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210512 ||| eng |
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|a 9783039439072
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|a 9783039439089
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|a books978-3-03943-908-9
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| 100 |
1 |
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|a Pascucci, Simone
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| 245 |
0 |
0 |
|a Hyperspectral Remote Sensing of Agriculture and Vegetation
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2021
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| 300 |
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|a 1 electronic resource (266 p.)
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| 653 |
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|a peanut
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| 653 |
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|a leaf chlorophyll content
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| 653 |
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|a multi-angle observation
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| 653 |
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|a platforms and sensors
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| 653 |
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|a hyperspectral LiDAR
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| 653 |
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|a BRDF
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| 653 |
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|a hyperspectral imaging for vegetation
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| 653 |
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|a hyperspectral
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| 653 |
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|a proximal sensor
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| 653 |
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|a feature selection
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| 653 |
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|a micronutrient
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| 653 |
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|a Eragrostis tef
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| 653 |
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|a vegetation
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| 653 |
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|a proximal sensing data
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| 653 |
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|a invasive species
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| 653 |
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|a PLS
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| 653 |
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|a vegetation classification
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| 653 |
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|a expansive species
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| 653 |
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|a hyperspectral imaging
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| 653 |
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|a Research & information: general / bicssc
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| 653 |
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|a MLR
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| 653 |
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|a product validation
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| 653 |
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|a waveband selection
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| 653 |
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|a support vector machine
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| 653 |
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|a soil characteristics
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| 653 |
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|a Environmental economics / bicssc
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| 653 |
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|a adaxial
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| 653 |
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|a grapevine
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| 653 |
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|a new hyperspectral technologies
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| 653 |
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|a plant traits
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| 653 |
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|a replicability
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| 653 |
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|a spectroscopy
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| 653 |
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|a chlorophyll content
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| 653 |
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|a spectral reflectance
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| 653 |
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|a hyperspectral databases for agricultural soils and vegetation
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| 653 |
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|a canopy spectra
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| 653 |
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|a crop properties
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| 653 |
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|a field spectroscopy
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| 653 |
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|a precision agriculture
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| 653 |
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|a random forest
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| 653 |
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|a partial least square regression (PLSR)
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| 653 |
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|a discrimination
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| 653 |
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|a correlation coefficient
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| 653 |
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|a Ethiopia
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| 653 |
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|a Natura 2000
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| 653 |
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|a plant
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| 653 |
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|a MDATT
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| 653 |
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|a partial least squares
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| 653 |
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|a DLARI
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| 653 |
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|a high-resolution spectroscopy for agricultural soils and vegetation
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| 653 |
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|a hyperspectral remote sensing
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| 653 |
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|a spectra
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| 653 |
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|a continuous wavelet transform (CWT)
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| 653 |
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|a abaxial
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| 653 |
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|a object-oriented segmentation
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| 653 |
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|a vegetation parameters
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| 653 |
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|a hyperspectral remote sensing for soil and crops in agriculture
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| 653 |
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|a analytical methods
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| 653 |
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|a future hyperspectral missions
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| 653 |
|
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|a reproducibility
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| 653 |
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|a Red Edge
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| 653 |
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|a AOTF
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| 653 |
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|a hyperspectral data as input for modelling soil, crop, and vegetation
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| 653 |
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|a remote sensing
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| 653 |
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|a classification of agricultural features
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| 653 |
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|a heavy metals
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| 653 |
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|a SVM
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| 653 |
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|a biodiversity
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| 653 |
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|a artificial intelligence
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| 653 |
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|a classification
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| 653 |
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|a macronutrient
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| 700 |
1 |
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|a Pignatti, Stefano
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| 700 |
1 |
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|a Casa, Raffaele
|
| 700 |
1 |
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|a Darvishzadeh, Roshanak
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
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|b DOAB
|a Directory of Open Access Books
|
| 500 |
|
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
|
| 028 |
5 |
0 |
|a 10.3390/books978-3-03943-908-9
|
| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/68321
|z DOAB: description of the publication
|
| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/3331
|7 0
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 363
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|a 000
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| 082 |
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|a 330
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|a 630
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|a 580
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|a 700
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|a 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.
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