From Drones to Decisions: How Integrated Remote Sensing Is Transforming Plantation Forestry

Plantation forestry is increasingly constrained by conventional inventory methods that are labour intensive, costly, spatially sparse, and conducted too infrequently to support timely, site-specific management. Over the past two decades, rapid advances in remote sensing have fundamentally transformed this paradigm. Modern sensing systems—including satellites, unmanned aerial vehicles (UAVs), airborne, terrestrial, and mobile laser scanning, and high-precision positioning technologies—can now provide complementary observations of forest structure, condition, and change across multiple spatial scales. At the same time, developments in photogrammetry, light detection and ranging (LiDAR), computer vision, machine learning, and artificial intelligence have shifted remote sensing beyond the production of maps and three-dimensional datasets towards the automated extraction of meaningful forest information. Individual trees, canopy and stem structure, survival, stocking, health, and productivity can now all be quantified objectively and repeatedly, providing the foundation for predictive models of establishment success, growth, stocking failure, and operational risk. Consequently, remote sensing is evolving from a measurement technology into an integrated decision-support framework capable of generating spatial decision intelligence through prediction, uncertainty quantification, scenario evaluation, and actionable management recommendations, while providing an increasingly capable foundation for automation and autonomous systems. This feature article examines how integrated remote sensing has evolved from a collection of measurement technologies into a comprehensive framework for operational decision support in plantation forestry. It reviews advances in integrated satellite, aerial, and terrestrial remote sensing, automated information extraction, predictive modelling, artificial intelligence, and precision silviculture, while considering the scientific, operational, regulatory, and data-governance challenges associated with their deployment. Finally, it examines how digital forest twins, autonomous systems, and technologies adapted from Agriculture 4.0 provide a technological roadmap towards an emerging Forestry 4.0 paradigm.