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Research article - Peer-reviewed, 2021

Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing

Noumonvi, Koffi Dodji; Oblisar, Gal; Zust, Ana; Vilhar, Ursa;

Abstract

Phenological events are good indicators of the effects of climate change, since phenological phases are sensitive to changes in environmental conditions. Although several national phenological networks monitor the phenology of different plant species, direct observations can only be conducted on individual trees, which cannot be easily extended over large and continuous areas. Remote sensing has often been applied to model phenology for large areas, focusing mostly on pure forests in which it is relatively easier to match vegetation indices with ground observations. In mixed forests, phenology modelling from remote sensing is often limited to land surface phenology, which consists of an overall phenology of all tree species present in a pixel. The potential of remote sensing for modelling the phenology of individual tree species in mixed forests remains underexplored. In this study, we applied the seasonal midpoint (SM) method with MODIS GPP to model the start of season (SOS) and the end of season (EOS) of six different tree species in Slovenian mixed forests. First, substitute locations were identified for each combination of observation station and plant species based on similar environmental conditions (aspect, slope, and altitude) and tree species of interest, and used to retrieve the remote sensing information used in the SM method after fitting the best of a Gaussian and two double logistic functions to each year of GPP time series. Then, the best thresholds were identified for SOS and EOS, and the results were validated using cross-validation. The results show clearly that the usual threshold of 0.5 is not best in most cases, especially for estimating the EOS. Despite the difficulty in modelling the phenology of different tree species in a mixed forest using remote sensing, it was possible to estimate SOS and EOS with moderate errors as low as <8 days (Fagus sylvatica and Tilia sp.) and <10 days (Fagus sylvatica and Populus tremula), respectively.

Keywords

phenology modelling; start of season; end of season; remote sensing; MODIS GPP; vegetation indices; threshold methods

Published in

Remote Sensing

2021, volume: 13, number: 15, article number: 3015
Publisher: MDPI

Authors' information

Swedish University of Agricultural Sciences, Department of Forest Ecology and Management
Slovenian Forestry Institute
Oblisar, Gal
Slovenian Forestry Institute
Zust, Ana
Slovenian Environment Agency (ARSO)
Vilhar, Ursa
Slovenian Forestry Institute

UKÄ Subject classification

Forest Science
Remote Sensing

Publication Identifiers

DOI: https://doi.org/10.3390/rs13153015

URI (permanent link to this page)

https://res.slu.se/id/publ/113255