Reprint

Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2019

Edited by
November 2020
184 pages
  • ISBN978-3-03936-754-2 (Hardback)
  • ISBN978-3-03936-755-9 (PDF)

This book is a reprint of the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2019 that was published in

Biology & Life Sciences
Environmental & Earth Sciences
Summary
Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. The main adopted image-based technologies are RGB, multispectral, and thermal infrared. LiDAR sensors are becoming commonly used to improve the estimation of relevant plant traits. In comparison with other permanent ecosystems, forests are particularly affected by climatic changes due to the longevity of the trees, and the primary objective is the conservation and protection of forests. Nevertheless, forestry and agriculture involve the cultivation of renewable raw materials, with the difference that forestry is less tied to economic aspects and this is reflected by the delay in using new monitoring technologies. The main forestry applications are aimed toward inventory of resources, map diseases, species classification, fire monitoring, and spatial gap estimation. This Special Issue focuses on new technologies (UAV and sensors) and innovative data elaboration methodologies (object recognition and machine vision) for applications in forestry.
Format
  • Hardback
License
© 2021 by the authors; CC BY license
Keywords
unmanned aerial vehicles; seedling detection; forest regeneration; reforestation; establishment survey; machine learning; multispectral classification; UAV photogrammetry; forest modeling; ancient trees measurement; tree age prediction; Mauritia flexuosa; semantic segmentation; end-to-end learning; convolutional neural network; forest inventory; Unmanned Aerial Systems (UAS); structure from motion (SfM); Unmanned Aerial Vehicles (UAV); Photogrammetry; Thematic Mapping; Accuracy Assessment; Reference Data; Forest Sampling; Remote Sensing; Robinia pseudoacacia L.; reproduction; spreading; short rotation coppice; unmanned aerial system (UAS); object-based image analysis (OBIA); convolutional neural network (CNN); juniper woodlands; ecohydrology; remote sensing; unmanned aerial systems; central Oregon; rangelands; seedling stand inventorying; photogrammetric point clouds; hyperspectral imagery; unmanned aerial vehicles; leaf-off; leaf-on; UAV; multispectral image; forest fire; burn severity; classification; unmanned aerial vehicles; precision agriculture; biomass evaluation; image processing; Castanea sativa; unmanned aerial vehicles (UAV); precision forestry; forestry applications; image processing; machine learning; RGB imagery