Spaceborne LiDAR has been widely used to obtain forest canopy heights over large areas, but it is still a challenge to obtain spatio-continuous forest canopy heights with this technology. In order to make up for this deficiency and take advantage of the complementary for multi-source remote sensing data in forest canopy height mapping, a new method to estimate forest canopy height was proposed by synergizing the spaceborne LiDAR (ICESat-2) data, Synthetic Aperture Radar (SAR) data, multi-spectral images, and topographic data considering forest types. In this study, National Geographical Condition Monitoring (NGCM) data was used to extract the distributions of coniferous forest (CF), broadleaf forest (BF), and mixed forest (MF) in Hua’ nan forest area in Heilongjiang Province, China. Accordingly, the forest canopy height estimation models for whole forest (all forests together without distinguishing types, WF), CF, BF, and MF were established, respectively, by Radom Forest (RF) and Gradient Boosting Decision Tree (GBDT). The accuracy for established models and the forest canopy height obtained based on estimation models were validated consequently. The results showed that the forest canopy height estimation models considering forest types had better performance than the model grouping all types of forest together. Compared with GBDT, RF with optimal variables had better performance in forest canopy height estimation with Pearson’s correlation coefficient (R) and the root-mean-squared error (RMSE) values for CF, BF, and MF of 0.72, 0.59, 0.62, and 3.15, 3.37, 3.26 m, respectively. It has been validated that a synergy of ICESat-2 with other remote sensing data can make a crucial contribution to spatio-continuous forest canopy height mapping, especially for areas covered by different types of forest.

Accurately measuring the moisture content (MC) of square timber is crucial for ensuring the quality and performance of wood products in wood processing. Traditional MC detection methods have certain limitations. Therefore, this study developed a one-dimensional convolutional neural network (1D-CNN) model based on the first 8 nanoseconds of ground-penetrating radar (GPR) signals to predict the MC of square timber. The study found that the mixed-species model exhibited effective predictive performance (R² = 0.9864, RMSE = 0.0393) across the tree species red spruce, Dahurian larch, European white birch, and Manchurian ash (MC range 0%–133.1%), while single-species models showed even higher accuracy (R² ≥ 0.9876, RMSE ≤ 0.0358). Additionally, the 1D-CNN model outperformed other algorithms in automatically capturing complex patterns in GPR full-waveform amplitude data. Moreover, the algorithms based on full-waveform amplitude data demonstrated significant advantages in detecting wood MC compared to those based on a traditional time–frequency feature parameter. These results indicate that the 1D-CNN model can be used to optimize the drying process and detect the MC of load-bearing timber in construction and bridge engineering. Future work will focus on expanding the dataset, further optimizing the algorithm, and validating the models in industrial applications to enhance their reliability and applicability.

Through research on the effects of soil and meteorological factors on poplar wood properties, poplar clones with enhanced cold tolerance, drought resistance, and salt–alkali tolerance were selected for large-scale cultivation in the Western Songnen Plain, Northern China. We evaluated wood physical properties (basic density) and anatomical characteristics (annual ring width—RW, vessel number—CNO, vessel lumen area—LA) of 15-year-old Populus simonii × P. nigra, Populus alba × P. berolinensis, P. euramericana N3016 × P. ussuriensis, and Populus pseudo-cathayana × P. deltodides clones in the typical black soil area and saline–alkali land. The results showed that black soil region was more suitable for poplar growth, which was influenced by both soil and meteorological factors. Among soil factors, pH was the primary factor influencing the radial growth of poplar clones, exhibiting a negative correlation for all clones except P. alba × P. berolinensis. Furthermore, P. euramericana N3016 × P. ussuriensis was affected by organic carbon, while P. simonii × P. nigra and P. alba × P. berolinensis were more influenced by potassium. Among climatic factors, basic wood density, annual ring characteristics, and vessel structural parameters in all clones were primarily influenced by wind speed and sunshine, with air temperature having the least effect. Among the four clones, P. alba × P. berolinensis displayed better growth performance (higher RW) and basic wood density (0.29–0.41 g/cm³) at both sites, while P. simonii × P. nigra proved suitable for cold regions. Both clones showing dual adaptability to saline–alkali and black soil environments in Northeast China.

Electromagnetic waves are used in wood inspection and treatment. Three important parameters need to be considered in the application of this method, including moisture content (MC), porosity, and dielectric constant (ε), which affect the effectiveness of application of electromagnetic waves, and these parameters can interact with each other. This study’ elucidated the effect of porosity on the ε of wood at different MCs (absolute dry to saturated) in the frequency (f) range of 1 Hz–10⁷ Hz. The results indicated that the porosity on the ε needs to consider both f and MC. Relationships between porosity and ε of absolute dry wood shows opposite patterns in different f bands. When f ≤ 200 Hz, the porosity was positively correlated with ε. When f > 200 Hz, the porosity was negatively correlated with ε. However, the effect of f on ε with the increased of MC was complicated., Correlations with MC, porosity and ε was not significant when f was low. As f increased, the relationship between MC and ε became linear. The results of this study provide a reference for use of ε to detect wood pore characteristics and MC.

To address the low accuracy of non-destructive detection of moisture content (MC) of logs (especially in small diameters) by ground penetrating radar (GPR) signals, the MC of 10–15 cm diameter spruce, Manchurian ash, and white birch logs were predicted using the time-frequency parameters of the GPR signals and a back-propagation neural network (BPNN) model. B-scan signals were obtained using tree radar on the barks of discs selected from fresh green logs. Then, 31 time-frequency parameters from the B-scan signals were optimised using the least absolute shrinkage and selection operator (Lasso) and principal component analysis (PCA). Finally, the log MCs of the single and hybrid models was predicted using the BPNN. The accuracy of the least absolute shrinkage and selection operator and back-propagation neural network (Lasso-BP) were higher than those of the principal component analysis and back-propagation neural network (PCA-BP), and the BPNN. The individual species and hybrid models both have good predictive capability; when the log MC is below 20%, the maximum residual errors are relatively small, almost within 6% and 10%, respectively. These models significantly improve the accuracy of non-destructive detection of log MC and are beneficial for efficient wood processing.