Coastal paleokarst landforms: A morphometric approach via UAV for coastal management (Algarve, Portugal case study)

Ocean and Coastal Management 167 (2019) 245–261

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Coastal paleokarst landforms: A morphometric approach via UAV for coastal management (Algarve, Portugal case study)

T

Sónia Oliveira∗, Delminda Moura, Tomasz Boski, João Horta Centre for Marine and Environmental Research (CIMA), University of the Algarve, Faro, Portugal

A R T I C LE I N FO

A B S T R A C T

Keywords: Rocky coasts Morphometry Karst landscapes Coastal management Unmanned aerial vehicle (UAV)

Karst landscapes display remarkable morphological diversity and raise challenging management questions. Understanding karst processes is particularly relevant to the management of densely populated rocky coastlines, since sea-level rise influences the erosion rate of potentially hazardous landforms (e.g. sinkholes). Appropriate management strategies are needed to mitigate against property loss and economic impacts on actively eroding karst. Coastal management in these areas should be based on accurate and reproducible measurements of karst features to better understand and predict their behaviour. Due to their inherent instability and frequent inaccessibility, detailed morphometric studies of exposed coastal sinkholes are limited. We demonstrate the utility of using an unmanned aerial vehicle (UAV) to provide rapid and accurate analysis of spatial data on a large density of sinkholes that would otherwise be inaccessible. UAV data were post-processed and analysed using geographical information (GIS) tools to characterize both individual sinkholes and their spatial distribution patterns. Our study was carried out on the rocky coast of the central Algarve (southern Portugal). As stated in many other previous works, sinkholes spatial distribution is mainly controlled by the network of fractures in the host rock that was also observed in our study area. In addition, the geometric properties and their differences between the studied sites are controlled by weathering processes and synoptic conditions. This research emphasises the geomorphic hazard associated with karst landscapes and reinforces the need to include knowledge about these landforms and their vulnerability to sea-level rise in integrated coastal management plans.

1. Introduction Karst landscapes result from chemical weathering of carbonate rocks and occupy approximately 20% of the planet's dry, ice-free land (Ford and Williams, 2007). Some landforms of similar genesis in noncarbonated rocks (siliceous sandstones, gypsum, salt or ultramafic rocks) may be also considered like karst (Ford and Williams, 2007; Galve et al., 2009). Porous biocalcarenites containing matrix- or cement-supported shells are highly permeable, enabling rapid percolation of pluvial water and dissolution, which leads to secondary porosity (Kaufmann, 2014; Kirstein et al., 2016). Structural discontinuities, such as bedding planes and fractures, combine with secondary porosity to determine where water flows through the rock and where dissolution occurs. Structure is thus a first order factor controlling the karstic network (Dubois et al., 2014). Rainfall penetrates the subsurface through a system of karstic depressions, among them the sinkholes. The latter exhibit various

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morphologies, including conical, cylindrical and bowl-shaped. The sinkholes size and quantity control the intensity of underground dissolution. Consequently, karst landscapes house a complex subterranean system of water circulation that can pose serious problems for land use and management (e.g., Cooper et al., 2011; Galve et al., 2009). Moreover, the aggressiveness of sea waters, especially when mixed with continental waters, impose great hazards that are created by the breakdown or gradual subsidence of dissolution voids of different sizes (Miao et al., 2013). These surface movements may cause severe property damage, adversely affect water quality in underlying aquifers and dramatically re-shape coastlines (Shaban and Darwich, 2011; Rahimi and Alexander, 2013; Galve et al., 2015; Taheri et al., 2015). The related processes of cliff recession and coastline retreat have an important bearing on the well-being and protection of life and property. This issue is particularly important in the context of rising sea level, which is expected to accelerate throughout the coming century (Antunes, 2011). Sinkholes can be classified based on genesis into dissolution

Corresponding author. Present/permanent address: Centre for Marine and Environmental Research (CIMA), Gab. 1.69, University of the Algarve, Faro, Portugal. E-mail address: [email protected] (S. Oliveira).

https://doi.org/10.1016/j.ocecoaman.2018.10.025 Received 23 May 2018; Received in revised form 2 October 2018; Accepted 28 October 2018 0964-5691/ © 2018 Elsevier Ltd. All rights reserved.

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Pleistocene fine-to-coarse sands, which, when exposed to wave action or heavy rain, are rapidly eroded. Furthermore, in terms of mechanical erosion, the presence of a siliciclastic cover and the associated vegetation is very important in terms of water aggressiveness and, consequently, of karsification. Sinkholes may be either partially or completely sediment-filled, the latter subsequently evolving into fully sealed cryptokarst (Gutiérrez et al., 2013; Silva et al., 2017). The loss of territory strengthened and accelerated by the density of sinkhole near the coastline, has socio-economic repercussions. The Algarve region is heavily impacted by anthropogenic pressures in the littoral zone caused by intensive construction along the coastline. Houses are frequently built on the detrital fill of sinkholes, making them extremely vulnerable to geomorphological and seismic hazards (Forth et al., 1999; Albardeiro and Moura, 2010). Karst is responsible for the higher values of retreat by mass lost mainly due to topples (Marques, 2008). Coastal erosion rates along the entire extension of the Algarve rock littoral zone increased substantially, doubling in several sectors over the last 4 decades (Teixeira, 2004, 2006; Marques, 2008; Nunes et al., 2009, 2011; Bezerra et al., 2011). The beach survival in this rocky coast depends on the artificial nourishment which is being done more frequently and in an increasing number of beaches. These nourishments that cost per sector nearly 3 million euros being a waste of financial and an exploitation of natural resources due to management inconsistencies. The Algarve experiences a Mediterranean-type climate, with warm dry summers. Mean monthly temperature varies between 14 °C in December and 26 °C in July–August (Miranda et al., 2002; Oliveira, 2013). Between 1986 and 2013, monthly potential evapotranspiration varied from 1.7 mm to 7.5 mm in July and January, respectively (Oliveira, 2013). Mean annual precipitation is as low as 500 mm/yr along the Algarve coast, 88% of which falls between October and April. Precipitation is a key factor for mass movements. Marques (1997) found a positive correlation between the mean annual precipitation and the number of mass movements. Between the 1st and the 6th of March 2018 in the Algarve region, 18 mass movements were recorded after intense and prolonged rains related to the Emma storm (field observations). The rainfall for this period could not be obtained but the average amount of rainfall in March, was 272 mm, about 4 times the monthly average value and was the 2nd rainier March since 1931 (IPMA, 2018). This coast experiences a semi-diurnal mesotidal regime ranging from 1.3 m during neap tides to 3.5 m during spring tides (Portuguese Hydrographic Institute data, http://www.hidrografico.pt/). The waves approach the southern coast of the Algarve from two dominant sectors: WSW and ESE, 71% and 23% of observations along the year respectively (Costa et al., 2001). The joint distribution of the waves' period and height are strongly influenced by the direction. The wave mean period is of 4.7 s ( ± 1.1 s) but values higher than 7 s are also observed for 4% of the year, associated to the WSW sector when the generation field is in the North Atlantic. In contrast, lower periods (< 11 s) are related to the waves incoming from the ESE sector, reflecting the influence of minor fetches from the Gibraltar Strait (Costa et al., 2001). The wave's height monthly averages are between 0.6 m and 1.5 m, with the most frequent significant heights below 1 m. Values > 3 m primarily occur in the winter months, associated to storm events from SW. These storm events are usually caused by W/SW situations of atmospheric circulation. In the central Algarve, these atmospheric conditions trigger storms from SW with offshore significant peak heights between 3 and 5 m (Pires, 1989; Costa et al., 2001). Four sites along the Algarve rocky coast, between 8° 40′17″W and 8° 17′ 52″W, were chosen as case studies for the morphometric description of individual sinkholes and spatial characteristics via UAV data acquisition (Fig. 1). The sites were: (i) Ponta da Piedade (near Lagos); (ii) Caniço (near Alvor); (iii) Albandeira (near Lagoa) and (iv) Castelo (near Albufeira). All sites were chosen taking in account the same geological substrate (Lagos-Portimão Formation; mainly carbonates) (Pais et al.,

sinkholes and collapse sinkholes. Solution sinkholes form by slow lowering of the surface due to the rock dissolution and display typical conical shape formed by water percolation (Waltham and Fookes, 2003), whereas collapse sinkholes result from failure of the host ground or overlying material into the karst cavity. Various subtypes of these two categories have been defined depending on the nature of the overlying material and mechanism of its downward migration (e.g., Waltham and Fookes, 2003; Gutiérrez et al., 2008; Kaufmann, 2014). To understand karst processes, mitigate geological hazards, design engineering solutions and predict coastal evolution in karst areas, it is critical to rely on sinkhole susceptibility and hazard maps developed from accurate and complete sinkhole inventories (Al-Kouri et al., 2013; Gutiérrez et al., 2014). Most methods used in the past for mapping sinkholes, such as visual interpretation of low-resolution topographic maps and aerial photographs, were extremely time-consuming and labour-intensive. Performing an accurate and complete field survey of individual sinkholes and sinkhole groups by a single worker was often dangerous and imprecise (Doctor and Young, 2013). Remote sensing technologies like airborne Light Detection and Ranging (LiDAR), Interferometric Synthetic Aperture Radar (InSAR), and other geophysical techniques in use during the last 40 years, have generated large volumes of high-accuracy and high-density topographical measurements, making regular update of sinkhole inventories and automated mapping achievable (Berardino et al., 2002; Lindsay and Creed, 2006; Filin et al., 2011; Kaufmann, 2014; Wu et al., 2016). Some of these technologies produce high-resolution digital elevation data, allowing an entirely new level of detailed delineation and permitting analyses of small-scale geomorphic features (Gutiérrez et al., 2011; Huang et al., 2014; Wu et al., 2014; Galve et al., 2015). These technologies have high costs that are not viable for many countries and cannot be achieved in a regular basis. Recently, the use of Unmanned Automated Vehicles (UAVs or ‘drones’) have further improved the accuracy and speed of land survey, enabling larger areas to be studied at low cost (e.g., Silva et al., 2017). Data acquired via UAV are ideal for morphometric analysis, the measurement and mathematical assessment of the configuration of the Earth surface's shape and dimensions (Bates and Jackson, 1987; Denizman, 2003). Several studies of karst regions have been carried out using morphometric techniques, proving their effectiveness in placing karst landforms in a more quantitative framework (Williams, 1972; Day, 1983; Troester et al., 1984; Magdalena and Alexander, 1995; Al-Kouri et al., 2013; Gutiérrez et al., 2014). But, to our knowledge the data acquired via UAV has not been used for karst landforms morphometric analyses. Thus, this study aims to demonstrate the application of UAV-based survey for rapid, cheap and accurate morphometric description of karst sinkholes. Coupling data from the UAV survey of the Algarve coast with GIS post-processing tools and field observations, we analysed the geometric properties and spatial distribution of sinkhole areas to identify patterns and drivers critical for hazard mapping and coastal planning. Thus, improving the characterization of the area and demonstrating the benefits of the approach for karst landform studies. 2. Study area The central Algarve rocky coast (Fig. 1) is characterised by strong karstification of Miocene carbonate rocks (Pais et al., 2012), leading to a very indented coastline and a complex coastal morphology with sinkholes, caves, arches and stacks. Within these Miocene strata, karst evolution is lithologically controlled by the occurrence of silty layers of low permeability (Moura et al., 2011a, b). Rainwater that infiltrates into the biocalcarenite emerges as freshwater springs along the contact with these silty layers. This may happen at the cliff face, or in the subtidal and intertidal zones, depending on the occurrence of the relatively impermeable silty layers leading to the development of a dense network of sinkholes chemokarst, produced by freshwater springs. The paleo-relief of the karstified carbonate substrate is filled by Plio246

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Fig. 1. Location of the study sites on the Algarve central rocky coast (Southern Portugal) and orthophotos acquired via UAV.

3. Methods

georeference the UAV imagery, 15 to 40 Ground Control Points (GCP) were positioned at each site depending on the land surface area. Each GCP consisted of an orange plate about 27 cm wide, marked with a black cross to distinguish it from the natural colour of the substrate. The GCPs were positioned in locations visible from the air and their ETRS 89 coordinates determined using an RTK GPS, allowing the UAV photographs to be georeferenced with an accuracy of ± 29 mm. UAV survey was carried out from a height of 30 m along transects spaced 30 m apart and running parallel to the shore. Images were acquired every 2 seconds. The number of photos taken within the transect grid ranged from 221 at the Castelo site to 694 at the Ponta da Piedade site (Table 1).

3.1. Field work

3.2. Data processing

2012) and provided conditions for the safe deployment of the UAV. The Ponta da Piedade site has part of its coastline orientated NW-SE (134°) and the other orientated N-S (6°), forming a pointed headland from which the site's name derives. The Caniço and Castelo sites are orientated NW-SE (115°), while Albandeira is orientated W-E (90°). The different geographical position and orientation of the studied sites leads to diverse synoptic conditions and exposure to waves (Oliveira, 2013). Albandeira is more protected from the Atlantic air masses, while Ponta da Piedade is the most exposed.

Coastal cliff outcrops were surveyed at each of the study sites to compare lithological and tectonic features with the sinkholes' morphology and spatial distribution. Furthermore, to compare our sinkhole measurements via UAV and its accuracy, 6 sinkholes (4 in Castelo and 2 in Albandeira) were surveyed in the field using the RTK GPS. Only these 6 were measured due to the inaccessibility and danger level associated to the other sinkholes in the study sites. Several points as close as possible to the sinkhole opening were measured to obtain the perimeter. When possible, the perimeter of the bottom of the sinkhole was also measured to calculate the maximum depth (Dmax). The remote survey was performed with a UAV Phantom 2 unit (DJI) equipped with a 14-megapixel digital camera, which enabled us to observe areas that could not be surveyed from the ground. To

Agisoft PhotoScan was used to produce a georeferenced 3D point Table 1 3D model inputs and error. Study Site

No. Photos

Surveyed Area (m2)

No. GCP

Alignment Error (m)

No. Dense Cloud Points

Ponta da Piedade Caniço Albandeira Castelo

694

179190

33

0.039

26629849

240 604 221

45992 91775 135290

10 21 8

0.034 0.038 0.005

6263767 16499223 20075651

GCP Ground Control Point. 247

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Coastal erosion has broken down numerous former sinkholes along this stretch of the coast, giving the coastline a crenulated outline. Vestiges of these formerly incomplete sinkholes, along with hanging valleys and karstic conduits, are frequently observed in this coastal sector (Fig. 2D and 2E). The original stratification of the calcarenites is frequently masked by dissolution, making it difficult to measure faults in the field.

cloud by overlapping aerial image data (Siebert and Teizer, 2014). This software has advanced UAV applications and generates Digital Elevation Models (DEM) and orthophotomaps that are more accurate than traditional cartographic materials. Prior elimination of distorted or blurred data produced a dense data cloud, ranging from 6263767 to 26629849 points, that was used to create the DEM (for more details, see Table 1). At the more extensive sites (i.e. Ponta da Piedade and Albandeira), the dense clouds were split into smaller, more manageable segments, which were re-merged after elevation modelling. The DEM and orthomosaic were subsequently processed using ARC Info 10.3.8 GIS to analyse sinkhole areas. Morphometric analysis was carried out on the area between the coastline and the inland limit acquired by UAV. The automation via filling methods and contour analyses could not be used in these study areas due to the amount of depressions associated to water flow and the eroded state of the sinkhole walls, leading to an overestimation of sinkholes in number. Therefore, the 3D model, orthophoto and DEM were used collectively to define the topographic contours of the sinkholes. Sinkhole delineation had to be performed manually due to irregular erosion patterns, overhanging vegetation and shadowing. Despite morphological indications that some sinkholes were the product of coalescence of several smaller sinkholes into a larger one, the applied criterion of unit delineation was a continuous external perimeter. Once sinkholes were identified, their area, 2D perimeter (2DP), 3D perimeter (3DP, i.e. the surface length of the sinkhole's outline), minor and major axes (Am and AM), maximum elevation (Zmax), maximum depth (Dmax) were measured according to the method proposed by Basso et al. (2013). This method captures the most significant parameters for sinkhole description (see Appendix A, Table A1). The resulting values were used to determine: i) the elongation ratio (ER), i.e. the ratio between major and minor axes; ii) the regularity index (RI), i.e. the ratio between the best fit 2D ellipse perimeter and the measured 3D perimeter of the sinkhole; iii) the sinkhole's maximum volume via GIS polygon filling below Zmax (Vmax); iv) distance from the centre of the sinkhole to the cliff edge (DC); and v) the direction of the major axis (AMA). Therefore, the directional analysis can be obtained by computing the orientation of the major axis that is completely contained within the polygon. Data are all available in Appendix A, Table A1. In addition, we calculated the pitting index (Rp) as proposed by Williams (1969, 1971), as well as advantaged GIS-based distribution pattern analysis based on Nearest Neighbour Ratio (Rnn) according to Bauer (2015). The Nearest Neighbour Index is expressed as the ratio of the Observed Mean Distance (La) to the Expected Mean Distance (Le). If the index is less than 1, the pattern exhibits clustering; if the index is greater than 1, the trend is toward dispersion. To further analyse morphometric parameters for each sector, a statistical analysis was carried out using Excel XLSTAT version 19.5.47892 (see Appendix B, Table B1). We followed normality testing of distribution, based on a Shapiro-Wilk test in order to establish the correct correlation analysis.

4.1.2. Caniço At the Caniço site (Fig. 1), the cliffs rise up to 30 m asl, exposing an interbedded sequence of biocalcarenite and siltite layers up to 2 m thick. This stratification is often obliterated by faults, mass movements and gullies. As at Ponta da Piedade, paleovalleys infilled with PlioPleistocene red sands form part of the plateau (Fig. 3A). Gullies that intersect the cliff face often continue for tens of meters inland, deeply dissecting the littoral plateau. The sector is strongly affected by fracturing. This structural control is expressed in littoral landforms such as arches, caves, headlands and embayments, and particularly in the nearshore, stacks and sinkholes (Fig. 3B). Faulting has caused vertical displacement of strata by up to 1 m (Fig. 3C). The direction of the main fault system is NNW-SSE. 4.1.3. Albandeira The Albandeira cliffs (Fig. 1) expose well-stratified siltite and biocalcarenite. The layers' thickness ranges between 0.70 m and 2.00 m and is uninterrupted by faults. The coastline is characterised by merging cliffs reaching up to 23 m asl, occasionally enclosing small pocket beaches and marine caves at the base (Fig. 4A). The most conspicuous karstic features are the spectacularly large sinkholes, which link the littoral plateau to the sea water below (Fig. 4B). In contrast to Ponta da Piedade and Caniço, the Albandeira coast follows a relatively straight line, lacking arches and stacks. 4.1.4. Castelo Like Albandeira, the Castelo site (Fig. 1) is characterised by large sinkholes without sedimentary infill. Here the sinkholes extend from the surface down to beach level (Fig. 4C and 4D.). The Castelo site shows a less complex karstic morphology than Ponta da Piedade and Caniço and, in contrast with the merging cliffs at the Albandeira site, the 18 m high cliffs are connected to shore platforms and sandy pocket beaches. Piles of rocks at the cliff bases provided evidence of erosion and cliff retreat. The preferential direction of the fault system here is NW-SE. 4.2. Individual sinkhole characterization 4.2.1. Area (A) From the total of 135 sinkholes analysed, 86 were at Ponta da Piedade, 15 at Caniço, 8 at Albandeira and 26 at the Castelo site (for detailed sinkhole parameters see Appendix A, Table A1). The area of the sinkhole aperture varied between 666 m2 and 0.40 m2. The largest sinkholes in terms of area were observed at Ponta da Piedade (7 sinkholes above 200 m2, maximum 666 m2), while the maximum was 321 m2 at Albandeira, 219 m2 at Castelo and 215 m2 at Caniço (Fig. 5). Total sinkhole area is also highest at Ponta da Piedade (5355 m2), corresponding to 7% of the studied area, followed by Castelo and Caniço with 1287 m2 (6%) and 948 m2 (4%) (Fig. 5 and Table 2). The Albandeira site has the lowest sinkhole area at 574 m2 (1%), most of which relates to one particularly large sinkhole (321 m2). The remaining sinkholes are much smaller (0.40–25 m2).

4. Results 4.1. Morphotectonics: field observations 4.1.1. Ponta da Piedade The Ponta da Piedade littoral plateau (Fig. 1) is formed by Miocene calcarenites whose elevation varies between 30 m and 45 m above sea level (asl). The plateau is heavily dissected by paleovalleys filled with the Plio-Pleistocene siliciclastic and are deeply incised by modern gullies produced by hydric erosion, that extend up to 100 m inland sediments, which highlight the importance of siliciclastic cover in the fluvial evolution of the relief (Fig. 2A). Coastal cliffs expose a Miocene sequence of alternating siltitic and bioclastic calcarenite facies. The sinkholes are empty near the cliff edges but are usually filled with PlioPleistocene red sand in more landward positions (Fig. 2B and 2C).

4.2.2. Elongation ratio (ER) and azimuth of the major axis (AMA) Although all the studied sinkholes have a broadly elliptical shape, analysis of the elongation ratio by Basso et al. (2013) allowed us to distinguish three subtypes: circular, elliptical and elongated. All three subtypes are present at Ponta da Piedade and Castelo, while Caniço and Albandeira only have circular and elliptical sinkholes (Table 2 and 248

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Fig. 2. Pictures of the Ponta da Piedade sector. A - host carbonate rock and ravine, B - free and isolated sinkholes, C - deposits of karst infill dismantled by water erosion, D - hanging valley, E - karst conduits demonstrating paleo sub-surface circulation.

Fig. 3. Pictures of the Caniço sector. A - coastal plateau, B - stacks, C – two fault systems.

indicate regularly shaped sinkhole walls. The Caniço sector has the least regular sinkhole margins with an average index of 0.59, followed by Ponta da Piedade with an average of 0.60, Castelo with 0.78 and Albandeira with the most regular sinkhole margins at 0.92 (Table 2).

Appendix A, Table A1). It is assumed that the elongation of the sinkholes follows structural and tectonic constraints (Bauer, 2015). The major axis of sinkhole apertures can reveal structural controls on their evolution by aligning with the trend of the fractures network. Sinkholes major axes at Ponta da Piedade are oriented between 0.6° and 180° with the largest (length of 37.93 m) oriented 94°. At Caniço, the major axis is oriented between 50° and 169.4°, with the largest (length of 16.8 m) oriented 151°. At Albandeira, sinkhole elongation varies between 2.2° and 142.6° (largest major axis with 26 m). At Castelo, orientation ranges from 0° to 143°, with the largest major axis (21.67 m) oriented 1° (Table 2 and Fig. 6). On a site-by-site basis (Table 2 and Fig. 6), Ponta da Piedade and Caniço have predominantly NW–SE oriented sinkhole elongations, with an average of 93° and 112° respectively. Albandeira and Castelo reveal a NE–SW trend (average of 59° and 54°, respectively).

4.2.4. Maximum depth (Dmax) and volume (Vmax) Average Dmax of the sinkholes is highest at Ponta da Piedade (11.76 m) and Caniço (12.97 m), and lower at Albandeira (5.61 m) and Castelo (5.47 m). This relates with the elevation of the littoral plateau which decreases eastward. Similarly, Ponta da Piedade site has the highest total sinkhole volume of 78751 m3, followed by Caniço (7311.2 m3), Castelo (6698.2 m3) and Albandeira (3966.9 m3) (Table 2). 4.3. Spatial properties 4.3.1. Nearest neighbour ratio (Rnn) Spatial distribution parameters of the four studied sites are summarized in Table 2. The dispersion pattern was obtained by a nearest neighbour analysis in GIS and varies from 0 (clustering) to 1 (dispersion). The statistical significance of the Rnn is exhibited by the z-score

4.2.3. Regularity index (RI) Erosion assessment on the surface in each sector was based on the regularity index (RI), translates the karren development and therefore the intensity of the superficial active weathering. Values close to 1 249

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Fig. 4. Pictures of the Albandeira and Castelo sector. A - Albandeira, merging cliffs of 23 m, B - Albandeira sinkholes above view, left with a major axes of 18 m and the right sinkhole with 27 m, C Castelo, sinkhole (major axes of 6 m) and block deposits resulting from cliff erosion, D - Castelo, morphology with cliffs of 18 m, sinkholes and beach after artificial renourishment.

evolution governed by dissolution and collapse, discussed further below.

and p-score. A very high or low z-score indicates that the observed pattern does not represent a spatial pattern of the null hypothesis (Ebdon, 1985). The calculated z-scores and p-values exhibit a minor likelihood that this clustered pattern could be the result of random chance (Table 2). Thus, the spatial statistic based on Rnn for our study sites shows great variations, from 0.5 (Ponta da Piedade) to 1.2 (Caniço), indicating a wide range of spatial distribution patterns of sinkholes per study site. Sinkholes are spatially clustered in Ponta da Piedade and Castelo (with a z-score of −8.15 and −4.32, respectively), while at Caniço and Albandeira the distribution is random (with a given z-score of 1.19 and −1.39, respectively) (Table 2).

4.3.4. Frequencies An analysis of the frequency of sinkhole properties (Dmax, Zmax and distance from the coastline, DC) were carried out at each of the sites. Most sinkholes are in the 0.4–25 m2 area interval, diminishing in frequency with increasing area. Sinkhole area classes at Ponta da Piedade, Caniço and Castelo are well distributed, while Albandeira lacks sinkholes of 50–100 m2 (Fig. 8A). This means that most sites are skewed towards small-sized sinkholes, except Caniço which has more medium-sized sinkholes. Albandeira is interesting because it has the same number of small-medium, medium-large and large sinkholes, but no medium-sized ones. Ponta da Piedade exhibits well-distributed depth classes (Dmax) and is the only site with sinkholes > 30 m depth. Caniço has sinkholes between 5 m and 30 m deep, Albandeira has no sinkholes between 5 and 10 m and Castelo only have sinkholes < 20 m deep (Fig. 8B). Maximum elevation (Zmax) intervals show that the greater values (30–40 and 40–50 m) are only represented at Ponta da Piedade. Sinkholes at Castelo occur in the 10–20 m and 1–10 m intervals, the latter interval being the most frequent (Fig. 8C). Sinkhole frequency relative to distance from the coastline (DC) shows that Ponta da Piedade has sinkholes at all distance intervals, while Caniço and Castelo have sinkholes within the first 20 m and primarily within the first 10 m (Fig. 8D). Albandeira only has sinkholes < 20 m and > 30 m from the coastline. Ponta da Piedade and Caniço exhibit a progressive decrease in the number of sinkholes inland, whereas there is an increase at Albandeira between 30 and 60 m and at Castelo between 5 and 10 m (Fig. 8D).

4.3.2. Pitting index We applied the pitting index of Bauer (2015), a simple measurement of surficial karstification that relates karstified area to total area. The closer the value of the pitting index to 1, the greater the influence of karstification in shaping the landscape (Bauer, 2015). Albandeira has the highest pitting index (71.38) and Ponta da Piedade the lowest, (14.09), while Caniço and Castelo show intermediate values (26.10 and 17.30 respectively) (Table 2). Thus, the joint analysis of Pitting Index and IR should reflect the degree of influence of active karsification on the surface (development of karren) that contributes to the irregularity of the opening of the sinkhole. The higher values of the Pitting index, that indicate greater influence of karsification in modelling the landscape, should correspond to the higher values of IR that indicate greater regularity of the apertures. 4.3.3. Correlations Pairwise correlations showed positive relationships between 3DP, Vmax, Dmax and Area (Spearman's rs = 0.70–1.0). Correlations between Dmax and Vmax varied between 0.78 and 0.95. Dmax and 2DP were also positively correlated (rs = 0.64–0.95), with stronger correlations at Albandeira compared to the other sectors (Fig. 7; Appendix B, Tables B3–B6). A strong negative correlation between the regularity index (RI) and Dmax (rs = −0.675) suggests that deeper sinkholes are less regular. Correlation analysis reveals differences in sinkhole

4.3.5. Validation of UAV data The field measurements, to validate the parameters obtained via UAV, were only possible in 6 sinkholes (4.4% of the studied sinkholes) because the remaining sinkholes (95.6%) were not accessible. The comparison between the UAV perimeter and the field measured perimeter shows a difference per sinkhole from 1% to 13% (Table 3). The 250

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Fig. 5. Sinkhole surface area details at each study site. A - Ponta da Piedade; B - Caniço; C - Albandeira, D - Castelo.

perimeter measured in the field is always greater than via UAV, except for the Castelo N_ID 5 that has 0.64 m less. This is due to the unstable state of the sinkholes that makes it impossible to reach their opening edge making the measured limit of the sinkhole a bit larger. In cases that it was possible to approach, the difference decreased to less than 1 m. This shows that the use of the UAV is not only accurate, but often the only possibility to study these landforms. Meanwhile, the direct measurement of the Dmax was only possible in the Castelo site and varied between 1 and 2% from the UAV measurements. These differences were of 0.02 and 0.05 m and are due to the GPS error.

5. Discussion The morphology of the calcareous rocky coasts depends on the intensity and duration of weathering and the structure and fracturing pattern of the rocks (Andriani et al., 2005; Thornton and Stephenson, 2006; Llopis, 2006; Naylor and Stephenson, 2010; Moura et al., 2011a). Coastal karst systems, like many environments around the world, are under increasing anthropogenic pressure, particularly those found along coastlines (Beynen and Townsend, 2005). Sinkholes generate geomorphological risk and pose land-use challenges. They are highly variable in terms of morphology and evolution, leading various authors to suggest different classification schemes (e.g. Waltham and Fookes, 2003; Gutiérrez et al., 2008; Kaufmann, 2014; 251

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are currently filled with detrital sediments (Fig. 2A and C). The four studied sites have significant variations in morphometric parameters and the spatial distribution of karst features. As these variations occur within a single morphoclimatic zone, there will be other key factors, besides the climate, such as lithology, structures, water availability, synoptic conditions, permeability of the rock and overburden thickness (Battiau-Queney, 1996; Denizman, 2003; Waltham and Fookes, 2003). Considering the number of sinkholes and elevation classes (Zmax) (Fig. 8. C), we can identify five elevation intervals that we hypothesise are due to the conjugation of differentiated tectonic movements and different stages of karst genesis. However, since that karst stages are impossible to date, we can only try to constrain, in general, the age of the oldest and of the most recent phase of karst development. The higher elevations, between 30-40 m and 40–50 m, are represented by sinkholes at Ponta da Piedade. These sinkholes tend to be very large, some with an area > 200 m2. Some of them are filled by Plio-Pleistocene sediment and others are partially destroyed. These features were also observed at the Caniço site where the individual characteristics of the sinkholes are similar to the ones at the Ponta da Piedade (Table 2), although occurring at lower heights. This may be interpreted as the oldest phase of karst genesis inherited from the latemost Miocene and Zanclean and represents ongoing weathering for long periods. In addition, allochtonous sediment, probably of different ages (post Miocene) is trapped inside the very frequent karstic conduits at the Ponta da Piedade and Caniço sites. These conduits occur at about 20 m above the mean sea level due to the continental uplift during the Quaternary, estimated between 0.11 and 0.15 mm/year, and were intercepted by the cliffs retreat (Fig. 2 C and E; Figueiredo et al., 2013; Moura et al., 2017). At the south Iberian Margin, several active structures were identified as a result of the convergence between the Eurasia and Nubian plates (Figueiredo et al., 2018 and cited literature). This neotectonic activity can be recognized through the displacement of some of the Late Pleistocene Marine terraces along the Algarve coast (Neves et al., 2015). The pitting index, that is an indirect density measurement, showed that Ponta da Piedade and Castelo are the sites, among those studied, where karst processes are most strongly expressed. Moreover, the Regularity Index, is lower at these two sites (Ponta da Piedade and Castelo) translating a superficial active weathering leading to the karren development, thus showing a good correlation to the Pitting index. Castelo has two generations of sinkholes: an older one open from the littoral plateau, the other, more recent, opened from a raised platform. In addition, the Ponta da Piedade sector has the highest percentage area of sinkholes (7%) and the greatest morphological diversity (Figs. 5 and 8A). The high values of Vmax (total Vmax 78751 m3) and area result from the coalescence of multiple sinkholes subjected to intense erosion. The west coast of the Algarve receives higher rainfall values than sites further east and, being more directly exposed to moist Atlantic air masses and storm fronts, leading to diverse synoptic conditions, Ponta da Piedade suffers the most intense dissolution and sinkhole erosion (Oliveira, 2013). Furthermore, in Ponta da Piedade, the 3DP is greater than all the other sites (50 m) pointing to intense superficial karstic processes. Sinkholes at the Albandeira and Castelo sites exist in the 1–10 m, 10–20 m and 20–30 m intervals of maximum elevation. The 1–10 m interval is the most frequent for Albandeira (more than 60%) and the 10–20 m interval (more than 70%) for the Castelo site. Marine terraces in both sites occur at 16–20 m elevation that, at the Southern coast of the Algarve, may be associated with high sea levels during Marine Isotopic Stage 5, sub-stages 5e, 5c and 5a (Figueiredo et al., 2013; Moura et al., 2017) or older ones. Hence, the more recent phase of karst formation occurred during the Late Quaternary. During the Last Interglacial highstand of the mean sea level, the discharge of freshwater from aquifers led to intense karstification in the intertidal zone, with the formation of a dense network of sinkholes (chemokarst), similar to

Table 2 Average sinkhole parameters for each study site. Study Site

Total Study Area No. Sinkholes Sinkhole Area Average 2DP Average 3DP Average AM Average Am Average AMA Mean Dmax Total Vmax Sinkhole Area per Study Area Pitting Index (Rp) Elongation Ratio (ER) Sinkholes with Elongated Shape Regularity Index (RI) Observed Mean Distance (La) Expected Mean distance (Le) Rnn Z-score p-value Distribution Pattern

Ponta da Piedade

Caniço

Albandeira

Castelo

m2 # m2 m m m m ° m m3 %

75468 86 5355 24.37 50.22 8.33 6.27 35.89 11.76 78751 7

24732 15 948 27.60 46.72 9.42 7.64 43.04 12.97 7311 4

40941 8 574 24.66 28.87 8.88 6.25 24.58 5.61 3967 1

22258 26 1287 24.68 33.84 8.56 6.08 22.99 5.47 6698 6

– AM/Am

14.09 1.35

26.10 1.29

71.38 1.32

17.30 1.41

–

Yes

No

No

Yes

2DP/3DP

0.60

0.59

0.92

0.78

m

11.96

22.55

12.04

11.88

m

22.13

19.43

16.16

21.3

– – – –

0.5 −8.15 0 Clustered

1.2 1.19 0.24 Random

0.8 −1.38 0.17 Random

0.6 −4.31 0 Clustered

2DP 2D perimeter, 3DP 3D perimeter, AM major axis length, Am minor axis length, AMA azimuth of major axis, Dmax maximum depth, Vmax maximum volume and Rnn nearest neighbour ratio. See text for more detail.

Kaufmann and Romanov, 2016). Following Waltham and Fookes (2003), all the studied sites belong to the mature karst kIII class, typical of temperate regions where sinkhole formation and evolution are dominated by suffusion mechanisms. With exception to the Albandeira site, where the bare rock is exposed, all the other sites belong to the buried or dropout sinkhole categories, the latter dominating at Ponta da Piedade. Several sinkholes close to the coastline are exhumed and undergoing erosion by marine action, having lost their sedimentary fill (Fig. 2B and Fig. 4C and D). Analysis of the morphometric data through UAV survey and GIS post-processing tools enabled a rapid and accurate characterization and comparison of sinkholes. This is visible in the comparison between the UAV and the field measurements with the average difference of 6% for the perimeter and 1% for the Dmax (Table 3). Therefore, the UAV approach can be a useful tool, both for assessing coastal karst hazards and studying coastal evolution. It should be kept in mind that dense vegetation and sediment cover may have obscured some smaller sinkholes, leading to a slight underestimation of their abundance. We could not apply automated filling methods or contour analyses at these study sites due to the large number of depressions created by hydric erosion, which would have otherwise caused an overestimation of sinkhole abundance (Bauer, 2015; Day, 1983). We therefore identified sinkholes individually, considering field observation, DTM and orthophoto interpretation (see section 3, Methods) to obtain accurate results. This study provides a quantitative expression of both the individual morphometry and spatial distribution of sinkholes. Due to its simplicity and low operating costs, the adopted methodology may be easily applied in other karst areas. Karst can survive several phases of repeated fossilization and exhumation, exhibiting a complicated poly-phase inheritance evident in many paleokarst features (Klimchouk, 2000). This is the case at Ponta da Piedade and Caniço sites, where both the dense network of paleokarst conduits and sinkholes at higher elevations of the littoral plateau 252

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Fig. 6. Sinkhole major axis orientations and lengths per study site. Left - Ponta da Piedade and Albandeira, Right - Caniço and Castelo.

and Castelo have 92 and 93% of their sinkholes within the first 10 m from the coastline (Fig. 8D.). The most inland sinkhole is at Ponta da Piedade, 55 m from the shore. Only 15 sinkholes (11%) have been mapped at distances close to or greater than 30 m, two at Albandeira and 13 at Ponta da Piedade. This supports our interpretation about

the intertidal sinkholes observed today Figs. 2 C and 3 B (Moura et al., 2011a; Gomes et al., 2017). The number of sinkholes in relation to their distance from the coastline (DC) shows that most (62%) are situated within the first 10 m, with a progressive decrease inland. It is noteworthy that both Caniço

Fig. 7. Correlation between geometric variables 3DP-Area, Vmax-Area, Dmax-Area, Dmax-Vmax, 2DP-Dmax and RI-Dmax at each of the study sites. 253

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Fig. 8. Frequencies of sinkhole properties at each site: A - area, B - Dmax, C – Zmax, D - DC.

between the centres of the sinkholes per study site is quite regular (La = 12 m), except for Caniço site (La = 23m), reinforcing the fracture network's role in determining sinkhole distribution (Table 2). Structural discontinuities, especially when these are pervasive within the rock mass, are of critical importance in sinkhole distribution and development. Dissolution is facilitated by discontinuities such as bedding planes, joints and faults, which collectively control sinkhole geometry and spatial distribution (e.g. Filin et al., 2011; Silva et al., 2017). These authors identified the fractures network as the main control of the karst development and spatial pattern and thus the geometry of the sinkholes should reflect the pattern of fractures. However, the ductile and fragile deformations of the sedimentary infill created by karst evolution makes it difficult to make a structural interpretation of the study area (Dias and Cabral, 2002). The Lagos-Portimão Formation is intercepted by a dense system of NW-SE faults that are in turn intersected by NNE-SSW transfer faults (Terrinha et al., 2013). At Ponta da Piedade and Caniço, the sinkholes' major axis follows a preferential NW-SE orientation, meaning that their elliptical shape is controlled by the main fault direction (Fig. 6 and Table 2). In addition, the Caniço sector exhibits a major axis multimodal orientation distribution, which Bauer (2015) relates to the merging of shallow features and sinkholes to create larger sinkholes. We also observed this process occurring at our study sites. Sinkholes at

chemokarst formation due to coastal aquifer discharge during sea-level high-stands. Other environmental parameters contribute to the genesis and evolution of karstic landscapes. Silva et al. (2017) correlated the main phases of karst development in NE Brazil with wet periods during the Quaternary. Several other authors have emphasised the importance of wet climatic conditions on karst evolution (Gutiérrez et al., 2014; Taheri et al., 2015; Linares et al., 2017). No relationship was found between sinkhole aperture area and distance to the coastline (top of the cliff), whereas maximum depth and the regularity index were strongly negatively correlated at Ponta da Piedade, Albandeira and Castelo (rs = −0.66, −0.69 and −0.57, respectively). This indicates that sinkhole depth and wall irregularity increase as the sinkholes age or with greater exposure to dissolution processes. A strong positive correlation also exists between perimeter (2DP) and maximum depth (Appendix B, Tables B3–B6 and Fig. 7), especially at Albandeira (rs = 0.95). The positive correlation in all study sites between these variables is assumed to reflect a dissolution origin (Bauer, 2015). Although dissolution is always the primary mechanism of karstification, at our study sites, rock structure, proximity to the sea and the collapse of walls and smaller sinkholes are now the determining geomorphic mechanisms. This holds true at Ponta da Piedade (rs = 0.87), Caniço (0.78) and Castelo (0.64). The measured distance

Table 3 Sinkhole parameters determined via UAV and field measurements and their difference. Obs

107 109 111 112 128 133

Site

Albandeira Albandeira Castelo Castelo Castelo Castelo

Perimeter 2DP (m)

Dmax (m)

UAV

Field

Difference

%

UAV

Field

Difference

%

68,61 47,86 14,59 43,62 35,58 19,07

71,40 50,98 15,88 42,98 36,49 21,57

−2,79 −3,12 −1,29 0,64 −0,91 −2,50

−4 −7 −9 1 −3 −13

21,70 13,66 1,55 2,88 4,04 2,25

Not accessible Not accessible 1,58 2,85 4,09 2,22

– – −0,02 0,04 −0,05 0,04

– – −1 −1 1 2

Obs observation, 2DP 2D perimeter, Dmax maximum depth. See text for more detail. 254

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of Algarve karst, emphasised the need to integrate sinkhole information into land-use planning as a key prerequisite for the successful development of karst terrains. Only now, almost two decades later, has a buffer of 500 m from the coastline been included in coastal zone planning. The Portuguese Government's Coastal Management Plans, expected to be concluded by 2020, are being evaluated and altered to include karst landscapes, mean sea-level rise and climatic changes. The present work, through a detailed examination of the morphometric features of sinkholes in the Algarve, is intended to contribute toward a deeper understanding of the karst features as a fundamental basis for facing the difficult problems related to sinkhole occurrence and evolution.

Albandeira and Castelo have a NE-SW elongated major axis that may be related to the NNE-SSW transfer faults, though further investigation must be carried out to verify this interpretation. Basso et al. (2013), in a study in Apulia (Italy), showed that fractures control sinkhole formation and their later evolution into compound landforms through the coalescence of individual sinkholes. The Albandeira sector presents randomly distributed individual sinkholes under incipient border erosion, with smooth margins (RI = 0.92) and pronounced conical shapes. The absence of faults and reduced karstification has preserved the original sedimentary stratification at Albandeira (Table 2 and Fig. 7). The fracture system here has less expression than at the other sites and consequently sinkhole density is lower. Analyses of the main trends in sinkhole distribution compared to coastline direction indicated that sinkholes play a major role in the formation of bays and inlets. Thus, karst landform evolution appears to be, along with wave erosion and gravity-related processes (rock falls and slope failures), one of the principal factors governing the present shape of the coastline. At our study sites, it is evident that fan-like decompression fractures lead to an increase in ground and superficial water flow efficiency and cause rotational failures, which contribute to sinkhole collapse and accelerated coastline retreat (Fig. 3A.). The number of mass movements and the mean precipitation along the year were positively correlated in our study area between 1947 and 1991 (Marques, 1997). Several authors that also studied coastal karst environments showed and assumed similar interactions and consequences that we observed in our study sites such as the influence of tectonics discontinuities, lithology and mass movements (e.g. Williams, 1972; Day, 1978; Ford and Williams, 2007; Basso et al., 2013), despite their study area being larger than ours and with smaller sinkhole density. Worldwide, coastal landforms have varying thresholds of geomorphic stability depending on intrinsic variables of the coastal system itself (e.g. geomorphology, sedimentary input, vegetation and sediment transport) and external forces (e.g. extreme events, sea-level rise and climate changes). The Algarve's karst landscape is largely responsible for the extremely crenulated physiography of the coastline, with its successive headlands and bay-beaches. The landscape pattern imposes a strong morphological control on longshore sediment transport (Oliveira et al., 2017). In these areas, with high density of sinkholes, mass movements will displace higher volumes and have greater displacement widths than those observed in sectors less affected by the sinkholes, as it has already happened in the past (Marques, 1997). This, along with the acceleration of the rate of sea-level rise, will create major problems for future land-use, cultural heritage and sustainability in coastal settlements. The current average of the mean sea level rise is 3.1 mm/yr, and the expected acceleration and predictive coastal planning for the 21st century should be performed to sea level rise to 2 m (Williams, 2013 and all the cited literature). Moreover, the increasing dwell times have direct impact both in artificial structures and cliffs. It is unequivocal that the mean sea level rise is accelerating (Antunes, 2011). A key point for coastal management is to identify how and where to adapt to changes forced by sea level rise (Williams, 2013). Studying sinkhole development and evolution in coastal areas is of paramount importance because of the potentially adverse effects that poor understanding could have on society, given humanity's heavy reliance on coastlines and coastal resources (Basso et al., 2013). The lack of scientific data on karst landforms in various coastal sectors means that this hazard is poorly understood and therefore poorly managed. Management errors in these unstable landscapes could lead to loss of property, cultural heritage, ecosystem services and human life. The geomorphological hazard of karstic landscapes translates directly into impacts on people and resources along densely populated coastlines. This impact reinforces the need to incorporate rigorous studies of these landforms and their response to sea-level rise into integrated coastal management plans. Forth et al. (1999), in their study

6. Conclusions This study applied a combination of UAV-acquired measurements, GIS analyses and field measurements to examine the morphometric and spatial distribution of sinkholes in the Algarve region. The joint use of UAV and GIS methodologies proved to be a useful tool, not only for the rapid analysis of spatial data from a large population of sinkholes, but also for providing an objective approach with consistent measurement and calculation processes. Furthermore, in many areas, such as ours, it is the only possibility for detail morphometric analysis, given the inaccessibility and hazard of the study areas. It provides collection of rapid, reliable and replicable data and answers the specific needs of coastal management. The direct field measurements, when possible, showed the accuracy of analysing morphometric data through UAV survey and GIS post-processing tools, enabling a rapid and accurate characterization and comparison of sinkholes. Using this methodology, we were able to conclude that: i) Considering individual and spatial sinkhole characteristics, our study sites are of two karstic systems: Ponta da Piedade and Caniço, karst inherited from the late Miocene to Zanclean; Albandeira and Castelo correspond to a karst formation occurred during the late Quaternary. ii) Our study sites exhibited great morphometric diversity in karst landforms due to local variations in tectonics, exposure to weathering processes and synoptic conditions (directly influencing the dissolution of carbonates); iii) Topple, due to mass loss from carbonate dissolution, is a major mechanism of cliff instability; iv) Karst landscapes control the current coastline's physiography, such that the acceleration of the rate of sea-level rise will alter karst processes, creating significant problems for coastal settlements, cultural heritage and environmental values in the region; v) Studying sinkhole development and evolution on densely populated coastlines is of fundamental importance, both to increase our knowledge of unstable coastal environments and to better incorporate these dynamic landscapes into integrated coastal management plans. Acknowledgements The first author was financially supported by the Erasmus Mundus programme MACOMA SGA 2015-1626/001-001-EMJD and UNESCO UNITWIN Wicop as part of their research activities at CEIMAR (Campus of International Excellence of the Sea), University of Cadiz. João Horta was supported by the Portuguese Foundation for Science and Technology (FCT, grant number SFRH/BD/104337/2014). This work was also supported by FCT grant UID/MAR/00350/2013, attributed to the Centre for Marine and Environmental Research (CIMA), University of the Algarve. The authors thank Ana Nascimento from the masters SIMCO who helped with the fieldwork and Simon Connor for the english proofreading. 255

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Appendix A Table A1 Morphometric parameters of the studied sinkholes Obs

Study Site

Area 2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69

Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta Ponta

da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da da

Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade Piedade

2DP

3DP

Zmax

Am

AM

AMA

Vmax 3

RI

Elongation Ratio

(m )

(m)

(m)

(m)

(m)

(m)

(°)

(m )

RI = 3DP/2DP

ER = AM/Am

4.60 16.52 5.17 18.56 2.30 4.07 37.82 11.33 23.18 169.67 21.05 197.73 21.38 25.80 17.77 82.64 38.03 84.20 49.21 25.26 84.00 38.55 69.48 36.11 22.20 279.12 16.62 70.94 241.74 266.10 103.81 32.42 666.23 54.68 25.45 36.53 26.30 55.24 5.59 36.96 15.12 85.77 7.88 70.75 273.15 494.88 124.36 74.79 47.18 9.37 16.73 4.84 3.61 14.65 37.25 58.83 70.44 19.98 14.88 11.32 6.13 1.74 3.10 11.63 7.68 6.79 0.64 464.51 12.07

7.94 15.00 8.44 15.79 5.56 7.77 22.60 12.41 17.55 52.36 17.12 58.31 17.65 19.51 15.43 40.52 22.75 35.86 26.03 19.77 34.05 23.51 30.87 22.29 17.18 62.39 15.91 30.32 66.27 66.78 40.64 21.23 105.57 28.27 18.55 23.03 19.07 28.24 9.71 23.27 14.48 34.98 10.38 31.25 60.79 85.86 41.15 32.74 25.50 11.12 16.60 8.15 7.39 15.54 22.88 30.74 33.58 16.42 14.73 12.49 10.97 4.86 6.38 12.44 10.41 10.40 2.99 104.52 15.20

13.12 23.59 9.82 17.99 7.25 11.85 61.79 19.19 53.68 82.88 52.48 162.53 51.35 22.99 20.35 52.24 46.15 59.28 34.57 29.87 46.49 32.43 81.18 42.84 40.18 104.97 25.68 38.91 131.77 108.62 59.25 33.57 187.49 58.07 21.97 27.95 37.72 47.42 13.84 27.75 23.48 44.07 19.99 90.25 194.84 144.87 82.80 220.50 49.08 14.64 20.16 10.92 11.64 43.77 28.08 41.58 50.68 31.50 51.73 29.10 19.89 8.35 7.96 17.76 15.16 16.27 3.14 253.20 16.04

30.57 29.28 16.84 14.18 16.21 7.79 34.64 18.04 11.58 22.16 22.29 29.35 32.92 22.30 37.39 29.12 35.78 35.70 33.24 24.63 25.56 21.75 30.51 31.64 28.25 24.75 29.23 21.45 29.92 35.66 39.20 21.98 35.09 27.79 16.24 17.56 25.98 19.96 4.81 29.39 14.28 17.27 40.46 39.85 45.76 48.91 41.30 15.96 25.15 37.00 12.80 20.06 35.68 36.77 37.63 20.69 19.31 24.60 18.65 15.66 19.38 33.02 36.26 34.44 6.85 7.77 2.83 19.10 7.93

2.17 4.14 2.12 4.82 1.68 1.95 6.31 3.40 5.34 13.27 4.85 13.17 4.55 5.50 4.66 8.36 6.44 9.87 7.09 4.17 9.41 5.74 8.93 5.82 5.21 18.40 3.96 9.19 15.19 19.08 11.54 6.22 23.49 7.26 5.34 5.33 5.25 6.81 1.81 5.04 4.30 9.25 2.92 8.58 16.95 23.68 12.05 8.17 7.67 3.45 3.30 2.17 1.99 4.56 6.03 7.35 6.72 4.46 3.22 3.13 2.13 1.44 1.84 3.51 3.03 2.36 0.85 26.40 3.18

2.65 5.31 2.89 4.93 1.71 2.90 7.54 4.25 5.42 18.95 5.33 20.22 6.13 6.33 4.91 13.27 7.50 11.77 9.03 7.73 11.51 8.61 9.99 7.30 5.41 19.20 5.79 9.78 24.77 20.42 12.08 6.96 37.93 9.89 5.65 8.50 6.76 10.57 3.83 8.76 4.36 12.12 3.41 10.08 19.67 28.37 12.79 11.98 8.02 3.53 6.47 2.84 2.50 4.65 8.26 10.73 13.04 5.72 5.39 4.59 4.67 1.56 2.12 4.29 3.28 3.79 1.01 30.92 5.63

18.40 82.60 160.60 111.80 3.60 38.20 98.10 5.10 120.10 44.10 15.40 38.30 38.80 160.20 8.20 171.60 121.00 164.40 170.70 108.90 137.30 143.90 170.30 82.80 54.20 40.60 127.90 155.60 57.40 179.60 142.30 55.80 93.80 49.80 143.40 86.90 61.10 176.20 138.70 5.00 95.40 50.40 7.10 173.40 124.70 57.40 161.50 150.10 136.80 128.40 129.10 69.40 130.00 165.20 117.20 97.30 42.20 164.50 169.40 169.00 153.90 91.30 27.60 139.20 44.00 0.70 99.60 97.40 23.50

12.31 82.97 6.95 41.41 2.90 10.09 448.67 31.77 182.93 2627.20 154.71 4214.87 177.15 94.06 50.33 707.97 428.42 1033.88 456.19 162.45 679.02 231.85 973.68 476.30 149.00 3614.48 101.39 527.19 4156.66 3508.64 662.15 176.19 16425.50 537.90 78.76 141.39 260.81 502.78 14.37 197.62 82.04 839.13 33.33 1357.25 5947.42 8999.15 1875.75 814.46 315.35 17.10 33.44 8.34 5.82 88.46 128.63 449.27 522.68 155.21 166.63 84.36 19.28 3.59 3.75 20.99 24.03 24.41 0.11 7462.92 11.21

0.60 0.64 0.86 0.88 0.77 0.66 0.37 0.65 0.33 0.63 0.33 0.36 0.34 0.85 0.76 0.78 0.49 0.60 0.75 0.66 0.73 0.73 0.38 0.52 0.43 0.59 0.62 0.78 0.50 0.61 0.69 0.63 0.56 0.49 0.84 0.82 0.51 0.60 0.70 0.84 0.62 0.79 0.52 0.35 0.31 0.59 0.50 0.15 0.52 0.76 0.82 0.75 0.64 0.36 0.81 0.74 0.66 0.52 0.28 0.43 0.55 0.58 0.80 0.70 0.69 0.64 0.95 0.41 0.95

1.22 1.28 1.36 1.02 1.01 1.49 1.19 1.25 1.01 1.43 1.10 1.54 1.35 1.15 1.05 1.59 1.17 1.19 1.27 1.85 1.22 1.50 1.12 1.25 1.04 1.04 1.46 1.06 1.63 1.07 1.05 1.12 1.61 1.36 1.06 1.59 1.29 1.55 2.12 1.74 1.01 1.31 1.17 1.18 1.16 1.20 1.06 1.47 1.05 1.02 1.96 1.31 1.26 1.02 1.37 1.46 1.94 1.28 1.67 1.47 2.19 1.09 1.16 1.22 1.08 1.61 1.18 1.17 1.77

Shape

Circular Sub-elliptical Sub-elliptical Circular Circular Sub-elliptical Circular Sub-elliptical Circular Sub-elliptical Circular Sub-elliptical Sub-elliptical Circular Circular Sub-elliptical Circular Circular Sub-elliptical Elongated Sub-elliptical Sub-elliptical Circular Sub-elliptical Circular Circular Sub-elliptical Circular Sub-elliptical Circular Circular Circular Sub-elliptical Sub-elliptical Circular Sub-elliptical Sub-elliptical Sub-elliptical Elongated Sub-elliptical Circular Sub-elliptical Circular Circular Circular Circular Circular Sub-elliptical Circular Circular Elongated Sub-elliptical Sub-elliptical Circular Sub-elliptical Sub-elliptical Elongated Sub-elliptical Sub-elliptical Sub-elliptical Elongated Circular Circular Sub-elliptical Circular Sub-elliptical Circular Circular Sub-elliptical

Dmax

DC

(m)

(m)

3.52 7.26 2.16 3.31 2.01 3.85 22.31 5.55 10.16 21.45 16.98 27.63 12.00 5.34 5.28 12.25 16.34 18.08 12.15 10.70 13.15 9.46 18.76 16.75 10.61 23.03 8.18 12.72 24.21 23.07 12.42 7.43 36.81 15.74 5.30 7.26 13.64 13.88 4.13 8.47 7.47 15.16 7.31 27.86 45.09 35.34 24.01 14.64 11.01 4.07 3.76 3.17 3.20 7.80 6.39 12.30 13.43 12.25 14.57 11.59 4.92 2.96 2.11 5.00 4.09 5.26 0.27 22.14 1.72

8.63 4.66 7.95 9.16 7.05 3.78 18.98 7.62 6.66 11.10 38.72 28.81 5.90 25.94 11.52 27.52 48.75 22.71 14.05 9.01 19.30 27.53 33.29 28.23 8.07 12.01 4.83 6.15 37.60 49.01 55.77 40.71 22.11 35.66 4.19 5.32 3.87 8.12 1.33 9.89 4.63 5.88 13.77 6.23 10.03 15.01 10.78 4.63 21.96 4.72 8.12 10.09 13.19 16.80 9.98 6.49 26.62 20.71 24.29 27.38 32.57 37.31 17.03 6.90 19.84 18.37 19.72 13.92 28.79

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Table A1 (continued) Obs

Study Site

Area 2

70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135

Ponta da Piedade Ponta da Piedade Ponta da Piedade Ponta da Piedade Ponta da Piedade Ponta da Piedade Ponta da Piedade Ponta da Piedade Ponta da Piedade Ponta da Piedade Ponta da Piedade Ponta da Piedade Ponta da Piedade Ponta da Piedade Ponta da Piedade Ponta da Piedade Ponta da Piedade Caniço Caniço Caniço Caniço Caniço Caniço Caniço Caniço Caniço Caniço Caniço Caniço Caniço Caniço Caniço Albandeira Albandeira Albandeira Albandeira Albandeira Albandeira Albandeira Albandeira Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo Castelo

2DP

3DP

Zmax

Am

AM

AMA

Vmax 3

RI

Elongation Ratio

(m )

(m)

(m)

(m)

(m)

(m)

(°)

(m )

RI = 3DP/2DP

ER = AM/Am

2.68 3.08 0.40 7.13 16.48 15.44 8.72 12.01 45.65 74.27 40.63 5.25 4.37 56.61 3.29 7.63 56.46 56.96 30.10 115.55 83.97 53.53 112.21 70.40 22.07 215.98 16.40 27.08 26.61 51.93 46.61 18.34 22.90 5.85 11.09 1.17 17.03 321.03 35.19 159.31 2.47 16.10 85.71 10.35 21.21 37.20 55.37 21.00 22.18 10.72 10.59 31.48 129.45 41.25 5.85 41.00 56.51 219.29 64.17 14.77 19.19 5.36 9.45 27.87 145.78 182.33

6.07 6.52 2.35 10.06 15.51 14.41 11.17 14.98 25.70 38.59 28.06 8.65 7.56 30.22 6.71 10.17 28.30 27.60 20.32 39.22 34.52 27.15 39.81 30.85 17.78 53.90 14.89 19.31 19.39 26.80 25.86 16.60 18.19 9.43 12.41 4.12 15.17 68.61 21.51 47.86 6.17 14.59 43.62 12.43 16.97 22.46 27.76 16.97 18.33 12.74 11.89 21.29 49.71 24.40 9.45 23.73 27.17 60.22 35.58 14.77 16.28 9.07 11.14 19.07 61.56 54.33

8.30 7.38 3.27 21.12 27.91 24.80 18.33 58.69 48.75 103.29 163.85 12.48 12.07 66.52 8.39 19.19 152.22 47.74 51.00 60.05 49.82 45.26 57.11 42.98 31.01 96.60 24.76 36.94 30.88 39.58 44.30 42.75 20.05 10.03 12.69 4.19 16.12 93.25 23.80 50.81 6.32 15.35 46.93 15.52 24.20 28.59 32.14 18.32 21.31 18.11 13.19 23.78 59.68 39.74 11.26 34.16 39.45 91.28 40.50 18.93 43.59 11.86 16.17 21.56 91.65 96.23

18.91 15.64 15.05 23.32 16.09 27.42 14.44 21.91 35.83 33.07 32.92 34.82 18.21 27.53 15.54 12.71 44.32 18.65 20.38 29.26 26.30 29.34 23.55 20.17 12.00 22.35 11.01 12.55 12.07 15.28 21.97 19.05 2.73 2.02 1.39 3.12 4.78 19.49 22.68 22.15 16.32 16.56 15.34 14.49 15.80 13.22 12.48 5.79 8.40 7.28 11.57 12.86 12.59 12.69 1.51 18.31 15.63 17.66 15.77 17.18 11.43 11.00 7.36 16.10 7.82 13.03

1.74 1.90 0.64 2.41 4.74 4.04 3.18 2.87 7.03 6.37 4.41 2.25 2.32 5.72 2.03 2.82 6.81 7.83 5.50 11.35 9.90 8.11 9.97 8.99 4.31 16.19 3.95 5.43 4.84 7.11 7.27 3.79 4.88 2.28 3.61 1.22 4.09 15.71 6.34 11.86 1.30 4.17 9.96 3.43 5.01 6.28 7.31 4.44 4.24 2.62 3.40 5.93 10.71 6.43 2.17 6.56 8.22 13.26 6.49 3.26 4.79 1.97 3.38 5.92 15.24 11.65

2.00 1.98 0.80 3.74 4.83 4.72 3.37 6.22 8.43 15.84 11.74 3.07 2.37 12.50 2.13 3.55 10.88 9.63 6.97 12.76 11.32 8.63 14.42 10.20 6.71 16.81 5.40 6.61 7.40 9.43 8.55 6.41 6.27 3.53 3.93 1.31 5.15 26.09 7.05 17.74 2.25 4.72 14.61 4.18 5.40 7.51 9.92 6.15 6.53 4.95 3.76 7.01 19.19 8.18 3.66 7.47 8.73 21.68 14.19 5.62 5.05 3.55 3.39 6.03 18.11 20.75

99.90 34.60 83.10 132.20 4.10 92.50 11.30 153.70 0.60 148.20 17.20 81.10 23.20 147.50 19.70 97.70 59.30 69.60 133.10 75.10 52.80 49.90 103.70 114.50 169.40 151.00 140.70 169.10 88.00 133.30 144.80 85.80 82.40 82.30 14.00 36.50 12.80 142.60 96.10 2.20 0.00 41.60 4.50 32.00 122.50 8.30 18.30 102.40 23.20 18.10 141.30 69.20 20.80 63.10 44.50 143.30 76.80 1.20 43.60 66.30 16.80 102.70 94.70 94.00 7.40 56.20

3.04 4.69 0.24 35.92 68.32 57.87 33.17 171.03 367.88 1172.30 494.31 10.95 12.90 795.45 4.90 34.83 1646.02 475.99 250.77 954.77 326.00 256.00 678.89 561.74 98.28 2582.18 77.46 178.28 89.57 342.31 316.72 122.24 35.51 6.22 7.02 0.21 25.63 3176.77 39.31 676.25 0.48 18.45 172.52 16.40 78.39 126.38 265.42 41.14 38.62 23.60 14.13 23.23 670.30 183.93 6.02 213.05 320.87 2048.47 161.24 29.08 66.17 3.99 10.43 46.09 731.39 1388.40

0.73 0.88 0.72 0.48 0.56 0.58 0.61 0.26 0.53 0.37 0.17 0.69 0.63 0.45 0.80 0.53 0.19 0.58 0.40 0.65 0.69 0.60 0.70 0.72 0.57 0.56 0.60 0.52 0.63 0.68 0.58 0.39 0.91 0.94 0.98 0.98 0.94 0.74 0.90 0.94 0.98 0.95 0.93 0.80 0.70 0.79 0.86 0.93 0.86 0.70 0.90 0.90 0.83 0.61 0.84 0.69 0.69 0.66 0.88 0.78 0.37 0.76 0.69 0.88 0.67 0.56

1.15 1.04 1.26 1.55 1.02 1.17 1.06 2.17 1.20 2.49 2.67 1.37 1.02 2.19 1.05 1.26 1.60 1.23 1.27 1.12 1.14 1.06 1.45 1.14 1.56 1.04 1.37 1.22 1.53 1.33 1.18 1.69 1.29 1.55 1.09 1.07 1.26 1.66 1.11 1.50 1.73 1.13 1.47 1.22 1.08 1.20 1.36 1.39 1.54 1.89 1.11 1.18 1.79 1.27 1.69 1.14 1.06 1.63 2.19 1.72 1.05 1.81 1.00 1.02 1.19 1.78

Shape

Circular Circular Sub-elliptical Sub-elliptical Circular Circular Circular Elongated Circular Elongated Elongated Sub-elliptical Circular Elongated Circular Sub-elliptical Sub-elliptical Sub-elliptical Sub-elliptical Circular Circular Circular Sub-elliptical Circular Sub-elliptical Circular Sub-elliptical Sub-elliptical Sub-elliptical Sub-elliptical Circular Sub-elliptical Sub-elliptical Sub-elliptical Circular Circular Sub-elliptical Sub-elliptical Circular Sub-elliptical Sub-elliptical Circular Sub-elliptical Sub-elliptical Circular Circular Sub-elliptical Sub-elliptical Sub-elliptical Elongated Circular Circular Sub-elliptical Sub-elliptical Sub-elliptical Circular Circular Sub-elliptical Elongated Sub-elliptical Circular Elongated Circular Circular Circular Sub-elliptical

Dmax

DC

(m)

(m)

2.35 2.55 1.05 7.46 5.81 5.56 5.96 20.49 15.03 18.76 15.37 2.68 3.70 21.87 2.20 6.41 41.46 14.57 14.42 16.10 12.19 13.67 15.57 16.36 6.80 23.62 7.40 10.41 5.20 11.66 13.75 12.80 1.78 1.64 0.92 0.27 1.91 21.70 3.03 13.66 0.37 1.55 2.88 2.83 6.37 7.94 9.56 4.75 3.71 4.69 2.01 2.49 13.23 7.11 1.49 10.78 9.94 16.53 4.04 3.60 4.21 2.60 3.01 2.25 2.65 11.58

1.40 0.83 1.40 8.99 2.41 9.71 1.46 2.27 38.18 39.93 42.45 12.70 3.84 12.34 4.13 2.53 3.90 5.10 4.76 13.12 5.23 5.33 7.03 6.05 3.13 8.12 3.39 4.38 4.60 7.12 6.62 2.45 3.64 3.44 3.78 19.82 33.55 36.75 9.07 8.04 6.20 8.20 8.90 1.52 3.12 6.36 8.71 5.23 7.04 8.22 11.74 8.88 14.82 4.86 3.57 4.02 5.54 12.02 3.09 8.37 5.22 8.70 2.68 3.77 4.88 5.08

Obs observation, 2DP 2D perimeter, 3DP 3D perimeter, Zmax maximum Z measured within the sinkhole, Am minor axis length, AM major axis length, AMA azimuth of major axis, Dmax maximum depth, Vmax maximum volume and Rnn nearest neighbour ratio.

Appendix B Table B1 257

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Summary statistics. Variable

Obs

Minimum

Maximum

Mean

Std. deviation

Area 2DP Zmax 3DP Am AM AMA Vmax RI ER Dmax DC

135 135 135 135 135 135 135 135 135 135 135 135

0.40 2.35 1.39 3.14 0.64 0.80 0.00 0.11 0.15 1.00 0.27 0.83

666.23 105.57 48.91 253.20 26.40 37.93 179.60 16425.50 0.98 2.67 45.09 55.77

60.46 24.80 21.22 45.41 6.38 8.53 85.68 716.50 0.65 1.36 10.31 12.97

97.21 18.72 10.48 44.05 4.69 6.38 54.84 1902.38 0.19 0.33 8.46 11.91

2DP 2D perimeter, 3DP 3D perimeter, Zmax maximum Z measured within the sinkhole, Am minor axis length, AM major axis length, AMA azimuth of major axis, RI regularity index and ER elongation ratio.

Table B2 Results of the Shapiro-Wilk normality test. Variable\Test

Shapiro-Wilk

Distribution

Area 2DP 3DP Zmax Am AM AMA Vmax RI ER Dmax DC

< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.024 < 0.0001 < 0.0001 < 0.0001

Does Does Does Does Does Does Does Does Does Does Does Does

not not not not not not not not not not not not

follow follow follow follow follow follow follow follow follow follow follow follow

a a a a a a a a a a a a

Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal

distribution distribution distribution distribution distribution distribution distribution distribution distribution distribution distribution distribution

2DP 2D perimeter, 3DP 3D perimeter, Zmax maximum Z measured within the sinkhole, Am minor axis length, AM major axis length, AMA azimuth of major axis, RI regularity index and ER elongation ratio.

Table B3 Proximity matrix observations at Ponta da Piedade (Spearman correlation coefficient). Variables

Area

2DP

Z_Max

3DP

Am

AM

AMA

Vmax

RI

ER

Dmax

DC

Area 2DP Zmax 3DP Am AM AMA Vmax RI ER Dmax DC

1 0.997 0.415 0.904 0.982 0.982 0.253 0.971 −0.311 0.139 0.871 0.372

1 0.408 0.908 0.973 0.989 0.256 0.970 −0.312 0.174 0.868 0.390

1 0.431 0.428 0.387 0.133 0.433 −0.322 −0.047 0.464 0.371

1 0.873 0.910 0.257 0.959 −0.650 0.193 0.956 0.378

1 0.936 0.243 0.947 −0.297 −0.004 0.844 0.363

1 0.262 0.964 −0.322 0.293 0.871 0.385

1 0.267 −0.104 0.115 0.239 0.125

1 −0.484 0.182 0.951 0.362

1 −0.095 −0.656 −0.204

1 0.198 0.138

1 0.336

1

2DP 2D perimeter, 3DP 3D perimeter, Zmax maximum Z measured within the sinkhole, Am minor axis length, AM major axis length, AMA azimuth of major axis, RI regularity index and ER elongation ratio.

Table B4 Proximity matrix observations at Caniço (Spearman correlation coefficient). Variables

Area

2DP

Zmax

3DP

Am

AM

AMA

Area 2DP Zmax 3DP Am AM AMA

1 0.993 0.761 0.850 0.989 0.975 −0.318

1 0.750 0.839 0.982 0.989 −0.336

1 0.839 0.789 0.686 −0.546

1 0.839 0.786 −0.332

1 0.957 −0.296

1 −0.311

1

Vmax

RI

ER

Dmax

DC

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Table B4 (continued) Variables

Area

2DP

Zmax

3DP

Am

AM

AMA

Vmax

RI

ER

Dmax

DC

Vmax RI ER Dmax DC

0.950 0.454 −0.711 0.789 0.861

0.936 0.489 −0.657 0.775 0.854

0.664 0.239 −0.664 0.668 0.654

0.821 0.064 −0.582 0.839 0.682

0.918 0.471 −0.761 0.779 0.868

0.925 0.539 −0.589 0.725 0.857

−0.200 −0.325 0.186 −0.200 −0.136

1 0.379 −0.629 0.868 0.875

1 −0.182 0.136 0.507

1 −0.611 −0.657

1 0.682

1

2DP 2D perimeter, 3DP 3D perimeter, Zmax maximum Z measured within the sinkhole, Am minor axis length, AM major axis length, AMA azimuth of major axis, RI regularity index and ER elongation ratio.

Table B5 Proximity matrix observations at Albandeira (Spearman correlation coefficient). Variables

Area

2DP

Zmax

3DP

Am

AM

AMA

Vmax

RI

ER

Dmax

DC

Area 2DP Zmax 3DP Am AM AMA Vmax RI ER Dmax DC

1 1.000 0.690 1.000 1.000 1.000 0.286 1.000 −0.67 0.571 0.952 0.357

1 0.690 1.000 1.000 1.000 0.286 1.000 −0.667 0.571 0.952 0.357

1 0.690 0.690 0.690 0.143 0.690 −0.405 0.167 0.762 0.571

1 1.000 1.000 0.286 1.000 −0.667 0.571 0.952 0.357

1 1.000 0.286 1.000 −0.667 0.571 0.952 0.357

1 0.286 1.000 −0.667 0.571 0.952 0.357

1 0.286 −0.786 0.286 0.238 0.119

1 −0.667 0.571 0.952 0.357

1 −0.643 −0.690 −0.143

1 0.667 −0.048

1 0.429

1

2DP 2D perimeter, 3DP 3D perimeter, Zmax maximum Z measured within the sinkhole, Am minor axis length, AM major axis length, AMA azimuth of major axis, RI regularity index and ER elongation ratio.

Table B6 Proximity matrix observations at Castelo (Spearman correlation coefficient). Variables

Area

2DP

Zmax

3DP

Am

AM

AMA

Vmax

RI

ER

Dmax

DC

Area 2DP Zmax 3DP Am AM AMA Vmax RI ER Dmax DC

1 0.995 0.255 0.932 0.977 0.986 −0.264 0.951 −0.293 0.069 0.657 0.188

1 0.223 0.932 0.975 0.988 −0.285 0.949 −0.287 0.082 0.642 0.173

1 0.187 0.244 0.216 −0.016 0.246 0.077 −0.096 0.142 0.032

1 0.928 0.912 −0.298 0.937 −0.504 0.017 0.688 0.085

1 0.945 −0.231 0.947 −0.323 −0.073 0.638 0.128

1 −0.300 0.938 −0.271 0.188 0.683 0.244

1 −0.233 −0.030 −0.342 −0.043 −0.279

1 −0.469 0.022 0.788 0.110

1 0.116 −0.571 0.264

1 0.120 0.292

1 0.106

1

2DP 2D perimeter, 3DP 3D perimeter, Zmax maximum Z measured within the sinkhole, Am minor axis length, AM major axis length, AMA azimuth of major axis, RI regularity index and ER elongation ratio.

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