The study of the temporal and spatial evolution of wetland landscapes and its driving factors is an important reference for wetland ecological restoration and protection. This article utilized seven periods of land use data in Henan Province from 1980 to 2015 to extract the spatial distribution characteristics of wetlands and analyze the temporal and spatial changes of wetlands in Henan Province. Transfer matrix, landscape metrics, correlation analysis, and redundancy analysis were applied to calculate and analyze the transformation types and area of wetland resources between all consecutive periods, and then the main driving factors of wetland expansion/contraction were explored. First, the total wetland area in Henan Province increased by 28% from 1980 to 2015, and the increased wetland area was mainly constructed wetlands, including paddy field, reservoir and pond, and canal. Natural wetlands such as marsh, lake, and floodplain decreased by 74%. Marsh area declined the most during 1990–1995, and was mainly transformed into floodplain and “Others” because of agricultural reclamation, low precipitation, and low Yellow River runoff. The floodplain area dropped the most from 2005 to 2010, mainly converted to canals and “Others” because of reclamation, exploitation of groundwater, the construction of the South–to–North Water Transfer Project, and recreational land development. Second, the results of correlation analysis and redundancy analysis indicated that economic factors were positively correlated with the area of some constructed wetlands and negatively correlated with the area of some natural wetlands. Socioeconomic development was the main driving factors for changes in wetland types. The proportion of wetland habitat in Henan Province in 2015 was only 0.3%, which is low compared to the Chinese average of 2.7%. The government should pay more attention to the restoration of natural wetlands in Henan Province.

The current study presents a detailed assessment of risk zones related to karst collapse in Wuhan by analytical hierarchy process (AHP) and logistic regression (LR) models. The results showed that the LR model was more accurate with an area under the receiver operating characteristic (ROC) curve of 0.911 compared to 0.812 derived from the AHP model. Both models performed well in identifying high-risk zones with only a 3% discrepancy in area. However, for the medium- and low-risk classes, although the spatial distribution of risk zoning results were similar between two approaches, the spatial extent of the risk areas varied between final models. The reliability of both methods were reduced significantly by excluding the InSAR-based ground subsidence map from the analysis, with the karst collapse presence falling into the high-risk zone being reduced by approximately 14%, and karst collapse absence falling into the karst area being increased by approximately 6.5% on the training samples. To evaluate the practicality of using only results from ground subsidence maps for the risk zonation, the results of AHP and LR are compared with a weighted angular distortion (WAD) method for karst risk zoning in Wuhan. We find that the areas with relatively large subsidence horizontal gradient values within the karst belts are generally spatially consistent with high-risk class areas identified by the AHP- and LR-based approaches. However, the WAD-based approach cannot be used alone as an ideal karst collapse risk assessment model as it does not include geological and natural factors into the risk zonation.

Matching multi-scale road networks in the same area is the first step in merging two road networks or updating one based upon the other. The quality of the merge or update depends greatly on the matching accuracy of the two road networks. We propose an improved probabilistic relaxation method, considering both local and global optimizations for matching multi-scale of road networks. The aim is to achieve local optimization, as well as to address the identification of the M:N matching pattern by means of inserting virtual nodes to achieve global optimization effects. Then, by adding two attribute-related evaluation indicators, we developed four evaluation indicators to evaluate the matching accuracy, considering both geographic and attribute information. This paper also provides instructions on how to identify the proper buffer threshold during matching procedures. Extensive experiments were conducted to compare the proposed method with the traditional approach. The results indicate that: (1) the overall matching accuracy of each evaluation indicator exceeds 90%; (2) the overall matching accuracy increases by 6–12% after an M:N matching pattern is added, and by 4–6% following the addition of topology indicators; and (3) the proper buffer threshold is about twice the average value of the closest distance from all nodes.

Road selection is a critical component of road network generalization that directly affects its accuracy. However, most conventional selection methods are based solely on either a linear or an areal representation mode, often resulting in low selection accuracy and biased structural selection. In this paper we propose an improved hybrid method combining the linear and areal representation modes to increase the accuracy of road selection. The proposed method offers two primary advantages. First, it improves the stroke generation algorithm in a linear representation mode by using an ordinary least square (OLS) model to consider overall information for the roads to be connected. Second, by taking advantage of the areal representation mode, the proposed method partitions road networks and calculates road density based on weighted Voronoi diagrams. Roads were selected using stroke importance and a density threshold. Finally, experiments were conducted comparing the proposed technique with conventional single representation methods. Results demonstrate the increased stroke generation accuracy and improved road selection achieved by this method.

Wetlands, as a regulator of water cycle and balance, play a key role in preventing flood and drought disasters and protecting biodiversity. The Yellow River Basin is an important ecological barrier and economic zone in our country. By predicting the distribution of wetland landscape and evaluating its evolutionary characteristics, strategies for wetland conservation and utilization in the Yellow River Basin can be formulated. Future land use simulation model can not only simulate the change of multiple land use types, but also reflect the uncertainty of real land use change. In this study, the model was improved and used to predict future wetland changes in the Yellow River Basin. Using the wetland distribution data in 2000 as the initial data, and the wetland distribution data in 2015 as the validation data, setting the parameters according to the land use data of the Yellow River Basin from 1980 to 2000 and the wetland type transfer matrix, after verifying the applicability of the model, the spatial distribution pattern of wetlands in the Yellow River Basin in 2030 was simulated. This research shows that the cost matrix setting method based on the wetland transfer matrix can effectively avoid the errors caused by subjective judgment assignment. By 2030, the wetland distribution in the Yellow River Basin will remain stable, with marshes, paddy fields, and beaches as the main types, similar to 2015. Of all the regions in the Yellow River Basin, Zhengzhou had the highest rate of wetland loss, down 31.94%. On the contrary, Sanmenxia had the highest growth rate of 24.44%.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research toward the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where the feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another for extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we use a set of metrics considering both quantitative and qualitative aspects of the road network. On road types with similar sample sizes, the HAN model outperforms other models in both transductive and inductive tasks. Its accuracy (ACC) is higher by 1.62% and 0.67%, and its F1-score is higher by 1.43% and 0.81%, respectively. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.