The　mountainous　vegetation　is　important　to　regional　sustainable　development.　However,　the　topographic　effect　is　the　main　obstacle　to　the　monitoring　of　mountainous　vegetation　using　remote　sensing.　Aiming　to　retrieve　the　reflectance　of　frequently-used　red-green-blue　and　near-infrared　(NIR)　wavebands　of　rugged　mountains　for　vegetation　mapping,　we　developed　a　new　integrated　topographic　correction　(ITC)　using　the　SCS　+　C　correction　and　the　shadow-eliminated　vegetation　index.　The　ITC　procedure　consists　of　image　processing,　data　training,　and　shadow　correction　and　uses　a　random　forest　machine　learning　algorithm.　Our　study　using　the　Landsat　8　Operational　Land　Imager　(OLI)　multi-spectral　images　in　Fujian　province,　China,　showed　that　the　ITC　achieved　high　performance　in　topographic　correction　of　regional　mountains　and　in　transferability　from　the　sunny　area　of　a　scene　to　the　shadow　area　of　three　scenes.　The　ITC-corrected　multi-spectral　image　with　an　NIR-red-green　composite　exhibited　flat　features　with　impressions　of　relief　and　topographic　shadow　removed.　The　linear　regression　of　corrected　waveband　reflectance　vs.　the　cosine　of　the　solar　incidence　angle　showed　an　inclination　that　nearly　reached　the　horizontal,　and　the　coefficient　of　determination　decreased　to　0.00　similar　to　0.01.　The　absolute　relative　errors　of　the　cast　shadow　and　the　self-shadow　all　dramatically　decreased　to　the　range　of　0.30-6.37%.　In　addition,　the　achieved　detection　rate　of　regional　vegetation　coverage　for　the　three　cities　of　Fuzhou,　Putian,　and　Xiamen　using　the　ITC-corrected　images　was　0.92-6.14%　higher　than　that　using　the　surface　reflectance　images　and　showed　a　positive　relationship　with　the　regional　topographic　factors,　e.g.,　the　elevation　and　slope.　The　ITC-corrected　multi-spectral　images　are　beneficial　for　monitoring　regional　mountainous　vegetation.　Future　improvements　can　focus　on　the　use　of　the　ITC　in　higher-resolution　imaging.