Cao et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-019-0314-x(2020) 7:2

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open AccessGeomorphometric characterisation ofnatural and anthropogenic land covers
Wenfang Cao1* , Giulia Sofia2 and Paolo Tarolli1

Abstract
The scientific community has widely discussed the role of abiotic and biotic forces in reshaping the Earth’s surface.
Currently, the literature is debating whether humans are leaving a topographic signature on the landscape. Apartfrom the influence of humans on processes, does the resulting landscape bear an unmistakable signature ofanthropogenic activities? This research analyses from a statistical point of view the morphological signature ofanthropogenic and natural land covers in different topographic context, as a fundamental challenge in theemerging debate of human-environment relationships and the modelling of global environmental change. It aims
to explore how intrinsically small-scale processes, related to land use, can influence the form of entire landscapesand to determine whether these processes create a distinctive topography. The work focusses on four study areasin floodplains, plain to hilly, hills and mountains, for which LiDAR-derived Digital Terrain Models (DTMs) areavailable. Surface morphology is described with different geomorphometric parameters (slope, mean curvature andsurface peak curvature) and their frequency distribution. The results show that the distribution of geomorphometricindices can reveal anthropogenic land covers and landscapes. In most cases, different land covers show statisticallysignificant differences (p < 0.05) in their morphology. Finally, this study demonstrates the possibility to use ageomorphic analysis to quantify anthropogenic impact based on land covers in different landscape contexts. This
provides useful insight into understanding the impact of human activities on the present morphology and offers acomprehensive understanding of coupling human-land interaction from a geomorphological point of view.
Keywords: Geomorphology, Geomorphometry, Anthropogenic impact, Land cover

Introduction
Landforms represent the long-term development of geologic and geomorphologic processes (Bolongaro-Crevenna
et al. 2005; Oldroyd and Grapes 2008; Kleman et al. 2016).
They tend to reflect the interaction of climate, tectonics,
erosion and deposition (Castelltort et al. 2015; Zhang et al.
2016; Marshall et al. 2017). An increasing amount of theresearch (Szabó et al. 2010; Hooke 2012; Ellis et al. 2013;
Goudie and Viles 2016; Tarolli and Sofia 2016; Tarolli
2016; Brown et al. 2017; Migoń and Latocha 2018; Goudie
2018; Tarolli et al. 2019) has pointed out that human activity has played a pivotal role as geomorphic forcing. For
instance, agriculture is susceptible to accelerate soil erosion (Tóth 2010; Curebal et al. 2015; Borrelli et al. 2017),
dams and reservoirs engineering interrupt the continuity* Correspondence: wenfang.cao@phd.unipd.it
1
Department of Land, Environment, Agriculture and Forestry, University of
Padova, Agripolis, viale dell’Università 16, Legnaro (PD) 35020, Italy
Full list of author information is available at the end of the articleof sediment transport in rivers system (Tessler et al. 2016;
Wang et al. 2016; Poeppl et al. 2017), road network construction is associated with slope stability of roadcut andother geological risks (Csima 2010; Sidle and Ziegler 2012;
Penna et al. 2014; Ramos-scharrón 2018).
With this literature, the concept of surface reshaping
from both abiotic and biotic forces has emerged (Ellis
2004; Steiger and Corenblit 2012; Pietrasiak et al. 2014;
Tarolli and Sofia 2016). As suggested by Dietrich andPerron (2006), small-scale biotic processes can influence
the form of landscapes and create a distinctive topography, but this has yet to be investigated for human-madelandforms. Identifying natural and anthropogenic featuresand further distinguishing the landform signatures still
poses a significant challenge for the geomorphologicalcommunity (Tarolli et al. 2019). Thanks to the progress inremote sensing techniques and open-access datasets, therecognition of large-scale geomorphic signatures is now
possible at various scales (Evans 1980; Nagel et al. 2014;© The Author(s). 2020 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.

Cao et al. Progress in Earth and Planetary Science(2020) 7:2

Page 2 of 17

Fig. 1 Considered four study areas: a floodplain, b plain to hilly, c hilly, d mountain

Sofia et al. 2014a; Tarolli 2014; Byun and Seong 2015;
Jordan et al. 2016). However, an explicit characterisation,from a morphological point of view, of natural and anthropogenic surfaces and for different landscape contexts,
is still missing. This study showcases how high-resolution
topographic data can offer the basis to (1) characterise
specific signatures with land covers on the basis of an objective geomorphometric analysis; (2) demonstrate thatthe anthropogenic and natural land covers show a statistically different underlining morphology and (3) understand(where present) the degree of anthropogenic impact due
to the various land covers.

Since a significant concern is how natural systems arebeing modified or transformed by anthropogenic land uses,
one crucial issue is how the different land surface should
be disaggregated for modelling and further analysis, and if
any generic relationships can be identified between land
uses and morphological transformations to the landscape.
Geosciences must advance towards empirical and theoretical frameworks that integrate the natural and socioculturalforces that are now among the leading shapers of Earth’s
surface processes (Tarolli et al. 2019) to understand thecauses and consequences of these transformations andcontribute to building a sustainable future. This work

Fig. 2 The field overview of the study areas: a floodplain, b plain to hilly, c hilly, d mountain (photo in 2a by P. Claudio; photo in 2b by M. Luca;
photo in 2c by B. Eros, photo in 2d from www.abfotografia.it)Cao et al. Progress in Earth and Planetary Science(2020) 7:2

offers an example of such an empirical framework, providing a diagnostic tool to infer objectively morphological differences within various landscapes. Processes happen inthree dimensions and observing the topographic differences among land covers offer a basis to potentiallyinfer differences in the processes happening in theselandscapes.

Study area
This study investigates four study areas of 10 × 10 km in
northeastern Italy, representing different landscapes,
from floodplains to mountains (Fig. 1): the Veneto floodplain (floodplain, Fig. 1a), Colli Euganei (plain to hilly,
Fig. 1b), Venetian Prealps (hilly, Fig. 1c) and Trentino(Alpine mountains, Fig. 1d). These sites were selected
because they share close geographic locations and distributions of land covers, but they differ in landforms.
The elevations in Veneto floodplain (Fig. 1a) range
from − 2 to 10 m a.s.l., with an average of 4 m a.s.l.
Seventy-five percent of the area has a height lower than

Page 3 of 17

5 m a.s.l. This area is characterised by a higher level ofanthropogenic pressure, especially agricultural landscapes, due to urbanisation and industrialisation. The
area is intensively drained for reclamation and irrigation purposes through a dense network of channels
and ditches (Fig. 2a). The plain to hilly area (Fig. 1b)
has an elevation ranging from 0.4 to 601 m a.s.l. (average 112 m a.s.l.). Seventy-eight percent of the area has
a height lower than 200m a.s.l. These hills are of volcanic origins and rise between 300 and 600 m. Vineyard cultivation is widespread in this area (Fig. 2b).
The elevation in hilly area (Fig. 1c) ranges between 88
and 889 m a.s.l. (average 251 m a.s.l.). Ninety-five percent of the area is concentrated on the height between
100 and 500 m a.s.l. As in the Euganei, vineyard is alsoa typical characteristic of this area (Fig. 2c). The
fourth area (Figs. 1d and 2d) is an Alpine landscape.
The elevation ranges from 541 to 2488 m a.s.l. with amean value of 1577 m a.s.l. Eighty percent of the area
has the height from 1000 to 2000 m a.s.l.

Fig. 3 LiDAR DTMs of the four study areas: a floodplain, b plain to hilly, c hilly, d mountainCao et al. Progress in Earth and Planetary Science(2020) 7:2

MethodData

Light detection and ranging–derived digital terrain models(DTMs) at 2-m resolution (Fig. 3) are available thanks topublic authorities in Italy (Italian Ministry for Environment,
Land and Sea; Treviso Province; Trentino Alto-Adige Autonomous Region). The datasets refer to the year’s range
2010–2012.
Information about land cover is available through theCorine-Land-Cover database (CLC, Coordination of Information on the Environment Land Cover) classification, as also reported by the local authorities. The
considered CLC data come from an updated version of
the Urban Atlas (European Environment Agency 2012)provided by the local government (Regione del Veneto2012). The original Urban Atlas is mainly based on the
combination of statistical image classification and visualinterpretation of very high resolution (VHR) satellite imagery. Multispectral SPOT 5 & 6 and Formosat-2 pansharpened imagery with a 2-to 2.5-m spatial resolution
is used as input data. The built-up classes are combinedwith density information on the level of sealed soil derived from the high-resolution layer imperviousness toprovide more detail in the density of the urban fabric(European Environment Agency 2012). The updated

Page 4 of 17

version was enriched by the local government (Regionedel Veneto) with functional information (road network,
services, utilities…) using ancillary data sources such as
regional cartography, forest inventories, road network
graphs, aerial photographs and ground surveys.
For the purpose of this work, we focused on artificial
surfaces, agriculture and forest (level 1 of the CLC classification). However, due to the large-scale cultivation ofvineyard in the plain to hilly and hilly areas, which we
expect to have a significant impact on the morphologyof the surfaces, we defined vineyard as an independentclassification from agriculture. As well, we consideredgrass as an independent land cover because it may occur
naturally or as the result of human activity (pastures,park and recreational sites), and this allows us to understand better the associated anthropogenic impact onland covers. The land cover classification can be seenfrom Fig. 4.

Geomorphometric parameters

To make quantitative measurements of landscape properties, we considered three geomorphometric parameters:
slope and mean curvature proposed by Evans (1980) andthe Spc developed by Sofia et al. (2014a).

Fig. 4 The land cover classification (Corine Land Cover related to 2012) in the study areas: a floodplain, b plain to hilly, c hilly, d mountain. The
black rectangle areas are the case studies in Fig. 12

Cao et al. Progress in Earth and Planetary Science(2020) 7:2

Page 5 of 17

Slope and curvature

Evans (1980) describes the DTM surface is approximated to a bivariate quadratic function in the form of:
Z ¼ ax þ by þ cxy þ dx þ ey þ f
2

2

ð1Þ

where x, y and z are local coordinates, and a to f arequadratic coefficients.
From such a surface, it is possible to compute the first(slope, Eq. (2)) and second (curvature, Eq. (5)) derivative.
Slope (Fig. 5) is calculated as:pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
Slope ¼ arctan d 2 þ e2
ð2Þ
where d and e are coefficients from Eq. s(1).
Curvature is the second derivative of the surface, also
referred to the change rate of slope gradient or direction(Wilson and Gallant 2000), and it emphasises convex
and concave elements in the landscape. Evans (1980)
proposes two measure of curvature, maximum and minimum, and Wood (1996) testifies that only the resolutionof the DTM and the neighbouring cells relevant to theseparameters and further defined as
qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
curvaturemax ¼ k  g −a−b þ ða−bÞ2 þ c2

ð3Þ


qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
curvaturemin ¼ k  g −a−b− ða−bÞ2 þ c2

ð4Þ

where a, b and c are quadratic coefficients from Eq.
(1), g is the grid resolution of the DTM (2 m) and k is
the size of the moving window.
From the Eq. (3) and (4), mean curvature (Fig. 6) canbe defined as:curvaturemean ¼

curvaturemax þ curvaturemin2ð5Þ

Surface peak curvature

The Spc is inversely correlated with anthropogenic pressure(Chen et al. 2015; Sofia et al. 2016; Xiang et al. 2018).
Surface morphology (slope) of regions presenting anthropogenic structures tends to be well organised (low Spc) and,in general, self-similar at a long distance. The basis for theevaluation of the Spc is the Slope Local Length of Auto-Fig. 5 Slope maps for the four study areas: a floodplain, b plain to hilly, c hilly, d mountain. Slope is colour-coded according to 1 to 2 times
intervals of standard deviation σ from the mean μCao et al. Progress in Earth and Planetary Science(2020) 7:2

Page 6 of 17

Fig. 6 Mean curvature maps for the four study areas: a floodplain, b plain to hilly, c hilly, d mountain. Mean curvature is colour-coded according
to 1 to 2 times intervals of standard deviation σ from the mean μ

Correlation (SLLAC). This index quantifies the local selfsimilarities of slope (Sofia et al. 2014a). It is based onthe (demonstrated) assumption that natural areas
present low correlations within a neighbourhood because they are inherently irregular, while artificial
surfaces to satisfy human needs for mobility and machine access tend to display a higher level of selfsimilarity with surroundings (Sofia et al. 2014a; Xiang
et al. 2019). Describing the algorithm in detail is beyond the scope of this study: the authors refer to Sofia
et al. (2014a) for a complete description of the procedure and to other examples of applications (Chenet al. 2015; Sofia et al. 2016; Tarolli and Sofia 2016;
Xiang et al. 2018; Xiang et al. 2019).
Briefly, the steps to obtain the Spc (Fig. 7) are as
follows:
1) Evaluate correlation between a moving
window (W) and a patch (T) centred at thecentre of the moving window. The implemented
algorithm computes a normalised crosscorrelation between a template and the patch, inthe spatial frequency domain, and reports astandardised value that ranges between 0(no correlation) and 1 (perfect correlation).

The larger the absolute values, the stronger ofthe correlation.



P 
u;v W ðiþu; jþvÞ −W i; j T u;v −TCorrði; jÞ ¼ P 
2 P 
2 0:5
u:v W ðiþu; jþvÞ −W i; ju;v T u;v −Tð6Þ2) Evaluate the correlation length (L) thresholding at
37% (ISO 2013; Whitehouse 2011), the maximum
correlation value (Eq. (6)). The length of correlationis the length of the longest line passing through thecentral pixel and connecting two boundary pixels
on the extracted area connected to the central pixel(SLLAC map in Additional file 1).
3) Evaluate the Spc (surface peak curvature) of the
SLLAC map defined as:

1 Xn
Spc ¼ −
i¼1
2n

 2
  2

∂ zðx; yÞ∂ zðx; yÞ
þ
;
∂2 x
∂2 y

ð7Þ

for every peak (pixel higher than its eight nearest neighbours). Where z stands for SLLAC value, x and y represent
the cell spacing, n is the number of considered peaks.

Cao et al. Progress in Earth and Planetary Science(2020) 7:2

Page 7 of 17

Fig. 7 Spc maps for the four study areas: a floodplain, b plain to hilly, c hilly, d mountain. Spc is colour-coded according to 1 to 2 times intervalsof standard deviation σ from the mean μPlease refer to the supplement to infer statistic values(mean, median, STD, MAD, skewness…) of each geomorphometric parameters within each land cover.
Statistical analysis

We expect that the topographic signature of anthropogenic
activities may be more subtle than the presence of a specificlandform and that it would likely be a signature on the frequency of occurrence of the various degrees of the investigated landscape properties (slope, curvature, Spc). Thatis, the frequency distributions of these measurements
would be very different, even though all observed landform types would be found in both natural or anthropogenically modified landscapes. Therefore, we observed theprobability density function (PDF) of the considered landscape parameters to (1) investigate statistical differences ingeomorphological surfaces between land covers under different landforms contexts and (2) explore the specifictopographic signatures of land uses. For this work, the
PDFs are a probability density estimate for the sampled
data. The estimate is based on a normal kernel function
and is evaluated at equally spaced points that cover therange of the sampled data. The distance between points ischosen automatically, based on the range of values. Thismeans that it can be very narrow (< 0.001) for landscapeparameter with small magnitude. In these cases, the PDFs
can reach values much greater than 1, but their integral
over any interval is always less or equal to 1.
After statistically ensuring that the datasets did notpresent a normal distribution and they exhibit heteroscedasticity, we decided to consider a Kruskal-Wallis test
(McKight and Najab 2010) to evaluate whether there
were significant differences between landscape properties underneath a specific land cover, across multiple
landscapes, and we set a p value threshold of 0.05 forsignificance. The null hypothesis for this test is that thedata for each group are statistically equal.
To investigate the similarities in PDFs between land
covers, we applied the two-sample Kolmogorov-Smirnov
test, which specifies the equality of probability distribution
between two samples (Wilcox 2005; Razali and Wah 2011).
One thousand points within each land cover were randomly selected and tested ten times to ensure the robustness of the results.

Results and discussion
Signatures recognition with different land covers

The PDF of slope (Fig. 8) exhibits very different appearances
throughout the investigated landscapes. The central tendency moves from lower value to higher value, and the PDF

Cao et al. Progress in Earth and Planetary Science(2020) 7:2

Page 8 of 17

Fig. 8 The PDFs of the slope with different land covers in the four study areas: a floodplain, b plain to hilly, c hilly, d mountain. The vineyard inplain to hilly (b) and hilly (c) areas present bimodal curve

itself tends to be more dispersed, as we increase landscapeelevation. Even though the slope PDFs are always skewed,
those in steeper topography present (as expected) a much
longer tail than that of more gentle landscapes (i.e. floodplain). Taking a closer view of land cover distributions, theforest distribution in hilly and mountain areas present lower
skewness respect to that in the floodplain. As well, most of
the land covers in the mountain site show lower asymmetry.
This could be an underlining symptom that humans activities are less marked in the mountains rather than in floodplains. Sofia et al. (2017) showed, for the Veneto region,different trends in anthropogenic expansion depending on
the topographic location, highlighting a significant pressurein floodplains rather than in high mountains. Other works
also highlighted how anthropogenic processes in the Alps
are not fundamentally different from the processes in thefloodplains, but they occur with a time lag and on a smallerscale (Perlik et al. 2001). Consequentially, the human signature on morphology might be less marked (Sofia et al.
2016). At the same time, the anthropogenic signatures onmorphology in the Alpine environments reflect the fact that
activities are generally shaped through valley bottoms andridges, and by limits due to the slope and the steepness ofthe terrain (Forman et al. 2003).
A further interesting result is the striking similarity between the PDFs of vineyards in the plain to hilly (b) and

Fig. 9 Overview of a typical vineyard in the plain to hilly landscape (from (a), the blue color represents the first slope peak value which is theterrace bench, and the green shows the second slope peak value which represents the terrace walls; (b) and (c) presents the corresponding
elevation and satellite image)Cao et al. Progress in Earth and Planetary Science(2020) 7:2

Page 9 of 17

Fig. 10 The PDFs of the mean curvature with different land covers in the four areas: a floodplain, b plain to hilly, c hilly, d mountainthe hilly sites (c): for these landscapes, the PDFs present
a double peak.
The first peak falls at a range of 0–3°, while the second
peak falls around 3–10°. It is possible to note that in thislandscape (Fig. 9), vineyards are constructed over terraces: the terrace walls present slope with the highest
values (the second peak); on the other hand, the slopewith lower values (the first peak) is related to the terrace
benches. This peculiar double peak in terraced landscapes was also showed by (Tarolli and Sofia 2016), forterraces related to urbanisation over hillslopes.
Focusing on mean curvature (Fig. 10), all landforms
present a distribution that peaks around 0. The extreme
values on the positive side are related to divergent-Fig. 11 The boxplot of mean curvature and the negative/positive threshold in the floodplain

Cao et al. Progress in Earth and Planetary Science(2020) 7:2

convex landforms, and they are generally associated withthe dominance of hillslopes. The presence of extreme
values on the negative side is related to convergentconcave landforms associated generally with fluvialdominated erosion (Tarolli et al. 2012; Evans 2013). As
shown in Fig. 10, the long tails of extreme value are
related to artificial land covers in the floodplain and toforests in the plain to hilly and hilly areas.
To better identify the reason behind these long tails, weused boxplots to detect the positive and negative outliers(Fig. 11) The idea behind this is that convex features canbe identified as curvature values above the upper bound,and on the contrary, concave features can be identified asvalue below the lower bound (Sofia et al. 2014b).
As we can see from the satellite image (Fig. 12), thenegative outliers of curvature in the floodplain aremostly related to channel networks, while the positive
outliers are related to scarps, levees and small banks

Page 10 of 17

around them. Some noise in the curvature map is givenby the footprint of the urban area, where negative andpositive outliers can be found around buildings. For the
hilly area, the outliers on the positive side are related toridges, while the negative ones are channelized valleys,
where forests are mostly present.

Statistic test on the morphology of different land covers
under various landforms

The results of Kruskal-Wallis test (Table 1) show thatsignificant differences (p < 0.05) exist among land covers
regarding their slope.
Looking at the p values for the Kolmogorov-Smirnov
test (Table 2), it is possible to highlight that (1) there are
significant differences in slope between any pair of land
covers in each case study areas, except for grass and vineyard in hilly sites (marked with asterisk symbol); (2)Fig. 12 The positive and negative outliers identified from mean curvature in the floodplain (a) and hilly area (c), the left side are the DTMs, andthe right side (b) and (d) are the satellite images. The location of the study areas is marked in black rectangle in Fig. 4

(2020) 7:2

Cao et al. Progress in Earth and Planetary Science

Page 11 of 17

Table 1 Kruskal-Wallis significance tests of difference in slope between different land covers in each study area. Source means theorigin of variance, and it includes the variance between groups (different land covers) and error (geomorphometric value in thesame land cover); SS means sum of square; MS represent standard deviation, and it could reflect the degree of dispersion of adataset; chi sq. is the H-statistic of the Kruskal-Wallis test, which is approximately chi-square distributed. The Prob. > chi sq. is the pvalue
Floodplain

Plain to hilly

Hilly

Mountain

SourceSSMS

Chi sq.

Prob. > chi sq.

Groups

1.22E+09

3.04E+08

583.76

5.06E-125

Error

9.20E+09

1.84E+06

Total

1.04E+10Groups

4.23E+09

1.06E+09

2031.160

Error

6.18E+09

1.24E+06

Total

1.04E+10Groups

5.86E+09

1.465E+09

2813.10

Error

4.55E+09

911886.3

449.184.90E-97

Total

1.04E+10Groups

5.99E+08

2.0E+08

Error

4.73E+09

1.18E+06

Total

5.33E+09

among all the p value, the most remarkable difference always relates to the forest and any of the other land covers.
We addressed the same analysis on mean curvature.
However, the results (Table 3) show no significant difference among land covers (p value > 0.05).

As a trial test, we randomly sampled 10,000 points
from the maps: with this enlarged dataset, the results
show a significant difference among various land covers
as a group or pairwise (Tables 4 and 5). Crops and vineyards give exceptions to this in the plain to the hilly area

Table 2 The significance level of two-sample Kolmogorov-Smirnov tests of pairwise differences in slope between land covers within
topographic types
Variable
Floodplain

Crop

Crop

ForestArtificial

Grass

Vineyard

1.55E-45

3.59E-27

1.23E-44

3.71E-36

1.55E-45

1.23E-44

1.55E-45

5.34E-42

1.75E-19

ForestArtificial
Grass

3.71E-36

Vineyard
Plain to hilly

Crop

2.21E-08

Forest

5.96E-06
4.26E-13

Artificial

8.24E-24

9.25E-05

2.69E-29

1.27E-12

1.40E-13

5.56E-01

Grass

9.13E-18

Vineyard
Hilly

Crop

5.06E-30

Forest

6.12E-07

1.34E-15

3.96E-16

3.64E-23

6.66E-19

3.28E-17

9.25E-05

2.45E-05

Artificial
Grass0.556*

Vineyard
Mountain

Crop
ForestArtificial

1.93E-39

1.68E-31
9.48E-44

5.96E-06
2.33E-35
1.44E-34

Grass*(1) there are significant differences in slope between any pair of land covers in each case study areas, except for grass and vineyard in hilly sites; (2) among all
the p value, the most remarkable difference always relates to the forest and any of the other land covers.

Cao et al. Progress in Earth and Planetary Science(2020) 7:2

Page 12 of 17

Table 3 Kruskal-Wallis significance tests of mean curvature in each study area based on different land covers
Floodplain

Plain to hilly

Hilly

Mountain

SourceSSMS

Chi sq.

Prob. > chi sq.

Groups

1.64E+07

4.11E+06

7.89

0.0958Error

1.04E+10

2.08E+06

Total

1.04E+10Groups

9.17E+06

2.29E+06

4.4

0.35

Error

1.04E+10

2.08E+06

Total

1.04E+102.2

0.69912.63

0.4516

Groups

4.58E+06

1.15E+06

Error

1.04E+10

2.08E+06

Total

1.04E+10Groups

3.51E+06

1.17E+06

Error

5.33E+09

1.33E+06

Total

5.33E+09

Table 4 Kruskal-Wallis significance tests of mean curvature in each study area based on different land covers with 10,000 sample
SourcePlain to hilly

Hilly

MountainSSMS

Chi sq.

Prob. > chi sq.

14.92

0.004923.21

0.00018.39

0.0386

Groups

3.11E+09

7.77E+08

Error

1.04E+13

2.08E+08

Total

1.04E+13Groups

4.83E+09

1.21E+09

Error

1.04E+13

2.08E+08

Total

1.04E+13Groups

1.12E+09

3.73E+08

Error

5.33E+12

1.33E+08

Total

5.33E+12

Table 5 The significant level of two-sample Kolmogorov-Smirnov tests of pairwise differences in mean curvature between land
covers within topographic types with 10,000 sample
Variable
Plain to hilly

Crop

Crop

ForestArtificial

Grass

Vineyard

1.19E-129

4.60E-03

1.66E-39

0.4132*

1.03E-100

1.59E-28

4.62E-132

1.60E-23

0.007

ForestArtificial
Grass

1.66E-39

Vineyard
Hilly

Crop

1.28E-270

Forest

4.66E-58
4.07E-108

Artificial

2.77E-63
3.62E-91

9.59E-140

5.95E-07

1.42E-07

Grass

3.28E-09

Vineyard
Mountain

Crop
ForestArtificial
Grass

1.83E-26

0.2791*

6.25E-08

1.70E-03

4.64E-11

6.11E-04
2.37E-19

Cao et al. Progress in Earth and Planetary Science(2020) 7:2

and also crop and forest in the mountain area (p value >0.05).
When observing the Spc and the result of the KruskalWallis test (Table 6), it is confirmed that the different
land covers present different topography signatures (pvalue < 0.05). The exceptional case which doesn't show
the significant difference makred with asterisk symbol.
As it is possible to infer from Table 7: (1) pairwise differences exist in all land covers except for grass and vineyards(marked with asterisk symbol). (2) Forest differentiates fromother land covers on the Spc, and this is evident for all landforms’ context considered.
The distinct anthropogenic impact analysis

The Spc is mathematically related to the percentage ofanthropogenic activity (Chen et al. 2015; Xiang et al.
2018; Xiang et al. 2019). As a consequence, it is a proxy
to illustrate the extent of human impact on morphology
for each land cover in different landforms (Fig. 13). The
most recognisable topographic signature is that given by
the forests in the floodplain (Fig. 13a). Besides, the peakof forest distribution (higher due to the small and uniform surface) falls within values of Spc on the range of
those obtained in literature for highly anthropogenic
surfaces. The forest distribution in both plain to hilly(Fig. 13b) and hilly areas (Fig. 13c) present a similar
trend, but the peak in the hilly area tends to be to theright side (where Spc values are referred to be more ‘natural’ if compared with the mentioned literature). When
moving to the mountain (Fig. 13d), the forest shapeappears less skewed, which might indicate that lower
human interference is present.
In Fig. 14a and b, the map of Spc and forest as seen fromthe satellite is shown in detail. The forest here is closed tothe channel and mostly on the levees. This also gives a reasonable interpretation of the lower value of Spc due to the

Page 13 of 17

human alteration on the forest in the floodplain. By contrast,
we highlight a small area with different Spc values (Fig. 14cand e, marked with different colours) and the LiDAR-derived
shaded relief map (Fig. 14d) in a different area where thetransition from plain to hilly is evident. The forests with relatively lower Spc value are located in areas surrounded byagricultural terraces and other anthropogenic surfaces. On
the other side, the forests with higher Spc value are distributed on the top of small hills and tend to be more natural.
Forest not only presents a remarkable difference with otherland covers but also shows a different morphology based on
the degree of anthropogenic disturbance.
Xiang et al. (2019) highlighted how morphological differences under forest cover emerged by considering natural forests or artificial plantations, with higher Spc fornatural forests. In actuality, the forest (mixed of shrubs
and medium trees) in the floodplain area considered inthis study have been altered in their structure and distribution, thus appearing as small patches surrounded byagricultural and urban areas, in lands highly disturbed byhuman activities. Forests in this floodplain are also managed to adopt peculiar forestry technique to preserve andmaintain the vegetation, through new plantations near theancient wood (Bellio and Pividori 2009). Our results confirm that, from a morphological perspective, the described
forest for the floodplain is related to a surface affected byanthropogenic pressure, while a lower anthropogenic disturbance might be present on forests on hilly places, withthe existence of more natural forests. Some land covers do
not exhibit apparent differences in specific landforms. For
example, grass and vineyards present some similarities inhilly areas (Table 2). As it is shown on Fig. 15, the floodplain grass (left side) has lower values of Spc, which implies that anthropogenic disturbance might be relatively
significant in this environment. Taking a closer view (Fig.
15d), this area is related to a sports field and a park. By

Table 6 Kruskal-Wallis significance tests of Spc in each study area based on different land covers
SS
Floodplain

Plain to hilly

Hilly

Mountain*MS

Chi sq.

Prob. > chi sq.

408.41

4.23E-87

1948.2201017.81

4.92E-219

1969.430Groups

8.51E+08

2.13E+08

Error

9.57E+09

1.92E+06

Total

1.04E+10Groups

4.06E+09

1.01E+09

Error

6.36E+09

1.27E+06

Total

1.04E+10Groups

2.12E+09

5.30E+08

Error

8.30E+09

1.66E+06

Total

1.04E+10Groups

2.63E+09

8.76E+08

Error

2.71E+09

677369.2

Total

5.33E+09

it is confirmed that the different land covers present different topography signatures (p value <0.05).

(2020) 7:2

Cao et al. Progress in Earth and Planetary Science

Page 14 of 17

Table 7 The significant level of two-sample Kolmogorov-Smirnov test of pairwise differences in Spc between land covers within
topographic types
Variable
Floodplain

Crop

Crop

ForestArtificial

Grass

Vineyard

1.55E-45

3.97E-25

1.23E-44

3.88E-41

1.55E-45

1.55E-45

1.55E-45

7.19E-43

1.43E-14

ForestArtificial
Grass

5.11E-33

Vineyard
Plain to hillyCrop

4.26E-13

Forest

2.45E-05
1.12E-20

Artificial

7.17E-28

1.80E-03

8.66E-34

4.48E-20

9.13E-18

0.677

Grass

6.67E-22

Vineyard
Hilly

Crop

5.06E-30

Forest

4.81E-05

4.41E-15

1.27E-12

8.44E-26

4.48E-20

1.15E-16

2.21E-08

2.85E-06

Artificial
Grass

0.26*

Vineyard
Mountain

Crop
ForestArtificial

5.77E-37

1.40E-28
1.23E-44

1.21E-07
2.33E-35
5.77E-37

Grass*(1) pairwise differences exist in all land covers except for grass and vineyards. (2) Forest differentiates from other land covers on the Spc, and this is evident for
all landforms’ context considered.

Fig. 13 The PDFs of the Spc. with different land covers in the four areas. a floodplain, b plain to hilly, c hilly, d mountainCao et al. Progress in Earth and Planetary Science(2020) 7:2

Page 15 of 17

Fig. 14 Forest with Spc value in floodplain (a) and 3D overview (b) of the surrounding from Google Earth (the red outline extracted from forest);
forest with Spc value in plain to hilly (c), LiDAR (d) and 3D overview (e); the outline with different colour denote the different Spc values

contrast, the grass in the hilly area (right side) shows arelatively higher Spc value than that of the floodplain. This
could imply that anthropogenic modifications in thisgrassland still exist, even though there are less marked
than in floodplain, and from the satellite image (Fig. 15h),
the grass in hilly can be identified as pastures.
The similarities in morphology (Spc) between grassand vineyard in hilly areas can be explained by the fact
these pastures are intensively farmed to maximise forage
production. Some of them have been abandoned (‘prati
vegri’) or converted to vineyards. Therefore, the humanmorphological signature is still evident and appears in
the Spc.

Conclusions
The primary goal of this work was to investigate the statistical differences of surface morphologies of anthropogenic

and natural land covers, testifying that human activities
alter the landforms from a statistical point of view. The
work highlights how, if we were to make quantitative measurements of landscape properties on a landscape without
human interference, and compare them to measurementsof a landscape where humans activities are preponderant;
the frequency distributions of these measurements would
be very different, even though all observed landform types
would be found in both realities. Possibly, if we had tomodel and describe a landscape without humans, it would
look much different from the one we are used to, but itwould not exhibit much different landforms. Rather, thesubtle differences would lie in the frequency distributionsof specific landform properties. This work also confirms
the possibility to recognise with a pure geomorphometricanalysis the signatures of anthropogenic activities within a
specific landscape context and further demonstrate howCao et al. Progress in Earth and Planetary Science(2020) 7:2

Page 16 of 17

Fig. 15 Grass with Spc value in floodplain (a) and hilly (d), LiDAR-derived shaded relief map in floodplain (b) and hilly (e), and satellite image infloodplain (c) and hilly (f); panels d and f are the 3D view from Google map in these two study areas

people use of the land changes the Earth surface in three
dimensions. Different utilisations of the same land covershow a different extent of anthropogenic impacts, underlining the opportunity for future analyses of the ‘magnitude’ and ‘type’ of human forcing on Earth. The results
provide robust evidence of the human activity’s impact onsome terrestrial surfaces, fostering therefore future studies
linking the relationship between humans, land use, andgeomorphological alterations. Our study offers a new
insight to understand the present geomorphology coupling the function of human activities and poses a challenge
for future research of the geomorphic and human systems
in a world increasingly affected by anthropogenic
activities.

Authors’ contributions
PT proposed the topic; PT and CWF designed the study; CWF performed
research and analysed data; CWF collaborated with GS in the construction
and writing the manuscript; PT supervised the entire work. All authors readand approve the final manuscript.
Funding
This work was supported by Chinese Scholarship Council (201606670005).
Availability of data and materials
The datasets supporting the results of this article are included within thearticle and its supplements.
Competing interests
The authors declare that they have no competing interest.

Supplementary information

Author details
1
Department of Land, Environment, Agriculture and Forestry, University of
Padova, Agripolis, viale dell’Università 16, Legnaro (PD) 35020, Italy.
2
Department of Civil & Environmental Engineering, University of Connecticut,
261 Glenbrook Rd, Storrs, USA.

Supplementary information accompanies this paper at https://doi.org/10.
1186/s40645-019-0314-x.

Received: 10 June 2019 Accepted: 22 November 2019

Additional file 1. Supplement 1: Statistic values (mean, median, STD,
MAD, skewness…) of each geomorphometric parameter within eachland cover Supplement 2: SLLAC maps for the four study areas (a)floodplain (b) plain to hilly (c) hilly (d) mountain.
Abbreviations
CLC: Corine-Land-Cover; DTMs: Digital Terrain Models; LiDAR: Light Detection
And Ranging; PDF: Probability Density Function; SLLAC: Slope Local Lengthof Auto-Correlation; Spc: Surface peak curvature
Acknowledgements
The authors thank Dr. Martino Bernard for the help with MATLAB coding and
Dr. Zihua Cheng for the fruitful discussion on the statistical analysis. The
authors are also grateful to the editor, Prof. Yuichi S. Hayakawa (The
University of Tokyo, Center for Spatial Information Science Japan) andanonymous reviewers for constructive review comments, which led tosignificant improvements in the manuscript.

References
Bellio B, Pividori P (2009) Caratteri strutturali in giovani impianti planiziali aprevalenza di farnia e carpino bianco nel Veneto. SISEF - Italian Society ofSilviculture and Forest Ecology. https://doi.org/10.3832/efor0554-006
Bolongaro-Crevenna A, Torres-Rodríguez V, Sorani V, Frame D, Arturo M (2005)
Geomorphometric analysis for characterizing landforms in Morelos State,
Mexico. Geomorphology 67:407–422
Borrelli P, Robinson DA, Fleischer LR, Lugato E, Ballabio C, Alewell C, Meusburger
K, Modugno S, Schütt B, Ferro V, Bagarello V, Oost KV, Montanarella
L,Panagos P (2017) An assessment of the global impact of 21st century land
use change on soil erosion. Nature Communications 8 (1): 2013
Brown AG, Tooth S, Bullard JE, Thomas D, Chiverrell RC, Plater AJ, Murton J,
Thorndycraft VR, Tarolli P, Rose J, Wainwright J, Downs P, Aalto R (2017) The
geomorphology of the Anthropocene: emergence, status and implications.
Earth Surface Processes and Landforms 42:71–90
Byun J, Seong YB (2015) An algorithm to extract more accurate stream
longitudinal profiles from unfilled DEMs. Geomorphology 242:38–48

Cao et al. Progress in Earth and Planetary Science(2020) 7:2

Castelltort S, Whittaker A, Vergés J (2015) Tectonics, sedimentation and surface
processes: from the erosional engine to basin deposition. Earth Surface
Processes and Landforms 40:1839–1846
Chen J, Li K, Chang KJ, Sofia G, Tarolli P (2015) Open-pit mining geomorphic
feature characterisation. International Journal of Applied Earth Observation
and Geoinformation 42:76–86
Csima P (2010) Urban development and anthropogenic geomorphology. In: Szabó J,
Dávid L, Lóczy D (eds) Anthropogenic geomorphology. Springer, Dordrecht
Curebal I, Efe R, Soykan A, Sonmez S (2015) Impacts of anthropogenic factors onland degradation during the anthropocene in Turkey. J Environ Biol 36:51
Dietrich WE, Perron JT (2006) The search for a topographic signature of life.
Nature 439:411
Ellis EC (2004) Long-term ecological changes in the densely populated rural landscapes ofChina. American Geophysical Union. https://doi.org/10.1029/153GM23
Ellis EC, Fuller DQ, Kaplan JO, Lutters WG (2013) Dating the Anthropocene:
towards an empirical global history of human transformation of theterrestrial biosphere. Elementa: Science of the Anthropocene 1, p.000018, doi:
10.12952/journal.elementa.000018
European Environment Agency (2012) Under the framework of the Copernicus
Programme. https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012?tab=metadata. Accessed 04 Aug 2018
Evans IS (2013) Land surface derivatives: history, calculation and further
development. Geomoprhometry Org:1–4
Evans S (1980) An integrated system of terrain analysis and slope mapping.
Geomorphologie, Suppl. – Bd 36: 274–295
Forman RTT, Sperling D, Bissonette JA, Clevenger AP, Cutshall CD, Dale VH (2003)
Road ecology: science and solutions. Isl. Press. Washington, D.C.,USA
Goudie A (2018) The human impact in geomorphology – 50 years of change.
Geomorphology. https://doi.org/10.1016/j.geomorph.2018.12.002
Goudie AS, Viles HA (2016) Geomorphology in the Anthropocene. Cambridge, UK
Hooke R (2012) Land transfomation by humans: a review. GSA Today 22:4–10
ISO (2013) ISO 25178-2:2013: Geometrical product specifications (GPS) – surface texture:areal –– Part 2: terms, definitions and surface texture parameters. ISO, London
Jordan H, Hamilton K, Lawley R, Price SJ (2016) Anthropogenic contribution to
the geological and geomorphological record: A case study from Great
Yarmouth, Norfolk, UK. Geomorphology 253:534–546
Kleman J, Borgström I, Skelton A, Hall A (2016) Landscape evolution and landform
inheritance in tectonically active regions: the case of the Southwestern
Peloponnese, Greece. Zeitschrift Für Geomorphologie 60:171–193
Marshall JA, Roering JJ, Gavin DG, Granger DE (2017) Late Quaternary climatic
controls on erosion rates and geomorphic processes in western Oregon,
USA. GSA Bulletin 129:715–731
McKight PE, Najab J (2010) Kruskal-Wallis Test. Corsini Encyclopedia of
Psychology. https://doi.org/10.1002/9780470479216.corpsy0491
Migoń P, Latocha A (2018) Human impact and geomorphic change throughtime in the Sudetes, Central Europe. Quaternary International 470:194–206
Nagel DE, Buffington, J M, Parkes SL, Wenger S, Goode JR (2014) A landscape scale valley
confinement algorithm: delineating unconfined valley bottoms for geomorphic,
aquatic, and riparian applications. Gen. Tech. Rep. RMRS-GTR-321 321:42
Oldroyd DR, Grapes, RH (2008) Contributions to the history of geomorphology
and Quaternary geology: an introduction:1–17
Penna D, Borga M, Aronica GT, Brigandì G, Tarolli P (2014) The influence of grid
resolution on the prediction of natural and road-related shallow landslides.
Hydrology and Earth System Sciences 18 (6):2127-2139
Perlik M, Messerli P, Batzing W (2001) Towns in the Alps: urbanization processes,
economic structure, and demarcation of European functional urban areas(EFUAs) in the Alps. Mt. Res. Dev. UNIV CALIF PRESS 21:243–252
Pietrasiak N, Drenovsky RE, Santiago LS, Graham RC (2014) Geomorphology
biogeomorphology of a Mojave Desert landscape — configurations andfeedbacks of abiotic and biotic land surfaces during landform evolution.
Geomorphology 206:23–36
Poeppl RE, Keesstra SD, Maroulis J (2017) A conceptual connectivity framework
for understanding geomorphic change in human-impacted fluvial systems.
Geomorphology 277:237–250
Ramos-Scharrón CE (2018) Land disturbance effects of roads in runoff andsediment production on dry-tropical settings. Geoderma 310:107-119
Razali NM, Wah YB (2011) Power comparisons of Shapiro-Wilk , KolmogorovSmirnov, Lilliefors and Anderson-Darling tests. Journal of Statistical Modelingand Analytics 2:21–33
Regione del Veneto (2012) Prezzario regionale on-line 2012. https://www.regione.
veneto.it. Accessed 20 May 2018

Page 17 of 17

Sidle RC, Ziegler AD (2012) The dilemma of mountain roads. Nature Geoscience
5 (7):437-438
Sofia G, Dalla Fontana G, Tarolli P (2014b) High-resolution topography andanthropogenic feature extraction: testing geomorphometric parameters infloodplains. Hydrological Processes 28:2046–2061
Sofia G, Marinello F, Tarolli P (2014a) A new landscape metric for theidentification of terraced sites: the slope local length of auto-correlation(SLLAC). ISPRS Journal of Photogrammetry and Remote Sensing 96:123–133
Sofia G, Marinello F, Tarolli P (2016) Metrics for quantifying anthropogenic
impacts on geomorphology: road networks. Earth Surface Processes andLandforms 41:240–255
Sofia G, Roder G, Dalla Fontana G, Tarolli P (2017) Flood dynamics in urbanised
landscapes: 100 years of climate and humans’ interaction. Scientific Reports 7:40527
Steiger J, Corenblit D (2012) The emergence of an “evolutionary
geomorphology”? Central European Journal of Geosciences 4:376–382
Szabó J, Dávid L, Lóczy D (2010) Anthropogenic geomorphology:a guide to manmade landforms. Springer Science & Business Media, Netherland
Tarolli P (2014) High-resolution topography for understanding Earth surface
processes: opportunities and challenges. Geomorphology 216:295–312
Tarolli P, Cao W, Sofia G, Evans D, Ellis EC (2019) From features to fingerprints: ageneral diagnostic framework for anthropogenic geomorphology. Progress
in Physical Geography: Earth and Environment 43:95–128
Tarolli P (2016) Humans and the Earth's surface. Earth Surface Processes andLandforms 41 (15):2301-2304
Tarolli P, Sofia G (2016) Human topographic signatures and derived geomorphic
processes across landscapes. Geomorphology 255:140–161
Tarolli P, Sofia G, Dalla Fontana G (2012) Geomorphic features extraction fromhigh-resolution topography: landslide crowns and bank erosion. Natural
Hazards 61:65–83
Tessler ZD, Vörösmarty CJ, Grossberg M, Gladkova I, Aizenman H (2016) A global
empirical typology of anthropogenic drivers of environmental change indeltas. Sustainability Science 11:525–537
Tóth C (2010) Agriculture: grazing lands and other grasslands. In Anthropogenic
Geomorphology (69–82). Springer
Wang S, Fu BJ, Piao S, Lü Y, Ciais P, Feng X, Wang Y (2016) Reduced sedimenttransport in the Yellow River due to anthropogenic changes. Nat Geosci 9:38
Whitehouse DJ. (2011) Characterization. In Handbook of surface andnanometrology, nanometrology, 2nd edn. CRC Press: Boca Raton, FL 5–170
Wilcox R (2005) Kolmogorov–smirnov test. Encyclopedia of Biostatistics. https://doi.org/10.1002/0470011815.b2a15064
Wilson JP, Gallant JC (2000) Digital terrain analysis. Terrain analysis: principles andapplications 1988:1–21
Wood J (1996) The geomorhological characterisation of digital elevation models.
Ph.D. Thesis, University of Leicester
Xiang J, Chen J, Sofia G, Tian Y, Tarolli P (2018) Open-pit mine geomorphic changes
analysis using multi-temporal UAV survey. Environmental Earth Science 77:220
Xiang J, Li S, Xiao K, Chen J, Sofia G, Tarolli P (2019) Quantitative analysis ofanthropogenic morphologies based on multi-temporal high-resolution
topography. Remote Sensing 11:1493
Zhang JY, Yin A, Liu WC, Ding L, Xu XM (2016) First geomorphological andsedimentological evidence for the combined tectonic and climate control
on Quaternary Yarlung river diversion in the eastern Himalaya. Lithosphere 8:
293–316

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.