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Lookup NU author(s): Dr Elias Berra,
Dr Rachel Gaulton
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
Vegetation phenology is the study of recurring plant life cycle stages, seasonality which is linked to many ecosystem processes and is an important proxy of climate and environmental change. Remote sensing has been playing an important and increasing role in the monitoring and assessment of vegetation phenology. The aim of this review is to critically examine key studies related to remote sensing of vegetation phenology, with a special focus on temperate and boreal forests. Specifically, we focus on how the latest ground, near-surface and aerial data have been used to assess the satellite-derived Land Surface Phenology (LSP) metrics and the agreements that has been achieved in the last 15 years. Results demonstrated that the timing of satellite-derived LSP events can be detected, in the best-case scenarios, with a certainty of around half-week for spring metrics (e.g. Day of Year -DOY- of start of growing season) and around one week for autumn metrics (e.g. DOY of end of growing season). With expected shifts in plant phenology averaging <1 day per decade, such LSP uncertainties (in terms of absolute phenological dates) could greatly over- or under-estimate these species-level shifts; but the spatial variation in phenology can be consistently monitored. An increasing number of studies have investigated autumn phenology in the last decade, but autumn phenological dates continue to be more challenging to retrieve and interpret than spring dates. Emerging opportunities to further advance remote sensing of forest phenology is presented that includes synergetic use of multiple orbital sensors and its LSP evaluation with data from new sensors at a ground, near-surface and airborne level; yet traditional ground-based observations will continue to be highly useful to accurately record the timing of species-specific phenological events. This review might provide a guide for planning and managing remote sensing of forest phenology.
Author(s): Berra EF, Gaulton R
Publication type: Article
Publication status: Published
Journal: Forest Ecology and Management
Print publication date: 15/01/2021
Online publication date: 12/10/2020
Acceptance date: 30/09/2020
Date deposited: 09/11/2020
ISSN (print): 0378-1127
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