Land use change detection (LUCD) is a critical technology with applications in various fields, including forest disturbance, cropland changes, and urban expansion. However, the current review articles on LUCD tend to be limited in scope, rendering a comprehensive review challenging due to the vast number of publications. This paper systematically reviewed 3512 articles retrieved from the Web of Science Core database between 1985 and 2022, utilizing a combination of bibliometric analysis and machine learning methods with LUCD as the main focus. The results indicated an exponential increase in the number of LUCD studies, indicating continued growth in this research field. Commonly used methods include classification-based, threshold-based, model-based, and deep learning-based change detection, with research themes encompassing forest logging and vegetation succession, urban landscape dynamics, and biodiversity conservation and management. To build an intelligent change detection system, researchers need to develop a flexible framework that integrates data preprocessing, feature extraction, land use type interpretation, and accuracy evaluation, given the continuous evolution and application of remote sensing data, deep learning, big data, and artificial intelligence.

Rapid urbanization and alterations in the built environment have exacerbated transportation energy consumption and environmental pollution, making transportation-related carbon emissions a significant barrier to low-carbon urban development. This study examines the influence of the built environment on commuting carbon emissions in the central urban area of Jinan, addressing the increasing challenge of transportation-induced emissions in rapidly urbanizing cities. Through the integration of multi-source big data, including travel trajectory, urban land use, and street view data, the research analyzes the spatial patterns of commuting behavior and emissions. Utilizing spatial autocorrelation, multiple linear regression, and geographically weighted regression (GWR), the study identifies critical factors influencing emissions, such as residential and commercial land area, transportation hubs, road network density, and floor area ratio. The results reveal that commuting emissions exhibit a monocentric pattern, with higher emissions in suburban areas due to lower population density and limited access to public transportation. Conversely, the central urban area of Jinan experience lower emissions, attributed to greater use of public transportation and shorter commuting distances. The GWR model uncovers spatial heterogeneity in the impact of the built environment, emphasizing the necessity for context-specific urban planning strategies. This research presents a comprehensive framework for reducing commuting carbon emissions, providing valuable insights for medium-sized cities striving to promote low-carbon transportation and optimize urban structures. The findings contribute to the formulation of targeted, data-driven policies for sustainable urban planning.

An accurate understanding of the structure of spatial correlation networks of land use carbon emissions (LUCEs) and carbon balance zoning plays a guiding role in promoting regional emission reductions and achieving high-quality coordinated development. In this study, 42 counties in the Tarim River Basin from 2002 to 2022 were chosen as samples (Corps cities were excluded due to missing statistics). The LUCE spatial correlation network characteristics and carbon balance zoning were analyzed by using the Ecological Support Coefficient (ESC), Social Network Analysis (SNA), and Spatial Clustering Data Analysis (SCDA), and a targeted optimization strategy was proposed for each zone. The results of the study indicate the following: (1) The LUCEs showed an overall upward trend, but the increase in LUCEs gradually slowed down, presenting a spatial characteristic of “high in the mid-north and low at the edges”. In addition, the ESC showed an overall decreasing trend, with a spatial characteristic opposite to that of the LUCEs. (2) With an increasingly close spatial LUCE correlation network in the Tarim River Basin, the network structure presented better accessibility and stability, but the individual network characteristics differed significantly. Aksu City, Korla City, Bachu County, Shache County, Hotan City, and Kuqa City, which were at the center of the network, displayed a remarkable ability to control and master the network correlation. (3) Based on the carbon balance analysis, the counties were subdivided into six carbon balance functional zones and targeted synergistic emission reduction strategies were proposed for each zone to promote fair and efficient low-carbon transformational development among the regions.

During the advancement of modern rural construction, traditional villages in the Xinjiang Oasis face the problem that uncoordinated system development affects scientific development and protection. Therefore, this study derives and constructs a coupling framework for the “Ecology–Humanities–Technology” system. Taking 53 traditional villages in Xinjiang as research objects, it uses the comprehensive evaluation model, the coupling coordination degree (CCD) model, and the geographic detector model to reveal the coupling coordination relationships and driving factors of the “Ecology–Humanities–Technology” system. The research results provide reference for evaluation methods and theoretical guidance for the balanced development of traditional villages in arid regions such as the Xinjiang Oasis. The results show the following: (1) The majority of the traditional villages in the Xinjiang Oasis are in the mild imbalance stage (71.7%). (2) The CCD rankings in various regions of Xinjiang are as follows: eastern Xinjiang > southern Xinjiang > northern Xinjiang. Humanities and technology have significantly different impacts on the traditional villages in different regions. (3) The inheritance level of the technology dimension and other factors are the main internal driving factors. The density of village road networks, the number of conservation and development projects, Baidu Index, and other factors are the main external driving factors. Nonlinear enhancement interaction effects are significant.

Characteristic villages play a pivotal role in revitalizing rural industries and advancing urban–rural tourism development. While numerous such villages have been designated across China, limited research has examined the spatial differentiation, inter-type linkages, and integrated tourism development potential of multiple village categories within a single regional system.By applying spatial pattern analysis, synergetic zoning method, and a multi-dimensional evaluation framework for tourism development potential, this study systematically investigates five types of characteristic villages in Xinjiang for the first time—Traditional villages, Forest Villages, Key Rural Tourism Villages, Ethnic Minority Characteristic Villages, and Historic and Cultural Villages.The study finds that the distribution of characteristic villages in Xinjiang exhibits significant clustering and imbalance. Traditional villages are concentrated in the Tianshan Mountains, while tourism-oriented villages are primarily located in metropolitan areas such as the Urumqi-Changji region. Through collaborative zoning analysis, it is observed that villages with ecological and cultural resources demonstrate strong synergistic effects in multi-ethnic regions. The tourism potential analysis reveals that cultural resources and climatic conditions are key factors, while population density and infrastructure development influence the effectiveness of resource conversion. Based on these findings, the study proposes zoning strategies tailored to different potential areas, aiming to promote high-quality tourism development and rural revitalization in Xinjiang’s characteristic villages.

Resource and environmental carrying capacity (RECC) has far-reaching significance in protecting the environment, managing resources, and achieving sustainable development. As a typical arid region, Xinjiang is rich in natural resources and fragile in its ecological environment. In recent years, rapid industrialization, urbanization, and water mismatch have resulted in socio-economic activities that have hurt the environment and sustainable development. This study used three-dimensional tetrahedral (3D Tetra) modeling to assess RECC in Xinjiang and analyzed its drivers in conjunction with geographically weighted regression (GWR) modeling. The research indicates that two countylevel administrative districts in Xinjiang are experiencing an overload state, accounting for 1.22% of the total area. Correspondingly, 2.11% of the population inhabits these regions. The comprehensive status of resource and environmental carrying capacity is generally good, and Northern Xinjiang is better than Southern Xinjiang, which aligns with the background conditions. The regression coefficient of the water resources carrying index is 0.63, which is the highest driving force for the RECC of Xinjiang. The prudent development and rational allocation of water resources are crucial to enhancing the RECC of Xinjiang. Spatially, the land resource carrying index is the most heterogeneous.