01138nam--2200385---450-9900006728002033163-540-60941-50067280USA010067280(ALEPH)000067280USA01006728020011012d1996----km-y0itay0103----baengDE||||||||001yyTime structuresformal description and algorithmic representationElzbieta HajniczBerlinSpringerc1996IX, 244 p.ill.24 cmLecture notes in artificialSerie princpale: Lecture notes in computer science2001Lecture notes in artificialIntelligenza artificialeAlgoritmi0063HAJNICZ,Elzbieta548570ITsalbcISBD990000672800203316006.3 HAJ19103 CBSBKSCIPATTY9020011012USA01203420020403USA011716PATRY9020040406USA011646Time structures960941UNISA05157nam 2201369z- 450 991055758410332120220111(CKB)5400000000043807(oapen)https://directory.doabooks.org/handle/20.500.12854/76364(oapen)doab76364(EXLCZ)99540000000004380720202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierOperationalization of Remote Sensing Solutions for Sustainable Forest ManagementBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (296 p.)3-0365-0982-8 3-0365-0983-6 The 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.Research & information: generalbicsscaccuracy assessmentairborne laser scanninganalytic hierarchy processanthropogenicArticbark beetlebark beetle infestationbeech-fir forestscanopy gapscanopy openings percentagechange detectiondamage mappingdeep learningdeforestation depletionDEMDJI droneearth observationsefficiencyElastic Netforest canopyforest classificationforest damageforest disturbanceforest inventoryforest managementforest maskforest monitoringforest road inventoryforested catchmentforestryGISglobal navigation satellite systemgray level cooccurrence matrix (GLCM)growing stock volumeharmonic regressionhydrological modelingIps typographus L.Landsatlandsat time seriesLarge Scale Mean-Shift Segmentation (LSMS)machine learningmangrovemangrove sustainabilityMaxENTmulti-scale analysismulti-temporal regressionmultispectral imageryn/anational forest inventorynatural water balancepestphenology modellingPhoracantha spp.pixel-based supervised classificationpoint cloudpositional accuracyprecision densityprincipal component analysis (PCA)probability samplingrandom forestRandom Forest (RF)remote sensingreplantingrestorationrisk modelingsatellite imagerysatellite indicesSentinel-2SiberiaSoutheast Asiasprucestand volumesupport vector machineSWAT modelthresholding analysistime series analysistotal stationUAVunmanned aerial vehicle (UAV)validationvegetation indexwildfiresWorldView-3YakutiaResearch & information: generalMozgeris Gintautasedt1302759Balenović IvanedtMozgeris GintautasothBalenović IvanothBOOK9910557584103321Operationalization of Remote Sensing Solutions for Sustainable Forest Management3026522UNINA