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Distributed and Collaborative Tree Architecture: A Low-cost Experimental Approach for Smart Forest Monitoring.

Title: Distributed and Collaborative Tree Architecture: A Low-cost Experimental Approach for Smart Forest Monitoring.
Authors: VARVERIS, Dimitrios; STYLIADIS, Athanasios; XOFIS, Panteleimon; DIMEN, Levente
Source: Baltic Journal of Modern Computing; 2023, Vol. 11 Issue 4, p653-685, 33p
Subject Terms: FOREST monitoring; BIOMASS estimation; LANDSCAPE architecture; LANDSCAPE design; ECOSYSTEM management; ARCHITECTURAL design; OPTICAL scanners; BIOMASS conversion
Abstract: Distributed and collaborative CAD recently gained great attention as a digital geodesign information exchange and management ecosystem, operating in a reliable, transparent, and secure way (CAD-Blockchain integration). Temporal-sensitive tree simulation, in collaborative design environments, offers advantages in the context of smart planning for virtual parks and forests, while DLT/Blockchain technology significantly reduces overhead and tree-modeling transaction costs, eliminates the need for third parties or middlemen to verify transactions, and provides data integrity and information confidence. A proof-of-concept 2.5D tree architecture and Blockchain integration technique was presented as a low-cost distributed and collaborative engineering case study that affects the digital landscape architecture design and monitoring infrastructure. As important findings were recorded, the add-in planning intelligence, the superior data integrity, and confidence, the superiority in terms of time and cost, as well as the satisfactory tree modeling accuracy for smart forest and landscape architecture applications, compared to traditional 3D tree modeling methods (laser scanning, close-range photogrammetry, etc.). The tree-model engineering added new value to the CAD-Blockchain integration industry because a simple and scalable "Blockchain/Merkle hash tree" temporarily tracks tree-geometry-growth and texture change with simple parametric transactions. Hence, decentralized, autonomous, coordinated, and parallel design; same-data sharing; data validation; smart forest distributed surveillance; and contractual collaborative frameworks are effectively supported. Stratification based on forest types improved woody above-ground biomass estimation, especially when biomass was greater than 350 Mg/ha, using the proposed "Internet-of-tree-images" technique. Therefore, this research provides new insight into the woody above-ground biomass modeling and monitoring. Finally, performance evaluation testing validates the technique's robustness. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index