Labeled as a fast-growing tree species, eucalyptus has outstanding carbon sequestration capacity. Forest stock volume (FSV) is regarded as an important parameter for evaluating the quality of planted eucalyptus forests. However, it is an intractable problem to map FSV of planted eucalyptus forests using optical images because of growth characteristics of the crown and low saturation levels. To improve the accuracy of FSV in planted eucalyptus forests, time series Landsat 8 OLI (LC8) images and ZY-3 stereo images were acquired in the study area. Additionally, then, three composite images were proposed using acquired Landsat 8 OLI images based on the size and shape of eucalyptus crowns, and several spectra variables were extracted from these composite images. Furthermore, corrected canopy height model (CCHM) was also extracted from ZY-3 stereo images. Meanwhile, four models (random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and multiple linear regression (MLR)) were used to estimate the FSV with various variable sets using the importance of the alternative variables ranked by RF. The results show that the sensitivity between proposed spectral variables and FSV is significantly improved using proposed composed images based on the growth characteristics of the crown, especially for young eucalyptus forests. After adding CCHM and stand age to the optimal variable set, the average relative root mean square error (rRMSE) of estimated FSV decreased from 41.01% to 29.94% for single LC8 images and from 32.64% to 26.47% for proposed composite LC8 images, respectively. After using the variable set extracted from composite LC8 images, the number of samples with overestimated FSV was significantly decreased for the young forest. Furthermore, forest height plays an important role in improving the accuracy of mapping FSV, whether young or mature eucalyptus forest. It was also proved that composite images related to crown close and CCHM have great potential to delay the saturation phenomenon for mapping FSV in planted eucalyptus forest.

Competition among trees for limited resources (e.g., sunlight, water, and nutrients) impacts their growth differently. Crown plays a crucial role in resource access for trees. However, uncertainties persist regarding crown size differences, tree growth, and tree competition levels between coniferous and broadleaf trees in mixed-age forests. We conducted a study on 3008 live trees across 28 plots in Hunan Province to measure individual crown size and tree growth increment. Introducing a new metric, the Crown Size Competition Index (CSCI), we assessed competition pressure among coniferous and broadleaf species in mixed-age forests. We examined the correlation between competition indices and tree growth increment while also investigating the influence of neighboring species diversity on tree competition. Our results revealed a significant negative correlation between the tree growth of Cunninghamia lanceolata (Lamb.) Hook.(CL) and Phoebe bournei (Hemsl.) Yang (PB) and the competition index. Increased competitive pressure on the focal tree corresponded to a decline in the growth of focal tree volume, with a more pronounced impact observed in PB compared to CL. The diversity of neighboring species contributed to variations in competition for the focal tree, with broadleaved species (PB) exerting a greater influence on the focal tree than coniferous species (CL). These findings underscore the competitive potential of PB in mature coniferous stands and advocate for the restoration of stratified mixes in CL broadleaf forests. Furthermore, our findings support the management strategies for the valuable tree species PB.

The spatial structure of forest stands significantly influences inter-tree competition and the overall stability of the stand’s ecological dynamics, and a profound understanding of forest stand spatial structure is essential for both effective forest management and ecological research. Previous studies on spatial structure have primarily employed single variables, offering only one-dimensional structural insights and lacking the capacity to interpret multidimensional information. In light of these limitations, our study introduces a novel approach founded on a six-variable distribution, aimed at conducting a comprehensive analysis and interpretation of the spatial attributes of forest stands. Diverging from conventional univariate or bivariate methods, the hexi-variate approach simultaneously considers six variables, facilitating a more intricate exploration of the intricate interrelationships within forest ecosystems from six distinct dimensions. We conducted an in-depth analysis of the spatial structural attributes within the forest stand, encompassing factors such as species diversity, size variation, spatial distribution patterns, openness, vertical stratification, and stand competition. To capture a comprehensive view of the trees’ spatial information, we employed the hexadecimal distribution method, effectively quantifying their characteristics across six dimensions. Our study unveiled a significant correlation between spatial structure and stand growth, establishing a connection by integrating the spatial structure with key structural features relevant to tree size. The outcomes of this study shed light on the effectiveness and superiority of the six-element distribution method when it comes to the analysis of forest structural characteristics. Our approach offers valuable insights into the optimization of forest management strategies, encompassing selective harvesting and biodiversity conservation, thereby establishing a solid footing for sustainable forest management practices.

Forest stand structure (the characteristics and interrelationships of live trees) and site conditions (the physical and environmental characteristics of a specific location) have been linked to forest regeneration, nutrient cycling, wildlife habitat, and climate regulation. While the effects of stand structure (i.e., spatial and non-spatial) and site conditions on the single function of Cunninghamia lanceolata and Phoebe bournei (CLPB) mixed forest have been studied in previous studies, the relative importance of stand structure and site conditions in terms of productivity, species diversity, and carbon sequestration remains unresolved. In this study, a structural equation model (SEM) was adopted to analyze the relative importance of stand structure and site conditions for the forest productivity, species diversity, and carbon sequestration of CLPB mixed forest in Jindong Forestry in Hunan Province. Our research demonstrates that site conditions have a greater influence on forest functions than stand structure, and that non-spatial structures have a greater overall impact on forest functions than spatial structures. Specifically, the intensity of the influence of site conditions and non-spatial structure on functions is greatest for productivity, followed by carbon sequestration and species diversity. In contrast, the intensity of the influence of spatial structure on functions is greatest for carbon sequestration, followed by species diversity and productivity. These findings provide valuable insights for the management of CLPB mixed forest in Jindong Forestry and have significant reference value for the close-to-natural forest management (CTNFM) of pure Cunninghamia lanceolata forests.

Forests, commonly viewed as the Earth’s lungs, play a crucial role in mitigating greenhouse gas emissions, regulating the globe, and maintaining ecological equilibrium. The assessment of aboveground biomass (AGB) serves as a pivotal indicator for evaluating forest quality. By integrating remote sensing images with a small number of ground-measured samples to map, forest AGBs can significantly reduce time and labor costs. Current research mainly focuses on improving the accuracy of mapping forest AGBs, such as integrating multiple-sensors remote sensing data and models. However, due to uncertainties associated with remote sensing images and complexities inherent in forest structures, the accuracy of mapping forest AGBs is constrained by both the quantity and distribution of ground samples available. The development of transfer learning methods can fully utilize ground-based measurement data and enable the application of samples across regions and time. To evaluate the potential of transfer learning methods in mapping forest AGBs, this study conducted a spatial–temporal transfer of spectral variables (SVs) and prediction models (PMs) using a direct-push transfer method, and a new evaluation metric, relative change of R-squared (RCRS), was proposed to assess the transferability of SVs and PMs. The results showed that the transferability of SVs and PMs in the spatial target domain is obviously greater than that in the temporal target domain. Compared to the temporal target domain, the RCRS for transfer SVs in the spatial target domain was lower by 20.89 (oak) and 20.88 (Chinese fir) and for transfer PMs by 24.16 (oak) and 24.79 (Chinese fir). Tree species is also one of the main factors affecting the spatial and temporal transfer of SVs, and it is challenging to transfer SVs between different tree species. The results also show that nonparametric models have better generalization performance, and their transferability is much greater than that of parametric models.

Unaffected by cloud cover and solar illumination, synthetic aperture radar (SAR) images coupled with quad-polarimetric techniques have significant potential for mapping forest aboveground biomass (AGB) in the mountains of southern China. To improve the accuracy of mapping forest AGB, it is necessary to accurately interpret and evaluate the sensitivity of polarimetric features related to polarimetric response in complex forests. In this study, several rotated polarimetric features were extracted from L-band quad-polarimetric ALOS PALSAR-2 images based on uniform polarimetric matrix rotation theory. In addition, the sensitivity of rotated polarimetric features with forest parameters was evaluated by the Pearson correlation coefficient, sensitivity index (SI), and saturation levels. Ultimately, the forest AGB was mapped with various combinatorial feature sets by a proposed feature selection method based on the sensitivity index. The results illustrated that rotated polarimetric features extracted from the rotational domain have higher sensitivity with various forest parameters and higher saturation levels for mapping forests than other traditional features. After using the proposed feature selection method and combinatorial feature sets, the rRMSE of mapped forest AGB ranged from 22.5% to 33.9% for two acquired images, and the best result was obtained from the combination of three types of polarimetric features (BC + C4 + Ro). It is also confirmed that different types of features extracted from quad-polarimetric SAR images have better compensation effects and the accuracy of mapped forest AGB is significantly improved.