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Gully erosion is one of the most prominent natural denudation processes of the Mediter-ranean. It causes significant soil degradation and sediment yield. Most traditional field methods for measurement of erosion-induced spatio-temporal changes are time and labor consuming, while their accuracy and precision are highly influenced by various factors. The main research question of this study was how the measurement approach of traditional field sampling methods can be automated and upgraded, while satisfying the required measurement accuracy. The VERTICAL method was developed as a fully automated raster-based method for detection and quantification of vertical spatio-temporal changes within a large number of gully cross-sections (GCs). The developed method was tested on the example of gully Santiš, located at Pag Island, Croatia. Repeat unmanned aerial vehicle (UAV) photogrammetry was used, as a cost-effective and practical method for the creation of very-high-resolution (VHR) digital surface models (DSMs) of the chosen gully site. A repeat aerophotogrammetric system (RAPS) was successfully assembled and integrated into one functional operating system. RAPS was successfully applied for derivation of interval (the two-year research period) DSMs (1.9 cm/pix) of gully Santiš with the accuracy of ±5 cm. VERTICAL generated and measured 2379 GCs, along the 110 m long thalweg of gully Santiš, within which 749 052 height points were sampled in total. VERTICAL proved to be a fast and reliable method for automated detection and calculation of spatio-temporal changes in a large number of GCs, which solved some significant shortcomings of traditional field methods. The versatility and adaptability of VERTICAL allow its application for other, similar scientific purposes, where multitemporal accurate measurement of spatio-temporal changes in GCs is required (e.g., river material dynamics, ice mass dynamics, tufa sedimentation and erosion).
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remote sensing
Article
New Method for Automated Quantification of Vertical
Spatio-Temporal Changes within Gully Cross-Sections Based
on Very-High-Resolution Models
Ante Šiljeg , Fran Domazetovi´c * , Ivan Mari´c , Nina Lonˇcar and Lovre Pan ¯
da


Citation: Šiljeg, A.; Domazetovi´c, F.;
Mari´c, I.; Lonˇcar, N.; Pan ¯
da, L. New
Method for Automated
Quantification of Vertical
Spatio-Temporal Changes within
Gully Cross-Sections Based on
Very-High-Resolution Models. Remote
Sens. 2021,13, 321. https://doi.org/
10.3390/rs13020321
Received: 28 October 2020
Accepted: 13 January 2021
Published: 19 January 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
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iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Department of Geography, Geospatial Analysis Laboratory, University of Zadar, Trg kneza Višeslava 9,
23000 Zadar, Croatia; asiljeg@unizd.hr (A.Š.); imaric1@unizd.hr (I.M.); nloncar@unizd.hr (N.L.);
lpanda@unizd.hr (L.P.)
*Correspondence: fdomazeto@unizd.hr
Abstract:
Gully erosion is one of the most prominent natural denudation processes of the Mediter-
ranean. It causes significant soil degradation and sediment yield. Most traditional field methods for
measurement of erosion-induced spatio-temporal changes are time and labor consuming, while their
accuracy and precision are highly influenced by various factors. The main research question of this
study was how the measurement approach of traditional field sampling methods can be automated
and upgraded, while satisfying the required measurement accuracy. The VERTICAL method was
developed as a fully automated raster-based method for detection and quantification of vertical
spatio-temporal changes within a large number of gully cross-sections (GCs). The developed method
was tested on the example of gully Santiš, located at Pag Island, Croatia. Repeat unmanned aerial
vehicle (UAV) photogrammetry was used, as a cost-effective and practical method for the creation
of very-high-resolution (VHR) digital surface models (DSMs) of the chosen gully site. A repeat
aerophotogrammetric system (RAPS) was successfully assembled and integrated into one functional
operating system. RAPS was successfully applied for derivation of interval (the two-year research
period) DSMs (1.9 cm/pix) of gully Santiš with the accuracy of
±
5 cm. VERTICAL generated and
measured 2379 GCs, along the 110 m long thalweg of gully Santiš, within which 749 052 height points
were sampled in total. VERTICAL proved to be a fast and reliable method for automated detection
and calculation of spatio-temporal changes in a large number of GCs, which solved some significant
shortcomings of traditional field methods. The versatility and adaptability of VERTICAL allow its
application for other, similar scientific purposes, where multitemporal accurate measurement of
spatio-temporal changes in GCs is required (e.g., river material dynamics, ice mass dynamics, tufa
sedimentation and erosion).
Keywords:
VERTICAL; repeat aerophotogrammetric system (RAPS); gully cross-sections (GCs);
spatio-temporal changes (STC); profilometer; soil erosion; Croatia
1. Introduction
Gully erosion is one of the most prominent natural denudation processes of the
Mediterranean [
1
9
], which causes significant soil degradation and sediment yield [
10
15
].
Active gullies tend to grow as long as predisposing factors, such as lithology, vegetation
cover, land use, terrain attributes, and climatic factors, sustain erosion processes and
soil removal [
16
18
]. Research about gully erosion is mostly aimed at quantification of
different aspects of spatio-temporal changes in gully geometry, for instance, gully headcut
retreat rate [
4
,
15
,
19
21
] or changes within gully cross-sections (GCs) [
22
25
]. Within this
research, we concentrated on the accurate quantification of spatio-temporal changes in GCs
as indicators of overall gully evolution. Spatio-temporal changes within GCs include all
changes in cross-section geometry caused by erosion and/or accumulation processes that
have occurred at the chosen study area, within a certain time period (e.g., day, month, year).
Remote Sens. 2021,13, 321. https://doi.org/10.3390/rs13020321 https://www.mdpi.com/journal/remotesensing
Remote Sens. 2021,13, 321 2 of 27
The choice of the most appropriate measurement method for the detection and quan-
tification of spatio-temporal changes depends primarily on the aim and spatial scope
of the research, as well as on the desirable and achievable accuracy of the measure-
ments [
26
,
27
]. A variety of traditional sampling methods for field study of changes in
gully cross-sections exist (e.g., pole [
27
], tape and ruler [
26
,
28
,
29
], total station [
27
,
30
],
profilometer
[22,2427,3134]).
These methods differ in terms of precision and accuracy,
time and cost-effectiveness, spatial coverage, complexity, and required expertise of re-
searchers [
26
,
27
]. Characteristics, as well as detailed accuracy assessment of various field
sampling methods, were given by [
26
]. While field sampling methods can have satisfactory
accuracy, their main lack is that they are, due to the high time ineffectiveness and difficulty
of field sampling [
26
,
35
], spatially limited to measurement of changes in a small number
of GCs per surveyed gully (Table 1). An additional disadvantage is that most traditional
sampling methods are direct measurement techniques, that require direct physical contact
between measurement probe and soil surface. Such direct contact can cause the occurrence
of soil surface compaction, resulting in measurement overestimation [29,36,37].
In recent years, different remote sensing methods have emerged as cost and time-
effective indirect techniques for accurate data collection [
20
,
27
,
38
41
] that allow for the
creation of very-high-resolution (VHR) digital surface models (DSMs). The use of Structure-
from-Motion (SfM) algorithms and unmanned aerial vehicles (UAV) photogrammetry has
been recently particularly popular for the creation of VHR DSMs and as such it has wide
application in various geomorphological researches [
42
63
]. In particular, the application
of UAV photogrammetry and SfM algorithms have found wide application in soil erosion
related research [
51
60
,
64
]. VHR DSMs provide the perfect basis for a pixel-orientated
analysis of morphological changes and quantification of spatio-temporal changes at a
sub-decimeter scale [
40
,
65
]. Although interval VHR DSMs are mostly used for detection
and quantification of areal and/or volumetric spatio-temporal changes, lately the interest
in the use of DSMs for accurate measurement and evaluation of GCs characteristics has
been growing [
29
,
66
70
]. From a scientific perspective, research of spatio-temporal changes
in GCs is still relevant and important, as cross-sections reveal valuable geomorphological
information that cannot be observed from volumetric measurements derived from continu-
ous surfaces [
24
,
68
]. While volumetric spatio-temporal changes indicate overall erosion
or accumulation of soil material, measurement of spatio-temporal changes within GCs
can provide insight into: cross-sectional metrics (e.g., cross-section width (W); gully top
width (TW); bottom width (BW); gully depth (D); gully cross-sectional area (CSA); width–
depth ratio (W/D ration); shape factor (SF)) [
24
,
70
,
71
], erosion rate [
24
], gully evolutionary
stage [
72
] (72 in 68), soil resistance [
73
,
74
] (73,74 in 68), dominance of certain types of
erosive processes [
75
,
76
] (75,76 in 68), etc. A detailed description of more than 20 different
geometric and morphological parameters that can be extracted for every measured GCs is
given in [24].
Table 1. Review of the recent studies focused on the measurement of GCs.
ID Case Study (Authors) Measurement Method
(Method Type)
Noof
Sampled
Gullies
Noof Sampled
GCs Per
Gully
Measured GCs
Application
1
Sapphire Mountains, Montana,
USA
[77]
Tape
(direct method) 6 5 Volume; SF; D; W;
W/D ratio
2Umbulo catchment, Ethiopia
[28]
Tape
(direct method) 15 1 Volume; W/D ratio;
D; W
3
Bardenas Reales, Navarre, Spain
[22]
Laser profiliometer
(indirect method);
Aerial photogrammetry
(indirect method)
5 4–6 D; TW; BW; CSA;
W/D
Remote Sens. 2021,13, 321 3 of 27
Table 1. Cont.
ID Case Study (Authors) Measurement Method
(Method Type)
Noof
Sampled
Gullies
Noof Sampled
GCs Per
Gully
Measured GCs
Application
4
Avon-Richardson Catchment,
Victoria, Australia
[36]
Aerial Photo Interpretation
(indirect method) 89 1 W; D; CSA
5Ethiopia
[71]
Tape
(direct method) 811 1 W; D; TW; BW; CSA;
W/D; SF; volume
6Pravara River, Western India
[78]
Profilometer; Erosion pins
(direct methods) 5 1 CSA; volume
7Belgium; Ethiopia
[66]
Ground photogrammetry
(indirect method); Tape
(direct method)
4 1–2 W; D; TW; BW; CSA
8
Yuanmou Dry-Hot Valley,
Yunnan Province, China
[24]
Laser distance meter
(indirect method) 152 3
26 different
morphological GCs
parameters
9Extremadura, SW Spain
[30]
Laser total station (indirect
method) 1 28 W; D; CSA; volume
10 Loess Plateau, China
[67]
Terestric laser scanning
(indirect method) 44 2–3 D; TW; BW; CSA;
W/D
11
Yuanmou Dry-Hot Valley,
Yunnan Province, China
[25]
Laser distance meter
(indirect method) 152 3
26 different
morphological GCs
parameters
12 Cordoba, Spain
[68]
FreeXSapp
(indirect method); tape
(direct method)
1 10 W; D; CSA; volume
13 Loess Plateau, China
[69]
Terestric laser scanning
(indirect method) 31 6 D; W/D
14 New Brunswick, Canada
[29]
Ground photogrammetry
(indirect method); Tape
(direct method)
1 10 CSA;
A review of various case studies in which direct and/or indirect measurement meth-
ods were applied for sampling and measurement of GCs is given in Table 1. It is evident
that measurement methods and Noof sampled GCs per gully vary considerably.
The main research question of this research was how to automate and improve the
measurement process of gully cross-sections (GCs), while achieving high measurement
accuracy. Therefore, the main research objective (1) was to develop the pixel-based (interval
VHR DSMs) methodological approach that will allow automated measurement of vertical
spatio-temporal changes in a large number of GCs. A conceptual basis for the development
of the VERTICAL method was based on the principle of profilometer, as one of the most
commonly used devices for manual field measurement of vertical spatio-temporal changes
within chosen cross-sections [
22
,
24
27
,
31
34
,
79
]. A profilometer is a simple device that
allows interval field measurement of depth at point samples distributed within identical
distance intervals along the chosen cross-section [
26
,
80
,
81
]. Older versions of profilometer
devices have aluminum pins (Figure 1) that allow mechanical measurement of depth by
lowering the pin until it reaches the surface [
79
,
81
,
82
]. Newer versions are equipped with
optical devices, e.g., laser distance gauge [22,34].
Accurate VHR DSMs of the chosen gully are essential for accurate measurement of
spatio-temporal changes in GCs by the developed VERTICAL method. Therefore, repeat
UAV photogrammetry was used, as a cost-effective and practical method [
83
85
] for the
derivation of interval (within a two-year research period) DSMs. Although numerous ready-
to-fly UAVs are most often used for different geomorphic surveys, they have limited flight
capabilities and camera characteristics [
85
88
]. Therefore, a repeat aerophotogrammetric
system (RAPS) was assembled and functionally integrated into a high-end UAV system
that was applied for two interval aerial surveys.
Remote Sens. 2021,13, 321 4 of 27
Remote Sens. 2021, 13, x FOR PEER REVIEW 4 of 28
Figure 1. Components of mechanical profilometer [81].
Accurate VHR DSMs of the chosen gully are essential for accurate measurement of
spatio-temporal changes in GCs by the developed VERTICAL method. Therefore, repeat
UAV photogrammetry was used, as a cost-effective and practical method [83–85] for the
derivation of interval (within a two-year research period) DSMs. Although numerous
ready-to-fly UAVs are most often used for different geomorphic surveys, they have lim-
ited flight capabilities and camera characteristics [85–88]. Therefore, a repeat aerophoto-
grammetric system (RAPS) was assembled and functionally integrated into a high-end
UAV system that was applied for two interval aerial surveys.
The second research objective (2) of our study was to determine and interpret the
intensity of spatio-temporal changes at GCs of the chosen gully within the 2-year research
period. For that purpose, the VERTICAL method was developed and applied on interval
VHR DSMs, of the chosen gully at Pag Island, Croatia. With several hundred recorded
active gullies [89,90] Pag Island can be considered as a very suitable location for gully
erosion research. Overall, studies considering soil erosion in Croatia are scarce [89–93],
especially in regard to the application of modern geospatial technologies and advanced
research methods for temporal monitoring of spatio-temporal changes. Therefore, thor-
ough longitudinal research of gully erosion is of crucial importance for a better under-
standing of the intensity of overall soil erosion dynamics.
2. Study Area
Pag Island (284 km²) is the largest island in the Northern Dalmatia archipelago (Cro-
atia) (Figure 2A). Structurally Pag is characterized by alteration of several folds of Dinaric
direction (NW-SE) [94]. Prevailing parent material is composed of Upper Cretaceous and
Eocene limestones, occasionally covered with Dalmatian Flysch and scarce Kalkocambisol
sediments [95]. Deposition of diluvial sediments occurred during the Pleistocene, while
most recent Holocene layers within Pag Island are represented by alluvium and organo-
genic-swamp sediments [95] (Figure 2B).
The island has a mild temperate climate with hot summers (Cfa) [96]. In the period
between 1981 and 2011 mean annual amount of precipitation was 977.5 mm, with pro-
nounced seasonal distribution during the autumn and early winter. The rainiest months
are November (124.1 mm) and September (115.6 mm) [97]. The average annual tempera-
ture is 15.5 °C [97]. The absence of protective vegetation cover leads to exposure of surface
soil materials to the influence of various exogenous processes and anthropogenic influ-
ences. Among the exogenous processes, climatic characteristics have a great significance
for relief formation [89]. A highly developed karstic landscape is characterized by shallow
Figure 1. Components of mechanical profilometer [81].
The second research objective (2) of our study was to determine and interpret the
intensity of spatio-temporal changes at GCs of the chosen gully within the 2-year research
period. For that purpose, the VERTICAL method was developed and applied on interval
VHR DSMs, of the chosen gully at Pag Island, Croatia. With several hundred recorded
active gullies [
89
,
90
] Pag Island can be considered as a very suitable location for gully
erosion research. Overall, studies considering soil erosion in Croatia are scarce [
89
93
],
especially in regard to the application of modern geospatial technologies and advanced
research methods for temporal monitoring of spatio-temporal changes. Therefore, thorough
longitudinal research of gully erosion is of crucial importance for a better understanding of
the intensity of overall soil erosion dynamics.
2. Study Area
Pag Island (284 km
2
) is the largest island in the Northern Dalmatia archipelago (Croa-
tia) (Figure 2A). Structurally Pag is characterized by alteration of several folds of Dinaric
direction (NW-SE) [
94
]. Prevailing parent material is composed of Upper Cretaceous and
Eocene limestones, occasionally covered with Dalmatian Flysch and scarce Kalkocam-
bisol sediments [
95
]. Deposition of diluvial sediments occurred during the Pleistocene,
while most recent Holocene layers within Pag Island are represented by alluvium and
organogenic-swamp sediments [95] (Figure 2B).
The island has a mild temperate climate with hot summers (Cfa) [
96
]. In the period
between 1981 and 2011 mean annual amount of precipitation was 977.5 mm, with pro-
nounced seasonal distribution during the autumn and early winter. The rainiest months are
November (124.1 mm) and September (115.6 mm) [
97
]. The average annual temperature is
15.5
C [
97
]. The absence of protective vegetation cover leads to exposure of surface soil
materials to the influence of various exogenous processes and anthropogenic influences.
Among the exogenous processes, climatic characteristics have a great significance for relief
formation [
89
]. A highly developed karstic landscape is characterized by shallow and
scarce soil cover. There is no permanent surface runoff, although periodic runoff frequently
occurs due to intense precipitation. Such intense periodic surface runoff is important for
the formation of gullies.
The chosen study site is gully Santiš (1163 m
2
) (Figure 2D), located within the south-
eastern part of Pag Island (Figure 2B). Gully Santiš is located at the very end of a larger
(0.18 km
2
) drainage basin (Figure 2C), where it is formed in relatively deep accumulated
soil sediments of Kalkocambisol, a soil type formed by the dissolution of limestone and/or
dolomite [
98
]. Steep, almost vertical headcut, about 15 m wide, forms the initial part of the
gully Santiš, where most intensive and complex gully erosion processes had been observed
Remote Sens. 2021,13, 321 5 of 27
during the field research. After the headcut, the gully narrows towards the direction of
the main channel, where parental material is less homogenous and where fewer traces
of active erosion can be found. Around 80 m from the headcut, at the contact zone with
the Adriatic Sea, a gully forms a small pebble beach. Gully Santiš was chosen for this
research as a simple, unbranched, and relatively short gully, with very scarce vegetation
cover (e.g., short grass). Recent intensive active gully erosion traces are visible and there
are no obvious anthropogenic influences that could disturb natural erosion processes.
Remote Sens. 2021, 13, x FOR PEER REVIEW 5 of 28
and scarce soil cover. There is no permanent surface runoff, although periodic runoff fre-
quently occurs due to intense precipitation. Such intense periodic surface runoff is im-
portant for the formation of gullies.
The chosen study site is gully Santiš (1163 m²) (Figure 2D), located within the south-
eastern part of Pag Island (Figure 2B). Gully Santiš is located at the very end of a larger
(0.18 km²) drainage basin (Figure 2C), where it is formed in relatively deep accumulated
soil sediments of Kalkocambisol, a soil type formed by the dissolution of limestone and/or
dolomite [98]. Steep, almost vertical headcut, about 15 m wide, forms the initial part of the
gully Santiš, where most intensive and complex gully erosion processes had been ob-
served during the field research. After the headcut, the gully narrows towards the direc-
tion of the main channel, where parental material is less homogenous and where fewer
traces of active erosion can be found. Around 80 m from the headcut, at the contact zone
with the Adriatic Sea, a gully forms a small pebble beach. Gully Santiš was chosen for this
research as a simple, unbranched, and relatively short gully, with very scarce vegetation
cover (e.g., short grass). Recent intensive active gully erosion traces are visible and there
are no obvious anthropogenic influences that could disturb natural erosion processes.
Figure 2. The study area (A) location of Pag Island within Croatia; (B) geological formations of Pag Island; (C) gully Santiš
and its catchment area; (D) study area.
3. Materials and Methods
3.1. Assembling and Functional Integration of Repeat Aerophotogrametric System (RAPS)
Repeat aerophotogrammetric system (RAPS) successfully integrates different high-
grade components (Figure 3). DJI Matrice 600 PRO (Figure 3A) is a professional UAV, that
Figure 2.
The study area (
A
) location of Pag Island within Croatia; (
B
) geological formations of Pag Island; (
C
) gully Santiš
and its catchment area; (D) study area.
3. Materials and Methods
3.1. Assembling and Functional Integration of Repeat Aerophotogrametric System (RAPS)
Repeat aerophotogrammetric system (RAPS) successfully integrates different high-
grade components (Figure 3). DJI Matrice 600 PRO (Figure 3A) is a professional UAV,
that represents a basis of developed RAPS, chosen for its advanced flight capabilities, the
ability to carry a payload up to 5.9 kg, and compatibility with various gimbals and add-ons
(e.g., professional-grade DSLR cameras, multispectral and thermal cameras, aeroLiDAR
solutions) [
99
]. Chosen UAV was upgraded with Gremsy T3 gimbal (Figure 3B), which was
chosen due to its advanced camera stabilization, 360
horizontal and 90
vertical rotation,
and compatibility with various professional cameras and different lenses [
100
]. During the
functional integration of RAPS, it was not possible to connect the DSLR camera to the UAV’s
GPS receiver, and thus collected images lacked the information needed for georeferencing.
Remote Sens. 2021,13, 321 6 of 27
Therefore, Reach M+ GNSS module for UAV mapping (Figure 3C), which allows accurate
determination of xyz coordinates of every collected aerial image [
101
] was added into the
RAPS setup. Professional Sony Alpha A7RII (42 MP) DSLR camera equipped with 20 mm
lens (Figure 3D), which allowed the acquisition of VHR aerial imagery, was mounted on
Gremsy T3 gimbal. Although all components of RAPS are important in the derivation
of VHR DSMs, the professional-grade Sony Alpha A7RII (42 MP) DSLR camera had the
highest impact on the quality of created models. Detailed characteristics of RAPS are given
in Table 2.
Remote Sens. 2021, 13, x FOR PEER REVIEW 7 of 28
Figure 3. Different components functionally integrated into RAPS (A–D); application of developed RAPS for aerial survey
of gully Santiš (E).
3.2. Field Data Acquisition
Repeat UAV photogrammetry was used as a cost-effective and practical method for
interval acquisition of aerial imagery [83–85]. Two aerial surveys were conducted with
developed RAPS within the interval of nearly 2 years. The initial survey was carried out
on 26 May 2017 (Survey A), while the final survey (Survey B) was conducted on 11 March
2019. UAVs flight missions for both surveys were planned and conducted through the
Universal Ground Control Software (UgCS) commercial UAV flight planning application.
It allows user to define various flight mission parameters (e.g., flight profiles, flight height,
percentage of side and forward overlap) [102] (Figure 4). Identical UAVs flight mission
was used for both aerial surveys. Double-grid flight profiles were chosen and 85% for-
ward and side overlap was set. In total 233 aerial images were collected per each survey.
Figure 3.
Different components functionally integrated into RAPS (
A–D
); application of developed RAPS for aerial survey
of gully Santiš (E).
Table 2.
Component (A), parameters (B) and characteristics (C) of repeat aerophotogrammetric
system (RAPS).
# Component (A) Parameter (B) Value (C)
1.
DJI Matrice 600 PRO
Flight time (min) 16–32 min
2. Max takeoff weight (kg) 15.5
3. Max wind resistance (m/s) 8
4. Max height above sea level (m) 2500
5. Max transmission distance (m) 5000
6.
Sony Alpha A7RII
Sensor size 861.6 mm2(35.90mm ×24.00mm)
7. Camera weight (kg) 0.64
8. Aperture f/3.5–f/22
9. Sensor (px) 7952 ×5304
10. ISO 100–25600
11. Shutter Speed 1/8000–30 sec
12. Focal Length (mm) 28–70
3.2. Field Data Acquisition
Repeat UAV photogrammetry was used as a cost-effective and practical method for
interval acquisition of aerial imagery [
83
85
]. Two aerial surveys were conducted with
Remote Sens. 2021,13, 321 7 of 27
developed RAPS within the interval of nearly 2 years. The initial survey was carried out
on 26 May 2017 (Survey A), while the final survey (Survey B) was conducted on 11 March
2019. UAVs flight missions for both surveys were planned and conducted through the
Universal Ground Control Software (UgCS) commercial UAV flight planning application.
It allows user to define various flight mission parameters (e.g., flight profiles, flight height,
percentage of side and forward overlap) [
102
] (Figure 4). Identical UAVs flight mission
was used for both aerial surveys. Double-grid flight profiles were chosen and 85% forward
and side overlap was set. In total 233 aerial images were collected per each survey.
Remote Sens. 2021, 13, x FOR PEER REVIEW 8 of 28
Figure 4. Parameters and spatial extent of photogrammetric survey planned within Universal Ground Control Software
(UgCS).
In order to reduce systematic error of DSMs created from acquired data, collected
aerial images were georeferenced accordingly to the seven fixed ground control points
(GCPs) scattered throughout the study area (Figure 2D). The number of GCPs was deter-
mined following examples of good practice [45,103]. Along with seven GCPs, additional
seven fixed checkpoints (CPs) were marked in the field as a quality measure [51]. The
number of GCPs and CPs is also conditioned by land cover type in the wider area of the
case study. Namely, shallow-brown soil predominates in this area. On such, extremely
dynamical and erosion susceptible surface, it was not possible to mark and construct a lot
of permanent, fixed geodetic points, i.e., to measure the GCPs and CPs using the proposed
methodology. Therefore, the GCPs were limited to predominantly rocky areas within the
study area. All GCPs and CPs were identical for both surveys, thus ensuring consistency
of created models. GCP and CP locations were marked before the initial and final aerial
survey by 15 × 15 cm X signs (Figure S1). Additionally, a Bosch hammer was used to drill
a small hole for the rod of the RTK-GPS rover at the center of the marked cross. Red
weather-resistant spray paint was used for marking X signs, which stands out well from
surrounding light carbonate rocks (Figure 2D). Spray paint applied on the carbonate rock
base remained visible after two years, but it was enhanced with new spray before the final
aerial survey. Precise coordinates (XYZ) of every selected GCP and CP were collected be-
fore every survey with Stonex S10 RTK GPS, with 0,8 cm horizontal and 1,5 cm vertical
precision [104], where every point was observed for a minimum of 5 minutes (300 epochs).
These data were used for the validation of RTK GPS precision. Validation of RTK GPS
coordinates has shown that the precision of collected coordinates slightly deviates from
the stated factory precision (horizontal precision = 1.2 cm; vertical precision = 1.8 cm).
Such deviation is probably caused by the remoteness of the study location, in relation to
the three closest base stations of the Croatian positioning system (CROPOS) used for the
correction of the collected XYZ coordinates. Identical GCPs and CPs were used in both
Figure 4.
Parameters and spatial extent of photogrammetric survey planned within Universal Ground Control Software (UgCS).
In order to reduce systematic error of DSMs created from acquired data, collected aerial
images were georeferenced accordingly to the seven fixed ground control points (GCPs)
scattered throughout the study area (Figure 2D). The number of GCPs was determined
following examples of good practice [
45
,
103
]. Along with seven GCPs, additional seven
fixed checkpoints (CPs) were marked in the field as a quality measure [
51
]. The number of
GCPs and CPs is also conditioned by land cover type in the wider area of the case study.
Namely, shallow-brown soil predominates in this area. On such, extremely dynamical and
erosion susceptible surface, it was not possible to mark and construct a lot of permanent,
fixed geodetic points, i.e., to measure the GCPs and CPs using the proposed methodology.
Therefore, the GCPs were limited to predominantly rocky areas within the study area.
All GCPs and CPs were identical for both surveys, thus ensuring consistency of created
models. GCP and CP locations were marked before the initial and final aerial survey by
15
×
15 cm X signs (Figure S1). Additionally, a Bosch hammer was used to drill a small hole
for the rod of the RTK-GPS rover at the center of the marked cross. Red weather-resistant
spray paint was used for marking X signs, which stands out well from surrounding light
carbonate rocks (Figure 2D). Spray paint applied on the carbonate rock base remained
visible after two years, but it was enhanced with new spray before the final aerial survey.
Remote Sens. 2021,13, 321 8 of 27
Precise coordinates (XYZ) of every selected GCP and CP were collected before every survey
with Stonex S10 RTK GPS, with 0,8 cm horizontal and 1,5 cm vertical precision [
104
], where
every point was observed for a minimum of 5 minutes (300 epochs). These data were used
for the validation of RTK GPS precision. Validation of RTK GPS coordinates has shown that
the precision of collected coordinates slightly deviates from the stated factory precision
(horizontal precision = 1.2 cm; vertical precision = 1.8 cm). Such deviation is probably
caused by the remoteness of the study location, in relation to the three closest base stations
of the Croatian positioning system (CROPOS) used for the correction of the collected XYZ
coordinates. Identical GCPs and CPs were used in both aerial surveys. Acquired aerial
photos were later used for photogrammetric processing and the derivation of VHR DSMs.
Along with aerial surveys, Hobo Onset RG3-M data logger, intended to record the
amount and intensity of precipitation was installed next to the study area. Unfortunately,
due to the weather-related mechanical failure, collected precipitation data are reliable only
for the sixth month period, between 30 April 2017, and 1 November 2017.
3.3. Aerial Data Processing and VHR DSM Creation
Imagery acquired through aerial surveys were processed in Agisoft Metashape 1.5.1.
software, which allows photogrammetric processing of aerial images and creation of
VHR DSMs [
105
]. Agisoft Metashape is image-based 3D modelling software, with an
implemented SfM algorithm and multi-view 3D reconstruction technology that enables the
creation of precise point clouds and 3D structures from 2D image sets [
106
,
107
]. Processing
steps and parameters, as well as user-defined options/values used for the derivation of
two-interval VHR DSMs, are given in Table 3. The image workflow process was performed
according to the guidelines of the SfM photogrammetry in geomorphic research [108].
Table 3.
Processing steps (A), parameters (B) and user-defined options/values (C) used for the
creation of very-high-resolution (VHR) digital surface models (DSMs) in Agisoft Metashape from
collected aerial imagery.
# Processing Step (A) Parameter (B) User-defined Option/Value (C)
1Selection of aerial
images Image quality (IQ) check Images with IQ < 0.8 removed
2Align photos
Accuracy High
Pair selection Reference
Key point limit 40.000
Tie point limit 10.000
3Sparse point cloud
filtration
(gradual selection)
Reprojection error < 0.27
Projection accuracy < 6
Reconstruction uncertainty < 23
4Point cloud
optimization Optimization parameters All parameters
5Build dense cloud Quality Low
Depth filtering Aggressive
6Build mesh
Surface type Arbitrary
Face count High
Interpolation Enabled
7Adding GCPs
and CPs 7 GCPs and 7 CPs added
Remote Sens. 2021,13, 321 9 of 27
Table 3. Cont.
# Processing Step (A) Parameter (B) User-defined Option/Value (C)
8Point cloud
optimization Optimization parameters All parameters
9Sparse point cloud
filtration
(gradual selection)
Reprojection error < 0.27
Projection accuracy < 6
Reconstruction uncertainty < 23
10 Point cloud
optimization Optimization parameters All parameters
11 Build dense cloud Quality High
Depth filtering Aggressive
12 Build mesh
Surface type Arbitrary
Face count High
Interpolation Enabled
13 Build texture
Mapping mode Generic
Blending mode Mosaic
Texture size 8096
Color correction Enabled
14 Build DEM
Coordinate system HTRS96
Source data Dense cloud
Interpolation Enabled
Point classes All
15 Build orthomosaic Surface mode DEM
Blending mode Mosaic
Validation of Model Accuracy and Uncertainty
Detection and interpretation of real spatio-temporal changes are directly affected by
the accuracy of created VHR models, as various errors and artifacts can lead to misin-
terpretations [
109
,
110
]. In order to detect and extract real morphological changes, DSM
uncertainty was accounted for through thorough accuracy assessment.
Within this research, accuracy assessment was based on seven CPs which were used
for the determination of model uncertainty, through the calculation of mean absolute error
(MAE) and root-mean-square error (RMSE) [
37
,
44
]. Calculated MAE and RMSE values
were used for the determination of a minimal level of detection (
LoDmin
), which was
applied as a threshold for the separation of real morphological spatio-temporal changes
from changes induced by systematic and/or nonsystematic errors in created models.
LoDmin
was determined for both created DSMs, as the root sum square of errors, based on
calculated MAE and RMSE values [111]:
LoDmin(RMSE)=qδxyzSurveyB 2+δxyzSurvey A 2(1)
LoDmin(MA E)=qδxyzSurvey B2+δxyzSurveyA2(2)
where:
LoDmin(RMSE)
= minimal level of detection calculated as root sum square of errors based
on RMSE values
LoDmin(MA E)
= minimal level of detection calculated as root sum square of errors based on
MAE values
Remote Sens. 2021,13, 321 10 of 27
δxyzSurveyA = error (RMSE or MAE) for survey A calculated in used CPs
δxyzSurveyB = error (RMSE or MAE) for survey B calculated in used CPs
Final absolute detection threshold
meanLoDmin
which represents the minimal inten-
sity of spatio-temporal changes that VERTICAL can detect and quantify from created
interval DSMs, was determined as the mean of two calculated minimal levels of detec-
tion
LoDmin(RMSE)
and
LoDmin(MA E)
), and applied to the erosion rates determined by
VERTICAL.
As CPs only demonstrate only point-based accuracy of created DSMs accuracy of
created models was further evaluated through the validation of point clouds with M2C3
plugin of CloudCompare software [
87
]. Using the M2C3 plugin, two-interval point clouds
were compared within three test sites (2
×
2 m) chosen over limestone bedrock, where
no spatio-temporal changes did not occur within the study period. On average around
12,000 points were used for the comparison of point clouds within every test site.
3.4. Development of the VERTICAL Method
VERTICAL was developed in ModelBuilder application within ArcGIS 10.1 soft-
ware [
112
], as a toolbox that combines various existing tools from 3D Analyst and Spatial
Analyst ArcGIS extensions. It requires the following input parameters for the automated
creation of GCs and quantification of spatio-temporal changes: main sampling line (MSL),
gully (study) area, and two interval DSMs (DSM 1 and DSM 2). MSL is a very important
parameter since it represents a user-defined line, whose vertices are used as starting points
for the derivation of perpendicular GCs. It can be either a straight line that divides the
whole study area into two identical sections, or it can be a specific, user-defined line, such
as gully thalweg (Figure 5). In this research 110 m long thalweg of gully Santiš, generated
from the flow accumulation model of the gully area is chosen as an MSL. The user-defined
gully area parameter restricts the processing extent for VERTICAL. The DSM 1 and DSM
2 are the interval DSMs used for comparison of heights within created GCs and calculation
of vertical spatio-temporal changes.
Remote Sens. 2021, 13, x FOR PEER REVIEW 11 of 28
3.4. Development of the VERTICAL Method
VERTICAL was developed in ModelBuilder application within ArcGIS 10.1 software
[112], as a toolbox that combines various existing tools from 3D Analyst and Spatial Ana-
lyst ArcGIS extensions. It requires the following input parameters for the automated cre-
ation of GCs and quantification of spatio-temporal changes: main sampling line (MSL), gully
(study) area, and two interval DSMs (DSM 1 and DSM 2). MSL is a very important parame-
ter since it represents a user-defined line, whose vertices are used as starting points for
the derivation of perpendicular GCs. It can be either a straight line that divides the whole
study area into two identical sections, or it can be a specific, user-defined line, such as
gully thalweg (Figure 5). In this research 110 m long thalweg of gully Santiš, generated
from the flow accumulation model of the gully area is chosen as an MSL. The user-defined
gully area parameter restricts the processing extent for VERTICAL. The DSM 1 and DSM
2 are the interval DSMs used for comparison of heights within created GCs and calculation
of vertical spatio-temporal changes.
The entire process of VERTICAL method application can be divided into four main
phases (Figure 5). In the first phase, the VERTICAL method determines the mean linear
direction (LDM) of the input MSL which is set as default orientation (°) of every vertex
within MSL (Figure 5A). Before the actual calculation of LDM input MSL is automatically
split at vertices, thus allowing calculation of the linear direction for all line segments, and
not only for the whole MSL.
Figure 5. Four processing phases of VERTICAL method application (A) determination of mean linear direction (LDM) of
the main sampling line (MSL); (B) extraction of MSL vertices; (C) construction of gully cross-sections (GCs) perpendicular
to the LDM; (D) sampling of height difference from input interval DSMs within created GCs. To make Figure 5. more
transparent only 32 GCs lines are displayed
In the second step, MSL vertices are extracted, along with their XY coordinates
(Figure 5B). Precise coordinates are needed for the later creation of GCs, perpendicular to
the determined LDM value. If gully thalweg is used as MSL (as in this study) then the
interval between created GCs is defined by extracted vertices that represent curves de-
fined by the flow direction. In that case interval between created GCs is not manually and
subjectively determined by the user, but it is rather determined by the morphological and
Figure 5.
Four processing phases of VERTICAL method application (
A
) determination of mean linear direction (LDM) of
the main sampling line (MSL); (
B
) extraction of MSL vertices; (
C
) construction of gully cross-sections (GCs) perpendicular
to the LDM; (
D
) sampling of height difference from input interval DSMs within created GCs. To make Figure 5. more
transparent only 32 GCs lines are displayed.
Remote Sens. 2021,13, 321 11 of 27
The entire process of VERTICAL method application can be divided into four main
phases (Figure 5). In the first phase, the VERTICAL method determines the mean linear
direction (LDM) of the input MSL which is set as default orientation (
) of every vertex
within MSL (Figure 5A). Before the actual calculation of LDM input MSL is automatically
split at vertices, thus allowing calculation of the linear direction for all line segments, and
not only for the whole MSL.
In the second step, MSL vertices are extracted, along with their XY coordinates
(Figure 5B).
Precise coordinates are needed for the later creation of GCs, perpendicular
to the determined LDM value. If gully thalweg is used as MSL (as in this study) then
the interval between created GCs is defined by extracted vertices that represent curves
defined by the flow direction. In that case interval between created GCs is not manually
and subjectively determined by the user, but it is rather determined by the morphological
and hydrological characteristics of the studied gully. Otherwise, if a straight line is used as
an MSL, the user has to define manually the interval at which GCs will be created.
Within the third step, for every extracted MSL vertex VERTICAL automatically con-
structs linear GCs perpendicular to the determined LDM (Figure 5C). Cross-section lines
are constructed from the MSL vertex as a starting point in two opposite perpendicular
directions (left and right), according to the following formulas:
A1= LDM () + 90(3)
A2= LDM () - 90(4)
where:
A1= GCs line constructed left from every MSL vertex
A2= GCs line constructed right from every MSL vertex
LDM = mean linear direction of MSL
Left and right parts of created GCs lines are then merged into a single GCs line and
a unique identification number (ID) is assigned to every created GCs. Unique IDs are
necessary for later analysis and extraction of separate statistics for every created GCs. The
width of the derived GCs is automatically limited by the input gully area.
Then, within the fourth step, VERTICAL uses the created GCs for a sampling of height
data (h) from two-interval DSMs (Figure 5D), where the sampling interval within created
GCs has to be defined manually by the user. Overall sampling density directly influences
the thoroughness of GCs morphology representation and quantification. We propose that
the sampling interval should be defined in regard to the research goals, spatial resolution,
and accuracy of used DSMs. In that way, the user can adjust the density of height point
samples within created GCs according to the research needs and goals, and the quality of
available interval digital surface models.
The height difference between two DSMs is calculated for every sampled point ac-
cording to the following formula:
h=hDSMAhDSMB(5)
where:
h= height difference at the sampled point
hDSMA= height of sampled point in initial DSM
hDSMB= height of sampled point in final DSM
The final step of VERTICAL application is an automated calculation of width/depth
(W/D) ratio. It is a dimensionless ratio that gives an indication of the GCs shape [
24
,
72
,
113
].
As one of the most frequently used cross-sectional metrics (Table 1), W/D ratio is used
for the evaluation of proportion between lateral erosion and incision rates within the GCs,
where higher values indicate the predominance of lateral erosion rate over incision rate [
24
].
Furthermore, the W/D ratio is commonly used for basic, shape-based GCs classification
(e.g., V-shaped cross-sections; U-shaped cross-sections). Values of W/D ratio usually vary
Remote Sens. 2021,13, 321 12 of 27
between 2.0 and 18.0, where lower ratio values are indicating narrower gully channels (V-
shaped) and higher values indicate wider channels (U-shaped) [
114
](114 in 24). VERTICAL
automatically calculates a unique W/D ratio for every sampled GCs. Calculation of the
W/D ratio (Figure 6) is performed by the VERTICAL accordingly to the following formula:
W/D ratio =WGCs
Dgcs (6)
where:
WGCs = width of the sampled GCs
Dgcs =maximal depth of the sampled GCs
Remote Sens. 2021, 13, x FOR PEER REVIEW 13 of 28
cs
D =maximal depth of the sampled GCs
Figure 6. Cross-sectional metrics used by VERTICAL for automated calculation of width–depth (W/D) ratio.
4. Results
4.1. Spatial Resolution and Accuracy of Created Interval VHR Models
Aerial imagery collected during the conducted aerial surveys was used for the deri-
vation of two-interval VHR DSMs with 1.9 centimeters spatial resolution and digital or-
thomosaics with 0.5 centimeters spatial resolution. Point density was 2980 points/m² for
Survey A and 3180 points/m² for Survey B. Created VHR models can be seen in Figure 7.
Figure 7. VHR DSMs derived for Survey A and Survey B
Results of accuracy assessment are demonstrating that model uncertainty is very sim-
ilar for both created VHR DSMs. Total RMSE values calculated for seven fixed checkpoints
(CPs) amount to 3.76 cm for Survey A and 3.51 cm for Survey B, resulting in a mean RMSE
of 3.63 cm. The total mean absolute error (MAE) was 3.37 cm for Survey A and 3.25 cm for
Survey B (mean MAE = 3.31 cm). Reprojection error (RE) for Survey A is 0.250 pix and
0.263 pix for Survey B. Minimal level of detection calculated based on RMSE and MAE
values was 5.143 cm (
()
min
LoD RMSE ) and 4.682 cm (
()
min
LoD
M
AE , resulting in an absolute
detection threshold ( min
meanLoD of 4.913 cm. Point cloud accuracy validation per-
formed by the M2C3 algorithm resulted in a 0.59 cm mean calculated distance between
two point clouds, with a 2.86 cm standard deviation. If obtained values (MAE; RMSE;
Figure 6. Cross-sectional metrics used by VERTICAL for automated calculation of width–depth (W/D) ratio.
4. Results
4.1. Spatial Resolution and Accuracy of Created Interval VHR Models
Aerial imagery collected during the conducted aerial surveys was used for the deriva-
tion of two-interval VHR DSMs with 1.9 centimeters spatial resolution and digital orthomo-
saics with 0.5 centimeters spatial resolution. Point density was 2980 points/m
2
for Survey
A and 3180 points/m2for Survey B. Created VHR models can be seen in Figure 7.
Remote Sens. 2021, 13, x FOR PEER REVIEW 13 of 28
cs
D =maximal depth of the sampled GCs
Figure 6. Cross-sectional metrics used by VERTICAL for automated calculation of width–depth (W/D) ratio.
4. Results
4.1. Spatial Resolution and Accuracy of Created Interval VHR Models
Aerial imagery collected during the conducted aerial surveys was used for the deri-
vation of two-interval VHR DSMs with 1.9 centimeters spatial resolution and digital or-
thomosaics with 0.5 centimeters spatial resolution. Point density was 2980 points/m² for
Survey A and 3180 points/m² for Survey B. Created VHR models can be seen in Figure 7.
Figure 7. VHR DSMs derived for Survey A and Survey B
Results of accuracy assessment are demonstrating that model uncertainty is very sim-
ilar for both created VHR DSMs. Total RMSE values calculated for seven fixed checkpoints
(CPs) amount to 3.76 cm for Survey A and 3.51 cm for Survey B, resulting in a mean RMSE
of 3.63 cm. The total mean absolute error (MAE) was 3.37 cm for Survey A and 3.25 cm for
Survey B (mean MAE = 3.31 cm). Reprojection error (RE) for Survey A is 0.250 pix and
0.263 pix for Survey B. Minimal level of detection calculated based on RMSE and MAE
values was 5.143 cm (
()
min
LoD RMSE ) and 4.682 cm (
()
min
LoD
M
AE , resulting in an absolute
detection threshold ( min
meanLoD of 4.913 cm. Point cloud accuracy validation per-
formed by the M2C3 algorithm resulted in a 0.59 cm mean calculated distance between
two point clouds, with a 2.86 cm standard deviation. If obtained values (MAE; RMSE;
Figure 7. VHR DSMs derived for Survey A and Survey B.
Remote Sens. 2021,13, 321 13 of 27
Results of accuracy assessment are demonstrating that model uncertainty is very simi-
lar for both created VHR DSMs. Total RMSE values calculated for seven fixed checkpoints
(CPs) amount to 3.76 cm for Survey A and 3.51 cm for Survey B, resulting in a mean RMSE
of 3.63 cm. The total mean absolute error (MAE) was 3.37 cm for Survey A and 3.25 cm
for Survey B (mean MAE = 3.31 cm). Reprojection error (RE) for Survey A is 0.250 pix
and 0.263 pix for Survey B. Minimal level of detection calculated based on RMSE and
MAE values was 5.143 cm (
LoDmin(RMSE)
) and 4.682 cm (
LoDmin(MA E)
, resulting in an
absolute detection threshold (
meanLoDmin
of 4.913 cm. Point cloud accuracy validation
performed by the M2C3 algorithm resulted in a 0.59 cm mean calculated distance between
two point clouds, with a 2.86 cm standard deviation. If obtained values (MAE; RMSE;
LoDmin)
are compared to the values from similar published geomorphic researches [
45
,
87
],
it can be concluded that the accuracy of created VHR models is sufficient for conducted
spatio-temporal changes analysis.
Based on carried accuracy assessment it can be concluded that uncertainty of created
interval VHR DSMs is within
±
5 cm. As a result, a minimal level of detection threshold for
spatio-temporal changes detected by VERTICAL was set to
±
5 cm accordingly. Application
of
±
5 cm threshold allowed separation of real morphological changes (erosion: values
above
5 cm; accumulation: values above 5 cm), from changes induced by errors in
created models.
4.2. Interpretation of Determined Spatio-Temporal Change
The VERTICAL method was applied for the comparison of DSMs representing the
2-year period between surveys A and B. In total VERTICAL method created and sampled
2379 GCs, along the 110 m long thalweg of gully Santiš (Figure 8). The total number of
height points sampled within all created GCs is 749,052, while on average 314.86 points
were sampled per every created GCs. Since the spatial resolution of interval DSMs in our
research was 1.9 cm and calculated accuracy metrics (RMSE; MAE;
meanLoDmin
) were
under 5 cm, we defined a 5 cm sampling interval. As a result, VERTICAL sampled the
height data from input DSMs along the created GCs lines at 5 cm intervals.
Furthermore, all sampled GCs under 5 cm were excluded from the determination of
spatio-temporal changes, as such these values cannot be considered accurate and reliable
enough for the analysis, due to the DSM uncertainty. Therefore, from 2379 sampled GCs,
only 922 GCs that had derived mean spatio-temporal changes values above the 5 cm
threshold were used for the analysis of spatio-temporal changes intensity.
Calculated mean spatio-temporal changes measured within 922 GCs vary significantly
from the gully headcut to the gully terminus (Figure 8). This correlates with the overall
heterogeneity of the gully erosion process [
92
]. Erosion prevails in 356 GCs (38.61%),
with the mean erosion rate per sampled GCs between 5.13
±
5 and 51.65
±
5 cm in the
study period
1
. On the other hand, accumulation prevails in 566 GCs (61.38%), with
the mean accumulation rate per sampled GCs between 5.04
±
5 and 13.43
±
5 cm in the
study period
1
. Variations in extracted spatio-temporal change values reflect the different
processes and trends that have occurred between the two surveys. Although gully Santiš
represents a relatively simple and short gully, results derived by VERTICAL are showing
that spatial distribution and intensity of the processes that influence its formation are
more complex than expected. For example, within the first 51 analyzed GCs (Figure 8A)
maximal mean erosion values were recorded, ranging between 9.21
±
5 and 51.64
±
5 cm
in the study period
1
. The maximal erosion value measured within point samples of
that 51 GCs is 1.43
±
5 m in the study period
1
. Such pronounced erosion is a result
of mass movements related to the collapse and uphill progression of the gully headcut
(Figures 8and 9).
Pronounced uphill progression and intensive erosion rates are typical for
most gully headcuts [
15
], and values of measured spatio-temporal changes within the first
51 GCs are strongly indicating that this is true for the headcut of gully Santiš.
Remote Sens. 2021,13, 321 14 of 27
Remote Sens. 2021, 13, x FOR PEER REVIEW 14 of 28
LoD) are compared to the values from similar published geomorphic researches
[45,87], it can be concluded that the accuracy of created VHR models is sufficient for con-
ducted spatio-temporal changes analysis.
Based on carried accuracy assessment it can be concluded that uncertainty of created
interval VHR DSMs is within ±5 cm. As a result, a minimal level of detection threshold for
spatio-temporal changes detected by VERTICAL was set to ±5 cm accordingly. Applica-
tion of ±5 cm threshold allowed separation of real morphological changes (erosion: values
above –5 cm; accumulation: values above 5 cm), from changes induced by errors in created
models.
4.2. Interpretation of Determined Spatio-Temporal Change
The VERTICAL method was applied for the comparison of DSMs representing the 2-
year period between surveys A and B. In total VERTICAL method created and sampled
2379 GCs, along the 110 m long thalweg of gully Santiš (Figure 8). The total number of
height points sampled within all created GCs is 749,052, while on average 314.86 points
were sampled per every created GCs. Since the spatial resolution of interval DSMs in our
research was 1.9 cm and calculated accuracy metrics (RMSE; MAE; 𝑚𝑒𝑎𝑛LoD ) were
under 5 cm, we defined a 5 cm sampling interval. As a result, VERTICAL sampled the
height data from input DSMs along the created GCs lines at 5 cm intervals.
Figure 8. Mean spatio-temporal changes within GCs sampled with the VERTICAL method; extracted predominant spatio-
temporal changes along the gully thalweg (A–E).
Furthermore, all sampled GCs under 5 cm were excluded from the determination of
spatio-temporal changes, as such these values cannot be considered accurate and reliable
enough for the analysis, due to the DSM uncertainty. Therefore, from 2379 sampled GCs,
Figure 8.
Mean spatio-temporal changes within GCs sampled with the VERTICAL method; extracted predominant
spatio-temporal changes along the gully thalweg (AE).
Remote Sens. 2021, 13, x FOR PEER REVIEW 15 of 28
only 922 GCs that had derived mean spatio-temporal changes values above the 5 cm
threshold were used for the analysis of spatio-temporal changes intensity.
Calculated mean spatio-temporal changes measured within 922 GCs vary signifi-
cantly from the gully headcut to the gully terminus (Figure 8). This correlates with the
overall heterogeneity of the gully erosion process [92]. Erosion prevails in 356 GCs
(38.61%), with the mean erosion rate per sampled GCs between 5.13 ± 5 and 51.65 ± 5 cm
in the study period
1
. On the other hand, accumulation prevails in 566 GCs (61.38%), with
the mean accumulation rate per sampled GCs between 5.04 ± 5 and 13.43 ± 5 cm in the
study period
1
. Variations in extracted spatio-temporal change values reflect the different
processes and trends that have occurred between the two surveys. Although gully Santiš
represents a relatively simple and short gully, results derived by VERTICAL are showing
that spatial distribution and intensity of the processes that influence its formation are
more complex than expected. For example, within the first 51 analyzed GCs (Figure 8 A)
maximal mean erosion values were recorded, ranging between 9.21 ± 5 and 51.64 ± 5 cm
in the study period
1
. The maximal erosion value measured within point samples of that
51 GCs is 1.43 ± 5 m in the study period
1
. Such pronounced erosion is a result of mass
movements related to the collapse and uphill progression of the gully headcut (Figures 8.
A,9). Pronounced uphill progression and intensive erosion rates are typical for most gully
headcuts [15], and values of measured spatio-temporal changes within the first 51 GCs
are strongly indicating that this is true for the headcut of gully Santiš.
Plotted point samples from
50
GCs
confirm that between two surveys, intensive
headcut collapse occurred within these GCs (Figure 9A,D,F), as well as accumulation of
eroded material at the headcut base (Figure 9B,C,E). Uphill progression of the gully head-
cut is also evident from the horizontal shift of the line representing headcut position in
Survey A and in Survey B (Figure 9). Due to the erosion-induced material collapse mean
linear gully headcut retreat (GHR) between Survey A and B was 10.85 ± 5 cm study pe-
riod
1
, while maximum values amounted up to the 49.71 ± 5 cm study period
1
. Since these
values represent the two-year period, the maximum annual GHR amounts to 24.85 ± 5 cm
year
1
. Determined annual GHR corresponds to the rates recorded in other researches,
with similar conditions [15,115,116].
Figure 9. Measured spatio-temporal changes within 236 height points sampled within GCs

; sighnificant spatio-temporal
changes detected within GCs

(A–F).
After the headcut and initial prevalence of erosion, accumulation prevails within the
next several hundred GCs, until the
886
GCs
(Figure 8B). In this section, the gully grad-
Figure 9.
Measured spatio-temporal changes within 236 height points sampled within
GCs50
; sighnificant spatio-temporal
changes detected within GCs50 (AF).
Plotted point samples from
GCs50
confirm that between two surveys, intensive head-
cut collapse occurred within these GCs (Figure 9A,D,F), as well as accumulation of eroded
material at the headcut base (Figure 9B,C,E). Uphill progression of the gully headcut is
also evident from the horizontal shift of the line representing headcut position in Survey A
and in Survey B (Figure 9). Due to the erosion-induced material collapse mean linear gully
Remote Sens. 2021,13, 321 15 of 27
headcut retreat (GHR) between Survey A and B was 10.85
±
5 cm study period
1
, while
maximum values amounted up to the 49.71
±
5 cm study period
1
. Since these values
represent the two-year period, the maximum annual GHR amounts to 24.85
±
5 cm year
1
.
Determined annual GHR corresponds to the rates recorded in other researches, with similar
conditions [15,115,116].
After the headcut and initial prevalence of erosion, accumulation prevails within the
next several hundred GCs, until the
GCs886
(Figure 8B). In this section, the gully gradually
narrows, forming one main channel. Steep sidewalls on both sides of the main channel are
being slowly eroded (Figure 10A), similarly as at the gully headcut, while eroded material
is accumulated within the channel (Figure 10B–E). The prevalence of accumulation within
this area is related to the accumulation of the soil material dispatched from the nearby
sidewalls and main headcut. Due to the lack of stronger surface flow eroded material
cannot be transported much further away from the base of sidewalls and headcut [
21
], thus
being accumulated within the first 20 meters of the gully. Experimental research conducted
by [
21
] have demonstrated the importance of intense rainfall-induced torrential surface
flows on transportation of discharged sediment. They concluded that lower-intensity
rainfall can be sufficient for the occurrence of mass movements at gully headcut, but at the
same time insufficient for further transportation of material from the headcut. This causes
a gradual accumulation of the eroded material. Our onsite precipitation measurements
between May and November 2017 registered 626 mm of rainfall with highly heterogenic
distribution, which is in very good concordance with the average values and seasonal
distribution of the rainfall recorded by [
97
]. Summer months (June-August) record very
low and relatively uniform amounts of precipitation, while autumn months recorded
uneven distribution and significantly increased precipitation values. Considering that
no torrential rain (I > 60 mm/h) was recorded, it can be presumed that also no torrential
surface runoff occurred within the studied period. Therefore, it is possible that surface
runoff intensity was insufficient for further transportation of the material. Consequently,
such rainfall intensity corroborates data on soil accumulation within the first 20 meters
after the headcut (Figure 8B). However, as noted by the field observations, the rain events
recorded in September 2017 (96.8 mm and 140 mm) had a significant influence on the
changes in gully morphology. Such changes proved the importance of the occurrence of
intense rainfall-induced surface runoff for reshaping the gully morphology [21,91].
Remote Sens. 2021, 13, x FOR PEER REVIEW 16 of 28
ually narrows, forming one main channel. Steep sidewalls on both sides of the main chan-
nel are being slowly eroded (Figure 10A), similarly as at the gully headcut, while eroded
material is accumulated within the channel (Figure 10B–E). The prevalence of accumula-
tion within this area is related to the accumulation of the soil material dispatched from
the nearby sidewalls and main headcut. Due to the lack of stronger surface flow eroded
material cannot be transported much further away from the base of sidewalls and headcut
[21], thus being accumulated within the first 20 meters of the gully. Experimental research
conducted by [21] have demonstrated the importance of intense rainfall-induced torren-
tial surface flows on transportation of discharged sediment. They concluded that lower-
intensity rainfall can be sufficient for the occurrence of mass movements at gully headcut,
but at the same time insufficient for further transportation of material from the headcut.
This causes a gradual accumulation of the eroded material. Our onsite precipitation meas-
urements between May and November 2017 registered 626 mm of rainfall with highly
heterogenic distribution, which is in very good concordance with the average values and
seasonal distribution of the rainfall recorded by [97]. Summer months (June-August) rec-
ord very low and relatively uniform amounts of precipitation, while autumn months rec-
orded uneven distribution and significantly increased precipitation values. Considering
that no torrential rain (I > 60 mm/h) was recorded, it can be presumed that also no torren-
tial surface runoff occurred within the studied period. Therefore, it is possible that surface
runoff intensity was insufficient for further transportation of the material. Consequently,
such rainfall intensity corroborates data on soil accumulation within the first 20 meters
after the headcut (Figure 8B). However, as noted by the field observations, the rain events
recorded in September 2017 (96.8 mm and 140 mm) had a significant influence on the
changes in gully morphology. Such changes proved the importance of the occurrence of
intense rainfall-induced surface runoff for reshaping the gully morphology [21,91].
Figure 10. Measured spatio-temporal changes within 339 height points sampled within GCs

; sighnificant spatio-tem-
poral changes detected within GCs

(A–E).
Sporadic stronger erosion values were recorded within the middle part of the gully
(Figure 8C), which is related to the intensive collapse of steep walls and incision of smaller
sub-channels, formed by concentrated periodic surface runoff within this part of the main
gully channel. Within the lower part of the gully (Figure 8D) low-intensity erosion pre-
vails, with erosion values in GCs up to the 11.61 ± 5 cm study period
1
. The mean erosion
value for this section (
1234 2057
GCs GCs
) of gully Santiš was 6.17 ± 5 cm study period
1
.
The absence of soil accumulation and less homogenous parental material have led to the
occurrence of selective erosion within this part of the gully.
Figure 10.
Measured spatio-temporal changes within 339 height points sampled within
GCs757
; sighnificant spatio-temporal
changes detected within GCs757 (AE).
Remote Sens. 2021,13, 321 16 of 27
Sporadic stronger erosion values were recorded within the middle part of the gully
(Figure 8C), which is related to the intensive collapse of steep walls and incision of smaller
sub-channels, formed by concentrated periodic surface runoff within this part of the main
gully channel. Within the lower part of the gully (Figure 8D) low-intensity erosion prevails,
with erosion values in GCs up to the 11.61
±
5 cm study period
1
. The mean erosion
value for this section (
GCs1234 GCs2057
) of gully Santiš was 6.17
±
5 cm study period
1
.
The absence of soil accumulation and less homogenous parental material have led to the
occurrence of selective erosion within this part of the gully.
The last section of gully Santiš (Figure 8E) is influenced by the fluctuations and
dynamics of the Adriatic Sea, primarily by the influence of wave activity. Generally, the
eastern Adriatic Sea has microtidal characteristics, with a tidal amplitude between 0.22 and
1.2 m [
117
] (117 in 119). According to [
118
], mean annual significant wave heights within
the Pag island quadrant are between 0.3 and 0.4 m, while the mean annual wind speed
(Bf) ranges from 1.5 m in S, SE, and SW part, up to 2.5 m in the rest of the Island. In spite
of the rather low values, the influence of waves on coastal areas is present. During the
field surveys accumulation of pebbles and soil sediment was observed at the beach, while
these sediments were later disrupted and dislocated by the Sea activity. Our observation
corresponds well with the time of percentage with no wind in the Pag island quadrant,
which is between 10 and 20% [
118
]. Moreover, even though the coastline is mostly formed
by carbonate rocks, which are more prone to karst processes with negligible effects of
coastal erosion [
119
], the existing pocket beach consisting of gravel deposits (Figure 11)
proves coastal dynamics and erosion of soil deposits.
Remote Sens. 2021, 13, x FOR PEER REVIEW 17 of 28
The last section of gully Santiš (Figure 8E) is influenced by the fluctuations and dy-
namics of the Adriatic Sea, primarily by the influence of wave activity. Generally, the east-
ern Adriatic Sea has microtidal characteristics, with a tidal amplitude between 0.22 and
1.2 m [117] (117 in 119). According to [118], mean annual significant wave heights within
the Pag island quadrant are between 0.3 and 0.4 m, while the mean annual wind speed
(Bf) ranges from 1.5 m in S, SE, and SW part, up to 2.5 m in the rest of the Island. In spite
of the rather low values, the influence of waves on coastal areas is present. During the
field surveys accumulation of pebbles and soil sediment was observed at the beach, while
these sediments were later disrupted and dislocated by the Sea activity. Our observation
corresponds well with the time of percentage with no wind in the Pag island quadrant,
which is between 10 and 20% [118]. Moreover, even though the coastline is mostly formed
by carbonate rocks, which are more prone to karst processes with negligible effects of
coastal erosion [119], the existing pocket beach consisting of gravel deposits (Figure 11)
proves coastal dynamics and erosion of soil deposits.
Results also indicate that smaller pebbles accumulated at the beach (Figure 11C,D)
were dislocated by Sea activity, while larger, heavier boulders remained at the same loca-
tion (Figure 11E). Another confirmation of the accuracy of used DSM models is the an-
thropogenic material (e.g., timber, plastic pipes), accumulated at the beach by Sea activity
between two surveys. This anthropogenic material was detected and measured by VER-
TICAL as the accumulation of material (Figure 11A,B).
Figure 11. Measured spatio-temporal changes within 184 height points sampled within GCs

; sighnificant spatio-tem-
poral changes detected within GCs

(A–E).
We assume that the strongest wave activity and related processes occur under the
influence of Bora wind [89] which in this part of the Adriatic raises waves up to 7.2 meters
[120]. Namely, Santiš is oriented in the NW direction and the average number of days
with wind from the NE and NNE quadrant is 85.4 and 144.6 respectively, with a mean
annual wind speed of 5.5 Bf and maximum of 30.4 Bf [97]. Field observations made a few
days after the significant rain event in September 2017 (140 mm) proved the connection
between rain-induced surface and subsurface runoff formation and reactivation of gully
hydrological function. During the field survey carried out a few days after the significant
rain event (140 mm), reactivation of spring at the pebble beach occurred, within the final
part of the gully, along with the reactivation of nearby submarine springs (Figure 12B).
Figure 11.
Measured spatio-temporal changes within 184 height points sampled within
GCs2145
; sighnificant spatio-temporal
changes detected within GCs2145 (AE).
Results also indicate that smaller pebbles accumulated at the beach (Figure 11C,D)
were dislocated by Sea activity, while larger, heavier boulders remained at the same
location (Figure 11E). Another confirmation of the accuracy of used DSM models is the
anthropogenic material (e.g., timber, plastic pipes), accumulated at the beach by Sea
activity between two surveys. This anthropogenic material was detected and measured by
VERTICAL as the accumulation of material (Figure 11A,B).
We assume that the strongest wave activity and related processes occur under the
influence of Bora wind [
89
] which in this part of the Adriatic raises waves up to 7.2 me-
ters [
120
]. Namely, Santiš is oriented in the NW direction and the average number of days
with wind from the NE and NNE quadrant is 85.4 and 144.6 respectively, with a mean
annual wind speed of 5.5 Bf and maximum of 30.4 Bf [
97
]. Field observations made a few
days after the significant rain event in September 2017 (140 mm) proved the connection
Remote Sens. 2021,13, 321 17 of 27
between rain-induced surface and subsurface runoff formation and reactivation of gully
hydrological function. During the field survey carried out a few days after the significant
rain event (140 mm), reactivation of spring at the pebble beach occurred, within the final
part of the gully, along with the reactivation of nearby submarine springs (Figure 12B).
Remote Sens. 2021, 13, x FOR PEER REVIEW 18 of 28
Figure 12. Comparison of wide initial part (headcut) of gully Santiš formed in homogenous soil sediment (A) with narrow
final part of gully formed in carbonate bedrock (B).
4.3. Interpretation of Derived W/D Ratio
VERTICAL calculated automatically W/D ratio for all 2379 sampled GCs. Values of
calculated W/D ratio range between 5.23 and 7.91 for survey A, and between 5.13 and 8.15
for survey B.
The highest W/D ratio values for both survey A and B were present at the initial part
of gully Santiš, from gully headcut towards the middle part. From the middle part, the
gully narrows, and W/D ratio values gradually decline towards the pebble beach at the
gully terminus. Such transition of W/D ratio values (from U-shaped GCs towards the V-
shaped GCs) is opposite to typical general gradual increase in W/D ratio values from
headcut towards the gully terminus, which was reported in some other similar case stud-
ies [e.g., 77] (77 in 24). This can be further explained by the overall heterogeneity of soil
sediments within the gully Santiš. While the initial part of the gully is formed in homog-
enous, erosion-prone soil sediments, the middle and final parts of the gully are formed in
sediments with pronounced erosion resistance. Thus, as calculated W/D ratio values indi-
cated, strong lateral erosion that is present in the initial part of the gully (Figure 12A) is
gradually subsided by more pronounced channel incision and occurrence of selective ero-
sion towards the middle and final parts of the gully (Figure 12B).
5. Discussion
5.1. Advantages of VERTICAL over the Profilometer
Regardless of its design, the profilometer has few significant shortcomings which af-
fect the practicality of its application and accuracy of its measurement. First, an important
shortcoming is a length constraint (1) of the GCs that the profilometer can measure. Due
to its design profilometer is suitable only for the measurement of smaller erosion features,
e.g., rill and ephemeral gullies. For gullies with wider cross-sections, such as gully Santiš,
a profilometer should be more than 10 m long, which is very impractical. A mechanical
profilometer is affected by depth constraint (2), as the maximal depth that the profilome-
ter’s pins can measure is restricted by the length (scale) of the pins. If the intensity of spa-
tio-temporal changes overcomes the maximal scale of measurement pins, a profilometer
cannot be used for the measurement of these changes. Furthermore, the whole measure-
ment process with a profilometer is time-consuming and labor-intensive (3), since meas-
urements are restricted to one GCs at a time. This significantly increases the duration of
field measurements, especially for longer gullies and researches that require sampling of
a larger number of GCs. The next shortcoming of a profilometer regards consistency, ac-
curacy, and precision (4) of its measurements. While optical profilometers tend to under-
estimate depth values [27], mechanical profilometers overestimate them [79]. Errors
caused by optical profilometer are sensor related [27], while errors caused by mechanical
Figure 12.
Comparison of wide initial part (headcut) of gully Santiš formed in homogenous soil sediment (
A
) with narrow
final part of gully formed in carbonate bedrock (B).
4.3. Interpretation of Derived W/D Ratio
VERTICAL calculated automatically W/D ratio for all 2379 sampled GCs. Values of
calculated W/D ratio range between 5.23 and 7.91 for survey A, and between 5.13 and
8.15 for survey B.
The highest W/D ratio values for both survey A and B were present at the initial
part of gully Santiš, from gully headcut towards the middle part. From the middle part,
the gully narrows, and W/D ratio values gradually decline towards the pebble beach at
the gully terminus. Such transition of W/D ratio values (from U-shaped GCs towards
the V-shaped GCs) is opposite to typical general gradual increase in W/D ratio values
from headcut towards the gully terminus, which was reported in some other similar case
studies [e.g., 77] (77 in 24). This can be further explained by the overall heterogeneity
of soil sediments within the gully Santiš. While the initial part of the gully is formed
in homogenous, erosion-prone soil sediments, the middle and final parts of the gully
are formed in sediments with pronounced erosion resistance. Thus, as calculated W/D
ratio values indicated, strong lateral erosion that is present in the initial part of the gully
(Figure 12A) is gradually subsided by more pronounced channel incision and occurrence
of selective erosion towards the middle and final parts of the gully (Figure 12B).
5. Discussion
5.1. Advantages of VERTICAL over the Profilometer
Regardless of its design, the profilometer has few significant shortcomings which
affect the practicality of its application and accuracy of its measurement. First, an impor-
tant shortcoming is a length constraint (1) of the GCs that the profilometer can measure.
Due to its design profilometer is suitable only for the measurement of smaller erosion
features, e.g., rill and ephemeral gullies. For gullies with wider cross-sections, such as
gully Santiš, a profilometer should be more than 10 m long, which is very impractical.
A mechanical profilometer is affected by depth constraint (2), as the maximal depth that
the profilometer’s pins can measure is restricted by the length (scale) of the pins. If the
intensity of spatio-temporal changes overcomes the maximal scale of measurement pins, a
profilometer cannot be used for the measurement of these changes. Furthermore, the whole
measurement process with a profilometer is time-consuming and labor-intensive (3), since
measurements are restricted to one GCs at a time. This significantly increases the duration
Remote Sens. 2021,13, 321 18 of 27
of field measurements, especially for longer gullies and researches that require sampling
of a larger number of GCs. The next shortcoming of a profilometer regards consistency,
accuracy, and precision (4) of its measurements. While optical profilometers tend to un-
derestimate depth values [
27
], mechanical profilometers overestimate them [
79
]. Errors
caused by optical profilometer are sensor related [
27
], while errors caused by mechanical
profilometer can be related to the direct measurement of depth values (e.g., compaction
problem
[29,121,122].
Furthermore, in order for interval measurements to be accurate and
precise, the profilometer has to be placed at the exact XYZ location, which is often difficult
to accomplish in the field. Even the slightest deviation along any axis (X, Y, or Z) in device
placement can lead to deviation of measurement values and occurrence of significant
measurement errors.
The developed VERTICAL method represents a fast and accurate measurement
method that allows automated measurement of vertical spatio-temporal changes within a
very large number of GCs. The main novelty and advantage of the VERTICAL method is
the automation of the whole measurement process (in comparison to both traditional field
techniques and most of existing raster-based methods), which significantly shortens and
simplifies the detection of spatio-temporal changes, while at the same time improving the
accuracy and repeatability of GCs measurement.
Furthermore, the application of VERTICAL demonstrated that it successfully resolves
most of the stated limitations of profilometer and similar traditional field sampling methods.
The main advantages of VERTICAL are:
Indirect measurement (1)—VERTICAL allows measurement of spatio-temporal changes
in GCs without direct physical contact with the measured surface. Measurements are non-
destructive, meaning that the measurement process will not alter the state of a measured
surface (e.g., compaction occurrence), as some other traditional methods would.
High sampling density (2)—this method measures far more GCs and height samples
that would be possible to achieve with traditional field methods. A total number of sampled
GCs and height points are defined by the user-defined MSL and sampling interval. In
theory, VERTICAL can measure an unlimited number of GCs and height points. On the
other hand, the measurement of such a large number of GCs and height samples would
be hardly achievable with any traditional field measurement technique. Studies that have
used profilometer measured significantly less GCs per one gully (e.g., 1 [
78
], 3 [
24
,
25
], 5 [
27
],
28 [
30
]) and less height samples within particular GCs (e.g., 46 [
78
], 50 [
26
], 100 [
27
,
34
])
(Table 1.).
Absence of length and depth constraint (3)—minimal and maximal lengths of GCs
that can be sampled by VERTICAL are restricted only by the user-defined MSL and gully
area. The profilometer’s depth constraint is also resolved, as VERTICAL can measure any
height difference from two defined DSMs. This means that no matter how wide GCs are,
or how intensive certain spatio-temporal change at a given location is, VERTICAL will
be able to measure it. The length of 2379 GCs sampled within gully Santiš varies from
just a few meters (minimum GCs length = 1.91 m), up to tens of meters (maximal GCs
length = 30.34 m). Measured height difference at some points sampled within created GCs
amounted up to over a meter (maximal height difference = 1.43 m). Measurement of GCs
with that variability in length and depth would be hardly achievable with a profilometer
(e.g., max GCs length = 1 m [26];2m[27];5m[34]).
Measurement speed and efficiency (4)—in comparison to traditional field techniques,
VERTICAL significantly simplifies and shortens the overall time-consuming and labor-
intensive process of GCs measurement, where the whole measurement process is fully
automated and the required processing time is restricted solely by available processing-
power.
Measurement consistency (5)—as VERTICAL uses identical GCs and points with
precise XYZ coordinates for measurement of height samples from both interval DSMs, all
user-influenced deviations in measurement accuracy and precision are eliminated. This
Remote Sens. 2021,13, 321 19 of 27
was hard to achieve in the field, as the profilometer had to be positioned et exactly the
same location for every measurement.
Adaptability and flexibility (6)—VERTICAL can be applied for different research
purposes and areas, not exclusively for measurement of gully erosion induced spatio-
temporal changes. This method can be applied on DSMs with different spatial resolution
and extend, from sub-millimeter resolution models (e.g., tufa dynamics monitoring, freeze
cracking) covering a few cm
2
, sub-meter resolution models (e.g., gully erosion, landslides,
river dynamics) covering hundreds of m
2
, up to the medium, or even low-resolution
models (e.g., ice mass dynamics, tectonics, bathymetry) covering tens or hundreds of km
2
.
Furthermore, VERTICAL can be applied on models created by various different geospatial
technologies (e.g., LiDAR, close-range photogrammetry, optical satellite stereo-imagery).
An example of successful VERTICAL application for monitoring of tufa formation dynamics
is given in Section 5.2.
Freely available (7)—this method is compatible with ArcToolbox and available as
an open-source toolset from the official web page of Geospatial Analysis Laboratory
(gal.unizd.hr).
5.2. Applicability of VERTICAL in the Study of Tufa Formation Dynamics (TFD)
The VERTICAL method can have prominent applicability in the monitoring of recent
tufa and travertine growth and erosion dynamics. This analysis can be carried out at sub
micro-level of research (<0.1 mm). Until recently, the tufa formation dynamics (TFD) was
predominantly analyzed using a direct measurement method of modified micro-erosion
meter (MEM) [
123
125
]. It gathers tufa elevation data in the form of individual points
(n = 50) and has various drawbacks [
37
]. In these researches, seasonal growth dynamics
from laminated tufa layers is often analyzed from specific cross-sections interpolating the
acquired MEM data. These “visible” sections are created by cutting and removing a part
of the formed tufa or travertine from a specific test plate (mostly limestone) which was
mounted in tufa forming watercourses. This procedure interrupts the tufa forming process
on the studied test surface, which in some cases lasted several years (Figure 13).
Remote Sens. 2021, 13, x FOR PEER REVIEW 20 of 28
extend, from sub-millimeter resolution models (e.g., tufa dynamics monitoring, freeze
cracking) covering a few cm², sub-meter resolution models (e.g., gully erosion, landslides,
river dynamics) covering hundreds of m², up to the medium, or even low-resolution mod-
els (e.g., ice mass dynamics, tectonics, bathymetry) covering tens or hundreds of km². Fur-
thermore, VERTICAL can be applied on models created by various different geospatial
technologies (e.g., LiDAR, close-range photogrammetry, optical satellite stereo-imagery).
An example of successful VERTICAL application for monitoring of tufa formation dy-
namics is given in Section 5.2.
Freely available (7)—this method is compatible with ArcToolbox and available as an
open-source toolset from the official web page of Geospatial Analysis Laboratory
(gal.unizd.hr).
5.2. Applicability of VERTICAL in the Study of Tufa Formation Dynamics (TFD)
The VERTICAL method can have prominent applicability in the monitoring of recent
tufa and travertine growth and erosion dynamics. This analysis can be carried out at sub
micro-level of research (<0.1 mm). Until recently, the tufa formation dynamics (TFD) was
predominantly analyzed using a direct measurement method of modified micro-erosion
meter (MEM) [123–125]. It gathers tufa elevation data in the form of individual points (n
= 50) and has various drawbacks [37]. In these researches, seasonal growth dynamics from
laminated tufa layers is often analyzed from specific cross-sections interpolating the ac-
quired MEM data. These “visible” sections are created by cutting and removing a part of
the formed tufa or travertine from a specific test plate (mostly limestone) which was
mounted in tufa forming watercourses. This procedure interrupts the tufa forming pro-
cess on the studied test surface, which in some cases lasted several years (Figure 13).
Figure 13. Examples of cutted formed tufa and determined cross-sections.
From the derived cross-section seasonal differences [125] in tufa formation dynamics
(changes in cross-section thickness), differences in structural and textural characteristics
of formed tufa, and specific events (macroinvertebrates accumulation and incrustation,
erosion due to extreme flood events, dry periods) in the tufa formation process can be
detected. However, [37] were the first to use SfM and interval high-resolution digital tufa
models (DTHRM) in TFD-a analysis. Given that this new methodological approach stud-
ies TFD in the fixed local coordinate system, it is now possible to continuously monitor
the dynamics of tufa formation from quantified cross-sections derived from DTHRM.
Therefore, VERTICAL, in this case, enables the creation and quantification of cross-sec-
tions from generated 2.5D and 3D tufa models thus eliminating the need to destroy the
recent tufa and to interrupts the tufa forming process.
Figures 14 and 15 show the elevation values within specific cross-section which were
derived from DTHRM using a VERTICAL tool. Two test plates were placed in different
fluvial environments and analyzed. The vertical spatio-temporal changes (increment +,
erosion -, no change) were tracked in each cross-section with 82 points. On the first test
plate, large variability in tufa growth between the initial and sequential DTHRMs can be
observed due to a wide range of factors. In the first period, the cross-sections have the
highest recorded mean spatio-temporal change (MSTC) because a large number of aquatic
insect larvae from the family Chironomidae (order: Diptera) accumulated on the surface.
However, since the accumulated organisms and plant material were not well-bounded to
Figure 13. Examples of cutted formed tufa and determined cross-sections.
From the derived cross-section seasonal differences [
125
] in tufa formation dynamics
(changes in cross-section thickness), differences in structural and textural characteristics
of formed tufa, and specific events (macroinvertebrates accumulation and incrustation,
erosion due to extreme flood events, dry periods) in the tufa formation process can be
detected. However, [
37
] were the first to use SfM and interval high-resolution digital
tufa models (DTHRM) in TFD-a analysis. Given that this new methodological approach
studies TFD in the fixed local coordinate system, it is now possible to continuously monitor
the dynamics of tufa formation from quantified cross-sections derived from DTHRM.
Therefore, VERTICAL, in this case, enables the creation and quantification of cross-sections
from generated 2.5D and 3D tufa models thus eliminating the need to destroy the recent
tufa and to interrupts the tufa forming process.
Figures 14 and 15 show the elevation values within specific cross-section which were
derived from DTHRM using a VERTICAL tool. Two test plates were placed in different
fluvial environments and analyzed. The vertical spatio-temporal changes (increment +,
erosion
, no change) were tracked in each cross-section with 82 points. On the first test
plate, large variability in tufa growth between the initial and sequential DTHRMs can be
Remote Sens. 2021,13, 321 20 of 27
observed due to a wide range of factors. In the first period, the cross-sections have the
highest recorded mean spatio-temporal change (MSTC) because a large number of aquatic
insect larvae from the family Chironomidae (order: Diptera) accumulated on the surface.
However, since the accumulated organisms and plant material were not well-bounded to
the substrate, significant erosion occurred which lowered down the MSTC values measured
in the following intervals.
Remote Sens. 2021, 13, x FOR PEER REVIEW 21 of 28
the substrate, significant erosion occurred which lowered down the MSTC values meas-
ured in the following intervals.
Figure 14. Example of two quantified cross sections on initial and sequential DTHRMs.
On the second test plate, the formed tufa was cut and partially removed following
the classical examples of studying the cross-sections. However, before this, the interval
SfM measurement of the formed tufa was made. Then, after the cutting, the remaining
tufa was measured and derived cross-sections were added to its 3D model (Figure 15).
This example shows how the identification of specific laminated layers (thickness), that
have pronounced seasonal characteristics (warm–cold period), can be improved. For ex-
ample, Figure 13 shows that it is not possible to accurately detect the thickness of the tufa
formed in a specific period (e.g., April–September). If the test plate of formed tufa is meas-
ured regularly with the SfM approach this drawback is easily addressed.
Figure 14. Example of two quantified cross sections on initial and sequential DTHRMs.
Remote Sens. 2021, 13, x FOR PEER REVIEW 22 of 28
Figure 15. Example of quantified cross-sections overlayed on 3D model of formed tufa.
5.3. Limitations of VERTICAL Method
Despite the stated advantages, the developed VERTICAL method has certain limita-
tions, which should be solved through future upgrades.
The first limitation is its dependence on DSM quality (1), as the accuracy and preci-
si on of us ed VHR DSMs di rectl y affec t the m easure ments per forme d by VER TICAL . Th us,
various systematic and non-systematic DSM errors can cause significant deviation in GCs
measurements. As VHR DSMs produced from SfM photogrammetry are influenced by
various different factors (e.g., camera calibration, surface texture, lighting conditions, GCP
characteristics, vegetation, complex terrain morphology) that can cause the occurrence of
errors [44], users should take care of possible influence of such errors on GCs measure-
ment. For example, if vegetation is not excluded from created interval DSMs, VERTICAL
can measure vegetation growth, which could then be misinterpreted as erosion-induced
STC. However, the application of VERTICAL for monitoring TFD has demonstrated that
highly accurate VHR DSMs, which are based on local coordinate systems, significantly
reduce the uncertainty of measurements.
The second important limitation of the developed VERTICAL method is its restricted
applicability to 2.5D models (2), and this segment will be addressed within future up-
grades. Currently VERTICAL is not suitable for the measurement of complex 3D gully
morphology, like undercuttings and overhangs, as these are not represented in 2D or 2.5D
models.
The third limitation is the required user expertise (3) needed for the derivation of
high-quality VHR DSMs that VERTICAL uses for measurement of GCs. Although the ap-
plication of VERTICAL is fast and user-friendly, the process of creation of VHR DSMs
with required accuracy is not simple and straightforward. However, if VERTICAL is ap-
plied to already created, available open-source DSMs, the amount of required user exper-
tise is significantly lower.
Figure 15. Example of quantified cross-sections overlayed on 3D model of formed tufa.
Remote Sens. 2021,13, 321 21 of 27
On the second test plate, the formed tufa was cut and partially removed following the
classical examples of studying the cross-sections. However, before this, the interval SfM
measurement of the formed tufa was made. Then, after the cutting, the remaining tufa
was measured and derived cross-sections were added to its 3D model (Figure 15). This
example shows how the identification of specific laminated layers (thickness), that have
pronounced seasonal characteristics (warm–cold period), can be improved. For example,
Figure 13 shows that it is not possible to accurately detect the thickness of the tufa formed
in a specific period (e.g., April–September). If the test plate of formed tufa is measured
regularly with the SfM approach this drawback is easily addressed.
5.3. Limitations of VERTICAL Method
Despite the stated advantages, the developed VERTICAL method has certain limita-
tions, which should be solved through future upgrades.
The first limitation is its dependence on DSM quality (1), as the accuracy and precision
of used VHR DSMs directly affect the measurements performed by VERTICAL. Thus,
various systematic and non-systematic DSM errors can cause significant deviation in GCs
measurements. As VHR DSMs produced from SfM photogrammetry are influenced by
various different factors (e.g., camera calibration, surface texture, lighting conditions, GCP
characteristics, vegetation, complex terrain morphology) that can cause the occurrence of
errors [
44
], users should take care of possible influence of such errors on GCs measurement.
For example, if vegetation is not excluded from created interval DSMs, VERTICAL can
measure vegetation growth, which could then be misinterpreted as erosion-induced STC.
However, the application of VERTICAL for monitoring TFD has demonstrated that highly
accurate VHR DSMs, which are based on local coordinate systems, significantly reduce the
uncertainty of measurements.
The second important limitation of the developed VERTICAL method is its restricted
applicability to 2.5D models (2), and this segment will be addressed within future upgrades.
Currently VERTICAL is not suitable for the measurement of complex 3D gully morphology,
like undercuttings and overhangs, as these are not represented in 2D or 2.5D models.
The third limitation is the required user expertise (3) needed for the derivation of
high-quality VHR DSMs that VERTICAL uses for measurement of GCs. Although the
application of VERTICAL is fast and user-friendly, the process of creation of VHR DSMs
with required accuracy is not simple and straightforward. However, if VERTICAL is
applied to already created, available open-source DSMs, the amount of required user
expertise is significantly lower.
6. Conclusions
Several important methodological conclusions can be highlighted on the basis of the
developed VERTICAL method:
(1)
VERTICAL method successfully overcomes the stated limitations of the profilometer
and similar traditional field measurement techniques, as its application facilitates
and simplifies the overall process of detection and measurement of vertical spatio-
temporal changes within GCs. The extent of the user’s required expertise and its
influence on error generation are minimalized with VERTICAL.
(2) A very large number of GCs sampled by VERTICAL allows a thorough representation
of overall spatio-temporal changes in gully geometry. Due to the high sampling
density detailed distinction of different complex erosion and accumulation induced
processes is possible. Interpretation of measured spatio-temporal changes is possible
within the whole gully (Figure 8), or within separate chosen GCs (Figures 911).
(3)
As demonstrated in Section 5.2, the VERTICAL method is potentially applicable for
other, similar scientific purposes, where multi-temporal accurate measurement of
spatio-temporal changes in cross-sectional geometry is required (e.g., river material
dynamics, ice mass dynamics, tufa sedimentation, and erosion).
Remote Sens. 2021,13, 321 22 of 27
Application of the developed VERTICAL method for measurement of spatio-temporal
changes allowed detailed reconstruction and quantification of gradual gully development.
Following conclusions about detected gully evolution can be highlighted:
(1)
Mean STC values vary significantly within 2379 GCs of gully Santiš from the gully
headcut until the gully terminus.
(2)
Highest erosion rates were recorded at the initial part of the gully, where intensive
collapse and uphill progression of gully headcut were observed.
(3)
Most of the material eroded from gully headcut and sidewalls is being accumulated
within the first 20 meters of the gully. Such accumulation could be related to the lack
of stronger surface flow, capable of further transportation of eroded material.
(4)
Less homogenous middle part of the gully is influenced by the occasional occurrence
of stronger selective erosion, manifested mainly through the channel incision and
sidewall collapse.
(5)
The final part of gully Santiš is influenced by the dynamics of the Adriatic Sea, which
are disrupting and dislocating accumulated sediments.
Within future research, the developed VERTICAL method will be further improved,
especially in regard to its current limitations (e.g., simplification of the method, less de-
pendency of results to errors in models). Special attention will be dedicated to the data
collection of ground control and checkpoints with a total station in order to achieve even
better measurement accuracy. Furthermore, VERTICAL will be tested for the detection of
spatio-temporal changes within other similar geomorphic purposes (e.g., detection of tufa
sedimentation and erosion).
Supplementary Materials:
The following are available online at https://www.mdpi.com/2072-429
2/13/2/321/s1, Figure S1: Permanent GCP for repeat UAV photogrammetric surveys.
Author Contributions:
Conceptualization, A.Š., F.D., I.M., N.L.and L.P.; Formal analysis, F.D. and
I.M.; Funding acquisition, A.Š. and Nina Lonˇcar; Investigation, F.D. and I.M.; Methodology, A.Š.,
F.D., I.M., N.L. and L.P.; Project administration, A.Š.; Resources, A.Š.; Software, F.D., I.M. and L.P.;
Supervision, A.Š. and N.L.; Validation, A.Š., F.D., I.M. and L.P.; Visualization, F.D. and L.P.; Writing-
original draft, F.D. and I.M.; Writing-review & editing, I.M., N.L.and L.P. All authors have read and
agreed to the published version of the manuscript.
Funding:
This research was performed within the project UIP-2017-05-2694 financially supported by
the Croatian Science Foundation.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author.
Acknowledgments:
Authors would like to thank Croatian Science Foundation for providing nec-
essary research funds. Specially thanks to the Burkhard Golla, Ralf Neukampf and Markus Möller
from Julius-Kühn-Institut (JKI) in Kleinmachnow, Germany, their help and expertise facilitated the
VERTICAL method development.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
STC spatio-temporal changes
GCs gully cross-sections
VHR very high resolution
UAV unmanned aerial vehicle
RAPS repeat aerophotogrammetric system
Remote Sens. 2021,13, 321 23 of 27
GHR gully headcut retreat
SfM structure from Motion
CP checkpoint
GCP ground control point
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... TLS is an indirect measuring technique that actively emits laser beams from a terrestrial, tripod-based station while simultaneously measuring Gully Santiš was chosen as the perfect site for testing and validation of the developed framework, due to its complex morphology and rapid soil erosion induced STCs. Gully Santiš was formed predominantly by water erosion in Kalkocambisol soil sediments a few meters deep, which are surrounded and underlined by limestone and dolomite carbonate rocks [43]. The dominant erosion type within gully Santiš is rainfall induced erosion, which causes an annual gully headcut retreat up to 24.85 ± 5 cm year −1 [43]. ...
... Gully Santiš was formed predominantly by water erosion in Kalkocambisol soil sediments a few meters deep, which are surrounded and underlined by limestone and dolomite carbonate rocks [43]. The dominant erosion type within gully Santiš is rainfall induced erosion, which causes an annual gully headcut retreat up to 24.85 ± 5 cm year −1 [43]. Although gully Santiš is relatively short (80 m), its morphology comprises an imposing, gully headcut over 140 m wide and a few meters deep, from which a single deeply incised channel leads to a pebble beach at the contact with the Adriatic Sea ( Figure 1). ...
... The second challenge posed for the performance of multi-temporal terrestrial LiDAR surveys over gully Santiš is the rapidity of STCs, which are constantly changing the shape of the gully, thus raising the probability for occurrence of obstructed areas. Rapid erosion induced STCs within gully Santiš are mainly occurring within the initial parts of the gully, due to the gradual collapse and uphill progression of the gully headcut [43]. ...
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Terrestrial LiDAR scanning (TLS) has in preceding years emerged as one of the most accurate and reliable geospatial methods for the creation of very-high resolution (VHR) models over gullies and other complex geomorphic features. Rough terrain morphology and rapid erosion induced spatio-temporal changes (STCs) can lead to significant challenges in multi-temporal field TLS surveys. In this study, we present a newly developed systematic framework for the optimization of multi-temporal terrestrial LiDAR surveys through the implementation of thorough systematic pre-survey planning and field preparation phases. The developed systematic framework is aimed at increase of accuracy and repeatability of multi-temporal TLS surveys, where optimal TLS positions are determined based on visibility analysis. The whole process of selection of optimal TLS positions was automated with the developed TLS positioning tool (TPT), which allows the user to adjust the parameters of visibility analysis to local terrain characteristics and the specifications of available terrestrial laser scanners. Application and validation of the developed framework were carried out over the gully Santiš (1226.97 m2), located at Pag Island (Croatia). Eight optimal TLS positions were determined by the TPT tool, from which planned coverage included almost 97% of the whole gully area and 99.10% of complex gully headcut morphology. In order to validate the performance of the applied framework, multi-temporal TLS surveys were carried out over the gully Santiš in December 2019 and 2020 using the Faro Focus M70 TLS. Field multi-temporal TLS surveys have confirmed the accuracy and reliability of the developed systematic framework, where very-high coverage (>95%) was achieved. Shadowing effects within the complex overhangs in the gully headcut and deeply incised sub-channels were successfully minimalized, thus allowing accurate detection and quantification of erosion induced STCs. Detection of intensive erosion induced STCs within the observed one-year period was carried out for the chosen part of the gully headcut. Most of the detected STCs were related to the mass collapse and gradual uphill retreat of the headcut, where in total 2.42 m2 of soil has been eroded. The developed optimization framework has significantly facilitated the implementation of multi-temporal TLS surveys, raising both their accuracy and repeatability. Therefore, it has great potential for further application over gullies and other complex geomorphic features where accurate multi-temporal TLS surveys are required for monitoring and detection of different STCs.
... However, there is a need to use various geoinformation tools, often in several programs. In order to automate the computation of the morphometric characteristics of concave forms, the authors developed their algorithms in the form of models, scripts, plug-ins, or extensions to facilitate work in GIS software [38,[42][43][44][45][46][47][48][49][50][51]. Based on the analysis of the functionalities of the mentioned tools, the following geometric parameters were considered as important for the morphometric evaluation of concave forms: area, perimeter, length, width, orientation, elongation ratio, circularity ratio, compactness coefficient, form factor, and the following hypsometric parameters: maximum, mean, and minimum height, elevation range, slope, aspect, Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), hypsometric integral (HI), volume. ...
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The rapid development of remote sensing technology for obtaining high-resolution digital elevation models (DEMs) in recent years has made them more and more widely available and has allowed them to be used for morphometric assessment of concave landforms, such as valleys, gullies, glacial cirques, sinkholes, craters, and others. The aim of this study was to develop a geographic information systems (GIS) toolbox for the automatic extraction of 26 morphometric characteristics, which include the geometry, hypsometry, and volume of concave landforms. The Morphometry Assessment Tools (MAT) toolbox in the ArcGIS software was developed. The required input data are a digital elevation model and the form boundary as a vector layer. The method was successfully tested on an example of 21 erosion-denudation valleys located in the young glacial area of northwest Poland. Calculations were based on elevation data collected in the field and LiDAR data. The results obtained with the tool showed differences in the assessment of the volume parameter at the average level of 12%, when comparing the field data and LiDAR data. The algorithm can also be applied to other types of concave forms, as well as being based on other DEM data sources, which makes it a universal tool for morphometric evaluation.
... The spatial analysis and simulation stage is conditioned by the quality and accuracy of the input databases within the spatial analysis and hydraulic calculation software for simulating the degree of evolution of the ravine. To complete this second stage, the HEC-RAS 5.07 software developed by the US Army Corps of Engineers was used to spatialize the necessary parameters (stream power, max depth, velocity, shear stress, WSE water surface elevation), to analyse stable and critical areas within the analysed ravine as well as for the simulation of the scenarios for different arrangement modes that aim at reducing the erosion within the ravine and, implicitly, its correct arrangement [40,41]. ...
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The accentuated degradation of agricultural lands as a result of deep erosion processes is the main problem identified in abandoned agricultural lands under the rainfall intensities, increasing number of hot days, indirectly under the impact processes derived from them (soil erosion, vegetation drying, etc.), as well as inadequate or poor management policies implemented by local authorities. The present study aims to develop and present a methodology based on GIS spatial analysis to choose the best hydro-amelioration solution for the arrangement of a complex ravine that negatively affects the entire agroecological area in its immediate vicinity. The proposed model is developed on spatial databases obtained based on UAV flights, the simulation of flow rate values and the establishment of three hydraulic analysis models through the HEC-RAS software with the main purpose of evaluating the results and databases, in order to identify the best implementing model for the stabilization and reduction in erosion within the analysed area. The comparative analysis of the three analysed scenarios highlighted the fact that a dam-type structure with overflow represents the best hydro-ameliorative solution to be implemented in the present study. The accuracy of the obtained results highlights the usefulness of developing GIS models of transdisciplinary spatial analysis to identify optimal solutions that can be implemented in territories with similar characteristics.
... First, the supporting equipment for such techniques is very expensive, and the operator needs have a high level of professional training [12,31,32]. Second, the earth surface in the study area should be free of shielding [25,33], which restricts the application of such techniques in medium-and high-vegetation areas. All the above limitations make it difficult for these methods to be used to measure gullies at a large spatial scale when high accuracy is required. ...
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The Rambla de Algeciras lake in Murcia is a reservoir for drinking water and contributes to the reduction of flooding. With a semi-arid climate and a very friable nature of the geological formations at the lakeshore level, the emergence and development of bank gullies is favored and poses a problem of silting of the dam. A study was conducted on these lakeshores to estimate the sediment input from the bank gullies. In 2018, three gullies of different types were the subject of three UAV photography missions to model in high resolution their low topographic change, using the SfM-MVS photogrammetry method. The combination of two configurations of nadir and oblique photography allowed us to obtain a complete high-resolution modeling of complex bank gullies with overhangs, as it was the case in site 3. To study annual lakeshore variability and sediment dynamics we used LiDAR data from the PNOA project taken in 2009 and 2016. For a better error analysis of UAV photogrammetry data we compared spatially variable and uniform uncertainty models, while taking into account the different sources of error. For LiDAR data, on the other hand, we used a spatially uniform error model. Depending on the geomorphology of the gullies and the configuration of the data capture, we chose the most appropriate method to detect geomorphological changes on the surfaces of the bank gullies. At site 3 the gully topography is complex, so we performed a 3D distance calculation between point clouds using the M3C2 algorithm to estimate the sediment budget. On sites 1 and 2 we used the DoD technique to estimate the sediment budget as it was the case for the LiDAR data. The results of the LiDAR and UAV data reveal significant lakeshore erosion activity by bank gullies since the annual inflow from the banks is estimated at 39 T/ha/year.
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Agricultural terraced landscapes, which are important historical heritage sites (e.g., UNESCO or Globally Important Agricultural Heritage Systems (GIAHS) sites) are under threat from increased soil degradation due to climate change and land abandonment. Remote sensing can assist in the assessment and monitoring of such cultural ecosystem services. However, due to the limitations imposed by rugged topography and the occurrence of vegetation, the application of a single high-resolution topography (HRT) technique is challenging in these particular agricultural environments. Therefore, data fusion of HRT techniques (terrestrial laser scanning (TLS) and aerial/terrestrial structure from motion (SfM)) was tested for the first time in this context (terraces), to the best of our knowledge, to overcome specific detection problems such as the complex topographic and landcover conditions of the terrace systems. SfM–TLS data fusion methodology was trialed in order to produce very high-resolution digital terrain models (DTMs) of two agricultural terrace areas, both characterized by the presence of vegetation that covers parts of the subvertical surfaces, complex morphology, and inaccessible areas. In the unreachable areas, it was necessary to find effective solutions to carry out HRT surveys; therefore, we tested the direct georeferencing (DG) method, exploiting onboard multifrequency GNSS receivers for unmanned aerial vehicles (UAVs) and postprocessing kinematic (PPK) data. The results showed that the fusion of data based on different methods and acquisition platforms is required to obtain accurate DTMs that reflect the real surface roughness of terrace systems without gaps in data. Moreover, in inaccessible or hazardous terrains, a combination of direct and indirect georeferencing was a useful solution to reduce the substantial inconvenience and cost of ground control point (GCP) placement. We show that in order to obtain a precise data fusion in these complex conditions, it is essential to utilize a complete and specific workflow. This workflow must incorporate all data merging issues and landcover condition problems, encompassing the survey planning step, the coregistration process, and the error analysis of the outputs. The high-resolution DTMs realized can provide a starting point for land degradation process assessment of these agriculture environments and supplies useful information to stakeholders for better management and protection of such important heritage landscapes.
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Three-dimensional (3D) morphological changes in rocky coasts need to be precisely measured for protecting coastal areas and evaluating the associated sediment dynamics, although volumetric measurements of bedrock erosion in rocky coasts have been limited due to the lack of appropriate measurement methods. Here we carried out repeat surveys of the 3D measurements of a small coastal island using terrestrial laser scanning (TLS) and structure-from-motion (SfM) photogrammetry with an unmanned aerial system (UAS) for 5 years. The UAS-SfM approach measures the entire shape of the island, whereas the TLS measurement enables to obtain more accurate morphological data at a scale of centimeters on the land side. The multitemporal TLS-derived data were first aligned in timeline by the iterative closest point (ICP) method and they were used as positionally correct references. The UAS-SfM data were then aligned to each of the TLS-derived data by ICP to improve its positional accuracy. The changed areas for each period was then extracted from the aligned UAS-derived point clouds and were converted to 3D mesh polygons, enabling a differential volume estimate (DVE). The DVE for each period was revealed to be from 3.1 to 77.2 m3/month. These changes are rapid enough to force the coastal bedrock island to disappear in 30 years. The temporal variations in the DVE is roughly associated with those in the frequency of high tidal waves.
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Water-induced channel is one of the main forms of soil erosion in cultivated fields. Channelized erosion is often measured by the volume of the channels. Traditionally, the measurements were conducted with rulers or measuring tapes. However, these traditional methods are generally time- and labor-consuming and can cause soil surface disturbance. Close-range photogrammetry with a Consumer-Grade Camera (CGC-CRP) provides an alternative way of measuring channel volume and can overcome limitations of traditional methods and provides much higher spatial resolution. However, quantitative information on the accuracy of this technique is rare. In this study, the accuracy of the CGC-CRP method under different settings were examined with an in-house experiment and validated with a field experiment. In the in-house experiment, a wood board surface with Artificial Channels (AC) of different shapes, orientations, and sizes were built. These ACs were surveyed using the CGC-CRP method with a series of settings of shooting angles and image overlapping rates. Selected cross-sectional areas were extracted to compare against manual measurements to assess the absolute and relative errors of the CGC-CRP method. The applicability of the CGC-CRP method with different settings was evaluated by comparing time consumption and the size of detection areas. The results indicated that in order to maintain an acceptable accuracy level, the image overlapping rate should be ≥70%, and the shooting angle should be in the range of 60° to 80°. For the channel shape, the accuracy for V-channel was ~15% higher than that for U-channel. For the U-channel, the impact of the channel orientation on the accuracy was not significant when the shooting angle was relatively high, whereas for the V-channel, the vertically oriented channel had higher accuracy than horizontal or angle channels. Last, channel size did not strongly affect accuracy when the channel was vertically orientated, and the shooting angle and image overlapping rate were set in the optimum ranges. However, when the shooting angle or image overlapping rate was low, or when the channel was angled or horizontally orientated, the accuracy was lower with larger channel size. In the field experiment, under the optimum camera setting, the error for the ten cross-sectional areas was about 1.6%. This result suggests that the CGC-CRP method is promising in volumetric assessment of rill and gully erosion. The quantitative information on the accuracy provided in this study can help researchers to select the setting of CGC-CRP methods to achieve their required accuracy level.