Smart Forest Modeling Behavioral for a Greener Future: An AI Text-by-Voice Blockchain Approach with Citizen Involvement in Sustainable Forestry Functionality

Abstract

This paper introduces a novel approach to tree modeling architecture integrated with blockchain technology, aimed at enhancing landscape spatial planning and forest monitoring systems. The primary objective is to develop a low-cost, automated tree CAD modeling methodology combined with blockchain functionalities to support smart forest projects and collaborative design processes. The proposed method utilizes a parametric tree CAD model consisting of four 2D tree-frames with a 45° division angle, enriched with recorded tree-leaves’ texture and color. An “AI Text-by-Voice CAD Programming” technique is employed to create tangible tree-model NFT tokens, forming the basis of a thematic “Internet-of-Trees” blockchain. The main results demonstrate the effectiveness of the blockchain/Merkle hash tree in tracking tree geometry growth and texture changes through parametric transactions, enabling decentralized design, data validation, and planning intelligence. Comparative analysis highlights the advantages in cost, time efficiency, and flexibility over traditional 3D modeling techniques, while providing acceptable accuracy for metaverse projects in smart forests and landscape architecture. Core contributions include the integration of AI-based user voice interaction with blockchain and behavioral data for distributed and collaborative tree modeling, the introduction of a scalable and secure “Merkle hash tree” for smart forest monitoring, and the facilitation of fintech adoption in environmental projects. This framework offers significant potential for advancing metaverse-based landscape architecture, smart forest surveillance, sustainable urban planning, and the improvement of citizen involvement in sustainable forestry paving the way for a greener future.

1. Introduction

The spatial planning and design of forests and landscapes can benefit from tree modeling architecture integrated with blockchain technology, which ensures information confidence, data integrity, and collaborative engineering. A proof-of-concept automated tree modeling architecture and blockchain integration technique was introduced as a low-cost application for digital landscape architecture design and forest monitoring systems. The parametric tree modeling process is based on a tree CAD model comprising four 2D tree-frames with a 45° division angle, enhanced with recorded tree texture.

An off-the-shelf CAD platform employs a user-defined routine using a fully automated “Text-by-voice CAD Programming” technique to create tangible tree-model NFT tokens for the proposed Internet-of-Tree Blockchain. This approach offers advantages such as enhanced planning intelligence, superior data integrity, offline error-free CAD design, and cost and time efficiency compared to traditional 3D tree modeling methods (e.g., terrestrial, drone, photogrammetry, laser scanning). It also provides acceptable tree modeling accuracy and supports smart forest fintech adoption and monitoring through Merkle hash tree magnification.

The core “Blockchain/Merkle hash tree” tracks temporary changes in tree geometry and texture using parametric transactions, enabling metaverse functionality such as decentralized design, data validation, modification, and planning intelligence. This makes the proposed architecture valuable for the global CAD-Blockchain integration industry, contributing to smart forest surveillance and distributed simulation.

Previous studies [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] highlight the need for tree geometry in spatial analysis for forests and urban parks, as well as blockchain’s role in facilitating data integrity, collaborative design, and information confidence. However, they lack a low-cost integration solution with fintech adoption functionality and citizen involvement in sustainable forestry potentiality. Of these studies, the most notable and closest to the present research is the publication [5] which addresses the issue of tree modeling based on the geometry created by two raster images and complicated key-in text commands. In comparison to this study, the present research approaches the issue of tree modeling based on four simple raster images that create more realistic tree geometry compared to the two images. Furthermore, the present research uses a pioneering approach to the issue of CAD-Blockchain integration and instead of key-in text commands, it relies on AI-based simple voice commands that allow in a very simple and friendly way citizen involvement in metaverse projects, e.g., sustainable forestry. Finally, the issue of tree architectural lighting enhances metaverse tree visualization is addressed in the present research effort as a new add-in functionality compared to previous publications [5,7,8,9,10,19,20,25,28].

While [31,32,33,34,35,36,37] discuss linking digital twins and assets to 3D models for structural monitoring and urban transformation, they do not address tree modeling as a symbol of urban green environments with citizen involvement functionality. Taxonomy and ontology-based methods, blockchain platforms, and decision-support models for sustainable urban investment optimization are noted in [38,39,40,41,42,43], but implementation details for distributed environments in sustainable forestry are missing.

The proposed method uses voice-to-text batch commands and event-driven routines for parametric tree CAD modeling relative to a ground reference point (GRP). This enables efficient blockchain operation and collaborative design, saving time and costs. Although the method yields less accurate tree models, this limitation does not affect applications in smart forests, agriculture, or landscape architecture [44,45,46,47,48,49]. Blockchain safety, fraud issues, hybrid learning, and data analytics frameworks are discussed in [50,51,52,53,54].

The primary goal is to create an experimental method for incorporating tree photos into 2D CAD frames and linking them to an “Internet-of-Trees” blockchain. Using a simple “Merkle hash tree,” the proposed model tracks tree geometry growth and texture changes with parametric transactions of tangible tree-model NFTs, adding value to the CAD-Blockchain integration industry [55,56,57,58,59,60,61,62,63,64,65,66]. This structure supports distributed and collaborative tree architecture for smart forests, VR, digital documentation, and landscape monitoring, where high accuracy is not required [67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85].

The following three research questions outline the central research concept and then addressed and discussed thought the text.

RQ-1: How does blockchain ensure data integrity in sustainable forestry?

RQ-2: What is the significance of the Internet-of-Trees blockchain?

RQ-3: How does the proposed method improve forest monitoring and citizen involvement in sustainable forestry?

The remainder of the paper is structured as follows: Section 2 (“Materials and Methods”) details the “Tree CAD modeling–Blockchain integration” technique and its implementation using “Text-by-voice batch command-line programming.” Section 3 (“Results”) presents an experimental blockchain case study on “Internet-of-Tree models” and a usability test for “IoTr-models.” Section 4 (“Discussion”) clearly addresses the three research questions, and Section 5 (“Conclusions”) summarizes the findings, contributions, limitations, and recommendations for future research.

2. Materials and Methods

Nowadays, a text-by-voice technique accurately converts speech into text using AI APIs powered by tech giants like Google. So, predefined human oral phrases (speech notes) like “place point”, “place block”, “rotate” can easily be converted to relative text sequences and get them transcribed automatically to key-in CAD commands for CAD programming.

In tree modeling architecture, the vector parametric modeling technique eliminates the need for a costly (time, resources, money) point-by-point design, offering at the same time great CAD flexibility (geometric transformations like tree model move, rotate, scale, mirror, etc.), excellent tree modeling functionality (tree dimensions, color, and texture), and a scalable “Internet-of-Trees” blockchain (plain tree model NFT tokens).

Even more, recently, CAD software platforms (like Bentley's “MicroStationPython 2024”, the V24-0-2 2024 release/version of MicroStation with Python V3.12 scripting used in our research and Autodesk's AutoCAD 2025) and tree generators (like Treelt) offer CAD/blockchain integration on top of the traditional vector parametric modeling [5,6,8,9,19,47].

The presented text-by-voice CAD programming approach for tree modeling architecture uses the Google’s speech-to-text AI API to produce personalized key-in commands, nested in a batch command ASCII text file, which in turn trigger dedicated event-driven routines for CAD tree modeling, tree-model NFT wallet setup, tangible token creation, and finally add to the thematic personalized “Internet-of-Trees” blockchain.

2.1. Tree CAD Modeling—Blockchain Integration Method

The GUI of the proposed tree CAD modeling—blockchain integration technique is composed by a pull-down GUI (Graphical User Interface) item in CAD platform’s main menu (“Smart Forest”) and its three sub-menus: A. The Tree Architecture FRAMES for tree modeling; B. Raster Images for tree coloring and texturing; and C. ADD the Tangible Tree Model NFT to the personalized “Internet-of-Trees” blockchain (Figure 1).

Geo-referenced capabilities for relative to ground reference point modeling deployment are used to precede the relative design of four rectangular tree-frames, perpendicular to one another, using this GUI for generic tree CAD modeling (Figure 2 and Figure 3).

The assignment of off-the-shelve photos, such as low-cost jpg images from smartphones, into these frames that function as tree-leaves texturing and coloring comes next (Figure 4 and Figure 5).

Following, this generic modeling format is stored, as a tangible tree-model NFT token, in the so-called “Internet-of-Trees” dedicated blockchain. For the implementation of this text-by-voice CAD programming for tree modeling architecture, a simple speech-to-text API (official Google API from Democritus University/Forestry Department) which converts audio into text transcriptions and integrates speech recognition with an easy-to-use GUI and a smart “Voice-to-text batch command-line programming” technique are applied [5,6,19].

Figure 6 and Figure 7 present the initial stage for a metaverse smart forest project (a multi tree modeling architecture).

Following, for metaverse “real-world” appearance, with rendering functionality, a tree architectural lighting is added (Figure 8, Figure 9 and Figure 10).

Finally, metaverse fly-through functionality, necessary for citizen involvement in sustainable forestry projects, is demonstrated in Figure 11 and Figure 12.

For metaverse and digital documentation projects involving tree landscapes, forests, monument landscapes with lots of trees, and other landscape architecture applications, the modeling and visualization accuracy is sufficient, satisfactory, and adequate (i.e., for applications without special tree-shape accuracy and visualization requirements).

2.2. Personalized CAD Software (Event-Driven Procedures)

The text-by-voice commercial API-Google’s cloud speech-to-text API; https://cloud.google.com/speech-to-text?hl=en (accessed on 21 August 2025)- produces a sequence of key-in commands, and the vector parametric modeling routines assigned to these key-ins, as user-defined CAD functions, were implemented in MDL (MicroStation Development Language) event-driven programming language. MDL code can be compiled using Microsoft Visual C++ as a native-code DLL ideal for object-oriented concepts like smart forests monitoring and landscape architecture planning [5,47].

The proposed tree CAD modeling procedure obeys a novel “adverbs-verb-nouns” programming metaphor which takes as “adverbs” parameters the CAD current drawing level (LV), the frame’s line color (CO), the line weight (WT), and the line style (LC), as “verb” a number of commands (Place Point, Place Block, Rotate, Assign, Attach, Setup, Create, Add), and as “nouns” parameters the frame’s geo-referencing point X, Y, Z coordinates (GRP), the tree’s dimensions height (Height) and widths (Width1, Width2), and the raster images for tree-leaves’ texture and color (Image1, Image2, Image3, and Image4) (Figure 13).

In more detail:

(i).

The tree-frame’s CAD “adverbs” LV, CO, LC, and WT are predefined.

(ii).

For generic geo-referencing modeling, the GRP is assigned to coordinates 0.0, 0.0, 0.0 (tree modeling universe space) (Figure 2).

(iii).

In the FRONT view of the host CAD platform a tree-frame is designed according to the tree’s height and width1 parameters (Height, Width1) (Figure 2).

(iv).

In the RIGHT view of the host CAD platform another tree-frame is designed according to the tree’s height and width2 parameters (Height, Width2) (Figure 3).

(v).

The last tree-frame is relocated in such a way that both frames cross each other perpendicularly. In this way compound tree-frame is constructed (Figure 3).

(vi).

Subsequently, using the “Rotate” verb with the “copy” option and the angle 45^o as a noun, a copy of the compound tree-frame is rotated for 45^o (Figure 4 and Figure 5).

(vii).

Four tree’s scalable raster images (jpg format), obtained from a user’s noise-free photography (such as a smartphone jpg picture), are assigned to these four tree-frames (as the unique geometry located in level LV and colored with color CO) using a user-defined GUI’s dialog setting box, in accordance with the predefined level (LV) and color (CO) parameters after the tree’s geometry construction, as well as for tree-leaves texture and color-imagery assignment (Figure 4, Figure 5 and Figure 6). In this manner, the GRP (0.0, 0.0, 0.0) is used to create a 4-images compound tree frame for a metaverse smart forest (Figure 7).

(viii).

The three architectural lighting channels enhancing metaverse tree and smart forest visualization (Figure 8, Figure 9 and Figure 10).

(ix).

The fly-through functionality (Figure 11) and the smart tree 2.5 CAD model for citizen involvement in sustainable forestry (Figure 12). Finally,

(x).

The tangible tree-model NFT is deployed step by step (i.e., wallet setup → tangible token creation → token addition to a thematic metaverse sustainable forestry blockchain) (Figure 13).

2.3. Batch Command-Line Programming (The Adverbs-VERB-Nouns Metaphor)

An ASCII text file is the foundation of the batch command-line programming implementation technique (NotePad/WordPad) located outside of the host CAD platform. For our case study the batch command file “Voice-to-Text-TreeCADModeling.bat” is used and the “tree CAD modeling-Blockchain integration” methodology is performed with a batch top-down job control according to this batch file (Figure 13).

The event-driven processes and command-line programming of the suggested framework were hosted in the low-cost CAD platform MicroStation^® Bentley Systems (an official academic license software issued for Melbourne Univ./Geomatics and Aristotle Univ./Topography was used. Bentley Systems Inc. has its corporate headquarters at Exton, Pennsylvania, USA) due to its versatility, ease of use, localization and add geospatial context functionality, and thematic customization capabilities. Because it uses real-time planning intelligence and Internet of Things (IoT) efficiency, the recently introduced unique “tree CAD modeling-Blockchain integration” approach, which is accomplished using the “voice-to-text batch command-line programming” technology, is a metaverse “smart modeling” function.

It is even more adaptable because it is carried out in a relaxing and safe manner offline, using straightforward English phrases as “Commands” for ASCII CAD-platform-offline coding that are connected to domain-dependent key-in procedures [5,12,47].

By entering a link-string, the “Voice-to-Text-TreeCADModeling.bat” batch file can be run from a CAD software platform prompt (key-in dialog box). E.g., @c://MetaverseSmartForest/Voice-to-Text-TreeCADModeling.bat.

Architectural lighting enhances tree visualization for metaverse projects. Particularly, three architectural lighting channels are supported by the “Voice-to-Text-TreeCADModeling.bat” batch file: Global (Figure 14), Source (Figure 15), and Solar lighting (Figure 16).

3. Results

Figure 17 illustrates the established distributed and collaborative smart tree landscape metaverse framework (method and implementation methodology) using a blockchain outline design for an urban landscape architecture project with a “Internet-of-Tree models” theme. A usability test (comparison study) is included in this section to validate the suggested technique.

3.1. “Internet-of-Tree Models” Blockchain: The Outline Design

The outline design of the “Internet-of-Tree Models” metaverse blockchain case study is displayed in Figure 17. Each tree, as a block in the IoTr-models chain, is referred to by a hash value created by the SHA256 cryptographic algorithm.

Accordingly, a linked list of transaction blocks that document changes in temporal texture and tree geometry growth makes up the “IoTr-models” thematic blockchain [13,20,69,70]. A “Merkle hash tree” series of transactions is shown by the “Root of Hash Tree” [13,16,18,26]. The “Merkle hash tree” in the blockchain under discussion is comparatively simple, compact, and readily managed (i.e., controlled magnification) [12,18,26,28,35,37,38,47] (Figure 17).

A bottom-up technique is used to build the “IoTr-models/Merkle hash tree” for urban parks and rich-in-trees forests. As a result, each leaf node (Hash-0, Hash-1, Hash-2, and Hash-3 in Figure 17) contains a hash of transactional data, such as the leaf color and texture changes (tree-leaves texturing) and the periodically recorded tree geometry growth (tree dimensions). Furthermore, the hash of its prior hashes is the non-leaf node (Hash-01 and Hash-23 in Figure 17).

• Hash-01 = hash (Hash-0 & Hash-1)
• Hash-23 = hash (Hash-2 & Hash-3)

Finally, since the suggested Merkle hash tree is binary, an even number of leaf nodes is always necessary.

• Notes
  • The cryptographic algorithm SHA256 has been employed for the hashing (hash values Hash-0, Hash-1, Hash-2, Hash-3, Hash-01, and Hash-23).
  • “Block 0” or the “genesis block” is the initial "IoTr-model" block that has been the foundation for the subsequent blocks in the suggested chain. This block, known as the fundamental block, serves as the starting point for the “IoTr-models” thematic blockchain (tree-model NFT tokens ledger).

3.2. Comparative Validation Analysis—Usability Test

This study compared the suggested tree CAD modeling technique with conventional 3D modeling techniques (terrestrial, drone, and close-range photogrammetry, as well as laser scanning techniques) for Above-Ground Biomass/AGB monitoring, using a variety of tree datasets and modeling algorithms [20,70]. AGB range and tree and forest types have an impact on tree modeling and are significant considerations in comparison analysis.

The results show the following fintech adoption functionalities:

(i).

Overestimation for small AGB values (<60 Mg/ha) and underestimation for large AGB values (>400 Mg/ha) are serious issues when using laser scanning, and terrestrial or close-range photogrammetry.

(ii).

Compared to the suggested “IoTr-models” technique, which yields AGB estimations of roughly 97 Mg/ha, drone photogrammetry footage yields more accurate estimates (RMSE values of about 25 Mg/ha).

(iii).

The readily available “Batch command-line programming” method is more flexible (ability to modify the design process and redesign functionality), quicker (time), less expensive (expenditure), and easier to use [11,12,47].

(iv).

When AGB > 600 Mg/he, stratification based on forest types improved AGB estimation by using the proposed “Voice-to-text IoTr-models” technique. Hence, the proposed technique provides new insight into AGB modeling [20].

(v).

The suggested method offers IoT efficiency in real-time, planning intelligence, smart forest monitoring, landscape architecture design, collaborative risk management analytics, and metaverse functionality (data validation) [25,44,47,68,71], and

(vi).

By utilizing an ASCII text editor to change the batch file, the end-user can alter the entire process offline and outside of the CAD environment, with the interpretation batch file at the client level (Figure 13). The change can be made offline (i.e., outside of a CAD platform software), with AI functionality, and without stress or the possibility of a design mishap [8,9,11,19].

3.3. Integrating Financial Theories

The paper does not explicitly discuss financial theories like the relationship between carbon credit valuation and token-based incentives (blockchain tokens). However, integrating such theories into the proposed framework could enhance its applicability in environmental finance and sustainability. Here’s how relevant financial theories could be incorporated [2,3,21,22,28,29,31]:

3.3.1. Carbon Credit Valuation

Theory: Carbon credits are financial instruments representing the reduction of greenhouse gas emissions [63,83,85]. Their valuation depends on market demand, regulatory frameworks, and the environmental impact of the projects generating them.

Application: The blockchain-based “Internet-of-Trees” could tokenize carbon credits by linking tree-model NFTs to measurable carbon sequestration data. Each token could represent a specific amount of carbon offset, enabling transparent and traceable transactions in carbon markets.

3.3.2. Token-Based Incentives

Theory: Blockchain tokens can act as incentives in decentralized systems, encouraging participation and investment. Token economics (or “tokenomics”) involves designing incentives to align user behavior with system goals [54,58,60].

Application: The proposed blockchain framework could issue tokens as rewards for activities like planting trees, maintaining forests, or contributing data to the system. These tokens could be traded or redeemed for financial benefits, creating a direct link between environmental actions and economic incentives.

3.3.3. Behavioral Finance

Theory: Behavioral finance examines how psychological factors influence financial decision-making. Incentives tied to environmental benefits can motivate individuals and organizations to adopt sustainable practices [36,39,51,67].

Application: The blockchain system could leverage gamification and token rewards to engage citizens and businesses, fostering a sense of ownership and responsibility toward forest conservation.

3.3.4. ESG (Environmental, Social, Governance) Integration

Theory: ESG metrics are increasingly used to evaluate the sustainability and ethical impact of investments. Blockchain tokens linked to tree models could serve as ESG assets, attracting green investors and fintech companies [14,23,44].

Application: The “Internet-of-Trees” blockchain could provide verifiable data on tree growth and carbon sequestration, enhancing the credibility of ESG reporting and driving investment in sustainable forestry projects [61,81].

3.3.5. Market Mechanisms

Theory: Market mechanisms like cap-and-trade systems rely on tradable permits or credits to regulate emissions. Blockchain tokens could integrate seamlessly into these systems.

Application: The blockchain framework could facilitate peer-to-peer trading of tree-model NFTs representing carbon credits, reducing transaction costs and increasing market efficiency.

Incorporating these financial theories would strengthen the proposed model’s ability to drive citizen engagement and improve forest outcomes by aligning environmental goals with economic incentives.

4. Discussion

The integration of blockchain enhances data transparency and trust in public and private sector forestry management by providing a secure, decentralized, and tamper-proof system for tracking and validating forestry-related data. Specifically, the following features collectively contribute to a more transparent, secure, and trustworthy system for managing forestry projects and improving citizen involvement in sustainable forestry.

4.1. Behavioral Model with Blockchain Functionalities

• Data Integrity and Confidence: Blockchain ensures cryptographic data security for tree geometry and AGB texture, preventing unauthorized modifications and ensuring reliable data for decision-making.
• Transparency: The blockchain records all transactions, such as changes in tree geometry growth and leaf texture, in a distributed ledger. This allows stakeholders to access and verify data without relying on central authority.
• Collaborative Design and Monitoring: Blockchain facilitates decentralized and autonomous design, enabling multiple stakeholders to share the same data and collaborate effectively in forestry management.
• Smart Contracts: Self-executing agreements between clients and designers can automate processes, reduce human error, and enhance trust in forestry management operations.
• Controlled Scaling: The “Merkle hash tree” structure in blockchain allows for simple and controlled scaling, making it easier to manage large datasets related to forestry.

4.2. AI-Driven Voice-to-Text Technique

The AI-based voice-to-text technique enhances CAD programming in several keyways, outlined as follows:

• Automation and efficiency: The technique converts predefined human oral phrases (e.g., “place point,” “rotate”) into text sequences that are automatically transcribed into CAD commands. This eliminates the need for manual input, speeding up the design process and reducing human error.
• Cost and time savings: By automating CAD programming tasks, the technique reduces the time, resources, and money required for point-by-point design, making it a low-cost solution for tree modeling and landscape architecture.
• Flexibility in design: The vector parametric modeling enabled by text-by-voice commands allows for geometric transformations such as move, rotate, scale, and mirror. This flexibility enhances the functionality and adaptability of CAD designs.
• User-friendly interface: Simple English phrases are used as commands, making the technique accessible to users with varying levels of technical expertise. This lowers the barrier to entry for CAD programming.
• Offline operation: The text-by-voice technique supports offline CAD programming, allowing users to work in a relaxed and error-free environment without requiring constant internet connectivity.
• Integration with blockchain: The technique facilitates the creation of tangible tree-model NFT tokens and their integration into the “Internet-of-Trees” blockchain. This adds value to CAD designs by enabling decentralized and collaborative monitoring.
• Batch command-Line programming: The technique generates personalized key-in commands nested in a batch command ASCII text file. This allows for efficient execution of event-driven routines, further enhancing the scalability and usability of CAD programming.
• Enhanced planning intelligence: The automated process supports real-time planning intelligence, making it suitable for applications like smart forest monitoring, landscape architecture, and collaborative design.

In summary, the text-by-voice technique streamlines CAD programming by automating tasks, improving efficiency, reducing costs, and enabling user-friendly and flexible design processes. It is particularly valuable for applications requiring decentralized collaboration and blockchain integration.

4.3. Research Questions

Three research questions have been outlined in Introduction, and they addressed as follows:

RQ-1: Blockchain ensures data integrity in sustainable forestry through the following mechanisms:

Cryptographic hashing: Blockchain uses cryptographic algorithms like SHA256 to generate unique hash values for each transaction or data entry. These hashes ensure that any alteration to the data is immediately detectable, preserving its integrity.

Immutable ledger: Once data is recorded in the blockchain, it cannot be altered or deleted. This immutability guarantees that forestry data, such as tree geometry growth or leaf texture changes, remains accurate and tamper-proof.

Merkle hash tree structure: The blockchain employs a Merkle hash tree to organize and validate transactions. Each leaf node contains a hash of transactional data (e.g., tree texture or geometry changes), and non-leaf nodes store hashes of their child nodes. This hierarchical structure ensures efficient verification and prevents data corruption.

Decentralization: Blockchain operates on a distributed network, where multiple nodes validate and store data. This decentralization eliminates the risk of a single point of failure and ensures that data integrity is maintained across the network.

Transparency and auditability: All transactions are recorded in a transparent ledger, allowing stakeholders to trace and verify data changes over time. This builds trust and confidence in the accuracy of forestry data.

By combining these features, blockchain provides a robust framework for maintaining the integrity of forestry data, supporting reliable metaverse monitoring, planning, and management.

RQ-2: The Internet-of-Trees blockchain holds significant importance in forestry and landscape management due to the following reasons:

Data integrity and security: The blockchain ensures that tree-related data, such as geometry growth and texture changes, is securely stored and tamper-proof. Cryptographic hashing and the immutable ledger guarantee data accuracy and reliability.

Collaborative monitoring: It enables distributed and collaborative design and monitoring of forests, allowing multiple stakeholders to contribute and validate data in real-time without relying on central authority.

Tangible tree-model NFTs: The blockchain stores tree models as NFT tokens, providing a unique and scalable way to document and track individual trees or forest landscapes. This supports metaverse smart forest monitoring and digital documentation.

Decentralized design and planning: The internet-of-trees blockchain facilitates decentralized and autonomous design processes, enhancing planning intelligence for urban parks, forests, and landscape architecture.

Cost efficiency: By integrating blockchain with CAD modeling, the system offers a low-cost solution for forest monitoring compared to traditional methods like laser scanning or photogrammetry.

Metaverse functionality: The blockchain supports metaverse applications, such as virtual reality-based forest simulations, enabling advanced data validation and immersive experiences.

Environmental impact: It contributes to a greener future by improving forest management, monitoring AGB and supporting sustainable urban transformation.

Overall, the thematic internet-of-trees blockchain is a transformative tool for enhancing metaverse forest monitoring, promoting collaboration, and ensuring data integrity, all while supporting citizen involvement, sustainable forestry, and landscape management.

RQ-3: The proposed method improves forest monitoring and citizen involvement in sustainable forestry through the following ways:

• Forest Metaverse Monitoring Improvements

Real-time data tracking: The blockchain tracks temporary changes in tree geometry and texture using parametric transactions, enabling real-time monitoring of forest dynamics.

Cost efficiency: The method is low-cost compared to traditional techniques like laser scanning or photogrammetry, making it accessible for widespread adoption.

Decentralized monitoring: The Internet-of-Trees blockchain allows distributed and collaborative monitoring, ensuring transparency and reducing reliance on centralized systems.

AGB estimation: The method provides insights into AGB modeling, improving accuracy for forest health assessments and carbon stock calculations. Error-Free Design: The text-by-voice CAD programming ensures offline, error-free modeling, enhancing the reliability of forest data.

• Citizen Involvement in Sustainable Forestry

Accessible technology: The use of smartphone images for tree modeling makes the technology accessible to citizens, encouraging participation in forest documentation and monitoring.

Collaborative design: Citizens can contribute to the blockchain by adding 2.5D tree models as NFT tokens, fostering community engagement in forest management.

Educational opportunities: The method can be used to create open-source simulations for educating citizens about the impact of natural disasters like forest fires, promoting awareness and proactive involvement.

Transparency and trust: Blockchain technology ensures data integrity and transparency, building trust among citizens in forestry initiatives.

Metaverse projects and applications: Virtual reality-based simulations and collaborative platforms enable citizens to interact with forest data, enhancing their understanding and involvement in sustainable practices.

By combining advanced monitoring capabilities with accessible and collaborative tools, the proposed method empowers both professionals and citizens to contribute to sustainable forestry and environmental conservation.

4.4. Outcome

(i).

A simple 2.5D parametric and relative tree CAD modeling methodology has been described for tangible tree-models as NFT tokens that guarantees uniqueness through a hash (metaverse IoTr-models thematic blockchain), enabling distributed design and collaborative metaverse monitoring for trees and smart forests where high tree-modeling accuracy is not required; and

(ii).

A clever, secure, simple, and error-free “Voice-to-text batch command-line programming” tree modeling approach has been implemented, using simple English language phrases as command-line commands for dedicated key-ins hooking and the IoTr-models metaverse thematic blockchain.

Additionally, MDL, a portable off-the-shelf event-driven CAD programming language, has code for hooking commands -simple English phrases- to CAD-domain dependent applications (system key-ins).

(iii).

Ability for citizen involvement in sustainable forestry using off-the-shelf equipment and software like smartphones and metaverse-supported apps.

4.5. Findings

(i)

Experimental data (findings) showed that the proposed strategy performed satisfactorily in terms of tree shape modeling when compared to manual terrestrial laser scanning or drone/terrestrial/close-range photogrammetry approaches.

(ii)

Parallel design, same-data sharing, and coordinated design -also known as decentralized and autonomous design efficiency- are made easier by the proposed technique.

(iii)

A batch file with simple ASCII plain-text commands can serve as an interpretation tool for the IoTr-models thematic blockchain case study, allowing for real-time tree modeling operations in a safe, relaxed, and error-free offline metaverse environment with flexibility in planning, design, and redesign.

4.6. Robustness, Scalability, and Metaverse Applications

The “Blockchain/Merkle hash tree” is a very effective tool that is especially helpful in applications related to digital entrepreneurship and a greener future. It’s simple, controlled scaling and magnification is the technique’s most significant contribution [72,73,74,75,76,77,78,79,80,81]. Additionally, the proposed “tree CAD modeling-blockchain integration” approach could be used to create an open-source web-based simulation that allows students to experience the effects of global natural disasters, such as unplanned, uncontrolled, and unpredictable forest fires [47,50,51,53,54,70].

The proposed AI text-by-voice blockchain model demonstrates significant robustness and scalability for metaverse sustainable forest applications, across varying levels of technological infrastructure, where high tree-modeling accuracy is not required:

• Robustness

Error-free design: The text-by-voice CAD programming ensures offline, error-free modeling, reducing the risk of design mishaps and making it suitable for environments with limited technological reliability.

Blockchain integrity: The use of blockchain technology ensures data immutability, transparency, and security, making the model robust against fraud and tampering.

Adaptability: The method supports decentralized and collaborative design, allowing it to function effectively in diverse operational contexts, including urban parks and remote forests.

Low-cost implementation: The reliance on inexpensive tools like smartphone images and off-the-shelf CAD platforms makes the model accessible and robust for regions with limited financial resources.

• Scalability

Distributed monitoring: The Internet-of-Trees blockchain enables scalable monitoring by allowing multiple stakeholders to contribute and validate data in a decentralized manner.

AI-based voice CAD programming commands: The use of AI-based voice CAD programming commands allows for efficient scaling of tree modeling operations without requiring high-end computational resources.

Controlled magnification: The Merkle hash tree structure in the blockchain supports controlled scaling, making it suitable for applications ranging from small urban parks to large forest landscapes.

IoT integration: The model’s compatibility with IoT devices ensures real-time data collection and analysis, enhancing scalability for advanced forest monitoring systems.

Flexibility across technological levels: The model can operate offline and does not require high-end infrastructure, making it adaptable to regions with varying levels of technological development.

• Metaverse applications

Urban parks: The model can be used for monitoring and designing green spaces in urban areas, ensuring sustainable urban transformation.

Remote forests: Its low-cost and decentralized nature make it suitable for large-scale forest monitoring in remote areas with limited technological infrastructure.

Educational and collaborative platforms: The model supports citizen involvement and educational initiatives, further scaling its impact on sustainable forestry.

In summary, the proposed model is both robust and scalable, capable of adapting to diverse technological environments while supporting sustainable forest applications at various scales.

4.7. CAD Integration

CAD integration improves forest monitoring systems in the following ways:

• Efficient tree modeling: CAD platforms enable the creation of parametric tree models using predefined routines, eliminating the need for costly and time-consuming point-by-point designs. This enhances the efficiency of forest monitoring systems.
• Scalable modeling: CAD integration allows for scalable tree modeling, where tree dimensions, color, and texture can be easily adjusted. This flexibility supports large-scale forest monitoring and planning.
• Automated processes: The use of text-by-voice CAD programming automates the generation of 2.5D tree models and their integration into metaverse monitoring systems, reducing human error and saving time.
• Blockchain integration: CAD platforms facilitate the creation of tangible tree-model NFT tokens, which can be added to a blockchain for secure and decentralized monitoring. This ensures data integrity and collaborative design.
• Visualization and analytics: CAD tools provide advanced visualization capabilities, such as architectural lighting and fly-through functionality, enabling better data analytics, metaverse analysis, and digital documentation of forest landscapes.
• Cost and time efficiency: Compared to traditional methods like terrestrial laser scanning or drone photogrammetry, CAD-Blockchain integration offers a low-cost and time-efficient solution for metaverse forest monitoring.

By combining these features, CAD-Blockchain integration enhances the accuracy, scalability, and efficiency of forest monitoring systems, supporting citizen involvement in sustainable forestry management and planning.

4.8. NFT Tokens Protection

Using NFT tokens in sustainable forestry guarantee uniqueness through a hash and offers several benefits:

• Unique tree identification: NFT tokens provide a unique digital representation of individual trees or forest landscapes, ensuring precise tracking and documentation.
• Data integrity: NFTs are stored on a blockchain, making the data immutable and tamper-proof. This ensures the reliability of tree-related information, such as geometry growth and texture changes.
• Scalability: NFT tokens allow for scalable forest monitoring and management, enabling the tracking of large numbers of trees in a decentralized manner.
• Collaborative design: NFTs facilitate distributed and collaborative forestry management, allowing multiple stakeholders to contribute to and validate data in real-time.
• Cost efficiency: Compared to traditional methods like laser scanning or photogrammetry, NFT-based systems are more cost-effective and time-efficient.
• Integration with blockchain: NFTs enable seamless integration with blockchain technology, supporting smart contracts, decentralized planning, and secure data sharing.
• Metaverse projects and applications: NFT tokens can be used in virtual reality or metaverse environments for immersive forest simulations and advanced planning intelligence.
• Environmental impact: By improving monitoring and management, NFTs contribute to sustainable forestry practices and a greener future.

Overall, NFT tokens enhance the accuracy, security, and efficiency of metaverse forestry management, while supporting innovative applications like blockchain integration and metaverse functionalities.

4.9. Limitations

The proposed blockchain model has several limitations:

Accuracy of tree modeling: The method yields less accurate tree models compared to advanced techniques like laser scanning or drone photogrammetry. This limitation may affect applications requiring high precision in tree geometry and visualization.

Dependency on CAD platforms: The user-defined source code and parametric CAD routines are dependent on specific CAD platforms, which may limit flexibility and compatibility across different software environments.

Limited visualization quality: The tree modeling approach relies on 2D frames and raster images, which may result in poor visibility and lower-quality visualization for applications requiring detailed 3D representations.

Lack of real-time spatial analytics: The current blockchain implementation does not include near-real-time spatial analytic features, which could enhance its utility for GIS-based applications and dynamic forest monitoring [84,85,86].

Binary Merkle hash tree structure: The binary nature of the Merkle hash tree requires an even number of leaf nodes, which may complicate the addition of new transactions or tree models in certain scenarios.

Technological limitations: While the model is low-cost and accessible, it may still require basic technological infrastructure (e.g., smartphones, CAD software) that might not be available in extremely resource-constrained environments.

Limited integration with advanced 3D data: The model does not currently support integration with raw videometry or scanned 3D tree data, which could improve accuracy and scalability for complex forest applications.

Offline operation constraints: Although offline operation is a strength, it may limit real-time collaboration and data sharing in scenarios requiring immediate updates or interactions.

Open-source development: The model is not yet fully open-source, which could restrict widespread adoption and collaborative improvements by the global community.

Educational and research limitations: The model’s reliance on raster images rather than advanced 3D data may limit its use in high-level educational or research applications requiring detailed simulations.

CAD platform dependency: The AI-based voice commands are pointed to user-defined source code (i.e., domain-dependent key-in operations), which is reliant on the CAD platform used.

Effectiveness of its behavioral model validation: The paper does not provide empirical or simulation-based evidence to validate the effectiveness of its behavioral model in changing citizen engagement or forest outcomes. While it discusses the conceptual framework, methodology, and implementation of the proposed blockchain-integrated tree modeling approach, the validation is primarily focused on technical aspects such as usability tests, comparative analysis with traditional methods, and the functionality of the blockchain model.

These limitations highlight areas for improvement, such as enhancing modeling accuracy, integrating advanced 3D data, and incorporating real-time spatial analytics for broader applicability in sustainable forestry.

4.10. Suggestions for Improvements and Further Study

Instead of using raster tree images, future research should examine distributed and collaborative “tree architecture” that is reconstructed from raw videometry (scanned) 3D tree data. Therefore, we require a video-driven tree architecture that uses a versatile and completely customizable template to apply video sequences to 3D model modifications. These days, the point clouds required for 3D tree geometry growth and 3D texture modification can be obtained via terrestrial videometry.

Furthermore, an upgrade (open research topic) is an IoTr-models Blockchain with near-real-time spatial analytic features for a secure and decentralized autonomous GIS. In this case, georeferenced data must be included in the tangible raster tree-image NFTs (ISO/TC 211 series of standards for geoinformation compliance).

The research highlights the potential benefits of the model, such as metaverse collaborative design, smart forest monitoring, and fintech adoption, but does not include specific empirical data or simulations demonstrating its impact on citizen engagement or forest outcomes. Future research could explore these aspects to provide more comprehensive validation.

5. Conclusions

The paper presents a low-cost, innovative approach integrating CAD modeling and blockchain technology to enhance metaverse smart forest monitoring and collaborative landscape design.

As main paper’s contribution is regarded the integration of AI-based voice interaction for 2.5D tree geometry creation with blockchain for creating an NFT token that guarantees protection and uniqueness through a hash, and behavioral data that can significantly improve citizen involvement in sustainable forestry and support metaverse projects (e.g., smart forest, pocket parks, landscape architecture, gaming) where high tree-modeling accuracy is not required. Additional contributions include the creation of tangible tree-model NFTs for the “Internet-of-Trees” blockchain, enabling decentralized design, data integrity, and efficient forest management.

The method demonstrates advantages in cost, scalability, and flexibility compared to traditional techniques, while supporting sustainable forestry and urban planning. Overall, the framework offers significant potential for advancing metaverse digital landscape architecture, environmental conservation, and citizen involvement in sustainable forestry.

Limitations such as modeling accuracy and visualization quality highlight areas for future research, including the integration of advanced 3D data and real-time spatial analytics.