Magnolia wufengensis is a new species of Magnolia L. and has considerable ornamental and economic value due to its unique characteristics. However, because of its characteristic of poor low temperature resistance, M. wufengensis is hardly popularization and application in the north of China. Furthermore, the mechanisms of gene regulation and signaling pathways involved in the cold-stress response remained unclear in this species. In order to solve the above-mentioned problems, we performed de novo transcriptome assembly and compared the gene expression under the natural (25 °C) and cold (4 °C) conditions for M. wufengensis seedlings. More than 46 million high-quality clean reads were produced from six samples (RNA was extracted from the leaves) and were used for performing de novo transcriptome assembly. A total of 59,764 non-redundant unigenes with an average length of 899 bp (N50 = 1,110) were generated. Among these unigenes, 31,038 unigenes exhibited significant sequence similarity to known genes, as determined by BLASTx searches (E-value ≤1.0E-05) against the Nr, SwissProt, String, GO, KEGG, and Cluster of COG databases. Based on a comparative transcriptome analysis, 3,910 unigenes were significantly differentially expressed (false discovery rate [FDR] < 0.05 and |log2FC (CT/CK)| ≥ 1) in the cold-treated samples, and 2,616 and 1,294 unigenes were up- and down-regulated by cold stress, respectively. Analysis of the expression patterns of 16 differentially expressed genes (DEGs) by quantitative real-time RT-PCR (qRT-PCR) confirmed the accuracy of the RNA-Seq results. Gene Ontology and KEGG pathway functional enrichment analyses allowed us to better understand these differentially expressed unigenes. The most significant transcriptomic changes observed under cold stress were related to plant hormone and signal transduction pathways, primary and secondary metabolism, and photosynthesis. In addition, 113 transcription factors, including members of the AP2-EREBP, bHLH, WRKY, MYB, NAC, HSF, and bZIP families, were identified as cold responsive. We generated a genome-wide transcript profile of M. wufengensis and a de novo-assembled transcriptome that can be used to analyze genes involved in biological processes. In this study, we provide the first report of transcriptome sequencing of cold-stressed M. wufengensis. Our findings provide important clues not only for understanding the molecular mechanisms of cold stress in plants but also for introducing cold hardiness into M. wufengensis.

Urban heat island (UHI) attenuation is an essential aspect for maintaining environmental sustainability at a local, regional, and global scale. Although impervious surfaces (IS) and green spaces have been confirmed to have a dominant effect on the spatial differentiation of the urban land surface temperature (LST), comprehensive temporal and quantitative analysis of their combined effects on LST and surface urban heat island intensity (SUHII) changes is still partly lacking. This study took the plain area of Beijing, China as an example. Here, rapid urbanization and a large-scale afforestation project have caused distinct IS and vegetation cover changes within a small range of years. Based on 8 scenes of Landsat 5 TM/7ETM/8OLI images (30 m × 30 m spatial resolution), 920 scenes of EOS-Aqua-MODIS LST images (1 km × 1 km spatial resolution), and other data/information collected by different approaches, this study characterized the interrelationship of the impervious surface area (ISA) dynamic, forest cover increase, and LST and SUHII changes in Beijing’s plain area during 2009–2018. An innovative controlled regression analysis and scenario prediction method was used to identify the contribution of ISA change and afforestation to SUHII changes. The results showed that percent ISA and forest cover increased by 6.6 and 10.0, respectively, during 2009–2018. SUHIIs had significant rising tendencies during the decade, according to the time division of warm season days (summer days included) and cold season nights (winter nights included). LST changes during warm season days responded positively to a regionalized ISA increase and negatively to a regionalized forest cover increase. However, during cold season nights, LST changes responded negatively to a slight regionalized ISA increase, but positively to an extensive regionalized ISA increase, and LST variations responded negatively to a regionalized forest cover increase. The effect of vegetation cooling was weaker than ISA warming on warm season days, but the effect of vegetation cooling was similar to that of ISA during cold season nights. When it was assumed that LST variations were only caused by the combined effects of ISA changes and the planting project, it was found that 82.9% of the SUHII rise on warm season days (and 73.6% on summer days) was induced by the planting project, while 80.6% of the SUHII increase during cold season nights (and 78.9% during winter nights) was caused by ISA change. The study presents novel insights on UHI alleviation concerning IS and green space planning, e.g., the importance of the joint planning of IS and green spaces, season-oriented UHI mitigation, and considering the thresholds of regional IS expansion in relation to LST changes.

Estimating above-ground biomass (AGB) is important for ecological assessment, carbon stock evaluation, and forest management. This research assesses the performance of the machine learning algorithms XGBoost, SVM, and RF using data from the Sentinel-2 and Landsat-9 satellites. The study assesses the influence of the significant spectral bands and vegetation indices on the accuracy of the AGB estimate. The results presented in the paper indicate that Sentinel-2 data were more effective than Landsat-9 data. This is mainly because it had higher spatial and spectral resolution, which enabled the model vegetation gradients and structural attributes more accurately. The XGBoost model performed the best with an R² of 0.82 and RMSE of 0.73 Mg/ha with Sentinel-2 and R² of 0.80 and RMSE of 0.71 Mg/ha with Landsat-9. In the current study, SVM also showed a substantial accuracy with an R² of 0.79 and RMSE of 0.73 Mg/ha for Sentinel-2 and R² of 0.76 and RMSE of 0.80 Mg/ha for Landsat-9. For Sentinel-2, the random forest achieved an R² of 0.74 and an RMSE of 0.93 Mg/ha, and Landsat 9 yielded an R² of 0.72 and an RMSE of 0.88 Mg/ha. Thus, using variable importance analysis, the results showed that vegetation indices and spectral bands have higher importance in predicting AGB. As expected from their application in biomass research, these predictors consistently emerged as highly significant across models and datasets. This study demonstrates the potential of integrating machine learning with remote sensing data to achieve accurate and efficient biomass assessment.