Forest　biomass　is　a　key　biophysical　parameter　for　climate　change,　ecological　modeling　and　forest　management.　Compared　with　discrete-return　LiDAR　data,　full-waveform　LiDAR　data　can　provide　more　accurate　and　abundant　vertical　structure　information　on　vegetation　and　thus　have　been　increasingly　applied　to　the　estimation　of　forest　aboveground　biomass　(AGB).　The　main　objective　of　this　research　is　to　estimate　forest　AGB　using　full　waveform　airborne　LiDAR　data.　In　this　study,　we　constructed　voxel-based　waveforms　(0.5　x　0.5　m)　using　small　footprint　full　waveform　LiDAR　data,　and　then　aggregated　voxel　based　waveforms　into　pseudo　large　footprint　waveforms　with　a　plot　size　of　20　x　20　m.　We　extracted　a　range　of　waveform　metrics　from　voxel-based　waveforms　and　pseudo-large-footprint　waveforms　(FWm),　respectively,　and　then　calculated　the　mean　of　the　voxel-based　waveform　metrics　within　a　plot　(FW　mu).　Based　on　the　Random　Forest　(RF)　regression,　the　forest　biomasses　were　estimated　using　two　types　of　waveform　metrics:　FWm　(R-2　=　0.84,　RMSE%　=　21.4%,　bias　=　-0.11　Mg　ha(-1))　and　FW　mu　(R-2　=　0.81,　RMSE%　=　23.3%,　bias　=　0.13　Mg　ha(-1)).　We　found　that　slightly　higher　biomass　estimation　accuracy　was　obtained　with　FW(m　)than　with　FW　mu.　In　addition,　a　comparison　between　the　biomasses　predicted　by　the　waveform　metrics　and　by　the　traditional　discrete-return　metrics　(R-2　=　0.80,　RMSE%　=　23.4%,　bias　=　0.20　Mg　ha(-1))　was　performed　to　explore　the　potential　to　improve　biomass　estimates　using　the　waveform　metrics,　and　the　results　showed　that　both　waveform　metrics　and　discrete-return　metrics　could　accurately　predict　forest　biomass.　However,　the　biomass　estimations　from　the　waveform　metrics　were　more　accurate　than　those　from　the　traditional　discrete-return　metrics.　We　concluded　that　the　method　proposed　in　this　study　has　the　potential　to　estimate　vegetation　structure　parameters　using　full-waveform　LiDAR　data.