Characterizing spatiotemporal non-stationarity in vegetation dynamics in China using MODIS EVI dataset

Bingwen Qiu, Canying Zeng, Zhenghong Tang, Chongcheng Chen

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

This paper evaluated the spatiotemporal non-stationarity in the vegetation dynamic based on 1-km resolution 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets in China during 2001-2011 through a wavelet transform method. First, it revealed from selected pixels that agricultural crops, natural forests, and meadows were characterized by their distinct intra-annual temporal variation patterns in different climate regions. The amplitude of intra-annual variability generally increased with latitude. Second, parameters calculated using a per-pixel strategy indicated that the natural forests had the strongest variation pattern from seasonal to semiannual scales, and the multiple-cropping croplands typically showed almost equal variances distributed at monthly, seasonal, and semiannual scales. Third, spatiotemporal non-stationarity induced from cloud cover was also evaluated. It revealed that the EVI temporal profiles were significantly distorted with regular summer cloud cover in tropical and subtropical regions. Nevertheless, no significant differences were observed from those statistical parameters related to the interannual and interannual components between the de-clouded and the original MODIS EVI datasets across the whole country. Finally, 12 vegetation zones were proposed based on spatiotemporal variability, as indicated by the magnitude of interannual and intra-annual dynamic components, normalized wavelet variances of detailed components from monthly to semiannual scale, and proportion of cloud cover in summer. This paper provides insightful solutions for addressing spatiotemporal non-stationarity by evaluating the magnitude and frequency of vegetation variability using monthly, seasonal, semiannual to interannual scales across the whole study area.

Original languageEnglish (US)
Pages (from-to)9019-9035
Number of pages17
JournalEnvironmental Monitoring and Assessment
Volume185
Issue number11
DOIs
StatePublished - May 7 2013

Fingerprint

vegetation dynamics
vegetation index
cloud cover
MODIS
Imaging techniques
wavelet
pixel
subtropical region
vegetation
summer
tropical region
meadow
cropping practice
temporal variation
transform
Pixels
crop
climate
Wavelet transforms
Crops

Keywords

  • China
  • Enhanced Vegetation Index
  • Pixel reliability
  • Spatiotemporal non-stationary
  • Wavelet transform

ASJC Scopus subject areas

  • Environmental Science(all)
  • Pollution
  • Management, Monitoring, Policy and Law

Cite this

Characterizing spatiotemporal non-stationarity in vegetation dynamics in China using MODIS EVI dataset. / Qiu, Bingwen; Zeng, Canying; Tang, Zhenghong; Chen, Chongcheng.

In: Environmental Monitoring and Assessment, Vol. 185, No. 11, 07.05.2013, p. 9019-9035.

Research output: Contribution to journalArticle

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