Designing a Cross-Platform Application That Employs Multi-Criteria Decision Making for Estimating the Value of Monumental Trees

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A cross-platform application that uses advanced technologies based on a combination of three different multi-criteria decision-making theories to evaluate monumental trees and olive groves.

Abstract

The rich history of the olive tree is deeply connected to the heritage of the Mediterranean region. There are olive trees that are still productive and their age has been calculated by the use of methods of increment core sampling, radiocarbon dating (C14) and luminescence dating (OSL) to be over two thousand years old. However, the age of these trees is not usually known and it is not easy to calculate. As a result, deciding whether an olive tree is monumental is a rather complicated task. The goal of this paper is to present the design and implementation of an intelligent system that uses multi-criteria decision-making to evaluate olive trees and make the decision of whether they are monumental. This information is further used by a system to decide whether an olive grove is monumental or not. The methodology is implemented in a cross-platform application called “Olea App”. The system evaluates different olive trees and evaluates trees and olive groves to select the one that is considered the best to be promoted. The system uses and combines three different multi-criteria decision-making theories, namely, analytical hierarchy process (AHP), simple additive weighting (SAW), and multicriteria optimization and compromise solution (VIKOR) and evaluates olive trees based on tangible and intangible criteria. The method proposed was used to evaluate trees in the Ionian Islands and has proven very effective. The cross-platform application could be used by other researchers to evaluate their olive trees and groves if they cannot apply methods for the estimation of the tree’s age such as the methods of OSL. This work introduces a novel, technology-driven solution for the identification, evaluation, and preservation of monumental olive trees. By integrating scientific, cultural, and technological perspectives, the study provides a sustainable and accessible methodology to ensure these ancient natural landmarks are protected for future generations. The Olea app represents a significant advancement in heritage tree conservation, offering a structured, transparent, and scalable approach to preserving olive tree ecosystems while supporting sustainable tourism and economic incentives for their protection.

1. Introduction

Monumental trees are distinguished from ordinary trees by their physical and socio-cultural characteristics [1]. These trees, which are called the heritage of nature, can be used in scientific studies, increasing environmental awareness of people and tourism activities [2]. These trees are significant for several reasons, both ecological and cultural, and play an important role in local communities [3]. As a result, a lot of research studies have been conducted to estimate the value of monumental trees [4,5,6]. Large heritage trees are keystone ecological features that play a crucial role in maintaining the structure and dynamics of natural communities [7,8,9]. As monumental trees are very important for the physical and cultural environment, they have to be protected, and their value should be highlighted. The highlight of their value is also important because they can be used for sustainable tourism.

Different monumental trees have been studied all over the world [10,11,12,13,14,15,16] and methods have been proposed for identifying these trees and estimating their value (e.g., [17]). The identification of these trees mainly relies on the trunk and the trunk rings. Olive trees have the special characteristic that the trunk has cavitations, distortion, and spiralization. The significance of monumental olive trees and their role in sustaining unique ecosystems, as emphasized by [18,19], goes beyond their scientific value; these trees seem to embody a rich blend of scientific and cultural importance, serving as living monuments with ties to history, culture, traditions, and mythology in various regions. The value of monumental olive trees is also highlighted by [20].

Deciding whether an olive tree is monumental is crucial for several reasons. First, these trees should be protected due to their great ecological and cultural value. Secondly, in some countries, e.g., Greece, they receive extra economic support for their protection. Last but not least, these trees may be used for sustainable tourism since they attract the audience’s interest. There are methods such as increment core sampling, radiocarbon dating (C14) and luminescence dating (OSL) that are used for the calculation of the age of olive trees. However, these methods are difficult to implement as they require specialized knowledge and equipment and stakeholders cannot have easy access to this information. Furthermore, there is no online system that can help estimate if an olive tree is monumental and its value. Given the above, it is our goal to implement an intelligent system that uses multi-criteria decision-making to evaluate olive trees and decide if they are monumental. The intelligent system will make the process easily accessible to all stakeholders who do not have an adequate background in criteria and multi-criteria decision-making.

In response to these challenges, the goal of this paper is threefold. First, this paper aims at defining a way of evaluating and classifying the monumental olive trees and groves taking into consideration criteria that represent tangible and intangible criteria. Secondly, this methodology is implemented in a cross-platform application so that it can be easily accessible to large audiences. Thirdly, the methodology is based on the research project of Kabassi et al. [21] which uses a combination of two multi-criteria decision-making theories and examines the combination of three different multi-criteria decision-making theories to improve the results.

The theories that are employed in the methodology proposed in this paper are analytic hierarchy process (AHP), VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and simple additive weighting (SAW) [22]. These theories are used for developing a forward-thinking approach to the evaluation and preservation of olive trees and groves. VIKOR, in combination with the SAW method, is employed to assess individual olive groves, providing a comprehensive analysis. Concurrently, the combination of the VIKOR and the SAW method is utilized to evaluate a set of monumental trees and olive groves, emphasizing both their tangible and intangible characteristics. This innovative methodology aligns seamlessly with the overarching objective of safeguarding and appraising the rich heritage of olive trees and olive groves dynamically and sustainably.

The methodology is implemented in a cross-platform application called “Olea App”. This cross-platform application was built using a cross-platform framework. The application is a central tool not only for assessing and comparing olive trees based on various characteristics but also for defining an olive tree similar to the work of Jim [23] or an olive grove as monumental. The “Olea App” extends its functionality beyond individual trees, enabling the comprehensive evaluation and comparison of olive trees, or even entire olive groves, offering a user-friendly interface for users. Cross-platform development is increasingly popular due to its efficiency, broader reach, and ability to deliver consistent user experience across different platforms. This means that the application can reach a wider audience as it can be used in different operating systems. Furthermore, the experience received by the user is the same regardless of the device or platform they are using.

The rest of the paper is organized as follows: Section 2 presents related work on the evaluation of olive trees as well as multi-criteria decision-making theories such as AHP, SAW, and TOPSIS. Section 3 analyses the design and implementation of the cross-platform application. Section 4 presents an example of the operation of the Olea app. The last two sections, Section 5 and Section 6, discuss the results and the conclusions drawn by this work.

2. Multi-Criteria Decision Making

2.1. Criteria for Olive Tree Evaluation

This study’s objective was to establish a comprehensive framework for evaluating the inherent value of olive trees. The methodology is based on a thorough literature analysis, considering works by Zapponi et al. [19], Jim [23], and Ritchie et al. [24] as well as field data collected while studying the olive trees in the Ionian Islands. The Ionian Islands are in the Ionian Sea, between Greece and Italy, a region that is intricately tied to the history and culture of the entire Mediterranean basin. The data collected during this process were used for defining the criteria used for the evaluation of the olive trees. The criteria were analyzed by Kabassi et al. [21]. The first category of criteria reflects the tangible characteristics of monumental olive trees, such as perimeter, diameter, height (measured in meters), and age (measured in years). The second category pertains to intangible values associated with olive trees and olive groves.

Tangible attributes consist of measurable physical features such as the perimeter, base perimeter, and height, expressed in meters. Additionally, the age of the monumental tree, though not always known, is an extraordinary characteristic. These physical features can be measured as follows:

Perimeter (${\mu }_1$): This measures the size and dimensions of the tree, reflecting its age. The perimeter is calculated at a height of 1.30 m from the base and is expressed in meters. Notably, some olive trees have reported perimeters exceeding 5 m.

Base Perimeter (${\mu }_2$): The root system of monumental olive trees, shaped by genetic, biological, environmental, and anthropogenic factors, often grows asymmetrically, forming impressive structures. Measuring the perimeter at the base is crucial for understanding these formations. The base perimeter is calculated in meters.

Height (${\mu }_3$): The height of the olive tree provides insights into cultivation practices over time in the studied area. For instance, in the Ionian Islands, the height of olive trees varies between islands due to different harvesting methods. Height is measured in meters, ranging from 4 to >20.

Beyond these morphological aspects, other tangible characteristics like the formation of caverns, the trunk shape, or the distortion are being evaluated with the assistance of the Likert scale. According to the Likert scale, the characteristics of an olive tree are measured as follows:

Trunk Cavitations (${\mu }_4$): Old olive trees typically develop internal cavities of various sizes and shapes in their trunks. By measuring the trunk’s perimeter and estimating the size of the caverns, the formation of caverns is assessed on the 5-point Likert scale: 1. No Cavern, 2. Low Cavern, 3. Medium Cavern, 4. High Cavern, 5. Very High Cavern.

Trunk Shapes (${\mu }_5$): The relief and shapes on the trunk of a perennial olive tree hold aesthetic and ecological value. The relief, characterized by folds, cavities, bumps, and unique shapes, is evaluated on the 5-point Likert scale: 1. No Relief, 2. Low Relief, 3. Medium Relief, 4. High Relief, 5. Very High Relief.

Tree Trunk Distortion and Trunk Spiralization (${\mu }_6$): Distortions and spiral formations in the trunk contribute to the special aesthetic value of perennial olive trees. The degree of crookedness is assessed on the 5-point Likert scale: 1. No Crookedness, 2. Low Crookedness, 3. Medium Crookedness, 4. High Crookedness, 5. Very High Crookedness. This evaluation considers factors such as the degree of observed spiralization, the regularity of the spiral, and the occupied area on the trunk.

When it comes to characteristics that cannot be measured, like the aesthetic, historical, cultural, or educational value connected to old olive trees, the evaluation could get a little bit complicated. Characteristics like old structures related to local activities and traditions, such as stone walls, traditional houses, bridges, and old olive mills have been also considered. The non-tangible criteria are:

Cultural value ($a_1$): The assessment of cultural value is based on a study related to cultural data, traditions, myths, and narratives conveyed by each olive tree to visitors. The study reveals a value using the Likert scale [19,23,24,25,26,27].

Educational value ($a_2$): A centuries-old olive grove or tree serves as an experiential laboratory for various aspects of ecology and biodiversity science. Educational value is measured using the Likert scale.

Historical value ($a_3$): Historical value is gauged by the extent of the centuries-old olive trees’ connection to historical events that have positively or negatively influenced the nation’s course [19,23,24,25,26,27]. The study reveals a value using the Likert scale.

Scientific value ($a_4$): The scientific value of Monumental Olive Trees is evaluated based on various parameters that could advance scientific knowledge, including ecological, environmental, historical, and cultural aspects. Agricultural science, exploring factors like longevity, varieties, genetic origins, and environmental conditions, is of scientific interest [23].

Aesthetic value ($a_5$): Elements with unique features and morphological characteristics enhance the aesthetic satisfaction of visitors [23,24,25,26,27]. The value is given in the Likert scale.

Touristic value ($a_6$): Touristic value is assessed based on the specific characteristics of the tree, the biodiversity it supports, and the overall landscape. The value is provided in the Likert scale.

Landscape value ($a_7$): Landscape, comprising natural and geomorphological diversity along with anthropogenic elements, is evaluated for its quality based on factors such as biodiversity, terrain, and anthropogenic activities (e.g., steps, dry boulders). Additionally, degradation elements like fires, changes in land use, and overgrazing are taken into account [23].

2.2. Combining AHP with SAW

Addressing the complexity of evaluating olive trees and groves necessitates the adoption of sophisticated methodologies, and among them, the multi-criteria decision-making (MCDM) methods emerge as a potent solution. From the available MCDM methods, we need methods that are easily applied and have valid results. For this purpose, we selected the simple additive weighting (SAW) method and the analytical hierarchy process (AHP) method. Both these multi-criteria evaluation approaches are widely applied in decision-making scenarios and offer systematic and insightful analyses ensuring a comprehensive ranking of alternatives. The versatility extends to various applications, making it a valuable tool for decision-makers dealing with intricate choices. The fundamental premise of SAW lies in systematically weighing criteria importance and their relative significance.

The application of the SAW method consists of the following steps:

• Criterion Scoring Scale Creation: The process commences with the establishment of a scoring scale for each criterion source. The final scores, situated in the (0, 1) interval, signify the performance of the criteria source, with 1 denoting optimal and 0 indicating the least favorable outcome. SAW does not have a predefined way of calculating weights. For this purpose, in this step, we used AHP. The application of AHP for the calculation of weights is presented in detail in [21].
• Application of Weights: SAW incorporates a weighted approach, acknowledging the varying importance of each criterion in the decision-making process. The weights, determined by evaluators’ perceptions, reflect the relative significance of criteria in the final decision.
• Summation of Values: Employing a summative methodology, SAW calculates a total score for each alternative by adding the products of criteria values and their corresponding weights. This comprehensive score offers a holistic evaluation, considering both the intrinsic merit of each criterion and the assigned importance by evaluators.

The formula for applying the SAW method used for the implementation needs of the application is as follows:

$$U\left(X_j\right)={\sum }_{i=1}^6{w}_{{\mu }_i}{\mu }_{ij}+{\sum }_{i=1}^7{w}_{{\alpha }_i}{\alpha }_{ij}$$

where ${\mu }_{ij}$ and ${\alpha }_{ij}$ are the tangible and intangible criteria for the tree $X_j$.

The application of AHP is presented in detail in [21] and AHP has been proven to be rather effective in estimating the weights of the criteria. The SAW method’s structured approach, on the other hand, ensures a nuanced evaluation, balancing the intricate interplay of criteria. Its application extends beyond olive trees and groves, proving valuable in diverse decision-making scenarios where multiple criteria contribute to the final choice.

In conclusion, the SAW method stands as a reliable and adaptable tool for multi-criteria decision-making, particularly in the nuanced evaluation of olive trees and groves. Its systematic approach, encompassing criterion scaling, weighted analysis, and summative scoring, contributes to informed and balanced decision processes.

In the application to be presented, the SAW method will be used to calculate not only the initial value of each olive tree but also the initial value of each olive. These values are further used by the system to select the final set of alternative olive trees and olive groves that are going to be evaluated by the third multi-criteria decision-making theory that is presented in the next section.

2.3. VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje): A Comprehensive Analysis

The VIKOR method, introduced to the academic realm in 2004 by Opricovic and Tzeng [28], stands as a pivotal approach in multi-criteria decision-making. Its inception aimed to tackle the intricate challenges of evaluation problems by presenting solutions that strike a balance between maximizing returns and minimizing risks. Over the years, VIKOR has become a cornerstone in decision-making processes, offering a nuanced perspective for assessing and selecting alternatives.

At its core, the VIKOR method operates on the principles of maximum performance and minimal risk. By normalizing data, calculating overall scores, and employing VIKOR indices, the method ensures a judicious equilibrium between achieving the highest possible return and mitigating associated risks. The VIKOR method has the following methodology.

• Data Normalization: The initial step involves standardizing the input data, ensuring uniformity and comparability across diverse criteria.
• Calculation of Overall Scores: VIKOR computes comprehensive scores for each alternative, synthesizing the normalized data into a coherent representation of their overall performance.
• VIKOR Indices: Leveraging specific indices, the method gauges the compromise solution for each alternative, offering a nuanced understanding of their relative standings.
• Final Selection Determination: The ultimate aim is to pinpoint the alternative that strikes the most balanced compromise between maximizing returns and minimizing risks.

Subsequently, the algorithm proceeds to calculate the values ${\mu }_{ij}$ and ${\alpha }_{ij}$ for the tangible and intangible criteria and $w_{{\mu }_i}, w_{{\alpha }_i}$ represent the weights of the criteria and signify their relative importance. Following this, the values Q, R, and S are calculated for the given values. S is the Utility Measure and represents the distance from an ideal solution. The lower the S value, the closer the alternative is to the ideal solution in an overall sense. R is the Regret Measure and represents the maximum normalized gap (or worst deviation) among all criteria for an alternative. Q is the VIKOR Index, which means the Compromise Measure and it is used as the final ranking score that balances both S and R using a compromise approach. The alternatives are then ranked based on the values of Q in ascending order using the values of S and R.

The analysis of the method’s stability relies on a combination of the results from three tables. Stability is confirmed when the best element in the Q list is also the best element in either the S or R list.

More specifically, the application of compromise ranking algorithm VIKOR [29] is based on the values of the criteria ${\mu }_1{\dots \mu }_6$ and $a_1\dots a_7$ and is briefly reviewed as follows:

Estimating the best ${\mu }_1^{+}\dots {\mu }_6^{+},{\alpha }_1^{+}\dots {\alpha }_7^{+}$ and worst ${\mu }_1^{-}\dots {\mu }_6^{-},{\alpha }_1^{-}\dots {\alpha }_7^{-}$ values of all criteria taking into account all values of ${\mu }_1{\dots \mu }_6$ and $a_1\dots a_7$

$$\begin{gathered} {\mu }_j^{+}=\underset{1\le i\le 6}{max}\left({\mu }_{ij}\right) \\ {\alpha }_j^{+}=\underset{1\le k\le 7}{max}\left(a_{kj}\right) \\ {\mu }_j^{-}=\underset{1\le i\le 6}{min}\left({\mu }_{ij}\right) \\ {\alpha }_j^{-}=\underset{1\le k\le 7}{min}\left(a_{kj}\right) \end{gathered}$$

Computing the values of $S_i$ and computing the values of $S_i$ and $R_i$

Where $w_{\mu }$ and $w_a$ are the weights of criteria $\mu$ and $\alpha$, representing the decision maker’s relative preference for the importance of the corresponding criteria.

Computing the values of $S^{\mathrm{*}}$, $S^{-}$ and $R^{\mathrm{*}}$, $R^{-}$

$$\begin{gathered} S^{\mathrm{*}}=min{S}_i, S^{-}=max{S}_i \\ {R}^{\mathrm{*}}=min{R}_i, R^{-}=max{R}_i \end{gathered}$$

Determining the value of $Q_i$ for the alternative trees i and ranking the alternatives by the values of $Q_i$.

$$Q_i=v\left({\displaystyle \frac{S_i-S^{\mathrm{*}}}{S^{-}-S^{\mathrm{*}}}}\right)+(1-v)\left({\displaystyle \frac{R_i-R^{\mathrm{*}}}{R^{-}-R^{\mathrm{*}}}}\right)$$

where $v$ was the weight for the strategy of maximum group utility and $1-v$ was the weight of the individual regret. Usually, $v=0.5$ and when $v>0.5$, the index of $Q_i$ tends to be majority agreement, and clearly when $v<0.5$, the index of $Q_i$ will indicate the majority negative attitude.

Considering all the above information, the VIKOR method emerges as a robust and adaptable framework for evaluating olive groves, providing a systematic and balanced approach to decision-making in complex scenarios. Its application holds significant promise in various domains, ensuring an optimal compromise between conflicting factors.

VIKOR has a more systematic way of evaluating the value of the olive trees and the olive groves combining the values of the criteria. This is the main reason why we used AHP for calculating the weights of the criteria and SAW for the initial selection of the olive trees and groves to set the set of alternatives as it is simple and quick in this implementation. Finally, we use VIKOR for the estimation of the final values to find the olive tree or the olive grove that has greater value and is more appropriate for promotion.

3. Design and Implementation of a Cross-Platform Application

The Olea app implements the above SAW method for selecting the trees and the olive groves that are going to be evaluated and VIKOR theory to perform the final evaluation and select the olive groves that are considered the best to be promoted. The Olea app is a cross-platform application and was implemented in Flutter. The choice of Flutter as the framework for Olea app development represents a forward-thinking approach, aligning with modern trends in technology. Flutter’s cross-platform capabilities ensure that the application is accessible across diverse devices and operating systems, including Windows, macOS, Linux, iOS, and Android. This versatility streamlines the user experience, allowing seamless interaction regardless of the platform.

A distinctive feature of cross-platform applications is the efficiency gained through writing a single set of code applicable to various platforms. This not only accelerates development but also reduces costs, enhancing the overall accessibility of the application. The consistency in user interface and experience ensures a seamless interaction for users across diverse platforms.

The development of a cross-platform application means building software that functions seamlessly on various types of devices and operating systems. This approach is beneficial because it enables developers to utilize a single codebase for multiple platforms, including mobile (such as iOS and Android), desktop (including Windows and Linux), and web platforms. For web platforms, developers often employ frameworks like React Native for Web, Flutter for Web, and technologies such as Electron. For example, using React Native for the Web allows developers to use the same React components for both mobile and web applications. The implementation of the Olea app was carried out using Microsoft’s Visual Studio Code 1.75 (January 2023), and the application is designed to run on Windows 10, Android, and Ubuntu (Linux). Therefore, the app can run both in desktop and mobile mode.

Like any software system, the initial step in developing a database system involves the collection and analysis of requirements [30]. According to Lazarinis and Mavrommatis [31], requirements fall into two categories:

Functional Requirements: These describe what the system should do from the user’s perspective, often referred to as capabilities.

Nonfunctional Requirements: These encompass characteristics that the system must have but do not relate to specific functionality. Essentially, they represent constraints on the implemented system.

Functional Requirements of the Application:

• Olive tree details, whether known or unknown, should be insertable into a database.
• Grove details should be insertable into a database.
• The application should calculate the value of each olive tree and grove using the simple additive weighting (SAW) method.
• After entering data for a tree or a grove, the application should display a data entry report with the option to save it as a PDF file.
• After entering data for a tree or a grove, the application should determine if the tree or grove is perennial.
• The application should be capable of comparing olive trees using the Vlsekriterijumska Optimizacija I KOmpromisno Resenje (VIKOR) model.
• After comparing trees or groves, the application should display a comparison report with the option to save it as a PDF file. The ranking of the comparison is a crucial element in the report.

Nonfunctional Requirements of the Application:

• All fields and text in the application’s user interface should be in English.
• The application should run on more than one operating system.
• The selection of a tree or grove location should be performed through a widget.
• The result of comparing a tree or a grove should dynamically appear after each selection of the object.

4. Example of Operation of Olea App

In this section, we explore how the Olea app assists in applying three powerful decision-making methods: AHP, SAW, and VIKOR to evaluate olive trees and olive groves. To do that, we have to insert some data on the olive trees and the olive groves in the database of the Olea app. We selected some olive trees from an olive grove located in Zakynthos. Zakynthos is in the Ionian Sea, between Greece and Italy. The island covers an area of 406 km² and has a population of about 42,000 people. One of the main characteristics of the island is the existence of many olive groves. The dataset of the values of the criteria of the olive trees used in our example is presented in

Table 1. The dataset of the values of the criteria of the olive trees evaluated in the example.

Tree Code	Perimeter at 1.30	Base Perimeter	Height	Ψ	X	Cavitation	Trunk Shapes	Trunk Torsion
AMB_KAM2	9.8	16.7	8.5	20.80993	37.68199	2	3	4
AMB_KAM3	9.3	12.8	7.2	20.80999	37.68184	5	4	3
AMB_KAM4	11.5	14.8	6.5	20.81018	37.68232	3	4	3
AMB_KAM5	7.8	11.8	6	20.80998	37.68291	5	5	4
AMB_KAM6	10.8	16.2	7	20.80988	37.68340	4	4	3
AMB_KAM7	9.1	12.5	6.5	20.80950	37.68357	3	3	3
AMB_KAM8	8.5	11.8	8.4	20.80995	37.68351	4	5	4
POR_KOUK_1	5.5	6.5	6.4	20.83933	37.70579	2	2	2
POR_KOUK_6	5.15	8.8	7	20.83614	37.72164	3	4	3
POR_KOUK_7	5.65	11.78	8	20.84197	37.71844	3	4	2
POR_KOUK_8	5.65	8.6	7	20.84224	37.71845	3	4	2
POR_KOUK_9	7.45	11.4	8	20.84231	37.71877	4	4	2
POR_KOUK_10	5.8	8.4	7.5	20.84246	37.71861	3	3	2

. In Table 1, for each tree, one can see the code of the tree, the perimeter at 1.30, the base perimeter, the height, the coordinates X and Ψ, the cavitation (in Likert scale), the trunk shapes, and torsion (in Likert scale)

When the user wants to insert new data for the criteria of an olive tree, the app will navigate to a screen where the user can input all the necessary olive grove data, as shown in Figure 1.

After inputting the data of the olive groves into the Olea app, by clicking on the “Save Data and Evaluate Olive Grove” button, the SAW method is initiated. Based on the inserted data, the SAW Value of Olive Grove is displayed in an insert report screen on the Likert scale as presented in Figure 2.

When the user selects Olive Grove comparison, the app will navigate to a screen where all Olive Groves from the Olea app’s database can be displayed. The user has the option to apply filter criteria, such as the olive grove SAW limit value, the location, or if the grove is monumental. After applying the filters, the comparison can be conducted by selecting the desired olive groves to be compared with each other. Finally, after the selection, the comparison can be made with the assistance of the VIKOR method.

Finally, the application gives the users the possibility to print a comparison report. In the final report, the ranking of the selected olive groves can be shown, with their name and the VIKOR values, as shown in Figure 3.

5. Discussion and Limitations

The key interpretations of this work rely on highlighting the significance of monumental trees, especially monumental olive trees, discussing the challenges in evaluating these trees and proposing an intelligent decision making system that is implemented in an application that can run both on PC and mobile mode. By integrating scientific, cultural, and technological perspectives, the study provides a sustainable and accessible methodology to ensure these ancient natural landmarks are protected for future generations.

Indeed, monumental trees are heritage assets, valued not only for their age and size but also for their cultural and ecological importance [1,19]. Ecologically, these ancient giants play a crucial role in their ecosystems. They provide habitat and shelter for numerous species, from birds to insects to fungi, contributing to biodiversity. Their large trunk also supports complex microhabitats that nurture diverse life forms. From a cultural perspective, monumental trees often hold deep historical and symbolic value. They can be landmarks, with some dating back thousands of years, serving as living witnesses to history. Many cultures around the world revere these trees for their spiritual or religious significance, considering them sacred or imbued with special powers. As a result, these trees contribute to biodiversity, landscape aesthetics, and local economies, making them valuable resources for sustainable tourism and environmental education. Various scientific methods (increment core sampling, radiocarbon dating, and luminescence dating) have been used to determine tree age, but they require expertise and specialized equipment, limiting accessibility. Research studies have been conducted to estimate the value of monumental trees [4,6] but are mainly for other kinds of trees. For example, the analysis of Menon et al. [6] focuses on the frequency and variability of tree-related microhabitats considering different types of protection and monumental trees. Other existing methods rely heavily on physical measurements (trunk size, rings, etc.), which may be inaccurate due to the cavitations and distortions in olive tree trunks (e.g., [17]) and there is no unified system for assessing monumental status or value, making it difficult for policymakers, landowners, and conservationists to make informed decisions.

Our study is quite different as it focuses on the evaluation of monumental trees using MCDM analysis and the stakeholder can perform evaluations of trees online using a simple application. Indeed, following Asciuto et al.’s [4] study, a two-step evaluation and classification method concerns the scientific community, considering both tangible and intangible aspects, providing us with a complete understanding not just of individual monumental olive trees but also entire groves.

The article presents the design and implementation of a system that combines different multi-criteria decision-making theories for estimating the value of monumental trees whose age is not already known. The use of MCDM allows for the consideration of multiple criteria simultaneously, such as historical significance, ecological impact, and cultural and heritage value. This ensures a holistic assessment rather than focusing on a single aspect. Furthermore, establishing standardized criteria for heritage or monumental tree status ensures consistent evaluations. The structured approach of MCDM provides transparency in decision-making processes and the use of these theories is rather important in evaluating the value of monumental olive trees. We used AHP, SAW, and VIKOR.

AHP provides a well-established procedure for weight calculation. The method is based on pairwise comparisons, which is a procedure that helps capture experts’ judgment. The main advantage of this theory is that it can be easily combined with MCDM methods, such as SAW and VIKOR.

The SAW method, with its structured approach and weighted analysis, offers a reliable framework for multi-criteria decision-making, particularly suited to the complex evaluation of olive trees and groves. Its systematic methodology ensures informed and balanced decision processes, making it an invaluable tool for experts and enthusiasts alike.

Furthermore, by using the VIKOR method, the Olea app becomes even smarter in how it evaluates olive trees and groves. It helps balance the pros and cons, ensuring we make smart decisions while minimizing risks. This method looks at all the data, calculates scores, and finds the best compromise, providing us with a clear picture even in complex situations.

As stated above, all these theories can be quite useful for evaluating olive trees but there are also some limitations. The main advantage of the MCDM methods is that they capture experts’ judgment. However, this may also be considered a drawback as these judgements may be subjective when assigning weights to criteria or evaluating alternatives. This subjectivity can affect the reliability of the results. The outcomes of MCDM methods can be sensitive to the input data. This is the main reason that the cross-platform application proposed in this paper can guide the stakeholder in data input. Furthermore, implementing MCDM for large-scale olive tree evaluations across multiple locations or farms can be challenging due to the scale and diversity of factors involved. However, our approach was tested along the Ionian Sea, on different islands and has proven successful. The Olea app represents a significant advancement in heritage tree conservation, offering a structured, transparent, and scalable approach to preserving olive tree ecosystems while supporting sustainable tourism and economic incentives for their protection.

6. Conclusions

Evaluating monumental olive trees with MCDM promotes sustainable management practices. It considers long-term impacts on biodiversity, landscape aesthetics, and socio-economic benefits, supporting preservation efforts for future generations. The estimation of the age of an olive tree is a difficult procedure that requires specialized staff and methods such as the methods of increment core sampling, radiocarbon dating (C14), and luminescence dating (OSL). Furthermore, until now there has been no online system for estimating the value of monumental olive trees.

In view of the above, the development and implementation of the Olea app contributes not only to the estimation of the value of monumental trees but also to sustainable tourism stakeholders that can utilize this information. The rich cultural, historical, and ecological significance of monumental olive groves demands a complex approach to their evaluation, one that goes beyond pure physical measurements. By combining multi-criteria decision-making methodologies such as the AHP, SAW, and VIKOR methods, Olea app offers a comprehensive solution to the complex challenges involved in evaluating and classifying these natural treasures.

AHP offers several advantages when used for decision-making, particularly in complex scenarios like evaluating the value of monumental olive trees. It breaks down complex decisions into a hierarchical structure, making it easier to analyze multiple criteria. However, its main advantages involve the way it considers criteria. It can consider both quantitative and qualitative criteria and uses pairwise comparisons to calculate the weights of the criteria, which is a well-defined procedure that incorporates expert judgment. Most MCDM methods do not have such a procedure for weight calculation. Since AHP provides a structured framework with mathematical calculations, it enhances transparency and allows decisions to be justified objectively. Additionally, one of the main advantages of this theory is that it can successfully be combined with other main MDCM methods such as SAW, VIKOR, and TOPSIS.

SAW can easily be applied and combined with AHP. SAW is one of the simplest MCDM methods, requiring only normalization and weighted summation of criteria values. Compared to more complex methods like AHP or VIKOR, SAW requires fewer calculations, making it fast and efficient. As a result, it may easily be applied in large datasets of olive trees where a quick decision is needed. These are the main reasons why it is selected for the first phase of assessment of the olive trees.

VIKOR, like AHP, can handle different types of criteria (qualitative and quantitative) and can easily be adapted for different contexts. The theory focuses on compromise ranking. This type of ranking helps select an option that is closest to the ideal solution while balancing different stakeholder interests. It ranks alternatives based on their performance across multiple criteria, making it easier to identify the most valuable monumental olive trees. It also classifies alternatives into acceptable and non-acceptable solutions, helping the identification of monumental olive trees that are worth promoting. VIKOR proved quite successful in evaluating monumental olive trees because it finds the best-balanced solution, considering both ideal and worst scenarios.

Additionally, the Olea app is easy to use on different devices and systems, thanks to Flutter. This means anyone can access it, making the process simpler and more inclusive. With its friendly interface, the app makes it easy for everyone to join in and help preserve our cultural and natural heritage. Using a cross-platform approach, the Olea app can run on multiple operating systems (like iOS, Android, Windows) which allows reaching a larger audience without developing separate native apps for each platform. Users get a consistent experience regardless of the device or platform they are using, which can enhance usability and user satisfaction. This is extremely important for users that do not have prior experience with similar applications. Since development is streamlined, cross-platform apps can often be launched quicker and, therefore, reach a wider audience quickly.

In summary, the Olea app is a big step forward in caring for olive trees and groves. It mixes old traditions with new technology, making it easier for us to protect these ancient treasures. By employing the power of technology and sophisticated methodologies, the app enables us to honor and protect these ancient living monuments for generations to come.