This research has used the L-band radar from ALOS-2 PALSAR-2 and field work data for evaluation of seasonal effects of backscattering intensity on retrieval forest biomass in the tropics. The effects of seasonality an...This research has used the L-band radar from ALOS-2 PALSAR-2 and field work data for evaluation of seasonal effects of backscattering intensity on retrieval forest biomass in the tropics. The effects of seasonality and HH, and HV polarizations of the SAR data on the biomass were analyzed. The dry season HV polarization could explain 61% of the biomass in this study region. The dry season HV backscattering intensity was highly sensitive to the biomass compared to the rainy season backscattering intensity. The SAR data acquired in the rainy season with humid and wet canopies were not very sensitive to the in situ biomass. Strong dependence of the biomass estimates with season of SAR data acquisition confirmed that the choice of right season SAR data is very important for improving the satellite based estimates of the biomass. This research expects that the results obtained in this research will contribute to monitoring of the quantity and quality of forest biomass in Vietnam and other tropical countries.展开更多
Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds.However,conventional encoding does not include angular information,which is...Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds.However,conventional encoding does not include angular information,which is critical for describing atomic arrangements in disordered systems.In this work,we extend the recently proposed ALIGNN(Atomistic Line Graph Neural Network)encoding,which incorporates bond angles,to also include dihedral angles(ALIGNN-d).This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures.ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II)aqua complexes,leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components.Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response,with distortions that represent transitions between more common geometries exhibiting the strongest absorption intensity.Future directions for further development of ALIGNN-d are discussed.展开更多
文摘This research has used the L-band radar from ALOS-2 PALSAR-2 and field work data for evaluation of seasonal effects of backscattering intensity on retrieval forest biomass in the tropics. The effects of seasonality and HH, and HV polarizations of the SAR data on the biomass were analyzed. The dry season HV polarization could explain 61% of the biomass in this study region. The dry season HV backscattering intensity was highly sensitive to the biomass compared to the rainy season backscattering intensity. The SAR data acquired in the rainy season with humid and wet canopies were not very sensitive to the in situ biomass. Strong dependence of the biomass estimates with season of SAR data acquisition confirmed that the choice of right season SAR data is very important for improving the satellite based estimates of the biomass. This research expects that the results obtained in this research will contribute to monitoring of the quantity and quality of forest biomass in Vietnam and other tropical countries.
基金The authors are partially supported by the Laboratory Directed Research and Development(LDRD)program(20-SI-004)at Lawrence Livermore National LaboratoryThis work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under contract No.DE-AC52-07NA27344.
文摘Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds.However,conventional encoding does not include angular information,which is critical for describing atomic arrangements in disordered systems.In this work,we extend the recently proposed ALIGNN(Atomistic Line Graph Neural Network)encoding,which incorporates bond angles,to also include dihedral angles(ALIGNN-d).This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures.ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II)aqua complexes,leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components.Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response,with distortions that represent transitions between more common geometries exhibiting the strongest absorption intensity.Future directions for further development of ALIGNN-d are discussed.
