Accurate, efficient, impersonal harvesting models play a very important role in optimizing stand spatial structural and guiding forest harvest practices. However, existing studies mainly focus on the single-objective optimization and evaluation of forest at the stand- or landscape-level, lacking considerations of tree-level neighborhood interactions. Therefore, the study explored the combination of the PSO algorithm and neighborhood indices to construct a tree-level multi-objective forest harvest model (MO-PSO) covering multi-dimensional spatial characteristics of stands. Taking five natural secondary forest plots and thirty simulated plots as the study area, the MO-PSO was used to simulate and evaluate the process of thinning operations. The results showed that the MO-PSO model was superior to the basic PSO model (PSO) and random thinning model Monte Carlo-based (RD-TH), DBH dominance (DOMI), uniform angle (ANGL), and species mingling (MING) were better than those before thinning. The multi-dimensional stand spatial structure index (L-index) increased by 1.0%~11.3%, indicating that the forest planning model (MO-PSO) could significantly improve the spatial distribution pattern, increase the tree species mixing, and reduce the degree of stand competition. In addition, under the four thinning intensities of 0% (T1), 15% (T2), 30% (T3), and 45% (T4), L-index increased and T2 was the optimal thinning intensity from the perspective of stand spatial structure overall optimization. The study explored the effect of thinning on forest spatial structure by constructing a multi-objective harvesting model, which can help to make reasonable and scientific forest management decisions under the concept of multi-objective forest management.

The Crested Ibis (Nipponia nippon) is an endangered animal with an extremely high ecological, humanistic, and scientific value. However, this species still faces survival challenges, due to rapidly shrinking foraging grounds, the serious interference of human behavior, and increased habitat requirements. Geographical environment is a significant factor affecting Crested Ibis behavior-pattern analysis and habitat protection. The spatial and temporal trajectory contains habitat location and period information, a vital record of the Crested Ibis’ habits, and the basis of all research. Nevertheless, there are only a handful of studies on the missing trajectory data and fusing multiple sources of environmental data-research methods. We studied the spatial and temporal habitat use of the tracked Crested lbis by fusing multiple data-sources in China. This paper adopts the LSTM (long short-term memory) model to supplement the missing trajectory data and perform cluster mining; and a random forest model is used to predict the habitat of the Crested Ibis with high fitting accuracy (R² = 84.9%). The results show that the Crested Ibis distribution-pattern is characterized by high altitude and proximity to woodland and rivers. Additionally, the habitat dependence on the village implicates human agricultural activities in positively impacting its reproduction. This paper provides a complete method for analyzing Crested Ibis’ spatial and temporal trajectory by fusing multi-source data, which is crucial for protecting the survival and reproduction of the Crested Ibis.

To address the current challenges of the heavy workload, time-consuming nature and labor-intensiveness involved in existing crested ibis’s (Nipponia nipponTemminck, 1835) habitat identification approaches, this paper proposes an automatic habitat identification method based on spatiotemporal density detection. With consideration of the characteristics of the crested ibis’s trajectory data, such as aggregation, repeatability, and uncertainty, this method achieves detecting the crested ibis’s stopping points by using the spatial characteristics of the trajectory data. On this basis, an improved spatiotemporal clustering-based DBSCAN method is proposed in this paper, incorporating temporal characteristics of the trajectory data. By combining the spatial and temporal features, the proposed method is able to accurately identify the roosting and foraging sites among the crested ibis’s stopping points. Supported by remote sensing images and field investigations, it was found that the method proposed in this paper has a good clustering effect and can effectively identify the crested ibis’s foraging sites and overnight roosting areas. Specifically, the woodland, farmland, and river areas are the common foraging sites for the crested ibis, while the woodland with large trees is their common overnight site. Therefore, the method proposed in this paper can provide technical support for identifying and protecting the crested ibis’s habitats.

Simulating animal movement has long been a central focus of study in the area of wildlife behaviour studies. Conventional modelling methods have difficulties in accurately representing changes over time and space in the data, and they generally do not effectively use telemetry data. Thus, this paper introduces a new and innovative deep reinforcement learning technique known as AnimalEnvNet. This approach combines historical trajectory data and remote sensing images to create an animal agent using deep reinforcement learning techniques. It overcomes the constraints of conventional modelling approaches. We selected pandas as the subject of our research and carried out research using GPS trajectory data, Google Earth images, and Sentinel-2A remote sensing images. The experimental findings indicate that AnimalEnvNet reaches convergence during supervised learning training, attaining a minimal mean absolute error (MAE) of 28.4 m in single-step prediction when compared to actual trajectories. During reinforcement learning training, the agent has the capability to replicate animal locomotion for a maximum of 12 iterations, while maintaining an error margin of 1000 m. This offers a novel approach and viewpoint for mimicking animal behaviour.

This study aims to explore the correlation between the activity trajectory of Crested Ibis and meteorological changes. The trajectory data of Crested Ibis were obtained using the HQBG3621L backpack-style tracker, and the spatiotemporal characteristics of the trajectory data were analyzed to obtain information on the activity range and habitat of Crested Ibis. The Scheirer–Ray–Hare test and Kruskal–Wallis test were used to investigate the impact of meteorological factors on the activity of Crested Ibis. The study found that meteorological factors have a certain influence on the habitat selection and activity patterns of Crested Ibis. Through this research, a better understanding of the interaction between Crested Ibis and the meteorological environment can be achieved, providing a scientific basis for the conservation and ecological management of Crested Ibis.