Spatio-temporal divergence in the responses of Finland’s boreal forests to climate variables

International Journal of Applied Earth Observations and Geoinformation

Yonghong Hu¹  Ari K. Venäläinen¹  Fuying Qin²  Meiting Hou²  Pentti Pirinen, I³  Yuxiang Zhu⁴  Yao Gao⁵  Shaofei Jin⁵  Linping Wang⁵ 

[1] Corresponding authors at: No. 46, Zhongguancun Nandajie, Haidian District, China.;China Meteorological Administration Training Centre, Beijing 100081, China;Department of Agricultural Sciences, University of Helsinki, Helsinki, FI-00014, Finland;Department of Geography, MinJiang University, Fuzhou, 350108, China;Finnish Meteorological Institute, Helsinki, FI-00101, Finland;

关键词: Monthly difference;    Plant phenology index (PPI);    Partial least squares (PLS) regression;    Boreal forests;    Climate variables;   

DOI  :  

来源: DOAJ

【 摘 要 】

Spring greening in boreal forest ecosystems has been widely linked to increasing temperature, but few studies have attempted to unravel the relative effects of climate variables such as maximum temperature (TMX), minimum temperature (TMN), mean temperature (TMP), precipitation (PRE) and radiation (RAD) on vegetation growth at different stages of growing season. However, clarifying these effects is fundamental to better understand the relationship between vegetation and climate change. This study investigated spatio-temporal divergence in the responses of Finland’s boreal forests to climate variables using the plant phenology index (PPI) calculated based on the latest Collection V006 MODIS BRDF-corrected surface reflectance products (MCD43C4) from 2002 to 2018, and identified the dominant climate variables controlling vegetation change during the growing season (May–September) on a monthly basis. Partial least squares (PLS) regression was used to quantify the response of PPI to climate variables and distinguish the separate impacts of different variables. The study results show the dominant effects of temperature on the PPI in May and June, with TMX, TMN and TMP being the most important explanatory variables for the variation of PPI depending on the location, respectively. Meanwhile, drought had an unexpectedly positive impact on vegetation in few areas. More than 50 % of the variation of PPI could be explained by climate variables for 68.5 % of the entire forest area in May and 87.7 % in June, respectively. During July to September, the PPI variance explained by climate and corresponding spatial extent rapidly decreased. Nevertheless, the RAD was found be the most important explanatory variable to July PPI in some areas. In contrast, the PPI in August and September was insensitive to climate in almost all of the regions studied. Our study gives useful insights on quantifying and identifying the relative importance of climate variables to boreal forest, which can be used to predict the possible response of forest under future warming.

【 授权许可】

Unknown