Inferring Grassland Drought Stress with Unsupervised Learning from Airborne Hyperspectral VNIR Imagery

Inferring Grassland Drought Stress with Unsupervised Learning from Airborne Hyperspectral VNIR Imagery

The 2018–2019 Central European drought had a grave impact on natural and managed ecosystems, affecting their health and productivity. We examined patterns in hyperspectral VNIR imagery using an unsupervised learning approach to improve ecosystem monitoring and the understanding of grassland drought...

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Bibliographic Details
Main Authors: Floris Hermanns, Felix Pohl, Corinna Rebmann, Gundula Schulz, Ulrike Werban, Angela Lausch
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Remote Sensing
Subjects:
vegetation stress detection
unsupervised machine learning
drought
hyperspectral VNIR data
monitoring solutions
photochemical reflectance index
Online Access:https://www.mdpi.com/2072-4292/13/10/1885

Internet

https://www.mdpi.com/2072-4292/13/10/1885

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