Forests　are　usually　distributed　on　rugged　mountains,　therefore　the　elimination　of　topographic　effects　becomes　one　of　the　essential　issues　to　improve　monitoring　accuracy　of　forest　cover　by　remote　sensing.　Here　we　introduced　a　novel　topography-adjusted　vegetation　index　(TAVI)　which　is　capable　of　removing　topographic　effects　in　rugged　terrain　based　only　on　infrared　and　red　wavebands　data　from　optical　remote　sensing　images,　without　support　of　digital　elevation　model　(DEM)　data.　The　Landsat　TM　images　acquired　in　1998　and　2008　respectively　were　utilized　in　case　study　to　validate　the　TAVI　performance　and　monitor　the　changes　of　forest　cover　with　TAVI.　From　the　regression　analysis　between　TAVI　and　the　solar　incidence　cosine　and　contrast　analysis　between　TAVI　and　normalized　difference　vegetation　index　(NDVI)　in　the　study　area,　it　was　discovered　that　TAVI　can　resist　the　topographic　effects　remarkably　and　was　much　better　than　NDVI　in　that　the　slope　of　linear　regression　equation　of　TAVI　and　the　solar　incidence　cosine　was　only　between　±0.04,　and　also　their　correlation　coefficient　was　just　between　±0.08.　The　forest　cover　images　in　rugged　areas　computed　from　TAVI　displayed　a　planar　spatial　distribution　instead　of　texture　pattern　resulting　from　topography.　The　dynamic　monitoring　results　turn　out　that　forests　in　the　research　area　grow　in　a　generally　sustainable　way　during　1998-2008.