Title
Quantifying the Uncertainty of Short-Term Vegetation Anomalies Detection Using Eo-Based Coarse-Resolution Vegetation Products
Author
Zhou, J. (TU Delft Optical and Laser Remote Sensing; Central China Normal University; Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Wuhan)
Liu, Xuan (Central China Normal University; Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Wuhan)
Xiong, Xuqian (Central China Normal University; Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Wuhan)
Jia, Li (Chinese Academy of Sciences)
Lu, Jing (Chinese Academy of Sciences)
Cui, Yilin (Central China Normal University; Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Wuhan)
Date
2022
Abstract
Satellite-based Earth Observation systems archived a variety of vegetation products during the last 50 years, which can reveal regional to global ecosystem dynamics across diverse spatiotemporal scales. The anomaly metrics such as Vegetation Condition Index (VCI) defined by comparing the current vegetation growth condition to historical average status based on long-term EO-based vegetation products were widely used to delineate abnormal vegetation variation exerted by either climatic or anthropogenic factors (e.g., droughts, wildfires). However, currently available long-term vegetation products may differ from each other in terms of sensors (observational platform or spectral bands), bio-physical definitions (e.g., NDVI, EVI, LAI, and VOD), spatiotemporal resolution, as well as the time-spans, which results in inconsistency across these vegetation products. Taking the VCI as an example, this study evaluated the uncertainty of vegetation anomalies detected based on different vegetation products over the middle reach of the Yangtze River by explicitly considering the effect of sensors, biophysical definitions, and time-spans. The preliminary results showed that VCI derived from NDVI products from different sensors (AVHRR vs. MODIS) induced significant inconsistent anomalies over most landscapes. The differences resulting from products with different biophysical definitions (NDVI vs. EVI, LAI, and VOD) are much lower than those from different sensors but still significant over specific areas. As for the time-spans, the 20-year NDVI based VCI presented a considerable reduction in variance over the study area on average compared to VCI calculated based on 5-year NDVI. In summary, caution should be taken when applying EO-based vegetation products for vegetation anomalies mapping, especially for quantitative assessment.
Subject
EO-based vegetation products
uncertainty
vegetation anomalies
Vegetation Condition Index
To reference this document use:
http://resolver.tudelft.nl/uuid:c75f864b-814f-4f8d-b749-0b503906af15
DOI
https://doi.org/10.1109/IGARSS46834.2022.9884555
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Embargo date
2023-07-01
ISBN
978-1-6654-2793-7
Source
Proceedings of the IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Event
2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022, 2022-07-17 → 2022-07-22, Kuala Lumpur, Malaysia
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
conference paper
Rights
© 2022 J. Zhou, Xuan Liu, Xuqian Xiong, Li Jia, Jing Lu, Yilin Cui