LEADER 05637nam 22014653a 450 001 9910367563203321 005 20250203235433.0 010 $a9783039212408 010 $a3039212400 024 8 $a10.3390/books978-3-03921-240-8 035 $a(CKB)4100000010106114 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/58176 035 $a(ScCtBLL)5119a169-6f25-4de1-bfa0-e9929ed1578c 035 $a(OCoLC)1163809745 035 $a(oapen)doab58176 035 $a(EXLCZ)994100000010106114 100 $a20250203i20192019 uu 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aRemote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters$fHongliang Fang, Juanma Lopez Sanchez, Francisco Javier García-Haro 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 210 1$aBasel, Switzerland :$cMDPI,$d2019. 215 $a1 electronic resource (334 p.) 311 08$a9783039212392 311 08$a3039212397 330 $aMonitoring of vegetation structure and functioning is critical to modeling terrestrial ecosystems and energy cycles. In particular, leaf area index (LAI) is an important structural property of vegetation used in many land surface vegetation, climate, and crop production models. Canopy structure (LAI, fCover, plant height, and biomass) and biochemical parameters (leaf pigmentation and water content) directly influence the radiative transfer process of sunlight in vegetation, determining the amount of radiation measured by passive sensors in the visible and infrared portions of the electromagnetic spectrum. Optical remote sensing (RS) methods build relationships exploiting in situ measurements and/or as outputs of physical canopy radiative transfer models. The increased availability of passive (radar and LiDAR) RS data has fostered their use in many applications for the analysis of land surface properties and processes, thanks also to their insensitivity to weather conditions and the capability to exploit rich structural and textural information. Data fusion and multi-sensor integration techniques are pressing topics to fully exploit the information conveyed by both optical and microwave bands. 610 $aartificial neural network 610 $adownscaling 610 $asimulation 610 $a3D point cloud 610 $aEuropean beech 610 $aconsistency 610 $aadaptive threshold 610 $aevaluation 610 $aphotosynthesis 610 $ageographic information system 610 $aP-band PolInSAR 610 $avalidation 610 $adensity-based clustering 610 $astructure from motion (SfM) 610 $aEPIC 610 $aTanzania 610 $asignal attenuation 610 $atrunk 610 $acanopy closure 610 $aREDD+ 610 $aunmanned aerial vehicle (UAV) 610 $aforest 610 $arecursive feature elimination 610 $aFraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) 610 $aaboveground biomass 610 $arandom forest 610 $auncertainty 610 $ahousehold survey 610 $aspectral information 610 $aforests biomass 610 $aroot biomass 610 $abiomass 610 $aunmanned aerial vehicle 610 $aBrazilian Amazon 610 $aVIIRS 610 $aglobal positioning system 610 $aLAI 610 $aphotochemical reflectance index (PRI) 610 $aallometric scaling and resource limitation 610 $aR690/R630 610 $amodelling aboveground biomass 610 $aleaf area index 610 $aforest degradation 610 $aspectral analyses 610 $aterrestrial laser scanning 610 $aBAAPA 610 $aleaf area index (LAI) 610 $astem volume estimation 610 $atomographic profiles 610 $apolarization coherence tomography (PCT) 610 $acanopy gap fraction 610 $aautomated classification 610 $aHemiView 610 $aremote sensing 610 $amultisource remote sensing 610 $aPléiades imagery 610 $aphotogrammetric point cloud 610 $afarm types 610 $aterrestrial LiDAR 610 $aaltitude 610 $aRapidEye 610 $aforest aboveground biomass 610 $arecovery 610 $asouthern U.S. forests 610 $aNDVI 610 $amachine-learning 610 $aconifer forest 610 $asatellite 610 $achlorophyll fluorescence (ChlF) 610 $atree heights 610 $aphenology 610 $apoint cloud 610 $alocal maxima 610 $aclumping index 610 $aMODIS 610 $adigital aerial photograph 610 $aMediterranean 610 $ahemispherical sky-oriented photo 610 $amanaged temperate coniferous forests 610 $afixed tree window size 610 $adrought 610 $aGLAS 610 $asmartphone-based method 610 $aforest above ground biomass (AGB) 610 $aforest inventory 610 $aover and understory cover 610 $asampling design 700 $aFang$b Hongliang$01787079 702 $aSanchez$b Juanma Lopez 702 $aGarcía-Haro$b Francisco Javier 801 0$bScCtBLL 801 1$bScCtBLL 906 $aBOOK 912 $a9910367563203321 996 $aRemote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters$94319700 997 $aUNINA