Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation:
Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspect...
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, FL
CRC Press
[2018]
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume I, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation introduces the fundamentals of hyperspectral or imaging spectroscopy data, including hyperspectral data processes, sensor systems, spectral libraries, and data mining and analysis, covering both the strengths and limitations of these topics. This book also presents and discusses hyperspectral narrowband data acquired in numerous unique spectral bands in the entire length of the spectrum from various ground-based, airborne, and spaceborne platforms. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume I through the editors' perspective. Key Features of Volume I: Provides the fundamentals of hyperspectral remote sensing used in agricultural crops and vegetation studies. Discusses the latest advances in hyperspectral remote sensing of ecosystems and croplands. Develops online hyperspectral libraries, proximal sensing and phenotyping for understanding, modeling, mapping, and monitoring crop and vegetation traits. Implements reflectance spectroscopy of soils and vegetation. Enumerates hyperspectral data mining and data processing methods, approaches, and machine learning algorithms. Explores methods and approaches for data mining and overcoming data redundancy; Highlights the advanced methods for hyperspectral data processing steps by developing or implementing appropriate algorithms and coding the same for processing on a cloud computing platform like the Google Earth Engine. Integrates hyperspectral with other data, such as the LiDAR data, in the study of vegetation. Includes best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, crop productivity and water productivity mapping, and modeling |
| Beschreibung: | 1 online resource (489 pages) 201 illustrations, text file, PDF |
| ISBN: | 9781315164151 |
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| 520 | |a Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume I, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation introduces the fundamentals of hyperspectral or imaging spectroscopy data, including hyperspectral data processes, sensor systems, spectral libraries, and data mining and analysis, covering both the strengths and limitations of these topics. This book also presents and discusses hyperspectral narrowband data acquired in numerous unique spectral bands in the entire length of the spectrum from various ground-based, airborne, and spaceborne platforms. | ||
| 520 | |a The concluding chapter provides readers with useful guidance on the highlights and essence of Volume I through the editors' perspective. Key Features of Volume I: Provides the fundamentals of hyperspectral remote sensing used in agricultural crops and vegetation studies. Discusses the latest advances in hyperspectral remote sensing of ecosystems and croplands. Develops online hyperspectral libraries, proximal sensing and phenotyping for understanding, modeling, mapping, and monitoring crop and vegetation traits. Implements reflectance spectroscopy of soils and vegetation. Enumerates hyperspectral data mining and data processing methods, approaches, and machine learning algorithms. Explores methods and approaches for data mining and overcoming data redundancy; Highlights the advanced methods for hyperspectral data processing steps by developing or implementing appropriate algorithms and coding the same for processing on a cloud computing platform like the Google Earth Engine. | ||
| 520 | |a Integrates hyperspectral with other data, such as the LiDAR data, in the study of vegetation. Includes best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, crop productivity and water productivity mapping, and modeling | ||
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| id | DE-604.BV046620866 |
| illustrated | Illustrated |
| index_date | 2024-07-03T14:08:07Z |
| indexdate | 2024-07-10T08:49:27Z |
| institution | BVB |
| isbn | 9781315164151 |
| language | English |
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| spelling | Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation edited by Prasad S. Thenkabail, John G. Lyon and Alfredo Huete Second edition Boca Raton, FL CRC Press [2018] © 2019 1 online resource (489 pages) 201 illustrations, text file, PDF txt rdacontent c rdamedia cr rdacarrier Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume I, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation introduces the fundamentals of hyperspectral or imaging spectroscopy data, including hyperspectral data processes, sensor systems, spectral libraries, and data mining and analysis, covering both the strengths and limitations of these topics. This book also presents and discusses hyperspectral narrowband data acquired in numerous unique spectral bands in the entire length of the spectrum from various ground-based, airborne, and spaceborne platforms. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume I through the editors' perspective. Key Features of Volume I: Provides the fundamentals of hyperspectral remote sensing used in agricultural crops and vegetation studies. Discusses the latest advances in hyperspectral remote sensing of ecosystems and croplands. Develops online hyperspectral libraries, proximal sensing and phenotyping for understanding, modeling, mapping, and monitoring crop and vegetation traits. Implements reflectance spectroscopy of soils and vegetation. Enumerates hyperspectral data mining and data processing methods, approaches, and machine learning algorithms. Explores methods and approaches for data mining and overcoming data redundancy; Highlights the advanced methods for hyperspectral data processing steps by developing or implementing appropriate algorithms and coding the same for processing on a cloud computing platform like the Google Earth Engine. Integrates hyperspectral with other data, such as the LiDAR data, in the study of vegetation. Includes best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, crop productivity and water productivity mapping, and modeling TECHNOLOGY & ENGINEERING / Agriculture / General bisacsh SCIENCE / Earth Sciences / General bisacsh Broad-band data from sensors; Landsat ETM+ bisacsh Cloud computing bisacsh Crop water use and water productivity modeling and mapping bisacsh Hyperspectral sensor systems bisacsh Spaceborne hyperspectral EO-1 Hyperion pre-processing bisacsh UAV and field hyperspectral data bisacsh Vegetation monitoring Plants / Remote sensing Crops / Remote sensing Multispectral imaging Thenkabail, Prasad S. edt Lyon, John G. edt Huete, Alfredo edt Taylor and Francis Sonstige oth https://www.taylorfrancis.com/books/9781315164151 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation TECHNOLOGY & ENGINEERING / Agriculture / General bisacsh SCIENCE / Earth Sciences / General bisacsh Broad-band data from sensors; Landsat ETM+ bisacsh Cloud computing bisacsh Crop water use and water productivity modeling and mapping bisacsh Hyperspectral sensor systems bisacsh Spaceborne hyperspectral EO-1 Hyperion pre-processing bisacsh UAV and field hyperspectral data bisacsh Vegetation monitoring Plants / Remote sensing Crops / Remote sensing Multispectral imaging |
| title | Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation |
| title_auth | Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation |
| title_exact_search | Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation |
| title_exact_search_txtP | Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation |
| title_full | Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation edited by Prasad S. Thenkabail, John G. Lyon and Alfredo Huete |
| title_fullStr | Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation edited by Prasad S. Thenkabail, John G. Lyon and Alfredo Huete |
| title_full_unstemmed | Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation edited by Prasad S. Thenkabail, John G. Lyon and Alfredo Huete |
| title_short | Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation |
| title_sort | fundamentals sensor systems spectral libraries and data mining for vegetation |
| topic | TECHNOLOGY & ENGINEERING / Agriculture / General bisacsh SCIENCE / Earth Sciences / General bisacsh Broad-band data from sensors; Landsat ETM+ bisacsh Cloud computing bisacsh Crop water use and water productivity modeling and mapping bisacsh Hyperspectral sensor systems bisacsh Spaceborne hyperspectral EO-1 Hyperion pre-processing bisacsh UAV and field hyperspectral data bisacsh Vegetation monitoring Plants / Remote sensing Crops / Remote sensing Multispectral imaging |
| topic_facet | TECHNOLOGY & ENGINEERING / Agriculture / General SCIENCE / Earth Sciences / General Broad-band data from sensors; Landsat ETM+ Cloud computing Crop water use and water productivity modeling and mapping Hyperspectral sensor systems Spaceborne hyperspectral EO-1 Hyperion pre-processing UAV and field hyperspectral data Vegetation monitoring Plants / Remote sensing Crops / Remote sensing Multispectral imaging |
| url | https://www.taylorfrancis.com/books/9781315164151 |
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