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.

As an important ecological barrier and a crucial base for animal husbandry in China, the forage–livestock balance in the Three-River-Source Region (TRSR) directly impacts both the degradation and recovery of grassland. This study examines the forage–livestock balance in the TRSR over the past 13 years (2010–2022) by calculating both the theoretical and actual livestock carrying capacity, thereby providing a scientific basis for regional animal husbandry policies. Firstly, the Carnegie–Ames–Stanford Approach (CASA) model was improved to fit the specific characteristics of alpine grassland ecosystem in the TRSR. This enhanced model was subsequently used to calculate the net primary productivity (NPP) of the grassland, from which the regional grassland yield and theoretical livestock carrying capacity were derived. Secondly, the actual livestock carrying capacity was calculated and spatialized based on the number of regional year-end livestock. Finally, the livestock carrying pressure index was determined using both the theoretical and actual livestock carrying capacity. The results revealed several key findings: (1) The average grassland NPP in the TRSR was 145.44 gC/m², the average grassland yield was 922.7 kg/hm², and the average theoretical livestock carrying capacity was 0.55 SU/hm² from 2010 to 2022. Notably, all three metrics showed an increasing trend over the past 13 years, which indicates the rise in grassland vegetation activities. (2) The average actual livestock carrying capacity over the 13-year period was 0.46 SU/hm², showing a decreasing trend on the whole. The spatial distribution displayed a pattern of higher capacity in the east and lower in the west. (3) Throughout the 13 years, the TRSR generally maintained a forage–livestock balance, with an average livestock carrying pressure index of 0.96 (insufficient). However, the trend of livestock carrying pressure is on the rise, with serious overloading observed in the western part of Qumalai County and the northern part of Tongde County. Slight overloading was also noted in Zhiduo, Maduo, and Zeku Counties. Notably, Tanggulashan Town, Zhiduo, Qumalai, and Maduo Counties showed significant increases in livestock carrying pressure, while Zaduo County and the eastern regions experienced significant decreases. In conclusion, this study not only provides feasible technical methods for assessing and managing the forage–livestock balance in the TRSR but also contributes significantly to the sustainable development of the region’s grassland ecosystem and animal husbandry industry.

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.