Tree Architecture & Blockchain Integration: An off-the-shelf Experimental Approach

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 featurebased 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 offthe-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.