Invited review article
Human topographic signatures and derived geomorphic processes
across landscapes
Paolo Tarolli ⁎, Giulia Soﬁa
Department of Land, Environment, Agriculture and Forestry, University of Padova, Agripolis, viale dell'Università 16, Italy
a b s t r a c ta r t i c l e i n f o
Article history:
Received 28 August 2015
Received in revised form 9 December 2015
Accepted 14 December 2015
Available online 17 December 2015
The Earth's surface morphology, in an abiotic context, is a consequence of major forcings such as tectonic uplift, erosion,
sediment transport, and climate. Recently, however, it has become essential for the geomorphological community
to also take into account biota as a geomorphological agent that has a role in shaping the landscape, even if at a
different scale and magnitude from that of geology. Although the modern literature is ﬂourishing on the impacts of
vegetation on geomorphic processes, the study of anthropogenic pressures on geomorphology is still in its early
stages. Topography emerges as a result of natural driving forces, but some human activities (such as mining, agricultural
practices and the construction of road networks) directly or indirectly move large quantities of soil, which leave
clear topographic signatures embedded on the Earth's morphology. These signatures can cause drastic changes to
the geomorphological organization of the landscape, with direct consequences on Earth surface processes. This review
provides an overview of the recent literature on the role of humans as a geological agent in shaping the morphology
of the landscape. We explore different contexts that are signiﬁcantly characterized by anthropogenic
topographic signatures: landscapes affected by mining activities, road networks and agricultural practices. We underline
the main characteristics of those landscapes and the implications of human impacts on Earth surface processes.
The ﬁnal section considers future challenges wherein we explore recent novelties and trials in the concept of
anthropogenic geomorphology. Herein, we focus on the role of high-resolution topographic and remote-sensing
technologies. The reconstruction or identiﬁcation of artiﬁcial or anthropogenic topographies provides a mechanism
for quantifying anthropogenic changes to landscape systems. This study may allow an improved understanding and
targeted mitigation of the processes driving geomorphic changes during anthropogenic development and help
guide future research directions for development-based watershed studies. Human society is deeply affecting the
environment with consequences on the landscape. Therefore, establishing improved management measures that
consider the Earth's rapidly changing systems is fundamental.
© 2015 Elsevier B.V. All rights reserved.
Keywords:
Geomorphology
Anthropogenic signatures
Anthropocene
Mining
Roads
Mountains
Floodplains
Remote sensing
High-resolution topography
Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
1.1. Humans as a geologic agent? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
2. Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
2.1. Mining and erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
2.2. Surface mining and land subsidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
2.3. Surface mining and landslides/debris-muddy ﬂows/rockfall hazards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
2.4. Surface mining and runoff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
3. Roads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
3.1. Roads in mountainous/hilly landscapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
3.1.1. Roads, runoff, erosion and sediment production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
3.1.2. Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
3.2. Roads in ﬂat landscapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
4. Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
4.1. Agriculture and soil erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
4.2. Land abandonment with special reference to the Mediterranean region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
Geomorphology 255 (2016) 140–161
⁎ Corresponding author.
E-mail address: paolo.tarolli@unipd.it (P. Tarolli).
http://dx.doi.org/10.1016/j.geomorph.2015.12.007
0169-555X/© 2015 Elsevier B.V. All rights reserved.
Contents lists available at ScienceDirect
Geomorphology
journal homepage: www.elsevier.com/locate/geomorph
4.3. Drainage systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
5. Future challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
5.1. Remote Sensing (RS) and high-resolution topography (HRT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
5.1.1. HRT for a quick comparison among different areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
5.1.2. Track/monitor changes over time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
5.1.3. Understanding the link between processes and anthropogenic elements . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
5.2. Land planning and management as a function of induced Earth surface processes . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
6. Final remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
1. Introduction
Landscapes are characterized by distinctive morphologies that are
mainly caused by major forcings such as tectonic uplift, erosion, sediment
transport, and climate that have shaped the Earth's surface and
left characteristic topographic signatures. In addition to these processes,
biota forcing might play a role in shaping the landscape, but, of course,
at a different scale and magnitude than geological forcing. In biotic landscapes,
vegetation through its roots inﬂuences soil formation and surface
erosion. Biota also inﬂuence climate and, as a consequence, the
mechanisms that control erosion rates and the evolution of the landscape.
Dietrich and Perron (2006) compared the hypothetical frequency
of occurrence of landform properties for the present Earth and an abiotic
Earth. They suggested that the frequency distributions of measurable
landform properties (such as mountain height, steepness, and curvature)
could greatly differ, although all observed landform types would
be found in both biotic and abiotic worlds (Fig. 1a). The question is, if
we can suppose that there is evidence of biota forcing, what is the role
of humans? Fig. 1b shows a real case study of the relative likelihoods
that landform slopes will take a given value (probability density
Fig. 1. (a) Hypothetical frequency distributions (PDFs) of landform properties for the present Earth and an abiotic Earth (from Dietrich and Perron, 2006); (b) PDFs of slope for natural and
terraced landscapes in northern (Trento province) and southern (Salerno province) Italy, respectively. Fig. 1b also shows shaded relief maps of the two study areas obtained from 2 m
LiDAR topography.
141P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
functions, PDFs) in a natural and a terraced landscape (the slope was
calculated using high-resolution topography derived from LiDAR
data). The result is interesting as the distributions seem to conﬁrm the
hypothesis of Dietrich and Perron (2006). Even if all slope values are
found in the natural and in the anthropogenic environments, humans
actively alter the frequency of occurrence of some speciﬁc slope
values. In the case presented in Fig. 1b, the natural morphology presents
a bell-shaped distribution of slopes, with a peak around the average
slope (~35°). On the other hand, the anthropogenic slope PDF presents
two peaks: one around the average value and one around low
slope values. These two peaks are caused by the walls (having high
values of slopes) and by the benches (low values of slopes) of the
terraces.
Society developed signiﬁcantly during the Holocene (from hunting–
gathering, to farming, to complex societies and metropolises), and the
increase in population was related to a progressive increase in intensive
agriculture, raw material demand and urbanization. As a consequence,
human activities are leaving a signiﬁcant signature on the Earth by altering
its morphology, processes, and ecosystems (Ellis, 2011). Anthropogenic
landscapes cover as great an extent of the Earth's land surface
as many other globally important ecosystems (Foley et al., 2005). In anthropogenic
landscapes, human alteration is reﬂected by artiﬁcial Earth
surface features (e.g., artiﬁcial channels, road networks, agricultural
practices and mining activities) that may affect natural processes
(Tarolli, 2014). Indeed, the scientiﬁc community is now debating the
fact that we are living in a new geological epoch, the Anthropocene
(Zalasiewicz et al., 2008, 2011a,b, 2014; Monastersky, 2015), where
human activities leave a signiﬁcant, if not dominant, signature on
the Earth. Whether this is true for the whole of mankind or for only
some civilizations (Crist, 2013) and whether human bioturbation
(Zalasiewicz et al., 2014) constitutes a negligible (Visconti, 2014) or
substantial fraction of long-term Earth geological activities are still up
for debate. Some authors have highlighted how human impacts are
often difﬁcult to separate from naturally driven activities (Fuller et al.,
2015), and others have argued that it might be too soon to determine
the human impact on geological records (Lewin and Macklin, 2014;
Visconti, 2014; Smil, 2015). Aside from this recent ongoing debate, geomorphologists
have long investigated the impacts of human societies on
Earth surface processes and landform records, without needing the motivation
of creating a new geological time interval (Brown et al., 2013a,b).
1.1. Humans as a geologic agent?
Deﬁning humans as a geologic agent might sound a little strange or
even a provocation. Further, of course, if we compare human-induced
changes to geologic forcing through the millennia, we are looking at a
different scale and magnitude. However, humans have the potential to
amplify geomorphic processes (Wolf et al., 2014). Bioturbation by
humans (‘anthroturbation’) is a phenomenon without precedent in
Earth history and is orders of magnitude greater in scale than any preceding
nonhuman type of bioturbation (Zalasiewicz et al., 2014).
Humans have become the dominant element in many Earth surface processes
at different scales (Steffen et al., 2007; Wohl, 2013), to the point
that human activities can be considered distinct from, but comparable
to, the effects of climatic or tectonic transformations (Macklin et al.,
2014). According to Wilkinson (2005), humans move increasingly
large amounts of rock and sediment during various construction
activities, and therefore become a geological agent. They modify the
spatial distribution and rates of hydraulic and geomorphic processes
(Fryirs and Brierley, 2012), which might result in land degradation,
and geomorphic changes (Doolittle, 2006). Furthermore, anthropogenic
transformations coupled with the natural system can cause geomorphological
processes to continue in a ‘genetically modiﬁed’ form
(Lewin, 2013), and the results of this combined forcing might be preserved
in long-term geological records, at least in some environments
(Brown et al., 2013a, 2013b; Jefferson et al., 2013).
Unlike natural geomorphic processes, the work done by humans is
focused in speciﬁc locations with well-deﬁned intent (Guthrie, 2015),
and as a consequence, speciﬁc anthropogenic topographic signatures
emerge. Looking at the different signatures of humans, we can mention
mining activities, road networks and agricultural practices such as terracing
and land reclamation. These signatures are very connected to current
and future societal changes. In recent years, there has been a rising
demand for base metals and industrial minerals, which is also connected
to research into new forms of energy production (Vidal et al., 2013).
Populations' needs for food will more than likely push the expansion
of arable lands (D'Odorico and Rulli, 2013). Irrigated lands are also expected
to expand (Foley et al., 2011). Terraced areas are greatly threatened
by abandonment (Tarolli et al., 2014) or, on the other hand, by
the intensiﬁcation and specialization of agriculture (Cots-Folch et al.,
2009). In addition, in the next few decades there may be a progressive
increase in transportation networks (Sidle and Ziegler, 2012).
These signatures can cause drastic changes to the geomorphic organization
of the landscape, with direct consequences on Earth surface
processes (e.g., Mossa and James, 2013; Soﬁa et al., 2014c; Tarolli
et al., 2014, 2015). Thus, this review provides an overview of the recent
challenging theme of the role of humans as a geological agent, speciﬁcally
as connected to those elements that leave a clear topographic signature
on the Earth's surface. The goal is to present the state of the
science of the analysis of human bioturbation that can serve as a basis
to improve environmental planning, mitigate the consequences of
anthropogenic alteration, and provide useful guidelines in the future
challenges section. Identifying signals connected to anthropogenic topographic
signatures within Earth surface processes might help provide
a better understanding of current settings, the future and past steps in
the evolution of our planet, and to schedule appropriate environmental
planning for sustainable development, to mitigate the consequences of
anthropogenic alteration.
2. Mining
Recent rapid urbanization in developing countries is spurring the demand
for industrial products and infrastructures such as electricity
grids, transportation systems and buildings (Kobayashi et al., 2014).
For developed countries, on the other hand, renewable energy requires
infrastructures built with metals (Vidal et al., 2013). Therefore, more
mining is almost unavoidable, and it is predicted that the demand for
base metals in 2050 will be several times the current demand (Halada
et al., 2008). Focusing on the quantities of mined minerals, Rogich and
Matos (2008) highlighted that there has been a constant increase over
the years in mineral extractions, mostly for construction materials.
However, increases in mineral extraction are concomitant with increases
in population, and the overall extraction per person in the
1980s was similar to that of the 2000s despite the fact that the gross extraction
has been increasing. Given these trends and the trends in our
society, it is important to account for the fact that mining can have a signiﬁcant
impact on catchments, during mining and for many years after
the cessation of mining (Hancock et al., 2008; Herrera et al., 2010),
which can inﬂuence landscape evolution (Haigh, 1978; Martín-Duque
et al., 2010), sediment transfer (Macklin and Lewin, 1989) and thus geomorphology
as a whole system (Mossa and James, 2013). The most recent
review about the impact of surface and underground mining on the
geomorphic system is by Mossa and James (2013). This section of the
paper will focus speciﬁcally on surface mining, which leaves an obvious
topographic signature on the landscape through the creation of large
landforms (Fig. 2) and the addition of mine tailings.
Table 1 collects the most signiﬁcant literature on surface mining
and connected geomorphic processes, and it focuses on four main issues:
(i) mining and consequent erosion, (ii) mining-induced effects
such as subsidence, (iii) landslide/debris-ﬂow/rockfall risk, and (iv) effects
on runoff. In the following sections, we will focus in detail on the
most critical issues connected to these four points.
142 P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
2.1. Mining and erosion
Land displacement can be seen as a form of direct erosion on a global
scale as the quantity of material mechanically moved within the mines
(Fig. 3a). However, mining activities also produce erosion issues at local
scales, when large quantities of sediment are transported by water erosion
(Fig. 3b).
The contribution of construction and mining activities to the excavation
and displacement of solid materials on the Earth's surface
is important. Quantifying such displacements is not obvious, but
some works in the literature allow a ﬁrst insight. However, the measurement
units are not always homogeneous. To allow a comparison,
for those works reporting the values of moved material in weight per
person, in this section we consider (i) the world or country population
at the time of the survey of each referenced work, (ii) the extent
of the considered country, and (iii) we considered that the affected
volumes were uniformly distributed over the area analysed, and to
account for an overall rate of erosion in mm y−1
we assumed a
bulk density of 2000 kg m−3
for construction/mining together. For
those works reporting the values in weight at the hillslope scale we
assumed a bulk density of 1500 kg m−3
. Hooke (2000) estimated
that construction in the past required moving (importing or excavating)
5 m of stone with an average density of 2350 kg m−3
m−2
; we
assumed a slightly lower value to account for mineral production
and excavation (e.g., coal, gravel, sand). At the hillslope scale, mine
soils commonly have higher bulk densities and lower porosities
than native soils due to heavy trafﬁc associated with grading
(Thurman and Sencindiver, 1986), thus we assumed a higher value
than that assumed by Montgomery (2007b) for soil erosion.
We are aware that these are a rough simpliﬁcation of reality and that
more information should be accounted for: however, the transformation
of the published values in mm y−1
allows for a direct comparison
between the works. The ﬁnal values should be considered to be only indicative
rather than exact measurements.
In the past, large scale mining activities together with construction
projects have been estimated to move approximately 20 t pers−1
y−1
in industrialized countries (Luttig, 1987). These numbers seem to have
been conﬁrmed, with an estimated rate of ~16.8 t pers−1
y−1
in the
Fig. 2. Worldwide surface mining from Google Earth imagery: (a) Minnesota (USA), (b) Germany, (c) Utah (USA), (d) Australia, (e) Chile, (f) Egypt, (g) India, (h) Indonesia, (i) Spain,
(l) Italy, (m) China, (n) Norway, (o) Russia and (p) South Africa.
143P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
United States in the 1990s (Hooke, 1999) and ~16.5 t pers−1
y−1
in
Britain (Douglas and Lawson, 2000). Other sources show a global rate
of ~9.3 t pers−1
y−1
, with ~15.7 t pers−1
y−1
in Europe (Douglas and
Lawson, 2000).
Soil erosion rates reported in various units in the literature and converted
to equivalent lowering rates provide an interesting overview. We
can estimate the rates of erosion connected to mines and/or construction
activities, which range from ~0.2–0.3 mm y−1
(USA and industrialized
countries in the 1980s) to ~0.6 mm y−1
in Europe, compared to a
natural European erosion of 0.05 mm y−1
. The global estimated erosion
rate was ~0.4 mm y−1
. Among the industrialized countries, given the
smaller extent of the area, the highest erosion rate apparently was for
Britain (~2 mm y−1
). Mining and construction are also responsible for
erosion at the plot and catchment scale with watershed scale rates
that range from 0.1 mm y−1
to 3 mm y−1
(Hancock et al., 2006; Rivas
et al., 2006; Meng et al., 2012; Trabucchi et al., 2012). At the hillslope
scale, erosion rates can reach N10 mm y−1
(Esling and Drake, 1988;
Biemelt et al., 2005). Clearly, local erosion rates are differently inﬂuenced
by vegetation, rocks, soils, local slopes, and roughness, and by
subsidence and restoration plans (Sanchez and Karl Wood, 1989;
Nicolau, 2002; Choi et al., 2008).
The numbers obtained above, although subject to uncertainties, can
be compared with different estimates of material transfer by geomorphic
agents reported in Montgomery (2007b) (Fig. 4).
Keeping in mind that the highest geomorphological effectiveness
can be achieved by events of moderate magnitudes that occur more frequently
(Guthrie, 2015), estimates from the literature suggest that the
direct or indirect displacement of sediments related to mining activities
varies between b0.1 and N10 mm y−1
. This is a relatively high contribution,
and it is considerable if compared with sediment production from
natural processes; these values, although at different spatial and temporal
scales, are in line with those of steep, tectonically active topographies
(Fig. 4).
2.2. Surface mining and land subsidence
Surface subsidence (Fig. 5) is recognized as a problem in most countries,
particularly those with signiﬁcant mining and other underground
resource extraction industries (Whittaker and Reddish, 1989).
Table 1
Literature on surface mining and the connected geomorphic processes.
Surface mining, land
displacement and erosion
Luttig (1987), Hooke (1994), Hooke (1999),
Esling and Drake (1988), Sanchez and Karl
Wood (1989), Douglas and Lawson (2000),
Nicolau (2002), Biemelt et al. (2005), Rivas
et al. (2006), Hancock et al. (2006), Choi et al.
(2008), Trabucchi et al. (2012), Meng et al.
(2012)
Surface mining and land
subsidence
Dunrud (1984), Whittaker and Reddish (1989),
Jambrik (1995), Scott and Statham (1998),
Wolkersdorfer and Thiem (1999), Bell et al.
(2000), Kircher et al. (2003), Panilas et al.
(2008), Donnelly (2009), Woldai et al. (2009),
Herrera et al. (2010)
Surface mining and
landslides/debris
ﬂows/rockfall hazards
Hearn (1995), Ramani (1995), Bentley and
Siddle (1996), Johnes (2000), Baroni et al.
(2000), Wei et al. (2008), Erginal et al. (2008),
Cortopassi et al. (2008), Guerrero et al. (2008),
Rico et al. (2008a,b), He et al. (2009), Peila
et al. (2011), Li et al. (2012), Laimer and
Mulleger (2012)
Surface mining, runoff and
ﬂoods
Kilmartin (1989), Holmes et al. (1993), Negley
and Eshleman (2006), Ferrari et al. (2009),
Katpatal and Patil (2010)
Fig. 3. Surface mining in the Rheinald-Pfalz region (Germany) (photo by G. Soﬁa) (a) and erosion of tailing piles in a copper mine (Agency, U.S.E.P., 1972).
Fig. 4. Soil erosion rates for mining landscapes reported in various units in the literature
and converted to equivalent lowering rates (assuming a soil bulk density of
2000 kg m−3
for construction/mining together, and 1500 kg m−3
for soil erosion at the
hillslope scale) compared to gently sloping lowland landscapes (cratons), moderate gradient
hillslopes of soil-mantled terrain (soil-mantled) and steep, tectonically active alpine
topography (Alpine) erosion rate ranges published in Montgomery (2007b).
144 P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
In surface mining, land subsidence is mainly induced by the lowering
of the water table caused by groundwater withdrawal (Jambrik,
1995; Wolkersdorfer and Thiem, 1999; Panilas et al., 2008; Woldai
et al., 2009), and the rates of withdrawal-induced subsidence have
been estimated in some areas to reach 50 mm y−1
(Kircher et al.,
2003). Mine-related subsidence causes the permanent inundation of
land, aggravates ﬂooding, changes topographic gradients, can induce
fault reactivation (Donnelly, 2009; Woldai et al., 2009) and has direct effects
on the natural (Bell et al., 2000) and built (Wolkersdorfer and
Thiem, 1999) environment. The signiﬁcance of mining subsidence as a
geomorphic agent is mainly due to its spatiotemporal scale. Considering
the time scale, subsidence can occur simultaneously to the mining activities
and long after the mines have been abandoned (Bell et al., 2000) or
actively restored. It is usually said that mines are somehow ‘living creatures’
that constantly change, even when their productive life has come
to an end, causing subsidence and slope instability phenomena (Herrera
et al., 2010). Regarding the spatial scale, the extent of the subsidence
area can range from nearly equal to many times the mining area,
where aquifers are dewatered and undergo compaction (Dunrud,
1984). Modern surface mining operations usually have signiﬁcant extents,
and they can also inﬂuence relatively large portions of the terrain
adjacent to the crest of the open pit or around the extraction areas. The
vertical magnitude of the subsidence itself typically does not cause
problems. However, it is the associated surface compressive and tensile
strains, curvature, tilts, and horizontal displacements that are the cause
of the worst damage to the natural environment, buildings, and infrastructure
(Herrera et al., 2012). The location and extent of the mine as
well as the mining techniques in respect to geometrical and geological
factors, play a key role in ground subsidence (Herrera et al., 2010).
However, the accumulation of these effects gives an additional qualiﬁcation
to mining as a geologic agent.
2.3. Surface mining and landslides/debris-muddy ﬂows/rockfall hazards
Rockfalls and landslides (Fig. 6) in surface mining are among the
most critical threats associated with the various types of geological instabilities
and also induce risks near the mine (e.g., Johnes et al., 2000;
and Fig. 6b). Some authors have argued that in the mining framework,
human beings are an active factor contributing to disasters (Ramani,
1995); and with the scale of mining extending, landslide disasters on
the Earth's surface will become increasingly serious (He et al., 2009).
Three types of induced-processes can be considered: (i) landslides/
debris/rockfalls within the mine itself, (ii) indirect landslides/debris/
rockfall hazards exacerbated or induced by mining activities in the surrounding
landscape, and (iii) further indirect hazards connected to the
Fig. 5. Baorixile, Hulunbeir, Inner Mongolia: grassland subsidence due to the lowering of the water table caused by coal mining (Greenpeace, 2014).
© Lu Guang/Greenpeace.
Fig. 6. The Aberfan (UK) Disaster (21 October 1966): a wave of coal deposits from surface mining slid down the Aberfan Valley (Richards, 1966).
145P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
movements of the mobilized material in areas away from the mine
itself.
Concerning direct hazards within mines, surface deformations and
slope stability in mining activities have been approached from a modelling
point of view because these issues are very important to the mining
industry as potential sources of danger to people and equipment. For
this type of events, stability analysis and comprehensive treatment
methods together with geotechnical data, sliding causes, and characteristics
about landslides should be gathered to prevent economic and life
loss (Wei et al., 2008; Peila et al., 2011; Li et al., 2012). An interesting aspect
from a geomorphological point of view is the effect of mining on
landslide/debris/rockfall hazards within the mined catchments. The
presence of mining/quarrying activities is apparently responsible for
very low frequency, catastrophic events but also for frequent events
that recur on a daily or weekly basis (Hearn, 1995) and events that
are triggered by medium-intensity rainstorms (Baroni et al., 2000;
Cortopassi et al., 2008). In some environments, mining activities have
been proven to be the cause of the reactivation of dormant slides
(Bentley and Siddle, 1996). Morphologies artiﬁcially modiﬁed by mining
in conjunction with climate inputs (e.g., intense rainfall, Erginal
et al., 2008) and geology (e.g., existing geological discontinuities,
Laimer and Mulleger, 2012) have also been underlined as a crucial element
that contributes to landslides.
In addition to landslide processes, mining can produce debris ﬂows
and muddy ﬂoodwaters from tailings failures, which results in ﬂoods
(Fig. 7) that are commonly composed of water with high sediment concentrations
(Guerrero et al., 2008; Rico et al., 2008a). These events generally
occur in active mines and only rarely occur in abandoned ponds
(Rico et al., 2008a,b).
Without denying the role of natural forcing, the frequency of induced
events (daily or weekly basis) and triggering connected with medium
magnitude climatic episodes or the exacerbation of events related
to intense climatic episodes seem to indicate that anthropogenic pressure
can indeed play a contributing role in the overall landscape
instability.
2.4. Surface mining and runoff
As in the case of subsidence and erosion, the effects of mining on
runoff can continue once the extraction activities have stopped. It has
been underlined how reclaimed surface-mines cause increased runoff
more rapidly than undisturbed areas (Kilmartin, 1989; Negley and
Eshleman, 2006). This can generally result in higher ﬂood peaks, reduced
base ﬂows, shorter lag times between the rainfall and ﬂood
peak, reduced groundwater recharge and higher sediment loads in the
affected catchments. In addition, the destruction of interﬂuves connected
to the removal of rocks below the water table can alter the natural
groundwater ﬂow paths, which can contribute to the overall increase
in ﬂow velocities and the shortening of ﬂow paths (Holmes et al.,
1993). Over large areas, surface mining can be correlated to increases
in ﬂood extent (Katpatal and Patil, 2010) and magnitude (Ferrari
et al., 2009), with greater rates observed for less frequent return intervals
(Ferrari et al., 2009). Interestingly, all the above ﬁndings seem to indicate
that mining leaves the landscape in a condition more similar to
urbanized areas than to natural reclaimed landscapes.
3. Roads
Among the anthropogenic modiﬁcations of landscapes, road construction
has increased dramatically, and new surfaces have appeared
in most areas of the world (Jimenez et al., 2013). Networks of roads interact
with water and sediment ﬂow paths in multiple ways (Wemple
et al., 1996; Jones et al., 2000; Forman et al., 2003). Roads inﬂuence a variety
of hydrologic and geomorphic processes, which has been well
known for a long time (Reid and Dunne, 1984; Luce and Cundy, 1994;
Montgomery, 1994; Luce and Wemple, 2001). Among the environmental
effects of roads, the effects on water quality, aquatic ecology (Luce,
2002) and ecological processes (Jones et al., 2000; Cofﬁn, 2007) are critical.
Table 2 reports the most signiﬁcant literature regarding roads and
issues related to runoff, sediment production, erosion, connectivity
and geomorphic processes.
3.1. Roads in mountainous/hilly landscapes
Roads in mountainous/hilly landscapes support agricultural development,
timber harvesting, local travel, trade and tourism, but
at the same time they present difﬁcult and pressing environmental
challenges in many locations around the world (Petley et al., 2007;
Sidle and Ziegler, 2012). Fig. 8 shows an example of a rural mountain
road in Nepal. Interestingly, in 2010 (Fig. 8b) the road suffered from
a series of large landslides along its path. Google Earth also provided
an image from 2008 (Fig. 8a), which was taken before the road was
constructed. In that year, there were no landslides at the same location.
It is clear that a landslide can result from the combination of a
whole range of natural factors; in this case, however, human involvement
is evident.
Fig. 7. Aznalcóllar (Seville, Spain). The tail of a mine broke in 1998 and polluted beyond recovery almost 5000 ha of natural land on both sides of a 63 km stretch of river, including a vast
swath of Doñana Natural Park (Jackson, 2014).
146 P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
3.1.1. Roads, runoff, erosion and sediment production
Roads, tracks and trails in mountainous/hilly landscapes collect runoff
from overland ﬂows (Fig. 9a) as well as from rain falling on road surfaces
(Fig. 9b). As a consequence, they can produce runoff much more
rapidly than the surrounding landscape.
Roads and geomorphic processes on hillslopes interact in multiple
ways: (i) roads lead to a decrease of inﬁltration due to the rerouted ﬂow
paths; thus, they lead to an increase in surface runoff (Wemple and
Jones, 2003; Hölzel and Diekkrüger, 2012); (ii) road segments can intercept
subsurface ﬂows and reroute them to ditches and previously unchanneled
hillslopes, thus increasing the potential for erosion (Wemple et al.,
2001; Borga et al., 2004; Ziegler et al., 2004; Tarolli et al., 2013, 2015);
(iii) roads act simultaneously to rainfall events; thus, erosion is controlled
both by the road and by the rainfall characteristics (MacDonald et al., 2001;
Wemple et al., 2001; Sugden and Woods, 2007; Van Meerveld et al., 2014);
(iv) they enhance the effects of other forcings (e.g., vegetation removal)
(Harr et al., 1975; Eisenbies et al., 2007); and (v) the relative position of
the road with respect to the landscape, road slope and maintenance status
have impacts on soil erosion (Sugden and Woods, 2007; Bochet et al.,
2010; Brown et al., 2013a, 2013b; Butzen et al., 2014).
As a consequence of the above interactions, roads can initiate debris
slides that increase the rate of mass wasting (Swanson and Dyrness,
1975), and they affect hydrological processes and water pathways
(Fig. 10a) (Luce, 2002), ﬂoods and debris ﬂows (Jones et al., 2000). In
addition, roads can function as both production (Sugden and Woods,
2007; Jordán-López et al., 2009) and depositional sites for sediments
(Fig. 10b), contributing to basin-wide production (Dunne and
Leopold, 1978; Reid and Dunne, 1984; Ziegler and Giambelluca, 1997;
Fu et al., 2010; Collins et al., 2010).
Some authors have emphasized that despite predicted increases in
sediment yields from road surfaces, annual sediment yields at the catchment
scale appeared to be within natural levels (Fransen et al., 2001).
Other authors have emphasized how the net effect of roads was an increase
in basin-wide sediment production, highlighting the simultaneous
importance of natural hydrologic factors (Wemple et al., 2001; Wemple
and Jones, 2003). Scale, however, matters. Road effects on streams vary
depending on the scale; low-order headwater streams can be heavily inﬂuenced
by roads, whereas this effect might be masked in higher-order
catchments (Thomaz et al., 2014). Additionally, in small channels, road
crossings can produced deposition, width/depth changes and bed ﬁning,
whereas larger channels produce different effects (Marion et al., 2014).
As a form of erosion, gully initiation is also affected by roads. In this
framework, roads can have a greater effect than vegetation or ground
cover, for example (Muñoz-Robles et al., 2010). Gully incision can be
signiﬁcant below culverts and near road drains (Wemple et al., 1996;
Nyssen et al., 2002; Takken et al., 2008), but it can also develop in locations
away from the road itself (Nyssen et al., 2002; Jungerius et al.,
2002). In fact, roads alter the thresholds for gullying erosion
(Muñoz-Robles et al., 2010, Katz et al., 2014) and also induce the development
of individual gullies and multi-branched gully systems at a watershed
scale (Superson et al., 2014).
3.1.2. Connectivity
Due to the induced effects on runoff, ﬂow paths, erosion and sediment
transport, roads can have an overall effect on the catchment connectivity.
We consider two main effects: direct connectivity through
channels and gullies and diffuse connectivity via overland ﬂow paths
(Croke et al., 2005). The lack of maintenance of anthropogenic features
Table 2
Literature on roads and geomorphic processes.
Roads in mountainous/hilly landscapes Luce and Wemple (2001), Croke and Mockler (2001), Fransen et al. (2001), La Marche and Lettenmaier (2001), Madej (2001),
Megahan et al. (2001), Tague and Band (2001)
Roads, runoff and erosion/sediment Harr et al. (1975), Jones et al. (2000), Fransen et al. (2001), Wemple et al. (2001), MacDonald et al. (2001), MacDonald et al.
(2001), Luce (2002), Wemple and Jones (2003), Ziegler et al. (2004), Borga et al. (2004), Eisenbies et al. (2007), Sugden and Woods
(2007), Jordán-López et al. (2009), Bochet et al. (2010), Fu et al. (2010), Collins et al. (2010), Hölzel and Diekkrüger (2012), Tarolli
et al. (2013), Penna et al. (2014), Thomaz et al. (2014), Butzen et al. (2014), Marion et al. (2014), Brown et al. (2013a, 2013b), van
Meerveld et al. (2014)
Gully erosion Wemple et al. (1996), Nyssen et al. (2002), Jungerius et al. (2002), Takken et al. (2008), Muñoz-Robles et al. (2010), Katz et al.
(2014), Superson et al. (2014), Dewitte et al. (2015)
Connectivity Croke et al. (2005), Latocha (2014), Pechenick et al. (2014)
Roads in ﬂat landscapes Florsheim et al. (2001), Blanton and Marcus (2009), Vis et al. (2003), Beevers et al. (2012), Douven et al. (2012), Douven and
Buurman (2013), Blanton and Marcus (2013), Buchanan et al. (2013), Kumar et al. (2014), Trizzino (2015)
Fig. 8. Nepal. (a) Forested landscapes without roads, as seen in 2008 from Google Earth, and (b) landslides along the newly constructed rural road, as shown in the 2010 Google Earth
image.
147P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
tends towards higher stability and lower efﬁciency of morphological
processes, connected with a sustained decrease in slope–channel coupling
(Latocha, 2014). The road network geometry also plays a key
role in connectivity (Pechenick et al., 2014). Independent of scale, the
proximity of the road to the stream can be a valuable driver that discriminates
the channel conditions, and the shape of the network can
create physical connections to the streams through short overland
ﬂow paths and gullies (Pechenick et al., 2014). Section 5.1.3 will further
examine the connection between road networks and connectivity.
3.2. Roads in ﬂat landscapes
Especially in ﬂoodplains, roads can have a signiﬁcant impact on natural
ﬂood patterns and their functions. In ﬂoodplains, road construction
generally follows two approaches: ‘resistance’ and ‘resilience’ alignment
(Vis et al., 2003). The ﬁrst approach increases the elevation of the road
to create a dyke that prevents ﬂood water from over-ﬂowing the structure.
In the second approach, the resilience strategy, roads are developed
both by increasing road elevations and the implementation of
ﬂow-through structures to allow ﬂoodwaters to permeate the road
alignment (Vis et al., 2003). In both cases, the ﬂood pathways, extent
and duration will be changed by road development (Beevers et al.,
2012), with possible effects on erosion as well (Fig. 11).
As in mountainous environments, the drainage system of roads in a
ﬂoodplain can have large geomorphic effects. The resistance and the resilience
approaches cause two main interactions with geomorphic processes
in ﬂoodplains: (i) impacts due to bridges, culverts and roadside
ditches and (ii) impacts where the road structures act as dams along
channels, causing connections or disconnections with the ﬂoodplain
(Blanton and Marcus, 2009, 2013) or altering the hydrological properties
of the landscape (Trizzino, 2015).
Regarding ﬂoods, culverts can facilitate the excavation of channels
and widening of small tributaries (Florsheim et al., 2001). The use of designed
ﬂow-through structures maintains inundation patterns, but with
increasing levels of road development more impacts are observed to hydraulic
components such as water levels, velocities, inundation durations
and extents (Beevers et al., 2012). Roads and roadside ditches
can also contribute to increasing the drainage density of watersheds,
with effects on ﬂoods (Buchanan et al., 2013; Douven and Buurman,
2013). Road-related sediment sources from culverts and road ditches
can contribute to rivers and tributaries (Florsheim et al., 2001; Zajac
et al., 2002), which make roads an efﬁcient means of transport for pollutants
(Buchanan et al., 2013). Road networks act as an anthropogenic
‘barrier’ for water and sediment ﬂuxes across the landscape (Kumar
et al., 2014). The presence of a road network can inﬂuence the size
and duration of inundated areas during ﬂoods, fragmenting habitats
Fig. 9. Road effects on runoff. (a) An agricultural road intercepting snowmelt in the Rheinald-Pfalz region (Germany) (photo by G. Soﬁa) (a), and an agricultural road intercepting and
concentrating runoff in Tuscany (Italy) (photo by S. Calligaro) (b).
Fig. 10. Road effects on streams and erosion. (a) Off-highway trail capturing a local stream. (b) Sand building up at a road crossing and washing down into the West Branch of the Sheepscot
River, as indicated by the gully between the third and fourth guard rails (Klamath Resource Information System—KRIS, 2015).
148 P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
and drying wetlands (Beevers et al., 2012). Channels near roads can be
narrower and simpler in form, and they generally lack depositional elements
such as bars and islands (Blanton and Marcus, 2013). Even if they
have a localized direct impact on the overall ﬂoodplain hydraulics
(Douven et al., 2012), small changes in ﬂood dynamics may have large
ecological impacts, especially if the cumulative impacts of more road developments
are taken into account.
4. Agriculture
Civilizations in ﬂoodplains tend to expand rapidly, as long as agriculture
in the fertile river valley bottoms allows populations to grow
(Montgomery, 2007a). However, as villages expand into cities, the construction
of canals, dikes, and earthworks for ﬂood protection, irrigation
and reclamation follows (Hooke, 2000). Agricultural activities, among
other activities, can signiﬁcantly alter the geomorphology of large territories
over the course of thousands of years and from the use of various
tillage methods (Gottschalk, 1945; Costa, 1975). Shifting human geography,
especially associated with changes in farming and agricultural
activities, is a main factor that drives major soil erosion processes
(Boardman and Poesen, 2006), and as a consequence of the exploding
world population, there has been an astonishing increase in the geomorphological
effects of agriculture. Agricultural lands have been subjected
to drastic shifts in land use, characterized by a high diversity of
trajectories depending on the local conditions, regional context and external
inﬂuences (Verburg et al., 2010). In many countries where the
economy has shifted from mainly agricultural to industrial, agricultural
lands have been reforested (Fig. 12a and b) and lost to urbanization in
highly populated areas (Fig. 12c and d), but in other cases, agriculture
has intensiﬁed (Fig. 12e and f) with changes in agricultural techniques
and consequent changes in the drainage system.
The interaction between agriculture and geomorphic processes is
the result of a combination of factors ranging from small to large scales.
At the local scale, tillage practices can inﬂuence runoff and erosion (Van
Oost et al., 2006), and agriculture can induce structural degradation of
the soil (Palmer and Smith, 2013), which results in increases in runoff
and transport capacity. At the local and medium scales, terracing practices
have a direct effect on processes (Tarolli et al., 2014; Arnáez
et al., 2015). Moving to a larger scale, land abandonment (see
Section 4.2) and land expansion in previously uncultivated lands
(Dotterweich, 2013; Dotterweich et al., 2013; Merten and Minella,
2013) can induce or alter geomorphological processes. In addition, hydrologic
alteration (Florsheim et al., 2011), which is also connected to
the alteration of the drainage network for irrigation purposes, plays a
key role in the overall system (see Section. 4.3). However, although it
is well known that agricultural activities are directly relevant to watershed
hydrology and geomorphology, the speciﬁc effects of artiﬁcial
drainage as contributors to processes are not well understood
(Schottler et al., 2014), but sometimes, their effects are critical (Soﬁa
et al., 2014c) (Fig. 13).
The following two sections, therefore, will focus on the direct effects
of agriculture on soil erosion, with a speciﬁc focus on land abandonment
and the indirect effects connected to changes in drainage systems.
Table 3 recalls review papers and speciﬁc scientiﬁc special issues, focusing
on exemplary case studies in different parts of the world and speciﬁc
cultivations that underline the deep connection between agriculture
and surface processes.
4.1. Agriculture and soil erosion
Recently, García-Ruiz et al. (2015) offered a detailed analysis of published
data on soil erosion rates (in units of mass per area and time)
under a large range of climatic conditions and land uses, taken from
more than 4000 sites worldwide. Their results revealed a number of
general features, including the signiﬁcant effect of land use, with agricultural
lands yielding the highest erosion rates. Similar to the work
on mining (Section 2.1), the soil erosion rates reported in García-Ruiz
et al. (2015) for agricultural landscapes were converted to equivalent
lowering rates, assuming a soil bulk density of 1200 kg m−3
, which provided
an update to the idea of Montgomery (2007b) (Fig. 14).
The data from Montgomery (2007b) that were updated using the
values of García-Ruiz et al. (2015) show how cultivated ﬁelds from different
regions mostly erode at rates typical of alpine terrains. These
numbers conﬁrm what others have found; erosion rates from agriculture
are among the highest rates found for land uses (García-Ruiz and
Lana-Renault, 2011), and they can exceed the rates of most natural erosion
processes (Massa et al., 2012). Compared to long-term natural
events, however, one should consider that agricultural techniques produce
this amount of sediments in shorter periods of time (Dotterweich
et al., 2015). This difference in time scale can become a critical problem,
especially considering that the natural production of soil is relatively
slow and that soil might not be ‘naturally’ renewable on a societal
time scale (Stockmann et al., 2014).
Erosion from agriculture also has indirect effects on river desiccation,
groundwater depletion, water pollution, sedimentation, salinization
and salt-water intrusion (Atapattu and Kodituwakku, 2009). In
addition, agricultural erosion can deliver sediment to the drainage network
(Borselli et al., 2008), and it can have consequences on the rates
Fig. 11. Nebraska (USA): receding ﬂoodwaters rush through a culvert and tear out the surrounding earth (a) and an aerial view of the damages showing the receding ﬂood waters
(b) (Nebraska State Patrol, 2002).
149P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
and magnitudes of ﬂoodplain sedimentation (Doolittle, 2006; Knox,
2006). Connected with soil erosion from agricultural lands, ﬂoods, especially
muddy ﬂoods, are also of growing concern in Western Europe
(Bielders et al., 2003; Boardman et al., 2003, 2006; O'Connell et al.,
2007; Evrard et al., 2007; Heitz et al., 2009; Boardman, 2010;
Boardman and Vandaele, 2010).
4.2. Land abandonment with special reference to the Mediterranean region
Land abandonment has signiﬁcant environmental consequences that
are not equally relevant in all regions of the world. The most evident effects
of farmland abandonment are shown in Mediterranean areas, especially
in semi-arid environments, which are primarily affected by erosion,
piping and gullying (García-Ruiz, 2010). Speciﬁcally on hillslopes, the
generalized abandonment of agricultural terraces can favour surface
runoff, erosion and soil sliding (Arnáez et al., 2011; García-Ruiz and
Lana-Renault, 2011; García-Ruiz et al., 2013; Tarolli et al., 2014, 2015).
Land abandonment in Mediterranean environments is expected to increase
the overall net soil losses and total erosion and decrease sediment
delivery ratios (Debolini et al., 2013). Aside from the direct negative consequences
of land abandonment, the recolonization of natural vegetation
can also result in a reduction in soil loss and the progressive improvement
of soil characteristics (García-Ruiz and Lana-Renault, 2011). However,
such evolution can also result in changing stream morphologies, especially
narrowing and incision, and a decline in sedimentation levels in
Mediterranean reservoirs (García-Ruiz and Lana-Renault, 2011).
4.3. Drainage systems
In many agricultural regions, more than 80% of the catchment basins
may be drained by surface ditches and subsurface drain pipes (Blann
et al., 2009). Among the environmental impacts caused by agricultural
drainage, we can mention habitat loss or alteration, reduced water quality
and hydrologic alterations (Blann et al., 2009). Regarding hydrologic
alterations, a coarsening of the drainage networks in ﬂoodplains might
result in signiﬁcant changes to total groundwater recharge (Krause
et al., 2007) as well as changes in the connectivity of the watersheds,
which can alter overland ﬂows and create excess water hazards (Kiss
and Benyhe, 2015). As a result, the presence of a speciﬁc drainage system
may also limit the extent to which land cover would ever resemble
historic hydrological conditions (Schilling et al., 2008). Connected to the
coarsening of drainage networks, the loss of ditches might also result in
an exacerbated ﬂood risk (O'Connell et al., 2007; Wheater and Evans,
2009; Soﬁa et al., 2014c). This latter effect might be greater for events
with shorter return times, with an increase in ﬂood risk for rather frequent
rainfall events that are not necessarily associated with extreme
meteorological conditions and that are not necessarily associated with
worst case scenarios (Soﬁa et al., 2014c). Furthermore, ﬂows from artiﬁcial
drainage networks affect stream baseﬂows and streamﬂow recession
characteristics (Schilling and Helmers, 2008), and they have the
potential to alter water budgets on a watershed scale (Schottler et al.,
2014). Reduced surface storage and increases in conveyance can also result
in increased ﬂows in streams and rivers (Blann et al., 2009), with
consequences on sediment transport capacities and increases in the
Fig. 12. Changes in agricultural practices in Italy: (a) terraces in 2006 that were (b) progressively recolonized by forest in 2014 (Monterosso al Mare, La Spezia, Italy); (c) agriculture in 1945 that
was (d) lost to urbanization in 2015 (Galluzzo Certosa, Florence, Italy). Changes in crop practices in Kansas (USA) (e) in 1991 and (f) after the implementation of centre-pivot irrigation in 2014.
Images from Google Earth.
150 P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
amount of sediments carried downstream (Lenhart et al., 2012). These
effects can be so prominent that in some environments, they exceed
the effects of precipitation (Soﬁa et al., 2014c) and land-use changes
(Schottler et al., 2014).
Fig. 13. Critical issues in the reclamation network in Veneto (Italy) (a) area as seen on Google Street View ® in normal condition and (b) after a few hours of rain in Nov. 2014.
Photo by G. Soﬁa.
Table 3
Collection of review papers and journal special issues that underline the deep connection
between agriculture and surface processes.
Agriculture and
soil erosion
Martínez-Casasnovas and Sánchez-Bosch (2000), Bielders
et al. (2003), Boardman et al. (2003, 2006), Van Oost et al.
(2006), Boardman and Poesen (2006), Knox (2006),
Doolittle (2006), O'Connell et al. (2007), Evrard et al.
(2007), Atapattu and Kodituwakku (2009), Heitz et al.
(2009), Cerdà et al. (2009), Boardman and Vandaele (2010),
Boardman (2010), García-Ruiz and Lana-Renault (2011),
Florsheim et al. (2011), Massa et al. (2012), Tetzlaff et al.
(2013), Cerdà et al. (2013), Palmer and Smith (2013),
Merten and Minella (2013), Dotterweich et al. (2013),
Dotterweich (2013), Boardman (2013), Lieskovský and
Kenderessy (2014), García-Ruiz et al. (2015), Arnáez et al.
(2015), Dotterweich et al. (2015)
Land abandonment García-Ruiz (2010), Arnáez et al. (2011), García-Ruiz and
Lana-Renault (2011), García-Ruiz et al. (2013), Debolini
et al. (2013), Tarolli et al. (2014)
Drainage systems Krause et al. (2007), O'Connell et al. (2007), Schilling and
Helmers (2008), Schilling et al. (2008), Blann et al. (2009),
Wheater and Evans (2009), Lenhart et al. (2012), Soﬁa et al.
(2014c), Schottler et al. (2014), Schottler et al. (2014), Kiss
and Benyhe (2015)
Fig. 14. Soil erosion rates for agriculture reported in García-Ruiz et al. (2015) converted to
equivalent lowering rates, assuming a soil bulk density of 1200 kg m−3
, compared to gently
sloping lowland landscapes (cratons), moderate gradient hillslopes of soil-mantled terrain
(soil-mantled) and steep tectonically active alpine topography (Alpine) erosion rate
ranges published in Montgomery (2007b).
151P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
5. Future challenges
5.1. Remote Sensing (RS) and high-resolution topography (HRT)
Remote Sensing and Digital Elevation Models (DEMs) have been
used in geomorphology for decades. Researchers in applied geomorphology
and geomorphologic surveying and mapping have always
relied upon these techniques. However, over the past decade, developments
in technology have enabled the general public to be increasingly
engaged with technology (Goodchild et al., 2012). Governments and
remote-sensing communities inside and outside the scientiﬁc world
are now seizing opportunities for sharing spatial data (Bernard et al.,
2005; Krishnan et al., 2011; Wulder and Coops, 2014), and scienceintensive
ﬁrms are involved with ‘open data’ initiatives (Perkmann
and Schildt, 2014). At the same time, due to the ease of processing
and obtaining data, a large amount of interest has arisen in obtaining
high-resolution DEMs using ﬂexible and affordable technologies. The
recent development of the Structure-From-Motion (SfM) photogrammetric
technique (Westoby et al., 2012) and its application on UAVs
(Martin-Vide, 2004; Chen et al., 2015; Francioni et al., 2015) and even
smartphones (Micheletti et al., 2015; Prosdocimi et al., 2015) has
opened new frontiers for the analysis of Earth surface processes and,
in particular, anthropogenic processes. Unlike natural geomorphic processes,
the work done by humans is focused on speciﬁc locations with
well-deﬁned intent (Guthrie, 2015). The resulting topographic signatures
can be actively modelled only with the global availability of ﬁnescale,
three-dimensional topographies, and today this framework offers
a large advantage in terms of spatiotemporal coverage. In this context,
the future challenge is the ability to actually model anthropogenic morphologies,
quantify them, and analyse the links between anthropogenic
elements and geomorphic processes. In addition, the involvement of
public authorities, land managers and common users in the creation
and dissemination of datasets might slowly allow the transfer the
knowledge of geomorphic processes from the scientiﬁc to the practical
world.
Tarolli (2014) and Passalacqua et al. (2015) proposed an overview of
how HRT can be useful in engineered landscapes and offered a review of
the most important works carried out in this ﬁeld by researchers. In addition
to the literature provided in the previously mentioned reviews,
Table 4 reports the most recent and interesting works that address
HRT and anthropogenic environments.
In the following sub-chapters, three examples will be provided regarding
how HRT can be useful for the analysis of anthropogenic geomorphologies.
The case studies are based on the Slope Local Length of
Auto-Correlation (SLLAC) proposed by Soﬁa et al. (2014b) and successfully
applied in mining landscapes in Chen et al., (2015) (Section 5.1.1),
and in a slightly modiﬁed version of the procedure and the technique
proposed in Soﬁa et al. (2015), to automatically detect anthropogenic
areas (Section 5.1.2). A further example (Section 5.1.3) will show the
connection between roads and geomorphic processes, which is based
on the analysis of the sediment Connectivity Index proposed in
Borselli et al. (2008) and Cavalli et al. (2013), and on the changes to
ﬂow paths induced by roads, measured according to the Relative Path
Impact Index (RPII) proposed by Tarolli et al. (2013).
The original SLLAC metric (Soﬁa et al., 2014b) is based on a 2D crosscorrelation
between a slope patch and its surrounding areas. From this
cross-correlation, it is possible to derive the correlation length as the
horizontal distance of the areal cross-correlation that corresponds to
37% of the correlation maximum value. A second parameter, the Surface
Peak Curvature (Spc), is the average of the principal curvature of the
local maximums on the SLLAC map, where each maximum is deﬁned
as a pixel higher than its 8 nearest neighbours. The Spc is statistically
able to differentiate a terraced area from a natural one; Spc increases
as the landscape becomes more natural. Chen et al. (2015) furthermore
proposed a polynomial (for mining landscapes) that related the value of
the Spc to the percentage of the terraced area within an analysed site.
The connectivity index by Borselli et al. (2008) and Cavalli et al.
(2013) is intended to represent the potential connectivity between different
parts of the catchment and aims, in particular, at evaluating the
potential connection between hillslopes and features, which act as targets
or sources for transported sediment. The RPII instead considers
the contributing area as a proxy of the ﬂow path distributions, and in
a logarithmic form emphasizes areas where the ﬂows are increased
due to the presence of anthropogenic features (Tarolli et al., 2013).
See Soﬁa et al. (2014b, 2015) and Chen et al. (2015) for a full description
of the cross-correlation computation and for the measure of the correlation
length, Borselli et al. (2008) and Cavalli et al. (2013) for a full description
of the Connectivity Index and Tarolli et al. (2014, 2015) for a
clear description of the RPII.
5.1.1. HRT for a quick comparison among different areas
The purpose of the following example is to show the beneﬁt of the
availability of free HRT data (in this case LiDAR) to allow a rapid automatic
comparison between areas that have different human pressures
to associate them with study areas in different parts of the world.
Fig. 15 shows the Bingham Canyon Mine (A) (UT, USA) and the HullRust-Mahoning
Mine (B) (MN, USA).
The Bingham Canyon mine (A in Fig. 15, Fig. 15a) is one of the largest
man-made excavations in the world (Pankow et al., 2013) and has an
open pit that is 0.97 km deep and 4 km wide. In 2013, the mine experienced
a massive landslide that moved ~65 million cubic metres of material;
this cumulative event was probably the largest non-volcanic
landslide in North America in modern times (Pankow et al., 2013). For
this area, the Utah Automated Geographic Reference Center offers a
free download of a 2 m DTM collected in 2006 (AGRC, 2015) (Fig. 15b).
The Hull-Rust-Mahoning Mine (B in Fig. 15, Fig. 15d) is one of the
widest open pit iron mines in the world; the mine has a 2.5 by ~6 km
footprint and a depth up to ~180 m. For this area, the Minnesota
Geospatial Information Ofﬁce of the Minnesota Department of Natural
Resources offers a free download of a 1 m LiDAR DTM, which has an accuracy
of 0.6 m (Minnesota Department of Natural Resources, 2015). To
ensure homogeneity with the other study site, we generated a reducedresolution
(2 m × 2 m per pixel) version of the Minnesota DTM, where
each output cell contained the mean of the input cells that were
encompassed by the extent of that cell (Fig. 15e). For the two areas, a
SLLAC map was derived (Soﬁa et al., 2014b) (Fig. 15c, f).
It is clear that the open pit areas leave a deep signature on the topography
that is captured by long ﬁbres in the SLLAC map and high values
of the correlation lengths, which underlines what Chen et al. (2015)
found for open-pit mines in China. For the Bingham Canyon mine
map, the Spc value is 4.4 × 10−2
m−1
, whereas for the Hull-RustMahoning
mine, it is 3.9 × 10−2
m−1
. These values are statistically similar
to Spc values of areas with greater than 10% terrace coverage in Chen
et al. (2015) (p-value = 0.2 and 0.8, respectively in a Mann–Whitney
test, thus indicating a failure to reject the null hypothesis that the samples
come from different populations).
According to the polynomial approach in Chen et al. (2015), the
Bingham Canyon mine Spc corresponds roughly to 21% of terrace coverage
within the site. The considered DTM is approximately 103 km2
;
thus, the estimate extent of the anthropogenic surface is approximately
Table. 4
Collection of the most recent works dealing with HRT and anthropogenic environments.
Overview Tarolli (2014), Jones et al. (2014), Passalacqua et al.
(2015), Wasklewicz and Scheinert (2016)
Mining landscape Francioni et al. (2015), Chen et al. (2015), Hancock et al.
(2015)
Road networks Tarolli et al. (2013), Sherba et al. (2014), Soﬁa et al.
(2015), Soﬁa et al. (2015)
Agricultural landscapes Martínez-Casasnovas et al. (2013), Tarolli et al. (2014),
Soﬁa et al. (2014a,b), Diaz-Varela et al. (2014), Tarolli
et al. (2015), Stöcker et al. (2015), Prosdocimi et al.
(2015)
152 P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
22 km2
. By roughly measuring the extent of the entire mine (not just the
open pit), the mine covers approximately 30 km2
. Therefore, the automatic
procedure produced an estimate that is approximately 70% of
the actual mine extent. However, by considering the conﬁdence bounds
of the Chen et al. (2015) relationship, the actual extent of the mine falls
within these bounds. For the Hull-Rust-Mahoning mine, the Spc corresponds
to 60% of the anthropogenic surface, approximately 240 km2
,
considering the extent of the analysed DTM. A rough estimate by hand
of the extent of the mine, including the areas covered by water, is
approximately 110 km2
. If we include the footprints of the towns in
the study area in the deﬁnition of the anthropogenic surface (Hibbing,
Keewatin and Chisholm), considering that buildings on LiDAR DTMs
leave a clear mark that is captured by the SLLAC (see Fig. 15f), as also
underlined by Chen et al. (2015), we measured an actual extent of approximately
250 km2
, which is very close to the estimated area.
In the Hull-Rust-Mahoning Mine case study, it is interesting to see
that the mine area itself presents numerous ponds. Clearly, on the
LiDAR data these appear as a completely ﬂat surfaces. The SLLAC is
Fig. 15. Bingham Canyon Mine (A) (UT, USA) and Hull-Rust-Mahoning Mine (B) (MN, USA) as seen from Google Earth (a and d, respectively) and depicted by a 2 m LiDAR DTM (b and d).
(c) and (f) present the SLLAC maps as derived according to the method of Soﬁa et al. (2014b). The water bodies are coloured in blue in (f); they are completely ﬂat areas where the crosscorrelation
is zero.
153P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
based on the normalized cross correlation (Soﬁa et al., 2014b), and this
measure is an undeﬁned operation in regions where there is zero variance
within the analysed area. This is exactly the case for completely
ﬂat surfaces derived from water bodies on LiDAR DTMs. A road or a
ﬂat ﬁeld would not be the same as a water body because their surfaces
have some roughness and, as a consequence, all the water bodies can be
automatically identiﬁed as ‘empty’ spaces on the SLLAC maps.
5.1.2. Track/monitor changes over time
Anthropogenic changes follow economic and societal changes and
thus occur rather quickly on a geological time scale. The availability of
multi-temporal HRT surveys with short revisiting times offers an opportunity
to effectively track those changes.
The SLLAC (Soﬁa et al., 2014b) is based on the spatial organization of
slopes rather than absolute values and can therefore be applied to different
topographic supports. As a matter of fact, this technique can potentially
be generalized for application to any type of imagery. If we
broaden the concept of slope, it is mathematically possible to compute
the ‘slope’ (or gradient) of a greyscale image as the directional change
in the intensity or colours (Eq. 1):
∇f ¼
∂f
∂x
x̂ þ
∂f
∂y
ŷ; ð1Þ
where ∂ f
∂x
x̂ and ∂f
∂y
ŷ are the gradient in the x and y direction, respectively,
and f is the colour intensity in a greyscale range. Having the
image gradient map, it is then possible to feed this input to the crosscorrelation
evaluation, thus deriving a Gradient Local Length of AutoCorrelation
(GLLAC). The following example (Fig. 16) shows a mining
area in Spain. For this area, aerial photographs at 2 m resolution are
available for 2005 (a) and 2008 (e).
From these images, we computed the gradient (b) and (f), and we
derived the GLLAC (c) and (g), considering the same parameters presented
in Soﬁa et al. (2014b).
Once the GLLAC was derived, we computed the Spc and the average
GLLAC using a moving window of 1 ha, and we clustered the area into
two parts. According to Soﬁa et al. (2015), the cluster with the lower
Spc or, alternatively, the higher average SLLAC, can be considered as potentially
anthropogenic; thus, we considered the same approach
(Fig. 16d and h).
Despite limitations due to image illumination/sun exposure that
might prevent some elements from being clear on the image, the
GLLAC, analogously to the SLLAC, well captured the presence of terraces
within the open pit mine and the presence of roads with elongated ﬁbres.
Comparing the two maps at two different times, it is possible to detect
changes within the study sites and identify, for example, the
expanded area of the mine. The area has an Spc of 2.08 × 10−2
in
2005 and 2.17 × 10−2
in 2008. Despite coming from a different source
(GLLAC instead of SLLAC), the two values are statistically similar to the
population of the Spc values of areas with more than 10% terrace coverage
in Chen et al. (2015) (p-value = 0.6 and 0.5, respectively, in a
Mann–Whitney test, which indicates a failure to reject the null hypothesis
that the samples came from different populations).
As in the previous example, using these values and considering the
Chen et al. (2015) relationship, we obtained a 17% coverage by anthropogenic
structures in 2005 vs. 33% in 2008, which is a gain of ~16% in
structures over the complete extent of the considered image. In this
case, the image covered approximately 1 km2
, therefore the estimated
change in the areas over time was approximately 16 km2
. Clearly, the
results may have been biased by the image quality and exposure to
light, and the capability of tracking anthropogenic structures depends
on their visibility on the map (e.g., this would not be possible under vegetation
cover). The detection of anthropogenic signatures from a DEM
has some conﬁdence bounds (e.g., 10% of terraced areas, according to
Soﬁa et al., 2014b or ~4 km/km2
of roads in Soﬁa et al., 2015). However,
Fig. 16. Mining area in Spain in 2005 (a) and 2008 (e); image gradients for the two years (b and f for 2005 and 2008, respectively) and the derived GLLAC (c) and (g), computed using the
same parameters presented in Soﬁa et al. (2014b). Fig. (d) and (h) show the area automatically identiﬁed as anthropogenic, starting from the GLLAC images.
154 P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
the SLLAC (and GLLAC) derived values, when considering natural or anthropogenic
areas, are signiﬁcantly different, and to our knowledge,
there is no other indicator currently in the literature that is able to automatically
relate human signatures to speciﬁc morphological characteristics.
Thus, the ﬂexibility of this image-based approach is promising.
5.1.3. Understanding the link between processes and anthropogenic
elements
HRT describes the landscape topography, which is both a product
and a driver of the activity of geomorphic processes. In the same way,
the connectivity of landscape units with respect to water and sediment
ﬂuxes can be seen as both a driver and an emergent property of hydrological
(Ali et al., 2014) and geomorphic processes. Road networks have
a large effect both on sediment production and sediment delivery (see
Section 3); thus, the availability of HRT can help and provide a better
understanding of road-related issues. The site chosen for this application
was the Lookout Creek watershed within the Andrew Experimental
Forest (OR, USA). In this area, numerous studies have underlined the relationship
between roads and erosion (landslides and debris ﬂows)
(Swanson and Dyrness, 1975; Wemple et al., 1996; Jones et al., 2000;
Wemple et al., 2001). For this site, the Experimental Forest website offers
a free download of a LiDAR DTM at 1 m resolution in addition to a
Fig. 17. Connectivity Index (Cavalli et al., 2013) for the Lookout Creek watershed (OR, USA). The connectivity is evaluated in terms of (a, b and h) sediment delivery across the entire
drainage system (as in the potential connection of sediment between the hillslopes and catchment outlet) and the sediment coupling–decouplings between the hillslopes and main
channels (c, f and i) and the hillslopes and the road network (d, g and l).
155P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
complete database of the slides and road network (Fig. 17a). The watershed
is mostly covered by vegetation; thus, the road network can be visible
only by having either the actual locations of the roads or LiDAR
datasets that capture the morphology under the forest cover.
For this study, the Connectivity Index (Cavalli et al., 2013) was evaluated
in terms of the (i) sediment delivery across the entire drainage
system (as in the potential connection of the sediment between the
hillslopes and catchment outlet) (Fig. 17a, b, h) and the sediment coupling–decoupling
between the hillslopes, (ii) main channels (Fig. 17c,
f and i) and (iii) the road network (Fig. 17d, g and l). The choice of
modelling these aspects results from the need to address some primary
sediment management issues: (i) what is the probability that sediment
from a certain sediment source (in this case, landslide processes) will
reach the catchment outlet?; (ii) what is the probability that sediment
eroded from the hillslopes will inﬂuence the drainage network?; and
(iii) what is the probability that a speciﬁc event will deliver sediment
to the road network.
There is no reference theory for the partitioning of the connectivity
index into classes, and we therefore adopted a relative classiﬁcation
into four classes (High, Medium-High, Medium-Low and Low) by identifying
break points that best grouped similar values and maximized the
differences between classes (natural breaks); this has been found to be
suitable for alpine watersheds, where the variability in the index ranges
from −8 to 2 (Cavalli et al., 2013). In our study case, the connectivity
value assumed similar ranges, therefore we deemed the same classiﬁcation
to be suitable.
For this watershed, Wemple et al. (2001) underlined how the roads
inﬂuenced the overall sediment production in two ways: by producing
sediments below the roads and by capturing sediments coming from
above the road and delivering them downstream. We therefore selected
two case studies: one where the landslides were located in the proximity
of a road, but in the downstream direction (Fig. 17e, f, g), and one
where the landslides were located above the roads (Fig. 17h, i, l). In
both cases, the roads were built before the landslide events. In the
ﬁrst example, the slides occurred from 1964 to 1972, but the two
roads within the area were built in 1959. In the second example, the
landslide occurred in 1953, but the road was built in 1952.
In the ﬁrst example, the analysed slides clearly did not deliver sediment
to the road (Fig. 17g). By analysing the connectivity to the outlet
(Fig. 17e) and to the network (Fig. 17f) it is possible to understand
that the sediment produced by the slides nevertheless could have contributed
to the total sediment of the watershed at the outlet. In the second
example (Fig. 17h, i and l), the slide was produced above the road,
and it had a high topographic connectivity to the road itself (Fig. 17l),
but it was disconnected from the network because the road itself served
as an obstacle, or buffer (Fryirs, 2013) (Fig. 17i). However, the connectivity
to the road contributed to the overall connectivity of the sediment
to the outlet (Fig. 17h) because the road itself deviated the ﬂows downstream,
thus increasing the probability of delivering the sediment to the
watershed outlet.
The analysis of the RPII (Fig. 18) for the two above examples gives
further interesting insights.
In the ﬁrst case (Fig. 18a), the high value of the RPII (N1σ) indicates
that the landslide (ﬁrst white arrow on the left in Fig. 18a) might have
been induced by the presence of the road network. However, it is interesting
to see that the deviation of the ﬂows did not start on the road in
the immediate proximity of the slide, but on another uphill section of
the road network (second white arrow in Fig. 18a).
In the second example (Fig. 18b), the RPII enabled an understanding
that the connectivity of the sediment to the outlet (Fig. 17h) was created
by the road itself (Fig. 18b). Without the road network, this connectivity
would probably not have existed.
The use of the Connectivity Index and RPII on both examples highlights
what was found for the same watershed by Wemple et al.
(2001) after an extensive ﬁeld survey and using empirical data. Those
authors argued that during extreme events, (i) the roads in the midslope
positions dominated the production and redistribution of sediment
(as underlined by the HRT analyses in Fig. 17e, f, g and Fig. 18a)
and that (ii) a road network may have major impacts on stream
channels far removed from the initiation sites (as underlined by HRT
analyses in Fig. 17h, i, l and Fig. 18b). Therefore, in the context of anthropogenic
geomorphologies, the use of HRT for the analysis of connectivity
and road-induced changes in ﬂows can provide a tool to better
address the possible outcomes of sediment production, for example,
and the impacts on the watershed in terms of how sediments can be delivered
and transported within it. In addition, it would enable an understanding
of where engineering efforts should be focused.
5.2. Land planning and management as a function of induced Earth surface
processes
According to the latest State of World Population 2014 report from
the UN, the global population is predicted to reach nearly 11 billion by
2100. The increase in population and the introduction of new technologies
will be inevitably linked to an increase in raw-material demand
(e.g., the global production of concrete, steel, aluminium, copper and
glass) (Vidal et al., 2013). In addition, if food production must keep
pace with the demands of an ever-expanding global population, arable
lands are expected to expand, with the expansion in developing countries
being offset by a decline in the developed countries. Lands
equipped for irrigation and harvested irrigated lands are also expected
to expand (Alexandratos and Bruinsma, 2012). On the other hand, the
results of recent studies suggest that there will likely be signiﬁcant
levels of farmland abandonment in Europe over the next 20–30 years
Fig. 18. RPII (Tarolli et al., 2013) evaluated for (a) the areas shown in (Fig. 17e, f and g) and (b) the areas shown in (Fig. 17h, i and l). The arrows in (a) highlight slides that were probably
induced by the presence of the road, and the road caused the ﬂow alteration. The arrows in (b) highlight the connection of ﬂows induced by the road.
156 P. Tarolli, G. Soﬁa / Geomorphology 255 (2016) 140–161
(Renwick et al., 2013). In addition, in the next few decades, there may
also be an increase in transportation networks in support of agricultural
development, local travel, trade and tourism (Sidle and Ziegler, 2012).
The result will be an increase in anthropogenic forcing with direct consequences
on Earth surface processes, especially soil erosion, runoff,
hazards and land degradation. A future challenge is to change the perspective
of land planning and management: anthropogenic changes in
landscapes (mining, agricultural activities and land-use changes)
should be controlled and managed as a function of the induced geomorphic
processes. Transportation networks need to be better planned and
constructed with due attention to minimizing erosion and landslides, so
that downstream communities and river systems are adequately
protected (Sidle and Ziegler, 2012). In addition, agricultural policy potentially
plays a key role in determining whether land is utilized for
agriculture (Renwick et al., 2013). The same applies for cities. Cities
need to be designed and planned with regard to potential runoff,
water storage capacity and soil erosion due to the demand on raw and
construction materials. The construction of new mining areas and the
conversion of existing ones should consider the overall effects on the
landscape, keeping in mind that a reclaimed mined area may never
function like a natural one, but rather in a condition that is more similar
to urbanized areas. Additionally, the rates of erosion that are induced by
mining and agricultural activities, despite being on a shorter time scale
than natural erosion rates, may become a critical problem considering
that soil might not be easily renewable on a societal time scale. Correct
land management involving the scientiﬁc and land-management
worlds is, in principle, an appropriate device to underpin this. Science
can inform land-managers by providing evidence-based means for
assessing the rates and scales of erosion and can assist land users and
policy makers in devising appropriate and effective management techniques
and systems. Although natural constraints cannot be inﬂuenced
but can only be compensated for, degradation processes can sometimes
be reversed through technical interventions and research innovations,
and the Earth science community will absolutely play a key role in this.
6. Final remarks
Natural processes can leave characteristic topographic signatures on
the Earth's surface. Abiotic forcing (e.g., climate and tectonics) have
played and will continue to play a signiﬁcant role across larger time
scales in shaping the landscape. However, biota forcing can also affect
geomorphic processes. Vegetation, through its roots, inﬂuences soil formation
and surface erosion. Biota also affect climate, and as a consequence,
the mechanisms and erosion rates that control the evolution
of the landscape. Humans are part of the biota as well. More than vegetation,
human activities are leaving a signiﬁcant signature on the Earth
by altering its morphology, processes and ecosystems. We are looking
at a different magnitude and scale with respect to abiotic forcing, however
the resulting topographic signatures and related consequences are
signiﬁcant. Humans are able to move large amounts of materials during
various construction activities (e.g., urban expansion and raw material
demand) and thus play a similar role as a geologic agent. The population
of the Earth is increasing; the last estimate debates the fact that we will
reach 11 billion people by the end of this century if fertility rates do not
drop. The consequences of this will result in an increase in urbanization,
road networks, mining activities due to demands in raw materials in
support of different needs (e.g., houses, technological devices, and
cars) and agriculture. At the end of this century, anthropogenic geomorphology
probably will cover a large part of the Earth, with direct consequences
to Earth surface processes, ﬁrst to geomorphology but also to
climate. Society, from a geomorphological point of view, should ﬁnd solutions
to minimize the following consequences: soil erosion and
landsliding related to surface water ﬂow interception by roads, soil erosion
and mass movements related to mining activities, runoff and soil
erosion related to land use changes and issues related to anthropogenic
drainage systems. The new remote sensing technologies (e.g., LiDAR
and low cost photogrammetric techniques) and the derived highresolution
topography can greatly assist in the understanding of anthropogenic
signatures and their effects on processes. Society also needs to
change its vision regarding land use planning and management to ﬁnd
sustainable solutions. Road networks and urban expansion should be
controlled and managed as a function of the induced potential for erosion
and ﬂoods. These will probably be the great challenges of this
century.
Acknowledgements
The LiDAR data in Fig. 1b were provided by the Italian Ministry for
Environment, Land and Sea (Ministero dell'Ambiente e della Tutela del
Territorio e del Mare, MATTM) as part of the PST-A framework. Data
for the example reported in Section 5.1.1 were provided by the Utah
Automated Geographic Reference Center (AGRC) and MnTOPO®
(funding was provided by the Clean Water Fund of the Clean Water,
Land and Legacy Amendment). The data considered in the example in
Section 5.1.3. for the Lookout Creek watershed were provided by the
HJ Andrews Experimental Forest research programme, which is funded
by the National Science Foundation's Long-Term Ecological Research
Programme (DEB 08-23380), US Forest Service Paciﬁc Northwest Research
Station and Oregon State University. The author especially
wishes to thank the Editor and four reviewers for the useful suggestions
raised during the review stage.
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