The Qilian Mountains (QLMs), an important ecological protective barrier and major water resource connotation area in the Hexi Corridor region, have an important impact on ecological security in western China due to their ecological changes. However, most existing studies have investigated vegetation changes and their main driving forces in the QLMs on the basis of a single scale. Thus, the interactions among multiple environmental factors in the QLMs are still unclear. This study was based on normalised difference vegetation index (NDVI) data from 2000 to 2019. We systematically analysed the spatial and temporal characteristics of the QLMs at multiple time scales using trend analysis, ensemble empirical mode decomposition, Geodetector, and correlation analysis methods. At different time scales under single-factor and multi-factor interactions, we examined the mechanisms of the vegetation changes and their drivers. Our results showed that the vegetation in the QLMs showed a trend of overall improvement in 2000–2019, at a rate of 0.88 × 10⁻³, mainly in the central western regions. The NDVI in the QLMs showed a short change cycle of 3 and 5 years and a long-term trend. Sunshine time and wind speed were the main drivers of the vegetation variation in the QLMs, followed by temperature. Precipitation affected the vegetation spatial variation within a certain altitude range. However, temperature and precipitation had stronger explanatory powers for the vegetation variation in the western QLMs than in the eastern part. Their interaction was the dominant factor in the regional differences in vegetation. The responses of the NDVI to temperature and precipitation were stronger in the long time series. The main drivers of vegetation variation were land surface temperature and precipitation in the east and temperature and evapotranspiration in the west. Precipitation was the main driver of vegetation growth in the northern and southwestern QLMs on both the short- and long-term scales. Vegetation changes were more significantly influenced by short-term temperature changes in the east but by a combination of temperature and precipitation in most parts of the QLMs on a 5-year time scale.

Drainage pattern recognition is crucial for geospatial understanding and hydrologic modelling. Currently, drainage pattern recognition methods employ geometric measures of overall and local features of river networks but lack measures of river basin unit shape features, so that potential correlations between river segments are usually ignored, resulting in poor drainage pattern recognition results. In order to overcome this problem, this paper proposes a supervised graph neural network method that considers the local basin unit shape of river networks. First, based on the overall hierarchy of the river networks, the confluence angle of river segments and the shape of river basin units, multiple drainage pattern classification features are extracted. Then, typical drainage pattern samples from the multi-scale NSDI and USGS databases are used to complete the training, validation and testing steps. Experimental results show that the drainage pattern indexes proposed can describe the characteristics of different drainage patterns. The method can effectively sample the adjacent river segments, flexibly transfer the associated pattern features among river segment neighbours, and aggregate the deeper characteristics of the river networks, thus improving the drainage pattern recognition accuracy relative to other methods and reliably distinguishing different drainage patterns.

Measuring similarity is essential for classifying, clustering, retrieving, and matching linear features in geospatial data. However, the complexity of linear features challenges the formalization of characteristics and determination of the weight of each characteristic in similarity measurements. Additionally, traditional methods have limited adaptability to the variety of linear features. To address these challenges, this study proposes a metric learning model that learns similarity metrics directly from linear features. The model’s ability to learn allows no pre-determined characteristics and supports adaptability to different levels of complex linear features. LineStringNet functions as a feature encoder that maps vector lines to embeddings without format conversion or feature engineering. With a Siamese architecture, the learning process minimizes the contrastive loss, which brings similar pairs closer and pushes dissimilar pairs away in the embedding space. Finally, the proposed model calculates the Euclidean distance to measure the similarity between learned embeddings. Experiments on common linear features and building shapes indicated that the learned similarity metrics effectively supported retrieving, matching, and classifying lines and polygons, with higher precision and accuracy than traditional measures. Furthermore, the model ensures desired metric properties, including rotation and starting point invariances, by adjusting labeling strategies or preprocessing input data.

Encryption of vector maps, used for copyright protection, is of importance in the community of geographic information sciences. However, some studies adopt one-to-one mapping to scramble vertices and permutate the coordinates one by one according to the coordinate position in a plain map. An attacker can easily obtain the key values by analyzing the relationship between the cipher vector map and the plain vector map, which will lead to the ineffectiveness of the scrambling operation. To solve the problem, a vector map encryption algorithm based on a double random position permutation strategy is proposed in this paper. First, the secret key sequence is generated using a four-dimensional quadratic autonomous hyperchaotic system. Then, all coordinates of the vector map are encrypted using the strategy of double random position permutation. Lastly, the encrypted coordinates are reorganized according to the vector map structure to obtain the cipher map. Experimental results show that: (1) one-to-one mapping between the plain vector map and cipher vector map is prevented from happening; (2) scrambling encryption between different map objects is achieved; (3) hackers cannot obtain the permutation key value by analyzing the pairs of the plain map and cipher map.

Security of vector maps is of importance in the community of geographic information sciences. Although some encryption techniques can protect vector maps from piracy, they need to decrypt the entire map in order to extract parts of the data, which obviously brings inconvenience to the users. To solve the problem, an encryption algorithm for vector maps based on Gaussian random and Haar transform is proposed in this paper. First, all vertices are randomized via the Gaussian random method to get the shuffled vector map coordinates. Then, the average coefficient and the differential coefficient generated by the Haar transform are encrypted. Lastly, the encrypted map data is obtained through performing the inverse Haar transformation. Experiments show that (1) the proposed algorithm guarantees authorized users can decrypt any parts (layers) of encrypted vector maps; and (2) the algorithm outperforms other existing encryption methods in security and efficiency.

Multi-criteria evaluation (MCE) method has been applied much in groundwater potential mapping researches. But when to data scarce areas, it will encounter lots of problems due to limited data. Digital Elevation Model (DEM) is the digital representations of the topography, and has many applications in various fields. Former researches had been approved that much information concerned to groundwater potential mapping (such as geological features, terrain features, hydrology features, etc.) can be extracted from DEM data. This made using DEM data for groundwater potential mapping is feasible. In this research, one of the most widely used and also easy to access data in GIS, DEM data was used to extract information for groundwater potential mapping in batter river basin in Alberta, Canada. First five determining factors for potential ground water mapping were put forward based on previous studies (lineaments and lineament density, drainage networks and its density, topographic wetness index (TWI), relief and convergence Index (CI)). Extraction methods of the five determining factors from DEM were put forward and thematic maps were produced accordingly. Cumulative effects matrix was used for weight assignment, a multi-criteria evaluation process was carried out by ArcGIS software to delineate the potential groundwater map. The final groundwater potential map was divided into five categories, viz., non-potential, poor, moderate, good, and excellent zones. Eventually, the success rate curve was drawn and the area under curve (AUC) was figured out for validation. Validation result showed that the success rate of the model was 79% and approved the method’s feasibility. The method afforded a new way for researches on groundwater management in areas suffers from data scarcity, and also broaden the application area of DEM data.