Forest and UAV: a bibliometric review

Since 2004, increasing attention has been focused on improving UAV applications in forestry. The technology related to the drones also allowed to prefigure new applications related to forest monitoring in real-time and timely, such as the monitoring of fire fronts during forest fires. Accurate information about forest composition, structure, volume, growth, and extent is essential for sustainable forest management. The aim of this paper is to compare the results obtained from Web of Science and Scopus databases in order to have a wide framework of the bibliography to explore between 2004 to date. The number of found publications in Scopus and Web of Science databases, underline that there is an increasing interesting on the investigated thematic; the comparison between the two databases show that WoS is more complete than Scopus. In conclusion, the results comparison, for each keywords combination in both databases, show that Web of Science is the best bibliographic database research for the explored thematic.

[1]  Michael A. Wulder,et al.  Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities , 2006 .

[2]  Simone Pascuzzi,et al.  Hazards assessment and technical actions due to the production of pressured hydrogen within a pilot photovoltaic-electrolyser-fuel cell power system for agricultural equipment , 2016 .

[3]  RD（翻译）.  Composition , 1885, Elementarbuch der Sanskrit-Sprache.

[4]  E. H. Lyons.  FIXED AIR-BASE 70 mm PHOTOGRAPHY, A NEW TOOL FOR FOREST SAMPLING , 1966 .

[5]  Philip J. Howarth,et al.  Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems , 1999 .

[6]  Guofan Shao,et al.  Forest cover types derived from Landsat Thematic Mapper imagery for Changbai Mountain area of China , 1996 .

[7]  Mary E. Martin,et al.  HIGH SPECTRAL RESOLUTION REMOTE SENSING OF FOREST CANOPY LIGNIN, NITROGEN, AND ECOSYSTEM PROCESSES , 1997 .

[8]  L. Tang,et al.  Forest degradation deepens around and within protected areas in East Asia , 2010 .

[9]  F. M. Danson,et al.  Satellite remote sensing of forest resources: three decades of research development , 2005 .

[10]  G. Burniske,et al.  International Journal of Sustainable Development & World Ecology Deforestation of Montane Cloud Forest in the Central Highlands of Guatemala: Contributing Factors and Implications for Sustainability in Q'eqchi' Communities Deforestation of Montane Cloud Forest in the Central Highlands of Guatemala: , 2022 .

[11]  J. Théau,et al.  Recent applications of unmanned aerial imagery in natural resource management , 2014 .

[12]  Composition, structure and diversity characterization of dry tropical forest of Chhattisgarh using satellite data , 2014, Journal of Forestry Research.

[13]  Barbara Koch,et al.  Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment , 2010 .

[14]  M. Keller,et al.  Selective Logging in the Brazilian Amazon , 2005, Science.

[15]  C.Y. Jim,et al.  Species adoption for sustainable forestry in Hong Kong’s degraded countryside , 2013 .

[16]  M Cecchini,et al.  Estimation of the risks of thermal stress due to the microclimate for manual fruit and vegetable harvesters in central Italy. , 2010, Journal of agricultural safety and health.

[17]  Aníbal Ollero,et al.  Journal of Intelligent & Robotic Systems manuscript No. (will be inserted by the editor) An Unmanned Aircraft System for Automatic Forest Fire Monitoring and Measurement , 2022 .

[18]  Isabel Cristina Pascual Castaño,et al.  Fire models and methods to map fuel types: The role of remote sensing. , 2008 .

[19]  Alvaro Marucci,et al.  The heat stress for workers employed in a dairy farm , 2013 .

[20]  D. Lu.  The potential and challenge of remote sensing‐based biomass estimation , 2006 .

[21]  Karin S. Fassnacht,et al.  Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites , 1999 .

[22]  L. Tang,et al.  Roles of digital technology in China's sustainable forestry development , 2009 .

[23]  Zhiliang Zhu,et al.  US forest types and predicted percent forest cover from AVHRR data , 1994 .

[24]  Rasmus Fensholt,et al.  Remote Sensing , 2008, Encyclopedia of GIS.

[25]  W. Cohen,et al.  Lidar Remote Sensing for Ecosystem Studies , 2002 .

[26]  Elena Allegrini,et al.  A model for musculoskeletal disorder-related fatigue in upper limb manipulation during industrial vegetables sorting , 2014 .

[27]  John Sessions,et al.  Eyes in the Sky: Remote Sensing Technology Development Using Small Unmanned Aircraft Systems , 2013 .

[28]  Alexandros Sotirios Anifantis,et al.  Environmental analysis of geothermal heat pump and LPG greenhouse heating systems , 2014 .

[29]  Danilo Monarca,et al.  Plant for the Production of Chips and Pellet: Technical and Economic Aspects of an Case Study in the Central Italy , 2007, ICCSA.

[30]  Simone Pascuzzi,et al.  Hydrogen and renewable energy sources integrated system for greenhouse heating , 2013 .

[31]  Simone Pascuzzi,et al.  Exposure of farm workers to electromagnetic radiation from cellular network radio base stations situated on rural agricultural land , 2015, International journal of occupational safety and ergonomics : JOSE.

[32]  R. Dubayah,et al.  Lidar Remote Sensing for Forestry , 2000, Journal of Forestry.