**3.3 Spectroscopic methods**

Various spectroscopic techniques such as Raman, nuclear magnetic reso¬nance (NMR), Fourier transform (FT-IR) infra-red spectroscopies, terahertz (THz) spectroscopic imaging, and various advanced applications of these methods are in practice to investigate the structure of co-crystals and find out the intermolecular forces between the components of co-crystals [45, 46].


**1. Introduction**

Currently, there are many types of navigation of autonomous unmanned robots. Satellite navigation is certainly a common way. However, it also has a number of applicability limitations. For example, indoors, the satellite signal usually does not reach devices through concrete and metal structures. In this case, due to the fact that the space inside buildings is often limited to relatively small areas, it is possible to use navigation tools such as triangulation [1], navigation through various markers such as QR codes [2,3], barcodes [4], ArUco markers for position correction [5–7], and SLAM navigation [8,9], as well as combinations of the above methods.

In unexplored spaces, the most promising of the mentioned technologies is the SLAM navigation method [10]. This method can be used in an unprepared and unknown to the robot room to create a map and then use it. At present, this concept is widely used in the field of robotics for solving problems of autonomous motion [11–13]. According to SLAM, the robot needs to know its location at every moment of time, and also gradually scans the surrounding space with the help of sensors, thus compiling a map of the area. The map is built gradually as the robot explores new areas.

The main source of information about the location of the robot is odometry obtained in one way or another (wheels, computer vision, IMU, or a combination of both). However, any sensory system has its weak points. Therefore, for example, cameras can fail when lighting changes, or wheel slip can occur, causing an odometry error. In this regard, it becomes necessary to support the navigation system in some way independent of the readings of the sensors. This explains the relevance of this study.

The paper proposes the use of model-based navigation as a parallel system. In this case, one system can correct its values relative to the other.

**Citation:** Prokopiev, I. ; Shmalko, E.; Diveev, A. Autonomous Navigation of Mobile Robot Assisted by Its Identified Neural Network Model. *Eng. Proc.* **2023**, *33*, 8. http://doi.org/10.3390/ engproc2023033008

Academic Editors: Ivan Zelinka, Arutun Avetisyan and Alexander Ilin

Published: 17 May 2023

**Copyright:** © 2023 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/).

Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 119333 Moscow, Russia<EMAIL_ADDRESS>(I.P<EMAIL_ADDRESS>(A.D.)

Receiving a mathematical model for the control object is often time-consuming and sometimes even impossible using traditional methods. To build a mathematical model of a robot, the laws of theoretical mechanics and the Lagrange equations of the second kind for generalized coordinates are used. It should be noted that the analytical derivation of the robot dynamics equations in most cases will not accurately determine some parameters of the model, for example, friction coefficients of the robot moving parts or moments of inertia of rotating structures. Thus, the analytical output of mechanics equations allows one to build models with accuracy to the values of some parameters.

In view of the increasing diversity and extremely complex nature of control objects, including the variety of modern robotic systems, the identification problem is becoming increasingly important, which allows one to build a mathematical model of the control object, having input and output data about the system. Conducting experiments in order to identify the correspondence of the mathematical model to a real object is the necessary stage of solving the identification problem.

The identification of a nonlinear system is of particular interest, since most real systems have nonlinear dynamics, and if, earlier, the identification of a system model consisted of the selection of optimal parameters for the selected structure, then the emergence of modern machine learning methods opens up broader prospects and allows one to automate the identification process itself. Thus, this process should also be automated and, therefore, must be considered as the task for machine learning.

A modern technique to identify a mathematical model of a robot is the use of artificial neural networks. To perform this, it is necessary, depending on the assumed complexity of the robot model, to choose the structure and type of the neural network, then, depending on the problem to be solved, select a set of controls and build a training sample. Next, it is necessary to train the neural network so that the resulting neural network reacts to control actions in the same way as a real object. It is this approach that is presented in this paper and applied in the described experiments for a mobile robot.

Further, the paper presents a mathematical formulation of the model identification problem. A universal approach for solving it based on modern methods of machine learning using neural networks is proposed. As an example, a solution to the navigation problem for a mobile wheeled robot based on the proposed approach using its identified model is presented.

#### **2. Problem Statement of the Control Object Identification by an Artificial Neural Network**

In the identification problem, the mathematical model of the control object is not fully or partially known, but the researcher has a real control object or its physical simulator. In this case, the real control object or physical simulator layout is an unknown function. The space of the input vectors of this function is the space of admissible controls for this object.

Let some control functions be set as functions of time

$$\mathbf{u}^{\dot{j}}(\cdot) = \mathbf{v}^{\dot{j}}(t), \ t \in [0; t^+], \tag{1}$$

where **u***j* ∈ U ⊆ R*m*, *j* = 1, ... , *K*, U is a compact set that takes into account control constraints, and *t* + is a limit value of time.

The control functions **v***j* (*t*), *j* = 1, ... , *K*, should be selected so that it is possible to determine dynamic abilities and speed limits in straight lines and at turns by the movement of the control object.

To obtain a training sample, it is necessary to conduct natural experiments. In the experiment, the control object is subjected to a given control **u***j* (·), *j* = 1, ... , *K* and at fixed times, *ti* = *i*Δ*t*, the state of the control object is stored, **x**(*ti*), *i* = 0, ... , *Dj*, where *Dj* is a number of time intervals for experiment with control **u***j* (·), *j* = 1, . . . , *K*.

A training sample for an artificial neural network is a set of stored values of the vectors of the control and the state in determined moments of time.

$$\mathbf{T}\_{\rangle} = (T\_{i}^{\dot{j}} = \{ \mathbf{u}^{\dot{j}}(t\_{i}), \mathbf{x}^{\dot{j}}(t\_{i}), t\_{i} = i\Delta t \}\_{i} : i = 0, \ldots, D\_{\dot{j}} \}. \tag{2}$$

The input vector for the artificial neural network is an element of the training sample **y**(*ti*) = *Tj i* , *i* = 1, . . . , *Dj* − 1, *j* ∈ {1; *K*}.

The output vector for the artificial neural network **z**(*ti*) must coincide with the state space vector for the next moment of time **x***j* (*ti*+1), *i* = 0..., *Dj* − 1, *j* ∈ {1; *K*}:

$$\mathbf{z}(t\_i) = \mathbf{f}\_{\text{NN}}(\mathbf{y}(t\_i)), \ i = 0, \ldots, D\_i - 1, \ j \in \{1; K\}. \tag{3}$$

where **f***NN*(**y**(*ti*)) is a function described by the artificial neural network.

A target functional of optimization in the machine learning process of the artificial neural network is

$$J\_0 = \sum\_{j=1}^{K} \sum\_{i=0}^{D\_j - 1} ||\mathbf{x}^j(t\_{i+1}) - \mathbf{z}(t\_i)|| \to \min. \tag{4}$$

In this case, the trained neural network is an approximation of the mathematical model of the control object in the form of a system of finite-difference recurrent equations.

If the time interval Δ*t* was selected to be quite small, then it is possible to use the artificial neural network as approximation of right parts of ordinary differential equation system in the Cauchy form. For this purpose, it is necessary to determine a rate of change of state variables for each time moment:

$$\dot{\mathbf{x}}^{j}(t\_{i}) = \frac{\mathbf{x}^{j}(t\_{i+1}) - \mathbf{x}^{j}(t\_{i})}{\Delta t}, \; i = 0, \ldots, D\_{j} - 1, \; j = 1, \ldots, \text{K}. \tag{5}$$

If the rate vector (5) is used as the output vector when training the neural network, then the mathematical model of the control object has the following form:

$$\mathbf{x}^{i}(t\_{i+1}) = \mathbf{x}^{i}(t\_{i}) + \Delta t \mathbf{f}\_{\text{NN}}(\mathbf{x}^{i}(t\_{i}), \mathbf{u}^{j}(t\_{i}), t\_{i}), \ i = 1, \ldots, D\_{j} - 1, \ j = 1, \ldots, \text{K}. \tag{6}$$

An important step in system identification is to determine the type of model used, since the overwhelming majority of methods implement the so-called parametric identification [14]. First, a certain model structure is selected, which is considered suitable for describing a given object. Next, an identification experiment is carried out in which the input and output signals are measured, and then the identification method implements the tuning of the model parameters in accordance with some adaptive laws, so that the response of the model to the input signal can approximately correspond to the response of the real system to the same input action. Most often, object identification is used using linear systems [15], since it is easy to determine the effect of various input signals on the output for them. Although linear models are attractive for many reasons, they have their limitations, especially since all physical systems are nonlinear to some extent, and in many cases linear models are not suitable for representing these systems. In this regard, there is currently a significant interest in methods for identifying nonlinear systems, especially using machine learning methods based on neural networks [16–18].

Neural networks, being a universal approximator, provide a powerful tool for identifying nonlinear systems. In view of the wide distribution and availability of software, neural networks have gained immense popularity. Combined approaches have also been developed, based, for example, on the reference model [19]. This article presents the implementation of a combined approach to the identification of a control object based on a neural network [20].

#### **3. Application Object Description**

A wheeled mobile robot shown in Figure 1 is taken as a control object.

**Figure 1.** A control object.

The work uses a mixed approach to identification, when part of the object model is known based on physical laws, and the other part is determined by a neural network trained on the basis of a specially formed training sample.

The robot motion model is described by a system of differential equations:

$$\begin{cases} \dot{\mathfrak{x}} = \upsilon \cos(\theta), \\ \dot{\mathfrak{y}} = \upsilon \sin(\theta), \\ \dot{\theta} = \omega. \end{cases} \tag{7}$$

where *x*, *y*, *andθ* define the object state, and *v*, *ω* are control signals.

Robot control is implemented using two signals: the desired linear velocity *uv* and the desired angular velocity *uω*. In the model (7), it is assumed that control signals are completely directly transmitted to the system *v* = *uv*, *ω* = *uω*. In fact, a completely different picture is observed: firstly, the speeds cannot change infinitely quickly, and the system always has a certain dynamics, and secondly, as a rule, there is some regulator at a lower level that directly controls the voltage supplied to the motors. Therefore, in order to adequately describe the robot motion model, we introduced two additional equations into the (7) system that describe an object's dynamics.

$$\begin{cases} \dot{\pi} = v \cos(\theta), \\ \dot{\nu} = v \sin(\theta), \\ \dot{\theta} = \omega, \\ \dot{\upsilon} = f^{v}(v, \omega, \mu^{v}, \mu^{\omega}), \\ \dot{\omega} = f^{\omega}(v, \omega, \mu^{v}, \mu^{\omega}), \end{cases} \tag{8}$$

where *f v* and *f ω* are unknown functions to be identified.

Factors such as friction of the wheels on the surface, inertia, and uneven distribution of the robot's mass do not allow writing the functions *f v*, *f ω* in an explicit form.

For the numerical implementation of the identification problem, Equation (8) is presented in the finite-difference form with the time step Δ*t*:

$$\begin{cases} \mathbf{x}\_{k+1} = \mathbf{x}\_k + \Delta t \cdot v\_k \cos(\theta\_k), \\ y\_{k+1} = y\_k + \Delta t \cdot v\_k \sin(\theta\_k), \\ \theta\_{k+1} = \theta\_k + \Delta t \cdot \omega\_{k'} \\ v\_{k+1} = F^\upsilon(v\_k, \omega\_{k'}, \mu\_{k'}^\upsilon, \mu\_{k'}^\upsilon, \Delta t), \\ \omega\_{k+1} = F^\omega(v\_k, \omega\_k, \mu\_{k'}^\upsilon, \mu\_{k'}^\upsilon, \Delta t). \end{cases} \tag{9}$$

To identify the system, we used the approximation of the function *F* by the parametric function *Fφ*. The learning function with parameters *φ* takes the current state of the robot, the control vector, and the time step as input, and outputs the state of the robot at the next moment of time:

$$[\upsilon\_{k+1}, \omega\_{k+1}] = F\_{\Phi}(\upsilon\_{k\prime} \omega\_{k\prime} \mathfrak{u}\_{k\prime}^{\upsilon} \mathfrak{u}\_{k\prime}^{\omega\prime} \Delta t). \tag{10}$$

Parameters *φ* are selected in such a way that the trajectory of the movement of the object described by the system of Equation (9) with the model (10) repeats the real trajectory of the robot as closely as possible.

#### **4. ANN Model Identification**

For training, the principle of supervised learning was chosen, in which the neural network is trained using the training dataset "input–reference output", and then checked using a set of validation and test data that did not fall into the training sample.

To collect data, multiple runs of the robot were implemented under various types of control. Figures 2 and 3 show the example trajectories and sample control data.

**Figure 2.** Sample 1: (**a**) trajectory; (**b**) control.

**Figure 3.** Sample 2: (**a**) trajectory; (**b**) control.

An artificial neural network with the multilayer perceptron architecture was chosen as the training model (10). PyTorch was used to train the neural network. A neural network with three layers was chosen, with 128 neurons on all hidden layers, an RELU activation function, and Adam optimization algorithm.

To assess the quality of the model, the accuracy of predicting the trajectory of movement by the model was evaluated on passages that were not included in the training sample. The neural network receives as input the initial state of the system and the control sequence for the test drive. As in the training phase, the trajectory of the robot's movement is predicted. To compare the predicted and actual trajectories, the *ATE* metric was used—the absolute translation error:

$$J\_{ATE} = \frac{1}{N} \sum\_{t=0}^{N} \sqrt{(\mathfrak{x}\_t - \mathfrak{x}\_t)^2 + (\mathfrak{y}\_t - \mathfrak{y}\_t)^2} \tag{11}$$

where *N* is the number of points in the trajectory; *xt* and *yt* are actual trajectory point coordinates; *x*ˆ*t* and *y*ˆ*t* are coordinates of the predicted trajectory point.

To assess the quality of the predicted angle, the *MAE* (mean absolute error) metric was used.

$$J\_{MAE} = \frac{1}{N} \sum\_{t=0}^{N} |yaw\_t - y\hat{a}w\_t|\_{\prime} \tag{12}$$

where *yawt* is the actual value of the yaw angle, and *yaw*ˆ *t* is the predicted value of the yaw angle.

The results of simulations of the trained neural network model are shown in Figures 4 and 5. As can be seen from the presented plots, the robot copes with the task of following the given trajectory even in the complex environment with strong surface slopes.

**Figure 4.** The trajectory to follow is given by green points at the distance of (**a**) 30 cm ; (**b**) 20 cm. The robots's trajectory is a red line.

**Figure 5.** The robot's trajectory (red line) in a complex environment of the Robotic Center of FRC CSC RAS.

#### **5. Discussion**

This paper presents an approach to the localization of a mobile robot based on the identified neural network model. The presented results show how the robot copes quite accurately with the task of following various trajectories. The resulting neural network model can be used as an independent method of localization or as an auxiliary algorithm for correcting the position in case of failure of sensors and cameras.

**Author Contributions:** Conceptualization, I.P. and E.S.; methodology, A.D. and E.S.; software, I.P.; validation, E.S.; formal analysis, A.D.; investigation, I.P. and E.S.; data curation, E.S.; writing—original draft preparation, E.S.; writing—review and editing, E.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflicts of interest.


**3. Superior local conductivity in self-organized nanodots on indium-tin-oxide films induced by femtosecond laser pulses**

**Figure 2a**–**f** shows the surface morphology of ITO films after normal-incidence irradiation with different pulse numbers from 0 to 5 × 103 (fluence: 0.1 mJ/cm2 ). The film only exhibits few small dots for *N* = 5 × 103 shots (**Figure 2b**). Meanwhile, the laser-induced periodic structure is clearly observed on the surface of ITO films for *N* ≥ 2.5 × 104 shots (**Figure 2c**–**f**). In the inset of **Figure 2f**, the submicron ripple structure is composed of self-organized line dots (size: 20–500 nm). The periodic ripple pattern has long axis perpendicular to the direction of the laser polarization (the arrow in **Figure 2f**). The mixture of the dotted and ripple structures is attributed to the interference of scattered and diffracted waves at the grains and/or defects. This is similar to ordered YBCO array structures, formed by the solidification of melted dot patterns, under conditions of constructive interference and minimized surface energy [11, 19].

The present periodic ripple structures have spacing of approximately 798 ± 15, 420 ± 14, and 230 ± 15 nm. The formation of the two large ripple spacing cases (i.e., 798 ± 15 and 420 ± 14 nm) can be easily explained by classical scattering model [20]:

$$
\Lambda = \frac{\lambda}{1 \pm \sin \theta} \tag{1}
$$

where Λ is the ripple spacing, *λ* is the laser wavelength, and *θ* is the incident angle of the laser beam onto the target. However, the classical scattering model with Eq. (1) cannot be used to predict for the shorter ripple spacing of 230 ± 15 nm, which is much smaller than the laser wavelength of 800 nm. We noted that the spacing value is close to ∼200 nm, thus it could be induced by second harmonic generation (SHG) with a shorter wavelength of 400 nm around the surface of ITO film. It is reasonable for occurring SHG owing to the surface asymmetry when the subwavelength ripple with ∼200 nm is indeed observed in high intensity regions, especially in the center of laser Gaussian beam (**Figure 2f**).

**Figure 3** shows the effects of number of pulses (*N*) from 0 to 3 × 106 shots on the carrier concentration (*n*), carrier mobility (*μ*), and resistivity (*ρ*) of the ITO films. For *N* ≤ 103 , the laser-treated ITO films almost remain the same as that of the asdeposited ITO films. As further increasing *N* from 5 × 103 to 3 × 106 , the *n* increases noticeably from ∼1 × 1019 to ∼1.6 × 1019 cm−3 . In contrast, the *μ* decreases correspondingly from 12.3 to 10.2 cm2 /V-s (i.e., 17% reduction), after fs laser annealing with 3 × 106 shots at a fluence of 0.1 mJ/cm2 . Reasonably, by using the Hall measurement, it is hard to detect a slight difference in *n* for *N* ≤ 103 . However, for larger *N* (>103 shots), the accumulated number of pulses induces the thicker laser-irradiated area for detecting the variation in *n* using the Hall measurement. Due to the aforementioned variation of *n* and *μ*, the resistivity presents a 14% reduction from 4.3 × 10−2 at *N* = 0 to 3.7 × 10−2 Ω-cm for *N* = 3 × 106 shots, which is primarily due to the increased *n*. Noticeably, the thickness the ITO films before and after irradiation are 30 ± 1.5 nm, and the thickness variation is not noticeable, and thus, the effect of thickness on the electrical properties can be eliminated.

The optical transmittance of the as-deposited ITO and fs-laser treated ITO films for 3 × 105 and 3 × 106 shots is shown in **Figure 3b**. Obviously, the optical transmittance of fs laser-treated ITO films remains about the same as that of the asdeposited ITO film, and they achieve a high transmittance of approximately 85% in the visible to near-infrared (NIR) range. The electrical and optical results indicate that fs laser annealing represents a new method to enhance the electrical properties of ITO films and retain their high optical transmittance.

To identify the cause of surface conductivity enhancement for fs laser-treated ITO films, we performed the X-ray photoelectron spectroscopy (XPS) experiments *Nanostructuring Indium-Tin-Oxide Thin Films by Femtosecond Laser Processing DOI: http://dx.doi.org/10.5772/intechopen.82790*

#### **Figure 2.**

*(a–f) SEM images of periodic surface structures induced by 800 nm fs laser pulses at a fluence of 0.1 mJ/cm2 and with various pulse numbers (N = 0, 5 × 103 , 2.5 × 104 , 1 × 105 , 3 × 105 , and 3 × 106 , respectively). The black-square inset shows the enlarged surface features at corresponding locations in (f). The arrow indicates the direction of the laser polarization [11].*

on the as-deposited and fs laser-treated ITO films in comparison with In2O3 powder. **Figure 4a** shows Donley's model of surface composition of an ITO film, which has In2O3-like sites, oxygen-deficient sites, and hydroxide and oxyhydroxide groups [21]. **Figure 4d**–**g** shows the O1s XPS spectra of fs laser-treated ITO films with *N* from 5 × 103 to 3 × 106 . **Figure 4c** presents the O1s XPS spectrum of

**Figure 3.**

*(a) The carrier concentration, mobility, and resistivity in the fs laser-treated ITO films as a function of the pulse numbers (the solid lines are a guide to the eyes). (b) The transmittance in the fs laser-treated ITO films as a function of wavelengths with various pulse numbers (N = 0, 3 × 105 , and 3 × 106 , respectively) at a fluence of 0.1 mJ/cm2 [11].*

as-deposited ITO films. It can be fitted well with three peaks, namely the In2O3-like oxygen at 529.6 ± 0.1 eV [21, 22], oxygen adjacent to the oxygen-deficient sites at 531.0 ± 0.1 eV [21, 22], and hydroxide and/or oxyhydroxide peak at 532.0 ± 0.1 eV [21–23]. Compared with a reference sample of In2O3 powder (**Figure 4b**), the oxygen peak (at 531.0 ± 0.1 eV) that is adjacent to the oxygen-deficient sites of as-deposited ITO film (**Figure 4c**) increases because of the formation of oxygen vacancies during the thin film growth using sputtering [23].

Furthermore, the XPS spectra of In 3d5/2, for In metals, In2O3 powders, and fs laser-treated ITO films, are shown in **Figure 5a**. The In 3d5/2 peak of In metal locates at a lower binding energy of 443.7 eV, corresponding to the In bonding state of In-In bonds [23]. However, the In 3d5/2 peak of the In2O3 powder shifts to the higher binding energy of 444.6 eV, corresponding to the In3+ bonding state of In2O3 [22, 24]. Thus, the valence states of In2O3 for the as-deposited ITO film can be demonstrated by the In 3d5/2 peak at 444.4 eV. As increasing the number of fs laser pulses, there is a gradual shift in the In 3d5/2 peak from the In-O bonding state to In-In bonding state. Particularly, the peak of In 3d5/2 for *N* = 3 × 106 is located at 443.9 eV, which is close to the binding energy of In-In bonds. This result strongly indicates that the existence of In metal-like clusters on the surfaces of fs laser-treated ITO films, particularly inside the self-organized nanodots that result in the enhanced electrical conductivity for the films. Similarly, upon fs laser irradiation, the aggregation of metal nanoparticles was observed in thin plasma polymer films due to the strong interaction via dipolar forces of the metals [25].

**Figure 5b** summarizes the findings that periodic self-organized nanodots are formed on the surfaces of the ITO films treated by fs laser pulses because of constructive interference of fs laser. The nanodots are In-like clusters, which induce superior local surface conductivity, and consequently offer reducing the electrical resistivity for the films. The approach for finding the self-organized nanodots with superior local surface conductivity may be suitable for applications such as nanolithography, nanophotoelectrons, and nanomechanics, in large-area nanotechnology.

In brief, periodic ripple structures with a multiperiodic spacing of ∼800, ∼400, and ∼200 nm were successfully fabricated by fs laser pulse irradiation. The ripple structure is composed of self-organized nanodots with a size of 20–500 nm, which are presumably formed by the solidification of melted dot patterns, under conditions of constructive interference and minimized surface energy. The ITO films with ripple structures on the surfaces exhibited significant enhancement in electrical conductivity, which is attributed to the formation of In metal-like nanodots/clusters.

*Nanostructuring Indium-Tin-Oxide Thin Films by Femtosecond Laser Processing DOI: http://dx.doi.org/10.5772/intechopen.82790*

#### **Figure 4.**

*(a) Schematic representation of as-deposited ITO surface composition based on Donley's model [21]. (b) The O1s XPS spectra of In2O3 powder and fs laser-treated ITO films with various pulse numbers (N = 0, 5 × 103 , 2.5 × 104 , 3 × 105 , and 3 × 106 , respectively) [11].*

#### **Figure 5.**

*(a) The In 3d5/2 XPS spectra of In2O3 powder, In metal, and fs laser-treated ITO films with various pulse numbers (N = 0, 5 × 103 , 1 × 105 , 3 × 105 , and 3 × 106 , respectively). (b) A schematic illustration for the formation of self-organized nanodots induced by the constructive interference of fs laser at near-surface region. The dot is composed of In-rich clusters with a height of* ∼*5 nm as a result of In-O bonding breaking into In-In under local-field enhancement [11].*


**3. Vegetation**

28 Soil Erosion Studies

Fig. 2. Precipitation histogram for the years 2006, 2007 and 2008 at Cadereyta, Queretaro,

One of the main factors related to the erosion potential of rain is the rainfall frequency and different magnitudes of events in combination with the soil geomorphic thresholds (Bisigato et al., 2009). High intensities of rainfall are the main factor driving the runoff processes

Mean, maximum and maximum intensity in 30 minutes are shown in Figure 3 for the Cadereyta area. Overall, the values of intensities varied from 0.71 to 88.2 mm h-1. Maximum

Fig. 3. Box plot of rainfall intensity by events measured in the semiarid of Mexico.

Mexico.

(Renard et al., 1974).

Vegetation patterns in arid and semiarid ecosystems are characterized by the size and spatial distribution of the higher plant-cover patches. These patches have its main distribution in spots or bands distributed as a mosaic (Aguiar & Sala, 1999). The spots are generally isodiametric or with irregular circular shapes, and the bands range from 1 to less than 100 m (Aguiar & Sala, 1999). Investigators have documented the presence of "islands of fertility" or "resource islands" in arid and semiarid ecosystems at individual plant scale (Moro et al., 1997; Aguiar & Salas 1999; Facelli & Temby, 2002; Rango et al., 2006).

These areas can also be considered as hydrologic islands, and they are observed at the individual plant scale as well as in large area patterns of banded vegetation, where playettes and beaded drainage networks run-on and water infiltration stimulates vegetation growth (Rango et al., 2006).

Fig. 4. Typical distribution of native vegetation in a semiarid environment of Central Mexico.

Soil Erosion Processes in Semiarid Areas: The Importance of Native Vegetation 31

Fig. 5. Canopy and surface LAI dynamics in native plants at a semiarid environment during

*Opuntia imbricata Prosopis laevigata Acacia farnesiana Opuntia* sp

13.00 9.00 8.00 9.00

0.67 2.05 2.57 3.08

0.20 0.69 0.86 0.45

23.60 60.00 53.50 86.70

22.10 35.30 36.60 76.90

Table 1. Characteristics of the vegetation patches studied (Vásquez-Méndez et al., 2010).

the rainfall season in Cadereyta, Queretaro, Mexico.

Plot Area (m2)

Total Height (m)

Trunk height (m)

Canopy cover (%)

Ground cover (%)

Water availability it is a limiting factor in the productivity of arid and semiarid ecosystems. Plants use the necessary water for their metabolic processes. Studies such as those of Fowler (1986) and Nobel (1990) have demonstrated that the species of arid and semiarid regions are characterized by several different patterns of roots distribution. Perennials root zones are wider and deeper than those of annuals. As a matter of fact, phreatophytes are often very deeprooted.

Simpson & Solbring (1977) recognize three major methods of native plant strategies:

The association of annual plants with woody shrubs has been recognized as to have higher organic soil contents, to trap windblown seeds and to protect the seeds or seedlings from predation (Fowler, 1986).

The general vegetation distribution is controlled by precipitation, and at the same time, distribution and amount of precipitation affects the development and distribution of vegetation, which also modifies the atmospheric energy and the storage of water (Xue, 2006; Dekker et al., 2007). Under some circumstances herbaceous layer productivity is lower under tree canopies than in nearby open grasslands, whereas in other instances grassland productivity is higher under tree canopies (Belsky et al., 1989). Studies suggest that tree canopies may improve the water – status of understory species. Native species such as *Prosopis laevigata*, *Acacia farnesiana*, *Opuntia* sp., and even for O*puntia imbricate*, contribute to the development of surface vegetation. The combination of canopy cover and the greatest development of surface vegetation in the rainfall period, when soils are more susceptible to soil erosion, diminish the erosive effect of precipitation at the highest intensities. Therefore some trees of semiarid environments like adult Acacias are considered as nurse plants for understory native species (Yang et al., 2009).

Figure 5 shows the dynamics of vegetation leaf area index (LAI) for four species and their understory. It is clearly observed that once precipitation falls in those areas, ground cover increases significantly. However, canopy cover of O*puntia imbricate* and *Opuntia* sp. does not increased significant in comparison with *Acacia* and *Prosopis*, because the morphology of its rackets.

Some morphometric characteristic of the four main types of vegetation patches in semiarid Cadereyta, are listed in Table 1.

Currently, trees in semi-arid regions are viewed as having the potential to increase crop productivity (agroforestry), to increase the forage production, to improve soil fertility and to reverse or at least stop desertification (Beslky et al., 1989).

On the other hand, plants of *Prosopis* and *Acacia* possess nitrogen-fixing bacteria in their root systems that accumulate nitrogen under the canopy soils. These trees tend to propagate in overgrazing soils (Tapia-Pastrana et al., 1999) and become free of grass competition (Mooney et al., 1977). These trees can be optimal for reforestation and for sustainable use in arid and semiarid zones (El-Keblawy & Ksiksi, 2005; Deans et al., 2003; Álvarez-Yépiz et al., 2008).

Water availability it is a limiting factor in the productivity of arid and semiarid ecosystems. Plants use the necessary water for their metabolic processes. Studies such as those of Fowler (1986) and Nobel (1990) have demonstrated that the species of arid and semiarid regions are characterized by several different patterns of roots distribution. Perennials root zones are wider and deeper than those of annuals. As a matter of fact, phreatophytes are often very

The association of annual plants with woody shrubs has been recognized as to have higher organic soil contents, to trap windblown seeds and to protect the seeds or seedlings from

The general vegetation distribution is controlled by precipitation, and at the same time, distribution and amount of precipitation affects the development and distribution of vegetation, which also modifies the atmospheric energy and the storage of water (Xue, 2006; Dekker et al., 2007). Under some circumstances herbaceous layer productivity is lower under tree canopies than in nearby open grasslands, whereas in other instances grassland productivity is higher under tree canopies (Belsky et al., 1989). Studies suggest that tree canopies may improve the water – status of understory species. Native species such as *Prosopis laevigata*, *Acacia farnesiana*, *Opuntia* sp., and even for O*puntia imbricate*, contribute to the development of surface vegetation. The combination of canopy cover and the greatest development of surface vegetation in the rainfall period, when soils are more susceptible to soil erosion, diminish the erosive effect of precipitation at the highest intensities. Therefore some trees of semiarid environments like adult Acacias are considered as nurse plants for

Figure 5 shows the dynamics of vegetation leaf area index (LAI) for four species and their understory. It is clearly observed that once precipitation falls in those areas, ground cover increases significantly. However, canopy cover of O*puntia imbricate* and *Opuntia* sp. does not increased significant in comparison with *Acacia* and *Prosopis*, because the morphology of its

Some morphometric characteristic of the four main types of vegetation patches in semiarid

Currently, trees in semi-arid regions are viewed as having the potential to increase crop productivity (agroforestry), to increase the forage production, to improve soil fertility and to

On the other hand, plants of *Prosopis* and *Acacia* possess nitrogen-fixing bacteria in their root systems that accumulate nitrogen under the canopy soils. These trees tend to propagate in overgrazing soils (Tapia-Pastrana et al., 1999) and become free of grass competition (Mooney et al., 1977). These trees can be optimal for reforestation and for sustainable use in arid and semiarid zones (El-Keblawy & Ksiksi, 2005; Deans et al., 2003; Álvarez-Yépiz et al., 2008).

Simpson & Solbring (1977) recognize three major methods of native plant strategies:

from almost unattainable zones at very deep phreatic levels.

water by transpiration can be reduced.

and extensive root networks.

understory native species (Yang et al., 2009).

reverse or at least stop desertification (Beslky et al., 1989).

Cadereyta, are listed in Table 1.

predation (Fowler, 1986).

rackets.

deeprooted.

Fig. 5. Canopy and surface LAI dynamics in native plants at a semiarid environment during the rainfall season in Cadereyta, Queretaro, Mexico.

Table 1. Characteristics of the vegetation patches studied (Vásquez-Méndez et al., 2010).

Soil Erosion Processes in Semiarid Areas: The Importance of Native Vegetation 33

Fig. 6. Box plot of runoff by native vegetation in a semiarid environment of Central Mexico

Fig. 7. Typical behavior between runoff and leaf area index of native vegetation in a

Erosion, the detachment of particles of soil and surficial sediments and rocks, occurs by hydrological (fluvial) processes of sheet erosion, rill and gully erosion, and through mass

(Vásquez-Méndez et al., 2010).

semiarid environment of Central Mexico.

**5. Soil erosion**


**1. Introduction**

Across the ages, Man has been harnessing water resources to suit his needs whether for power generation or for food, fishing and aquaculture. The first aquaculture tryouts started more than 4000 years ago in Egypt with the production of the famous fish: Tilapia [1]. Till to date, tilapia has remained an important food resource, in the family Cichlidae, with two species being predominantly cultivated: Mozambique Tilapia (*Oreochromis mosambicus*) and Nile Tilapia (*O. niloticus*), which alone

accounts for more than 1/5 of the total global aquaculture production; and 85% of tilapia production [1–4]. Nile tilapia, a species native to the Nile River, also inhabits the Niger, Volta, and Senegal River Basins [1, 5, 6]. Thanks to its suitability for aquaculture and given its quick adjustment to extreme and varied environments, its ease of reproduction in captivity and its wide ecological valence, this fish has been the subject of various breeding attempts and has been widely spread in all continents since the 1960s [7, 8].

Around the world, several methods have been used to breed tilapia, including extensive (mostly in ponds), semi-intensive, intensive and mostly hyper-intensive systems. These methods are characterized by the use of selected strains and a highperformance compound feed [4, 9]. Nile Tilapia is widely known for its adaptability to different farming systems linked to its spawning period which spreads over the whole year. Likewise, this species is known for its resistance to pathogens as well as its ability to withstand stressful environments and the different manipulations associated with aquaculture [10].

In addition to the afore-mentioned advantages, *O. niloticus* has an intriguing growth rate, unique when compared to other species of the cichlid family. On top of a good food conversion ratio associated with an excellent ability to accept artificial feed [11–13], its diet corresponds to the lowest levels of the food chain (phytoplankton, detritus...). All these attributes make the production costs of this species relatively moderate and adequate. In Tunisia, in addition to mullet, pikeperch and carp, the rearing of *O. niloticus* could prove to be an interesting future alternative for the partial replacement of the production of other freshwater fish, given its very remarkable biological characteristics in inland fish farming.

Tilapia was first introduced in 1966 in Kebilli Oases, where aquaculture is often connected with agricultural practices, taking advantage of the warm climate and the abundant geothermal water resources in southern Tunisia. However, despite the unerring documentation of Tilipia's survival and reproduction [14], it was not until 1999, that the idea of breeding this species was pursued. This took place after the experimental research station in geothermal pisciculture of Bechima in the governorate of Gabes was founded by the National Institute of Sciences and Technologies of the Sea (NISTS). Since then, several experiments have reported effective management and success of the different rearing cycles of this species, from spawning to grow-out [15]. In parallel, various acclimatization experiments of tilapia have been conducted in the southern oases, as well as in the reservoirs in the north and center of Tunisia, where the low winter temperature hinders the growth and survival of this species. These experiments have shown encouraging results and breeding success especially at the reservoirs of Lebna, Lahma, Ghezala and Sidi Saâd [16, 17].

The present study was commissioned by the Technical Center of Aquaculture (TCA) and the Higher Institute of Fishery and Aquaculture of Bizerte (HIFAB), within the framework of a convention signed in 2015 between the TCA and the NISTS which aims to ensure the annual production of 300,000 single-sex male Nile tilapia fry. In other respects, the researchers sought to identify the most suitable environment for the rearing of this species in Tunisia through pinpointing the strengths and weaknesses associated with this species' farming in two quite different environments (fresh and geothermal waters). To achieve this goal, the physicochemical parameters of the study areas, zootechnical parameters of the fish and pathologies were considered on top of the monitoring and evaluation of the different rearing phases (hatching, larval rearing, and grow-out) and the economic study.


**4.2 Front-end delivery**

#### *4.2.1 Communication of cultural heritage and cultural tourism*

The introduction of technologies for cultural heritage communication has revolutionised the concept of "museum experience" and "historic site experience", leading to the emergence of novel services powered by the **digitisation of cultural heritage artefacts**. In recent decades many GLAM organisations (i.e. Galleries, Libraries, Archives, and Museums) and historic sites have launched initiatives to improve representation, engage visitors with content in new, innovative ways and support cultural revitalisation. Technology is increasingly used to support novel forms of narration and improve the **historic interpretation** of cultural heritage, i.e. the ways visitors make meanings and connections to the past, in order to experience culture [57, 58]. Digital technologies have the power to transform history and cultural heritage into a living resource, also in the form of embodied Interactions [59]. Complex interconnected cyber-physical systems for experiencing cultural heritage can thus emerge [60].

#### *4.2.2 Hybrid onsite experiences*

Digital technologies have the potential to redefine the way visitors experience and connect with museums and cultural sites, as well as expand the on-site visit with prior and post experiences [61]. Most heritage sites are multidimensional and dense with meaning. Different cultural heritage contexts can coexist and serve as a backdrop for many overlapping services and experiences, suitable for diverse audiences. As the motivations, interests and degree of engagement of audiences vary, creating relevant cultural heritage experiences that engage and resonate with each visitor represents a challenge for the sector. The early "electronic" tour guide model was based on predefined itineraries, complemented by the synchronised delivery of textual and/or multimedia content that was curated by museum experts.

#### *Novel Ways of Discovering, Capturing and Experiencing Cultural Heritage: A Review… DOI: http://dx.doi.org/10.5772/intechopen.99170*

The evolution of Ubiquitous Computing technologies enabled the introduction of context-aware and location-aware features that offer the user increased degrees of freedom (e.g. through the combined use of mobile phones and RFID technologies). Traditional linear narratives (e.g. in the form of typical guided tours) are thus replaced by **visitor-directed narratives**, in which the visitor is in control of the content they consume [62]. The former involved mainly applications that mimic the purposes and experience of a **traditional guided tour** (e.g. mobile tour guides and museum websites), while the latter is facilitated by technologies that help create interactive and immersive experiences [63, 64]. The challenge is to not merely communicate scientific information, but to develop audience-centric experiences that achieve a **narrative and emotional engagement** of the audience [65, 66]. Better cultural accessibility and inclusion has the potential to enhance citizens' **well-being** [67].

Digital storytelling represents an effective way to deliver content in cultural heritage [68], achieve emotional resonance and create human connection. Immersive experiences can put the visitor in control of the content and make them feel as if they are a part of the exhibit or program [63, 64]. The combination of advanced immersive technologies with storytelling techniques, can help create **emotive digital experiences** that bring cultural heritage sites and events alive [69].

For example, the GIFT project developed a portfolio of free, open-source tools and methods that museums can use to enrich the physical experiences of their visitors, such as a Mobile Game to encourage collaborative storytelling within the museum [70]. The EMOTIVE project developed immersive storylines using a range of technologies including virtual and augmented reality and mobile phone apps, to create more 'emotive' cultural site visits [69].

**Audio augmented environments**, featuring concepts like sonic narratives, soundscapes and binaural spatialisation have also been explored in the cultural heritage context [71].

Digital technologies can help enrich a physical visit to a cultural site or a museum, with rich complementary content tailored to the needs of the visitor. Heritage sites can be enriched by scanning and overlaying virtual annotations on top of these places. Devices can be directed at the point of interest, and 2D/3D e.g. texts, sounds, icons, videos are added to the users' view. These applications provide visitors with immediate cultural location-based information regarding specific points of interest [72], they allow them to explore personal cultural locations and points of interest [73] and give them opportunities to discover new or unknown knowledge [74]. Every cultural site has a specific "wow-factor" that has to be captured and transported via digital technology and digital communication channels. From the provider view, new business models and opportunities can be identified and initiated using novel digitisation technologies [75], which can help increase the competitiveness of the cultural site [76] with significant spillover effects on the local economy. Recent research efforts go beyond visual, sound, or narrative enhancements, to provide visitors with multisensory stimulations [77].

For people with **disabilities**, digital innovations, such as sign language video avatars, tactile artwork reliefs, barrier-free apps for museum visits, etc., can help them overcome access barriers to cultural spaces [78].

#### *4.2.3 Online experiences—virtual museums*

The online presence of museums is changing, with traditional museum websites evolving into **online "walk-through" museums,** featuring dynamic exhibitions and versatile multimedia explorations of cultural heritage [79]. Advances in digitisation technologies provide the means for new forms of engagement with

museum-held heritage via 3D multimedia-rich museum websites, online "walkthrough" museum explorations [80], virtual museum exhibitions [81], virtual environment system installed within a real museum [82], online community platforms [83, 84], etc. This has also led to novel context-aware representations of cultural heritage, produced from merging 3D models of artefacts, like in the case of the MUVI - Virtual Museum of Daily Life [85].

Ambient intelligence technologies (AmI) provide the means for personalizing content and user interfaces (UIs) to each individual user [86].

Virtual museums have also emerged. The term **virtual museum** describes "a collection of digitally recorded images, sound files, text documents, and other data of historical, scientific, or cultural interest that are accessed through electronic media" [87]. Unlike a traditional museum, a virtual museum does not house actual objects. Instead, it channels digitised representations of artefacts from one or several cultural institutions (e.g. Google Cultural Institute). A virtual museum can also be set up, in order to provide access to cultural sites that are otherwise invisible to the general public and to cultural artefacts that no longer exist or are impossible to view physically [88–94].

For example, the "Underwater Malta", virtual museum for submerged cultural heritage, provides access to numerous inaccessible underwater archaeological sites [95]. This is facilitated by recent advances in underwater imaging and processing software and the development of 3D photogrammetry of submerged sites. The GRAVITATE project developed software tools to allow archaeologists and curators to reconstruct shattered or broken cultural objects and to identify and re-unify parts of a cultural object that has been separated across collections [96]. Similarly, the Time Machine project [25] combines digitised archives from museums and libraries, with Artificial Intelligence and Big Data mining, to offer richer interpretations of our past. The project developed a 4D (3D plus time) engine which 'recreates' past cities, as digital twins of our cities ('Mirror Worlds') that is accessible from mobile phones or through specific Augmented Reality interfaces. 3D models can "**time-travel**" users to historical places, cities or buildings in a specific historic period to deliver a feeling of how daily life was [97, 98]. While in the past, 3D visualisation content was merely used to digitally visualise historic artefacts (e.g. replace damaged or missing physical artefacts), presently realistic **virtual heritage environments** can be developed to contain 3D models of heritage object, thus visualising three-dimensional contexts as well, in order to offer a much richer user experience. For example, the INCEPTION project revolved around the development of heritage"spaces"(complex architectures and sites) and semantic enrichment for creating 3d Models to cater for multiple purposes, in line with the specific needs and level of knowledge of the end-users [99].

In the last two years the Covid-19 pandemic, which disrupted the daily routine of museums and brought the global tourism industry to a standstill prompted the adoption of innovative approaches building on digital instruments, such as **virtual tours** [100, 101] that build on geo-referenced sequences of panoramic images and three-dimensional models of the actual site [102]. According to Nemtinov et al. [103] "Virtual trips to memorable places allow experiencing history in an interactive form; they attract the audience and promote interest in museums and, accordingly, strengthen their cultural and educational functions".

#### *4.2.4 Onlife cultural experiences*

Advances in digital technologies are helping connect a person's cultural heritage experiences to their "daily" life, i.e. they provide the means to enhance other experiences outside the museum site, based on experiences at the museum site [61].

*Novel Ways of Discovering, Capturing and Experiencing Cultural Heritage: A Review… DOI: http://dx.doi.org/10.5772/intechopen.99170*

Overall, immersive experiences for cultural heritage can take shape in a variety of forms, including the use of augmented reality, virtual reality, serious games and gamification, embodied interaction etc. Central ls the role of Virtual Reality and Augmented Reality which allow for new ways of experiencing cultural heritage. Augmented reality can overlay additional information onto existing artefacts, while virtual reality facilitates fully immersive virtual explorations [104]. Virtual Reality head-mounted displays (HMD) provide a first-person stereoscopic view of the environment and the ability to physically change the looking direction with head rotations. Mixed reality and semi-immersive VR applications combine the real and virtual environments [26]. Audio augmented environments, featuring concepts like sonic narratives, soundscapes and binaural spatialisation are also being explored in the cultural heritage context [71].

#### *4.2.5 Mobile applications*

Central to this shift is the rise of mobile communications and the rapid uptake of smartphone technology. Users today are increasingly connected to the world of digital information while "on the go" via mobile devices. GSMA [105] estimates that the number of unique mobile subscribers will reach 5.8 billion by 2025, equivalent to 70% of the world's population. Τhe European Travel Commission (ETC) stressing that ownership of mobile devices and mobile online access is high and increasing, reached the conclusion that in the future those travelling within and to Europe will be smartphone-equipped and will have both the technological capability and the online access to engage with online content that will make their travel and experiences richer and smoother. Smartphones, tablets and other mobile and handheld technologies are increasingly playing a central role in touristic experience mediation [106] and as a travel tool during all stages of tourism consumption [84]. Wang et al. [106] noted that "the instant information support of smartphones enables tourists to more effectively solve problems, share experiences, and "store"memories". Dickinson et al. [84] concluded that smartphones are enhancing temporal and spatial awareness, i.e. are evolving society's contemporary understandings of time and relationships with place and things in significant ways for travel by (a) enhancing the temporal alignment between people, the things they need, destinations and attractions, and activity options and (b) providing tourists with enhanced spatial tools and awareness (place-related information and content), ultimately leading to knowledge-rich visitors.

## **5. Conclusions and way forward**

The present chapter discussed the emerging paradigm of cultural heritage experience, as shaped by the continuous advances in information technologies. Cultural heritage experiences can benefit greatly from the current trend towards digitalisation, systematisation and accessibility of digital cultural resources. Advancements in technology are creating new opportunities to digitise cultural heritage for preservation, conservation, restoration, research, as well as for broader online access and re-use by citizens and various sectors, such as tourism. The amount of digitised cultural material is growing very rapidly, also thanks to numerous initiatives for the digitisation of cultural heritage content belonging to museums, libraries, archives etc. The experience of cultural heritage is constantly evolving and is expected to continue to develop in complexity and sophistication, as new opportunities for the technological representation and communication of culture emerge and the types of transaction and encounter with tangible and intangible cultural heritage increase.

The experience of cultural heritage will continue to develop in complexity [9]. The shift towards more sophisticated technology-enhanced cultural heritage experiences is facilitated by the growing digitisation of cultural heritage, continuous innovations in 2D/3D digital scanning, in image enhancement and 3D reproduction, and in immersion technologies, the emergence of advanced equipment (such as head-mounted displays (HMD), the affordances of ubiquitous computing and mobile applications and novel easy-to-use authoring tools. Advanced digitisation, and digital preservation and accessibility have been instrumental in transforming people's experience of cultural heritage assets, relics, and monuments, as well as of intangible heritage. The ViMM project concluded that harnessing additional technologies will have increasing relevance for museums and cultural heritage institutions, including: artificial intelligence; computer vision; deep learning/machine learning; and adaptive cognitive methods [107].

Given the strong economic spill over effect of cultural heritage, interest in advancing the communication of cultural heritage goes beyond the traditional players of the sector: GLAM organisations (i.e. Galleries, Libraries, Archives, and Museums), cultural and historic sites etc.

Regions and local communities increasingly pursue the valorisation of their local heritage, leveraging what makes their societies unique, to promote their cultural identity and boost economic growth. Communicating cultural heritage to visitors in understandable and engaging ways is challenging, yet it represents an increasingly important aspect of tourism destination marketing. Presently, the relationship between tourism and culture is transformed by the affordances of new technologies. Advanced digital technologies can accommodate the provision of value-added learning experiences to visitors to increase the attractiveness of heritage sites.

Developing cultural heritage experiences is a **complex socio-technical, multidisciplinary exercise** that spans several distinct areas: **Back-office** capabilities, in terms of accessible, quality digital cultural content, need to be in place, to subsequently be able to **design, develop and provide** cultural heritage experiences, and to **align** these with the requirements and expectations of the intended users.

Required technical capabilities thus range from back-office infrastructures for digitisation, standardisation, storage and retrieval, computing, connectivity, instrumentation and online accessibility of cultural materia, to authoring, service development and collaboration tools, to digital solutions for the technology-enabled experiencing of cultural events, arts and heritage and the creation of new applications on new devices, for different audiences and for different purposes.

Offerings should be expert-driven and **user-oriented** at the same time. Meeting the need for customised cultural heritage communication offerings implies continuously developing cultural heritage experience services and new processes of value creation. This implies a need for rapid development, and continuous adaptation and enrichment of the offerings. On the backend side, this translates into tailored content creation and easy content reuse, re-purposing and improvement, which in turn call for access to sources of relevant digital content (Cultural Heritage repositories) and domain expertise, besides technical skills and authoring environment capabilities. As value shifts to visitor experiences, cultural heritage experience development essentially should be regarded as a **co-creation** exercise, in which cultural heritage experience services are shaped and continuously adapted through the interactions between consumers and service providers [108].

Of particular importance is to increase awareness and acceptance of the "Digital Turn" within the cultural heritage community. Increasingly cultural institutions will have to incorporate technology solutions within their day-to-day responsibilities. Skills requirements and organisational aspects also need to be considered.

*Novel Ways of Discovering, Capturing and Experiencing Cultural Heritage: A Review… DOI: http://dx.doi.org/10.5772/intechopen.99170*


*Edited by Georgios Tsoulfas*

Hepatocellular carcinoma (HCC) represents one of the most significant global health issues, given its high prevalence and the challenging nature and physiology of the liver and hepatic surgery, in its many forms. This means that the most appropriate management for HCC should incorporate a multidisciplinary approach, combining the expertise from several different specialties. This book showcases the various steps in the development, diagnosis, staging, and management of HCC and provides views and thoughts from true experts in the field. As such, it is a useful resource for any physician or surgeon, whether training or practicing, who is interested in caring for patients with HCC.

Published in London, UK © 2022 IntechOpen © Sinhyu / iStock

Hepatocellular Carcinoma - Challenges and Opportunities of a Multidisciplinary Approach

Hepatocellular Carcinoma

Challenges and Opportunities of a

Multidisciplinary Approach

*Edited by Georgios Tsoulfas*


**Biogeomorphologic Approaches to a Study of Hillslope Processes Using Non-Destructive Methods**

Pavel Raška *Jan Evangelista Purkyně University in Ústí nad Labem, Czech Republic*

#### **1. Introduction**

20 Studies on Environmental and Applied Geomorphology

Whalley, W. B. (2009), On the interpretation of discrete debris accumulations associated with

Whalley, W. B. & Martin, H. E. (1992), Rock glaciers: II model and mechanisms, *Progress in*

Whalley, W. B., and Azizi, F. (1994), Models of flow of rock glaciers: analysis, critique and a

Whalley, W. B., and Azizi, F. (2003), Rock glaciers and protalus landforms: analogous forms

Whalley, W. B.; Douglas, G. R. & Jonsson, A. (1983), The magnitude and frequency of large rockslides in Iceland in the Postglacial, *Geografiska Annaler*, *65A*, 99-110. Whalley, W. B.; Palmer, C. F., Hamilton, S.J. & Kitchen, D. (1996), Supraglacial debris

Whalley, W. B., Hamilton, S. J., Palmer, C. F., Gordon, J. E., & Martin, H. E. (1995), The

Wilson, P. (2009), Rockfall talus slopes and associated talus-foot features in the glaciated

Wilson, P. (2010), *Lake District Mountain Landforms*, Scotforth Books, Lancaster.

*Geomorphology*, Edited by O. Slaymaker, pp. 129-145, Wiley, Chichester. Wilson, P. (2004), Relict rock glaciers, slope failure deposits, or polygenetic features? A

possible test, *Permafrost and Periglacial Processes*, Vol.5, pp. 37-51.

*London, Special Publications*, *320,* pp. 85-102.

108(E4), 8032, (DOI: 8010.1029/2002JE001864).

*Physical Geography*, *16*, 127-186.

*Glaciology*, Vol.22, pp. 181-186.

*Publications*, Vol.320, pp. 133-144.

glaciers with special reference to the British Isles in Periglacial and Paraglacial Processes and Environments, edited by J. Knight and S. Harrison, *Geological Society,*

and ice sources on Earth and Mars, *Journal of Geophysical Research, Planets*, Vol.

transport, variability over time: examples from Switzerland and Iceland, *Annals of*

dynamics of rock glaciers: data from Tröllaskagi, north Iceland, in *Steepland*

reassessment of some Donegal debris landforms, *Irish Geography*, Vol.37, pp. 77-87.

uplands of Great Britain and Ireland in Periglacial and Paraglacial Processes and Environments, edited by J. Knight and S. Harrison, *Geological Society, London, Special*  The aim of this chapter is to present new non-destructive methods and techniques used in the biogeomorphologic study of hillslope processes, particularly sheet erosion and shallow landslides. These processes belong to a broad spectre of natural hazards that have significant impacts on landscape and society and their research represents the fundamental issue for applied geomorphology (Panizza, 1996; Alcántara-Ayala, Goudie eds., 2010). Nondestructive methods are not yet well established in biogeomorphologic research despite their relevance in areas protected under conservation law, in fragile habitats and considering their simple field application. To introduce some of these methods in case studies within the context of biogeomorphology, we first give an introduction to the main concepts regarding landform-biota interactions followed by a focus on hillslope processes. In sections 3 and 4, we present two case studies of the application of non-destructive methods to quantify the bioprotective role of fallen trees (trunk dams, log dams) and to analyse short-term surface stability. In the final section, we suggest possible directions for the future development of non-destructive methods in the biogeomorphology of hillslope processes.

The evolution of Earth's surface in contrast with other planets in the Solar System is characterised by the fundamental role played by organisms, which act directly by creating, modifying and destroying landforms and indirectly by changing other factors that influence surface processes, such as climate and the distribution of energy. Looking at the history of research in this field of expertise, it seems that the significance ascribed to organisms (and particularly to vegetation) within a short history of biogeomorphology grew as rapidly as other fundamental concepts within a hundred year history of geomorphology. Most recently, this trend has led to the assumption that vegetation can indeed be a leading factor in global geomorphic change, and understanding its evolution is crucial to establishing an evolutionary view in geomorphology (Corenblit & Steiger, 2009). From a case study in eastern Kentucky, for instance, Phillips (2009) concluded that if only 0.1 % of net primary production of biomass is assumed to be geologically active, it still exceeds the energy of uplift and denudation. In spite of these results, one can hardly imagine vegetation being

Biogeomorphologic Approaches to

author)

2011).

a Study of Hillslope Processes Using Non-Destructive Methods 23

Fig. 2. The thematic framework of biogeomorphology (Naylor et al., 2002; modified by

The major aims of biogeomorphology were set by Viles et al. (1988) as (i) the influence of landforms on the distribution of organisms and (ii) the influence of organisms on Earth surface processes, but in fact, the two-way linkages were not fully pursued in the book (cf. Wainwright & Parsons, 2010). The "revision" of developments in biogeomorphology presented by Naylor et al. (2002) emphasises three main processes interlinking landforms and organisms: (i) bioconstruction, (ii) bioprotection and (iii) bioerosion. The authors call for studies of the complexity of landform-biota interactions. However, organisms were still considered a static factor, and geomorphologists were often unable to avoid the unidirectional appreciation of the landform-organism (vegetation) relation, which has changed only in past few years (e.g., Marston, 2010; Reinhardt et al., 2010; Corenblit et al.,

The problem of a biogeomorphologic focus on two-way linkages is twofold. First, the development of biogeomorphology was accelerated as geomorphology moved from global and regional research scales aiming at historical interpretations of landscape (Church, 2010) to local scales and individual sites as a result of the quantitative revolution and the establishment of the process approach paradigm. The physical background of the process approach emphasised analyses of Earth surface processes and frequently neglected the feedbacks from organisms. Furthermore, a focus on a local scale did not allow the effective modelling of vegetation changes as a response to geomorphic processes because these changes are also influenced by decision-making effects, which are variable and can be better assessed at a regional scale (see Wainwright & Millington, 2010). One of the possible integrating views to resolve these scale-related problems could be offered by Quaternary landscape ecology, which emphasises evolutionary concepts together with changing patterns of biota and developments in human societies that influence the primary modes of

The second problem is methodological and emerges from the different backgrounds of the two disciplines integrated within biogeomorphology: geomorphology and ecology. While geomorphologic conclusions are frequently drawn from theoretical considerations combined with detailed field surveys and measurements, ecological conclusions usually result from statistical analyses of extensive datasets (Haussmann, 2011). The prevailing geomorphologic approach, then, only anticipates the real situation, where biota is frequently understood as a static factor or as a source of proxy data for the study of Earth surface processes. Fig. 2 shows the thematic framework of biogeomorphology emerging from

decision making (e.g., Delcourt & Delcourt, 1988).

responsible for creating the global geomorphic patterns comprising everything from mountain ranges to valleys (cf. Scheidegger, 2007) even though the absence or presence and character of vegetation can modify the rate of landform evolution by protecting the Earth's surface (factor being limited by extent of rhizosphere) or by changes in energy diversification (assuming land cover pattern scale, which is sufficient to influence continental to global climate). Moreover, the role of vegetation in different time horizons (i.e., scale dependency) is unclear. The classic work of Schumm & Lichty (1965) shows how vegetation changes from a dependent to independent factor through time. More recently, Phillips (1995), at a more local scale, has shown that vegetation may in fact be a dependent variable rather than a controlling factor, which has also been discussed by Raska & Orsulak (2009).

Fig. 1. Major publications devoted to the interaction of organisms and geomorphology shown in historical perspective.

The abovementioned uncertainties in geomorphic significance of organisms are partly caused by the development of the scientific background of biogeomorphology, the conceptualisation of which as a young subdiscipline of geomorphology is still developing. An overview of the main achievements in biogeomorphology as represented by fundamental publications is shown in Figure 1. The first works to discuss the landformorganism relation date back to the 19th century; however, it was not until 1960s that biogeomorphology was established as a research approach by Hack & Goodlett (1960). The very first attempt to give an overview of the field was in the 1980s, when Viles (in Viles et al. 1988) defined biogeomorphology as a "concept of an approach to geomorphology, which explicitly considers role of organisms". Since then, biogeomorphology has been increasingly pursued in studies across varying environments. According to the Web of Science database (Thomson-Reuters), the annual number of works with biogeomorphology as a focal topic increased from less than five to more than 15 during the last 20 years (Fig. 1). This trend is also apparent in dendrogeomorphologic studies, while the number of zoogeomorphologic papers remains consistently low. This pattern indicates the prevailing appreciation of vegetation, the role of which can be studied at different scales and can be generalised. In contrast to the increasing number of biogeomorphologic papers, it is somewhat surprising that the amount of biogeomorphologic content has decreased in textbooks, being lowest in 1970s (see Stine & Butler, 2011).

responsible for creating the global geomorphic patterns comprising everything from mountain ranges to valleys (cf. Scheidegger, 2007) even though the absence or presence and character of vegetation can modify the rate of landform evolution by protecting the Earth's surface (factor being limited by extent of rhizosphere) or by changes in energy diversification (assuming land cover pattern scale, which is sufficient to influence continental to global climate). Moreover, the role of vegetation in different time horizons (i.e., scale dependency) is unclear. The classic work of Schumm & Lichty (1965) shows how vegetation changes from a dependent to independent factor through time. More recently, Phillips (1995), at a more local scale, has shown that vegetation may in fact be a dependent variable rather than a controlling

Fig. 1. Major publications devoted to the interaction of organisms and geomorphology

The abovementioned uncertainties in geomorphic significance of organisms are partly caused by the development of the scientific background of biogeomorphology, the conceptualisation of which as a young subdiscipline of geomorphology is still developing. An overview of the main achievements in biogeomorphology as represented by fundamental publications is shown in Figure 1. The first works to discuss the landformorganism relation date back to the 19th century; however, it was not until 1960s that biogeomorphology was established as a research approach by Hack & Goodlett (1960). The very first attempt to give an overview of the field was in the 1980s, when Viles (in Viles et al. 1988) defined biogeomorphology as a "concept of an approach to geomorphology, which explicitly considers role of organisms". Since then, biogeomorphology has been increasingly pursued in studies across varying environments. According to the Web of Science database (Thomson-Reuters), the annual number of works with biogeomorphology as a focal topic increased from less than five to more than 15 during the last 20 years (Fig. 1). This trend is also apparent in dendrogeomorphologic studies, while the number of zoogeomorphologic papers remains consistently low. This pattern indicates the prevailing appreciation of vegetation, the role of which can be studied at different scales and can be generalised. In contrast to the increasing number of biogeomorphologic papers, it is somewhat surprising that the amount of biogeomorphologic content has decreased in textbooks, being lowest in

factor, which has also been discussed by Raska & Orsulak (2009).

shown in historical perspective.

1970s (see Stine & Butler, 2011).

Fig. 2. The thematic framework of biogeomorphology (Naylor et al., 2002; modified by author)

The major aims of biogeomorphology were set by Viles et al. (1988) as (i) the influence of landforms on the distribution of organisms and (ii) the influence of organisms on Earth surface processes, but in fact, the two-way linkages were not fully pursued in the book (cf. Wainwright & Parsons, 2010). The "revision" of developments in biogeomorphology presented by Naylor et al. (2002) emphasises three main processes interlinking landforms and organisms: (i) bioconstruction, (ii) bioprotection and (iii) bioerosion. The authors call for studies of the complexity of landform-biota interactions. However, organisms were still considered a static factor, and geomorphologists were often unable to avoid the unidirectional appreciation of the landform-organism (vegetation) relation, which has changed only in past few years (e.g., Marston, 2010; Reinhardt et al., 2010; Corenblit et al., 2011).

The problem of a biogeomorphologic focus on two-way linkages is twofold. First, the development of biogeomorphology was accelerated as geomorphology moved from global and regional research scales aiming at historical interpretations of landscape (Church, 2010) to local scales and individual sites as a result of the quantitative revolution and the establishment of the process approach paradigm. The physical background of the process approach emphasised analyses of Earth surface processes and frequently neglected the feedbacks from organisms. Furthermore, a focus on a local scale did not allow the effective modelling of vegetation changes as a response to geomorphic processes because these changes are also influenced by decision-making effects, which are variable and can be better assessed at a regional scale (see Wainwright & Millington, 2010). One of the possible integrating views to resolve these scale-related problems could be offered by Quaternary landscape ecology, which emphasises evolutionary concepts together with changing patterns of biota and developments in human societies that influence the primary modes of decision making (e.g., Delcourt & Delcourt, 1988).

The second problem is methodological and emerges from the different backgrounds of the two disciplines integrated within biogeomorphology: geomorphology and ecology. While geomorphologic conclusions are frequently drawn from theoretical considerations combined with detailed field surveys and measurements, ecological conclusions usually result from statistical analyses of extensive datasets (Haussmann, 2011). The prevailing geomorphologic approach, then, only anticipates the real situation, where biota is frequently understood as a static factor or as a source of proxy data for the study of Earth surface processes. Fig. 2 shows the thematic framework of biogeomorphology emerging from

Biogeomorphologic Approaches to

sheet erosion - detection, dating

sheet erosion

gully erosion - detection, dating

shallow landslides - effect of roots on slope

shallow landslides protective function of

protective effect of fallen

disturbances by animals

advances in the field of expertise)

vegetation

stability

trees

landslides

uprooting - effect on soils

debris flows - detection, dating

soil and regolith

effect of fallen trees - shaping debris flows

rockfall

trees

Aim (process or effect) Studied object

(approach)

vegetation (modelling)

roots (anatomy analyses)

roots (anatomy analyses)

forest cover (modelling)

survey)

trunk (tree ring analyses)

roots, trunk (field

trunk (scars, tree ring analyses)

trunk (scars, tree ring analyses)

different species (field survey and experiments)

Table 1. Overview of selected biogeomorphologic approaches to a study of hillslope processes (references are selected to show the different approaches and methodological

The second group of approaches can be called non-destructive. These approaches focus on measurements and analyses of visible features that represent past or current interactions between vegetation and hillslope processes. To analyse rockfall activity and patterns in protected forests, Stoffel (2005) performed analyses of the distribution and visibility of scars on trees. Attention was also given to log jams shaping debris flows trajectories (Lancaster et

modelling)

trunk (field survey,

a Study of Hillslope Processes Using Non-Destructive Methods 25

based on physical laws, which are quite often calibrated by the results of limited field surveys or laboratory measurements. These approaches are applied mostly in the modelling of changes in vegetation patterns and the related influence on sediment supply, and they often exploit specially developed GIS-based modelling software, such as MIKE 11 or HEC-RAS, although the incorporation of vegetation parameters into these models is limited.

Reference

Gärtner et al. (2001), Bodoque et al. (2005), Gärtner (2007), Rubiales et al. (2008)

Dorren et al. (2004), Vanacker et al. (2007)

Preston & Crozier (1999), Schwarz et al.

Sidle et al. (1985), Bathurst et al. (2010)

Schaetzel et al. (1989), Phillips & Marion

Perret et al. (2006), Stoffel & Perret (2006)

Bollschweiler et al. (2008), Stoffel (2010),

Trimble & Mendel (1995), Gover & Poesen

Fantucci (1999), Gers et al. (2001)

Šilhán & Pánek (2010)

trunk (field survey) Lancaster & Hayes (2003), Matyja (2007)

Raska & Orsulak (2009)

(1998), Hall & Lamont (2003)

Vandekerckhove et al. (2001)

roots (modelling) Wu et al. (1979), Abe & Ziemer (1991),

(2010)

(2006)

Naylor et al. (2002) and modified to emphasise the mutual linkages between biota and landforms.

## **2. Biogeomorphology of hillslope processes: Main themes and recent advances**

The abovementioned constraints of a study of landform-biota interactions are quite apparent in the research of hillslope processes, as these represent one of the primary focuses of geomorphologists, and the evolution of biogeomorphology was tightly connected with advances in hillslope studies. Hillslopes represent the most common landforms across environments varying from periglacial to tropic regions (e.g., Anderson & Brooks, 1996 eds.), and they were a key landform in designing fundamental models of landscape evolution, ranging from the classic models of W.M. Davis, W. Penck and L.C. King (for overview see Summerfield, 1991) to modern ones, such as backwearing model by R.V. Ruhe, R.B. Daniels and J.G. Cady, nine-unit model by A.J. Conachre and J.B. Dalrymple, COSLOP by F. Ahnert, and many others. Much of the Earth's surface represented by hillslopes is covered by vegetation, whether a sparse cover of lichens, mosses, grass or less or a more continuous cover of shrubs and trees. Furthermore, the hillslopes are habitats for various animals, some of which prefer gentle slopes covered with a deep soil layer and others that developed a preference for rock-mantled slopes (see Butler, 1995, for overview). At the same time, hillslopes belong among the most dynamic landforms of the Earth's surface, enabling the occurrence and acceleration of different natural hazards, such as sheet and gully erosion, landslides, rockfalls, rock avalanches, debris flows, snow avalanches, and others, which have impacts on the manmade objects and human activities in a landscape (Alcántara-Ayala & Goudie, 2010 eds.). The enormous share of hillslopes on the Earth's surface together with their dynamics implies the necessity of intense applied research as well as opportunities for the development of new, effective research techniques. Finally, considering the abovementioned distribution of organisms on hillslopes, these techniques frequently draw upon analyses of organisms, whose distribution, activity or physiognomic modifications serve as proxy indicators of hillslope processes, and the primary attention is devoted to vegetation in this respect.

The first work on dendrogeomorphologic responses to geomorphic processes in terms of their chronology was by Alestalo (1971). Since then, many studies have focused mainly on mass movements and erosion. Concerning research on erosion, Thorns (1985) summarised not only the state of the art but also established a framework to understand feedbacks between vegetation and erosion, thus extending the traditional unidirectional approach in biogeomorphology; this was also enabled by his attention to non-linear dynamic systems in biogeomorphology. Most recently, Marston (2010) presents a comprehensive overview of research on hillslope-vegetation linkages, including research history, main functions of vegetation, feedbacks between vegetation and landforms within the disturbance regimes, and suggestions for future research directions.

An overview of the basic approaches and techniques used in the biogeomorphologic study of hillslope processes is presented in Table 1 along with references to some of the major papers published within this scope. Regarding the methods used, these approaches could be divided into three groups. The first one has in common the use of modelling techniques

Naylor et al. (2002) and modified to emphasise the mutual linkages between biota and

The abovementioned constraints of a study of landform-biota interactions are quite apparent in the research of hillslope processes, as these represent one of the primary focuses of geomorphologists, and the evolution of biogeomorphology was tightly connected with advances in hillslope studies. Hillslopes represent the most common landforms across environments varying from periglacial to tropic regions (e.g., Anderson & Brooks, 1996 eds.), and they were a key landform in designing fundamental models of landscape evolution, ranging from the classic models of W.M. Davis, W. Penck and L.C. King (for overview see Summerfield, 1991) to modern ones, such as backwearing model by R.V. Ruhe, R.B. Daniels and J.G. Cady, nine-unit model by A.J. Conachre and J.B. Dalrymple, COSLOP by F. Ahnert, and many others. Much of the Earth's surface represented by hillslopes is covered by vegetation, whether a sparse cover of lichens, mosses, grass or less or a more continuous cover of shrubs and trees. Furthermore, the hillslopes are habitats for various animals, some of which prefer gentle slopes covered with a deep soil layer and others that developed a preference for rock-mantled slopes (see Butler, 1995, for overview). At the same time, hillslopes belong among the most dynamic landforms of the Earth's surface, enabling the occurrence and acceleration of different natural hazards, such as sheet and gully erosion, landslides, rockfalls, rock avalanches, debris flows, snow avalanches, and others, which have impacts on the manmade objects and human activities in a landscape (Alcántara-Ayala & Goudie, 2010 eds.). The enormous share of hillslopes on the Earth's surface together with their dynamics implies the necessity of intense applied research as well as opportunities for the development of new, effective research techniques. Finally, considering the abovementioned distribution of organisms on hillslopes, these techniques frequently draw upon analyses of organisms, whose distribution, activity or physiognomic modifications serve as proxy indicators of hillslope processes, and the primary attention is devoted to

The first work on dendrogeomorphologic responses to geomorphic processes in terms of their chronology was by Alestalo (1971). Since then, many studies have focused mainly on mass movements and erosion. Concerning research on erosion, Thorns (1985) summarised not only the state of the art but also established a framework to understand feedbacks between vegetation and erosion, thus extending the traditional unidirectional approach in biogeomorphology; this was also enabled by his attention to non-linear dynamic systems in biogeomorphology. Most recently, Marston (2010) presents a comprehensive overview of research on hillslope-vegetation linkages, including research history, main functions of vegetation, feedbacks between vegetation and landforms within the disturbance regimes,

An overview of the basic approaches and techniques used in the biogeomorphologic study of hillslope processes is presented in Table 1 along with references to some of the major papers published within this scope. Regarding the methods used, these approaches could be divided into three groups. The first one has in common the use of modelling techniques

**2. Biogeomorphology of hillslope processes: Main themes and recent**

landforms.

**advances**

vegetation in this respect.

and suggestions for future research directions.

based on physical laws, which are quite often calibrated by the results of limited field surveys or laboratory measurements. These approaches are applied mostly in the modelling of changes in vegetation patterns and the related influence on sediment supply, and they often exploit specially developed GIS-based modelling software, such as MIKE 11 or HEC-RAS, although the incorporation of vegetation parameters into these models is limited.

Table 1. Overview of selected biogeomorphologic approaches to a study of hillslope processes (references are selected to show the different approaches and methodological advances in the field of expertise)

The second group of approaches can be called non-destructive. These approaches focus on measurements and analyses of visible features that represent past or current interactions between vegetation and hillslope processes. To analyse rockfall activity and patterns in protected forests, Stoffel (2005) performed analyses of the distribution and visibility of scars on trees. Attention was also given to log jams shaping debris flows trajectories (Lancaster et

Biogeomorphologic Approaches to

trees or trunks.

**3.1 The concept**

a Study of Hillslope Processes Using Non-Destructive Methods 27

In this section, we will define and characterise trunk dams in terms of their structure, dynamics and protective effects. Then, we will present the recent advances in implementing the results of field research to regional models of the bioprotective effect of trunk dams at a catchment scale using the geographic information systems (GIS). The aim of the developed model is to transfer the local information about the protective effect of individual trunk dams to the regional scale and to estimate the total volume of material retained by fallen

The major recent focus of biogeomorphologic (dendrogeomorphologic) studies of hillslope processes is on the protective effects of trees and shrubs. The research is especially carried out in protection forests. The delimitation of protection forests emerges from their general ability to control or modify the natural hazards connected with Earth's surface dynamics (Berger & Rey, 2004). Different national approaches and nomenclatures have been adopted for the zonation of protection forests and for their management, the difficulty of which emerges from the requirement to maintain both the ecosystem's integrity and the protective function of these forests (Dorren et al., 2004). The protection forests in mountainous areas are predominantly determined for rockfall impact reduction. Most protection forests in highlands and slightly undulating terrains without rock faces have a protective function against soil erosion. In the Czech Republic, where the present study was performed, these forests are called soil-protecting forests (Collective, 2007). In many studies and applied works on protection forests, the focus is on standing trees and shrubs, although Dorren et al. (2007), for instance, also explicitly mention the significance of lying trunks for increasing surface roughness and reducing the velocity of bouncing and rolling clasts of rock. The overall number of biogeomorphologic papers dealing with fallen trees, however, is small,

and the issue has been much more in the focus of forest ecologists and biologists.

formerly was the removal of all woody debris (cf. Harmon, 2002).

In forest ecology and management, fallen trees and their large parts are referred to as coarse woody debris (CWD), woody detritus, or downed wood, and a significant ecological role is ascribed to the process of tree death and decomposition (Masser et al., 1984; Harmon et al., 1986; Franklin et al., 1987). Lying trees and their parts increase the habitat diversity in forests, thus increasing biodiversity, as they offer good conditions for the development of mosses, lichens, plants and fungi communities as well as insect (*Insecta*) and other organisms. Furthermore, the importance of fallen trees lies in their influence on channel diversity in water streams, in the increase of the production function of forests and in the positive influence on the carbon cycle in forests. The ecological significance of fallen trees has led to changes in the traditional forest management approach, the aim of which

The trunk dam concept presented here was originally described in Raska & Orsulak (2009), resulting from the typology of the bioprotective effects of trees at rock-mantled slopes in the Ceske stredohori volcanic mountain range in the NW Czech Republic (Raska, 2007, 2010). In contrast to studies of allochtonous log jams, which often form as a result of mass movements on hillslopes, the present concept emphasises the role of individual trunks (or logs), which, depending on the local surface roughness and density of standing trees, are rather

**3. Structure, dynamics and protective effects of trunk dams**

al., 2003; Matyja, 2007). However, most of these techniques are usually combined with sampling techniques in the field.

The third and most exploited group of approaches, beginning with the classic work of Alestalo (1971), draws upon extensive field research and sampling using different strategies. The samples are usually taken as cores, wedges or cross-sections from stems, stumps and roots (for nomenclature see Gschwantner et al., 2009). Sampling and tree ring analyses are frequently supported by measurements of stem or root deformation, and studies based on this approach have already offered good results regarding the spatiotemporal patterns of rockfall (e.g., Stoffel & Perret, 2006), debris flow (Bollschweiler et al., 2008) and landslide (e.g., Fantucci, 1999) activity. The application of the approach encounters several problems, however. These problems consist of the number and suitability of tree species for cross dating (Grissino-Mayer, 1993), the availability of reference datasets and other issues (see Stoffel & Perret, 2006). The number of samples differs according to the extent of the area under research, the time span and type of process being studied (Fig. 3), which result in the varying efficiency of the sampling strategy as expressed by the number of samples per one detected event.

Fig. 3. Number of samples used for the detection and dating of debris flows, landslides and rockfall events in relation to the length of the monitored period in years and to the number of species analysed. Source: based on own review of 25 selected papers published in international journals between 2001 and 2011.

In some areas, however, it is not possible or suitable to take cross-sections and wedges from roots and stems or to measure root geometry deformations after uncovering the soil layer. In these cases, one has to employ non-destructive techniques, which enable the detection of processes and the estimation of their rates from the current positions and visible deformations of vegetation in the field. Moreover, several of the abovementioned destructive approaches focus the enormous potential of information recorded in vegetation species as proxy indicators, which oftentimes leads to maintaining the unidirectional approach to a study of landform-biota relations as discussed in the first section.