Rainwater-induced　soil　erosion　occurring　in　the　forest　is　a　special　phenomenon　of　soil　erosion　in　many　red　soil　areas.　Detection　of　such　soil　erosion　is　essential　for　developing　land　management　to　reduce　soil　loss　in　areas　including　southern　China　and　other　red　soil　regions　of　the　world.　Remotely　sensed　canopy　cover　is　often　used　to　determine　the　potential　of　soil　erosion　over　a　large　spatial　scale,　which,　however,　becomes　less　useful　in　forest　areas.　This　study　proposes　a　new　remote　sensing　method　to　detect　soil　erosion　under　forest　canopy　and　presents　a　case　study　in　a　forest　area　in　southern　China.　Five　factors　that　are　closely　related　to　soil　erosion　in　forest　were　used　as　discriminators　to　develop　the　model.　These　factors　include　fractional　vegetation　coverage,　nitrogen　reflectance　index,　yellow　leaf　index,　bare　soil　index　and　slope.　They　quantitatively　represent　vegetation　density,　vegetation　health　status,　soil　exposure　intensity　and　terrain　steepness　that　are　considered　relevant　to　forest　soil　erosion.　These　five　factors　can　all　be　derived　from　remote　sensing　imagery　based　on　related　thematic　indices　or　algorithms.　The　five　factors　were　integrated　to　create　the　soil　erosion　under　forest　model　(SEUFM)　through　Principal　Components　Analysis　(PCA)　or　a　multiplication　method.　The　case　study　in　the　forest　area　in　Changting　County　of　southern　China　with　a　Landsat　8　image　shows　that　the　first　principal　component-based　SEUFM　achieves　an　overall　accuracy　close　to　90%,　while　the　multiplication-based　model　reaches　81%.　The　detected　locations　of　soil　erosion　in　forest　provide　the　target　areas　to　be　managed　from　further　soil　loss.　The　proposed　method　provides　a　tool　to　understand　more　about　soil　erosion　in　forested　areas　where　soil　erosion　is　usually　not　considered　an　issue.　Therefore,　the　method　is　useful　for　soil　conservation　in　forest.