Number of the records: 1
Incorporating high-resolution climate, remote sensing and topographic data to map annual forest growth in central and eastern Europe
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SYSNO ASEP 0583881 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Incorporating high-resolution climate, remote sensing and topographic data to map annual forest growth in central and eastern Europe Author(s) Jevšenak, J. (SI)
Klisz, M. (PL)
Mašek, J. (CZ)
Čada, V. (CZ)
Janda, P. (CZ)
Svoboda, M. (CZ)
Vostárek, O. (CZ)
Treml, V. (CZ)
van der Maaten, E. (DE)
Popa, A. (CZ)
Popa, I. (RO)
Van der Maaten-Theunissen, M. (DE)
Zlatanov, T. (BG)
Scharnweber, T. (DE)
Ahlgrimm, S. (DE)
Stolz, J. (DE)
Sochová, Irena (UEK-B) SAI, ORCID, RID
Roibu, C. C. (RO)
Pretzsch, H. (DE)
Schmied, G. (DE)
Uhl, E. (DE)
Kaczka, R. (CZ)
Wrzesiński, P. (PL)
Šenfeldr, M. (CZ)
Jakubowski, M. (PL)
Tumajer, J. (CZ)
Wilmking, M. (DE)
Obojes, N. (IT)
Rybníček, Michal (UEK-B) RID, ORCID, SAI
Lévesque, M. (CH)
Potapov, A. (EE)
Basu, S. (IL)
Stojanović, Marko (UEK-B) ORCID, RID, SAI
Stjepanović, S. (BA)
Vitas, A. (LV)
Arnič, D. (SI)
Metslaid, S. (EE)
Neycken, A. (CH)
Prislan, P. (SI)
Hartl, C. (DE)
Ziche, D. (DE)
Horáček, Petr (UEK-B) RID, ORCID, SAI
Krejza, Jan (UEK-B) RID, ORCID, SAI
Mikhailov, Sergei (UEK-B) SAI
Světlík, Jan (UEK-B) ORCID, SAI, RID
Kalisty, A. (PL)
Kolář, Tomáš (UEK-B) RID, ORCID, SAI
Lavnyy, V. (UA)
Hordo, M. (EE)
Oberhuber, W. (AT)
Levanič, T. (SI)
Mészáros, I. (HU)
Schneider, L. (DE)
Lehejček, J. (CZ)
Shetti, R. (CZ)
Bošeľa, M. (SK)
Copini, P. (NL)
Koprowski, M. (PL)
Sass-Klaassen, U. (NL)
Izmir, Ş. C. (TR)
Bakys, R. (LT)
Entner, H. (AT)
Esper, Jan (UEK-B) SAI, ORCID, RID
Janecka, K. (DE)
Martinez del Castillo, E. (DE)
Verbylaite, R. (LV)
Árvai, M. (HU)
de Sauvage, J. C. (CH)
Čufar, K. (SI)
Finner, M. (AT)
Hilmers, T. (DE)
Kern, Z. (HU)
Novak, K. (SI)
Ponjarac, R. (RS)
Puchałka, R. (PL)
Schuldt, B. (DE)
Škrk Dolar, N. (SI)
Tanovski, V. (MK)
Zang, C. (DE)
Žmegač, A. (DE)
Kuithan, C. (DE)
Metslaid, M. (EE)
Thurm, E. (AT)
Hafner, P. (SI)
Krajnc, L. (SI)
Bernabei, M. (IT)
Bojić, S. (BA)
Brus, R. (SI)
Burger, A. (DE)
D'Andrea, E. (IT)
Đorem, T. (BA)
Gławęda, M. (PL)
Gričar, J. (BA)
Gutalj, M. (BA)
Author(s) Horváth, E. (HU)
Kostić, S. (RS)
Matović, B. (RS)
Merela, M. (SI)
Miletić, B. (BA)
Morgós, A. (HU)Article number 169692 Source Title Science of the Total Environment. - : Elsevier - ISSN 0048-9697
Roč. 913, FEB (2024)Number of pages 14 s. Publication form Online - E Language eng - English Country NL - Netherlands Keywords ndmi ; ndre ; Random forest ; Sentinel-1 ; Sentinel-2 ; Tree rings Subject RIV GK - Forestry OECD category Remote sensing R&D Projects LM2023048 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) TO01000345 GA TA ČR - Technology Agency of the Czech Republic (TA ČR) GA23-07583S GA ČR - Czech Science Foundation (CSF) Method of publishing Open access Institutional support UEK-B - RVO:86652079 UT WOS 001158139800001 EID SCOPUS 85181767010 DOI 10.1016/j.scitotenv.2023.169692 Annotation 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. Workplace Global Change Research Institute Contact Nikola Šviková, svikova.n@czechglobe.cz, Tel.: 511 192 268 Year of Publishing 2025 Electronic address https://www.sciencedirect.com/science/article/pii/S0048969723083225?ref=pdf_download&fr=RR-2&rr=8612eccecfc1b353
Number of the records: 1