Knowledge　of　vegetation　distribution　patterns　is　very　important.　Their　relationships　with　topography　and　climate　were　explored　through　a　geographically　weighted　regression　(GWR)　framework　in　a　subtropical　mountainous　and　hilly　region,　Minjiang　River　Basin　of　Fujian　in　China.　The　HJ-1　satellite　image　acquired　on　December　9,　2010　was　utilized　and　NDVI　index　was　calculated　representing　the　range　of　vegetation　greenness.　Proper　analysis　units　were　achieved　through　segregation　based　on　small　sub-basins　and　altitudinal　bands.　Results　indicated　that　the　GWR　model　was　more　powerful　than　ordinary　linear　least　square　(OLS)　regression　in　interpreting　vegetation-environmental　relationship,　indicated　by　higher　adjusted　R2　and　lower　Akaike　information　criterion　values.　On　one　side,　the　OLS　analysis　revealed　dominant　positive　influence　from　parameters　of　elevation　and　slope　on　vegetation　distribution.　On　the　other　side,　GWR　analysis　indicated　that　spatially,　the　parameters　of　topography　had　a　very　complex　relationship　with　the　vegetation　distribution,　as　results　of　the　various　combinations　of　environmental　factors,　vegetation　composition　and　also　anthropogenic　impact.　The　influences　of　elevation　and　slope　generally　decreased,　from　strongly　positive　to　nearly　zero,　with　increasing　altitude　and　slope.　Specially,　most　rapid　changes　of　coefficients　between　NDVI　and　elevation　or　slope　were　observed　in　relatively　flat　and　low-lying　areas.　This　paper　confirmed　that　the　non-stationary　analysis　through　the　framework　of　GWR　could　lead　to　a　better　understanding　of　vegetation　distribution　in　subtropical　mountainous　and　hilly　region.　It　was　hoped　that　the　proposed　scale　selection　method　combined　with　GWR　framework　would　provide　some　guidelines　on　dealing　with　both　spatial　(horizontal)　and　altitudinal　(vertical)　non-stationarity　in　the　dataset,　and　it　could　easily　be　applied　in　characterizing　vegetation　distribution　patterns　in　other　mountainous　and　hilly　river　basins　and　related　research.