Reprint

Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters

Edited by
September 2019
334 pages
  • ISBN978-3-03921-239-2 (Paperback)
  • ISBN978-3-03921-240-8 (PDF)

This book is a reprint of the Special Issue Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters that was published in

Biology & Life Sciences
Environmental & Earth Sciences
Summary

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.

Format
  • Paperback
License
© 2019 by the authors; CC BY-NC-ND license
Keywords
conifer forest; leaf area index; smartphone-based method; canopy gap fraction; terrestrial laser scanning; forest inventory; density-based clustering; forest aboveground biomass; root biomass; tree heights; GLAS; artificial neural network; allometric scaling and resource limitation; structure from motion (SfM); 3D point cloud; remote sensing; local maxima; fixed tree window size; managed temperate coniferous forests; point cloud; spectral information; structure from motion (SfM); unmanned aerial vehicle (UAV); chlorophyll fluorescence (ChlF); drought; Mediterranean; photochemical reflectance index (PRI); photosynthesis; R690/R630; recovery; BAAPA; remote sensing; household survey; forest; farm types; automated classification; sampling design; adaptive threshold; over and understory cover; LAI; leaf area index; EPIC; simulation; satellite; MODIS; biomass; evaluation; southern U.S. forests; VIIRS; leaf area index (LAI); Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR); MODIS; consistency; uncertainty; evaluation; downscaling; Pléiades imagery; unmanned aerial vehicle; stem volume estimation; remote sensing; clumping index; leaf area index; trunk; terrestrial LiDAR; HemiView; forest above ground biomass (AGB); polarization coherence tomography (PCT); P-band PolInSAR; tomographic profiles; canopy closure; global positioning system; hemispherical sky-oriented photo; signal attenuation; geographic information system; digital aerial photograph; aboveground biomass; leaf area index; photogrammetric point cloud; recursive feature elimination; machine-learning; forest degradation; multisource remote sensing; modelling aboveground biomass; random forest; Brazilian Amazon; validation; phenology; NDVI; LAI; spectral analyses; European beech; altitude; forests biomass; remote sensing; REDD+; random forest; Tanzania; RapidEye