LEADER 05157nam 2201369z- 450 001 9910557584103321 005 20220111 035 $a(CKB)5400000000043807 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76364 035 $a(oapen)doab76364 035 $a(EXLCZ)995400000000043807 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aOperationalization of Remote Sensing Solutions for Sustainable Forest Management 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (296 p.) 311 08$a3-0365-0982-8 311 08$a3-0365-0983-6 330 $aThe great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue "Operationalization of Remote Sensing Solutions for Sustainable Forest Management". The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry. 606 $aResearch & information: general$2bicssc 610 $aaccuracy assessment 610 $aairborne laser scanning 610 $aanalytic hierarchy process 610 $aanthropogenic 610 $aArtic 610 $abark beetle 610 $abark beetle infestation 610 $abeech-fir forests 610 $acanopy gaps 610 $acanopy openings percentage 610 $achange detection 610 $adamage mapping 610 $adeep learning 610 $adeforestation depletion 610 $aDEM 610 $aDJI drone 610 $aearth observations 610 $aefficiency 610 $aElastic Net 610 $aforest canopy 610 $aforest classification 610 $aforest damage 610 $aforest disturbance 610 $aforest inventory 610 $aforest management 610 $aforest mask 610 $aforest monitoring 610 $aforest road inventory 610 $aforested catchment 610 $aforestry 610 $aGIS 610 $aglobal navigation satellite system 610 $agray level cooccurrence matrix (GLCM) 610 $agrowing stock volume 610 $aharmonic regression 610 $ahydrological modeling 610 $aIps typographus L. 610 $aLandsat 610 $alandsat time series 610 $aLarge Scale Mean-Shift Segmentation (LSMS) 610 $amachine learning 610 $amangrove 610 $amangrove sustainability 610 $aMaxENT 610 $amulti-scale analysis 610 $amulti-temporal regression 610 $amultispectral imagery 610 $an/a 610 $anational forest inventory 610 $anatural water balance 610 $apest 610 $aphenology modelling 610 $aPhoracantha spp. 610 $apixel-based supervised classification 610 $apoint cloud 610 $apositional accuracy 610 $aprecision density 610 $aprincipal component analysis (PCA) 610 $aprobability sampling 610 $arandom forest 610 $aRandom Forest (RF) 610 $aremote sensing 610 $areplanting 610 $arestoration 610 $arisk modeling 610 $asatellite imagery 610 $asatellite indices 610 $aSentinel-2 610 $aSiberia 610 $aSoutheast Asia 610 $aspruce 610 $astand volume 610 $asupport vector machine 610 $aSWAT model 610 $athresholding analysis 610 $atime series analysis 610 $atotal station 610 $aUAV 610 $aunmanned aerial vehicle (UAV) 610 $avalidation 610 $avegetation index 610 $awildfires 610 $aWorldView-3 610 $aYakutia 615 7$aResearch & information: general 700 $aMozgeris$b Gintautas$4edt$01302759 702 $aBalenovic?$b Ivan$4edt 702 $aMozgeris$b Gintautas$4oth 702 $aBalenovic?$b Ivan$4oth 906 $aBOOK 912 $a9910557584103321 996 $aOperationalization of Remote Sensing Solutions for Sustainable Forest Management$93026522 997 $aUNINA