LEADER 01176nam0-22003251i-450- 001 990001801850403321 005 20040225114811.0 035 $a000180185 035 $aFED01000180185 035 $a(Aleph)000180185FED01 035 $a000180185 100 $a20030910d1996----km-y0itay50------ba 101 0 $aita 200 1 $aEffetti di diversi tipi di fertilizzanti sulla composizione floristica e sul valore nutritivo di un nardeto$fDiego Orlandi, Fabrizio Clementel, Alessandro Bezzi 210 $aVillazzano di Trento$cISAFA$d1996 215 $ap. 15-22$d30 cm 225 1 $aComunicazioni di ricerca$fIstituto sperimentale per l'assestamento forestale e per l'alpicoltura$v96-2 610 0 $aNardetum 610 0 $aPascoli 676 $a634.99 700 1$aOrlandi,$bDiego$079018 701 1$aBezzi,$bAlessandro$079016 701 1$aClementel,$bFabrizio$0357109 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001801850403321 952 $a60 634.95 A 3/96-2A$b6035$fFAGBC 959 $aFAGBC 996 $aEffetti di diversi tipi di fertilizzanti sulla composizione floristica e sul valore nutritivo di un nardeto$9411258 997 $aUNINA LEADER 01331nam 2200385 450 001 9910293157503321 005 20230814225902.0 010 $a1-5386-4198-4 035 $a(CKB)4100000007125748 035 $a(WaSeSS)IndRDA00121898 035 $a(EXLCZ)994100000007125748 100 $a20200415d2018 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$a2018 IEEE 18th International Power Electronics and Motion Control Conference $e26-30 August 2018, Budapest, Hungary /$fIEEE Industry Applications Society 210 1$aPiscataway, New Jersey :$cInstitute of Electrical and Electronics Engineers,$d2018. 215 $a1 online resource (144 pages) 311 $a1-5386-4199-2 606 $aMotion control devices$vCongresses 606 $aPower electronics$vCongresses 606 $aElectronic control$vCongresses 615 0$aMotion control devices 615 0$aPower electronics 615 0$aElectronic control 676 $a621.317 712 02$aIEEE Industry Applications Society, 801 0$bWaSeSS 801 1$bWaSeSS 906 $aPROCEEDING 912 $a9910293157503321 996 $a2018 IEEE 18th International Power Electronics and Motion Control Conference$92543576 997 $aUNINA LEADER 04708nam 2201081z- 450 001 9910557427903321 005 20210501 035 $a(CKB)5400000000043445 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68491 035 $a(oapen)doab68491 035 $a(EXLCZ)995400000000043445 100 $a20202105d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAssessment of Renewable Energy Resources with Remote Sensing 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (244 p.) 311 08$a3-0365-0480-X 311 08$a3-0365-0481-8 330 $aThe book "Assessment of Renewable Energy Resources with Remote Sensing" focuses on disseminating scientific knowledge and technological developments for the assessment and forecasting of renewable energy resources using remote sensing techniques. The eleven papers inside the book provide an overview of remote sensing applications on hydro, solar, wind and geothermal energy resources and their major goal is to provide state of art knowledge to contribute with the renewable energy resource deployment, especially in regions where energy demand is rapidly expanding. Renewable energy resources have an intrinsic relationship with local environmental features and the regional climate. Even small and fast environment and/or climate changes can cause significant variability in power generation at different time and space scales. Methodologies based on remote sensing are the primary source of information for the development of numerical models that aim to support the planning and operation of an electric system with a substantial contribution of intermittent energy sources. In addition, reliable data and knowledge on renewable energy resource assessment are fundamental to ensure sustainable expansion considering environmental, financial and energetic security. 606 $aResearch & information: general$2bicssc 610 $aartificial neural networks 610 $aBaltic area 610 $aclimate 610 $acloud 610 $acloud coverage 610 $acloud detection 610 $acoastal wind measurements 610 $acoastline 610 $acomputational design method 610 $aconvection 610 $aCSP plants 610 $adata processing 610 $adigitized image processing 610 $aelectrical resistivity tomography 610 $aextreme value analysis 610 $afeature engineering 610 $afeature importance 610 $aforecasting 610 $ageophysical prospecting 610 $ageothermal energy 610 $aGES-CAL software 610 $aglobal radiation 610 $agraphical user interface software 610 $aHazaki Oceanographical Research Station 610 $ahydropower reservoir 610 $aimage processing 610 $alake breeze influence 610 $alight gradient boosting machine 610 $amachine learning 610 $amachine learning techniques 610 $ametaheuristic 610 $amultistep-ahead prediction 610 $aparameter extraction 610 $apassive design strategy 610 $aphotovoltaic power plant 610 $aplan position indicator 610 $apoint cloud data 610 $apotential well field location 610 $aremote sensing 610 $aremote sensing data acquisition 610 $arenewable energy resource assessment and forecasting 610 $asatellite 610 $ascanning LiDAR 610 $ascatterometer 610 $ashading envelopes 610 $asky camera 610 $asmart island 610 $asolar energy 610 $asolar energy resource 610 $asolar irradiance enhancement 610 $asolar irradiance estimation 610 $asolar irradiance forecasting 610 $asolar photovoltaic 610 $asolar radiation forecasting 610 $astatistical analysis 610 $asurface solar radiation 610 $atime domain electromagnetic method 610 $atotal sky imagery 610 $avelocity volume processing 610 $avoxel-design approach 610 $awhale optimization algorithm 610 $awind speed 615 7$aResearch & information: general 700 $aMartins$b Fernando Ramos$4edt$01297532 702 $aMartins$b Fernando Ramos$4oth 906 $aBOOK 912 $a9910557427903321 996 $aAssessment of Renewable Energy Resources with Remote Sensing$93024506 997 $aUNINA