Urban vegetation classification based on phenology using HJ-1A/B time series imagery

Li Feng, Liujun Zhu, Han Liu, Yinyou Huang, Peijun Du, Ebenezer Adaku

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

4 Citations (Scopus)


Urban vegetation classification need vegetation index especially temporal information of vegetation, thus high spatio-temporal NDVI product is necessary. NDVI time-series data derived from HJ 1A/B time series imagery (HJ NDVI) have relatively high spatio-temporal resolution. In this research, HJ NDVI time series of typical vegetation types in the city of Nanjing are established, the S-G filter is chosen to filtering. Taking filtered HJ NDVI time-series data as 'simulated Hyperspectral data', the linear spectral mixture unmixing algorithm is used to carry out vegetation mapping. The results indicate that unmixing algorithm of linear spectral mixture model can obtain the distribution information of the five kinds of vegetation sub-classes including shrub, grassland, evergreen needle forest, broadleaved deciduous forest, evergreen and deciduous broad-leaved mixed forest in the research area.

Original languageEnglish
Title of host publication2015 Joint Urban Remote Sensing Event, JURSE 2015
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781479966523
Publication statusPublished - 9 Jun 2015
Externally publishedYes
EventJoint Urban Remote Sensing Event 2015 - Lausanne, Switzerland
Duration: 30 Mar 20151 Apr 2015

Publication series

Name2015 Joint Urban Remote Sensing Event, JURSE 2015


ConferenceJoint Urban Remote Sensing Event 2015
Abbreviated titleJURSE 2015


  • HJ NDVI time series
  • Linear spectral mixture model
  • Nanjing city
  • Simulated Hyperspectral data
  • Urban vegetation

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