05637nam 22014653a 450 991036756320332120250203235433.09783039212408303921240010.3390/books978-3-03921-240-8(CKB)4100000010106114(oapen)https://directory.doabooks.org/handle/20.500.12854/58176(ScCtBLL)5119a169-6f25-4de1-bfa0-e9929ed1578c(OCoLC)1163809745(oapen)doab58176(EXLCZ)99410000001010611420250203i20192019 uu engurmn|---annantxtrdacontentcrdamediacrrdacarrierRemote Sensing of Leaf Area Index (LAI) and Other Vegetation ParametersHongliang Fang, Juanma Lopez Sanchez, Francisco Javier García-HaroMDPI - Multidisciplinary Digital Publishing Institute2019Basel, Switzerland :MDPI,2019.1 electronic resource (334 p.)9783039212392 3039212397 Monitoring 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.artificial neural networkdownscalingsimulation3D point cloudEuropean beechconsistencyadaptive thresholdevaluationphotosynthesisgeographic information systemP-band PolInSARvalidationdensity-based clusteringstructure from motion (SfM)EPICTanzaniasignal attenuationtrunkcanopy closureREDD+unmanned aerial vehicle (UAV)forestrecursive feature eliminationFraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR)aboveground biomassrandom forestuncertaintyhousehold surveyspectral informationforests biomassroot biomassbiomassunmanned aerial vehicleBrazilian AmazonVIIRSglobal positioning systemLAIphotochemical reflectance index (PRI)allometric scaling and resource limitationR690/R630modelling aboveground biomassleaf area indexforest degradationspectral analysesterrestrial laser scanningBAAPAleaf area index (LAI)stem volume estimationtomographic profilespolarization coherence tomography (PCT)canopy gap fractionautomated classificationHemiViewremote sensingmultisource remote sensingPléiades imageryphotogrammetric point cloudfarm typesterrestrial LiDARaltitudeRapidEyeforest aboveground biomassrecoverysouthern U.S. forestsNDVImachine-learningconifer forestsatellitechlorophyll fluorescence (ChlF)tree heightsphenologypoint cloudlocal maximaclumping indexMODISdigital aerial photographMediterraneanhemispherical sky-oriented photomanaged temperate coniferous forestsfixed tree window sizedroughtGLASsmartphone-based methodforest above ground biomass (AGB)forest inventoryover and understory coversampling designFang Hongliang1787079Sanchez Juanma LopezGarcía-Haro Francisco JavierScCtBLLScCtBLLBOOK9910367563203321Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters4319700UNINA