TY - BOOK AU - Sanchez, Juanma Lopez AU - Fang, Hongliang AU - García-Haro, Francisco Javier AB - 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. DO - 10.3390/books978-3-03921-240-8 ID - OAPEN ID: 42517 KW - artificial neural network KW - downscaling KW - simulation KW - 3D point cloud KW - European beech KW - consistency KW - adaptive threshold KW - evaluation KW - photosynthesis KW - geographic information system KW - P-band PolInSAR KW - validation KW - density-based clustering KW - structure from motion (SfM) KW - EPIC KW - Tanzania KW - signal attenuation KW - trunk KW - canopy closure KW - REDD+ KW - unmanned aerial vehicle (UAV) KW - forest KW - recursive feature elimination KW - Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) KW - aboveground biomass KW - random forest KW - uncertainty KW - household survey KW - spectral information KW - forests biomass KW - root biomass KW - biomass KW - unmanned aerial vehicle KW - Brazilian Amazon KW - VIIRS KW - global positioning system KW - LAI KW - photochemical reflectance index (PRI) KW - allometric scaling and resource limitation KW - R690/R630 KW - modelling aboveground biomass KW - leaf area index KW - forest degradation KW - spectral analyses KW - terrestrial laser scanning KW - BAAPA KW - leaf area index (LAI) KW - stem volume estimation KW - tomographic profiles KW - polarization coherence tomography (PCT) KW - canopy gap fraction KW - automated classification KW - HemiView KW - remote sensing KW - multisource remote sensing KW - Pléiades imagery KW - photogrammetric point cloud KW - farm types KW - terrestrial LiDAR KW - altitude KW - RapidEye KW - forest aboveground biomass KW - recovery KW - southern U.S. forests KW - NDVI KW - machine-learning KW - conifer forest KW - satellite KW - chlorophyll fluorescence (ChlF) KW - tree heights KW - phenology KW - point cloud KW - local maxima KW - clumping index KW - MODIS KW - digital aerial photograph KW - Mediterranean KW - hemispherical sky-oriented photo KW - managed temperate coniferous forests KW - fixed tree window size KW - drought KW - GLAS KW - smartphone-based method KW - forest above ground biomass (AGB) KW - forest inventory KW - over and understory cover KW - sampling design L1 - https://mdpi.com/books/pdfview/book/1542 LA - English LK - https://directory.doabooks.org/handle/20.500.12854/58176 PB - MDPI - Multidisciplinary Digital Publishing Institute PY - 2019 SN - 9783039212392 SN - 9783039212408 TI - Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parametersnull ER -