LEADER 04577nam 2201057z- 450 001 9910557112103321 005 20231214132816.0 035 $a(CKB)5400000000040925 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69331 035 $a(EXLCZ)995400000000040925 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aForestry Applications of Unmanned Aerial Vehicles (UAVs) 2019 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 electronic resource (184 p.) 311 $a3-03936-754-4 311 $a3-03936-755-2 330 $aUnmanned 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. 517 $aForestry Applications of Unmanned Aerial Vehicles 606 $aResearch & information: general$2bicssc 606 $aBiology, life sciences$2bicssc 606 $aForestry & related industries$2bicssc 610 $aunmanned aerial vehicles 610 $aseedling detection 610 $aforest regeneration 610 $areforestation 610 $aestablishment survey 610 $amachine learning 610 $amultispectral classification 610 $aUAV photogrammetry 610 $aforest modeling 610 $aancient trees measurement 610 $atree age prediction 610 $aMauritia flexuosa 610 $asemantic segmentation 610 $aend-to-end learning 610 $aconvolutional neural network 610 $aforest inventory 610 $aUnmanned Aerial Systems (UAS) 610 $astructure from motion (SfM) 610 $aUnmanned Aerial Vehicles (UAV) 610 $aPhotogrammetry 610 $aThematic Mapping 610 $aAccuracy Assessment 610 $aReference Data 610 $aForest Sampling 610 $aRemote Sensing 610 $aRobinia pseudoacacia L. 610 $areproduction 610 $aspreading 610 $ashort rotation coppice 610 $aunmanned aerial system (UAS) 610 $aobject-based image analysis (OBIA) 610 $aconvolutional neural network (CNN) 610 $ajuniper woodlands 610 $aecohydrology 610 $aremote sensing 610 $aunmanned aerial systems 610 $acentral Oregon 610 $arangelands 610 $aseedling stand inventorying 610 $aphotogrammetric point clouds 610 $ahyperspectral imagery 610 $aleaf-off 610 $aleaf-on 610 $aUAV 610 $amultispectral image 610 $aforest fire 610 $aburn severity 610 $aclassification 610 $aprecision agriculture 610 $abiomass evaluation 610 $aimage processing 610 $aCastanea sativa 610 $aunmanned aerial vehicles (UAV) 610 $aprecision forestry 610 $aforestry applications 610 $aRGB imagery 615 7$aResearch & information: general 615 7$aBiology, life sciences 615 7$aForestry & related industries 700 $aMatese$b Alessandro$4edt$01293412 702 $aMatese$b Alessandro$4oth 906 $aBOOK 912 $a9910557112103321 996 $aForestry Applications of Unmanned Aerial Vehicles (UAVs) 2019$93022594 997 $aUNINA