In recent years, the frequent occurrence of eutrophication problems in water bodies has been caused by changes in the climate environment and overexploitation of natural resources by humans. Chlorophyll-a, as a key indicator for water body assessment, plays an important role in eutrophication research and has a profound impact on the global biogeochemical cycle of the climate process. Studies have shown that temperature can directly or indirectly affect the concentration of chlorophyll-a by influencing the growth of algae and water quality indicators in water bodies. Considering the temperature factor in the inversion of chlorophyll-a concentration is a novel research approach. Based on the influence of temperature on chlorophyll-a concentration, we propose the idea of inverting global ocean chlorophyll-a concentration based on temperature zoning. Using monthly average remote sensing reflectance data from VIIRS (Visible and Infrared Imaging Radiometer Suite), combined with the results of temperature zoning, the OC3V_(SST) model was constructed to invert the monthly average chlorophyll-a concentration in the global ocean in October 2018. The OC3V_(SST) model has been validated by applying it to the remaining 11 months of January, April, July, and October in 2017, 2018, and 2019, as well as the entire 31-day dataset of October 2018. The results indicate that temperature zonation can significantly improve the inversion accuracy of chlorophyll-a and further explore the spatial distribution patterns of global chlorophyll-a concentrations across various temperature ranges based on monthly averages from the global ocean. Additionally, the study investigates the continuity issues of various models and the correlation between temperature and chlorophyll-a.

Changes in river plane shapes are called river planform changes (RPCs). Such changes can impact sustainable human development (e.g., human habitations, industrial and agricultural development, and national border security). RPCs can be identified through field surveys—a method that is highly precise but time-consuming, or through remote sensing (RS) and geographic information system (GIS), which are less precise but more efficient. Previous studies that have addressed RPCs often used RS, GIS, or digital elevation models (DEMs) and focused on only one or a few rivers in specific areas with the goal of identifying the reasons underlying these changes. In contrast, in this paper, we developed a combinatorial reasoning mechanism based on topological and metric relations that can be used to classify RPCs. This approach does not require DEMs and can eliminate most false-change information caused by varying river water levels. First, we present GIS models of river planforms based on their natural properties and, then, modify these models into simple GIS river planform models (SGRPMs) using straight lines rather than common lines to facilitate computational and human understanding. Second, we used double straight line 4-intersection models (DSL4IMs) and intersection and difference models (IDMs) of the regions to represent the topological relations between the SGRPMs and used double-start-point 8-distance models (DS8DMs) to express the metric relations between the SGRPMs. Then, we combined topological and metric relations to analyse the changes in the SGRPMs. Finally, to compensate for the complexity of common river planforms in nature, we proposed three segmentation rules to turn common river planforms into SGRPMs and used combinatorial reasoning mechanism tables (CRMTs) to describe the spatial relations among different river planforms. Based on our method, users can describe common river planforms and their changes in detail and confidently reject false changes. Future work should develop a method to automatically or semi-automatically adjust the segmentation rules and the combinatorial reasoning mechanism.

Current models cannot distinguish detailed topological relations between regions with holes. In order to solve this problem, a new detailed representation model for topological relations is proposed in this study. In this model, the key is to describe topological relations caused by multi-holes. Definitions of regions with holes and real objects expressed by elements of regions with holes are comprehensively analyzed, and then a practical definition of regions with holes is presented. Based on the 9-intersection model, a generalized 9-intersection model is proposed, which can completely describe detailed topological relations between regions with holes. For showing the description ability, the model is applied to describe detailed topological relations between regions with holes and detailed topological relations between objects of different complexities with the same major categories of topological relations, and a case study is designed to show the practicality of the model. The results show that the model is valid.

The dynamic evolution of shoal-bar systems in fluvial geomorphology remains challenging to fully capture through conventional observation methods. While multi-dimensional dynamic data enables qualitative analysis of riverine morphology, existing approaches lack effective spatiotemporal topological analysis. This study proposes a novel spatiotemporal modelling framework based on the topological invariance principles of three-dimensional perforated complexes. Focusing on the connectivity preservation in river systems under rotational and scaling transformations, we develop a dual-cavity complex topology model to characterize the dynamic transitions between shoals and sandbars. Compared with traditional single-cavity models, the proposed approach incorporates dynamic cavity features that precisely quantify thalweg elevation changes while resolving the spatiotemporal coupling mechanisms of bar morphology migration. Case study results demonstrate that this approach not only preserves spatial correlations among geomorphic elements but also enables evolutionary trend inversion through topological relation analysis. The methodology provides new theoretical foundations for developing digital twin watershed systems, offering enhanced capabilities for simulating complex fluvial processes.

Urban forests play a crucial role in sustaining habitable urban environments, and tree canopy height (TCH) is a key parameter for characterizing urban forest structure and ecological functions. However, accurate TCH retrieval using spaceborne lidar remains challenging in urban built-up areas because of the high spatial heterogeneity of urban land cover. This study proposes an urban-oriented method for ICESat-2 data processing and TCH extraction and evaluates it in Peoria, Illinois, USA. By integrating spectral constraints with photon spatial distribution patterns, the method further separates non-ground photons into vegetation photons and building photons. Individual tree crown polygons are then introduced as spatial constraints for TCH extraction, helping to overcome the limitations of regular grids in representing both the laser footprint scale and fine urban landscape detail. In addition, a non-exclusive photon-to-crown assignment strategy based on energy weighting allowed each photon to contribute to multiple crowns in proportion to the footprint energy intercepted by each crown. Among the 1391 crowns associated with vegetation photons, 286 met the thresholds for both total and high-weight photon counts and were retained for accuracy assessment. Validation against canopy heights derived from airborne laser scanning (ALS) reference data shows that the proposed method achieves an R² of 0.55 and an RMSE of 2.71 m. Further analysis indicates that retrieval accuracy is primarily controlled by beam strength: strong beams yield higher accuracy (R² ≈ 0.60), whereas daytime and nighttime observations differ only slightly. Overall, after targeted processing, ICESat-2 can provide a set of individual tree canopy height samples in urban built-up areas, which may support local calibration and serve as potential labels for subsequent regional canopy height mapping.

Existing point cloud semantic segmentation models tend to favor classes with abundant points and salient structures, while sparse, small-scale, and confusable targets remain insufficiently represented. To address this issue, we propose an offline teacher–student point cloud segmentation framework for challenging features. The framework uses challenging-class-centered sampling to strengthen the teacher’s representation of hard-to-learn targets and their local contexts, and generates class-response priors that are point-wise aligned with the original point cloud. A Point Foundation Adapter (PFA) then selectively injects teacher priors at the feature level, while challenging-point selective knowledge distillation imposes targeted constraints at the output level. Experiments on WHU3D and Toronto3D show consistent improvements across different teacher–student backbone combinations. For Sonata–OA-CNNs, the mIoU and Hard mIoU increase from 45.54% and 22.58% to 51.91% and 32.84% on WHU3D, and from 73.03% and 55.41% to 80.73% and 68.51% on Toronto3D, respectively. These results demonstrate that specialized teacher learning and selective prior transfer improve challenging-class segmentation across different backbone architectures, with teacher priors supporting both student training and inference.