577
Views
51
CrossRef citations to date
0
Altmetric
Articles

Detecting the effects of climate change on canopy phenology in coniferous forests in semi-arid mountain regions of China

, , , &
Pages 6490-6507 | Received 12 Nov 2013, Accepted 23 Jul 2014, Published online: 23 Sep 2014
 

Abstract

An increased understanding of the responses of forest phenology to climate on regional scales is critical to the evaluation of biochemical cycles (i.e. carbon, water, heat, and nutrient) under environmental changes. In this study, we aimed to identify climatic constraints on phenological events in an evergreen coniferous forest in semi-arid mountain regions of northern China. We quantified the start of season (SOS), end of season (EOS), and growing season length (GSL) based on satellite-derived data sets (normalized difference vegetation index (NDVI)) and investigated the relationships between these phenological events and climate factors. The results revealed discontinuous trends in phenological events throughout the study period, with neither an obvious extension nor decrement in GSL. We demonstrated that minimum temperatures controlled the dynamics of SOS and EOS, thus providing strong evidence for the need to include minimum temperature as a control on phenology in simulation models. Additionally, precipitation was coupled to the shift in maximum NDVI, as rainfall is a major climatic limitation to vegetation growth in semi-arid regions. It appears that selecting appropriate timescales to analyse the relationships between phenology and climate is critical. We illustrated that NDVI was an effective tool in an effort to gain greater understanding of the effects of environmental change on ecosystem functioning in this forest. Our results may be used as reference to track local changes in the evergreen coniferous forest dynamics under different climate change scenarios for semi-arid mountain regions.

Acknowledgements

We wish to thank the Land Processes Distributed Active Archive Center (LPDAAC) for providing remote-sensing data and the Meteorological Information Center of the China Meteorological Administration for climate data. We thank the three anonymous reviewers for providing most useful comments on the submitted version of this article.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.