Tree Architecture & Blockchain Integration:
An off-the-shelf Experimental Approach
DIMITRIOS VARVERIS1, ATHANASIOS STYLIADIS1*, PANTELEIMON XOFIS1,
LEVENTE DIMEN2
1Department of Forest & Natural Environment Sciences,
International Hellenic University,
14th km Thessaloniki, Nea Moudania 570 01, Thessaloniki,
GREECE
2Faculty of Informatics and Engineering
“1 Decembrie 1918” University of Alba Iulia,
15-17 Uniri Street, Alba Iulia 510009, Alba Iulia,
ROMANIA
*Corresponding Author
Abstract: - Temporally sensitive tree modeling and urban park spatially explicit simulation offer advantages to
large-scale landscape planning and design, especially in the context of smart applications for virtual parks and
forests, while Blockchain technology provides collaborative engineering, data integrity, and information
confidence. A proof-of-concept 2.5D tree architecture and Blockchain integration technique (distributed
Internet-of-Trees images, “IoTr-images”) was presented as a low-cost metaverse case study that affects the
forest monitoring and digital landscape architecture design infrastructures. At the core of the proposed feature-
based parametric modeling methodology is a 2.5D tree CAD model composed of two perpendicular 2D tree
frames on which recorded tree texture has been assigned. A “Batch command-line programming” technique has
been implemented, as a user-defined routine at the top of a commercial CAD platform, to describe the proposed
off-the-self method and to create tangible tree-image NFT tokens (Internet-of-Trees-images Blockchain). As
important findings were recorded, the add-in planning intelligence, the superior data integrity, and confidence,
the offline relaxed error-free CAD design, and the superiority in terms of time and cost compared to traditional
3D tree modeling methods (laser scanning, close-range photogrammetry, etc.); as well as the satisfactory tree
modeling accuracy for smart forest monitoring and landscape architecture applications. The proposed 2.5D
parametric tree model added new value to the CAD-Blockchain integration industry because a plain
“Blockchain/Merkle hash tree” tracks tree geometry growth and texture change temporarily with simple
parametric transactions (i.e. controlled hash tree magnification/scaling). So, metaverse functionality
(decentralized, autonomous, coordinated, and parallel design; same-data sharing; data validation), modification
and redesign ability, and planning intelligence are effectively supported by the proposed technique. Main
contributions are regarded as the ability for smart forest distributed surveillance and collaborative parallel
landscape architecture design, open-source Web-based educational simulations, as well as the potential for off-
the-shelf contractual collaborative frameworks (smart contracts between designers and clients). Stratification
based on forest types improved above-ground biomass (AGB) estimation, especially when AGB was greater
than 500 Mg/ha, using the proposed “IoTr-images” technique. So, this research provides new insight into AGB
modeling and monitoring. Finally, the proposed method’s robustness has been validated by performance
evaluation testing.
Key-Words: - Environmental modeling, geodesign, tree modeling architecture, distributed and collaborative
CAD, smart forest monitoring, landscape architecture, AGB, Metaverse, Blockchain
functionalities.
Received: April 29, 2023. Revised: July 17, 2023. Accepted: September 12, 2023. Published: October 10, 2023.
1 Introduction
This is a descriptive paper on a complex process
regarding tree architecture and Blockchain. Here,
we report the results of an experiential approach to
applying spatially explicit tree modeling and
environmental simulation to forests and urban parks
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Dimitrios Varveris, Athanasios Styliadis,
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monitoring in the design and planning process. The
length of this paper does not allow for an in-depth
discussion about the specifics of our approach.
Instead, we focus on the application of the results in
distributed and collaborative design and planning
with metaverse Blockchain functionality.
3D tree modeling from mobile laser scanning
and terrestrial close-range photogrammetry
techniques provides the necessary data for tree
architecture, forest and park monitoring, and urban
transformations. In, [1], the authors declare that an
adaptive urban transformation needs tree and flora
visualization data, and according to, [2], tree
geometry is necessary for spatial GIS analysis
regarding urban parks. According to, [3],
Blockchain in distributed CAD environments
supports data integrity, distributed, collaborative,
and validated digital design, and information
confidence, and as stated by, [4], a distributed
digital design needs a CAD-Blockchain integration
strategy. This literature approaches the “Trees/Flora
Forest/Urban parks collaborative
design/Blockchain” problem in detail but without
describing a low-cost integration solution. Similarly,
in, [5], the authors discuss data for structural
monitoring in asynchronous communication, while
in, [6], the authors discuss the consequences of
Blockchain technology on the building information
modeling (BIM) process but without reference to
trees as the basic representation element of an
urban, green-oriented environment.
According to the current study, monitoring in
nearly real-time 3D tree geometry growth and
tracking tree texture change temporarily is an ideal
off-the-shelf distributed and collaborative
environment but means time, cost, and an
exhausting “Blockchain/Merkle hash tree”. In, [7],
the authors describe in detail a method for linking
image-based metrics to 3D model-based ones for the
assessment of visual landscape quality, but no
information is given about a potential Blockchain
integration. Finally, according to, [8], decision-
support models, for sustainable urban investment
optimization, could be useful in urban
transformation processes, but no implementation
details for collaborative and distributed
environments are given.
The proposed technique uses batch commands
and event-driven routines (design level/layer, color,
weight, style, reference point, height, and width) for
2.5D parametric tree CAD modeling relative to a
ground reference point (GRP) for tree CAD
geometry deployment (geo-referenced tree-CAD
frame’s). Thus, with the proposed technique we
save time, greatly reduce costs, and achieve the
operation and maintenance of a simple and
functional Blockchain with all that this implies at
the level of distributed and collaborative design.
Certainly, the proposed technique produces less
accurate tree models, but this disadvantage does not
affect smart forest or landscape architecture
applications, [1], [2].
For the proposed technique the research
questions are described as follows: Describe
parametric and relative 2.5D easy-to-design tree
CAD modeling methodology based on simple tree
images (e.g. jpg format); Design Blockchain data
structures for tangible tree-image and tree-model
NFTs respectively; and Code batch command-line
programming for hooking user-defined commands
(simple English phrases) to CAD domain-dependent
software routines (system key-ins). The commands
refer to both 2.5D tree modeling, as well as building
and maintaining the Blockchain structure, [9].
The article’s main aim is to develop an
experimental technique for integrating tree images
in mutually perpendicular 2D CAD frames and
connecting them into a dedicated “Internet-of-Tree”
Blockchain. So, after describing the general research
questions, this article’s specific research objectives
are defined as (1) The implementation details of the
tree CAD modeling methodology; (2) The
implementation details of the Metaverse/Blockchain
“tree display file” structure/metadata, in DXF
ASCII format, for tangible tree-image and tree-
model NFT tokens; (3) The smart and user-friendly
“Batch command-line programming”
implementation technique with command-line
commands (ASCII text) hooked to CAD-domain
dependent (Bentley’s MicroStation CAD platform
was selected) KEY-Ins; and (4) The implementation
of the “Internet-of-Tree” Blockchain app case study.
The 2.5D models are decentralized among
several nodes that hold identical information, and at
the same time, none (designer or client) holds the
complete authority (digitally distributed consensus).
This enables transparency of design activity and
enhancement of 2.5D data security.
Novelties: (i) The proposed 2.5D tree model added
new value to the CAD-Blockchain integration
industry, thanks to a very plain “Merkle hash Tree”
that tracks tree geometry growth and texture change
temporarily with simple parametric transactions. So,
smart forest surveillance; decentralized,
autonomous, coordinated and parallel design; same-
data sharing; tree modeling data validation; design
files transaction management with analytics
functionality; and contractual frameworks (e.g.
smart and performance-based contracts) are
effectively supported; (ii) Smart forest add-in app
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with simple Blockchain transactions (tracking tree
growth in the course of time); (iii) The innovative
2.5D tree CAD modeling Blockchain
integration” methodology for parametric, relative,
and untagged geo-referenced tree modeling; (iv)
The smart “Batch command-line programming”
implementation technique, associated with key-ins
(domain-dependent dedicated CAD software tools)
for 2.5D generic and parametric tree modeling by
simply writing -in an online text editor (Notepad)-
commands with phrases from the English language
(plain ASCII text data files for job control); and (v)
The “tree display file” Blockchain structure storing
the metadata of a DXF/ASCII representation of
generic tangible tree-image NFTs, [7], [9].
The particular significance of this study lies in
the plain “Blockchain/Merkle hash tree” structure
supporting distributed and collaborative tree
architecture for monitoring landscape architecture,
smart forest, and digital documentation apps where
there are no requirements on the accuracy of the tree
representation, [10], [11], [12].
The rest of the paper is organized as follows. In
Section 2 (“Method and Technique”) the proposed
low-cost 2.5D tree CAD modeling Blockchain
integration method and the Batch command-line
programming implementation technique are
introduced. In Section 3 (“Results”) the outline
design of an “Internet-of-Tree images” Blockchain
case study (experimental approach) is presented
followed by a comparative validation analysis as a
“IoTr-images” usability test. Finally, in Section 4
(“Discussion and Conclusion”) the results, major
findings, article’s main significance and
contributions, as well as spotted limitations and
suggestions for improvements and further study are
discussed, followed by concluded remarks.
2 Method and Technique
Parametric modeling lets designers modify the
entire shape of the design at once, not just individual
dimensions one at a time. Also, a feature-based
parametric modeling CAD software design tool
saves time as it eliminates the need for a design
engineer to constantly redraw a design every time
one of the design’s dimensions changes.
MicroStation, AutoCAD, Pro/ENGINEER, and
SolidWorks offer direct modeling CAD platforms
on top of existing feature-based parametric
modeling, [12].
2.1 2.5D Tree CAD Modeling Blockchain
Integration Method
For the so-called 2.5D generic tree modeling, the
relative design of two rectangular tree-frames,
perpendicular to each other, starting with a GRP
with coordinates 0,0,0 is preceded (geo-referenced
functionality for relative to ground reference point
modeling deployment) (Figure 1).
Next is the assignment of low-cost tree images
(e.g. smartphone’s jpg images) in these frames
(Figure 2), and the storage of this generic tree
format as a 2.5D tree CAD model (at a local hard
disk), and as a tangible tree image NFT token as
well (“Internet-of-Trees image”/IoTr-images chain
at the metaverse cloud). For the implementation of
the 2.5D tree CAD modeling Blockchain
integration” method, a smart Batch command-line
programming technique is used, [1], [9], [12], [13],
[14].
In more detail, the proposed 2.5D tree CAD
modeling procedure takes as parameters the CAD
current drawing level (LV), frame’s line color (CO),
line weight (WT), line style (LC), frame’s geo-
referencing point coordinates (GRP), and tree’s
height (H) and width (W), and is developed as
follows:
(a) The tree-frame’s CAD parameters LV, CO, WT,
and LC are predefined.
(b) In the CAD platform (FRONT view) a tree
frame is designed according to the tree’s height
parameter (H) and for a generic geo-referencing,
the GRP is assigned to coordinates 0,0,0 (CAD
universe space) (Figure 1, bottom-left window).
(c) In the CAD platform (RIGHT view) another
tree-frame is designed according to the tree’s
width parameter (W) (Figure 1, bottom-right
window).
(d) The last tree frame is relocated in such a way
that both frames cross each other
perpendicularly (Figure 1, top-left window).
In this way, a compound tree frame is designed
starting from the GRP (0,0,0) (Figure 1, top-right
window).
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Fig. 1: The two orthogonal tree frames cantered
perpendicularly (FRONT and RIGHT view)
(e) A tree’s scalable raster image (jpg), acquired
from (i) the CAD platform’s system palette with
flora images (e.g. “Flora.pal”), or (ii) the user’s
noise-free photography (e.g. a smartphone jpg
image), or (iii) the proposed IoTr-images
ecosystem, is assigned to both tree-frames (as
the unique geometry located in level LV and
colored with color CO), utilizing a user-defined
GUI’s dialog setting box, according to the
predefined level (LV) and color (CO)
parameters (Figure 2).
Fig. 2: Tree texture image assignment to FRONT
and RIGHT view tree-frames.
In this way, the modeling is referred to as “2.5D
tree CAD modeling” and the visualization accuracy
is adequate, sufficient, and satisfactory for digital
documentation and visualization purposes regarding
tree landscapes, forests, monument landscapes rich
in trees, and landscape architecture applications.
Figure 3 presents a 2.5D tree CAD model
(ISOMETRIC view). The modeling accuracy is
adequate for projects and applications without
special tree-shape accuracy and visualization
requirements.
Fig. 3: A 2.5D tree CAD model.
2.2 CAD Software (Event-Driven
Procedures & Key-Ins)
The parametric modeling routines assigned to Icon
tools (user-defined event-driven PROCEDURES)
were implemented in MDL (MicroStation
Development Library). MDL code can be compiled
using Microsoft Visual C++ as a native-code DLL.
This both enhances programmer productivity with
C++ object-oriented concepts and provides better
performance. The command-line BATCH
COMMANDS were implemented as plain text in an
online text editor (Notepad). For batch job control,
these commands are organized in batch files and use
phrases from the English language (plain ASCII text
data). The CAD platform MicroStation was used as
the hosting software environment for the event-
driven procedures and the command-line
programming of the proposed framework.
MicroStation® is Bentley Systems' CAD product
and one of its many strengths is its adaptability.
Inherent to that adaptability are tools to customize
and extend MicroStation.
User customization and task-specific tools:
MicroStation lets an administrator modify its user
interface and create custom menus, palettes
(toolboxes), and icon tools (icon buttons) that
provide a fast track to commands and functions used
frequently. The icon tools are grouped into
modeling thematic palettes, and they are assigned to
event-driven procedures (user-defined CAD s/w) or
key-ins (CAD system s/w / Bentley’s propriety
MDL source code). In the presented research,
several event-driven procedures were written in
MDL/C++ as .mc source code, linked to appropriate
libraries, compiled to new .ma executable routines
(dedicated to smart tree landscape framework user-
defined procedures) and then they are grouped into
the “TREE.ma” exe file.
Figure 4 presents the development process for
the graphic representation of the new icon tool
“PLACE Tree Frame”. The modeling duty of this
icon tool is to design a rectangular parametric and
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relative tree frame in any CAD view (Front, Right,
Left, etc.).
Fig. 4: The “PLACE Tree Frame” icon tool:
Graphic design and assignment to user-defined key-
in “PLACE Parametric Relative Tree Frame”.
The “PLACE Tree Frame” icon tool is assigned
to a dedicated user-defined event-driven procedure
embedded into the new 2.5DTREE.ma MDL
application, and has been grouped, as the 3rd tool on
the 2nd row, into the thematic palette “Icon Tools
for 2.5D TREE” (menu: Smart Tree Landscapes
(Customized GUI technique) / Trees / 2.5D tree
CAD geometry/setup FRONT Frame) and on a
“left-button” event (cursor hit activation) the
dedicated user-defined software procedure “PLACE
Parametric Relative Tree Frame” is called (Figure
5).
Fig. 5: The pull-down menus: “Smart Tree
Landscapes (Customized GUI)”, Setup NFT
Wallet”, “create NFT”, and “Add NFT” for adding a
tangible tree-image NFT to MetaMak WAX
Blockchain.
2.3 Batch Command-Line Programming
Technique
At the heart of the batch command-line
programming implementation technique is a
NotePad or WordPad ASCII text file located outside
of the CAD platform, e.g., the batch command file
TreeModeling.bat located at the local Hard Disk
(Figure 6). The 2.5D tree CAD modeling
Blockchain integration” methodology is performed
with a batch top-down job control (Figure 6).
So, initially, a design file segmentation is
performed by allocating discrete tree model parts to
design session layers/levels (e.g. in our experiment
level 22 was chosen to host the GRP and level 11 to
host the tree CAD model). With that
“segmentation”, we gain design independence,
modification functionality, and redesign flexibility,
because by withdrawing some levels we can
denominate a design unit and focus on it. Following,
the ground reference point and the tree modeling
settings are defined by level, line color, line weight,
and the 0,0,0 coordinates for the GRP.
Subsequently, in segmentation level (LV) 11,
with line color (CO) 0, line weight (WT) 1, and line
style (LC) 0, the 2.5D CAD geometry of the tree,
the tree imagery and texture assignment, and the
tangible tree-image NFT (cloud wallet, token,
“IoTr-images” Blockchain) were deployed step by
step (Figure 6).
Fig. 6: The batch command file TreeModeling.bat
(ASCII text file with command-line COMMANDS).
The TreeModeling.bat batch file can be executed
from a CAD software platform prompt (key-in
dialog box) by typing a link string. E.g.
@c://PhD-Research/Smart-TreeLandscapes/TreeModeling.bat
The introduced 2.5D tree CAD modeling
Blockchain integration” method, implemented with
the Batch command-line programming” technique,
is a smart and distributed CAD modeling
procedure because operates in near real-time with
planning intelligence (modeling and design process
modification ability in near real-time) and Internet
of Things (IoT) metaverse efficiency. Also, it is
flexible because it is performed offline in a friendly
safety and relaxed way, with simple phrases from
the English language as ASCII CAD-platform-
offline coding “Commands” hooked to domain-
dependent key-in routines.
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3 Results
For the demonstration of the introduced distributed
and collaborative smart tree landscape framework
(method and implementation technique), an
“Internet-of-Tree images Blockchain outline
design, for an urban landscape architecture design
project, is presented in Fig 7. Follows a comparative
analysis’s usability test for validation purposes.
3.1 “Internet-of-Tree images” Blockchain:
The Outline Design
The outline design of the “Internet-of-Tree images
Blockchain case study (metaverse application for an
urban park rich in trees) is displayed in Figure 7.
Each tree, as a block in the IoTr-images chain, is
referred to by a hash value created by the SHA256
cryptographic algorithm. Hence, the “IoTr-images”
Blockchain is composed of a linked list of blocks of
transactions (tree-frame dimensions and raster
image data in autonomous collaborative design)
tracking tree geometry growth and AGB texture
change over time, [3], [4]. The “Root of Hash Tree”
points to a “Merkle hash tree” chain of transactions,
[6].
It is important to know that, in the discussed
Blockchain the “Merkle hash tree” is relatively
compact in its extension (small-scale Blockchain
functionality), plain, and easily manageable
(controlled magnification), [4], [6], [13], [15], [16]
(Figure 7).
The jpg tree-photography “Tree-image.0”,
“Tree-image.1”, “Tree-image.2”, and “Tree-
image.3” together with relative tree geometry
measures, are regarded as tree texture and tree
geometry “Data Blocks” respectively (i.e.
transactions for the “IoTr-images” Blockchain).
Fig. 7: The proposed “Blockchain/Merkle hash
tree” (The Root of Hash Tree is pointing to a
“Merkle hash tree” chain of transactions).
The “IoTr-images/Merkle hash tree”, for the
rich-in-trees urban park, is constructed with a
bottom-up approach. Hence, every leaf node (the
Hash-0, Hash-1, Hash-2, and Hash-3 in Figure 7) is
a hash of transactional data, i.e. the periodically
recorded tree geometry growth (tree dimensions),
and the AGB texture change (tree images), and the
non-leaf node (the Hash-01 and Hash-23 in Figure
7) is a hash of its previous hashes. The proposed
Merkle hash tree is a binary one, so it always
requires an even number of leaf nodes.
Hash-01 = hash (Hash-0 & Hash-1)
Hash-23 = hash (Hash-2 & Hash-3)
Hash-0 = hash (tree-geometry.0 & tree-image.0)
Hash-1 = hash (tree-geometry.1 & tree-image.1)
Hash-2 = hash (tree-geometry.2 & tree-image.2)
Hash-3 = hash (tree-geometry.3 & tree-image.3)
Notes
1. For the hashing (hash values Hash-0, Hash-1,
Hash-2, Hash-3, Hash-01, and Hash-23) the hash
function SHA256 (cryptographic algorithm) has
been used.
2. The very first “IoTr-image” block upon which
additional blocks, in the proposed chain, have been
added is called block 0 or genesis block”. This
block represents the starting point of the “IoTr-
images” Blockchain (ledger). It is hardcoded into
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the Blockchain/Bitcoin software as the so-called
foundational block.
3. On January 3, 2009, pseudonymous Bitcoin
creator “Satoshi Nakamoto” mined the genesis
Bitcoin, which led to the mining of the first 50
Bitcoins.
4. The reference to the genesis Bitcoin is made
because, in self-executed agreements (smart
contracts under the proposed “IoTr-images”
Blockchain) between management (as urban
planning project managers), landscape architects (as
designers), and local authorities (as clients), the
payments decided to be made with Bitcoin (BTC).
3.2 Comparative Validation Analysis
(“IoTr-images” usability test)
This research conducted a comparative analysis of
different tree datasets and modeling algorithms,
between the proposed 2.5D tree modeling technique
and traditional 3D modeling methods (laser
scanning, terrestrial close-range photogrammetry for
Above-Ground Biomass/AGB monitoring), [16].
Tree and forest type, as well as AGB range,
influence tree modeling and they are important
factors in comparative analysis.
The results show the following:
(i) Laser scanning imagery provides more accurate
AGB estimates (RMSE values in about 28 Mg/ha)
than the proposed “IoTr-images” technique (about
95 Mg/ha).
(ii) Overestimation for small AGB values (<50
Mg/ha) and underestimation for large AGB values
(>300 Mg/ha) are major problems when using
terrestrial close-range photogrammetry.
(iii) Stratification based on forest types improved
AGB estimation, when AGB>500 Mg/ha, using the
proposed “IoTr-images” technique. So, this research
provides new insight into AGB modeling, [17].
(iv) The off-the-shelf “Batch command-line
programming” technique is cheaper (expenditure),
faster (time), user-friendly, and more flexible
(design process modification ability and redesign
functionality), [14].
(v) The proposed technique provides metaverse
functionality (data validation), collaborative risk
management analytics, planning intelligence (smart
forest monitoring, landscape architecture design),
and IoT efficiency in (nearly) real-time, [15], and
(vi) The end-user, with the interpretation batch file
at a client level, can modify the offline and outside
of the CAD environment the whole process by using
a NotePad for editing the batch file (Fig. 6). The
modification was performed with AI functionality,
in a simple way (text editor), offline (CAD
platform), and without stress/risk of a design
accident, [10], [12].
4 Discussion and Conclusion
Results: (a) A simple 2.5D parametric and relative
tree CAD modeling methodology has been
described for tangible tree images as NFT tokens
(IoTr-images Blockchain) facilitating trees and
smart forests distributed design and collaborative
monitoring; and
(b) A smart, safe, relaxed, and error-free “Batch
command-line programming tree modeling
approach has been implemented, with simple
English language phrases as command-line
commands for dedicated key-ins hooking, and the
IoTr-images Metaverse/Blockchain.
Also, code has been written in MDL, an event-
driven CAD programming language, for hooking
Commands (simple English phrases) to CAD-
domain dependent s/w (system Key-ins).
Findings: (i) Experimental data (findings) proved
the satisfactory time performance, in tree shape
modeling, of the proposed technique compared to
manual terrestrial laser scanning and close-range
photogrammetry methods.
(ii) A batch file, as an interpretation tool for the
IoTr-images Blockchain case study, with simple
ASCII plain-text commands, can support near real-
time 2.5D tree modeling operations in a safe,
relaxed, and error-free offline environment, with
redesign flexibility, planning, and design
functionality; and
(iii) The proposed technique supports coordinated
design, same-data sharing, and parallel design (i.e.
characterized by decentralized and autonomous
design efficiency).
Significance, contributions, and limitations: The
simple, plain, controlled magnification/scaling
“Blockchain/Merkle hash tree” is considered the
most important significance of the technique. Also,
the proposed 2.5D tree CAD modeling -
Blockchain integration” method could be studied as
an open-source, web-based simulation, that provides
students with virtual experiences of the impact of
global natural disasters, such as unplanned,
uncontrolled, and unpredictable forest fires, [18],
[19].
Also, the most important contributions are
considered the ability for low-cost smart forest
monitoring and collaborative landscape architecture
design, the superior data integrity and confidence
(cryptographic data for tree geometry and AGB
texture), as well as the potential for contractual
frameworks for self-executing agreements (smart
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contracts) between manager, designers, and clients
to automate and control design and AGB
monitoring, and to document and validate contract
transactions in a secure, low-cost, and transparent
manner without the need for superintendence by a
central authority (i.e. track the design process and
verify the tree and AGB data authenticity and
ownership, create and manage decentralized AGB
identity and authorization, verify ownership of AGB
data as a digital asset, and support applications that
run at the top of the “IoTr-images” decentralized
Blockchain environment). An obvious limitation of
the proposed “Batch command-line programming”
technique is the poor tree modeling accuracy and
visualization. Also, a limitation regards the CAD
platform dependency of the user-defined source
code (domain-dependent key-in procedures).
However, feature-based parametric CAD routine
adaptation, for compatibility reasons, is not a major
problem.
Suggestions for improvements and further study:
An improvement (open research issue) is an IoTr-
images Blockchain with spatial analysis
functionalities in near real-time for a secure and
decentralized autonomous GIS. For this case, we
need to incorporate georeferenced data into the
tangible raster tree-image NFTs (ISO/TC 211 series
of standards for geoinformation compliance).
Also, future research should study distributed and
collaborative “tree architecturereconstructed from
rough videometry (scanned) 3D tree data instead of
raster tree images. So, we need a video-driven tree
architecture that implements video sequences to 3D
model transformations using a flexible and fully
configurable template. Nowadays, terrestrial
videometry can provide the necessary point clouds
for 3D tree geometry growth and 3D texture change.
Conclusion. The described conceptual case study
(method and implementation technique) is a low-
cost metaverse application that supports smart forest
monitoring, coordinated and parallel design, plain
and controlled hash tree scaling, same-data sharing,
and a trustworthy collaborative design process
(digitally distributed consensus); enabling
coordinated activity, transparency, data security
enhancement, and contractual framework
functionality for smart contracts (self-executed
agreements between designers and clients).
Acknowledgement:
We would like to acknowledge the support of the
Department of Forest & Natural Environment
Sciences, International Hellenic University
(Greece).
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed to the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare.
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(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2023.19.91
Dimitrios Varveris, Athanasios Styliadis,
Panteleimon Xofis, Levente Dimen
E-ISSN: 2224-3496
977
Volume 19, 2023