TY - BOOK ID - 138504933 TI - Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2019 PY - 2020 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - unmanned aerial vehicles KW - seedling detection KW - forest regeneration KW - reforestation KW - establishment survey KW - machine learning KW - multispectral classification KW - UAV photogrammetry KW - forest modeling KW - ancient trees measurement KW - tree age prediction KW - Mauritia flexuosa KW - semantic segmentation KW - end-to-end learning KW - convolutional neural network KW - forest inventory KW - Unmanned Aerial Systems (UAS) KW - structure from motion (SfM) KW - Unmanned Aerial Vehicles (UAV) KW - Photogrammetry KW - Thematic Mapping KW - Accuracy Assessment KW - Reference Data KW - Forest Sampling KW - Remote Sensing KW - Robinia pseudoacacia L. KW - reproduction KW - spreading KW - short rotation coppice KW - unmanned aerial system (UAS) KW - object-based image analysis (OBIA) KW - convolutional neural network (CNN) KW - juniper woodlands KW - ecohydrology KW - remote sensing KW - unmanned aerial systems KW - central Oregon KW - rangelands KW - seedling stand inventorying KW - photogrammetric point clouds KW - hyperspectral imagery KW - leaf-off KW - leaf-on KW - UAV KW - multispectral image KW - forest fire KW - burn severity KW - classification KW - precision agriculture KW - biomass evaluation KW - image processing KW - Castanea sativa KW - unmanned aerial vehicles (UAV) KW - precision forestry KW - forestry applications KW - RGB imagery UR - https://www.unicat.be/uniCat?func=search&query=sysid:138504933 AB - 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. ER -