Plant mitochondrial (mt) genomes possess several complex features, including a variable size, a dynamic genome structure, and complicated patterns of gene loss and gain throughout evolutionary history. Studies of plant mt genomes can, therefore, provide unique insights into organelle evolution. We assembled the complete Salix purpurea L. mt genome by screening genomic sequence reads generated by a Roche-454 pyrosequencing platform. The pseudo-molecule obtained has a typical circular structure 598,970 bp long, with an overall GC content of 55.06%. The S. purpurea mt genome contains 52 genes: 31 protein-coding, 18 tRNAs, and three rRNAs. Eighteen tandem repeats and 404 microsatellites are distributed unevenly throughout the S. purpurea mt genome. A phylogenetic tree of 23 representative terrestrial plants strongly supports S. purpurea inclusion in the Malpighiales clade. Our analysis contributes toward understanding the organization and evolution of organelle genomes in Salicaceae species.

Individual tree canopy extraction plays an important role in downstream studies such as plant phenotyping, panoptic segmentation and growth monitoring. Canopy volume calculation is an essential part of these studies. However, existing volume calculation methods based on LiDAR or based on UAV-RGB imagery cannot balance accuracy and real-time performance. Thus, we propose a two-step individual tree volumetric modeling method: first, we use RGB remote sensing images to obtain the crown volume information, and then we use spatially aligned point cloud data to obtain the height information to automate the calculation of the crown volume. After introducing the point cloud information, our method outperforms the RGB image-only based method in 62.5% of the volumetric accuracy. The $AbsoluteError$ of tree crown volume is decreased by 8.304. Compared with the traditional 2.5D volume calculation method using cloud point data only, the proposed method is decreased by 93.306. Our method also achieves fast extraction of vegetation over a large area. Moreover, the proposed YOLOTree model is more comprehensive than the existing YOLO series in tree detection, with 0.81% improvement in precision, and ranks second in the whole series for $mAP50\text{-}95$ metrics. We sample and open-source the TreeLD dataset to contribute to research migration.

Forest fires represent a significant menace to both the ecological equilibrium of forests and the safety of human life and property. Upon ignition, fires frequently generate billowing smoke. The prompt identification and management of fire sources and smoke can efficiently avert the occurrence of extensive forest fires, thereby safeguarding both forest resources and human well-being. Although drone patrols have emerged as a primary method for forest-fire prevention, the unique characteristics of forest-fire images captured from high altitudes present challenges. These include remote distances, small fire points, smoke targets with light hues, and complex, ever-changing background environments. Consequently, traditional target-detection networks frequently exhibit diminished accuracy when handling such images. In this study, we introduce a cutting-edge drone-based network designed for the detection of forest fires and smoke, named FSNet. To begin, FSNet employs the YOCO data-augmentation method to enhance image processing, thereby augmenting both local and overall diversity within forest-fire images. Next, building upon the transformer framework, we introduce the EBblock attention module. Within this module, we introduce the notion of “groups”, maximizing the utilization of the interplay between patch tokens and groups to compute the attention map. This approach facilitates the extraction of correlations among patch tokens, between patch tokens and groups, and among groups. This approach enables the comprehensive feature extraction of fire points and smoke within the image, minimizing background interference. Across the four stages of the EBblock, we leverage a feature pyramid to integrate the outputs from each stage, thereby mitigating the loss of small target features. Simultaneously, we introduce a tailored loss function, denoted as $L_{forest}$, specifically designed for FSNet. This ensures the model’s ability to learn effectively and produce high-quality prediction boxes. We assess the performance of the FSNet model across three publicly available forest-fire datasets, utilizing mAP, Recall, and FPS as evaluation metrics. The outcomes reveal that FSNet achieves remarkable results: on the Flame, Corsican, and D-Fire datasets, it attains mAP scores of 97.2%, 87.5%, and 94.3%, respectively, with Recall rates of 93.9%, 87.3%, and 90.8%, respectively, and FPS values of 91.2, 90.7, and 92.6, respectively. Furthermore, extensive comparative and ablation experiments validate the superior performance of the FSNet model.

Semantic segmentation of vegetation in aerial remote sensing images is a critical aspect of vegetation mapping. Accurate vegetation segmentation effectively informs real-world production and construction activities. However, the presence of species heterogeneity, seasonal variations, and feature disparities within remote sensing images poses significant challenges for vision tasks. Traditional machine learning-based methods often struggle to capture deep-level features for the segmentation. This work proposes a novel deep learning network named FA-HRNet that leverages the fusion of attention mechanism and a multi-branch network structure for vegetation detection and segmentation. Quantitative analysis from multiple datasets reveals that our method outperforms existing approaches, with improvements in MIoU and PA by 2.17% and 4.85%, respectively, compared with the baseline network. Our approach exhibits significant advantages over the other methods regarding cross-region and cross-scale capabilities, providing a reliable vegetation coverage ratio for ecological analysis.

With the advancement of remote sensing technology, high-resolution images are widely used in the field of computer vision. However, image quality is often degraded due to hardware limitations and environmental interference. This paper proposes a Residual Separable Super-Resolution Reconstruction Generative Adversarial Network (RSSRGAN) for remote sensing image super-resolution. The model aims to enhance the resolution and edge information of low-resolution images without hardware improvements. The main contributions include (1) designing an optimized generator network by improving the residual dense network and introducing depthwise separable convolutions to remove BN layers, thereby increasing training efficiency—two PatchGAN discriminators are designed to enhance multi-scale detail capture—and (2) introducing content loss and joint perceptual loss on top of adversarial loss to improve global feature representation. Experimental results show that compared to the widely used SRGAN model in remote sensing (exemplified by the satellite-specific SRGAN in this study), this model improves PSNR by approximately 18.8%, SSIM by 8.0%, reduces MSE by 3.6%, and enhances the PI metric by 13.6%. It effectively enhances object information, color, and brightness in images, making it more suitable for remote sensing image super-resolution.

The Northeast China Cold Vortex (NCCV) is a crucial local synoptic system influencing the weather and climate of Northeast China. However, the application of artificial intelligence techniques in NCCV prediction remains limited. Based on ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), this study constructs a 23-year multi-modal spatiotemporal sequence dataset of NCCV via an objective identification method, focusing on NCCV trajectory prediction. An improved generative adversarial network model is proposed, which adopts a multi-encoder architecture to extract spatiotemporal features of multi-modal NCCV data and introduces a multi-generator structure to address the insufficient prediction capability of a single generator. A selector module is added to enable the model to adaptively select the optimal generation path. Ablation experiments show that compared with single-trajectory data input, multi-modal data input in our model reduces the average prediction error by 67.96 km, representing a 34.0% improvement, and the 24-h prediction error improvement reaches 39.7%. Ultimately, the proposed model achieves superior prediction accuracy and stability in the NCCV trajectory prediction tasks at 6 h, 12 h, 18 h, and 24 h, with prediction distance errors reduced by 21.4%, 29.2%, 34.0%, and 37.0% compared to LSTM.