Ground filtering is crucial for airborne Light Detection and Ranging (LiDAR) data post-processing. The progressive triangulated irregular network densification (PTD) algorithm and its variants outperform others in accuracy, stability, and robustness, using grid-based seed point selection, TIN construction, and iterative rules for ground point identification. However, these methods still face limitations in removing low points and accurately preserving terrain details, primarily due to their sensitivity to grid size. To overcome this issue, a novel PTD filtering algorithm based on an adaptive grid (AGPTD) was proposed. The main contributions of the proposed method include an outlier removal method using a radius outlier removal algorithm and Kd-tree, a method for establishing an adaptive two-level grid based on point cloud density and terrain slope, and an adaptive selection method for angle and distance thresholds in the iterative densification processing. The performance of the AGPTD algorithm was assessed based on widely used benchmark datasets. Results show that the AGPTD algorithm outperforms the classical PTD algorithm in retaining ground feature points, especially in reducing Type I error and average total error significantly. In comparison with other advanced algorithms developed in recent years, the novel algorithm showed the lowest average Type I error, the minimal average total error, and the greatest average Kappa coefficient, which were 1.11%, 2.28%, and 90.86%, respectively. Additionally, the average accuracy, precision, and recall of AGPTD were 97.69%, 97.52%, and 98.98%, respectively.

High spatial resolution land surface temperature (LST) has widespread applications in many fields. Unmanned aerial vehicle (UAV) thermal infrared (TIR) remote sensing is a crucial means of obtaining such data. UAV thermal cameras typically need to capture numerous images to create a comprehensive TIR mosaic covering the target region, which is then converted into an LST map. However, LST can change rapidly over time, leading to temporal inconsistencies within the LST map, thereby affecting subsequent analysis and decision-making. Although reducing the UAV flight duration can minimize such inconsistencies, most practical applications cannot meet this requirement. Therefore, acquiring the time information of UAV TIR mosaic pixels is essential for developing temporal normalization methods and for assessing temperature data quality. Here, we propose a so-called equivalent acquisition time estimation for mosaics (EATEMs) method, designed to estimate the equivalent acquisition time (EAT) of UAV TIR mosaic pixels. This method integrates principles of UAV photogrammetry and image fusion. In our experiments, the estimated time map accurately reflects the UAV’s flight path and landing situation. Additionally, evaluation results based on ground-measured data indicate that the estimated time has an uncertainty of less than 5 min when there is a good linear relationship between LST and time. The more significant the linear relationship, the smaller the uncertainty. These promising results demonstrate the potential of the EATEM method in addressing issues related to temporal variations in UAV TIR remote sensing.

The South China Sea (SCS) is an important region of fishery resources. However, its fishery resources have been threatened, mainly because of overfishing. In this study, we explored the distribution of night-time fishing boats and analyzed the relationship between fishing activities and marine environmental factors in the northwestern SCS (NWSCS). Firstly, the spatiotemporal variations in nighttime fishing boats in each month of 2021 in the NWSCS were studied. Meanwhile, a fishery activity center index was used to analyze the overall fishery activity trend in the NWSCS. Finally, the spatiotemporal distribution patterns of corresponding environmental factors (i.e., Chl-a, SSS, SST, latitude, longitude) were analyzed, and the nonlinear relationship between environmental factors and fishery activities was quantitatively studied using the generalized additive model. The results showed that fishery activities were mainly distributed in the waters of Beibu Gulf and the southwest of Hainan Island. Meanwhile, there were obvious seasonal differences (i.e., trimodal distribution) in the intensity of fishery activities in the NWSCS. Chl-a was the most important impact factor with a contribution of 21.7%, followed by SSS, longitude, SST, and latitude, with contributions of 12.8%, 9.4%, 4.2%, and 0.5%, respectively. Fishery activities in the NWSCS were mainly distributed in the area with Chl-a of 0~0.35 mg/m³, SST of 21.2~26.4 °C, and SSS of 32.9~33.8 Practical Salinity Unit. This study reveals that more efforts are required to prevent IUU fishing activities for the sustainable development of marine ecosystems in the NWSCS. It is also necessary to improve remote sensing technology to support making sustainable fishing plans.

Sunspots and filaments are critical indicators of solar activity. While the Chinese H α Solar Explorer (CHASE) provides massive full-disk spectral data, existing automated detection algorithms are primarily optimized for other solar observatories, such as the Solar Dynamics Observatory/Helioseismic and Magnetic Imager. The significant discrepancies in instrument responses between different solar observatories render the direct transfer of existing algorithms ineffective, failing to yield robust results when processing CHASE data. To address these challenges, an automatic detection framework based on an Improved Simulated Annealing Genetic Algorithm (ISAGA) is proposed. First, a standardized preprocessing system tailored to CHASE imaging characteristics is established to effectively eliminate limb darkening and transmission artifacts in the Fe I and H α bands. Second, the ISAGA algorithm introduces a physical prior reward mechanism based on the Sigmoid function into the conventional Kapur–Sahoo–Wong (KSW) entropy model, to overcome the shortcomings of handling unimodal histograms, significantly improving the threshold search robustness. Finally, topological postprocessing is used to achieve refined feature extraction. Experiments on the 2022–2023 CHASE dataset demonstrate that ISAGA achieves an intersection over union (IoU) of 98.43% and an F1 score of 99.20% for sunspot detection, outperforming other representative methods. For solar filaments, the framework yields an average IoU of 60.93% and an F1 score of 74.95%. By achieving an effective balance between superior precision and high computational efficiency, ISAGA is suitable for the automated detection of sunspots in massive CHASE data and serves as an effective tool for the rapid generation of large-scale filament datasets.