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Article

A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia

1
Centre for Tropical Environmental and Sustainability Science, Cairns, QLD 4878, Australia
2
College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia
3
Department of Entomology, Tree Fruit Research and Extension Center, Washington State University, 110 N Western Ave., Wenatchee, WA 98801, USA
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Maitec, P.O. Box U19, Charles Darwin University, Darwin, NT 0815, Australia
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Centre for Tropical Water and Aquatic Ecosystem Research (TropWATER), James Cook University, Townsville, QLD 4811, Australia
6
Geoscience Australia, CNR Jerrabomberra Ave and Hindmarsh Drive, Symonston, ACT 2609, Australia
*
Author to whom correspondence should be addressed.
Current address: Fenner School of Environment and Society, Australian National University, Linnaeus Way, Acton ACT 2601, Australia.
Remote Sens. 2020, 12(24), 4008; https://doi.org/10.3390/rs12244008
Received: 22 September 2020 / Revised: 29 November 2020 / Accepted: 30 November 2020 / Published: 8 December 2020
(This article belongs to the Special Issue Remote Sensing in Mangroves)
Around the world, the effects of changing plant phenology are evident in many ways: from earlier and longer growing seasons to altering the relationships between plants and their natural pollinators. Plant phenology is often monitored using satellite images and parametric methods. Parametric methods assume that ecosystems have unimodal phenologies and that the phenology model is invariant through space and time. In evergreen ecosystems such as mangrove forests, these assumptions may not hold true. Here we present a novel, data-driven approach to extract plant phenology from Landsat imagery using Generalized Additive Models (GAMs). Using GAMs, we created models for six different mangrove forests across Australia. In contrast to parametric methods, GAMs let the data define the shape of the phenological curve, hence showing the unique characteristics of each study site. We found that the Enhanced Vegetation Index (EVI) model is related to leaf production rate (from in situ data), leaf gain and net leaf production (from the published literature). We also found that EVI does not respond immediately to leaf gain in most cases, but has a two- to three-month lag. We also identified the start of season and peak growing season dates at our field site. The former occurs between September and October and the latter May and July. The GAMs allowed us to identify dual phenology events in our study sites, indicated by two instances of high EVI and two instances of low EVI values throughout the year. We contribute to a better understanding of mangrove phenology by presenting a data-driven method that allows us to link physical changes of mangrove forests with satellite imagery. In the future, we will use GAMs to (1) relate phenology to environmental variables (e.g., temperature and rainfall) and (2) predict phenological changes. View Full-Text
Keywords: GAMs; Generalized Additive Models; EVI; Landsat; mangrove forests; phenology; time series analysis GAMs; Generalized Additive Models; EVI; Landsat; mangrove forests; phenology; time series analysis
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MDPI and ACS Style

Younes, N.; Northfield, T.D.; Joyce, K.E.; Maier, S.W.; Duke, N.C.; Lymburner, L. A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia. Remote Sens. 2020, 12, 4008. https://doi.org/10.3390/rs12244008

AMA Style

Younes N, Northfield TD, Joyce KE, Maier SW, Duke NC, Lymburner L. A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia. Remote Sensing. 2020; 12(24):4008. https://doi.org/10.3390/rs12244008

Chicago/Turabian Style

Younes, Nicolas, Tobin D. Northfield, Karen E. Joyce, Stefan W. Maier, Norman C. Duke, and Leo Lymburner. 2020. "A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia" Remote Sensing 12, no. 24: 4008. https://doi.org/10.3390/rs12244008

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