Spatiotemporal vegetation dynamic patterns in a subtropical humid region of China

Bingwen Qiu, Min Feng, Ming Zhong, Zhenghong Tang, Chongcheng Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper aims to improve our knowledge of the complex vegetation-climate relationship in subtropical humid region in the context of global warming, by taking into considerations of spatio-temporal variation of both vegetation and climate change. A multi-resolution analysis (MRA) based on the wavelet transform (WT) is applied to examine the vegetation growth and its relationship with climate factors based on 250m 16-day composites MODIS vegetation EVI dataset in subtropical humid region of China over the period 2001-2010. A general greening up (68%) was observed over the period 2001-2010, as well as rather local negative trends. A trend toward global warming was also observed for the whole study region, whereas no obvious trend of precipitation is examined in most areas. Temperature generally has a positive influence on vegetation; with only very few negative EVI-temperature coefficients observed on the south portion principally due to changes in land use, land degradation, and cloud noise. However, nearly equally positive and negative EVI-rainfall relationship is observed on the inter-annual level, with negative coefficients principally observed in the northwest portion with abundant precipitation. Very strong positive relationship is observed between both EVI-temperature and EVI-precipitation at seasonal level. It is revealed that spatio-temporal variation of both vegetation and climate should be taken into considerations when analyzing the long-term effects of global climate change. Interactions between vegetation dynamics and climate variability must be studied through spatially and temporally explicit multi-scale analysis to investigate the influence of long-term climate change on the vegetation growth.

Original languageEnglish (US)
Title of host publicationICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services
EditorsChongcheng Chen, Diansheng Guo, Yee Leung
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages93-98
Number of pages6
ISBN (Electronic)9781479977482
DOIs
StatePublished - Oct 13 2015
Event2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM 2015 - Fuzhou, China
Duration: Jul 8 2015Jul 10 2015

Publication series

NameICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services

Other

Other2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM 2015
CountryChina
CityFuzhou
Period7/8/157/10/15

Fingerprint

Climate change
Global warming
Negative temperature coefficient
Multiresolution analysis
Land use
Wavelet transforms
Rain
Degradation
Temperature
Composite materials

Keywords

  • Climate change
  • Enhanced Vegetation Index (EVI)
  • Non-stationary
  • Subtropical humid region
  • Vegetation dynamic
  • Wavelet transform

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

Cite this

Qiu, B., Feng, M., Zhong, M., Tang, Z., & Chen, C. (2015). Spatiotemporal vegetation dynamic patterns in a subtropical humid region of China. In C. Chen, D. Guo, & Y. Leung (Eds.), ICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (pp. 93-98). [7298032] (ICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSDM.2015.7298032

Spatiotemporal vegetation dynamic patterns in a subtropical humid region of China. / Qiu, Bingwen; Feng, Min; Zhong, Ming; Tang, Zhenghong; Chen, Chongcheng.

ICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services. ed. / Chongcheng Chen; Diansheng Guo; Yee Leung. Institute of Electrical and Electronics Engineers Inc., 2015. p. 93-98 7298032 (ICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Qiu, B, Feng, M, Zhong, M, Tang, Z & Chen, C 2015, Spatiotemporal vegetation dynamic patterns in a subtropical humid region of China. in C Chen, D Guo & Y Leung (eds), ICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services., 7298032, ICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, Institute of Electrical and Electronics Engineers Inc., pp. 93-98, 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM 2015, Fuzhou, China, 7/8/15. https://doi.org/10.1109/ICSDM.2015.7298032
Qiu B, Feng M, Zhong M, Tang Z, Chen C. Spatiotemporal vegetation dynamic patterns in a subtropical humid region of China. In Chen C, Guo D, Leung Y, editors, ICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services. Institute of Electrical and Electronics Engineers Inc. 2015. p. 93-98. 7298032. (ICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services). https://doi.org/10.1109/ICSDM.2015.7298032
Qiu, Bingwen ; Feng, Min ; Zhong, Ming ; Tang, Zhenghong ; Chen, Chongcheng. / Spatiotemporal vegetation dynamic patterns in a subtropical humid region of China. ICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services. editor / Chongcheng Chen ; Diansheng Guo ; Yee Leung. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 93-98 (ICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services).
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