Estimating　forest　above-ground　biomass　(AGB)　and　monitoring　its　variation　are　relevant　for　sustainable　forest　management,　monitoring　global　change,　carbon　accounting,　particularly　for　the　Qilian　Mountains　(QMs),　a　water　resource　protection　zone.　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.　An　optimized　k-Nearest　Neighbor　(k-NN)　method　was　determined　by　varying　both　the　mathematical　formulation　of　the　algorithm　and　remote　sensing　data　input　which　resulted　in　3,000　different　model　configurations.　Following　the　sun-canopy-sensor　plus　C　(SCS+C)　topographic　correction,　performance　of　the　optimized　k-NN　method　was　satisfied　(R2=0.59,　RMSE=24.92　ton/ha)　which　indicated　that　the　optimized　k-NN　is　capable　of　operational　applications　of　forest　AGB　estimates　in　regions　where　only　a　few　inventory　data　are　available.　Afterwards,　the　calibrated　BIOME-BGC　was　applied　to　simulate　the　carbon　fluxes　over　QMs　forests　with　satisfactory　accuracy.　Finally,　the　dynamic　analysis　and　modeling　of　forest　AGB　was　conducted　based　on　the　remotely　sensed　estimation　of　forest　AGB　and　the　annual　forest　AGB　increment　from　the　ecological　process　model.　©　2014　IEEE.