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Title:Empirical approach for modelling tree phenology in mixed forests using remote sensing
Authors:Noumonvi, Koffi Dodji (Author)
Oblišar, Gal (Author)
Žust, Ana (Author)
Vilhar, Urša (Author)
Language:English
Tipology:1.01 - Original Scientific Article
Organisation:Logo SciVie - Slovenian Forestry Institute
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
Year of publishing:2021
COBISS_ID:73513731 Link is opened in a new window
UDC:630*18
ISSN on article:2072-4292
DOI:10.3390/rs13153015 Link is opened in a new window
Note:Nasl. z nasl. zaslona; Opis vira z dne 19. 8. 2021;
Views:363
Downloads:233
Files:.pdf PDF - Presentation file, download (2,63 MB)
URL URL - Source URL, visit https://doi.org/10.3390/rs13153015
 
Journal:Remote sens.
MDPI
 
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Document is financed by a project

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project no.:P4-0107
Name:Gozdna biologija, ekologija in tehnologija

Funder:EC - European Commission
Funding Programme:H2020
Project no.:862221
Name:Improving access to FORest genetic Resources, information and services for end-users
Acronym:FORGENIUS

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:23.08.2021

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