Drone remote sensing for forestry research and practices

Drones of various shapes, sizes, and functionalities have emerged over the past few decades, and their civilian applications are becoming increasingly appealing. Flexible, low-cost, and high-resolution remote sensing systems that use drones as platforms are important for filling data gaps and supplementing the capabilities of crewed/manned aircraft and satellite remote sensing systems. Here, we refer to this growing remote sensing initiative as drone remote sensing and explain its unique advantages in forestry research and practices. Furthermore, we summarize the various approaches of drone remote sensing to surveying forests, mapping canopy gaps, measuring forest canopy height, tracking forest wildfires, and supporting intensive forest management. The benefits of drone remote sensing include low material and operational costs, flexible control of spatial and temporal resolution, high-intensity data collection, and the absence of risk to crews. The current forestry applications of drone remote sensing are still at an experimental stage, but they are expected to expand rapidly. To better guide the development of drone remote sensing for sustainable forestry, it is important to systematically and continuously conduct comparative studies to determine the appropriate drone remote sensing technologies for various forest conditions and/or forestry applications.

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

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

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

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

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

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

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

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

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

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

[11]  M. Wimberly,et al.  Digital Forestry: A White Paper , 2005, Journal of Forestry.

[12]  Ian A. Munn,et al.  Evaluating forest management intensity: A comparison among major forest landowner types , 2006 .

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

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

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

[16]  Lara A. Arroyo,et al.  Fire models and methods to map fuel types: The role of remote sensing , 2008 .

[17]  S. Frolking,et al.  Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure , 2009 .

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

[19]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

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

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

[22]  Aníbal Ollero,et al.  Automatic Forest-Fire Measuring Using Ground Stations and Unmanned Aerial Systems , 2011, Sensors.

[23]  Eve Hinkley,et al.  USDA forest service–NASA: unmanned aerial systems demonstrations – pushing the leading edge in fire mapping , 2011 .

[24]  Francis Y. Enomoto,et al.  The Ikhana unmanned airborne system (UAS) western states fire imaging missions: from concept to reality (2006–2010) , 2011 .

[25]  D. Gillieson,et al.  Near-infrared imagery from unmanned aerial systems and satellites can be used to specify fertilizer application rates in tree crops , 2011 .

[26]  Heikki Saari,et al.  Unmanned Aerial Vehicle (UAV) operated spectral camera system for forest and agriculture applications , 2011, Remote Sensing.

[27]  S. Wich,et al.  Dawn of Drone Ecology: Low-Cost Autonomous Aerial Vehicles for Conservation , 2012 .

[28]  Vincent G. Ambrosia,et al.  Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use , 2012, Remote. Sens..

[29]  Stephan Getzin,et al.  Assessing biodiversity in forests using very high‐resolution images and unmanned aerial vehicles , 2012 .

[30]  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 .

[31]  M. Dudek,et al.  Hybrid Fuel Cell – Battery System as a Main Power Unit for Small Unmanned Aerial Vehicles (UAV) , 2013, International Journal of Electrochemical Science.

[32]  Karen Anderson,et al.  Lightweight unmanned aerial vehicles will revolutionize spatial ecology , 2013 .

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

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

[35]  Erle C. Ellis,et al.  High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision , 2013 .

[36]  M. Pierrot-Deseilligny,et al.  A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery , 2013 .

[37]  H. Michael Tulldahl,et al.  Lidar on small UAV for 3D mapping , 2014, Security and Defence.

[38]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[39]  Jochen Teizer,et al.  Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system , 2014 .

[40]  Lian Pin Koh,et al.  Small Drones for Community-Based Forest Monitoring: An Assessment of Their Feasibility and Potential in Tropical Areas , 2014 .

[41]  Lina Tang,et al.  Market-oriented forestry in China promotes forestland productivity , 2014, New Forests.

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

[43]  An analysis of potential investment returns and their determinants of poplar plantations in state-owned forest enterprises of China , 2014, New Forests.

[44]  Arko Lucieer,et al.  Detecting pruning of individual stems using Airborne Laser Scanning data captured from an Unmanned Aerial Vehicle , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[45]  Pablo J. Zarco-Tejada,et al.  Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods , 2014 .

[46]  John Sessions,et al.  Remote Sensing and Unmanned Aerial System Technology for Monitoring and Quantifying Forest Fire Impacts , 2014 .

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

[48]  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 .

[49]  Franklin Ginn The International Encyclopedia of Geography: People, The Earth, Environment and Technology , 2017 .

[50]  Guofan Shao,et al.  Satellite Data , 2022 .