LEADER 05124nam 2201345z- 450 001 9910557584103321 005 20231214133548.0 035 $a(CKB)5400000000043807 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76364 035 $a(EXLCZ)995400000000043807 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOperationalization of Remote Sensing Solutions for Sustainable Forest Management 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (296 p.) 311 $a3-0365-0982-8 311 $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 $aforest road inventory 610 $atotal station 610 $aglobal navigation satellite system 610 $apoint cloud 610 $aprecision density 610 $apositional accuracy 610 $aefficiency 610 $amangrove sustainability 610 $adeforestation depletion 610 $aanthropogenic 610 $anatural water balance 610 $aSoutheast Asia 610 $aPhoracantha spp. 610 $aunmanned aerial vehicle (UAV) 610 $amultispectral imagery 610 $avegetation index 610 $athresholding analysis 610 $aLarge Scale Mean-Shift Segmentation (LSMS) 610 $aRandom Forest (RF) 610 $aforest mask 610 $avalidation 610 $aprobability sampling 610 $aremote sensing 610 $aearth observations 610 $aforestry 610 $aaccuracy assessment 610 $aforest classification 610 $aforested catchment 610 $ahydrological modeling 610 $aSWAT model 610 $aDEM 610 $aairborne laser scanning 610 $adeep learning 610 $aLandsat 610 $anational forest inventory 610 $astand volume 610 $abark beetle 610 $aIps typographus L. 610 $apest 610 $achange detection 610 $aforest damage 610 $aspruce 610 $aSentinel-2 610 $adamage mapping 610 $amulti-temporal regression 610 $amangrove 610 $areplanting 610 $arestoration 610 $aanalytic hierarchy process 610 $aUAV 610 $aDJI drone 610 $amachine learning 610 $aforest canopy 610 $acanopy gaps 610 $acanopy openings percentage 610 $asatellite indices 610 $aElastic Net 610 $abeech-fir forests 610 $apixel-based supervised classification 610 $arandom forest 610 $asupport vector machine 610 $agray level cooccurrence matrix (GLCM) 610 $aprincipal component analysis (PCA) 610 $aWorldView-3 610 $awildfires 610 $aMaxENT 610 $arisk modeling 610 $aGIS 610 $amulti-scale analysis 610 $aYakutia 610 $aArtic 610 $aSiberia 610 $aphenology modelling 610 $aforest disturbance 610 $aforest monitoring 610 $abark beetle infestation 610 $aforest management 610 $atime series analysis 610 $asatellite imagery 610 $alandsat time series 610 $agrowing stock volume 610 $aforest inventory 610 $aharmonic regression 615 7$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