Incorporating high-resolution climate, remote sensing and topographic data to map annual forest growth in central and eastern Europe

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F24%3A00583881" target="_blank" >RIV/86652079:_____/24:00583881 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62156489:43410/24:43924501 RIV/60460709:41320/24:100564 RIV/44555601:13520/24:43898398 RIV/00216208:11310/24:10488696

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0048969723083225?ref=pdf_download&fr=RR-2&rr=8612eccecfc1b353" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0048969723083225?ref=pdf_download&fr=RR-2&rr=8612eccecfc1b353</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.scitotenv.2023.169692" target="_blank" >10.1016/j.scitotenv.2023.169692</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Incorporating high-resolution climate, remote sensing and topographic data to map annual forest growth in central and eastern Europe

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Incorporating high-resolution climate, remote sensing and topographic data to map annual forest growth in central and eastern Europe

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20705 - Remote sensing

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Science of the Total Environment

ISSN

0048-9697

e-ISSN

1879-1026

Svazek periodika

913

Číslo periodika v rámci svazku

FEB

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

169692

Kód UT WoS článku

001158139800001

EID výsledku v databázi Scopus

2-s2.0-85181767010