Precision Forestry Revisited

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dc.authorid0000-0001-5552-5670
dc.authorid0000-0003-1413-0036
dc.authorid0000-0001-6558-9029
dc.contributor.authorVatandaslar, Can
dc.contributor.authorBoston, Kevin
dc.contributor.authorUcar, Zennure
dc.contributor.authorNarine, Lana L.
dc.contributor.authorMadden, Marguerite
dc.contributor.authorAkay, Abdullah Emin
dc.date.accessioned2026-02-08T15:16:02Z
dc.date.available2026-02-08T15:16:02Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractHighlights What are the main findings? Precision forestry has grown substantially since the early 2010s, driven by advances in UAV and LiDAR technologies. Nearly half of the reviewed studies focus on forest management and planning, with remote sensing platforms and sensors being the dominant tools. What are the implications of the main findings? Although data collection and analysis in forestry have advanced significantly, the translation of these tools into fully automated, integrated, and widely adopted practices is lagging. Geographic disparities and an aging, undertrained workforce continue to limit adoption, underscoring the need for updated forestry curricula and stronger industry-academia collaboration.Highlights What are the main findings? Precision forestry has grown substantially since the early 2010s, driven by advances in UAV and LiDAR technologies. Nearly half of the reviewed studies focus on forest management and planning, with remote sensing platforms and sensors being the dominant tools. What are the implications of the main findings? Although data collection and analysis in forestry have advanced significantly, the translation of these tools into fully automated, integrated, and widely adopted practices is lagging. Geographic disparities and an aging, undertrained workforce continue to limit adoption, underscoring the need for updated forestry curricula and stronger industry-academia collaboration.Abstract This review presents a synthesis of global research on precision forestry, a field that integrates advanced technologies to enhance-rather than replace-established tools and methods used in the operational forest management and the wood products industry. By evaluating 210 peer-reviewed publications indexed in Web of Science (up to 2025), the study identifies six main categories and eight components of precision forestry. The findings indicate that forest management and planning is the most common category, with nearly half of the studies focusing on this topic. Remote sensing platforms and sensors emerged as the most frequently used component, with unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) systems being the most widely adopted tools. The analysis also reveals a notable increase in precision forestry research since the early 2010s, coinciding with rapid developments in small UAVs and mobile sensor technologies. Despite growing interest, robotics and real-time process control systems remain underutilized, mainly due to challenging forest conditions and high implementation costs. The research highlights geographical disparities, with Europe, Asia, and North America hosting the majority of studies. Italy, China, Finland, and the United States stand out as the most active countries in terms of research output. Notably, the review emphasizes the need to integrate precision forestry into academic curricula and support industry adoption through dedicated information and technology specialists. As the forestry workforce ages and technology advances rapidly, a growing skills gap exists between industry needs and traditional forestry education. Equipping the next generation with hands-on experience in big data analysis, geospatial technologies, automation, and Artificial Intelligence (AI) is critical for ensuring the effective adoption and application of precision forestry.
dc.identifier.doi10.3390/rs17203465
dc.identifier.issn2072-4292
dc.identifier.issue20
dc.identifier.scopus2-s2.0-105020266413
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/rs17203465
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6076
dc.identifier.volume17
dc.identifier.wosWOS:001603012900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofRemote Sensing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectrobotic technologies
dc.subjectsmart forestry
dc.subjectforest industry
dc.subjectdrone
dc.subjectprecision agriculture
dc.subjectdigital forestry
dc.titlePrecision Forestry Revisited
dc.typeReview Article

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