaya has led to a need for a prior planning to face landslide hazard. A comprehensive study for the identification of landslide hazard zones using landslide frequency ratio and fuzzy logic in GIS environment has been presented for the Lachung valley, Sikkim, India, where a number of hydroelectric projects are proposed.

Temporal remote sensing data was used to generate significant landslide causative factors in addition to landslide inventory. Primary topographic attributes namely slope, aspect and relative relief were derived from digital elevation model. Landslide frequency ratio approach was adopted to correlate landslide causal factors with landslide incidences. Further, fuzzy logic method was used for the integration of landslide causative factors in order to delineate the landslide hazard zones. Fuzzy memberships were derived from the landslide frequency ratio values. Different gamma values were used in fuzzy gamma integration process, which resulted different landslide hazard index maps. Receiver operating characteristic curves were prepared to analyze consistency of the resulting landslide hazard index maps.

Landslide frequency ratio values have emphasised the importance of factors/classes in landsliding. High slope angle (35°-45°), very high slope category (>45°), High and very high relative relief categories; south, southeast and southwest aspects; drainage and lineaments buffer range of 0-50m, 50-100m and 100 to 150m; quartzite/garnet schist and migmatite type of lithology; Sandy loam and Rock/loam classes of soils; fallow land and sparse vegetation classes of land use/land cover were found to be associated with landsliding. Five landslide hazard zonation maps with each comprising five relative landslide hazard zones namely; very low, low, moderate, high and very high hazard zones were prepared by using five fuzzy gamma operators. Maps indicated that steep talus slopes, close proximity to drainages, ridges and spars fall under high hazard zones. Settlement areas were observed in low to moderate hazard zones. Very high hazard zones were observed in steep slopes, cliffs and cut slopes excavated for the roads. Low hazard zones were observed in agricultural terraces and permafrost areas.

Conclusions

Hence it can be concluded that landslide causative factor’s integration using fuzzy logic has yielded good results for Lachung valley. Frequency ratio method for determination of fuzzy membership value has reduced subjectivity in the model. The final LHZ map (γ = 0.92) can be used for the planning of future infrastructure, settlement and ecological development in Lachung region.

Landslide is a result of a wide variety of geo-environmental processes, which include geological, meteorological and human factors. The main factors which influence landslides were discussed by Varnes ( 1984 ) and Hutchinson ( 1995 ). Most important inherent factors are bedrock geology (lithology, structure, degree of weathering), geomorphology (slope gradient, aspect, and relative relief), soil (depth, structure, permeability, and porosity), land use-land cover, and hydrologic conditions. Landslides are triggered by many extrinsic causative factors such as rainfall, earthquake, blasting and drilling, cloudburst, flash-floods (Anbalagan 1992 ). Present study area is a part of Sikkim Himalaya, which is consistently subjected to landslides during monsoon season. The Himalaya has highly undulating terrain, which is witnessing ongoing orogeny. In addition to that, during monsoon period the present area receives high precipitation. In this part of Sikkim Himalaya, a number of hydroelectric projects are in planning or construction phase and it has substantially increased the anthropogenic activities. Combination of inherent, external and orogenic factors has made this terrain highly vulnerable to landslides. A landslide hazard zonation (LHZ) map is prepared in advance to facilitate mitigation strategies in the wake of any landslide hazard. It provides a prior knowledge of landslide probable zones on the basis of a set of geo-environmental factors suitable for landslides locally. Assumption of LHZ is based on an analogy that the future landslide is expected on those locations which has same set of geo-environmental conditions as that of past and present landslide locations (Varnes 1984 ; Kanungo et al. 2009a ). Choices of factors depend upon the exhaustive field work, data availability and professional experience. Advent of machine learning, fast computation packages, easy data availability and GIS has propelled the landslide hazard research to a new high. The outcome can be seen in terms of the quantum of literature regarding landslide hazard available at present. A number of terms such as landslide hazard zonation, landslide susceptibility mapping (LSM), landslide hazard mapping (LHM), landslide susceptibility zonation (LSZ), landslide probability etc. are in practice (Varnes 1984 ; Anbalagan 1992 ; Gupta et al. 1999 ; Arora et al. 2004 ; Brabb 1984 ; Guzetti et al. 1999 ; Lee et al. 2002 ;Ayalew and Yamagishi 2005 ; Mathew et al. 2007 ; Yalcin 2008 ; Yilmaz 2009 ; Pachauri and Pant 1992 ; Guzzetti et al. 2005 ; Van Westen et al. 2006 ; Lee and Pradhan 2007 ; Dahal et al. 2008 ; Dahal et al. 2009 ; Clerici et al. 2002 ; Saha et al. 2005 ; Kanungo et al. 2006 ; Gupta et al. 2008 ; Mathew et al. 2009 ; Chauhan et al. 2010 ; Ohlmacher 2007 ) for the landslide hazard related studies. Coinfusion still prevails among the researchers about the choice of the use of the term for landslide hazard studies. Varnes ( 1984 ) defined the term ‘Zonation’ in context of landslide. It applies in general terms to division of the land surface into areas and ranking these areas according to degree of actual or potential hazard from landslides or other mass movement on slopes. Landslide hazard is considered under the natural hazard category, which is defined as the probability of occurrence within a specified period of time and within a given area of potentially damaging phenomenon (Varnes 1984 ). In 1980s, 1990s and early 2000s, a number of authors used LHZ mapping (Gupta and Joshi 1990 ; Gupta and Anbalagan 1997 ; Nagarajan et al. 1998 ; Saha et al. 2002 ). Another term ‘landslide susceptibility’, in this context was given as spatial probability of occurrence of landslides based on a set of geo-environmental factors (Brabb 1984 ; Sarkar and Kanungo 2004 ; Lee and Sambath 2006 ; Kundu et al. 2013 ; Kayastha et al. 2013 ). Some authors are using the term ‘landslide hazard mapping’ in accordance with the definition of natural hazard given by UNO. Temporal factors such as rainfall, earthquake, and temperature variations etc. has been considered in landslide hazard mapping studies (Guzzetti et al . 2006 ; Pradhan et al. 2010 ; Dahal et al. 2012 ). Landslide susceptibility zonation (LSZ) is a compromise term and is practiced now a day’s very often (Kanungo et al. 2009a ). In the present study LHZ, LSM and LSZ has been perceived as the same.

In LHZ studies, remote sensing along with GIS provides great advantages. Remote sensing images are helpful in factor characterization and landslide inventory mapping. Temporal capability of remote sensing imageries are of a great help in acquiring past and present landslide incidences locally which further has a great significance in LHZ. GIS is very effective in data handling, manipulation and statistical measures. A number of methodologies are in practice for the identification of landslide hazard zone. Broadly, it can be classified into three groups namely, qualitative, semi-quantitative and quantitative method. In qualitative methods, scores are assigned to factors on the basis of professional knowledge. Semi quantitative methods assume weights and ranking on the basis of logical tools such as AHP, fuzzy logic and weighted linear combination (WLC). Quantitative methods are landslide inventory driven statistical methods and it considers association of landslide factors with landslide inventory. Based on landslide densities present in factor classes, weights/ratings are calculated mathematically. It can further be divided into bivariate and multivariate methods. Another quantitative method is the deterministic slope instability mapping, which is based on the geotechnical properties of the particular slope. Detailed review of the above mentioned methodologies can be found in the works of Guzetti et al. ( 1999 ), Aleotti and Chowdhury ( 1999 ), Kanungo et al. ( 2009a ) and Pardeshi et al . ( 2013 ). LHZ techniques have been applied in Himalayan region by a number of authors. LHEF (Landslide Hazard Evaluation Factor) based LHZ was carried out by Anbalagan ( 1992 ), Landslide hazard mapping based on geological attributes (Pachauri and Pant 1992 ), GIS based landslide hazard zonation (Gupta et al. 1999 ), integrated approach for landslide hazard zonation (Sarkar and Kanungo 2004 ) and GIS-based statistical landslide susceptibility zonation (Saha et al. 2005 ). Some authors adopted other techniques namely landslide hazard zonation based on meso scale for town planning (Anbalagan et al . 2008 ), fuzzy logic based LSZ mapping (Kanungo et al . 2006 ; Champatiray et al . 2007 ), predictive modeling of landslide hazard in lesser Himalaya by Dahal et al. ( 2008 ). Several quantitative and semi-quantitative techniques were applied for landslide susceptibility/hazard modelling in Himalayan terrain. Logistic regression technique for data integration of geo-environmental factors (Das et al . 2010 ), empirical modelling of landslide susceptibility in the Darjeeling Himalayas (Ghosh et al. 2011 ) and several others (Das et al. 2012 ; Kayastha et al . 2013 ; Kundu et al . 2013 ).

Lachung valley is physiographically narrow and elongated and it forms a crescent shape, which provides suitable conditions for trapping the nimbus clouds in the narrow gullies leading to cloud burst conditions. Numerous instances of cloud bursts or concentrated rain fall is commonly reported in this valley, which often results landslides and consequent transportation of huge quantum of debris down the slope. These debris materials, deposited by the side of the river course forming cones of debris. It is a striking factor that successive cones of debris are seen throughout the valley by the side of Lachung river course. The width of the debris cones are more on the right bank as compared to the left bank. This has resulted in a continuous presence of debris materials on both sides of the river course with rocks exposed much away from the river in the entire length of the basin Lachung. Major settlements are situated on these debris cones which are very prone to mass movements during the rainfall. Lachung valley is drained by the river Lachung Chu which is a major tributary of the Teesta river. A number of hydroelectric power plants are in construction phase in the Lachung and Teesta basin (just downstream to Lachung basin). In view of the existing settlements, infrastructures and upcoming infrastructures in the region, landslide hazard zonation is a necessity. In this paper, fuzzy logic technique was used to integrate the causative factors of landslide. Fuzzy membership values were derived from landslide frequency ratio. The frequency ratio is a ratio between the occurrence and absence of landslides in each cell/class of causative factors (Lee and Sambath 2006 ). A fuzzy membership value has a range (0, 1), where 0 is for the minimum fuzzy relation and 1 is for maximum. A membership value between 0 and 1 indicate the degree of fuzzy relationship. Fuzzy gamma operator was selected for the integration of factors using five different gamma values. ROC curves were prepared to validate the resulting maps.

The Lachung valley is located in the upper north-eastern reaches of Teesta river in Sikkim state of India. It has central longitude/latitude value of 88.65°E and 27.61°N. The valley has temperate climate in the lower reaches of the valley, whereas high mountainous region in the north is characterized by low temperature Tundra type of climate. The valley receives an average monthly rainfall of 52 mm and also snowfall in the month of December, January and occasionally in the month of March. Figure  1 shows the location of the study area.

Study area.

Geological setting

An overview of the area indicates that the Eastern Himalaya covers the Sikkim-Darjeeling-Bhutan and Arunachal Pradesh sectors, extending from the eastern Nepal to Western Burma. Higher Himalaya is a zone of crystalline rocks dividing two distinct lithofacies association in the South and the North. It is designated as the Axial belt. The Northern zone comprising the Tethyan Palaeo-Mesozoic sedimentary sequence forms the Trans- Axial belt. To the south of the Axial belt occurs the Inner belt, comprising thrust sheets of Proterozoic-Upper Palaeozoic formations, while the foothill belt is represented by para-autochthonous Siwaliks. This geological framework is valid for the entire Eastern Himalaya, upto the Lohit District of Arunachal Pradesh, where the geological picture does not conform to this general scheme. The stratigraphic sequence provided by GSI indicates that the Lachung region from South to North is occupied by Gondwana, Daling, Chungthang and Central Crystalline Gneissic group of rocks (Figure  2 ). Rock types belonging to Chungthang Formation and Kanchenjunga gneiss of Central Crystallines of Higher Himalaya occupy in and around the area of study. These rocks are seen in Chungthang area at the mouth of the basin. In the central area, the Kanchanjunga group of rocks comprising gneisses are exposed. The contact between the two is reported to be thrusted. Due to complex folding, gneissic and schistose bands are intricately folded with meta-sedimentary units. In general, the rock type trends in NW-SE to N-S direction dipping towards northeast to east direction.

Geological map of the Lachung area modified after GSI 2001.

Data preparation

A spatial data set containing landslide causative factors namely, slope, aspect, relative relief, lithology, distance to photo-lineament, distance to drainage, land/use land/cover (LULC) and soil cover was used to apply fuzzy logic method for LHZ. LISS-IV image of 5.6 meter spatial resolution was used to generate LULC (Figure  3 ), photo-lineament and landslide inventory of the Lachung valley. Landsat ETM+, ASTER and IRS LISS-IV DATA were further used to delineate landslide incidents by means of visual image interpretation. Cartosat-1 DEM of 2.5 meter spatial resolution was used to generate drainage network, slope, aspect and relative relief maps. Ancillary data such as geological map, topographic map, soil map and landslide inventory map of varying scales were obtained from different concerning departments. All data set were rasterized to 5 m × 5 m grid cell. Finally a spatial data set of 5009 columns and 6239 rows were prepared. Table  1 shows different data types used in present study. Figure  4 refers to some of the data layers worked out in this study.

Land use/Land cover map of Lachung valley.

Refers to a) Landslide inventory, b) Aspect, c) Relative relief, d) Slope map of Lachun g valley.

Rock types of Lachung valley belongs to Chungthang Formation and Kanchenjunga gneiss of Central Crystallines of Higher Himalaya. Chungthang Formation comprises quartz-biotite schist, calc-silicate rocks and graphite schists. The quartzites at places have intrusions of amphibolites and pegmatite veins. These rocks are seen in Chungthang area at the mouth of the valley. These rocks are less prone to landslides. In the Lachung area, the Kanchenjunga group of rocks, comprising gneisses is exposed. These rocks are hard, compact and well jointed and at places intruded by tourmaline granites and pegmatite. The rock types are represented mainly by high-grade metamorphic of central crystalline gneisses complex. In general these rocks are also less prone to weathering. The contact between the two is reported to be thrusted. (Acharya and Shastry 1979 ; Ray 1976 ; Sinha-Roy 1982 ). Due to complex folding, gneissic and schistose bands are intricately folded with meta-sedimentary units. In general, the rock type trends in NW-SE to N-S direction dipping towards northeast to east direction. The implications of tectonics and lithological attributes have been considered in formulating concepts as regards to landslides in Sikkim Himalayas. Geological map of the area is presented in Figure  2.

Land use land cover

Image classification resulted into 8 land/use land/cover classes namely, dense vegetation, sparse vegetation, lake, drainage, settlement, cloud-cover, fallow/barren land, and snow cover (Figure  3 ). It is found that basin in general has a good vegetation cover with thick vegetation covering an area of about 98 sq km. It is mainly concentrated on the left bank of the Lachung river in the lower reaches between Chungthang and Lachung. On the right bank the thick vegetation is seen adjoining to the river up to Lachung with patches of sparse vegetation and barren land. A part of middle and top slopes close to the ridge are generally barren in nature due to snow cover. On the left bank the top slopes close to the ridge are barren in the upper reaches of the basin. Sparse vegetation is seen as patches and well distributed within the basin.

Soils of the Lachung valley are mainly constituted of sandy loam, loamy sand, sandy clay loam and sandy rocky loam. These soils are prone to sheet erosion, gully erosion and creeping.

Topographic attributes

Aspect is an important factor considered in LHZ (Nagarajan et al. 1998 ; Saha et al . 2002 ; Kanungo et al. 2009b ) studies. Aspect is the direction a slope faces with respect to north. Aspect determines the effect of solar heating, soil moisture and dryness of air (Yalcin 2008 ). Aspect map of the area was prepared on the basis of DEM manifesting nine classes namely, flat (−1), north (0° – 22.5° and 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°) and northwest (292.5°-337.5°) (Figure  4 b). Slope angle substantially impact the landslide incidences (Kanungo et al. 2006 ; Gupta et al . 2008 ; Dahal et al . 2009 ). Slope map was prepared covering six classes: very low/flat (0° -5°), low (5°-15°), moderate (15° -25°), moderately high (25°- 35°) and high (35° - 45°) and very high (>45°) (Figure  4 d). Relative relief is the difference between maximum and minimum elevation point within a facet or area and it is widely used in LHZ model (Gupta et al . 1999 ; Saha et al . 2005 ; Kanungo et al. 2009b ). In the present study, relative relief was found to be varying between 0 to 320 m. Following five classes of relative relief: very low relief (0–30 m), low relief (30 m–60 m), moderate relief (60 m–100 m), high relief (100 m–150 m) and very high relief (>150 m) were considered for landslide LHZ study (Figure  4 c).

Photo-lineaments

Linear geological discontinuities can be delineated from multispectral image and DEM and are called photo-lineament. Landslides are associated with the proximity to photo-lineament (Gupta et al. 1999 ). A distance to lineament map (also called lineament buffer map) covering 50 m, 100 m, 150 m and 200 m distances was prepared complying with field evidences of landslides. The overall lineament pattern of Lachung basin shows a nearly similar trend as that of drainage pattern. Distance from these structural features have relative influence on the landslide, accordingly buffer map is prepared for landslide hazard zonation.

Landslide inventory

Landslide inventory map is prepared from satellite imageries and field investigations. Temporal ASTER and IRS LISS-IV remote sensing data of pre and post earthquake is used to map the landslides. Based on the size, they have been divided visually into large, medium and small. These landslides are shallow in nature (Figure  4 a). Since the depth of the slides is limited to few meters it is mainly affecting the overlying debris materials and a small part of the rocks below which seems to be intact. The resultant debris can be seen lying on the slope below. A few medium size slides are seen mainly in the middle portions of the valley. The small landslides are commonly seen in many places, though they seem to be concentrated in the lower reaches where the debris cone materials are present. Moreover, debris materials are consistently present on either side of the river. Hence wherever the river takes sharp turns locally the toe erosion had resulted in a series of shallow landslides by the side of the river on either bank. Landslide data is used for the validation of landslide hazard zonation map.

Landslide casual factors and data processing

In the present study, fuzzy logic technique was used to perform LHZ mapping. First step was the preparation of landslide causative factor layers. It is very common to assume causal factors to predict landslide occurrences in the absence of any universally defined set of factors. The assumption behind this is that future landslides will occur under similar conditions as past and present landslides (Lee and Talib 2005 ). In the present study, causal factors included remote sensing imagery derived land/use land/cover map, photo-lineament map and DEM derived slope, aspect and relative relief map. LULC of the present area was extracted by applying supervised classification of LISS -IV image in ERDAS Imaging software. Photo-lineament was extracted from visual interpretation of remote sensing imagery. Proximity to drainage and photo-lineament are important causative factors (Gupta et al. 1999 ). In this study distance to photo-lineament and distance to drainage layer was used as causal factors. Furthermore, ancillary data which includes geology and soil maps were co-registered with imagery derived data in GIS environment and vector layers were generated.

Fuzzy modeling

In the next step, data integration was performed using fuzzy logic technique. Fuzzy set theory was introduced by Zadeh ( 1965 ). It facilitates analysis of non-discrete natural processes as mathematical formulae (Zimmermann 1996 ). According to this theory, membership value of elements (x) has varying degree of support and confidence (ƒ(x)) in the range (0, 1) (Ercanoglu and Gokceoglu 2002 ). A fuzzy set can be described by formula given below as

Where A is a fuzzy set, x is an element of universal set R, and ƒ(x) is the fuzzy membership function. A crisp set range (0, 1) has either membership value of 1 or non-membership value of 0 whereas a fuzzy set inherit continuous membership in the range (0, 1).

Fuzzy membership determination using frequency ratio approach

Landslide hazard zonation mapping requires determination of fuzzy membership function of causative factors. Fuzzy membership function can be determined subjectively or objectively. There is no universal approach available for the determination of fuzzy membership function (Champatiray et al. 2007 ). A suitable and universally acceptable approach may enhance information accuracy (prediction capability). For LHZ, several authors used knowledge based approach for assigning fuzzy membership function (Chung and Fabbiri 2001 , Champatiray et al. 2007 ). Depending upon the data type (ordered or categorical) a membership function can be assigned quantitatively. In the present study, categorical factor layers were considered for fuzzy integration. Mathematical methods of fuzzy membership determination are not fit for categorical data. Landslide factors were compared with landslide inventory and a correlation between them were quantitatively analyzed by landslide frequency ratio method.

Landslide frequency ratio

The assumption behind LHZ is that future landslides will occur under similar conditions as past and present landslides (Lee and Talib 2005 ). Following the same assumption, a relationship can be determined between landslides related casual factors with the landslide occurrences and non-occurrences spatially. This relationship can be quantified using frequency ratio. Landslide frequency ratio can be calculated by the ratio of percent domain of a factor class and percent landslide in that class (Lee and Sambath 2006 ; Poudyal et al. 2010 ; Pradhan 2010 ; Pourghasemi et al. 2013 ). It follows the principle of conditional probability, in which if the ratio is >1 then there is a strong relationship between landslides and factor classes whereas ratio <1 represents weak relationship. Normalized value of landslide frequency ratio was used as fuzzy membership function by (Pradhan et al. 2010 ). In this study also, frequency ratio results were normalized in the range (0, 1). Table  2 refers to frequency ratio and fuzzy membership value of each attribute.

Fuzzy integration/operation

Next step of fuzzy logic technique is fuzzy operation. Fuzzy OR, fuzzy AND, fuzzy algebraic sum, fuzzy algebraic product and fuzzy gamma operator are important fuzzy operators (Chung and Fabbiri 2001 ). In case of fuzzy OR and fuzzy AND, only one of the contributing fuzzy set has an effect on the resultant value. The fuzzy algebraic sum and fuzzy algebraic product operators make the resultant set larger than, or equal to the maximum value and smaller than, or equal to the minimum value among all fuzzy sets respectively (Chi et al. 2002 ). Fuzzy gamma (γ) operator calculates values which range between fuzzy algebraic product and fuzzy algebraic sum. Gamma (γ) value has a range between 0 (No compensation) and 1(full compensation). Determination of optimum γ value is dependent on the degree of compensation between two extreme confidence levels.

Choice of suitable fuzzy operator for the data integration is required to achieve optimum result in landslide prediction studies. Choice of a fuzzy operator depends upon the types of spatial data to be integrated (Choi et al. 2000 ). Fuzzy gamma operator was chosen to integrate factors using the formula given below:

where x denotes the membership functions and R i denotes fuzzy membership function of i -th map, i = 1, 2…n. Using equation 2, 3 and 4 LHI maps were prepared. Further LHI maps were classified in five hazard zones namely, very low, Low, Moderate, high and very high hazed high hazard zones using Jenks natural break classifier in Arc GIS 10.1. Figure  5 refers to complete methodology flow chart.'

Methodology flowchart for LHZ mapping.

Result and discussion

Landslide frequency ratio.

Landslide frequency ratio was used as fuzzy membership function. Results of frequency ratio have been presented in Table  2 . Analysis of landslide frequency ratio indicates the importance of factors/classes on landslides. Topographic attributes are found to have good association with landslide incidences. Among the slope categories, high landslide frequency ratio is observed in high slope (35°-45°) and very high slope category (>45°). In steep slopes, the weight of the possible mobilized material under gravity will be more as compared to a moderate slope. Shear strength being same in both the cases, a steep slope with more mobilizing force may fail early. High and very high relative relief categories have resulted in high frequency values. Frequency ratio of the relative relief categories also indicates the increasing tendency in very low relief to very high relative relief classes. High relative reliefs are surface manifestation of cliffs and ridges, which are often rendered unstable by the influence of triggering factors such as rainfall and earthquakes. Topographic aspect is also found to be an important factor in this area. Very high frequency ratio; 3.71, 1.24 and 0.94 are found for south, southeast and southwest aspect respectively. Southern aspect of the study area, which is receiving excessive sun radiation and high rainfall, are more prone to landslides. In view of LHZ, drainage and lineaments buffer maps of , 0-50 m, 50 -100 m, 100–150 m, 150-200 m and >200 m were prepared. Frequency ratio for the range: 0-50 m, 50-100 m and 100 to 150 m are found to be high in case of drainage buffer and it can be attributed to the stream bank erosion due to the river flow such as gulling, toe cutting which further leads to landslides. Lithology of the area belongs to different formations as mentioned in the previous section. Each formation is represented by characteristic rock type, which might govern landslide incidence. Frequency ratio results of geology layer have reflected that quartzite/garnet schist and migmatite are more prone to landslide in view of frequency ratio values. Among the soil categories, Sandy loam and Rock/loam has resulted in high frequency value where as other categories resulted low values. Within the LULC classes, high landslide frequency value is observed in fallow land and sparse vegetation classes and can be attributed to the inherent physical properties of the LULC classes.

Landslide Hazard Zonation

LHZ maps were prepared by classifying LHI map. Each cell of LHI map contains hazard information in continuous form of range (0, 1). A statistical classification based on Jenk’s natural breaks method was used for LHI maps. Natural Breaks classes are based on natural clustering inherent in the data. Class breaks are identified that best group similar values and that maximize the differences between classes (ESRI FAQ 2012 ). Five LHI maps were prepared by applying five different gamma values in fuzzy gamma operator function. LHI maps were further divided into five classes (very low, low, moderate, high and very high hazard zone) on the basis of natural break of LHI values. In all the five cases natural break points were taken as threshold value for the hazard zones (Figure  6 ). In the first case (γ = 0.75) LHI value was found to be varying between 0.00357 and 0.06635. Threshold value of 0.003575, 0.006332, 0.01034, 0.01863, and 0.0663 were chosen on the basis of natural breaks to classify the LHI map into LHZ. In this case, 84.06 sq. km. area was occupied by very low hazard zone, 69.77 sq. km. area was under low hazard zone, 51.13 sq. km. area found in moderate hazard zone, 32.82 sq. km. area found in high hazard zone. In case of γ value of 0.8, LHI value was varying between 0.001166 and 0.114158 and threshold value of 0.00853. 0.014475. 0.022638, 0.03798, and 0.11415 were chosen for LHZ. In case of γ value of 0.85, LHI values were varying from 0.004089 to 0.1963. Threshold value of 0.02144, 0.03390, 0.0496, 0.0768, and 0.1963 were chosen to obtain LHZ map. In case of γ value of 0.92, LHI values were varying between 0.01432 to 0.3378 and threshold value of 0.05069, 0.0754, 0.10637, 0.1556, and 0.3378 were chosen for hazard classes. In case of 0.975, LHI values were found in the range of 0.08295 to 0.72214 and threshold values of 0.1947, 0.25576, 0.3211, 0.4071, and 0.7221. LHZ of five gamma cases are shown in Figure  7 . These results shows increasing trend of LHI values as the γ value increases. Very high and high hazard area obtained in case of γ value of 0.975 and 0.92 were larger in comparison to γ value of 0.75, 0.8, and 0.85. Figure  8 shows the area occupied by hazard zones for different γ values. Very high hazard area of 12.63 and 7.5 sq. km. was found in case of gamma values 0.975 and 0.92 respectively, whereas 5.75, 6.2 and 6.93 sq. km. area found for gamma values of 0.75, 0.8, and 0.85. Figure  8 refers to area covered under different hazard zones for different gamma values selected for fuzzy integration. A judicious choice of gamma value: 0.92 was selected for the final LHZ. Results indicate that area occupied by debris cone (terraces), generally falls under moderate hazard zone, where as steep talus slopes fall under high hazard zone. High hazard zones are also observed in close proximity to drainages, ridges and spars. Settlement areas are generally situated on the flat terraces and are less prone to the landslides. On the contrary, these terraces are made up of RBM (River Bourne Materials) or debris and may be subjected to mass movements such as gullying, sheet erosion in case of intense rain. Very high hazard zones are generally found near the steep slopes, cliffs and cut slopes adjoining the roads. Low hazard zones are observed in agricultural terraces, settlement area and permafrost areas.

Threshold values chosen for classification of LSI map a) γ =0.75, b) γ = 0.8, c) γ = 0.85 d) γ = 0.92, e) γ = 0.975.

LHZ map for different γ values, a) 0.75, b) 0.8, c) 0.85, d) 0.92, e) 0.975.

Bar chart showing area covered under different hazard zones for different gamma values.

Validation of Landslide Hazard Zonation Maps

Prediction accuracy assessment was performed to obtain the consistency of LHZ. Accuracy of LHZ is the capability of map to delineate landslide free and landslide prone areas. Comparison of different models and model parameter variables can also be done from validation (Begueria 2006 ). Accuracy and objectivity depend on model accuracy, input data, and experience of earth scientist and size of the study area (Soeters and Van Westen 1996 ). Validation of landslide susceptibility/hazard zonation maps are mainly based on the confusion matrix or contingency table (Bonham-Carter 1994 ). Confusion matrix consists of the calculation of overlap areas between the two binary maps. For confusion matrix, continuous susceptibility/hazard maps are compared with the landslide inventory map. There are two types of error found in LHZ, 1) landslides may occur in areas that are predicted to be stable, and 2) landslides may actually not occur in areas that are predicted to be unstable (Soeters and van Westen 1996 ). Prediction accuracy of LHZ were performed on the basis of receiver operating characteristic (ROC) curves in the present study. The ROC curve technique is based on plotting model sensitivity, true positive fraction values calculated for different threshold values versus model specificity, true negative fraction values on a graph (Deleo 1993 ). Model sensitivity—true positive fraction is the ratio between correctly classified presence data and all presence data, while model specificity—true negative fraction is the ratio between correctly classified grid cells without landslides and all grid cells without landslide (Pradhan and Lee 2010 ). Area under the ROC curve has peak value of 1 for perfect prediction where as value near 0.5 suggests failure of the model. A comparison result of the present study is shown in Figure  9 . It shows five different curves with varying degree of smoothness. LHZ for the gamma value of 0.92 shows better smoothness than other gamma values Figure  10 . ROC curve was prepared by dividing the LHI map into 18 successive susceptible classes on the basis of standard deviation and arranging them in descending order against the corresponding cumulative landslide area. Quantitative validation was performed by calculating the AUC value of ROC graph. AUC for 5 different gamma values are shown in Table  3 . Highest AUC value-0.876 was found in the case of gamma- 0.975, so it can be said that accuracy of model was 87.6%. In the same manner prediction accuracy of 85.23%, 82.11%, 80.245 and 74.43% was observed in the case of gamma - 0.92, 0.85, 0.8, and 0.75 respectively. These results suggested good prediction accuracy of the model.

ROC graph representing curves for different gamma cases. Horizontal axis and vertical axis representing specificity and sensitivity respectively.

ROC curve of best suitable gamma (0.92) value.

Fuzzy logic relations and fuzzy operation based landslide hazard zonation mapping have achieved acceptable results. Fuzzy membership values were determined by frequency ratio approach. Frequency ratio of each factor’s attributes was determined. High frequency ratio values were observed for drainage buffer, relative relief and slope. Fuzzy gamma operator was successfully applied for the LHI map. Model suggested that higher gamma values (0.92, 0.975) yielded better prediction of LHZ than low gamma values (0.75, 0.8. 0.85). Results had shown increasing tendency of hazard prediction corresponding to increasing gamma values. LHZ map indicated the importance to factors in landsliding. Among the slope classes, most of the high LHZ is observed in very high and high slope angle classes. Generally, in a terrain having high slope angle, the weight of the possible mobilized material under gravity will be more as compared to a moderate slope angle. Shear strength being same in both the cases, a steep slope with more mobilizing force may fail early. High hazard zone is observed in high and very high relative relief classes. High relative reliefs are surface manifestation of cliffs and ridges, which are often rendered unstable by the influence of triggering factors such as rainfall and earthquakes. Southern aspect of the study area, which is receiving excessive sun radiation and high rainfall, are observed under higher hazard zone categories. High hazard zones are also observed in the areas in closer proximity to drainages (drainage buffer) and it can be attributed to the stream bank erosion due to the river flow such as gulling, toe cutting which further leads to landslides. Lithology of the area belongs to different Formations and is represented by characteristic rock type, which might govern landslide incidence. High Hazard zones were observed in the rocks belonging to Kanchenjunga Formation as compared to Chungthang Formation. Alluvial sandy loamy soil has been observed at lower elevations along the drainage network and are not well compacted and are more prone to landslides. These areas are manifested in form of high hazard zone in the LHZ map. Model validity was performed using ROC curves. Smooth curves suggested good prediction results, whereas AUC values of ROC curves also indicated better prediction. Gamma: 0.92 was chosen for the final LHZ generation, because of smoothest ROC curve. Hence it can be concluded that landslide causative factor’s integration using fuzzy logic has yielded good results for Lachung valley. Frequency ratio method for determination of fuzzy membership value has reduced subjectivity in the model. The final LHZ map (γ = 0.92) can be used for the planning of future infrastructure, settlement and ecological development in Lachung region.

Abbreviations

Disaster studies

Geoinformatics

Industrial Research

Information

Intelligent system

Internatioal

Metamorphic

Observational

Observation

Photogrammetry

Remote sensing

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Acknowledgements

We acknowledge Lachung Hydro Power Pvt. Ltd., Teesta Hydro Power Pvt. Ltd. and Chungthang Hydro Power Pvt. Ltd., Noida, Uttarpradesh, India for their ancillary support for managing our stay during the course of field investigations and logistics. We also acknowledge Department f Earth Sciences, IIT Roorkee, Uttarakhand, India for facilitating advanced softwares required to implement the research methodology.

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Rathinam Anbalagan, Rohan Kumar, Kalamegam Lakshmanan, Sujata Parida & Sasidharan Neethu

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RA has carried out the detailed field investigations which included geological investigation, landslide identification along with contributions for the report writing of geological and physiographic conditions of the area. RK has carried out remote sensing data interpretation and conceptualization of methodology. KL has carried out geotechnical analysis of vulnerable slopes and field validation of land use land cover map. SP has carried out GIS analysis and lithological data interpretation. SN has helped in the preparation of ROC curve and also to draft the entire manuscript. All authors read and approved the final manuscript.

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Anbalagan, R., Kumar, R., Lakshmanan, K. et al. Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung Valley, Sikkim. GEOENVIRON DISASTERS 2 , 6 (2015). https://doi.org/10.1186/s40677-014-0009-y

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The Landslide Blog is written by Dave Petley, who is widely recognized as a world leader in the study and management of landslides.

Image of a landslide partially covered with a transparent sand-colored overlay and the words “The Landslide Blog,” centered, in white

The 4 October 2023 Glacial Outburst Flood (GLOF) in Sikkim was triggered by a landslide into a glacial lake, that in turn triggered an overtopping event that generated the appalling debris flow and flood. I have previously highlighted the amazing work that Praful Rao of the Save the Hills blog has undertaken to document the impact of the flood between Teesta Bazar and NHPC’s Teesta Low Dam Project . He has now posted a further three fascinating articles that document damage along other sections of the Teesta River, first, at Chunthang and its vicinity ; second, at Singtam ; and third, along the NH10 road . All three are remarkable records of the high level of damage – I strongly recommend that you take a look.

The first documents damage along the line of the Teesta River, but it is especially interesting in that it was the site of the 60 metre high Teesta III dam, which collapsed during the flood. Praful has posted an image of the remains of the dam:-

Drone image of the Teesta 3 dam in Sikkim, taken from Chungthang town looking downstream.

Clearly the dam has been completely destroyed, but note also the size of the boulder lodged on the left side of the structure.

The view in the other direction is equally as remarkable:-

Drone image from the site of the Teesta III dam in Sikkim, looking towards Chungthang town.

The scale of the destruction portrayed in the image is clear. There are many more of this area on the Save the Hills site .

Meanwhile, the Teesta stage VI dam at Sirwani, Singtam in Sikkim has also suffered huge amounts of damage . The image below appears to show severe damage to the infrastructure of the dam and a large amount of timber debris having been deposited:-

Drone image of damage to the Teesta VI dam in Sikkim.

Praful also highlights damage to the NH10 highway , a key arterial route for Sikkim, and the issues that this has caused. The image below shows damage to just one section of the road:-

Drone photo of NH10 (a key arterial route for Sikkim), opposite Melli bazaar.  I

The scale of the challenges that this area now faces is very stark.

Finally, as Praful has pointed out in his most recent post , the next monsoon season is starting to hove into view. There is much to be done to be ready for another long period of heavy rainfall.

Text © 2023. The authors.  CC BY-NC-ND 3.0 Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

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International Conference on Case Histories in Geotechnical Engineering

Session 2 - Case Histories of Slopes, Dams, and Embankments

Major Landslides in Sikkim − Analysis, Correction and Protective Measures: A Case Study

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Paper No. 2.15

St. Louis, Missouri

10 Mar 1998, 2:30 pm - 5:30 pm

The paper presents the case history of two major landslides in Sikkim which had proved to be a source of continual trouble for many years since 1973, causing considerable recurring financial strain on the Border Roads Organisation in order to keep the communications through Highway cuttings in the Sikkim Himalaya are exposed to hazardous landslides of a bewildering variety. Geotechnical investigations have often revealed their complex and violent nature usually associated with high and steep cuttings in immature geological settings, severe river-erosion of toe slopes particularly due to flash floods, deforestation and faulty subsurface and surface drainage conditions during heavy rains. Most landslide cause road blockades thereby badly disrupting the communication system of the region. Some of the important highways, particularly without alternative routes therefore require heavy maintenance inputs. The paper gives an exclusive report on geological, geotechnical field and laboratory investigations of the two selected slides in Sikkim, India. The results of remedial measures suggested and implemented to protect the slope from sliding are discussed in the paper.

A. K. Gupta , Central Road Research Institute, New Delhi, India D. S. Tolia , Central Road Research Institute, New Delhi, India

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Gupta, A. K. and Tolia, D. S., "Major Landslides in Sikkim − Analysis, Correction and Protective Measures: A Case Study" (1998). International Conference on Case Histories in Geotechnical Engineering . 13. https://scholarsmine.mst.edu/icchge/4icchge/4icchge-session02/13

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Landslides in Sikkim

October 19, 2011 JPEG

October 19, 2011 TIFF

On September 18, 2011, a 6.9-magnitude earthquake struck the India-Nepal border region. According to news reports, impacts of the earthquake included landslides in in the northern Indian state of Sikkim, between Nepal and Bhutan.

The Advanced Land Imager (ALI) on NASA’s Earth Observing-1 (EO-1) satellite acquired this natural-color image of Sikkim on October 19, 2011. Landslides look like giant scratches on the landscape, where dirt and rock have displaced vegetation. The avalanches of earth terminate in river valleys. (Because of the angle of sunlight, this image may cause an optical illusion known as relief inversion. )

In early October 2011, geologist David Petley of the International Landslide Centre at Durham University noted that most of the landslides occurred on east- and south-facing slopes—away from the epicenter of the quake about 45 kilometers (28 miles) to the northwest.

As with most landslides occurring around earthquakes, researchers must ask whether those slides definitely resulted from the quakes. After comparing recent images of the Sikkim region to imagery from 2007, Petley stated that these slides most likely resulted from the September 18 earthquake, although other causes could not be ruled out, such as monsoon rains.

NASA Earth Observatory image created by Jesse Allen and Robert Simmon, using EO-1 ALI data provided courtesy of the NASA EO-1 team. Caption by Michon Scott.

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Acquired October 19, 2011, this natural-color image shows landslides in the northern Indian state of Sikkim. The landslides likely resulted from an earthquake to the northwest.

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References & Resources

  • Petley, D. (2011, October 6). A first look at NASA satellite imagery of the landslides from the Sikkim earthquake. The Landslide Blog. Accessed October 24, 2011.
  • U.S. Geological Survey. (2011, September 18). Magnitude 6.9 – India-Nepal Border Region. Accessed October 24, 2011.

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  • Francesco Troiani   ORCID: orcid.org/0000-0002-4554-1332 4 &
  • José Miguel Azañón   ORCID: orcid.org/0000-0001-7834-5816 2  

Landslide research has benefited greatly from advances in remote sensing techniques. However, the recent increase in available data on land surface movement provided by InSAR techniques can lead to identifying only those areas that were active during data acquisition as hazardous, overlooking other potentially unsafe areas or neglecting landslide-specific geological settings in hazard assessments. Here, we present a case study that serves as a reminder for landslide researchers to carefully consider the geology and geomorphology of study areas where complex active movements are detected using InSAR technology. In an area extensively studied using InSAR and UAV-related techniques, we provide new insights by applying classical approaches. The area is the coastal stretch of La Herradura, and its importance lies in the fact that it has served as an illustrative example in the Product User Manual of the European Ground Motion Service, a platform that provides ground motion data on a European scale. Our approach is to revisit the area and carry out qualitative geological and geomorphological assessments supported by UAV surveys and GIS spatial analysis on a broader scale than previously published investigations. Our classical approach has yielded the following new observations, crucial for risk assessment and land management: active landslides identified by InSAR techniques since 2015 are bodies nested within large mass movements that affect entire slopes. A variety of processes contribute to slope dynamics, such as large slumps, marble rock spreading and block sliding, and surface rock falls and topples. The revised delineation of the landslide bodies reveals an area almost five times larger than previously mapped. These new findings in a well-known area highlight (1) the importance of updating and downscaling previous maps and (2) the ongoing importance of classical fieldwork and desk studies as basic complements to modern InSAR analyses.

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Introduction

The knowledge of the geomorphological processes of an area is fundamental to understand changes in the territory for risk identification and mitigation as well as urban development (Howard 2013 ; Sreenivasan and Jha 2022 ). In the present day, geomorphological assessments are assuming an increasingly important role in supporting engineering geology (Hearn 2019 ; Laimer 2021 ). Contemporary remote sensing methodologies are facilitating the acquisition and analysis of spatio-temporal data. In this sense, Synthetic Aperture Radar Interferometry (InSAR) data (e.g., Cigna et al. 2011 ; Bianchini et al. 2013 ; Barra et al. 2022 ; Reyes-Carmona et al. 2023 ) and photogrammetric low-altitude aerial photos acquired from Unmanned Aerial Vehicles (UAVs) (e.g., Hackney and Clayton 2015 ; Tziavou et al. 2018 ; Giordan et al. 2020 ) have emerged as common tools for geological and geomorphological mapping and ground characterization.

The increasing accessibility of remote data, now readily available, may lead to base landslide research mainly on remote sensing information alone. However, it is crucial always to consider the specific geological and geomorphological conditions, which can only be known by developing comprehensive studies integrating classical field surveys and desk studies with remote sensing analyses as Griffiths ( 2019 ) or Hearn ( 2019 ) point out. In the landslide research environment, fieldwork and desk study are considered essential for obtaining sound results but, as methodological procedures, these classical techniques nowadays have been relegated to a secondary role. In some cases, field work is perceived laboriousness or difficult to develop in the time frames within which contemporary science operates. This particularly occurs under challenging conditions due to field accessibility problems of diverse nature and, in this case, fieldwork is reduced and left out of the picture to only underscore the technologies or methods applied to analyze data acquired by remote sensors. In our experience in other professional spheres, classical approaches are sometimes considered obsolete if not coupled with new technology or dismissed when there is existing data in the study areas. In the latter scenario, problems may arise as the prior information may be outdated or not refined to the necessary detail required by the research scale. Although classical approaches remain theoretically fundamental for the surface dynamics scientific community, there is a growing trend in practice to decrease the focus on qualitative information acquired by classical techniques such as geological and geomorphological observations, although, it is widely accepted that this information is a basic complement to remote sensing analysis in order to interpret results, and it should be given its due importance. Here, we present a case study that reflects the above described, in which some authors of the study themselves have realized that they overlooked a more general research based on classical surveys in an area extensively studied using InSAR and UAV technologies. Observations obtained through traditional approaches, which aim for a thorough understanding of the terrain of the study area, have yielded a more detailed model of the geological context that helps to interpret the ground motion detected by InSAR techniques. Through this study, we can highlight the importance of considering the “big picture” and updating geological and geomorphological data for an accurate diagnosis of an area affected by complex landslides.

Background of the case study

Remote sensing techniques play a crucial role in assessing coastal areas (Sreenivasan and Jha 2022 ). These regions, characterized by high population density, cultural significance, valuable ecosystems, and critical freshwater resources face substantial impacts from many interactions between natural and human processes. Global Change factors, including sea-level rise, escalating aridity, and intense and sudden rainfall (López-Fernández et al. 2022 ), further contribute to the challenges. In this context, one of the most human-altered areas globally is the Mediterranean shoreline (Falcucci et al. 2007 ), with half of it composed of rocky coasts (Furlani et al. 2014 ), where unstable slopes are common (e.g., Mantovani et al. 2016 ; Polcari et al. 2018 ; Svigkas et al. 2020 ). Identification and monitoring of coastal landslides are a priority in this setting, and the research using remote sensing techniques focused on the coast of Granada province in the Mediterranean shoreline of Southern Spain has attracted international attention on this topic (Notti et al. 2015 ; Galve et al. 2017 ; Mateos et al. 2017 ; Barra et al. 2022 ). So much so that the European Ground Motion Service (EGMS), a Europe-wide project on InSAR applications, has taken this stretch of coastline as one of its example cases to show the results of the service (Crosetto et al. 2020 ; Kotzerke et al. 2022 ). The EGMS is an ambitious initiative of the Copernicus Land Monitoring Service (CLMS) that provides information on ground motion occurring in the majority of European countries. The European Space Agency (ESA), the European Union, and other national organizations and academic institutions are working together on it to identify and track ground motion events including earthquakes, landslides, and subsidence. This initiative is focused on providing ground motion data, but the interpretation of the results is the concern of those who use the platform.

The present study returns to the abovementioned stretch of the Granada’s coast, albeit with a complete approach that underlines the importance of classical geomorphological assessments as an important asset for remote sensing studies. For that, in-depth geological-geomorphological surveys have been conducted in the La Herradura section revealing meaningful changes in the previous interpretations of the ground movements detected by InSAR and photogrammetry (Notti et al. 2015 ; Galve et al. 2017 ; Mateos et al. 2017 ; Barra et al. 2022 ). This contribution includes a wide range analysis of the landforms, as well as a comprehensive geomorphological map, which serves as a basis for the subsequent interpretation of the movements detected by the aforementioned techniques. In that sense, we have (i) enlarged landslide extensions compared to the previously mapped landslide area and, amongst other observations, (ii) determined the process underlying a deformation of unknown origin detected by Notti et al. ( 2015 ). These two points have important implications for land-use planning, urban development, the design of structural engineering measures as well as superficial and groundwater management, especially considering the occurrence of local aquifers within marble units (Andreo et al. 2018 ; Montiel et al. 2018 ) affected by slope instabilities. The main purpose of this work was to refine the conceptual model of landslides along the coastline of La Herradura to validate interpretations derived from EGMS, which will also help to design effective landslide mitigation measures there and in other coastal areas around the Globe.

The results of the work hold particularly significant, especially considering that previous research, including contributions from some authors of this paper, extensively examined the study area in recent years, leading to the perception that the area was fully understood from a geological/geomorphological point of view. It appeared that existing knowledge and evidence regarding the landslides in La Herradura were comprehensive and conclusive, leaving little expectation for significant new discoveries. However, it is crucial to note that previous research has relied on prior information based on 1:50,000-scale geological maps and site-scale geomorphological assessments that did not allow the geomorphic “big picture” to be seen. This is the first medium-scale geomorphological assessment of the area that brings to light new aspects of the slopes. The new perspective gained from this assessment has the potential to reshape the interpretation of monitoring data in an area of high interest for InSAR studies, particularly within the EGMS.

The coastal section of La Herradura (36°44′ N, 3°44′ W; Fig.  1 ) is a 3 km long bay located in the province of Granada (Southern Spain). The bay is bounded by two promontories, Cerro Gordo (ca. 343 m a.s.l.) and Punta de la Mona (ca. 126 m a.s.l.), which are included in two Special Areas of Conservation (SAC): Maro-Cerro Gordo Cliff Natural Site (MCGCNS) and Cliffs and Seabed of La Punta de la Mona (CSPM), stated within the framework of the European Union Habitats Directive (9242/EEC). The area is heavily anthropized with agricultural terraces, housing, resorts, and hotels, which contribute to the regional economic vitality. The inner part of La Herradura bay is the most densely urbanized sector of the study area, while both promontories also feature urban developments. Notably, the resorts of Cármenes del Mar (on the Cerro Gordo promontory) and Marina del Este (on the Punta La Mona promontory) were built on the eastern slopes of the capes, which are affected by active slope movements.

figure 1

La Herradura coastal section, showing major urban areas, main roads (A-7 highway, N-340 national road), and special conservation areas. The map of Spain with zones of Betic-Rif orogen is after Moragues et al. ( 2021 ). The geological map is after Marín-Lechado et al. ( 2009 ). La Herradura fault is after Ruano ( 2003 ) and Ruano et al. ( 2004 ). Preferred wave movement direction in percent and wave height by colour range in the range 2011–2021 (is from State Port, Ministry of Transport, Mobility and Urban Agenda of Spanish on https://www.puertos.es/es-es )

The coastal sector under study shows a NW-SE orientation, with local N-S variations due to the promontories morphology (Fig.  1 ). It is characterized by a Mediterranean climate, a humid temperate climate with dry and hot summer (Csa, according to the Köppen-Geiger classification) (Cunha et al. 2011 ), with a mean annual precipitation of about 430 mm (Chacón et al. 2019 ). Rainfall in this coastal area above the yearly average occurred in the wet seasons of 1995–1997, 2000–2003, and 2009–2010 (Irigaray et al. 2000 ; Vicente-Serrano et al. 2011 ) breaking all the previous records in 2010, with an annual average value of about 857 mm (Chacón et al. 2019 ); the greatest amount was reported in 1973 with 350 mm in 24 h (Notti et al. 2015 ). Due to the latitude, the coastal orientation, and the Betic Cordillera, which protects the coastline from the Northern winds (Mooser et al. 2021 ), temperatures are mild both during winter and summer with a mean annual range of 18–20 °C (Chica Ruiz and Barragán Muñoz 2011 ; Consejería de Medio Ambiente y Ordenación del Territorio Junta de Andalucía 2015 ). Dominant winds approach from E-SE with a speed up to 9 m/s (Manno et al. 2016 ; Molina et al. 2019 ) causing storm waves. The coast is dominated by E-SE high-frequency waves (significant wave height 1 m) (Guisado and Malvárez 2009 ) that generate a littoral drift towards W (Fig.  1 ); the average tidal range is less than 20 cm (Manno et al. 2016 ), and the ongoing local sea-level rising is estimated in 2.65 mm per year (Serrano et al. 2020 ).

Geological and geomorphological setting

La Herradura coastal sector is settled in the Alpujárride Complex, formed by deformed metamorphic rocks belonging to the Alboran Domain of the Betic Cordillera (Azañón et al. 1994 ; Azañón and Crespo-Blanc 2000 ). The main structures of the Alpujárride Complex are Alpine regional-scale anticline folds, with a mean E-W trend and NW vergence (Simancas and Campos 1993 ; Azañón et al. 1997 ; Williams and Platt 2017 ). These compressive structures are cut by top-to-the-SW low-angle normal faults and later high-angle transtensional faults and other structures, formed since the Miocene (Azañón et al. 1997 ; Ruano 2003 ; Ruano et al.  2004 ; Simancas 2018 ). However, a NE-SW extensional phase characterizes the whole region at present (Azañón et al. 2015 ; Galindo-Zaldívar et al. 2015 ).

The study area is settled in the tectonic unit of La Herradura, formed by the following lithostratigraphic sequence, from bottom to top (Azañón and Crespo-Blanc 2000 ): (i) Dark Schists Formation (Fm.), dark-coloured schists and graphitic micaschists (Paleozoic), with Migmatite Gneisses at the bottom; (ii) Light Schists Fm., light-coloured fine-grained schists with calcschists in the uppermost part (Permian-Triassic); (iii) Marble Fm., calcareous and dolomitic marbles with interbedded phyllites and schists (Middle-Upper Triassic). The geological structure of the coast stretch is complex and exhibits a km-scale recumbent syncline affected by SW-dipping low-angle normal faults and other Alpine structures (Simancas and Campos 1993 ). The normal fault displaced the Marble Fm. belonging to the hanging wall towards the S-SW.

Marble Fm. is classically considered a karst aquifer with high fissuration and a low degree of karstification, while the remaining rocks, such as schists, are aquitards (Andreo et al. 2018 ; Calvache et al. 2020 ). The main recharge of the aquifer occurs by rainfall infiltration inland and the discharge takes place via submarine outlets or through karst springs located at sea level (Montiel et al. 2018 ). Both promontories are associated with Triassic marble that exhibits karst features often related to fractures and faults, especially in Cerro Gordo cape, where 11 caves totalling 330 m in length have been reported by speleological teams.

The plunging cliff (Sunamura 2015 ), which reaches a depth of over 40 m below the sea level (Consejería de Medio Ambiente y Ordenación del Territorio Junta de Andalucía 2015 ), is the main coastal morphology along the shoreline. Alternating with the plunging cliff, there are small coves with beach deposits and larger pebbly and sandy beaches such as La Herradura bay. The seabed in front of the coast consists of a continental shelf that slopes dipping 1.4° to the continental escarpment zone located at about 4 km from the coast. The promontories are dominated by small torrential streams within short and narrow valleys and slope/run-off processes in slopes. Slope processes involve prominent landslides impacting Cármenes del Mar and Marina del Este resorts (Fig.  1 ). Chacón et al. ( 2014 ) reported the occurrence of landslides with slide kinematics mostly at the contact between marble and schist or phyllite units. The activity of these instabilities was directly correlated with extreme rainfall events (Notti et al. 2015 ; Mateos et al. 2017 ; Chacón et al. 2019 ).

Previous landslides studies

The terrain instability studies of the Cerro Gordo and Punta de la Mona promontories started with their urban development of Cármenes del Mar and Marina del Este resorts. From 1977 onwards, the first buildings were constructed, with urbanization increasing very rapidly from 1997 to the 2000s (Notti et al. 2015 ; Mateos et al. 2017 ). Signs of coastal instability in this area were firstly described in 1988. From 2005 onwards, unpublished technical reports, described in Chacón et al. ( 2019 ), analyzing landslide areas were carried out, focusing on the urbanization of Cármenes del Mar. After those reports centred on geotechnical aspects, scientific research was developed focused on remote sensing techniques. Thus, the recent activity of landslides in both the Cerro Gordo and Punta de la Mona promontories have been largely confirmed by applying Differential Interferometric Synthetic Aperture Radar (DInSAR) (Notti et al. 2015 ; Mateos et al. 2017 ; Chacón et al. 2019 ; Barra et al. 2022 ). Regarding the Cerro Gordo promontory, the most studied is Calaiza landslide previously reported by several unpublished geotechnic studies, by Landslide Database of the Spanish Geological Survey (CN IGME 2016 ), the inventory of the Granada Province (Chacón et al. 2007 ) and by Azañón et al. ( 2016 ), Mateos et al. ( 2017 ), Chacón et al. ( 2019 ), and Barra et al. ( 2022 ). A Line-Of-Sight (LOS) displacement rate of up to 2.5 mm/yr was obtained in the period 1997–2000, related to the Calaiza landslide activity (Chacón et al. 2019 ). Mateos et al. ( 2017 ) estimated a maximum LOS displacement rate of − 10 mm/yr for this landslide from May 2003 to December 2009, a period in which the first damages of the Cármenes del Mar resort appeared. These authors also estimated displacements of up to 1.98 m in 8 years through UAV digital photogrammetry in relation to the intense rainfall period during 2009–2010. Regarding the Punta de la Mona promontory, Marina del Este and Peñón del Lobo landslides were reported by unpublished geotechnic studies, Chacón et al. ( 2007 ), CN IGME ( 2016 ), Azañón et al. ( 2016 ) and Notti et al. ( 2015 ); an average LOS displacement rate of − 11 mm/yr was registered on the landslide affecting the Marina del Este resort (Notti et al. 2015 ). More recently, Barra et al. ( 2022 ) confirmed the activity of the Marina del Este and Punta de la Mona landslides from November 2015 to May 2020, using InSAR techniques and without delimiting landslide extension, both with LOS average displacement rates ranging from − 10 to − 20 mm/yr. The ground displacement was inferred to be around 10 mm/yr affecting the Marina del Este resort.

Finally, landslides have resulted in a dramatic situation. The authorities have currently proclaimed an emergency state after ground movement damage forced the evacuation of 42 buildings in the resort of Cármenes del Mar (Chacón et al. 2019 ). For these reasons, all the investigations bring the La Herradura coastal section to be one of the selected illustrative cases of the Product User Manual of EGMS (Kotzerke et al. 2022 ; Crosetto and Solari 2023 ).

Materials and methods

The geomorphological and geological map of La Herradura coastal section was carried out at a scale of 1:20,000 to identify gravity-induced deposits, to recognize surface processes acting on the rocky coastline, and to establish a relationship between slope processes and bedrock lithology and structure. The cartography allowed to know the landslide extent and dynamics and to elaborate cross-sections along the main slope instabilities, as the basis for performing a conceptual model of the slope processes previously detected by DInSAR studies.

The approach used for this study involved consulting the literature, geotechnical reports, and historical pre-urbanization aerial images. Geological and geomorphological terrain models were developed based on various in situ and remote sensing surveys. Moreover, a comparison was made with studies that investigated the same areas using InSAR data from the EGMS Explorer (European Environment Agency 2023 ). The calibrated displacement data in ascending and descending orbits from the period January 2018–September 2022 were downloaded by using the EGMStream app (Festa and Del Soldato 2023 ), which facilitated the data management. For both geometries, the stability range was established as two times the standard deviation of the data (Barra et al. 2017 ), and satellite Line-Of-Sight (LOS) velocity maps were displayed. Moreover, the average time series of accumulated displacement of the main unstable areas was extracted and analyzed.

In the laboratory, aerial photography, land, and submarine Digital Elevation Models (DEM), and previous geological maps were performed and analyzed using ArcGIS 10.8.2 (ESRI ® , 2022/2023-licensed “DISPEA University of Urbino”). Aerial photos were downloaded by the National Geographic Institute (IGN) of Spain ( https://centrodedescargas.cnig.es/ ; https://fototeca.cnig.es/fototeca/ ) resulting from photogrammetric flights conducted by the USA over the years 1945–1946 and 1956–1957 and by Spanish administrations since 1973; these were used to visualize the area previously to its heavy urbanization and to define the recent pre-urbanization landslide activity. The images were interpreted using a stereoscope for a 3D interpretation of landforms in combination with Google Earth optical satellite images providing a multitemporal analysis. The IGN also provided base maps and the 3D LiDAR point cloud in LAZ format; this zip file was decompressed into LAS archives and filtered with LAStools (Isenburg 2014 ), generating a DEM with a 1 m cell-size resolution. Data for bathymetric reconstruction were downloaded from the Ministry for the Ecologic Transition and Demographic Challenge of Spain ( https://www.miteco.gob.es/es/costas/temas/proteccion-costa/ecocartografias/ecocartografia-granada.aspx ), generating a 5 m-resolution bathymetric DEM. Previous geological cartography is from Avidad et al. ( 1973 ), and the Continuous Digital Geological Map of Spain performed at a 1:50,000 by Marín-Lechado et al. ( 2009 ).

Lineaments (related to landslide failure surfaces, faults, and/or fractures) and landforms were identified by analyzing also the hillshade model derived by the DEM and were later verified by fieldwork carried out from September to December 2022. The landslide mechanisms were classified according to Cruden and Varnes ( 1996 ) and Hungr et al. ( 2014 ). The field surveys were novelty supported by UAV flights to visualize hard-to-reach spots such as the rocky cliffs along the coast. The drone model used was a DJI Mavic 2 pro equipped with a 20 MP Hasselblad camera with a 1’’ CMOS sensor size; the camera is fastened on a 3-axis gimbal for image stabilization and a 28 mm lens, with a FOV of ca. 77° and an aperture of f/2.8-f/11, allowing a 20 Mpixel photo resolution. Furthermore, this drone is equipped with a GPS/Glonass system for image georeferencing. Mobile device applications (e.g., FieldMove Clino) were also utilized to collect data directly in digital form.

Desk study and field investigations provided the first comprehensive geological-geomorphological map of La Herradura coastal section at a scale of 1:20,000 (Fig.  2 ). Three landslides (named Cantarriján, Calaiza, and Las Palomas) have been identified, surveyed, and/or studied in Cerro Gordo promontory. Additionally, four major instabilities (namely Playa, Punta de la Mona, Marina del Este, and Peñón del Lobo landslides) have been delimited and studied in the surroundings of Punta de la Monta promontory. In our work, we examined Calaiza, Las Palomas, Punta de la Mona, and Marina del Este landslides in detail by means of three geological cross-sections to synthesize all newly gathered observations in the vertical dimension (Fig.  3 ). The studied instabilities are affecting or could impact resorts, infrastructures (highway, national road) and other urban areas. Subsequently, we compared the results with previous works that have analyzed only some of these landslides, particularly through InSAR data.

figure 2

Detailed geological and geomorphological map of La Herradura coastal section. Bedrock geology is after Avidad et al. ( 1973 ) and Marín-Lechado et al. ( 2009 ). In the legend and in the map light-coloured features remain inactive/quiescent, dashed-line features are supposed/uncertain, and barbs (in normal fault) and triangles (in thrusts) are in the hanging wall

figure 3

Geological-geomorphological cross-sections along La Herradura coastal section. The legend and position of the sections are depicted in Fig.  2 and the zoom position of the aerial image in profile b can be seen in Fig.  4 a. a Cross-section along the active Calaiza landslides, where Cármenes del Mar resort was built on the eastern slope of Cerro Gordo promontory. An inferred shear zone associated with a larger gravitational slope deformation is also shown. b Cross-section of Las Palomas landslide that showcases the foundations of Las Palomas resort, at the vicinity of Cerro Gordo hill. While the majority of this landslide is currently inactive, there is activity observed in its lower part, where Marble blocks are sliding at the present day. Aerial image of the 1957 flight displays trenches revealing the occurrence of large, slipped marble blocks (highlighted by red ellipses). Vertical scale is ca. two times exaggerated. c Cross-section along Punta de la Mona promontory exhibiting active Punta de la Mona and Marina del Este landslides

Landslides in the Cerro Gordo promontory: reconfiguring the conceptual model of slope failures

Cerro Gordo promontory (343 m altitude) is mainly composed of Marble Fm. exhibiting the typical massive morphology influenced by the occurrence of a recumbent syncline (Simancas and Campos 1993 ), with a NW-SE to E-W trend. Here, bedding dips 15° to the SE at the summit, while it is subvertical in the southern plunging cliff of the cape. The fold shows an axial plane tectonic foliation towards S-SE dipping 30–40°. Both Marble and Schists Fms. are partially weathered, although the schists show a more extreme alteration. The deformation that the Dark Schists Fm. has undergone, and the high degree of fracturing, due to the tectonics present in the area, led to a more ductile than brittle rheology.

The eastern side of the Cerro Gordo promontory before the urban development is shown in Fig.  4 a, and the corresponding improved geological-geomorphological map (Fig.  4 b) shows a zoom of Fig.  2 . Here, Dark Schists Fm. has been identified in areas previously mapped as Marble Fm. in previous works. Moreover, we observed that Paleozoic Dark Schist Fm. lies over the Marble Fm. through a contact of tectonic origin. This evidence and the absence of the Permian-Lower Triassic Light Schists Fm. (which stratigraphically appears between the Marbles and the Dark Schists Fms.; Azañón and Crespo-Blanc 2000 ) suggest that the contact between Dark Schists and Marble Fms. is a thrust in line with observation carried out by Sanz de Galdeano and López-Garrido ( 2003 ) in Betic Cordillera.

figure 4

a Aerial image of 1957 exhibiting landslides and their detachment areas, where the landslides are clearly visible without anthropization. Continuous red lines indicate landslide bodies, orange lines indicate the detachment areas, black rectangle indicates zoom in Fig.  3 b; b Zoom of the geological and geomorphological map (Fig.  2 ) on the Cerro Gordo promontory. The survey details each single slipped block of marble. The legend for b is in Fig.  2 ; c Panoramic view of the eastern slope of Cerro Gordo promontory with the villages built on Calaiza and Las Palomas landslides. The point of view of c is shown in a

Cantarriján landslide stands out in the western slope of the Cerro Gordo promontory (Fig.  4 ). This inactive landslide is a rock avalanche, has a minimum volume of 3 × 10 6 m 3 , and produces a significant effect on the littoral relief and the bathymetry until the depth of − 50 m visible in the Visor de Cartografiado Marino ( https://sig.mapama.gob.es/marino/ ). The landslide deposit is quiescent/inactive, except for sporadic rock falls along the landslide scarp and along the cliff of the landslide deposit generated by marine erosion.

The eastern slope of the Cerro Gordo promontory is affected by the Calaiza landslide, which has impacted on Cármenes del Mar resort up to the present (Chacón et al. 2014 , 2016 , 2019 ; Notti et al. 2015 ; Azañon et al. 2016 ; Mateos et al. 2017 ). The landslide volume was estimated by Chacón et al. ( 2019 ) in ca. 8 × 10 5 m 3 , and its cartography has been updated in our work (Figs. 2 , 3 , and 4 ). Lateral spread and block sliding of marble blocks with volumes over 300 m 3 (within and above the landslide) have been detailed in the southernmost sector, detached from the 25 m height escarpment (Fig.  5 a–c), some of them already mapped by Mateos et al. ( 2017 ). Just below the ridge, two escarpments have been discovered using the hillshade model of 1 m resolution and later confirmed by fieldwork (Fig.  5 d). The uppermost, with an approximately 10 m height and a N30° E mean direction can be recognized along the entire slope (Fig.  4 b). The second escarpment at lower altitudes is definitely shorter and discontinuous. The origin of this landform will be explored in the discussion section.

figure 5

a Contact between a detached marble block and the Dark Schists Fm. marked by a shear zone made of a sandy gouge. In the insert frame are visible drag folds in the foot of the deformation zone; b Marble block detached from the escarpment c and slipped above the Calaiza landslide deposit in the southern part; c Panoramic view of the Calaiza landslide (dashed red area) in the Cerro Gordo promontory. The right flank escarpment of the Calaiza landslide is also visible; d Main upper escarpment (ca. 10 m height) above the Calaiza landslide; e Las Palomas landslide body involving up to metric marble blocks, falling into the sea through marine erosive action; f Detail on the Las Palomas landslide body, with medium-fine matrix and up to metric marble blocks

Las Palomas landslide has been discovered towards the NE of the Calaiza landslide. It is almost masked by the urban area, and the observation of geological and geomorphological features here is really difficult. Specifically, the northern limit of the landslide is now largely obscured from view, making it challenging to delimit. Fortunately, historical aerial photographs taken in 1957 (Fig.  4 a) display key morphological features to map the sliding mass. In addition, the sea cliff face provides a clear view of the landslide deposit, allowing for an accurate understanding of its structure and composition. Thanks to low-altitude aerial photos captured using a UAV, the inaccessible cliff has been examined revealing (i) the fallen marble rocks from the landslide deposit into the sea (Fig.  5 e), which are not found anywhere else on the promontory where landslides were not reported; (ii) the alternation of massive marble outcrops (huge blocks on Las Palomas landslide) to the landslide deposit. These observations were crucial for developing a more comprehensive understanding of the processes that contribute to landslides in this area. Las Palomas landslide, had a short runout, with a general composite rock slump kinematic and nested movements of marble block local lateral spreads and block slides (Figs. 3 b and 4 b). With an estimated minimum volume of about 6 × 10 6 m 3 , the main body involves a large part of the slope reaching the N-340 national road. The crown, which is not very discernible within the landscape, encompasses the two different types of nested bodies, with the largest gravitational process that involves the entire slopes with a very large and deep sliding surface (Fig.  3 b). The structure of the landslide with up to metric-scale marble blocks embedded in a medium-fine matrix derived from the weathering schists and phyllites (Fig.  5 f) does not fit with the current source area of the landslide (i.e., crown area) composed of schists. Particularly, it is clear how the Las Palomas landslide changed the previous coastline (as for the Cantarriján landslide), which was about 150 m further back.

In some cases, a reddish-brown sandy-rich sediment consisting of fine matrix and angular centimetric marble clasts, not previously described in the area, has been found at the contact between marble blocks and schists (Fig.  5 a). Field evidence observed in an excavated talus reports that this sediment is probably a gouge as a result of large marble blocks sliding process on schists (Fig.  5 a). The outcrop exhibits a marble block situated above the layer in question, as well as significantly deformed schist displaying a drag folding zone associated with the movement of the block (zoom in Fig.  5 a). According to this, marble blocks are comparable to ploughing blocks commonly observed in periglacial environments (Goudie 2004 ). These blocks exhibit faster downslope movement compared to the underlying sediment due to solifluction processes, which produce inner deformation within the sediment.

Gravitational collapse of the Punta de la Mona promontory: redefining the landslide areas

Punta de la Mona promontory (Fig.  6 ) shows similar lithologies as the previous promontory, with the additional presence of sporadic outcrops of highly altered and fractured Migmatite Gneisses in the eastern slope. Here too, the strata dip towards the S, and the recumbent synclinal axis turns in an E-W direction following the coastline. One of the regional extensional faults, with a NW-SE direction, here cutting across the promontory and generating a partially visible tectonic contact in the ridge between Marble and Dark Schists Fms. (Fig.  7 a). The main difference from the Cerro Gordo is the scarcity of Marble Fm., which crops out in situ only in the southernmost promontory and at the ridge (Fig.  6 a). This is also related to the lower altitude of the promontory itself, which reaches about 126 m in contrast with over 300 m of the Cerro Gordo cape.

figure 6

a Aerial image taken in 1957 exhibiting landslides and their detachment areas, which are easily recognizable in the pre-urbanized Punta de la Mona promontory. Continuous red lines indicate landslide bodies, orange lines indicate the detachment areas, the green arrow indicates the part of the body landslide flattened for the parking; b Zoom of the geological and geomorphological map (Fig.  2 ) on the Punta de la Mona promontory. It can be seen the detail with which the landslide deposits and their shapes have been mapped. The legend of b is shown in Fig.  2 ; c Panoramic view of the eastern slope of the Punta de la Mona promontory with Marina del Este resort, after which the landslide was named. The rock fall deposit with the up to 30 m height escarpment in the southeastern part of the promontory is clearly visible. The point of view of c is shown in a

figure 7

a Contact between Marble and Dark Schists Fms. that is associated with a tectonic shear zone composed by brecciated and well-consolidated material. Here, one of the extensional faults cuts across the promontory, exhibiting this tectonic contact in the ridge; b Plunging cliffs of Punta de la Mona promontory, obtained from a drone video of the Inmobiliaria de La Cuesta. Indicated by a red arrow is an open fracture that coincides with the escarpment (dashed orange line) of the landslide affecting the western slope; c The escarpment (30 m height) of Marina del Este landslide showing vertical open fractures (red arrow) related to the detachment of marble blocks; d Peñón de las Caballas rock, a huge marble block slipped into the sea, where the harbour of Marina del Este was built

Landslides involve both sides of the Punta de la Mona promontory, which is heavily urbanized. Two landslides are now being analyzed that had so far not been catalogued, neither in the Landslide Database of the Spanish Geological Survey (CN IGME 2016 ) nor in the inventory of the Granada Province (Chacón et al. 2007 ). Punta de la Mona landslide, with composite rock slump kinematics, affects the entire western slope and is characterized by active superficial reactivations in its central part, exhibiting a minimum volume of 1.5 × 10 6 m 3 . The landslide scarp is not evident, but it would be recognizable S of the landslide body and continues along the southern flank, coinciding with a large open fracture on the plunging cliff (Fig.  7 b). In the northern part of the western flank, Playa landslide was recognized, which caused some damages to the overlying buildings and roads. This active landslide has a slow-moving flow kinematic and developed entirely on Dark Schists Fm.

In the eastern sector of the promontory, the Marina del Este landslide shows a minimum volume of about 2.5 × 10 6 m 3 and has been much studied in geotechnics reports and through InSAR analysis (Notti et al. 2015 and reference therein). The landslide escarpment follows the entire slope showing heights of up to 30 m (Fig.  7 c), vertical fractures, and detached large marble blocks up to 100 m in diameter such as the Peñón de las Caballas block in the marina (Fig.  7 d).

Looking at the aerial images of the 1957 flight, it is clear how the promontory was modified when the resorts were constructed (Fig.  6 a). For instance, the bottom of the Marina del Este landslide was flattened for the construction of the harbour car park (green arrow in Fig.  6 a) or the innumerable gullies engraving the landslide body. Furthermore, as in the case of Las Palomas landslide, huge slipped marble blocks were found along the coastline and on the landslide deposit along the slope, mapped in detail in the geological-geomorphological map (Fig.  6 b). In this context, it is noteworthy that an accumulation of marble rocks can be observed at the bottom of the southeastern slope of the promontory, just situated beneath the crown scarp. Based on their distribution pattern, it is possible to infer that the rocks have been dislodged and fallen into the sea as a result of a topple or rockfall event, rather than through sliding over the underlying schist. This inference is supported by the occurrence here of marble outcrops without preserved marble blocks inland.

EGMS InSAR data in La Herradura coastal section

Figure  8 shows the InSAR calibrated data in ascending and descending orbit, extracted from the EGMS, at the Cerro Gordo (Fig.  8 a, b) and Punta de la Mona (Fig.  8 c, d) promontories. The stability range was settled from 2.5 to − 2.5 mm/yr for both geometries. All the landslides exhibit ground motion through at least one geometry, with the exception of Cantarriján, Las Palomas, and Peñón del Lobo landslides where clear ground displacement was not captured. The LOS maximum velocity registered in ascending orbit is 14 mm/yr in the Calaiza landslide (Fig.  8 a), while in descending geometry, LOS velocities reach 15.3 mm/yr in the Punta de la Mona landslide (Fig.  8 d). The average velocities of the Calaiza, Punta de la Mona, and Marina del Este landslides range from 3.5 to 8.1 mm/yr (absolute values), while the Playa landslide shows a lower mean LOS velocity of − 2.8 mm/yr. Moreover, the displacement pattern provided by EGMS in these four landslides follows a linear and constant trend during the measured period of time (from January 2018 to September 2022), as evidenced by the time series of accumulated displacement (Fig.  9 ). There is no significant acceleration of the movement in any of the landslides, and the total LOS accumulated displacement is up to − 30 mm in the Calaiza (Fig.  9 a) and Punta de la Mona (Fig.  9 b) landslides, in ascending and descending orbit, respectively.

figure 8

Line-Of-Sight (LOS) velocity maps in ascending ( a ) and descending ( b ) orbits in the Cerro Gordo promontory; and in ascending ( c ) and descending ( d ) orbits in the Punta de la Mona promontory (data downloaded from https://egms.land.copernicus.eu/ ). The main landslide boundaries delimited in this research (Fig.  2 ) are also indicated in order to compare current active areas according to EGMS with the entire area of landslides

figure 9

Times series of accumulated displacement, measured along the satellite Line-Of-Sight (LOS), of the Calaiza and Marina del Este landslides in ascending geometry ( a ) and Punta de la Mona and Playa landslides in descending orbit ( b ). In both graphs, daily precipitation recorded by the closer meteorological station of the Spanish State Meteorological Agency (Station Salobreña 6267X, 36°44′54″ N 3°34′43″ W) to the study area is represented. No clear accelerations of landslide displacements are observed in relation to daily rainfall peaks or periods of prolonged rainfall in the timeframe analyzed

New landslide conceptual model of La Herradura coastal section

The field survey developed in the present investigation has corroborated that the evolution of the eastern slope of the Cerro Gordo promontory was undoubtedly influenced by the outcropping Dark Schists Fm. as previous investigations had highlighted. Notwithstanding, the new geological-geomorphological map (Fig.  2 ) and cross-sections (Fig.  3 a, b) have yielded dissimilar results compared to earlier works, which were based on the geological map of Avidad et al. ( 1973 ). This map was created prior to significant advancements in tectonics during the 1980s (a period when, for instance, the conceptual understanding of thrust systems underwent significant development) and suggested that the promontory has a core of dark schists covered by the marble unit. Our investigation conducted at a scale of 1:20,000 (utilizing high-resolution aerial images, LiDAR DEM, and an extensive field survey supported by UAV technology) unveiled the occurrence of a NW-direct thrust dipping to the SE. This geological structure is parallel to the eastern slope of Cerro Gordo promontory and, therefore, would influence widely the slope processes here. These novel insights and new geomorphological evidence allowed us to redefine the geological and geomorphological conception of the study area and subsequently to revise the local landslide conceptualization made by Notti et al. ( 2015 ), Mateos et al. ( 2017 ), and Chacón et al. ( 2019 ).

The new landslide conceptual model of Cerro Gordo and Punta de la Mona promontories is shown in Fig.  10 . In both cases, the general attitude of the strata dips to the S-SE. The contact between the geological formations would be an Alpine thrust (Fig.  10 a) although this is not evident in Punta de la Mona promontory. According to this, the contact here is represented by a vertical fault in Fig.  10 b.

figure 10

Conceptual landscape model. 3D illustrations show the morphodynamic style that characterizes the promontories of Cerro Gordo a and Punta de la Mona b . In addition to the large landslides present, the process of lateral spreading and block sliding is highlighted. For some features (such as marble blocks or detachment areas) vertical scale is exaggerated more or less two times for a better view of the single block

In general, the geological structure of the bedrock favoured the primary development of landslides on the eastern hillsides, creating an apparent suitable smooth area for building. The landslides are really complex and involve three main clear movements: (i) a deepest movement characterized by rock slump kinematics at a 40 m depth in Cerro Gordo promontory (Mateos et al. 2017 ) and 20 to 25 m depth in Punta la Mona Promontory (Notti et al. 2015 ); (ii) superficial movements composed of a slow-moving earthflow in combination with lateral spreads and sliding of large marble blocks, and (iii) local toppling and rockfall processes on scarps and detached marble blocks, confined in areas proximal to escarpments and cliffs. Another potential main movement is related to the escarpments recognized at the top of Cerro Gordo promontory and a hypothetic shear zone in depth (Fig.  10 a). The scarps would be formed by a gravitational process, and it is related to one of the most persistent joint family with a NE-SW orientation (Mateos et al. 2017 ). This joint family is also correlated with the NNE-SSW extensional tectonic structures that characterize the Alpujarride complex (Azañón et al. 1997 ; Simancas 2018 ). The presence of enormous, detached marble blocks and the mentioned escarpment suggests that the entire eastern slope of Cerro Gordo promontory is affected by a complex and old gravitational movement. The morphological features resulting from this gravitational process appear to have been largely obliterated by watershed erosion, which implies that the development of such phenomena has been notably slow and over a long period of time.

Internal discontinuity planes of the marble bedrock manifested as bedding, tectonic foliation, and joint families, constrained the mobilized blocks. Meanwhile, the presence of schist interbedded into the Marble Fm. would act as a sliding layer for marble blocks, leading to their eventual mingling with the underlying weathered and slid-down Dark Schists mass. In addition, the Dark Schists Fm. is of low permeability and concentrated groundwater on its top, favouring landslide movements. This agrees with the InSAR monitoring that reordered higher ground movement velocities during wet periods (Notti et al. 2015 ; Mateos et al. 2017 ; Chacón et al. 2019 ).

Landslides affected both sides of the Punta de la Mona promontory (Fig.  10 b) according to Notti et al. ( 2015 ) and Barra et al. ( 2022 ), showing detached large marble boulders. Again, the landslides that are highlighted in the literature are only the active bodies. From the morphological evidence, the eastern side, where Marina del Este is located, was completely involved in a very extensive mass movement, with only the central portion currently active, above which the Marina del Este resort was built. From the bathymetric trend, it is visible that the seabed slopes more gently than on the western side of the promontory, most likely due to the transport of sediments from the landslide deposit to the seabed close to the shoreline. As a further indication of the above, the − 50 m isobath also has an extension in the area below the slope (Fig.  6 b). The western side is also entirely affected by gravitational processes, which activity was recently displayed by Barra et al. ( 2022 ) and mapped in our research for the first time.

As can be seen, the evolution of both promontories is very similar, with equal landslide kinematics and quite similar sliding surface depths. Surface movements, detected by InSAR, are nothing more than reactivations of previous landslide deposits, caused by a combination of exceptional rainfall events and rapid and massive urban development. Another important common feature is the presence of marble blocks of varying size (from metric to decametric scale) that lie along the slope or on the coastline (Figs. 4  and  6 ). Due to the development of major discontinuities (Fig.  7 c) within the marbles, with an approximately NNE-SSW trend, the detachment of blocks, even large ones, occurs through a process similar to lateral spreading. The weathering of the schists and the landslide deposits means that the block can slide, thus highlighting the evolution from lateral spreading to block sliding (Fig.  10 ). Examples of this type of kinematics have already been studied on Mediterranean coasts made of sedimentary rocks (Mantovani et al. 2013 ; Devoto et al. 2021 ). In the La Herradura coastal section, we present one of the few comprehensible examples of an Alpine metamorphic setting, highlighted just by few contributions until now in other areas (Poisel et al. 2009 ).

Marine erosive action may have played a key role in the main landslide activities. In fact, the highest concentration of landslide phenomena is found on the eastern slopes, where the prevailing waves and storm waves with the highest frequency are active (Fig.  1 ).

In relation to the existing mapping, that is the BD-MOVES catalogue of the Geological and Mining Institute of Spain (CN IGME 2016 ), the total landslide areas in the La Herradura coastal stretch would be 2 × 10 5 m 2 . According to our surveys and to the new landslide mapping, the landslide area would amount to about 1 × 10 6 m 2 , five times more extension than the previously mapped landslides (Fig.  11 ). It is important to clarify that not all the areas affected by landslides are presently exhibiting clear signs of instability. Nevertheless, it should be noted that landslide deposits typically lack consolidation, and as such, an area currently deemed stable, such as those discernible by InSAR, may undergo reactivation in the future. This is particularly true during periods of exceptional rainfall events or earthquakes, which are common in the region. These extraordinary events could also alter the linear constant trend of the active landslides (Fig.  9 ) and trigger catastrophic accelerations of these movements as it has already occurred in the extraordinary wet period between 2009 and 2010 (Notti et al. 2015 ; Chacón et al 2019 ).

figure 11

Comparison of landslide boundaries defined by this study (red lines) and previous works (blue lines Notti et al. 2015 ; orange lines Mateos et al. 2017 ; green lines Chacón et al. 2019 ) in both Cerro Gordo ( a ) and Punta de la Mona ( b ) promontories. It is clearly visible how the use of different techniques leads to a much more defined picture of an area, mapping not only the active landslide portions but also those that could reactivate

Classical methods offer fresh insights in areas extensively explored by advanced technologies

Calaiza landslide is one of the most studied landslides in Spain because of the countless significant damages to the overlying buildings and the forced evacuation of 42 dwellings (Chacón et al. 2019 ). This led to analyzing the activity, speed, and extent of landslide displacement with all the latest techniques, particularly remotely and from the geotechnical point of view. By concentrating only on the analysis of the abovementioned things and not carrying out a detailed geological and geomorphological survey at the scale of the entire slope, the basis on which all the following analyses can then be carried out has been lacking. This is also partly demonstrated by the Las Palomas landslide. It is noteworthy that photointerpretation of historical images allowed the novel identification of the latter landslide, which with a short runout, a large part of the landslide body is mainly lying on the slope. This observation can be attributed to the fact that all investigations thus far have been limited to the damaged area of Cármenes del Mar resort, without any broader research being undertaken in the region. Furthermore, in the historical images, it can be recognized that the lower part of the landslide shows two large marble blocks affected by an internal process of lateral spread and block sliding provoked by the coastal erosion (zoom of the aerial image in Fig.  3 b). Nowadays, this zone is covered by urban areas which prevents us from observing these features. These features suggest an active movement in the date when those photographs were taken. Currently, the landslide seems to be inactive, which is also indicated by the calibrated EGMS InSAR data (Fig.  8 ), as movement has not been detected in that area up to now. However, this should be treated with caution because the InSAR data cover a limited range of time and ground movements (few millimetres), and the technique could be blind for movements with N-S orientations. Another feature worth mentioning in the area of the Las Palomas landslide is the perturbation of the drainage network caused by this landslide. The path of the main impluvium of the slope is deflected showing a sharp bend. In the longitudinal profile of the ravine, it is clear how there is an anomaly, in part certainly due to the anthropic component, but with a change in convexity downstream of the anomaly (Fig.  12 ). This could indicate a diversion of the ravine due to lithological contrast between schist and marbles, due to the existence of a slipped marble block, or the recent advance of the landslide body, in a centennial temporal scale. The impluvium, in the section previous to the anomaly, engraved the main rock slump landslide deposit by crossing it and is diverted due to the subsequent reactivation of the deposit and lithological change of the marble block.

figure 12

Longitudinal profile of a ravine perturbated by Las Palomas landslide. The red arrow shows a relief anomaly (sharp slope) defined by a profile-changing curvature. Other relief anomalies are related to anthropic works. In the aerial image, the diverted path of the impluvium is clearly visible (red circle)

Thanks to the geological and geomorphological field survey, the use of multi-temporal aerial images and InSAR data, it was possible to give a complete evolutionary picture of the La Herradura coastal section. In Table  1 , the present work is compared with the previous scientific papers, which describe researches in this coastal section apply advanced technologies, through the methods used, work products, and results obtained. Notti et al. ( 2015 ) have analyzed landslides in Punta de la Mona promontory using Permanent Scatterers (PS)InSAR technique, mapping all the active processes and made a building damages assessment; furthermore, they compared recent photos with historical pre-urbanization images but focalizing only on the active portion of Marina del Este landslide. Mateos et al. ( 2017 ) have examined the Calaiza landslide on Cerro Gordo promontory combining PSInSAR and UAV photogrammetry; they mapped the active landslide deposits and smaller, shallower marble blocks, studied the recent reactivations of the landslide body in relation to exceptional rainfall events, and compiled a building damages inventory. Chacón et al. ( 2019 ) focused on the Calaiza landslide, meticulously describing all the interventions carried out to stabilize the slope over the years and compiling an inventory of damages. Barra et al. ( 2022 ) chose the two promontories as study areas, starting from the PSInSAR data and providing a map of damages for the Calaiza, Punta de la Mona, and Marina del Este landslides while always considering recent activity. The application of a large variety of remote sensing techniques but without the knowledge provided by a more extensive survey (Notti et al. 2015 ; Mateos et al. 2017 ; Chacón et al. 2019 ; Barra et al. 2022 ) allowed to describe in detail only 40% of the existing landslides in La Herradura coastal section (Fig.  11 ). All the landslides previously reported correspond to active instabilities, which occurrence is also evidenced by damages observed in urban areas. However, the geological and geomorphological mapping at the 1:20,000-scale conducted in our work has enabled the identification of additional landslide bodies in the same area. It is crucial to consider the occurrence of a priori inactive landslides in land management as they have the potential to be reactivated by human activities (e.g., excavations, new constructions, drainage network modifications). Consequently, their reactivation could significantly impact urban areas and infrastructures. In addition, the meticulous field surveying has led to the recognition of novel instability processes, including the lateral-spreading and sliding of marble blocks. The presence of these boulders has been previously documented by Notti et al. ( 2015 ) and Mateos et al. ( 2017 ), but these researches do not link these blocks with the landslides’ dynamics. Two escarpments have been also discovered in Cerro Gordo promontory through fieldwork, suggesting the development of a major potential instability that affects the entire slope.

New technologies supporting classical methods and their synergies

In the previous section, the advantages of carrying out geomorphological and geological surveys as a complement to remote sensing surveys have been outlined. However, it is also important to underline the importance of remote sensing techniques to support classical methods. For example, site accessibility, logistical problems, and the presence of the sea are some of the aspects that make classical field surveys difficult on rocky coasts, as in the case study described. For this reason, remote surveys are widely used in these environments, both for their convenience and their lower cost. Furthermore, they have, for example, taken advantage of UAVs and Land Surface Quantitative (LSQ) analysis based on high-resolution DEMs, in particular when studying gravitational processes (Devoto et al. 2020 ; Troiani et al. 2020 ; Piacentini et al. 2021 ). In our case, the coastal stretch of La Herradura revealed the important role of UAVs in the exploration of cliff areas, as well as in providing additional perspectives on the ground. In addition, with the help of high-resolution DEM, we were able to discern geological and geomorphological features and understand the landscape from different points of view. Thanks to InSAR techniques, researchers in previous works were able to identify active zones where imperceptible movements developed. In this sense, active landslides can be remotely analyzed, monitored, and mapped using InSAR data and nowadays, more easily through web-based digital platforms that provide already processed data (Galve et al. 2017 ; EGMS 2023 ). By exploiting the InSAR data from the EGMS Explorer (Fig.  8 ), it can be seen that ground displacement is registered in the most important landslides described in this work: Calaiza, Playa, Punta de la Mona, and Marina del Este. Notice that the detection sensitivity varies between the ascending and descending orbit data, and displacement is not equally captured in both geometries for all landslides. In this regard, the availability of InSAR data in the two geometries of the EGMS was truly valuable, preventing the oversight of active landslides that could be undetected using a single geometry. However, as demonstrated by this study, all the aforementioned techniques are complementary. Each one provides information at varying spatial and temporal scales with precisions that collectively enable a more certain diagnosis of slope conditions.

In conclusion, the present work shows the immense value of traditional geomorphological and geological survey approaches that complement novel techniques and facilitate multidisciplinary investigations, as well as the great advances made in mapping techniques assisted by remote sensing technologies. The accessibility of InSAR data through EGMS has provided widespread access to this valuable information. However, it is essential to emphasize that detailed geological and geomorphological investigations combining fieldwork and remote sensors are crucial to avoid misinterpretations. These investigations play a critical role in understanding ground motions detected by InSAR techniques and developing accurate conceptual models of slope instabilities. Furthermore, geological maps typically prioritize tectonics and stratigraphy and may not specifically address slope instability investigations. Therefore, it is necessary to conduct a thorough review of previous geological data at the local scale to check them as the first step to gain a comprehensive understanding of slope processes and enable effective risk assessment. By incorporating these investigations, we can ensure a more robust and reliable analysis of slope instabilities.

A new comprehensive conceptual model has been developed for the La Herradura coastal section, which serves as an example case in the landslide paragraph of the Product User Manual of the EGMS. The model incorporates instability mechanisms and their extension in relation to the lithology and structure of the bedrock. The conceptual model identifies three main types of instabilities: (i) a deep rock slump movement occurring along a failure surface at a depth of 20–40 m, (ii) surface movements resulting from slow-moving earth flows, lateral spreads, and sliding of large marble blocks resembling ploughing boulders found in periglacial environments, and (iii) local toppling and rockfall processes involving scarps and detached marble blocks. Furthermore, a potential major movement affecting the entire slope has also been deducted. All these landslides are associated with the tectonic contact between Triassic marbles and Permian-Triassic schists. Intensive schist weathering has resulted in a surface deposit with a more plastic behaviour, facilitating the sliding of marble blocks. These blocks were detached following the natural bedding, tectonic foliation, and fissures of the marble, which derived from an intense tectonic deformation.

The development of a new comprehensive model was made possible through an in-depth geological and geomorphological investigation supported by fieldwork, historical and recent aerial photography (1956–2022), a LiDAR 1-m resolution DEM, and advanced technologies such as unmanned aerial vehicle (UAV) imagery and InSAR. The geological map was meticulously revised with the study objective in mind, and the geomorphological mapping focused specifically on slope processes. These techniques have led to the discovery of previously unknown landslides along the La Herradura coast, which were not detected using remote sensing alone over the past two decades. Remote sensing methods only identified approximately 40% of the existing landslides, whereas their actual extent is five times greater than previously known. The detailed geological and geomorphological survey also revealed the presence of apparently inactive landslides, some of which have been urbanized. The occurrence of these inactive or quiescent landslides must be considered in land management due to the potential for reactivation resulting from anthropic activities or extreme rainfalls.

In summary, we illustrate the importance of thorough geological and geomorphological studies focused on understanding the context of landslides in ensuring an accurate interpretation of remotely sensed information. This work serves as a call to action for geologists and geomorphologists to meticulously review existing mapping and update it as needed, thereby enhancing the reliability of terrain information and instilling greater confidence in the interpretation of remotely sensed data. It also highlights the growing reliance on InSAR and other advanced technologies, which may lead to a focus solely on areas in motion during data acquisition, potentially underestimating the true extent of landslides as exemplified in the described case study. In this regard, the incorporation of qualitative geological and geomorphological information can prevent misinterpretations, ensuring the effectiveness of landslide risk assessment.

Data availability

The data employed in this work are available on request from the corresponding authors.

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Acknowledgements

We would like to thank Inmobiliaria de La Cuesta for providing a UAV video of a part of Punta de La Mona’s rocky coastline. Consejería de Sostenibilidad , Medio Ambiente y Economía Azul ( Junta de Andalucía ) authorized the research within the Special Area of Conservation: Natural Site of Maro-Cerro Gordo Cliffs (ES6170002) and Cliff and Seabed of Punta de la Mona cape (ES6140016).

Funding for open access publishing: Universidad de Granada/CBUA. Funding for open access publishing: Universidad de Granada/CBUA. The research was supported by the following funds: the “Ramón y Cajal” Programme (RYC-2017-23335) of the Spanish Ministry of Science; the project “MORPHOMED” (PID2019-107138RB-I00) funded by MCIN/SRA (State Research Agency/ https://doi.org/10.13039/501100011033 ); and FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades/Projects (A-RNM-508-UGR20 and P18-RT-3632). The work of DB and DA-J is funded by Plan Andaluz de Investigación , Desarrollo e Innovación 2020 ( Junta de Andalucía ), and by the programme Garantía Juvenil of the Spanish Government, respectively.

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Torre, D., Galve, J.P., Reyes-Carmona, C. et al. Geomorphological assessment as basic complement of InSAR analysis for landslide processes understanding. Landslides (2024). https://doi.org/10.1007/s10346-024-02216-w

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