Year: 2023
Type: Article
Title: Incorporating high-resolution climate, remote sensing and topographic data to map annual forest growth in central and eastern Europe
Author: Jevšenak, Jernej
Author: Klisz, Marcin
Author: Mašek, Jiří
Author: Čada, Vojtěch
Author: Janda, Pavel
Author: Svoboda, Miroslav
Author: Vostarek, Ondřej
Author: Treml, Vaclav
Author: van der Maaten, Ernst
Author: Popa, Andrei
Author: Popa, Ionel
Author: van der Maaten-Theunissen, Marieke
Author: Zlatanov, Tzvetan
Author: Scharnweber, Tobias
Author: Ahlgrimm, Svenja
Author: Stolz, Juliane
Author: Sochová, Irena
Author: Roibu, Cătălin-Constantin
Author: Pretzsch, Hans
Author: Schmied, Gerhard
Author: Uhl, Enno
Author: Kaczka, Ryszard
Author: Wrzesiński, Piotr
Author: Šenfeldr, Martin
Author: Jakubowski, Marcin
Author: Tumajer, Jan
Author: Wilmking, Martin
Author: Obojes, Nikolaus
Author: Rybníček, Michal
Author: Lévesque, Mathieu
Author: Potapov, Aleksei
Author: Basu, Soham
Author: Stojanović, Marko
Author: Stjepanović, Stefan
Author: Vitas, Adomas
Author: Arnič, Domen
Author: Metslaid, Sandra
Author: Neycken, Anna
Author: Prislan, Peter
Author: Hartl, Claudia
Author: Ziche, Daniel
Author: Horáček, Petr
Author: Krejza, Jan
Author: Mikhailov, Sergei
Author: Světlík, Jan
Author: Kalisty, Aleksandra
Author: Kolář, Tomáš
Author: Lavnyy, Vasyl
Author: Hordo, Maris
Author: Oberhuber, Walter
Author: Levanič, Tom
Author: Mészáros, Ilona
Author: Schneider, Lea
Author: Lehejček, Jiří
Author: Shetti, Rohan
Author: Bošeľa, Michal
Author: Copini, Paul
Author: Koprowski, Marcin
Author: Sass-Klaassen, Ute
Author: Izmir, Şule Ceyda
Author: Bakys, Remigijus
Author: Entner, Hannes
Author: Esper, Jan
Author: Janecka, Karolina
Author: Martinez Del Castillo, Edurne
Author: Verbylaite, Rita
Author: Árvai, Mátyás
Author: de Sauvage, Justine Charlet
Author: Čufar, Katarina
Author: Finner, Markus
Author: Hilmers, Torben
Author: Kern, Zoltán
Author: Novak, Klemen
Author: Ponjarac, Radenko
Author: Puchałka, Radosław
Author: Schuldt, Bernhard
Author: Škrk Dolar, Nina
Author: Tanovski, Vladimir
Author: Zang, Christian
Author: Žmegač, Anja
Author: Kuithan, Cornell
Author: Metslaid, Marek
Author: Thurm, Eric
Author: Hafner, Polona
Author: Krajnc, Luka
Author: Bernabei, Mauro
Author: Bojić, Stefan
Author: Brus, Robert
Author: Burger, Andreas
Author: D'Andrea, Ettore
Author: Đorem, Todor
Author: Gławęda, Mariusz
Author: Gričar, Jožica
Author: Gutalj, Marko
Author: Horváth, Emil
Author: Kostić, Saša
Author: Matović, Bratislav
Author: Merela, Maks
Author: Miletić, Boban
Author: Morgós, András
Author: Paluch, Rafał
Author: Pilch, Kamil
Author: Rezaie, Negar
Author: Rieder, Julia
Author: Schwab, Niels
Author: Sewerniak, Piotr
Author: Stojanović, Dejan
Author: Ullmann, Tobias
Author: Waszak, Nella
Author: Zin, Ewa
Author: Skudnik, Mitja
Author: Oštir, Krištof
Author: Rammig, Anja
Author: Buras, Allan
Abstract: To enhance our understanding of forest carbon sequestration, climate change mitigation and drought impact on forest ecosystems, the availability of high-resolution annual forest growth maps based on tree-ring width (TRW) would provide a significant advancement to the field. Site-specific characteristics, which can be approximated by high-resolution Earth observation by satellites (EOS), emerge as crucial drivers of forest growth, influencing how climate translates into tree growth. EOS provides information on surface reflectance related to forest characteristics and thus can potentially improve the accuracy of forest growth models based on TRW. Through the modelling of TRW using EOS, climate and topography data, we showed that species-specific models can explain up to 52 % of model variance (Quercus petraea), while combining different species results in relatively poor model performance (R2 = 13 %). The integration of EOS into models based solely on climate and elevation data improved the explained variance by 6 % on average. Leveraging these insights, we successfully generated a map of annual TRW for the year 2021. We employed the area of applicability (AOA) approach to delineate the range in which our models are deemed valid. The calculated AOA for the established forest-type models was 73 % of the study region, indicating robust spatial applicability. Notably, unreliable predictions predominantly occurred in the climate margins of our dataset. In conclusion, our large-scale assessment underscores the efficacy of combining climate, EOS and topographic data to develop robust models for mapping annual TRW. This research not only fills a critical void in the current understanding of forest growth dynamics but also highlights the potential of integrated data sources for comprehensive ecosystem assessments.
Publisher: Elsevier BV
Relation: Science of The Total Environment
Identifier: oai:repository.ukim.mk:20.500.12188/29000
Identifier: http://hdl.handle.net/20.500.12188/29000Identifier: 10.1016/j.scitotenv.2023.169692
Identifier: https://api.elsevier.com/content/article/PII:S0048969723083225?httpAccept=text/xmlIdentifier: https://api.elsevier.com/content/article/PII:S0048969723083225?httpAccept=text/plainIdentifier: 913
Identifier: 169692
