Gomez-Heras et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-019-0275-0(2019) 6:29

REVIEW

Progress in Earth andPlanetary Science

Open Access

Morphometric measurements of bedrock
rivers at different spatial scales andapplications to geomorphological heritage
research
Miguel Gomez-Heras1* , Jose A. Ortega-Becerril1, Julio Garrote2, Rafael Fort3 and Laura Lopez-Gonzalez4

Abstract
Morphometric characterisation is particularly relevant in the study of geomorphological heritage. 3D modelling
techniques have been proven as very useful tools to recognise, characterise and valorise geomorphosites. Bedrock
rivers account for one of the most outstanding aspects within geomorphological heritage due to the amount ofdistinctive and attractive geomorphological features associated to them and the high preservation of sculpted
forms. Digital elevation models (DEMs) have made it increasingly possible to establish accurate morphometricindices and establish clearer connections between forms and processes. This paper reviews different methodologies
to obtain DEMs on bedrock rivers. This review goes from DEM analysis at multiple spatial scales to introduce optical
microrugosimeter as the latest technical development to facilitate micromorphometric analysis. Micromorphometricanalysis opens the scope for improving the knowledge we have on trans-scale issues in bedrock rivers.
Micromorphometric analysis also opens a new layer of information that enriches the public’s valuation ofgeodiversity of geomorphosites by increasing its didactic and interpretative potential.
Keywords: Bedrock rivers, Geomorphological heritage, Non-destructive techniques, Digital elevation models, LiDAR,
SfM, Photogrammetry, Microroughness

Introduction: geomorphological heritage andbedrock rivers
Geomorphological heritage is a very distinctive topic that isreceiving considerable interest in the last few years amonggeoscientists involved in geoheritage studies (Coratza andHoblea 2018). The progress in remote sensing technologies
over the last 50 years has been central in the developmentof geomorphology (Wohl et al. 2017) and promoted a toolfor increasingly detailed morphometric characterisation.
Morphometry is indeed particularly relevant in the study ofgeomorphological heritage and, above all, 3D modelling
techniques have been proven as very useful tools to recognise, characterise and valorise geomorphosites (Ravanel etal. 2014, 2015). The term “geomorphosite” was suggested
by Panizza (2001) to refer to landforms that have acquired* Correspondence: miguel.gomezheras@uam.es
1
Department of Geology and Geochemistry, Universidad Autónoma de
Madrid, 28049 Madrid, Spain
Full list of author information is available at the end of the article

a scientific, cultural/historical, aesthetic and/or social/economic value due to human perception or exploitation.
Morphometric characterisation at different scales is
crucial for a better knowledge and valorisation of geomorphological heritage. Cayla et al. (2012) introduced
the concept of “perceptive invisibility” as the lack of visibility of a geomorphosite conditioned by the scale of observation. They associated this concept to large
geomorphological structures, but it could also be extrapolated to very small weathering forms whose perception
and interpretation might be difficult for the observer
and, at times, are essential for the understanding (andtherefore valorisation) of geomorphological processes
taking place at a particular location. Nonintrusive andhigh-resolution spatial monitoring are, as Hoblea et al.
(2014) recognised, excellent tools for conservation andpromotion of “neglected” geomorphological features.
Bedrock rivers are a fine example of geomorphologicalsites with forms of various scales. Bedrock rivers account© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.

Gomez-Heras et al. Progress in Earth and Planetary Science(2019) 6:29

for one of the most outstanding aspects within geomorphological heritage due to high preservation of sculpted
forms in bedrocks (Garzón-Heydt et al. 2012). Besides
playing a critical role in landscape evolution, these rivers
constitute important features in protected areas, due tothe amount of distinctive and attractive geomorphological features associated with them, from large-scale
canyons and gorges to small-scale grooves, flutes andfurrows. This is reflected, for example, in the myriad ofterms in many languages associated with bedrock rivers
which are not only part of scientific jargon but commonplace terms used in daily language, such as waterfall,canyon, narrow, and rapids. Furthermore, some names
given to sculpted forms in bedrock rivers are rather symbolic, as for example Spanish and Italian “Giant’s marmites” for potholes or “pans” and “scallops” in English.
The importance of some bedrock river forms goes far
beyond scientific and natural values to have a place inpopular culture, as for example the large-scale Grand
Canyon (Colorado River), the number one tourist destination in the USA, but also smaller features, such as
Antelope Canyon, a narrow bedrock river managed by
Navajo Native Americans as a tourist attraction. Waterfalls associated with bedrock rivers are among the mostdemanded tourist destinations, such as Iguazu waterfalls
in the Argentina-Brazilian border, one of the most visited waterfalls in the world (Hudson 2013) along with
Niagara Falls (USA-Canada), Victoria Falls (Zimbabwe)
and Salto del Angel (Venezuela). This interaction between geomorphological components of a territory andcultural features frames in Panizza and Piacente’s (2008)
definition of cultural geomorphology and makes of bedrock rivers one of the most important features to be included in this term. Bedrock rivers are among the mostvalued features in some protected areas, such as
Sobrarbe Geopark in the Pyrenees (Ortega-Becerril et al.
2017a) because their high variety of features at different
scales is an added value when designing geotouristicroutes.
This paper aims to review different techniques to getmorphometric measurements at different scales. Building
on a review of commonly-used techniques, such as LiDAR(Light Detection And Ranging) and photogrammetry, thispaper intends to show the value of innovative microphotogrammetric techniques (optical rugosimeter) in contributing
to the understanding and valorisation of trans-scale
flow-related geomorphological processes of bedrock rivers.
Bedrock rivers are a relatively poorly-understood environment in which the interpretation of features is very
dependent on the scale. To demonstrate the scope of thesetechniques, a particularly relevant geosite within a bedrock
river in central Spain (upper Manzanares) will be used. In
addition, this paper shows the potential of high-resolution
micromorphometric techniques as a tool for visibilisation

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and valorisation of small-scale erosive features in this particular geomorphosite.

Bedrock rivers: morphometry and scale issues
Quantitative morphometric aspects of bedrock rivers areof great importance. Morphometry, i.e. the measurementof forms of the ground surface is consubstantial to thebirth of geomorphology as a preliminary step to interpret processes. As early as 1899, Davis (p.481) stated “allthe varied forms of the lands are dependent upon (…)
structure, process and time”, showing that early in thedevelopment of geomorphology as a science came therealisation that only after understanding forms one caninterpret them as the result of processes.
In bedrock rivers, as in other environments, morphometry is one of the main tools to discriminate forms. Yet,some of those forms resemble, but are genetically different,
as for example in several features described by Richardson
and Carling (2005), e.g. compound parallel-side furrows
and runnels with alternating scour. Nevertheless, this variability and complexity in forms and processes is one of themain scientific values of bedrock rivers (and therefore also
an asset for their geomorphological heritage status).
The relationship between morphometry and processes
in bedrock rivers is considered an under-researched
area, due to the patchiness and morphological discontinuity of bedrock rivers and because, echoing J.W.N.
Sullivan’s famous quote, “it is much easier to make measurements than to know exactly what you are measuring”. Richardson and Carling (2005, p.1) stated, “themorphology of surface bedrock channels is still a relatively neglected area of research within the study of fluvial systems”. Anyhow, there has been a big evolution inthis research area from Wohl’s (1998) and Richardson
and Carling’s (2005) seminal attempts to describe bedrock river forms to more recent efforts by Lamb et al.
(2015), Beer et al. (2017), Velázquez et al. (2016) to derive processes from forms in this environment.
The formation and development of bedrock river features at different spatial scales are the result of a combination of extrinsic factors (such as stream hydraulics)
and intrinsic bedrock factors such as lithology and heterogeneities (Wohl 1998) in combination with surface
alteration processes, as for example wetting-drying cycles (Inoue et al. 2017). As Hall et al. (2012) suggested,“rock decay” encompasses energy transfer processes (aterm they prefer to weathering), the influence of rockproperties and product removal (erosion). As an example, Ortega-Becerril et al. (2017b) demonstrated how
the balance between these factors led to variations in thegeneration of potholes, a typical bedrock river feature.
Geomorphologists define bedrock rivers as patchyreaches within a fluvial system (Whipple et al. 2013).
Therefore, scale issues are deeply related to bedrockGomez-Heras et al. Progress in Earth and Planetary Science(2019) 6:29

rivers by definition, as large-scale processes are often inferred from small-scale forms. A fluvial system to beconsidered a bedrock river must have long-term capacity
to transport bedload vs. sediment supply and persistent
incision (Whipple et al. 2013), and it must do so at a
rate greater than sediment supply. Usually, the riverbed
must be clean of sediment and have a high ratio (> 50%)of either exposed rock or a thin alluvial cover (Tinkler
and Wohl 1998). This means bedrock reaches are frequently patchy and relatively short, and the incision
rates are deeply dependent on the interaction between
alluvial cover and erosive mobile grains (Sklar and Dietrich 2004; Inoue et al. 2014).
Much work has been done regarding the use of multiscale techniques in bedrock rivers (e.g. Springer et al.
2005; Ortega et al. 2014; Pelletier et al. 2014). Some ofthese models like Pelletier’s et al. (2014) use the aspectratio of potholes to predict flood sizes that would generate their optimal growth. Hence, large-scale processes
are connected with small-scale measurements. Modelsof river flow in bedrock rivers (e.g. Venditti et al. 2014)
are also a tool to understand processes at different
scales. This is shown for example in the studies by
Anton et al. (2015) describing how small-scale flooding
can generate large-scale erosion and Beer et al. (2017)
determining the importance of the spatial distribution ofmoving and static sediment in determining small-scaleerosion, which in turn will result in gorge evolution at
the landscape scale.
The complex balance of factors leading to changes
in bedrock river forms counsels morphometrical characterisation at different scales to understand the response of different occurring lithologies to river flow
and the relationships between erosion and weathering
processes. Nonintrusive spatial monitoring can be also
performed at different times as a way, not only forunderstanding processes, but also for assessing the influence of climatic and human impacts and consequently planning conservation strategies, which areespecially relevant in bedrock rivers in protectedareas. Methods like LiDAR and increasingly affordable
photogrammetry are among other technical developments that have had a strong impact on the ability tocollect data in geomorphology (Viles 2016). Hence,these techniques and how they have been used in recent times to understand processes at different scales
in bedrock rivers are reviewed.

DEMs from LiDAR in bedrock fluvial
geomorphology and hydraulic modelling
Burrough (1986) defined DEMs as the representation of
the continuous variation of relief over space. DEMs havemade it increasingly possible to establish accurate morphometric indices and establish clearer connections

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between forms and processes. The advancement in theknowledge of interactions between surface forms andgeomorphological processes in rivers in the last decades
led to an increasing need of having more precise andspatially continuous 3D topographical data (Lane et al.
1998).
LiDAR, which mainly includes ALS (Aerial Laser
Scanning) and TLS (Terrestrial Laser Scanning), is nowadays one of the most precise systems to obtain data tobuild large-scale DEMs (Cavalli et al. 2008; Marks andBates 2000). This technique is particularly useful in geoheritage, both in the phase of developing scientificknowledge of a site as well as in the interpretation anddevelopment of geotourism (Cayla 2014).
Hohenthal et al. (2011) classify LiDAR data in fourmain groups: ALS, ALB (airborne LiDAR bathymetry),
TLS and MLS (mobile laser scanning), all of them withdifferent characteristics in terms of spatial resolution
and ability to penetrate water.
In addition to many other documented uses (Lohani
and Mason 2001; Thoma et al. 2005; Carey et al. 2006;
Milan et al. 2007; Alho et al. 2009a; Cook and Merwade
2009; Oguchi et al. 2013; Garrote et al. 2018), LiDAR
data has proven especially useful when representing bedrock river morphology, as for example Baggs Sargood etal. (2015), Sweeney and Roering (2017) or Beer et al.
(2017) pointed out.
LiDAR shows some advantages in relation to othertopographic techniques. For example, ALS offer quicker
data, with higher spatial density at a lower cost than
traditional methods, such as theodolites and total stations (Charlton et al. 2003; Gomes-Pereira and Wicherson 1999). LiDAR advantages in comparison to otherremote sensing techniques (such as aerial and satellite
images) include the ability to measure elevation in surfaces covered by vegetation (Charlton et al. 2003;
Gomes-Pereira and Wicherson 1999). In addition,
LiDAR does not depend upon illumination condition(Baltsavias 1999a).
However, not everything is positive about LiDAR
data. The variability in the characteristics of the sediments and forms in bedrock rivers can produce errors
in the DEMs generated from LiDAR data (Hodgson
and Bresnahan 2004; Hodgson et al. 2003). Thus, Heritage and Hetherington (2007) observed how the best
precisions in the elevation data are obtained in gently
sloped riverbeds, while the precision decreases significantly on riverbeds covered by pebbles and blocks because of shadowing. Another source of precision
decrease is vegetation cover (Fig. 1), which has a positive correlation with errors in elevation of LiDAR data
(Charlton et al. 2003; Hodgson et al. 2003). Heritageand Hetherington (2007) estimated in 0.07 m the error
between TLS data and data obtained with theodolites

Gomez-Heras et al. Progress in Earth and Planetary Science(2019) 6:29

for areas with variable vegetation cover. Other works (e.g.
Hodgson and Bresnahan 2004; Baltsavias 1999b; Overton
et al. 2009) have also analysed the effect on the accuracyof LiDAR data of other types of vegetation cover, density
and height and its influence on laser pulses discrimination.
Furthermore, as Jones et al. (2007) stated, the use of automatic techniques for filtering LiDAR data can affect significantly the identification of fluvial forms (areas without
surface topographical data appear and need to be interpolated). In a bedrock environment like the strath terraces,
automatic filtering of LiDAR data may reduce the qualityof resulting DEMs (Reusser and Bierman 2007).
Spatial resolution of ALS data is adequate for visual
recognition of typical fluvial macro-forms, such as channels, paleo-channels, pointbars, alluvial fans and levees(Aggett and Wilson 2009; Jones et al. 2007; Notebaert etal. 2009; Kasvi et al. 2012). Less work has been done in
bedrock channels, with some works of Reusser and Bierman (2007) and Finnegan and Dietrich (2011) mostly on
medium- and large-scale features such as strath terraces.

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Cartography and interpretation of fluvial forms
from ALS data is limited by different factors. Mainly,
spatial density of data may limit the identification ofsmall-scale forms (i.e. when topographical variations
are very small in relation to the surrounding terrain)on DEMs created from the interpolation of ALS data
(Notebaert et al. 2009). Thus, the spatial resolution
and vertical error of ALS data determine the minimum size of forms that could be identified. Notebaert et al. (2009) show an example of theabovementioned when comparing fluvial forms thatcan be identified with two DEMs of different spatial
resolution (5 and 1 m), indicating how the use of
shading models derived from the ALS data helps inboth cases to the identification of fluvial forms(Fig. 2).
The use of LiDAR data has become widespread for hydraulic modelling, because it improves the definition ofchannel geometry, both in 1D studies (e.g. Aggett andWilson 2009; Omer et al. 2003; Casas et al. 2006) and in

Fig. 1 Effect of vegetation cover on ALS data (National Geographic Institute, Government of Spain) corresponding to the case study area in “La
Pedriza” (Madrid, Spain). a Elevation profile from ALS considering all the ALS points (up), only the points classified as terrain (middle), and only the
points classified as vegetation (bottom). The number of ALS points giving information on the terrain surface decreases in the areas with more
vegetation. Effect of vegetation on a DEM considering all the ALS points (b) and only the points classified as terrain surface (c). The red line
corresponds to topographic profiles in (a)Gomez-Heras et al. Progress in Earth and Planetary Science(2019) 6:29

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Fig. 2 Hillshade DEM obtained from ALS data, with spatial
resolutions of 5 m (a) and 1 m (b); detailed digital photogrammetry(c) obtained from UAV image (resolution 0.25 m). a–c the case study
area in “La Pedriza” (Madrid, Spain). Panels b and c are enlargementsfrom marked areas in a and b. d TLS data interpolated with a spatial
resolution of 0.05 m. Panel d is obtained from UNAVCO organisation
TLS free data of an area near Prescott (Arizona, USA, http://
opentopo.sdsc.edu/datasets?id=OT.122010.26912.1&host=ot). Thissequence shows how ALS resolution does not allow identifying
small-scale elements in the channel, something that can only beachieved from TLS and detailed digital photogrammetry data

2D hydraulic models (e.g. French 2003; Marks and Bates
2000; Straatsma and Baptist 2008). These LiDAR data
have also been used to define terrain surface roughness,
as an improvement from other, more traditional, techniques based on photo-interpretation or granulometricstudies of sediment samples (Mason et al. 2003; Heritageand Milan 2009; Milan 2009). Milan (2009) shows that
one of the main advantages of using LiDAR data to determine surface roughness is the ability of this data to
reproduce roughness spatial variability.
The use of LiDAR has allowed significant improvement in DEM accuracy in bedrock rivers and therefore abetter representation of the highly-variable topographyof bed and banks in these rivers (mainly by using TLS
data, and both aerial and terrestrial detailed photogrammetry). As the topography obtained in the DEM is moredetailed, the effect of even small-scale features on flow
diversion can be reflected in flow direction vector fieldmaps at most scales, which leads to a more realistic reconstruction of actual flows.
The availability of ALS data to cover large terrain areas
has been shown as an essential aspect for a correct mapping of flood areas on the floodplain, where topographic
variations in elevation are small but they affect the flowof shallow water (French 2003). Thus, several authors
have investigated the potential for the use of ALS data
for flood mapping in recent years (e.g. Cook and Merwade 2009; Overton et al. 2009). One of the main conclusions obtained by authors like Cobby et al. (2003) and
Cook and Merwade (2009) is that using ALS data generally implies a decrease in the extent of modelled floodzones. This is due to the increment of vertical accuracyof topographic data (which is now more capable ofrepresenting small-scale topographic variations in thefloodplain). The use of a precise channel bathymetry further enhances the reduction of the modelled flood inundation area.
It is only now, when LiDAR results are more faithful
to real topography, that it is possible to locate and assess
fluvial process like cavitation and plucking, which aredecisive in the initiation and development of typical bedrock river morphologies like potholes and grooves. Another improvement from the use of these more realistic

Gomez-Heras et al. Progress in Earth and Planetary Science(2019) 6:29

surfaces is related to flow direction pattern variations
and its relationship with peak flow values.
The use of TLS data meant a significant improvement for the quantification of meso- and micro-scale
forms in relation to the use of traditional contact
profilers, such as erosion pins or microtopographic
profilers (Hodge et al. 2009a, 2009b; Wang et al.
2013), by improving spatial resolution of the data obtained. It is also, as pointed out by Entwistle andFuller (2009), a much faster method for the characterisation of the sediment than the taking of samples
and its granulometric analysis. To limit topographic
noise, Hodge et al. (2009a) recommended setting
spatial resolution of TLS data to centimetre scale(even when these techniques are capable of producing
millimetre resolutions), with the aim that the variations in the topography are not masked by the noise
existing in the data. Associated with this limit in thespatial resolution of TLS data, Alho et al. (2009b)
stated that even when sediment characteristics could
not allow their individual identification by TLS, it
would still be possible to map variables as importantas the height, length, volume and orientation of fluvial forms such as ripples and dunes.

Structure from motion: A cost-effective multiscaletechniqueRock weathering and erosion operate at different temporal and spatial scales (Viles 2001). Therefore, morphometric investigation needs to be able to accommodate todifferent scales. As mentioned elsewhere, considering
small-scale sculpted forms can give evidence oflarger-scale morphological change in bedrock rivers,
both large-scale features and small-scale forms to beassessed together to get the whole picture of how “energy transfer” (in the sense of Hall et al. 2012) interacts
with rock properties.
Chandler and Buckley (2016) state that SfM photogrammetry can be a good technique to overcome themain challenges associated with TLS: cost, occlusions
and the lack of flexibility of scale. Structure form motion photogrammetry can use photographs taken witha conventional camera, and processing may be accomplished through inexpensive (or even free) software. This contributed to the extensive use of SfM
photogrammetry over TLS (Almagro 2010; Westoby
et al. 2012).
Photogrammetry is a long-used technique in geology
and geomorphology. Laussedat (1854) already noticed
that accurate measurements could be made from multiple images taken with slight offset. From then throughthe increasing relevance of photogrammetry from the1940s in mapping (e.g. Birdseye 1940; Eardley 1942) tonowadays, structure from motion (SfM) methods used

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in the most advanced photogrammetry software, photogrammetry has consolidated as a very suitable technique for the generation of DEMs at low cost.
“Structure from motion” (or “multi-image photogrammetry” as it is sometimes called) is the photogrammetrictechnique in which datasets of overlapping digital images of a feature taken from different positions areloaded into a software to obtain high-resolution 3Dmodels (McCarthy 2014). Currently, the speed of calculation of the computers and the great development ofdigital cameras allows the immediate restitution of thephotographed object, getting some rectified images, withactual measurements.
Multi-image photogrammetry has been used for thelast 20 years in geomorphological studies (e.g. Chandler
1999; Lane 2000; Westoby et al. 2012; Saito et al. 2018)
and in heritage applications, such as archaeology (e.g.
Bryan and Clowes 1997; De Reu et al. 2013, 2014), cultural heritage (e.g. Lopez-Gonzalez et al. 2016; Fujii et al.
2018) and geoheritage (e.g. Fernández-Lozano andGutiérrez-Alonso 2017; Santos et al. 2018).
The development of cameras, inertial units, UAV andsmartphones in this same timespan now makes it
possible to find examples of multi-image photogrammetry using large-scale images taken with UAV (e.g.
Domínguez Pérez et al. 2017), as well as close-range
photogrammetry (e.g. Fraser and Cronk 2009; Jordá etal. 2011; McCarthy 2014) and even smartphones (e.g.
Micheletti et al. 2015).
The possibility of obtaining results with accessible
low-cost equipment makes photogrammetry one of themost widespread methods to obtain DEM, especially atclose-range (Almagro 2010). Photogrammetry allows
obtaining DEMs of both large and small features withthe same equipment (camera and software), so it can beused in its own right or as an inexpensive way of complementing more expensive LiDAR.
As precision in SfM is dependent upon scale (thecloser the images are taken the more precise is themodel), for small-scale features, a SfM can generate ahigh-accuracy point cloud at a significantly lower costthan TLS. The use of targets allows scaling, orientation and geo-referencing, which can remain relative
or be made absolute through a GPS or total station.
Geo-referencing is particularly relevant when analytical data will be superimposed on the model, thus, creating a high-detail small-scale GIS (Gomez-Heras et al.
2014; Lopez-Gonzalez et al. 2016). This also allows a 4D
approach, i.e. the addition of time to the three spatial dimensions X, Y and Z, by comparing and operating with aseries of surveys obtained through time (Lopez-Gonzalez
et al. 2016), as a step forward from the mere evaluation of landform changes (e.g. Lin and Oguchi 2002;
Hayakawa et al. 2008).

Gomez-Heras et al. Progress in Earth and Planetary Science(2019) 6:29

Contribution of micromorphometry to the studyof bedrock rivers and small-scalegeomorphodiversity
Up to this point, this paper has reviewed the evolutionof techniques, which have been commonly used in 3D
Modelling for the research and monitoring of fluvial environments (particularly bedrock rivers) and are also
amply used for visualisation and valorisation of geoheritage (Cayla 2014; Ghiraldi et al. 2014; Ravanel et al.
2014, 2015).
Ortega-Becerril et al. (2017a) discussed how the assessment of the geomorphodiversity of bedrock rivers
should include larger spatial scales rather than individual
river reaches. Likewise, the assessment of geomorphodiversity should also include microscale because of thelarge variety of bedrock sculpted forms (some of them at
microscale) in this environment.
This is particularly true considering the definition ofgeodiversity, which includes the features together withthe processes that form and alter them (e.g. Dudley2008; Németh et al. 2017). As previously discussed, microforms are key for the understanding of geomorphological processes in bedrock rivers. However, these forms
remain “invisible” analogously to the sense in which
Cayla et al. (2012) used this term for large geomorphological structures.
The previously reviewed techniques may allow morphological analysis up to a centimetre accuracy, but their resolution level usually does not permit morphometricanalysis of sculpted forms below this scale (Fig. 3). Particularly, the previous reviewed techniques would not bepractical to analyse large-scale forms at the same timethan microforms, which are relevant for the scientificknowledge of geomorphological processes in bedrock rivers and also for the public understanding (and therefore

Fig. 3 Millimetre-scale pits and grooves in a microdiorite dyke. Darkcoloured area corresponds to moisture accumulated in and aroundpits. This is likely to cause weathering through wetting-drying cycles

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valorisation) of geomorphosites. For this level of detailed
analysis, this paper reviews a novel application to geomorphology of a technique already used in cultural heritage: the optical rugosimeter.
Surface microroughness is one of the main controls in
the initiation of weathering processes. The term “micro”
in roughness refers to variations in the relief in order ofmagnitudes of ten of micrometres. The formation of
microrelief is controlled, much like the formation of relief at larger scales, by a combination of lithological andenvironmental factors. In igneous rocks, compositional
heterogeneity of the rocks at the mineral scale contributes to increasing surface roughness due to the different
resistance to deterioration agents of each mineral(Gomez-Heras et al. 2008; Sousa and Gonçalves 2013).
This is particularly relevant in coarse texture granites,
which tend to have a greater roughness (Aydin et al.
2011) that increases during the process of deteriorationof the rock.
Surface porosity, as a consequence of higher roughness,favours water penetration into the rock and can trigger
deterioration by salt crystalisation, freezing-thawing andwetting-drying cycles. Likewise, water or wind can generate microroughness due to microturbulence and impactsof particles in suspension.
Surface microroughness can also favour surface deposits, which either can increase the deterioration
process or can form a crust on its surface becoming
more resistant to erosion, as is the case in the formationof carbonate or iron oxide crusts. Microroughness is alsoa contributing factor to the colonisation of endolithic
microorganisms, helping their adherence on the surfaceof rocks (Etienne 2002; Bergey 2006; Prieto et al. 2006;
Viles 2012).
Igneous rock porosity is mainly fissural. Microcracks
develop both in the interface between rock minerals (intercrystalline porosity) or inside minerals (intracrystalline porosity), such as along crystallographic planes inbiotite (López-Arce et al. 2010) and twin planes in feldspars (Vázquez et al. 2013). Moropoulou et al. (2007) defined a friability index, considering fracture density andsurface roughness. Friability increases (and therefore resistance decreases) when increasing the roughness.
Therefore, when a rock has high roughness, it is more
prone to deterioration (Ribeiro et al. 2011). Roughness
difference in igneous rocks depends on fissural anisotropy at outcrop (López-Arce et al. 2010; Sousa and
Gonçalves 2013; Freire-Lista and Fort 2017) and the effects of weathering agents, such as thermal fatigue, salt
crystallisation or frost action (Gomez-Heras et al. 2008;
Freire-Lista et al. 2015).
The study of microroughness not only allows us tounderstand the process of deterioration of rocks but also
allows us to provide information on weathering/erosion

Gomez-Heras et al. Progress in Earth and Planetary Science(2019) 6:29

rates by different deterioration agents being influenced
by the hardness of each mineralogical component of therock. They can indicate preferential directions of erosion
such as in the action of glacial polish (Dąbski 2015), andbedrock outcrops related to, fluvial water flow whenabrasion, plucking and cavitation processes are frequent(Whipple et al. 2012) and recognised in the studied area
(Ortega-Becerril et al. 2017b).
There are different techniques to measure microroughness: contact (profilometers) and non-contact rugosimeters(optical rugosimeters) (Grissom et al. 2000). In addition tothese, it is possible to measure the roughness of the surfaceof the materials by means of high-resolution microscopic
techniques, such as scanning electron microscopy (SEM),
atomic force microscopy (AFM) and tunnelling microscopy(STM). Profilometers and optical rugosimeters have theadvantage of being non-destructive and portable,
while techniques such as AFM, SEM and STM have ahigher resolution but they are destructive, and measures must be carried out in a laboratory at a muchhigher cost (Fort et al. 2013).
Profilometers have been the most common techniqueso far for microroughness determination in geomorphology (e.g. McCarroll 1992; Hubbard and Glasser 2005;
Dąbski 2015). A fine tip crosses the surface of the object
in these contact rugosimeters. Height variations of thetip moving at a controlled speed are transformed into
electrical impulses that result in a profile showing surface topography.
Optical rugosimeters have been amply used in cultural
heritage science (e.g. Fort et al. 2012; Vazquez-Calvo et
al. 2012) and only more recently on geological heritage(e.g. Esteve et al. 2018). However, to this day, they havenot been used in geomorphological analysis nor in thevalorisation of geoheritage.
Optical rugosimeters (Fig. 4) share principles withmulti-image photogrammetry. Three collimated whitelight sources incise angularly (70°) to the surface andthree images are taken at 0°, 120° and 240°. These images are integrated automatically with photogrammetrictechniques offering a 3D image of the surface. Thisequipment marketed as TraceIT (Fig. 5) allows measuring the surface roughness of a 5 × 5 mm area with aresolution in the three directions of the 2.5 μm space.
The topography (roughness) of the analysed surface (25mm2) is obtained by processing the results from between
500 and 1500 profiles for each of the X and Y-axes ofthe measured plane. The whole illumination and image
acquisition system is integrated in a hand-held portablemeasuring unit. A fixed array of illumination and sensors facilitates repeatability, diminishing errors causedby these factors in subsequent measurements. Other
sources of variability between subsequent measurements
or operators would be comparable to other surveying

Page 8 of 18

Fig. 4 Innowep TraceIT optical rugosimeter. The acquisition unit
(centre) contains the three collimated light beams and cameras thatallow a photogrammetric reconstruction of a 5 × 5 mm area. North ison the right of the image

methods and negligible, as suggested by Scott et al.
(2016).
There is a great variety of parameters to quantify theroughness of a surface or its roughness profiles. McCarroll (1992) establishes roughness parameters that determine the standard deviation of the slope between the
two adjacent measurement points on the surface of therock (index A), and the absolute difference between theadjacent slope values (index B), but at this time otherparameters are used that allow a more visual interpretation of the results (Santos and Júlio 2013). Currently,
the following parameters (see Fig. 5 for the elements in a
profile) are used:– Average roughness—Ra: average of the absolutevalues of deviations from the base line of the profile.
– Root-mean-squared roughness—Rq: root meansquare of deviations from the base line of the profile.
– Total roughness—Rt: height between the highest
and lowest point in the profile.
– Mean peak-to-valley height—Rz: sum of the fivehighest peaks and five lowest valleys in the profile.
– Among these parameters, Rz is considered the best
discriminating profiles (Dąbski and Tittenbrun2013).

Case study: the upper Manzanares river as an
example of multiscale geomorphosite
A landform to be defined as a geomorphosite needs tohave scientific, cultural/historical, aesthetic and/or social/economic value (Panizza 2001). The upper Manzanares river is one of the most outstanding bedrock rivers
in Spain, and probably in Europe, for its relevant scientific value (particularly, due to the variety of features atdifferent scales contributing to the understanding ofGomez-Heras et al. Progress in Earth and Planetary Science(2019) 6:29

Page 9 of 18

Fig. 5 Schematic view of a topographic profile showing the elements to define Ra and Rt roughness parameters. Blue dotted line: base line ofthe profile; Zp: height of the highest peak; Zv: deepest valley; Zmax: parallel line of the baseline that marks; Zmin: line parallel to the baseline thatmarks the deepest valley; brown arrows: the five highest peaks; green arrows: the five deepest valleys

bedrock river geomorphology) in addition to numerous
cultural/historical values.
The upper Manzanares river is a bedrock reach locatedin “La Pedriza” (Fig. 6) within the Sierra de Guadarrama
National Park (Madrid, Spain). La Pedriza constitutes thelargest granitic outcrop in Europe, and its importance, together with the rest of the national park, is seminal for thedevelopment of geology in Spain and granite geomorphology worldwide. For example, several granite weatheringforms were illustrated for the first time in this area, suchas tafoni (De Prado 1864). Geomorphological studies inthis area extend to nowadays (e.g. Pedraza et al. 2014), being particularly relevant to those associated with gnammas
formation (e.g. Domínguez-Villar et al. 2008), weathering
processes (e.g. García-Rodríguez et al. 2015, 2017) andbedrock river features (Ortega et al. 2014; Ortega-Becerril
et al., 2017b).
This area is also outstanding in terms of the culturalvalues associated with it. La Pedriza has been part ofpaintings throughout history (Bernaldo de Quiros 1923;

Pena 2016) and is also important in the development in
Spain of fieldwork as an educational tool in the late nineteenth century (Otero 2004). The Sierra de Guadarrama
was therefore listed as a national park in 2013 because of
its outstanding natural, scientific and cultural values.
Under the light of its exceptional heritage status, theupper Manzanares river will be used as a case study toshow the potential of an optical rugosimeter. Specifically,
an area listed as a geosite (Code TMP126 in the Spanish
Inventory of Places of Geological Interest—IELIG) wherea series of microdioritic dikes intrude a coarse-grained Variscan leucogranite (Fig. 7).
The upper Manzanares river is a perennial river which
drains an area of around 50 km2. The selected area consists of a mixed bedrock alluvial channel (10–15 m wide)that contains large boulder obstacles in-channel, a knickpoint, potholes, step-pool sections and some smoothrock platforms. The reach-scale channel gradient is 0.12
m/m. The sediment cover at the site is very scarce.
Floods occur mainly in winter caused by frontal systems.

Fig. 6 Location map of La Pedriza in the Spanish Central System mountain range

Gomez-Heras et al. Progress in Earth and Planetary Science(2019) 6:29

Fig. 7 Test area at the upper Manzanares river in La Pedriza

Other flood events are related to snowmelt during latespring and intense convective storms during autumn.
The flood-peak discharge with a 500 year recurrence(Q500 = 145 m3·s−1) exceeds around six times the annual
peak discharge (Q2 = 24 m3·s−1) at the study site (Ortega
et al. 2014).
The outcropping microdioritic dikes generate a varietyof large-scale sculpted features, such as waterfalls, pools,
potholes and furrows. Figure 8 shows a 2D hydraulicmodelling at the study site including results of principal
variables like flow depth and velocity, and flow direction.
This model was obtained using Iber, a free 2D hydrodynamic modelling software (Bladé et al. 2014). Surface
topography was obtained with a 3D mesh from a DEM(0.25 m pixel size), which was created by interpolation of
UAV digital photogrammetry point cloud data (0.05 m

Page 10 of 18

pixel size). This model shows the complex macro-scale
flow patterns generated by a highly-variable topography.
Small-scale SfM was also carried out at the site from64 photographs of a 2.47 m2 (1.58 × 1.56 m) area wherereference axes were determined using six coded targets.
Then, a point cloud of 6,612,543 points was generated
with Agisoft Photoscan Edition (1.1.6) software, whichtranslated into a 1,333,581 faces model, with a size between 1 and 0.1 mm. Software manipulation of the 3D
mesh was be used to obtain profiles of the model(Fig. 9).
In addition to this, small-scale crests, troughs, submillimetre pits and scratch marks were observed at the outcropping microdioritic dikes (Fig. 10). A TraceIT opticalrugosimeter was therefore used to obtain a 3D modeland roughness parameters of these surface marks.
Both the stoss slope and the lee slope were measured
keeping Y-axis oriented towards the north. Two oriented
surface scans were therefore obtained (Fig. 11). The scanon the stoss slope (Fig. 11a) shows asymmetrical impact
scratches elongated in a northeast-southwest direction.
On the lee slope (Fig. 11b), round pits with diameters
less than 0.9 mm and depth less than 0.38 mm that could
be related to cavitation are observed. Roughness parameters calculated on these surfaces (Table 1) show, there
is much more variability on roughness indices between
E-W and N-S axis on the stoss slope than on the leeslope, because of the more isotropic shape of cavitation
pits. The dominant E-W component of the impact
scratches on the stoss slope results in a higher roughness

Fig. 8 Results of the 2D hydraulic model developed in the area of La Pedriza. This model refers to the variables flow depth (left) and velocity(centre). Noticeable variations observed in both variables are the result of large-scale roughness of bedrock and may be responsible for thedevelopment of large-scale bedrock forms, such as pools, potholes and grooves. The image on the right represents flow directions vectors, which
could be correlated to occurrence of the abovementioned forms

Gomez-Heras et al. Progress in Earth and Planetary Science(2019) 6:29

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Fig. 9 Textured point cloud 3D model (a). Polygonal mesh model of the same area with profile line I-I′ marked (b). I-I′ profile where Leucogranite(LG) and Microdiorite (MD) areas are marked (c). Detail of mesh in the area of the profile marked with an arrow (d). Microdiorite dykes run east
to westin the N-S direction than in the E-W direction and thanin the lee slope.
These preliminary results will be discussed in relation
to the information obtained from previously discussed
techniques to obtain DEM and on the basis of its potential to be used to obtain information on flow directions
as well as from cavitation, abrasion or plucking processes in bedrock rivers. In addition, based on how thistechnique reveals these, otherwise invisible, microforms,
it will be discussed how a new layer of microscale information enriches the geomorphological heritage values ofthis site. Either establishing it as a model of trans-scalegeomorphological processes or as an educational resource, by giving to the public new tools to understandthe geomorphological processes behind landscape at several scales.

Fig. 10 Microdiorite dyke with a series of crests and troughs.
Scratches are superimposed to stoss slopes and steep, concave, leeslopes show pits. North is on the top right of the image

Discussion: integrating high-resolution and ultradetailed topographic data in a bedrock river
Bedrock rivers are a fine example to demonstrate theinterconnection between small-scale features and(2019) 6:29

Gomez-Heras et al. Progress in Earth and Planetary Science

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Fig. 11 Surface scans of a 5 × 5 mm area on crest slopes. The stoss slopes (a) show elongated scratches corresponding to the main northeastsouthwest flow direction, which superimposes to the secondary flow forming the crests and troughs. Pits of various sizes on lee slopes (b) areformed by the action of the secondary flow forming crests and troughslarge-scale behaviour in natural complex systems.
Erosional processes dominate the morphology of mostbedrock channels and to be able to understanderosion fully, the scale factor needs to be taken into
account. Erosion ranges from small spatial scale (micro-scale, millimetre to centimetre) with erosional
characteristics as abrasion, flacking or plucking (small
size grains), meso-scale (centimetre to metre) with selective erosion that produce potholes, grooves, knickpoints, inner-channels and other meso-features andfinally macro-scale (metre to kilometre) with morphologies at reach, basin-scale (Wohl 1998).
Several studies have dealt with the influence ofsmall-scale features and processes on large-scale ones aspreviously cited Anton et al. (2015) and Beer et al.
(2017), as well as more recent works by Ortega-Becerril
et al. (2017b) and the synthesis work of Scott and Wohl
(2018). However, the level of sub-millimetre detail obtained by means of a microrugosimeter is unprecedented
in bedrock river geomorphology.
Large-scale techniques, such as ALS or airborne
photogrammetry are useful for the understanding oflarge sections of bedrock rivers. An example of this in
the upper Manzanares river area is shown in Fig. 8. In
this model, the interaction of the river system with its
surroundings and the impact of discharge on how flow
directions may change can be ascertained. Each flow
stage, or “shaping flow”, will be related to different erosional levels, from small-scale processes, such as
Table 1 Roughness parameters for both N-S and E-W axes
obtained from surface scans of stoss and lee slopes
E-W axis

N-S axis

Ra (μm)Rq (μm)Rz (μm)Ra (μm)Rq (μm)Rz (μm)Stoss slope

6±1

8±2

26 ± 59±2

12 ± 236 ± 7

Lee slope9±2

11 ± 333 ± 88±2

10 ± 328 ± 6

cavitation or micro-abrasion through medium-scale
plucking or, if flow increases, large-scale erosional tool
removal, highlighting the interconnection of scales inerosion. ALS LiDAR models may be used to assert the
influence of large-scale lithological changes; however,this technique is not detailed enough to understandsmall-scale flow diversions and the influence of localised
lithological changes on river dynamics.
The shaping flow could imply erosion at various
flow levels instead of dominant discharge that imply
that general river landscape is controlled by a certain
flow level. Some studies have examined the idea of adominant discharge that might shape bedrock channels. Although literature on this topic in alluvial
channels is extensive (e.g. Lamb and Fonstad 2010;
Venditti et al. 2014; Beer et al. 2017), we still are in
the early stages of understanding whether the conceptof a dominant discharge/shaping flows could be applied to bedrock channels. This is particularly important, as small-scale flows (and therefore small-scaleerosion) should be considered an agent in bedrock
river evolution. One of the keys to understand thechannel morphology in bedrock rivers is to use newtools to measure channel erosion rates by ordinary
flows instead the classic idea of larger flows generating catastrophic changes. These flows have a lot ofenergy and potential for geomorphic change, but therecurrence period is also large. Further detailed studies should be carried out to test this hypothesis because abrasion of hard bedrock might not besignificantly effective until flow depths and sedimentloads are quite high, although abrasion generates extremely small-scale features. On the contrary, the observed pitting in the lee side of pseudo-ripples seems
to be due to the cavitation process that has been
formed during low water flows and has been able todevelop bedrock erosion. This is inferred from theGomez-Heras et al. Progress in Earth and Planetary Science(2019) 6:29

observed direction divergence between low (N186E)
and high flow (N125E) under flood conditions, as wellas the location of pitting, just downstream frompseudo-ripples crests. This observation diverges fromgeneral knowledge (e.g. Hancock et al. 1998) that indicated high flows and speeds as necessary for the existence of cavitation. In using the microrugosimeter,
one of the key findings is indeed the occurrence ofpitting related to low flow levels that lightly cover thepseudo-ripples. Nevertheless, this process is still
under-researched according to Hancock et al. (1998)
and this point should still be considered a hypothesis.
A higher level of detail is obtained with SfM models in
the example area (Fig. 6). This digital model shows thesurface effects of localised lithological changes, such as
the occurrence of microdiorite dykes. This technique
has proved useful for the observation of mesoscale forms
and surface differences between two adjacent lithologies:
hard, apparently smooth, microdiorite standing slightly
proud from host leucogranite, which shows block removal. At this scale, it is noticeable the existence of local
flow diversions, which in turn establish themselves as
different “shaping flows”, indicated by centimetre-scalesurface marks.
Figure 12 summarises the transition between DEM
scales from ALS LiDAR to the microrugosimeter. While
the flow model obtained from a large-scale DEM

Page 13 of 18

indicates a dominant northwest-southeast flow for thisarea, the DEM obtained with SfM reveals a series ofcrests and troughs at small scale that means flow separation and cavitation (identified by small-scale pits).
These features are smaller and different in origin than
upstream facing convex surfaces (UFCS defined by Richardson and Carling 2005; Beer et al. 2017) and indicate asecondary north-south local flow direction. Crests andtroughs operate at two different scales (Fig. 6C): decimetric crests that extend throughout leucogranite andmicrodiorite, and centimetric crests confined to microdiorite dykes.
Optical microrugosimeter confirms the trans-scalecomplexity allowing a more detailed observation of thesurface of microdiorite centimetric crests and troughs.
Stoss slopes show millimetric scratch marks (Figs. 11A
and 12C), which reflect small-scale predominantlarge-scale northwest-southeast flow, while lee slopes
show a totally different pattern that could be related tocavitation associated with the lower intensity north-southsecondary “shaping flow”. These marks, invisible to thenaked eye, highlight once more the complex patterns offlow found in bedrock rivers. The variability of small-scalefeatures (Fig. 13) in these crests and troughs (sharp-crested hummocky forms in the classification of Richardson and Carling (2005)) reflect what could be called“fluvial micro-systems” within the large-scale system.

Fig. 12 An example of how different morphometric techniques operate indicating flow directions (red arrows, white arrows in c) at different scales. Macroscale DEM (a) and mesoscale DEM (b) from SfM and 5 × 5 mm microscan obtained with optical microrugosimeter on the stoss slope of a microdiorite crest

Gomez-Heras et al. Progress in Earth and Planetary Science(2019) 6:29

Page 14 of 18

Brilha (2016) proposed the assessment of scientific,
educational and touristic values for the inventory of geosites. Scientific knowledge, didactic and interpretative
potential are three of the items in these categories. Detailed DEMs, such as those presented for rugosimeter,
not only increase an improvement of scientific knowledge and trans-scale issues, but also open up new possibilities in terms of the didactic and interpretative
potential of the site, enrichening its value and the perception of its geodiversity by the public.

Fig. 13 An interpretation of the image shown in Fig. 10. Crests andtroughs in a microdiorite dyke. Scratch marks superimposed on
gentle convex stoss slopes form following the direction of the main
flow. Pseudo-ripples crests develop perpendicularly to a secondary
flow. The secondary flow could generate cavitation on steep
concave lee slopes, forming pits as a consequence of the small
crystal size and higher weatherability of microdiorite

Meso-scale SfM images showed microdiorite dykes standing slightly proud of host granite with an apparently
smoother surface as a result of higher resistance to erosion (Schmidt hammer values of 62 for microdiorite vs. 55
for granite, according to previous works (Ortega et al.
2014, 2017b)). However, small-scale surface scans with optical rugosimeter show a rough surface (indeed rougher in
microdiorite (Rz values from 15 to 35 μm) than in granite(Rz values ranging from 5 to 20 μm)), which may be a reflection of its higher weatherability. This is a fine exampleof how variations of lithology and “energy transfer”, in theabove-discussed sense given by Hall et al. (2012), may influence sculpted forms even in an area of a few centimetres as it does at larger scales (Ortega-Becerril et al.
2017b).
The highly detailed observation scale offered by the
microrugosimeter has also direct and indirect implications
for considerations on geodiversity. For example, the casestudy used to demonstrate this technique is listed as a geosite mainly on petrological and geochemical criteria becauseof the intruding microdiorite dykes. However, the impact
these dykes generate on meso- and particularly micro-scaleerosion processes, and therefore its richness in relation togeomorphological values, could go unnoticed (in the senseof Cayla et al. 2012) without the advancement on DEM acquisition offered by the microrugosimeter.

Conclusions
This paper highlights the importance of bedrock rivers
as an outstanding feature within geodiversity and how
the evolution of methods for downscaling morphologicalanalysis adds new layers to their value. As Ferrer et al.
(2017) state, spatial scales introduce complexity into
geomorphology, but one also can say it introduces richness. Bedrock rivers epitomise the relationship between
scales in geomorphological research and their richness
because the diversity of small-scale features illustrates
how the complex relation of energy transfer processes atthe small-scale impacts on landscapes. The technology
improvements for obtaining DEMs has allowed quantifying topography at an improved range of scales “to see
which was formerly invisible” (Viles 2016).
While the value of LiDAR (mainly ALS) and airborne
photogrammetry cannot be diminished in understanding
the large-scale components of bedrock rivers systems,
SfM offered easy and cheap solutions to obtain DEMs formeso-scale analysis. Although technically possible using
LiDAR, SfM’s lower cost facilitates adding time to thethree spatial dimensions X, Y and Z by generating several
DEMs over time, creating then a four-dimensional (4D)
space.
The use of optical microrugosimeters in geomorphological research represents a big development as it
facilitates drastically micromorphometric analysis andoffers the possibility of 3D analysis of microforms ina way a series of profiles obtained through contact
profilometers could not allow. All of this means theuse of microrugosimeters are particularly useful tostudy bedrock rivers as they allow us to detectchanges in bedrock incision rates, which usually operate in very small-scale from 0.01 mm/yr. Microrugosimeters also allow a detailed water flow direction
assessment from morphological analysis, a key pointin steep and complex bedrock rivers.
In addition to this, microrugosimeter reveal microforms, otherwise invisible in the concept Cayla et al.
(2012) introduced for large geomorphological structures.
This tool, therefore, opens the scope for improving theknowledge we have on trans-scale issues in bedrock rivers as well as it opens a new layer of information toGomez-Heras et al. Progress in Earth and Planetary Science(2019) 6:29

enrich the geomorphological heritage values of a site byincreasing its didactic and interpretative potential andthe perception of geodiversity by the public.
All this said, the multiscale understanding of complex
systems is of great importance not only to interpret theevolution of a natural system, but also to manage thehuman-nature interactions and the sense of value of
geodiversity.
Abbreviations
ALB: Airbone LiDAR bathymetry; ALS: Airborne laser scanner; DEM: Digital
elevation model; LiDAR: Light detection and ranging; MLS: Mobile laser
scanner; SfM: Structure from motion; TLS: Terrestrial laser scanner;
UAV: Unmanned aerial vehicle (drone)
Acknowledgements
We thank Geohazards InSAR Modelling group for assistance on airborne scanused on large-scale DEM models. MG wishes to thank Japan Geosciences
Union for support on presenting this research in session M-IS07 “Conservation of natural geosites and cultural heritage: weathering process and damage assessment” at the JpGU general meeting 2017 held at Tokyo, Japan. In
addition, MG is indebted to Dr. Chiaki Oguchi for facilitating the attendance
to the JpGU general meeting and her ongoing professional and personal
support. Finally, the authors would like to thank M Hogan, DN Scott andother, anonymous reviewer whose comments helped to improve themanuscript.
Funding
This work was supported by Geomateriales 2 (S2013/MIT-2914) and Top
Heritage (P2018/NMT-4372) programmes from the Regional Government ofMadrid (Spain).
Availability of data and materials
Data sharing is not applicable to this article as no datasets were generated
or analysed during the current study.
Authors’ contributions
MG and JAO conceived, designed and supervised the study. JG carried out
and interpreted LiDAR models, LL did so with SfM analysis and RF withmicroroughness. JAO and MG undertook the general parts of the overview
and interpretation. All authors contributed to drafting the manuscript. All
authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Author details
1
Department of Geology and Geochemistry, Universidad Autónoma de
Madrid, 28049 Madrid, Spain. 2Department of Geodynamics, Stratigraphy andPalaeontology, Universidad Complutense de Madrid, 28040 Madrid, Spain.
3
Instituto de Geociencias (CSIC, UCM), 28040 Madrid, Spain. 4Artfabrik, 28029
Madrid, Spain.
Received: 28 March 2018 Accepted: 1 March 2019

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