Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The integration of multi-source forest inventories with automated remote sensing workflows presents significant challenges for sustainable forest management and global climate mitigation initiatives. This review examines unified data warehouse architectures as critical frameworks for harmonizing disparate data streams—including satellite imagery, airborne LiDAR, UAV datasets, ground surveys, and historical records—into scalable analytical systems. We synthesize methodologies enabling near real-time inventory updates, focusing on spectral-index chronosequence analysis for disturbance detection, multi-sensor fusion strategies for canopy-penetrating observations, and dynamic growth-model recalibration. The paper assesses how optimized data warehousing supports machine learning and deep learning applications in automated attribute estimation, change detection, and error propagation management. Key challenges in data standardization, model transferability, and scalable cloud-based implementations are critically evaluated. Furthermore, the role of integrated architectures in advancing carbon monitoring, precision conservation, and long-term forest sustainability is analyzed. The synthesis concludes that unified data repositories are essential for operationalizing next-generation forest monitoring paradigms aligned with carbon neutrality objectives, providing robust foundations for policy-relevant decision-making through enhanced spatiotemporal analytics