An　accurate　and　updated　natural　vegetation　map　is　imperative　for　sustainable　environmental　management.　This　paper　proposed　a　novel　natural　vegetation　mapping　algorithm　based　on　time　series　images.　Several　indices　of　temporal　dispersion　and　continuity　were　established　for　this　purpose:　low　density　(LD),　medium　density　(MD),　high　density　(HD)　and　medium　continuity　(MC).　These　indices　were　developed　based　on　the　particular　percentiles-determined　section　of　the　EVI2　temporal　profiles　obtained　through　continuous　wavelet　transform.　The　natural　vegetation　was　generally　characterized　as　with　lower　temporal　dispersion　and　greater　temporal　continuity　compared　with　agricultural　crops.　The　proposed　methodology　incorporated　the　indices　of　temporal　dispersion　and　continuity　and　was　applied　to　13　provinces　in　central　East　China　based　on　500　m　8-day　composite　Moderate　Resolution　Imaging　Spectroradiometer　(MODIS)　Enhanced　Vegetation　Index　with　two　bands　(EVI2)　in　2013.　An　overall　accuracy　of　92.97%　was　obtained　when　compared　with　2715　ground　truth　sites.　There　was　also　a　good　agreement　(kappa　index　=　0.8049)　on　the　distribution　and　areas　of　different　vegetation　types　between　the　MODIS-estimated　image　and　the　Landsat　8　OLI　interpreted　data　on　two　test　regions.　This　study　demonstrated　the　efficiency　of　the　transform　and　metric　integrated　time　series　classification　approaches　in　the　fields　of　land　and　vegetation　cover　mapping.　©　2016　Elsevier　Ltd.　All　rights　reserved.