The vertical structure of forest ecosystems influences and reflects ecosystem functioning. Terrestrial laser scanning (TLS) enables the rapid acquisition of 3D forest information and subsequent reconstruction of the vertical structure, which provides new support for acquiring forest vertical structure information. We focused on artificial forest sample plots in the north-central of Nanning, Guangxi, China as the research area. Forest sample point cloud data were obtained through TLS. By accurately capturing the gradient information of the forest vertical structure, a classification boundary was delineated. A complex forest vertical structure segmentation method was proposed based on the Forest-PointNet model. This method comprehensively utilized the spatial and shape features of the point cloud. The study accurately segmented four types of vertical structure features in the forest sample location cloud data: ground, bushes, trunks, and leaves. With optimal training, the average classification accuracy reaches 90.98%. The results indicated that segmentation errors are mainly concentrated at the branch intersections of the canopy. Our model demonstrates significant advantages, including effective segmentation of vertical structures, strong generalization ability, and feature extraction capability.

Research on wildlife monitoring methods is a crucial tool for the conservation of rare wildlife in China. However, the fact that rare wildlife monitoring images in field scenes are easily affected by complex scene information, poorly illuminated, obscured, and blurred limits their use. This often results in unstable recognition and low accuracy levels. To address this issue, this paper proposes a novel wildlife identification model for rare animals in Giant Panda National Park (GPNP). We redesigned the C3 module of YOLOv5 using NAMAttention and the MemoryEfficientMish activation function to decrease the weight of field scene features. Additionally, we integrated the WIoU boundary loss function to mitigate the influence of low-quality images during training, resulting in the development of the NMW-YOLOv5 model. Our model achieved 97.3% for mAP50 and 83.3% for mAP50:95 in the LoTE-Animal dataset. When comparing the model with some classical YOLO models for the purpose of conducting comparison experiments, it surpasses the current best-performing model by 1.6% for mAP50:95, showcasing a high level of recognition accuracy. In the generalization ability test, the model has a low error rate for most rare wildlife species and is generally able to identify wildlife in the wild environment of the GPNP with greater accuracy. It has been demonstrated that NMW-YOLOv5 significantly enhances wildlife recognition accuracy in field environments by eliminating irrelevant features and extracting deep, effective features. Furthermore, it exhibits strong detection and recognition capabilities for rare wildlife in GPNP field environments. This could offer a new and effective tool for rare wildlife monitoring in GPNP.

The Amur tiger is an important endangered species in the world, and its re-identification (re-ID) plays an important role in regional biodiversity assessment and wildlife resource statistics. This paper focuses on the task of Amur tiger re-ID based on visible light images from screenshots of surveillance videos or camera traps, aiming to solve the problem of low accuracy caused by camera perspective, noisy background noise, changes in motion posture, and deformation of Amur tiger body patterns during the re-ID process. To overcome this challenge, we propose a serial multi-scale feature fusion and enhancement re-ID network of Amur tiger for this task, in which global and local branches are constructed. Specifically, we design a global inverted pyramid multi-scale feature fusion method in the global branch to effectively fuse multi-scale global features and achieve high-level, fine-grained, and deep semantic feature preservation. We also design a local dual-domain attention feature enhancement method in the local branch, further enhancing local feature extraction and fusion by dividing local feature blocks. Based on the above model structure, we evaluated the effectiveness and feasibility of the model on the public dataset of the Amur Tiger Re-identification in the Wild (ATRW), and achieved good results on mAP, Rank-1, and Rank-5, demonstrating a certain competitiveness. In addition, since our proposed model does not require the introduction of additional expensive annotation information and does not incorporate other pre-training modules, it has important advantages such as strong transferability and simple training.

Deriving meaningful mineralization information from raw geospatial datasets is fundamental to the sustainable evaluation and management of mineral resources. As a cornerstone of mineral resource evaluation, identifying geochemical anomalies often faces the significant challenge of sampling bias in practical applications. Strong spatial unevenness often leads to information loss in traditional geostatistical models, where critical anomaly structures may be over-smoothed or obscured. To address this limitation, this study proposes a knowledge-driven adaptive direct sampling (KD-ADS) framework. This approach functions as a geospatial context-aware reconstruction engine. It integrates a multi-factor knowledge-driven weighting system to prioritize regions with high information value and incorporates a dynamic context-aware neighborhood module that adapts to local statistical characteristics. Using 1268 samples from the Jiulian Mountains tungsten metallogenic belt, ablation studies demonstrate the individual contributions of the knowledge-driven weighting and adaptive neighborhood modules to improving reconstruction accuracy and spatial connectivity. Comparative experiments with the traditional direct sampling (DS) algorithm demonstrate that KD-ADS achieves a more accurate reconstruction of geochemical fields and better preserves discrete high-value mineralization anomalies and spatial heterogeneity under sampling-bias conditions. This approach improves the reproducibility of mineralization enrichment patterns and enhances computational efficiency, providing data science-driven support for sustainable mineral exploration and resource allocation.

Recognizing fine-grained giant panda behaviors from long-term infrared camera-trap videos remains challenging due to subtle motion variations, complex natural environments, and the inability of vision-only models to exploit high-level semantic information. To address these challenges, we propose VTS-PANDA, a unified multimodal framework that jointly models temporal visual dynamics and ecology-aware semantic priors. The framework introduces three complementary components: a depthwise temporal convolution module that enhances local inter-frame motion cues at the input stage, an MLLM-guided semantic prior module that generates structured ecological descriptions through constrained prompting and integrates them with visual features via a gated fusion mechanism, and a CLIP-based similarity enhancement module that explicitly aligns visual representations with behavioral text prompts to improve discriminability. Extensive experiments on wild giant panda infrared video datasets demonstrate that the proposed framework effectively captures temporal motion patterns, spatial structures, and high-level ecological semantics, achieving robust and interpretable behavior recognition in challenging real-world environments. This work provides a semantic-guided framework for automated wildlife behavior understanding and offers potential support for intelligent long-term ecological monitoring and precision conservation.