Hyperspectral Remote Sensing of Agriculture and Vegetation
This book shows recent and innovative applications of the use of hyperspectral technology for optimal quantification of crop, vegetation, and soil biophysical variables at various spatial scales, which can be an important aspect in agricultural management practices and monitoring. The articles colle...
I tiakina i:
| Hōputu: | Online |
|---|---|
| Reo: | Ingarihi |
| I whakaputaina: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Ngā marau: | |
| Urunga tuihono: | ONIX_20210501_9783039439072_65 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| _version_ | 1869517764094328832 |
|---|---|
| collection | Directory of Open Access Books |
| description | This book shows recent and innovative applications of the use of hyperspectral technology for optimal quantification of crop, vegetation, and soil biophysical variables at various spatial scales, which can be an important aspect in agricultural management practices and monitoring. The articles collected inside the book are intended to help researchers and farmers involved in precision agriculture techniques and practices, as well as in plant nutrient prediction, to a higher comprehension of strengths and limitations of the application of hyperspectral imaging to agriculture and vegetation. Hyperspectral remote sensing for studying agriculture and natural vegetation is a challenging research topic that will remain of great interest for different sciences communities in decades. |
| format | Online |
| id | doab-20.500.12854ir-68321 |
| 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-683212024-03-28T03:33:55Z Hyperspectral Remote Sensing of Agriculture and Vegetation Pascucci, Simone Pignatti, Stefano Casa, Raffaele Darvishzadeh, Roshanak Huang, Wenjiang hyperspectral LiDAR Red Edge AOTF vegetation parameters leaf chlorophyll content DLARI MDATT adaxial abaxial spectral reflectance peanut field spectroscopy hyperspectral heavy metals grapevine PLS SVM MLR multi-angle observation hyperspectral remote sensing BRDF vegetation classification object-oriented segmentation spectroscopy artificial intelligence proximal sensing data precision agriculture spectra vegetation plant classification discrimination feature selection waveband selection support vector machine random forest Natura 2000 invasive species expansive species biodiversity proximal sensor macronutrient micronutrient remote sensing hyperspectral imaging platforms and sensors analytical methods crop properties soil characteristics classification of agricultural features canopy spectra chlorophyll content continuous wavelet transform (CWT) correlation coefficient partial least square regression (PLSR) reproducibility replicability partial least squares Ethiopia Eragrostis tef hyperspectral remote sensing for soil and crops in agriculture hyperspectral imaging for vegetation plant traits high-resolution spectroscopy for agricultural soils and vegetation hyperspectral databases for agricultural soils and vegetation hyperspectral data as input for modelling soil, crop, and vegetation product validation new hyperspectral technologies future hyperspectral missions thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::K Economics, Finance, Business and Management::KC Economics::KCV Economics of specific sectors::KCVG Environmental economics This book shows recent and innovative applications of the use of hyperspectral technology for optimal quantification of crop, vegetation, and soil biophysical variables at various spatial scales, which can be an important aspect in agricultural management practices and monitoring. The articles collected inside the book are intended to help researchers and farmers involved in precision agriculture techniques and practices, as well as in plant nutrient prediction, to a higher comprehension of strengths and limitations of the application of hyperspectral imaging to agriculture and vegetation. Hyperspectral remote sensing for studying agriculture and natural vegetation is a challenging research topic that will remain of great interest for different sciences communities in decades. 2021-05-01T15:06:53Z 2021-05-01T15:06:53Z 2021 book ONIX_20210501_9783039439072_65 9783039439072 9783039439089 https://directory.doabooks.org/handle/20.500.12854/68321 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/3331 https://mdpi.com/books/pdfview/book/3331 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03943-908-9 10.3390/books978-3-03943-908-9 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039439072 9783039439089 266 Basel, Switzerland open access |
| spellingShingle | hyperspectral LiDAR Red Edge AOTF vegetation parameters leaf chlorophyll content DLARI MDATT adaxial abaxial spectral reflectance peanut field spectroscopy hyperspectral heavy metals grapevine PLS SVM MLR multi-angle observation hyperspectral remote sensing BRDF vegetation classification object-oriented segmentation spectroscopy artificial intelligence proximal sensing data precision agriculture spectra vegetation plant classification discrimination feature selection waveband selection support vector machine random forest Natura 2000 invasive species expansive species biodiversity proximal sensor macronutrient micronutrient remote sensing hyperspectral imaging platforms and sensors analytical methods crop properties soil characteristics classification of agricultural features canopy spectra chlorophyll content continuous wavelet transform (CWT) correlation coefficient partial least square regression (PLSR) reproducibility replicability partial least squares Ethiopia Eragrostis tef hyperspectral remote sensing for soil and crops in agriculture hyperspectral imaging for vegetation plant traits high-resolution spectroscopy for agricultural soils and vegetation hyperspectral databases for agricultural soils and vegetation hyperspectral data as input for modelling soil, crop, and vegetation product validation new hyperspectral technologies future hyperspectral missions thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::K Economics, Finance, Business and Management::KC Economics::KCV Economics of specific sectors::KCVG Environmental economics Hyperspectral Remote Sensing of Agriculture and Vegetation |
| title | Hyperspectral Remote Sensing of Agriculture and Vegetation |
| title_full | Hyperspectral Remote Sensing of Agriculture and Vegetation |
| title_fullStr | Hyperspectral Remote Sensing of Agriculture and Vegetation |
| title_full_unstemmed | Hyperspectral Remote Sensing of Agriculture and Vegetation |
| title_short | Hyperspectral Remote Sensing of Agriculture and Vegetation |
| title_sort | hyperspectral remote sensing of agriculture and vegetation |
| topic | hyperspectral LiDAR Red Edge AOTF vegetation parameters leaf chlorophyll content DLARI MDATT adaxial abaxial spectral reflectance peanut field spectroscopy hyperspectral heavy metals grapevine PLS SVM MLR multi-angle observation hyperspectral remote sensing BRDF vegetation classification object-oriented segmentation spectroscopy artificial intelligence proximal sensing data precision agriculture spectra vegetation plant classification discrimination feature selection waveband selection support vector machine random forest Natura 2000 invasive species expansive species biodiversity proximal sensor macronutrient micronutrient remote sensing hyperspectral imaging platforms and sensors analytical methods crop properties soil characteristics classification of agricultural features canopy spectra chlorophyll content continuous wavelet transform (CWT) correlation coefficient partial least square regression (PLSR) reproducibility replicability partial least squares Ethiopia Eragrostis tef hyperspectral remote sensing for soil and crops in agriculture hyperspectral imaging for vegetation plant traits high-resolution spectroscopy for agricultural soils and vegetation hyperspectral databases for agricultural soils and vegetation hyperspectral data as input for modelling soil, crop, and vegetation product validation new hyperspectral technologies future hyperspectral missions thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::K Economics, Finance, Business and Management::KC Economics::KCV Economics of specific sectors::KCVG Environmental economics |
| topic_facet | hyperspectral LiDAR Red Edge AOTF vegetation parameters leaf chlorophyll content DLARI MDATT adaxial abaxial spectral reflectance peanut field spectroscopy hyperspectral heavy metals grapevine PLS SVM MLR multi-angle observation hyperspectral remote sensing BRDF vegetation classification object-oriented segmentation spectroscopy artificial intelligence proximal sensing data precision agriculture spectra vegetation plant classification discrimination feature selection waveband selection support vector machine random forest Natura 2000 invasive species expansive species biodiversity proximal sensor macronutrient micronutrient remote sensing hyperspectral imaging platforms and sensors analytical methods crop properties soil characteristics classification of agricultural features canopy spectra chlorophyll content continuous wavelet transform (CWT) correlation coefficient partial least square regression (PLSR) reproducibility replicability partial least squares Ethiopia Eragrostis tef hyperspectral remote sensing for soil and crops in agriculture hyperspectral imaging for vegetation plant traits high-resolution spectroscopy for agricultural soils and vegetation hyperspectral databases for agricultural soils and vegetation hyperspectral data as input for modelling soil, crop, and vegetation product validation new hyperspectral technologies future hyperspectral missions thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::K Economics, Finance, Business and Management::KC Economics::KCV Economics of specific sectors::KCVG Environmental economics |
| url | ONIX_20210501_9783039439072_65 |