Export citation

Show simple item record

dc.contributor.authorSanchez, Juanma Lopez*
dc.contributor.authorFang, Hongliang*
dc.contributor.authorGarcía-Haro, Francisco Javier*
dc.date.accessioned2021-02-12T01:48:02Z
dc.date.available2021-02-12T01:48:02Z
dc.date.issued2019*
dc.date.submitted2019-12-09 11:49:15*
dc.identifier42517*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/58176
dc.description.abstractMonitoring 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.*
dc.languageEnglish*
dc.subjectQ1-390*
dc.subject.otherartificial neural network*
dc.subject.otherdownscaling*
dc.subject.othersimulation*
dc.subject.other3D point cloud*
dc.subject.otherEuropean beech*
dc.subject.otherconsistency*
dc.subject.otheradaptive threshold*
dc.subject.otherevaluation*
dc.subject.otherphotosynthesis*
dc.subject.othergeographic information system*
dc.subject.otherP-band PolInSAR*
dc.subject.othervalidation*
dc.subject.otherdensity-based clustering*
dc.subject.otherstructure from motion (SfM)*
dc.subject.otherEPIC*
dc.subject.otherTanzania*
dc.subject.othersignal attenuation*
dc.subject.othertrunk*
dc.subject.othercanopy closure*
dc.subject.otherREDD+*
dc.subject.otherunmanned aerial vehicle (UAV)*
dc.subject.otherforest*
dc.subject.otherrecursive feature elimination*
dc.subject.otherFraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR)*
dc.subject.otheraboveground biomass*
dc.subject.otherrandom forest*
dc.subject.otheruncertainty*
dc.subject.otherhousehold survey*
dc.subject.otherspectral information*
dc.subject.otherforests biomass*
dc.subject.otherroot biomass*
dc.subject.otherbiomass*
dc.subject.otherunmanned aerial vehicle*
dc.subject.otherBrazilian Amazon*
dc.subject.otherVIIRS*
dc.subject.otherglobal positioning system*
dc.subject.otherLAI*
dc.subject.otherphotochemical reflectance index (PRI)*
dc.subject.otherallometric scaling and resource limitation*
dc.subject.otherR690/R630*
dc.subject.othermodelling aboveground biomass*
dc.subject.otherleaf area index*
dc.subject.otherforest degradation*
dc.subject.otherspectral analyses*
dc.subject.otherterrestrial laser scanning*
dc.subject.otherBAAPA*
dc.subject.otherleaf area index (LAI)*
dc.subject.otherstem volume estimation*
dc.subject.othertomographic profiles*
dc.subject.otherpolarization coherence tomography (PCT)*
dc.subject.othercanopy gap fraction*
dc.subject.otherautomated classification*
dc.subject.otherHemiView*
dc.subject.otherremote sensing*
dc.subject.othermultisource remote sensing*
dc.subject.otherPléiades imagery*
dc.subject.otherphotogrammetric point cloud*
dc.subject.otherfarm types*
dc.subject.otherterrestrial LiDAR*
dc.subject.otheraltitude*
dc.subject.otherRapidEye*
dc.subject.otherforest aboveground biomass*
dc.subject.otherrecovery*
dc.subject.othersouthern U.S. forests*
dc.subject.otherNDVI*
dc.subject.othermachine-learning*
dc.subject.otherconifer forest*
dc.subject.othersatellite*
dc.subject.otherchlorophyll fluorescence (ChlF)*
dc.subject.othertree heights*
dc.subject.otherphenology*
dc.subject.otherpoint cloud*
dc.subject.otherlocal maxima*
dc.subject.otherclumping index*
dc.subject.otherMODIS*
dc.subject.otherdigital aerial photograph*
dc.subject.otherMediterranean*
dc.subject.otherhemispherical sky-oriented photo*
dc.subject.othermanaged temperate coniferous forests*
dc.subject.otherfixed tree window size*
dc.subject.otherdrought*
dc.subject.otherGLAS*
dc.subject.othersmartphone-based method*
dc.subject.otherforest above ground biomass (AGB)*
dc.subject.otherforest inventory*
dc.subject.otherover and understory cover*
dc.subject.othersampling design*
dc.titleRemote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters*
dc.typebook
oapen.identifier.doi10.3390/books978-3-03921-240-8*
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0*
virtual.oapen_relation_isPublishedBy.publisher_nameMDPI - Multidisciplinary Digital Publishing Institute
virtual.oapen_relation_isPublishedBy.publisher_websitewww.mdpi.com/books
oapen.relation.isbn9783039212392*
oapen.relation.isbn9783039212408*
oapen.pages334*
oapen.edition1st*


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

https://creativecommons.org/licenses/by-nc-nd/4.0/
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/