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Naslov:Empirical approach for modelling tree phenology in mixed forests using remote sensing
Avtorji:ID Noumonvi, Koffi Dodji (Avtor)
ID Oblišar, Gal (Avtor)
ID Žust, Ana (Avtor)
ID Vilhar, Urša (Avtor)
Datoteke:.pdf PDF - Predstavitvena datoteka, prenos (2,63 MB)
MD5: D36692496B5256672038F0A54E28AC89
 
URL URL - Izvorni URL, za dostop obiščite https://www.mdpi.com/2072-4292/13/15/3015
 
Jezik:Angleški jezik
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:Logo SciVie - Gozdarski inštitut Slovenije
Povzetek:: 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.
Ključne besede:phenology modelling, start of season, end of season, remote sensing, MODIS GPP, vegetation indices, threshold methods
Leto izida:2021
Status objave pri reviji:Objavljeno
Verzija članka:Založnikova različica članka
Št. strani:15 str.
Številčenje:Vol. 13, iss. 15
UDK:630*18
ISSN pri članku:2072-4292
DOI:10.3390/rs13153015 Novo okno
COBISS.SI-ID:73513731 Novo okno
Opomba:Nasl. z nasl. zaslona; Opis vira z dne 19. 8. 2021;
Datum objave v DiRROS:23.08.2021
Število ogledov:467
Število prenosov:312
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Gradivo je del revije

Naslov:Remote sensing
Skrajšan naslov:Remote sens.
Založnik:MDPI
ISSN:2072-4292
COBISS.SI-ID:32345133 Novo okno

Gradivo je financirano iz projekta

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Številka projekta:P4-0107
Naslov:Gozdna biologija, ekologija in tehnologija

Financer:EC - European Commission
Program financ.:H2020
Številka projekta:862221
Naslov:Improving access to FORest genetic Resources, information and services for end-users
Akronim:FORGENIUS

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Začetek licenciranja:01.08.2021

Sekundarni jezik

Jezik:Ni določen
Ključne besede:fenološko modeliranje, daljinsko zaznavanje, MODIS GPP, vegetacijski indekki


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