Given　the　complexity　of　vegetation　dynamic　patterns　under　global　climate　change,　multi-scale　spatiotemporal　explicit　models　are　necessary　in　order　to　account　for　environmental　heterogeneity.　However,　there　is　no　efficient　time-series　tool　to　extract,　reconstruct　and　analyze　the　multi-scale　vegetation　dynamic　patterns　under　global　climate　change.　To　fill　this　gap,　a　Multi-Scale　Spatio-Temporal　Modeling　(MSSTM)　framework　which　can　incorporate　the　pixel,　scale,　and　time-specific　heterogeneity　was　proposed.　The　MSSTM　method　was　defined　on　proper　time-series　models　for　multi-temporal　components　through　wavelet　transforms.　The　proposed　MSSTM　approach　was　applied　to　a　subtropical　mountainous　and　hilly　agro-forestry　ecosystem　in　southeast　China　using　the　moderate　resolution　imaging　spectroradiometer　enhanced　vegetation　index　(EVI)　time-series　data　sets　from　2001　to　2011.　The　MSSTM　approach　was　proved　to　be　efficient　in　characterizing　and　forecasting　the　complex　vegetation　dynamic　patterns.　It　provided　good　estimates　of　the　peaks　and　valleys　of　the　observed　EVI　and　its　average　percentages　of　relative　absolute　errors　of　reconstruction　was　low　(6.65).　The　complexity　of　the　relationship　between　vegetation　dynamics　and　meteorological　parameters　was　also　revealed　through　the　MSSTM　method:　(1)　at　seasonal　level,　vegetation　dynamic　patterns　are　strongly　associated　with　climatic　variables,　primarily　the　temperature　and　then　precipitation,　with　correlations　slight　decreasing　(EVI-temperature)/increasing　(EVI-precipitation)　with　altitudinal　gradients.　(2)　At　inter-annual　scale,　obvious　positive　correlations　were　primarily　observed　between　EVI　and　temperature.　(3)　Despite　very　low-correlation　coefficients　observed　at　intra-seasonal　scales,　considerable　proportions　of　EVI　anomalies　are　associated　with　climatic　variables,　principally　the　precipitation　and　sunshine　durations.