LEADER 01356cam0-22003371i-450- 001 990001153560403321 005 20160714111001.0 035 $a000115356 035 $aFED01000115356 035 $a(Aleph)000115356FED01 035 $a000115356 100 $a20001205d1589----km-y0itay50------ba 101 0 $alat 140 $aa-------------------bb0----- 200 1 $aHistoria naturale di G. Plinio Secondo, tradotta per M. Lodouicho Domenichi. Con le additioni in margine, nelle quali, ò vengono segnate le cose notabili, ò citati altri auttori, che della stessa materia habbiano scritto, ò dichiarati i luoghi difficili, ò posti i nomi di geografia moderni. Di nuouo ristampate, riuiste, & ricorrette. Con le sue tauole copiosissime di tutto quel che nell'opera si contiene 210 $aIn Venetia$cappresso Gio. Battista Vscio$d1589 215 $a55, [1], 1189, [3] p.$d4° 500 10$aHistoria naturalis$m 620 $aItalia.$dVenezia 676 $a870$zita 700 1$aPlinius Secundus,$bGaius$f<23-70>$0208975 702 1$aDomenichi,$bLodovico$f<1515-1564> 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aAQ 912 $a990001153560403321 952 $aSG 870/A 85$bARCH. 14206$fFLFBC 952 $a211-B-21$b02857$fMA1 959 $aFLFBC 959 $aMA1 996 $aHistoria naturale$9346280 997 $aUNINA LEADER 01059nam0-22003371i-450- 001 990005559050403321 005 19990530 010 $a87-12-155888-7 010 $a87-12-15889-5 035 $a000555905 035 $aFED01000555905 035 $a(Aleph)000555905FED01 035 $a000555905 100 $a19990530d1976----km-y0itay50------ba 101 0 $afre 105 $ay-------001yy 200 1 $aTopica, Opuscula$fBoethius Dacus$gnunc primum ediderunt Nicolaus Georgius Green-Pedersen & Joannes Pinborg$gschedis usi Alfredi Otto 210 $aHauniae$cTypis Fr. 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Foster 250 $a2nd ed. 260 $aNew York :$bVan Nostrand Reinhold Co.,$cc1976 300 $axvii, 300 p. :$bill. ;$c24 cm 490 0 $aComputer science series 500 $aIncludes bibliographical references and index 650 0$aComputer architecture 650 0$aComputer system organization 907 $a.b10755664$b23-02-17$c28-06-02 912 $a991000776759707536 945 $aLE013 68M FOS11 (1976)$g1$i2013000095226$lle013$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10849919$z28-06-02 996 $aComputer architecture$9876947 997 $aUNISALENTO 998 $ale013$b01-01-98$cm$da $e-$feng$gus $h0$i1 LEADER 04788nam 2201285z- 450 001 9910557691803321 005 20210501 035 $a(CKB)5400000000044613 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68321 035 $a(oapen)doab68321 035 $a(EXLCZ)995400000000044613 100 $a20202105d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aHyperspectral Remote Sensing of Agriculture and Vegetation 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (266 p.) 311 08$a3-03943-907-3 311 08$a3-03943-908-1 330 $aThis book shows recent and innovative applications of the use of hyperspectral technology for optimal quantification of crop, vegetation, and soil biophysical variables at various spatial scales, which can be an important aspect in agricultural management practices and monitoring. The articles collected inside the book are intended to help researchers and farmers involved in precision agriculture techniques and practices, as well as in plant nutrient prediction, to a higher comprehension of strengths and limitations of the application of hyperspectral imaging to agriculture and vegetation. Hyperspectral remote sensing for studying agriculture and natural vegetation is a challenging research topic that will remain of great interest for different sciences communities in decades. 606 $aEnvironmental economics$2bicssc 606 $aResearch & information: general$2bicssc 610 $aabaxial 610 $aadaxial 610 $aanalytical methods 610 $aAOTF 610 $aartificial intelligence 610 $abiodiversity 610 $aBRDF 610 $acanopy spectra 610 $achlorophyll content 610 $aclassification 610 $aclassification of agricultural features 610 $acontinuous wavelet transform (CWT) 610 $acorrelation coefficient 610 $acrop properties 610 $adiscrimination 610 $aDLARI 610 $aEragrostis tef 610 $aEthiopia 610 $aexpansive species 610 $afeature selection 610 $afield spectroscopy 610 $afuture hyperspectral missions 610 $agrapevine 610 $aheavy metals 610 $ahigh-resolution spectroscopy for agricultural soils and vegetation 610 $ahyperspectral 610 $ahyperspectral data as input for modelling soil, crop, and vegetation 610 $ahyperspectral databases for agricultural soils and vegetation 610 $ahyperspectral imaging 610 $ahyperspectral imaging for vegetation 610 $ahyperspectral LiDAR 610 $ahyperspectral remote sensing 610 $ahyperspectral remote sensing for soil and crops in agriculture 610 $ainvasive species 610 $aleaf chlorophyll content 610 $amacronutrient 610 $aMDATT 610 $amicronutrient 610 $aMLR 610 $amulti-angle observation 610 $aNatura 2000 610 $anew hyperspectral technologies 610 $aobject-oriented segmentation 610 $apartial least square regression (PLSR) 610 $apartial least squares 610 $apeanut 610 $aplant 610 $aplant traits 610 $aplatforms and sensors 610 $aPLS 610 $aprecision agriculture 610 $aproduct validation 610 $aproximal sensing data 610 $aproximal sensor 610 $arandom forest 610 $aRed Edge 610 $aremote sensing 610 $areplicability 610 $areproducibility 610 $asoil characteristics 610 $aspectra 610 $aspectral reflectance 610 $aspectroscopy 610 $asupport vector machine 610 $aSVM 610 $avegetation 610 $avegetation classification 610 $avegetation parameters 610 $awaveband selection 615 7$aEnvironmental economics 615 7$aResearch & information: general 700 $aPascucci$b Simone$4edt$01311963 702 $aPignatti$b Stefano$4edt 702 $aCasa$b Raffaele$4edt 702 $aDarvishzadeh$b Roshanak$4edt 702 $aHuang$b Wenjiang$4edt 702 $aPascucci$b Simone$4oth 702 $aPignatti$b Stefano$4oth 702 $aCasa$b Raffaele$4oth 702 $aDarvishzadeh$b Roshanak$4oth 702 $aHuang$b Wenjiang$4oth 906 $aBOOK 912 $a9910557691803321 996 $aHyperspectral Remote Sensing of Agriculture and Vegetation$93030627 997 $aUNINA