In　this　work,　the　results　of　above-ground　biomass　(AGB)　estimates　from　Landsat　Thematic　Mapper　5　(TM)　images　and　field　data　from　the　fragmented　landscape　of　the　upper　reaches　of　the　Heihe　River　Basin　(HRB),　located　in　the　Qilian　Mountains　of　Gansu　province　in　northwest　China,　are　presented.　Estimates　of　AGB　are　relevant　for　sustainable　forest　management,　monitoring　global　change,　and　carbon　accounting.　This　is　particularly　true　for　the　Qilian　Mountains,　which　are　a　water　resource　protection　zone.　We　combined　forest　inventory　data　from　133　plots　with　TM　images　and　Advanced　Spaceborne　Thermal　Emission　and　Reflection　Radiometer　(ASTER)　global　digital　elevation　model (GDEM)　V2　products　(GDEM)　in　order　to　analyse　the　influence　of　the　sun-canopy-sensor　plus　C　(SCS+C)　topographic　correction　on　estimations　of　forest　AGB　using　the　stepwise　multiple　linear　regression　(SMLR)　and　k-nearest　neighbour　(k-NN)　methods.　For　both　methods,　our　results　indicated　that　the　SCS+C　correction　was　necessary　for　getting　more　reliable　forest　AGB　estimates　within　this　complex　terrain.　Remotely　sensed　AGB　estimates　were　validated　against　forest　inventory　data　using　the　leave-one-out　(LOO)　method.　An　optimized　k-NN　method　was　designed　by　varying　both　mathematical　formulation　of　the　algorithm　and　remote-sensing　data　input,　which　resulted　in　3000　different　model　configurations.　Following　topographic　correction,　performance　of　the　optimized　k-NN　method　was　compared　to　that　of　the　regression　method.　The　optimized　k-NN　method　(R2 = 0.59,　root　mean　square　error　(RMSE) = 24.92 tonnes ha–1)　was　found　to　perform　much　better　than　the　regression　method　(R2 = 0.42,　RMSE = 29.74 tonnes ha–1)　for　forest　AGB　retrieval　over　this　montane　area.　Our　results　indicated　that　the　optimized　k-NN　method　is　capable　of　operational　application　to　forest　AGB　estimates　in　regions　where　few　inventory　data　are　available.　©　2014,　©　2014　Taylor　&　Francis.