0601463 - ÚVGZ 2025 RIV SI eng C - Conference Paper (international conference)
Missarov, A. - Kašpar, J. - Král, K. - Brovkina, Olga - Švik, Marian
Predicting GPP in Carpathian Beech Forests: Uncovering spatial and temporal patterns using a regression model with climatic, topographic and additional features.
Predicting future trends – responses of beech and fir in the Carpathian region. Lublaň: Slovenian Forestry Institute, The Silva Slovenica Publishing Centre, 2024 - (Čater, M.; Dařenová, E.), s. 67-71. ISBN 978-961-6993-87-6.
[Predicting future trends – responses of beech and fir in the Carpathian region. Ljublaň (SI), 05.09.2024-05.09.2024]
R&D Projects: GA ČR(CZ) GF21-47163L
Institutional support: RVO:86652079
Keywords : gross primary product * remote sensing * regression model * temperature * precipitation * digital elevation model
OECD category: Forestry
DOI: https://doi.org/https://dirros.openscience.si/Dokument.php?id=28410

Climate change impact ecosystems globally, including the mixed forests of the Carpathian Mountains (Kruhlov et al. 2017). The primary manifestations of climate change are shifts in temperature and precipitation regimes, which undoubtedly affect biomass growth in complex ways. Since direct observations of the future are impossible, we rely on various modeling methods. Machine learning is the most popular contemporary approach for addressing such tasks. The aim of our study is to develop a regression model that predicts the behavior of Gross Primary Product (GPP) based on a range of climatic, topographic, and other variables. We use this model to forecast the growth of beech forests over the next 20 years under different climate scenarios.
Permanent Link: https://hdl.handle.net/11104/0358630