The Rapid expansion of the Lanzhou–Xining (Lanxi) urban cluster in China during recent decades poses a threat to the fragile arid environment. Quantitatively assessing the impact of urban expansion on vegetation in the Lanxi urban cluster has profound implications for future sustainable urban planning. This study investigated the urban expansion dynamics of the Lanxi urban cluster and its impacts on regional vegetation between 2001 and 2021 based on time series land cover data and auxiliary remote sensing data, such as digital elevation model (DEM) data, nighttime light data, and administrative boundary data. Thereinto, urban expansion dynamics were evaluated using the annual China Land Cover Dataset (CLCD, 2001–2021). Urban expansion impacts on regional vegetation were assessed via the Vegetation Disturbance Index (VDI), an index capable of quantitatively assessing the positive and negative impacts of urban expansion at the pixel level, which can be obtained by overlaying the Enhanced Vegetation Index (EVI) and rainfall data. The major findings indicate that: (1) Over the past two decades, the Lanxi region has experienced rapid urban expansion, with the built-up area expanding from 183.50 km² to 294.30 km², which is an average annual expansion rate of 2.39%. Notably, Lanzhou, Baiyin, and Xining dominated the expansion. (2) Urban expansion negatively affected approximately 53.50 km² of vegetation, while about 39.56 km² saw positive impacts. The negative effects were mainly due to the loss of cropland and grassland. Therefore, cities in drylands should balance urban development and vegetation conservation by strictly controlling cropland and grassland occupancy and promoting intelligent urban growth.

In recent years, rapid urbanization in the Southern Jiangsu urban area has significantly altered land use patterns, impacting local ecosystem service value (ESV). This study examines land use changes (LUCs) from 2000 to 2020 and assesses ESV changes with the equivalent factor method. The PLUS model is also used to project future LUCs in line with the “Land and Space Planning of Jiangsu Province (2021–2035)”. The simulation considers scenarios for natural development (NDS), ecological protection (EPS), farmland protection (FPS), and comprehensive development (CDS) for the year 2035. The research findings show that over the past two decades, urbanized areas have persistently grown, whereas there has been a steady decline in the extents of farmland, forested areas, and grasslands. The total ESV has shown a decreasing trend, with the spatial distribution of ESV exhibiting higher values in the eastern regions and lower values in the west. The relationship between land use and ESV is negative, as evidenced by both the comprehensive land use (LA) and the Moran’s I index. Scenario predictions reveal that farmland, forest land, and grassland show a decreasing trend under the NDS, while it is the opposite under the EPS. The FPS is predominant in farmland area, and all land types in the CDS comply with national land planning requirements. Overall, the research findings reveal and predict the influence of LUCs on ESV in the study area under four scenarios, which is beneficial optimizing regional resource planning schemes and formulating ecological compensation policies.

Urban expansion and its driving factors are frequently analyzed within administrative regions to inform regional urban planning, yet such analyses often fall short at the natural basin scale (referring to the spatial extent defined by hydrological drainage boundaries) due to the scarcity of statistical data. Geographic and socio-economic spatial data can offer more detailed information across various research scales compared to traditional data (such as administrative statistical data, survey-based data, etc.), providing a potential solution to this limitation. Thus, this study took the Indus Basin as an example to reveal its urban expansion patterns and driving mechanism based on natural–economic–social time-series (2000–2020) spatial data, landscape expansion index, and geographical detector model (GDM). Future urban expansion distribution under different scenarios was also projected using Cellular Automata and Markov model (CA-Markov). The results indicated the following: (1) The Indus River Basin experienced rapid urban expansion during 2000–2020 dominated by edge-expansion, with urban expansion intensity showing a continuous increase. (2) Between 2000 and 2010 as well as 2010 and 2020, the dominant factor influencing urban expansion shifted from altitude to population (Pop), while the strongest interacting factors shifted from fine particulate matter (PM_2.5) and altitude to Gross Domestic Product (GDP) and Pop. (3) Future urban expansion probably occupies substantial mountainous area under the normal scenario, while the expansion region shifts towards the central plains to protect more ecological zones under a sustainable development scenario. Findings in this study would deepen the understanding of urban expansion characteristics of the Indus Basin and benefit its future urban planning.