Accurate quantification of forest biomass (FB) is the key to assessing the carbon budget of terrestrial ecosystems. Using remote sensing to apply inversion techniques to the estimation of FBs has recently become a research trend. However, the limitations of vertical scale analysis methods and the nonlinear distribution of forest biomass stratification have led to significant uncertainties in FB estimation. In this study, the biomass characteristics of forest vertical stratification were considered, and based on the integration of random forest and least squares (RF-LS) models, the FB prediction potential improved. The results indicated that compared with traditional biomass estimation methods, the overall R² of FB retrieval increased by 12.01%, and the root mean square error (RMSE) decreased by 7.50 Mg·hm⁻². The RF-LS model we established exhibited better performance in FB inversion and simulation assessments. The indicators of forest canopy height, soil organic matter content, and red-edge chlorophyll vegetation index had greater impacts on FB estimation. These indexes could be the focus of consideration in FB estimation using the integrated RF-LS model. Overall, this study provided an optimization method to map and evaluate FB by fine stratification of above-ground forest and reveals important indicators for FB inversion and the applicability of the RF-LS model. The results could be used as a reference for the accurate inversion of subtropical forest biomass parameters and estimation of carbon storage.

Guangxi Zhuang bronze drums are important material carriers of ethnic cultural heritage, yet their digital images often suffer from pattern wear, edge fracture, surface corrosion, color degradation, and local information loss caused by long-term preservation, environmental erosion, and acquisition limitations. To support the sustainable digital conservation and dissemination of bronze drum cultural heritage, this paper proposes an image restoration method that integrates degradation prior guidance with a damaged-region structure–texture collaborative constraint. Based on a generative adversarial network, the proposed method explicitly models complex degradation cues, including weathering, corrosion, wear, and color attenuation, on the generation side, thereby improving the perception and restoration of degraded cultural heritage image regions. On the discrimination side, local structural continuity and texture consistency are jointly constrained to enhance the visual authenticity of restored decorative patterns and metallic surface details. In addition, a Guangxi Zhuang bronze drum image restoration dataset is constructed to provide data support for intelligent cultural heritage conservation research. Experimental results show that the proposed method achieves an MSE of 27.318, an MAE of 2.431, a PSNR of 36.284, and an SSIM of 0.956, outperforming representative restoration methods in pixel fidelity, structural similarity, and visual coherence. The results demonstrate that the proposed method can provide an effective non-contact digital restoration tool for damaged bronze drum images, reduce reliance on repeated physical handling of cultural objects, and support the long-term preservation, digital archiving, visual interpretation, and public dissemination of ethnic cultural heritage within a sustainable development framework.

Prunus mume, a traditional ornamental species native to China, is highly valued for both its captivating weeping variety and economic value. The glycosylation of metabolites, which is mediated by UDP-glycosyltransferases (UGTs), is essential for the regulation of secondary metabolic pathways in plants. Here, we systematically identified and analyzed the UGTs in P. mume. A total of 182 PmUGTs were identified using genomic data and categorized into 16 distinct subfamilies (A–P). All PmUGTs were distributed unevenly across the eight chromosomes, with clear evidence of tandem duplication. Additionally, synteny analysis revealed a close evolutionary relationship between P. mume and Prunus persica. A promoter cis-acting element analysis indicated that PmUGTs may respond to light, hormones, and external stresses. A heatmap analysis revealed that PmUGTs had specific expression patterns across different tissues, under various hormone treatments, and in different developmental stages of stem lignification. Notably, qRT-PCR verification showed significant differences in PmUGT163 expression between straight and weeping stems, underscoring its role in regulating plant architecture formation. Taken together, our study elucidates the evolutionary trajectory of PmUGTs and lays the groundwork for the further validation of the candidate genes involved in plant architectural formation.

Double-flowered cultivars are generally considered more attractive than single-flowered varieties in ornamental plants. The AP2/ERF transcription factors superfamily plays pivotal roles in plant development, including floral organ formation. Here, a total of 248 AP2/ERF genes were identified in the genome of L. speciosa, and these genes were unevenly distributed on the 24 chromosomes. Phylogenetic analysis classified LsAP2/ERF genes into five distinct groups; the ERF subfamily was the largest, whereas the AP2 subfamily was associated with floral development. Gene duplication events contributed to the expansion of LsAP2/ERF family members, with segmental duplication identified as the primary contributor. Promoter cis-element analysis revealed an abundance of light-responsive and hormone-responsive elements. Spatiotemporal expression profiling showed that core AP2 subfamily members (LsAP2/ERF4/9/10/13/219) exhibited distinct expression patterns during flower bud development. Moreover, heterologous overexpression of LsAP2/ERF10 in tobacco resulted in transgenic lines with altered petal morphology, supporting its potential functional involvement in floral development. This study provides comprehensive characterization of the AP2/ERF family in L. speciosa, laying the foundation for elucidating its molecular mechanisms in floral morphogenesis.