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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| Главные авторы: | , , |
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| Формат: | Online |
| Язык: | английский |
| Опубликовано: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Предметы: | |
| Online-ссылка: | 42517 |
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| _version_ | 1869517806789197824 |
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| author | Sanchez, Juanma Lopez Fang, Hongliang García-Haro, Francisco Javier |
| author_browse | Fang, Hongliang García-Haro, Francisco Javier Sanchez, Juanma Lopez |
| author_facet | Sanchez, Juanma Lopez Fang, Hongliang García-Haro, Francisco Javier |
| author_sort | Sanchez, Juanma Lopez |
| collection | Directory of Open Access Books |
| description | 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 | Online |
| id | doab-20.500.12854ir-58176 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-581762023-12-20T18:40:22Z Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters Sanchez, Juanma Lopez Fang, Hongliang García-Haro, Francisco Javier Q1-390 artificial neural network downscaling simulation 3D point cloud European beech consistency adaptive threshold evaluation photosynthesis geographic information system P-band PolInSAR validation density-based clustering structure from motion (SfM) EPIC Tanzania signal attenuation trunk canopy closure REDD+ unmanned aerial vehicle (UAV) forest recursive feature elimination Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) aboveground biomass random forest uncertainty household survey spectral information forests biomass root biomass biomass unmanned aerial vehicle Brazilian Amazon VIIRS global positioning system LAI photochemical reflectance index (PRI) allometric scaling and resource limitation R690/R630 modelling aboveground biomass leaf area index forest degradation spectral analyses terrestrial laser scanning BAAPA leaf area index (LAI) stem volume estimation tomographic profiles polarization coherence tomography (PCT) canopy gap fraction automated classification HemiView remote sensing multisource remote sensing Pléiades imagery photogrammetric point cloud farm types terrestrial LiDAR altitude RapidEye forest aboveground biomass recovery southern U.S. forests NDVI machine-learning conifer forest satellite chlorophyll fluorescence (ChlF) tree heights phenology point cloud local maxima clumping index MODIS digital aerial photograph Mediterranean hemispherical sky-oriented photo managed temperate coniferous forests fixed tree window size drought GLAS smartphone-based method forest above ground biomass (AGB) forest inventory over and understory cover sampling design bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general 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. 2021-02-12T01:48:02Z 2021-02-12T01:48:02Z 2019-12-09 11:49:15 2019 book 42517 9783039212392 9783039212408 https://directory.doabooks.org/handle/20.500.12854/58176 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/1542 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03921-240-8 10.3390/books978-3-03921-240-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039212392 9783039212408 334 open access |
| spellingShingle | Q1-390 artificial neural network downscaling simulation 3D point cloud European beech consistency adaptive threshold evaluation photosynthesis geographic information system P-band PolInSAR validation density-based clustering structure from motion (SfM) EPIC Tanzania signal attenuation trunk canopy closure REDD+ unmanned aerial vehicle (UAV) forest recursive feature elimination Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) aboveground biomass random forest uncertainty household survey spectral information forests biomass root biomass biomass unmanned aerial vehicle Brazilian Amazon VIIRS global positioning system LAI photochemical reflectance index (PRI) allometric scaling and resource limitation R690/R630 modelling aboveground biomass leaf area index forest degradation spectral analyses terrestrial laser scanning BAAPA leaf area index (LAI) stem volume estimation tomographic profiles polarization coherence tomography (PCT) canopy gap fraction automated classification HemiView remote sensing multisource remote sensing Pléiades imagery photogrammetric point cloud farm types terrestrial LiDAR altitude RapidEye forest aboveground biomass recovery southern U.S. forests NDVI machine-learning conifer forest satellite chlorophyll fluorescence (ChlF) tree heights phenology point cloud local maxima clumping index MODIS digital aerial photograph Mediterranean hemispherical sky-oriented photo managed temperate coniferous forests fixed tree window size drought GLAS smartphone-based method forest above ground biomass (AGB) forest inventory over and understory cover sampling design bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general Sanchez, Juanma Lopez Fang, Hongliang García-Haro, Francisco Javier Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters |
| title | Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters |
| title_full | Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters |
| title_fullStr | Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters |
| title_full_unstemmed | Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters |
| title_short | Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters |
| title_sort | remote sensing of leaf area index lai and other vegetation parameters |
| topic | Q1-390 artificial neural network downscaling simulation 3D point cloud European beech consistency adaptive threshold evaluation photosynthesis geographic information system P-band PolInSAR validation density-based clustering structure from motion (SfM) EPIC Tanzania signal attenuation trunk canopy closure REDD+ unmanned aerial vehicle (UAV) forest recursive feature elimination Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) aboveground biomass random forest uncertainty household survey spectral information forests biomass root biomass biomass unmanned aerial vehicle Brazilian Amazon VIIRS global positioning system LAI photochemical reflectance index (PRI) allometric scaling and resource limitation R690/R630 modelling aboveground biomass leaf area index forest degradation spectral analyses terrestrial laser scanning BAAPA leaf area index (LAI) stem volume estimation tomographic profiles polarization coherence tomography (PCT) canopy gap fraction automated classification HemiView remote sensing multisource remote sensing Pléiades imagery photogrammetric point cloud farm types terrestrial LiDAR altitude RapidEye forest aboveground biomass recovery southern U.S. forests NDVI machine-learning conifer forest satellite chlorophyll fluorescence (ChlF) tree heights phenology point cloud local maxima clumping index MODIS digital aerial photograph Mediterranean hemispherical sky-oriented photo managed temperate coniferous forests fixed tree window size drought GLAS smartphone-based method forest above ground biomass (AGB) forest inventory over and understory cover sampling design bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general |
| topic_facet | Q1-390 artificial neural network downscaling simulation 3D point cloud European beech consistency adaptive threshold evaluation photosynthesis geographic information system P-band PolInSAR validation density-based clustering structure from motion (SfM) EPIC Tanzania signal attenuation trunk canopy closure REDD+ unmanned aerial vehicle (UAV) forest recursive feature elimination Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) aboveground biomass random forest uncertainty household survey spectral information forests biomass root biomass biomass unmanned aerial vehicle Brazilian Amazon VIIRS global positioning system LAI photochemical reflectance index (PRI) allometric scaling and resource limitation R690/R630 modelling aboveground biomass leaf area index forest degradation spectral analyses terrestrial laser scanning BAAPA leaf area index (LAI) stem volume estimation tomographic profiles polarization coherence tomography (PCT) canopy gap fraction automated classification HemiView remote sensing multisource remote sensing Pléiades imagery photogrammetric point cloud farm types terrestrial LiDAR altitude RapidEye forest aboveground biomass recovery southern U.S. forests NDVI machine-learning conifer forest satellite chlorophyll fluorescence (ChlF) tree heights phenology point cloud local maxima clumping index MODIS digital aerial photograph Mediterranean hemispherical sky-oriented photo managed temperate coniferous forests fixed tree window size drought GLAS smartphone-based method forest above ground biomass (AGB) forest inventory over and understory cover sampling design bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general |
| url | 42517 |
| work_keys_str_mv | AT sanchezjuanmalopez remotesensingofleafareaindexlaiandothervegetationparameters AT fanghongliang remotesensingofleafareaindexlaiandothervegetationparameters AT garciaharofranciscojavier remotesensingofleafareaindexlaiandothervegetationparameters |