Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters
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...
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| Format: | Book Chapter |
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MDPI - Multidisciplinary Digital Publishing Institute
2019
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 05310naaaa2201309uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_58176 | ||
| 005 | 20210212 | ||
| 020 | |a books978-3-03921-240-8 | ||
| 020 | |a 9783039212392 | ||
| 020 | |a 9783039212408 | ||
| 024 | 7 | |a 10.3390/books978-3-03921-240-8 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Sanchez, Juanma Lopez |4 auth | |
| 700 | 1 | |a Fang, Hongliang |4 auth | |
| 700 | 1 | |a García-Haro, Francisco Javier |4 auth | |
| 245 | 1 | 0 | |a Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2019 | ||
| 300 | |a 1 electronic resource (334 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a 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. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
| 546 | |a English | ||
| 653 | |a artificial neural network | ||
| 653 | |a downscaling | ||
| 653 | |a simulation | ||
| 653 | |a 3D point cloud | ||
| 653 | |a European beech | ||
| 653 | |a consistency | ||
| 653 | |a adaptive threshold | ||
| 653 | |a evaluation | ||
| 653 | |a photosynthesis | ||
| 653 | |a geographic information system | ||
| 653 | |a P-band PolInSAR | ||
| 653 | |a validation | ||
| 653 | |a density-based clustering | ||
| 653 | |a structure from motion (SfM) | ||
| 653 | |a EPIC | ||
| 653 | |a Tanzania | ||
| 653 | |a signal attenuation | ||
| 653 | |a trunk | ||
| 653 | |a canopy closure | ||
| 653 | |a REDD+ | ||
| 653 | |a unmanned aerial vehicle (UAV) | ||
| 653 | |a forest | ||
| 653 | |a recursive feature elimination | ||
| 653 | |a Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) | ||
| 653 | |a aboveground biomass | ||
| 653 | |a random forest | ||
| 653 | |a uncertainty | ||
| 653 | |a household survey | ||
| 653 | |a spectral information | ||
| 653 | |a forests biomass | ||
| 653 | |a root biomass | ||
| 653 | |a biomass | ||
| 653 | |a unmanned aerial vehicle | ||
| 653 | |a Brazilian Amazon | ||
| 653 | |a VIIRS | ||
| 653 | |a global positioning system | ||
| 653 | |a LAI | ||
| 653 | |a photochemical reflectance index (PRI) | ||
| 653 | |a allometric scaling and resource limitation | ||
| 653 | |a R690/R630 | ||
| 653 | |a modelling aboveground biomass | ||
| 653 | |a leaf area index | ||
| 653 | |a forest degradation | ||
| 653 | |a spectral analyses | ||
| 653 | |a terrestrial laser scanning | ||
| 653 | |a BAAPA | ||
| 653 | |a leaf area index (LAI) | ||
| 653 | |a stem volume estimation | ||
| 653 | |a tomographic profiles | ||
| 653 | |a polarization coherence tomography (PCT) | ||
| 653 | |a canopy gap fraction | ||
| 653 | |a automated classification | ||
| 653 | |a HemiView | ||
| 653 | |a remote sensing | ||
| 653 | |a multisource remote sensing | ||
| 653 | |a Pléiades imagery | ||
| 653 | |a photogrammetric point cloud | ||
| 653 | |a farm types | ||
| 653 | |a terrestrial LiDAR | ||
| 653 | |a altitude | ||
| 653 | |a RapidEye | ||
| 653 | |a forest aboveground biomass | ||
| 653 | |a recovery | ||
| 653 | |a southern U.S. forests | ||
| 653 | |a NDVI | ||
| 653 | |a machine-learning | ||
| 653 | |a conifer forest | ||
| 653 | |a satellite | ||
| 653 | |a chlorophyll fluorescence (ChlF) | ||
| 653 | |a tree heights | ||
| 653 | |a phenology | ||
| 653 | |a point cloud | ||
| 653 | |a local maxima | ||
| 653 | |a clumping index | ||
| 653 | |a MODIS | ||
| 653 | |a digital aerial photograph | ||
| 653 | |a Mediterranean | ||
| 653 | |a hemispherical sky-oriented photo | ||
| 653 | |a managed temperate coniferous forests | ||
| 653 | |a fixed tree window size | ||
| 653 | |a drought | ||
| 653 | |a GLAS | ||
| 653 | |a smartphone-based method | ||
| 653 | |a forest above ground biomass (AGB) | ||
| 653 | |a forest inventory | ||
| 653 | |a over and understory cover | ||
| 653 | |a sampling design | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/1542 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/58176 |7 0 |z DOAB: description of the publication |