Building change detection is a prominent topic in remote sensing applications. Scholars have proposed a variety of fully-convolutional-network-based change detection methods for high-resolution remote sensing images, achieving impressive results on several building datasets. However, existing methods cannot solve the problem of pseudo-changes caused by factors such as “same object with different spectrums” and “different objects with same spectrums” in high-resolution remote sensing images because their networks are constructed using simple similarity measures. To increase the ability of the model to resist pseudo-changes and improve detection accuracy, we propose an improved method based on fully convolutional network, called multitask difference-enhanced Siamese network (MDESNet) for building change detection in high-resolution remote sensing images. We improved its feature extraction ability by adding semantic constraints and effectively utilized features while improving its recognition performance. Furthermore, we proposed a similarity measure combining concatenation and difference, called the feature difference enhancement (FDE) module, and designed comparative experiments to demonstrate its effectiveness in resisting pseudo-changes. Using the building change detection dataset (BCDD), we demonstrate that our method outperforms other state-of-the-art change detection methods, achieving the highest F1-score (0.9124) and OA (0.9874), indicating its advantages for high-resolution remote sensing image building change detection tasks.

The accurate mapping of crop types and rotation patterns is essential for promoting sustainable agricultural development, particularly in ecologically fragile regions such as the farming–pastoral ecotone of China (FPEC). This study focuses on Zhangjiakou, a representative area of the FPEC, to develop a multi-sensor remote sensing framework for monitoring crop distribution and analyzing rotation dynamics. After cloud removal and Savitzky–Golay filtering were applied to correct noise, we selected vegetation index features with maximum inter-class separability during the optimal classification window (June 15–August 20) and generated quarterly Sentinel-1 SAR composites. A Random Forest classifier was employed to perform crop classification based on these optimized features, enabling 10 m resolution crop mapping from 2019 to 2023. The proposed method achieved high classification accuracy (overall accuracy and Kappa > 0.90), with strong agreement between mapped and statistical crop areas (R²: 0.85–0.88; RMSE: 0.42–0.58 × 10⁴ ha). Spatial analysis revealed distinct distribution patterns: oats, potato, sesame, and vegetables were predominantly cultivated in northern Zhangjiakou, while maize dominated southern regions. We observed significant annual variations in crop area proportions and identified specific altitudinal preferences: maize, potato, and sesame were mainly grown at 480–520 m, while oats and other crops at 520–600 m. Slope analysis showed that most crops were cultivated on gentle slopes of 0–5°, with sesame extending to 4–10° slopes. Temporal analysis from 2019 to 2023 indicated that sesame, oats, and potato predominantly followed rotation patterns, while maize cultivation was primarily monoculture. Key drivers of rotation change included water scarcity, economic incentives, and continuous cropping constraints. These findings provide critical insights for optimizing crop rotation strategies, enhancing agricultural sustainability, and improving land-use efficiency in ecologically fragile regions.

Assessing the supply and demand of urban green space (UGS) ecosystem services (ESs) can provide relevant insights for urban planning. This study presents an analysis method for the spatial distribution of UGS ES supply and demand at administrative unit and 1-m grid scales and directly compares the matches of ES supply and demand in spatially explicit maps at two scales. Based on the analysis results at administrative unit scale, administrative units with an unbalanced UGS ES supply and demand were divided into three types: (Ⅰ) lack of green space; (Ⅱ) unreasonable green space structure; (Ⅲ) comprehensive, and different optimization schemes were put forward. According to the analysis results at 1-m scale, the regions with an unbalanced ES supply and demand of an administrative unit were divided into the following: (1) severe ES shortage area; (2) moderate ES shortage area; (3) mild ES shortage area, and the severe ES shortage area was taken as the UGS optimization area. We take the UGS within the 5th Ring Road of Beijing as an example and propose suggestions for optimizing the UGS pattern based on the evaluation of the supply and demand of UGS carbon sequestration services and purification services for particulate matter with an aerodynamic diameter <2.5 µm (PM_2.5). This study provides an easy-to-use evaluation method for the spatial distribution of UGS ES supply and demand and proposes different optimization suggestions for the unbalanced area, thus playing a role in UGS construction activities and green space structure optimization.

Greenhouse gases, such as carbon dioxide (CO₂), released from excessive fossil fuel consumption, are major contributors to global warming. Understanding the spatial distribution of CO₂ emissions on a refined scale is crucial for promoting green economic development. Xi’an, a key central city in China, serves as the case study for this research. Using nighttime light data from Black Marble, combined with energy statistics and socio-economic information, this study employed spatial analysis to simulate CO₂ emissions on the district and county levels in Xi’an for the years 2012 and 2022. The results indicated that nighttime light data were significantly correlated with CO₂ emissions (linear function; coefficients of determination: 0.7838 and 0.7941 for 2012 and 2022, respectively). The spatial distribution analysis revealed a clear pattern in CO₂ emissions, with higher emissions concentrated in central urban areas and lower emissions in peripheral regions. Additionally, a comparative analysis of carbon emissions and carbon emission intensity across districts and counties between 2012 and 2022 showed that CO₂ emissions in central urban areas had continued to grow and expand, while emission intensity had declined. These findings suggest that the socio-economic development, policy interventions, and industrial structure in Xi’an influence the spatial distribution of CO₂ emissions.

We collected data from approximately 500 tourism destinations and revenues of Xinjiang region, China, to assess tourism resources and improve tourism industry development. We analyzed the characteristics, geographical distributions, and environmental conditions of these tourism destinations, summarized changes in regional tourism revenue over the past 34 years, and proposed strategic suggestions for the region’s tourism development. Historical sites and architectures were found to be dominant, followed by natural scenic sites, with major hotspots located in the Altay Mountains, Urumqi-Turpan-Hami line, Ili region, and Aksu-Kashgar-Hotan line. A total of 65% of tourism destinations are located mainly in low altitudes (2,000 m). They correlate spatially with rivers and roads, often in areas with high heatwave index values. Although tourism revenues have grown rapidly in the past decades, with 1998, 2010, and 2015 as breakpoint years, Xinjiang’s tourism volume remains relatively low compared to China’s, especially in international tourism. In 2015, Xinjiang’s total tourism revenue made up 2.9% of China’s total, with international tourism accounting for just 0.5%. Based on the analysis, there is significant potential for tourism development in the Xinjiang region. Hence, we suggest developing mountainous forest tourism based on forest parks in middle and high-altitude areas with relatively lower heatwave index values, targeting the European market, expanding cultural tourism based on diverse ethnic groups, and creating “Silk Road” tourism routes with other western Chinese provinces.

As urbanization accelerates in China, the protection and renewal of historical and cultural districts have become key issues. The Beiyuanmen Historical and Cultural District in Xi’an, with its long history and cultural significance, is a prime example. This study uses Beiyuanmen as a case study, employing Baidu heatmap data, Point of Interest (POI) data, and space syntax theory to examine the district’s spatial layout, crowd activity distribution, and functional structure. The purpose is to quantify its vitality and spatial characteristics, providing a basis for scientific planning. The methods involve analyzing spatiotemporal crowd activity intensity via heatmaps, assessing street network configuration through integration and choice values, and comparing POI data from 2014 and 2024 to track functional evolution. The research identifies the distinctive spatiotemporal patterns of crowd activity, revealing not only a southeast concentration correlated with urban functions but also distinct diurnal rhythms—a bimodal pattern on weekdays versus a sustained leisure-oriented pattern on weekends, underscoring a functional shift. It also explores the directed permeability of the spatial structure, identifying streets like Miaohou Street that form a highly integrated “cross-shaped backbone”. Analysis of POI data shows that commercial services dominate and have expanded outward, with the growth rate of POI density in the control area surpassing that of the core, indicating a trend of functional diffusion. Finally, the study highlights Miaohou Street, Beiguangji Street, Damai Market Street, Beiyuanmen, and Sajinqiao as key areas, and it concludes by proposing integrated planning recommendations that focus on four strategic aspects—spatial and crowd activity distribution management, functional zoning guidance, enhancement of public services and cultural displays, and alignment with broader urban policies—for prioritized landscape enhancement and tourism.