05124nam 2201345z- 450 991055758410332120231214133548.0(CKB)5400000000043807(oapen)https://directory.doabooks.org/handle/20.500.12854/76364(EXLCZ)99540000000004380720202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierOperationalization of Remote Sensing Solutions for Sustainable Forest ManagementBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 electronic 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: generalbicsscforest road inventorytotal stationglobal navigation satellite systempoint cloudprecision densitypositional accuracyefficiencymangrove sustainabilitydeforestation depletionanthropogenicnatural water balanceSoutheast AsiaPhoracantha spp.unmanned aerial vehicle (UAV)multispectral imageryvegetation indexthresholding analysisLarge Scale Mean-Shift Segmentation (LSMS)Random Forest (RF)forest maskvalidationprobability samplingremote sensingearth observationsforestryaccuracy assessmentforest classificationforested catchmenthydrological modelingSWAT modelDEMairborne laser scanningdeep learningLandsatnational forest inventorystand volumebark beetleIps typographus L.pestchange detectionforest damagespruceSentinel-2damage mappingmulti-temporal regressionmangrovereplantingrestorationanalytic hierarchy processUAVDJI dronemachine learningforest canopycanopy gapscanopy openings percentagesatellite indicesElastic Netbeech-fir forestspixel-based supervised classificationrandom forestsupport vector machinegray level cooccurrence matrix (GLCM)principal component analysis (PCA)WorldView-3wildfiresMaxENTrisk modelingGISmulti-scale analysisYakutiaArticSiberiaphenology modellingforest disturbanceforest monitoringbark beetle infestationforest managementtime series analysissatellite imagerylandsat time seriesgrowing stock volumeforest inventoryharmonic regressionResearch & information: generalMozgeris Gintautasedt1302759Balenović IvanedtMozgeris GintautasothBalenović IvanothBOOK9910557584103321Operationalization of Remote Sensing Solutions for Sustainable Forest Management3026522UNINA