Blockchain Papers

Jüri Sildam

The EU Deforestation Regulation (EUDR), Regulation (EU) 2023/1115, requires operators and traders placing cattle, cocoa, coffee, palm oil, soya, wood, rubber, charcoal and their derived products on the EU market to demonstrate that the underlying commodities are deforestation-free after 31 December 2020, are legally produced, and are covered by a due diligence statement. While deforestation detection is often treated as the technically dominant requirement, EUDR compliance is broader: it also requires traceability across supply chains, provenance of production, country-specific legal context, and auditable due diligence processes. Translating these obligations into an inspectable, reproducible, software-supported workflow raises three coupled problems: (i) the regulation itself is a moving artefact whose definitions, country risk classifications and implementing acts evolve; (ii) the geospatial evidence used to satisfy Article 3(a) depends on upstream datasets-primarily the Hansen Global Forest Change product-whose versions, tile schemes and methodological conventions also change; and (iii) the resulting compliance interpretations cannot be ethically delegated to a fully autonomous agent because they affect market access, livelihoods and the legal exposure of operators. This paper describes an architecture that addresses these problems jointly through a closed feedback loop linking regulation, data dependencies, implementation, validation, and governance. We separate authoritative deterministic generation of evidence from a public, non-authoritative Digital Twin portal that exposes system state, dependencies, and example outputs for inspection. A procedural Decentralized Autonomous Organization (DAO)implemented in this work as a file-grounded YAML proposal workflow, but compatible with optional blockchain anchoring of evidence digests and proposal records-closes the governance loop between stakeholders, developers, and evolving regulatory interpretation. The procedural design is deliberate: as we discuss below, the kind of DAO appropriate for governing truth claims about the physical world differs in object, voting subject and failure mode from the protocol-governance DAOs commonly associated with the term, and the choice to run the governance layer off-chain reflects that difference rather than a rejection of distributed-ledger technology as such. An LLM-based Digital Twin Engineer (DTE) agent supports inspection and proposal drafting under strict grounding rules, but never executes code or makes compliance determinations. We describe the multi-repository implementation, the deterministic evidence bundle contract, the public/private trust-zone separation that protects per-operator plot data while allowing example reports to be inspected publicly, and the regulation-as-dependency feedback loop that forces reruns of impacted methods when upstream artefacts change. Although the current implementation focuses primarily on geospatial deforestation evidence, the proposed pipeline is intended as a practical starting point for progressive enrichment as new forms of land intelligence, supply-chain transparency, legal provenance data, and business-network evidence become available. We argue that this design is a generalisable pattern for compliance domains in which regulatory requirements evolve over time and implementations must remain inspectable, reproducible, extensible, and corrigible by humans.

Nophea Sasaki, Issei Abe

Meeting global forest restoration targets by 2030 requires a transition from labor-intensive and opaque practices to scalable, intelligent, and verifiable systems. This paper introduces a cyber–physical digital twin architecture for forest restoration, structured across four layers: (i) a Physical Layer with drones and IoT-enabled sensors for in situ environmental monitoring; (ii) a Data Layer for secure and structured transmission of spatiotemporal data; (iii) an Intelligence Layer applying AI-driven modeling, simulation, and predictive analytics to forecast biomass, biodiversity, and risk; and (iv) an Application Layer providing stakeholder dashboards, milestone-based smart contracts, and automated climate finance flows. Evidence from Dronecoria, Flash Forest, and AirSeed Technologies shows that digital twins can reduce per-tree planting costs from USD 2.00–3.75 to USD 0.11–1.08, while enhancing accuracy, scalability, and community participation. The paper further outlines policy directions for integrating digital MRV systems into the Enhanced Transparency Framework (ETF) and Article 5 of the Paris Agreement. By embedding simulation, automation, and participatory finance into a unified ecosystem, digital twins offer a resilient, interoperable, and climate-aligned pathway for next-generation forest restoration.

Dimitrios Varveris, Athanasios D. Styliadis, Panteleimon Xofis, Levente Dimén

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.

Luca Buonocore, Jim Yates, Riccardo Valentini

The increasing importance of forest ecosystems for human society and planetary health is widely recognized, and the advancement of data collection technologies enables new and integrated ways for forest ecosystems monitoring. Therefore, the target of this paper is to propose a framework to design a forest digital twin (FDT) that, by integrating different state variables at both tree and forest levels, creates a virtual copy of the forest. The integration of these data sets could be used for scientific purposes, for reporting the health status of forests, and ultimately for implementing sustainable forest management practices on the basis of the use cases that a specific implementation of the framework would underpin. Achieving such outcomes requires the twinning of single trees as a core element of the FDT by recording the physical and biotic state variables of the tree and of the near environment via real–virtual digital sockets. Following a nested approach, the twinned trees and the related physical and physiological processes are then part of a broader twinning of the entire forest realized by capturing data at forest scale from sources such as remote sensing technologies and flux towers. Ultimately, to unlock the economic value of forest ecosystem services, the FDT should implement a distributed ledger-based on blockchain and smart contracts to ensure the highest transparency, reliability, and thoroughness of the data and the related transactions and to sharpen forest risk management with the final goal to improve the capital flow towards sustainable practices of forest management.

Muhammad Zawish, Nouman Ashraf, Rafay Iqbal Ansari, Steven Davy · 7 authors

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality of service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI, and blockchain for agricultural supply chain management with the purpose of ensuring traceability and transparency, tracking inventories, and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAVs, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G-enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements.

Luc Girod, Christopher Nuth, Andreas Kääb, Bernd Etzelmüller · 5 authors

Abstract. Acquiring data to analyse change in topography is often a costly endeavour requiring either extensive, potentially risky, fieldwork and/or expensive equipment or commercial data. Bringing the cost down while keeping the precision and accuracy has been a focus in geoscience in recent years. Structure from motion (SfM) photogrammetric techniques are emerging as powerful tools for surveying, with modern algorithm and large computing power allowing for the production of accurate and detailed data from low-cost, informal surveys. The high spatial and temporal resolution permits the monitoring of geomorphological features undergoing relatively rapid change, such as glaciers, moraines, or landslides. We present a method that takes advantage of light-transport flights conducting other missions to opportunistically collect imagery for geomorphological analysis. We test and validate an approach in which we attach a consumer-grade camera and a simple code-based Global Navigation Satellite System (GNSS) receiver to a helicopter to collect data when the flight path covers an area of interest. Our method is based and builds upon Welty et al. (2013), showing the ability to link GNSS data to images without a complex physical or electronic link, even with imprecise camera clocks and irregular time lapses. As a proof of concept, we conducted two test surveys, in September 2014 and 2015, over the glacier Midtre Lovénbreen and its forefield, in northwestern Svalbard. We were able to derive elevation change estimates comparable to in situ mass balance stake measurements. The accuracy and precision of our DEMs allow detection and analysis of a number of processes in the proglacial area, including the presence of thermokarst and the evolution of water channels.