| 报告题名: | Sentinel4Nature: Estimating environmental gradients and properties using remote sensing |
| 作者: | Blumentrath, Stefan; Nowell, Megan; Salberg, Arnt-Børre; Kermit, Martin; Bakkestuen, Vegar; Erikstad, Lars; Bernhardt, Jacob Jeff
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| 出版年: | 2016
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| 发表日期: | 2016-05-02
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| 国家: | 挪威
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| 语种: | 英语
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| 英文关键词: | NINA Kortrapport
; Norway
; Oslofjord
; Lurøykalven
; Hjerkinn
; Sunndalen
; environmental gradients
; reduced growing season due to prolonged snow-lie
; tree canopy cover
; remote sensing
; sentinel imagery
; data fusion
; modelling
; NiN
; PRODEX
; ESA
; NRS
; Norge
; lokale komplekse miljøvariabler
; LKM
; snødekkebetinget vekstsesongreduksjon
; tresjiktstetthet
; fjernmåling
; modellering
; NINA Kortrapport
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| 英文摘要: | Blumentrath, S., Nowell, M, Salberg, A., Kermit, M., Bakkestuen, V., Erikstad, L., Bernhardt, J. J. 2016. Sentinel4Nature: Estimating environmental gradients and properties using remote sensing. - NINA Kortrapport 6. 20 pp.
The Sentinel4Nature project was launched in 2014 with the objective of exploring the potential of using remote sensing techniques to detect and model environmental gradients identified by the Nature in Norway (NiN) classification system. The following report describes the progress of the Sentinel4Nature project to date. Two environmental gradients were selected, namely 1) Re-duced growing season due to prolonged snow-lie and 2) Tree canopy cover. The methodology for modelling these two gradients is discussed. The suitability of Sentinel imagery for the objec-tives of this project has also been explored. |
| URL: | https://brage.bibsys.no/xmlui/bitstream/handle/11250/2388191/Seks.pdf?sequence=4&isAllowed=y
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| 资源类型: | 研究报告
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/28861
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| Appears in Collections: | 过去全球变化的重建
影响、适应和脆弱性
科学计划与规划
气候变化与战略
全球变化的国际研究计划
气候减缓与适应
气候变化事实与影响
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| Seks.pdf（2274KB） | 研究报告 | -- | 开放获取 | | View
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| Recommended Citation: | |
Blumentrath, Stefan,Nowell, Megan,Salberg, Arnt-Børre,et al. Sentinel4Nature: Estimating environmental gradients and properties using remote sensing. 2016-01-01.
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