NDLI: Forest classification based on MODIS time series and vegetation phenology

Content Provider	IEEE Xplore Digital Library
Author	Xinfang Yui Dafang Zhuang Hua Chen Xiyong Hou
Copyright Year	2004
Description	Author affiliation: Inst. of Geogr. Sci. & Natural Resources Res., Chinese Acad. of Sci., Beijing, China (Xinfang Yui; Dafang Zhuang; Hua Chen; Xiyong Hou)
Abstract	The distribution and phenologies of vegetation are largely associated with climate, terrain characteristics and human activities. Satellite data provide an opportunity to map vegetation and monitor its dynamics continuously. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for forest classification in Northeast China. Fourier analysis was used to identify forest types based on temporal changes in MODIS NDVI values. Firstly, Fourier transform was applied to 36 MODIS 10-day maximum NDVI composite images during 2002 in the study area. Then the amplitude and phase data from the first and second harmonics of the Fourier transform were analyzed in amplitude-phase space. It shows that the introduction of these characteristic phenology parameters extracted from this series of MODIS NDVI into feature space improves the separabilities of forest types. Finally, the mean NDVI, first- and second-order amplitude and phase were used to produce an unsupervised classification map of basic forest formations in the study area. The Fourier analysis approach shows promising for identifying vegetation types based on multi-temporal remotely sensed data.
Starting Page	2369
Ending Page	2372
File Size	604046
Page Count	4
File Format	PDF
ISBN	0780387422
DOI	10.1109/IGARSS.2004.1369764
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-09-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	MODIS Vegetation mapping Remote sensing Satellites Fourier transforms Remote monitoring Fluctuations Humans Harmonic analysis Data mining
Content Type	Text
Resource Type	Article

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