Rapid urbanisation and increasing heat extremes pose significant challenges for megacities in the Global South. This study develops a configuration-sensitive assessment of blue-green space (BGS) cooling in Delhi, a Global South megacity facing intensified heat. Using satellite imagery and statistical modelling, we quantify how land cover and patch structure regulate land surface temperature (LST). Satellite imagery was used to derive LST, and six land-cover classes were mapped using supervised classification. Spectral indices and proximity metrics were calculated, land-cover patches were delineated, and their thermal behaviour was analysed using patch-level LST statistics. Delhi exhibits a heterogeneous urban heat island (UHI) surface, with LST spanning 19.8–38.6 °C and built-up land dominating (743.50 km2), while BGS remains limited and fragmented. Warming scaled almost linearly with built-up patch size (R2 = 0.98), with mean LST rising from 22.6 °C (<20,000 m2) to 27.4 °C (>500,000 m2). Cooling strengthened with BGS spatial dominance as dense vegetation declined from 23.8 to 22.1 °C (R2 = 0.98), sparse vegetation from 24.3 to 22.2 °C, and water bodies from 21.4 to 18.8 °C (R2 = 0.89) across increasing size classes. Correlations identified impervious surfaces as primary warming controls, while moisture and vegetation were cooling indicators. Random Forest-SHAP confirmed modified bare soil index (MBSI) and normalised difference built-up index (NDBI) as dominant predictors, with cooling from modified normalised difference water index (MNDWI) and comparatively conditional effects of normalised difference vegetation index (NDVI). Impervious and exposed surfaces govern Delhi’s thermal baseline, while BGS acts as a modifier whose benefits emerge when patches are large, connected, and integrated. These findings support shifting from area-based greening targets to morphology-based planning that protects connected blue-green corridors.

Under global warming, the intensification of the hydrological cycle highlights evapotranspiration (ET) as a key process governing land–atmosphere water and energy exchanges. Understanding the spatiotemporal variability of ET and its driving mechanisms is essential for regional hydrological and ecological studies. Based on MOD16 evapotranspiration products, meteorological data, and multi-source remote sensing datasets, this study systematically analyzed the spatiotemporal characteristics of evapotranspiration (ET) and its driving mechanisms in the Yellow River Basin during 2001–2022 using trend analysis, correlation analysis, and geographical detector methods. Results showed that ET exhibited a significant increasing trend across the YRB (5.29 mm·year−1), with extremely significant increases (p < 0.01) observed in 61.93% of the basin. Among climatic factors, precipitation, temperature, and wind speed exhibited significant increasing trends. Human activities were characterized by a significant increase in NDVI and land-use transitions toward forest and built-up land. Geographical detector results identified NDVI and precipitation as the strongest explanatory factors controlling ET spatial heterogeneity, with distinct driving mechanisms across the upper, middle, and lower reaches. Interaction effects among factors were stronger than individual effects, indicating that the spatial differentiation of ET is jointly controlled by climatic conditions and human activities. These findings empirically characterize the spatial heterogeneity, temporal trends, factor hierarchy, and interaction strength of ET variability at the basin scale and provide basin-scale evidence for understanding hydrological cycle responses under the combined influences of climate change and anthropogenic activities.

Across India, medium-sized towns frequently experience unplanned urban growth and the ensuing environmental problems. In addition to notable reductions in water bodies and vegetation cover, unchecked, rapid urbanisation has led to a marked expansion of built-up areas. Such transformations of once-green urban landscapes have intensified environmental risks and exacerbated climate-related issues. Geospatial technology is a powerful tool for quantifying land-cover transformations and the resulting temperature increase. The present study investigates the spatio-temporal dynamics of urban temperature rise and its association with accelerated urban growth and resultant land-use/land-cover (LULC) transformations in the Kozhikode city region, from 1993 to 2023. To quantify these LULC changes, a combination of remote sensing and Geographic Information System (GIS)-based analytical techniques (Various spectral indices (NDMI, NDII, GNDVI, NDbaI) and Land surface temperature) was employed. The study reveals a pronounced intensification of built-up land and a corresponding depletion of vegetation cover. The mean GNDVI value decreased from 0.53 in 1993 to 0.51 in 2023. Similarly, the moisture index showed a declining trend. It transformed from 0.27 in 1993 to 0.23 in 2023. Conversely the bareness index and impervious index showed a marked acceleration over the research period. It transformed into -0.43 in 1993 to -0.17 in 2023 and -0.43 in 1993 to -0.17 in 2023 respectively. The steadily increasing land-surface temperature readings during the research period provide further evidence of accelerating urban warming in the study area. The mean Land Surface Temperature value accelerated into 35.59°C in 2023 from 29.24°C in 1993. The spatial analysis of Land Surface Temperature (LST) reveals that elevated temperature zones are predominantly concentrated in intensively built-up areas, particularly within the 5 km buffer surrounding the city core. The findings clearly indicate that regions characterised by higher built-up density function as urban heat island (UHI) zones within the city.

Rapid urbanisation is a major driver of land use and land cover (LULC) transformation in urban lake catchments, often threatening the long-term sustainability of lake ecosystems. This study aimed to quantify and analyse long-term spatio-temporal LULC dynamics within the Shahpura Lake catchment, Bhopal, India, over 40 years (1980–2021). The lake catchment was delineated using a DEM-based hydrological approach derived from Shuttle Radar Topography Mission (SRTM) data. Multi-temporal Landsat imagery was analysed using supervised classification with a Maximum Likelihood algorithm to generate LULC maps for five reference years. Temporal changes were assessed using area statistics, linear trend analysis, and correlation analysis at the catchment scale. The results reveal a statistically significant expansion of built-up land, increasing by 220.7% (+0.062 km²/year; R² = 0.94; p < 0.01), accompanied by substantial declines in vegetation (−40.1%) and barren land (−39.6%). A strong inverse correlation between built-up and vegetation cover (r = −0.96; p < 0.05) confirms urban expansion as the dominant driver of land transformation. The surface area of Shahpura Lake remained relatively stable throughout the study period, showing no significant long-term trend. The study emphasises the importance of catchment-scale assessment for understanding urban lake dynamics. It also presents a remote sensing and GIS-based framework to support sustainable planning and management of urban lake ecosystems in rapidly urbanising regions.

This study examines the spatial imbalance and driving mechanisms of human–environment coupling in the Xiuhe River Basin, an important agricultural–ecological watershed in the middle and lower reaches of the Yangtze River. We integrated the coupling-coordination degree (C–D) model, Coupling Elasticity Index (CEI), spatial autocorrelation analyses (global and local Moran’s I, LISA, and Getis–Ord Gi*), and GeoDetector to assess spatial heterogeneity, classify coupling types, and identify key human–ecosystem. The results reveal marked spatial variation in coordination levels, which are higher in the central–western mountains and lower in the southeastern plains (global Moran’s I = 0.7344, p < 0.01), indicating significant spatial clustering. Using the CEI, the basin was classified into ecology-dominated zones (ecological advancement with human retreat), human-dominated zones (human advancement with ecological retreat), and relatively balanced zones. Upstream areas (CEI < 0.6) exhibit ecological advantages, whereas downstream plains (CEI > 1.5) experience dominant human pressures. GeoDetector analysis identified population density, proportion of built-up land, and water quality indicators (COD and TP) as the primary drivers, with interactions substantially enhancing explanatory power (e.g., PD × COD q = 0.86). These findings underscore nonlinear feedbacks and cross-scale interactions that influence coordinated ecosystem services in agricultural landscapes. We recommend differentiated management strategies: conserving upstream ecological functions, promoting balanced development in midstream areas, and regulating development intensity and pollution downstream to sustain human–environment coordination. This study provides quantitative evidence and methodological insights to improve understanding of ecological complexity and optimize governance of the agricultural landscape.

Background: Barong Tongkok Subdistrict, West Kutai Regency, East Kalimantan, is experiencing increasing ecological pressure driven by population concentration, land-use change, and rising resource consumption, raising concerns about the region's environmental carrying capacity and long-term sustainability. Objective: This study aims to assess regional sustainability in Barong Tongkok Subdistrict using a spatially explicit approach that integrates the Ecological Footprint (EF), Carbon Footprint (CF), and Biocapacity (BC). Method: A quantitative approach was applied using household consumption surveys, land-cover data, emission factors, and Geographic Information Systems (GIS). The EF was calculated based on food consumption, resource use, and built-up land. The CF was estimated from household electricity consumption, LPG use, transportation fuel, and waste burning. BC was derived from land-cover-based productivity using yield and equivalence factors. Sustainability was evaluated through a Sustainability Index (SI), defined as the ratio between BC and the combined EF and CF. Result: The results indicate significant spatial variation in sustainability across villages. Geleo Baru Village exhibits the highest SI value (31.57), reflecting a strong ecological surplus supported by extensive natural land cover and low population pressure. Conversely, Rejo Basuki Village records the lowest SI value (0.023), indicating a severe ecological deficit due to limited land availability and intensive residential land use. Peripheral villages tend to show ecological surplus, while densely populated areas exceed local carrying capacity. Conclusion: The integration of EF, CF, and BC within a GIS framework effectively reveals spatial sustainability patterns, providing valuable insights for evidence-based regional planning and targeted strategies to improve local sustainability.

Sedimentation in the Ranu Pane Volcanic Lake (RPVL) has increased due to land use change (LUC) in the surrounding area and the considerable outflow of materials from the Semeru volcanic eruption. The continued sedimentation will have negative consequences for humans and the sustainability of the RPVL. This research was aimed at building a LUC simulation model for 2024-2036 using a land change modeler (LCM) with the Logistic Regression/Markov Chain algorithm and a built-up land scenario based on historical land use (LU) data to investigate future sedimentation. The secondary data were mosaics from Planet Labs® Norway's International Climate and Forest Initiative (NICFI) imagery, roads, rivers, slopes, elevation, rainfall, and soil types. The primary data collected through fieldwork for validation of the LU were processed using remote sensing and the Geographic Information System (GIS). The simulation model for 2024-2036 was built using the LU data for 2016, 2020, and 2024. The LCM model detected that there would be a decrease in forest area but an increase in built-up land and agriculture in the study area between 2024–2036. The findings indicate that the increased land area and agriculture around Mount Semeru will lead to further sedimentation. The study findings can help planners understand LU patterns and their impacts, which can be useful in designing sustainable lake development strategies and management plans for the area.

GeoDetector-based assessment of DOC-metal risk hotspots and LULC-climate projections in the Upper Meghna River system.

Exploring the impacts of spatial and temporal land use evolution on carbon stock in the urban agglomeration around Taihu Lake is of great significance for the management of regional ecosystem carbon pools and the realization of regional low-carbon and high-quality development. Using the PLUS-InVEST-Geodetector model， we explored the spatial and temporal patterns of land use and carbon stock and their different characteristics from 2000 to 2020； predicted and simulated the distribution characteristics of spatial patterns of land use and carbon stock under the scenarios of natural development， urban development， and ecological protection in 2030； and revealed the reasons for the impacts on the changes of carbon stock. The results showed that： ① From 2000 to 2020， the cultivated land area of the urban agglomeration around Taihu Lake decreased by 26.3%， and the built-up land area increased by 147.5%， with the main driving factors being the population size， elevation， and the relative distance to secondary roads. ② The carbon stock in the urban agglomeration around Taihu Lake showed a spatial distribution characteristic of high in the southwest and low in the northeast， with an overall decrease of 8.17%， in which cropland and forest land lost 48.84 and 5.36 Mt， respectively. The shift in land use type was the main reason for the decrease in carbon stock. ③ Based on the comparison of the three scenarios in 2030， the ecological protection scenario had higher carbon stock （469.865 Mt） and lower carbon loss （-11.545 Mt）. The high carbon stock areas were mainly distributed in the western and southern parts of Taihu Lake， while the low value areas were concentrated in the eastern and northern parts. ④ The attribution analysis showed that the natural environmental factors had a significantly higher influence on carbon stock than the socioeconomic factors.

Rapid urbanization heightens the risk of land subsidence in Makassar City. Aim: This study analyzes and maps land surface deformation alongside Land Use and Land Cover (LULC) dynamics from 2020–2024. Methodology and Results by integrating multitemporal Sentinel-1 SAR (VV polarization) and supervised LULC classification from Landsat-8 on the Google Earth Engine platform. Annual LULC maps were produced using an SVM, yielding high accuracy (Kappa 0.893–0.988). Built-up land expanded mainly at the expense of vegetation and bare land. Deformation was inferred from temporal differences in VV backscatter (VV_diff); statistics were computed for each class. Negative VV_diff values were frequently observed in built-up and bareland areas, indicating subsidence. Significant interannual variability was observed in 2023-2024, particularly within the vegetation zones. Linear regression confirmed a strong negative deformation trend in built-up areas (slope −0.0123 dB/year). These results demonstrate a linkage between urban expansion and ground deformation. Conclusion, significance, and impact study: The approach provides a repeatable and cost-effective framework for continuous subsidence monitoring using open satellite data. GEE facilitates the open replication of workflows. The findings contribute to the field of urban planning and policy by identifying vulnerable zones, promoting risk-aware land allocation, infrastructure maintenance, and sustainable development methods for Indonesian coastal megacities and other rapidly expanding metropolitan areas.

Despite cropland changes' critical role in ensuring food security, fostering socio-economic development, and balancing ecological protection, the regional variability in cropland dynamics within large-scale watershed economic belts remains understudied. To address this gap, we mapped land use change intensity to analyze cropland change patterns and stability in the Yangtze River Economic Belt (YREB). Through statistical analysis, we identified key driving factors and predicted cropland changes for 2030 using the FLUS model. We also proposed region-specific sustainable use scenarios. The results reveal a systematic trend of cropland conversion to built-up land and water transforming into cropland in the YREB. Despite a steady decline in cropland area over the past two decades, the intensity of cropland inflows and outflows has remained balanced. The cropland center of gravity follows an east-north trajectory, with local meandering, isotropic shifts, and transverse displacements. Transport networks significantly influence cropland changes in the upper YREB, while climate impacts on cropland in the lower YREB are increasing. Future projections suggest the cropland center of gravity will shift toward southern mountainous regions in the upper and middle YREB and toward northern plains in the lower YREB. Among the proposed scenarios, only the Cropland Protection Scenarios (CPS) align closely with the cropland coverage targets of the Sustainable Pathways in Shared Socio-Economic Development Pathways (SPP1). Urban expansion encroaching on agricultural land is evident in Deyang and Mianyang, while competition with ecological space is pronounced in the middle and lower reaches and in riverine and coastal areas. This study provides a novel perspective on cropland changes in the YREB, emphasizing regional variability and the need for tailored policy interventions, thereby offering a scientific foundation for cropland management and planning.

Itanagar Capital Region (ICR) is more commonly referred to as the capital of Arunachal Pradesh. Over the past few decades, ICR has undergone a wide range of changes Land Use and Land Cover (LULC). The present paper divulges with aim examine the status of LULC and urban built-up growth using geo-spatial tools. Landsat 5, 8 and 9 images from different years have been utilised with a time span of 10 years from 1986, 1996, 2006, 2016, and 2024. The Land use and land cover information was extracted from the satellite images using the Maximum Likelihood Classification algorithm through Supervised image classification in ArcGIS 10.8. The LULC has been done in six classes as waterbodies, dense forest, bare land, shrubland/open forest, built-up and agriculture. Accuracy assessment has been carried out for validating the supervised images. The result shows that there is a decrease in the area of dense forest, open forest, and agricultural land, while built-up area land increased between 1986-2024. From 1.74% (3.72 Km2) to 30.2% (64.58 Km2) in 2024. Normalized Difference Built-up Index (NDBI) has also been used to evaluate built-up and non-built-up areas. The evaluation indicates a sharp rise of 4.82 Km2 (2.25%) built-up in 1986 to 49.10 Km2 (22.96%) built-up in 2024 within a span of 38 years. The change in built-up is attributed to factors like population explosion, socio-economic change, land use transformation, urban built-up and urbanization. A GIS spatio-temporal study provides crucial data on the transformation of the rapidly urbanizing environmentally sensitive mountainous urban centre located in the frontiers of India. The study findings align with providing a road map towards achieving Sustainable Development Goals (SDGs), particularly SDG 11 which to build sustainable cities and communities particularly in sensitive Himalayan state of Arunachal Pradesh.

Prosopis juliflora species was introduced in the Kenyan drylands as part of an afforestation program to rehabilitate rangelands and supply fuelwood in the 1980s. However, the species has since spread beyond areas of intervention, altering ecosystem integrity and threatening the livelihoods of pastoralists. This study analysed the spatial and temporal dynamics of P . juliflora in Cherab Ward, Isiolo County, to provide empirical evidence for the management and utilisation of this species. High-resolution satellite imagery was used to assess land-use and land-cover changes between 2017 and 2024, complemented by participatory socio-ecological approaches to elicit pastoralists’ local knowledge of the species' invasion patterns and impacts. The results show that P. juliflora cover increased by approximately 706.1 km 2 between 2017 and 2024. Equally, shrubland and crop land declined by approximately 414.9 km 2 and 122.8 km 2 , respectively. Bare land decreased by 397.4 km 2 , whereas built-up land increased slightly by 26.2 km 2 . These trends were corroborated by maps generated through participatory approaches with communities, which showed that P. juliflora invaded riverine and roadside areas, making it difficult for livestock to access pasture and water in the affected area. These results imply both ecological and socioeconomic consequences, with expected negative impacts on livestock production in the study area. The observed rate of spread of P. juliflora (103%) from 2017 to 2024 indicates that, if the invasion continues unabated, grazing resources in the area will diminish, leading to the loss of ecosystem services and, consequently, impacting pastoral livelihoods. These findings highlight the need for context-specific, co-developed management approaches that integrate spatial evidence with local knowledge to ensure the sustainable control and exploitation of the species, thereby maximising ecological and economic benefits.

Accurate identification of built-up land from remotely sensed imagery is essential for urban planning, environmental monitoring, and disaster response. However, binary built-up maps derived from single-date classifications often contain semantic noise—misclassified pixels resulting from shadows, bare soil confusion, or seasonal conditions. Common denoising methodologies, such as smoothing or filtering, are designed for continuous imagery and can distort small or fragmented features and fail to correct underlying classification errors. To overcome these limitations, this study evaluated a multi-date summation and thresholding workflow as a denoising alternative. Five Sentinel-2 images per site were classified as built-up maps, summed into a composite “built-up frequency” raster, and thresholded using Otsu, adaptive, and voting methods to produce refined binary maps. The results across nine international study sites show that the Otsu thresholding method outperformed the other methods in most locations when comparing their accuracies using the Matthews Correlation Coefficient (MCC), showing that using multiple images can improve identification of built-up land.

The uneven distribution of populations within urban and rural built-up areas restricts balanced regional development. This study employs statistical data from 296 cities in China covering the years 2010, 2015, and 2020 to analyze the coupling coordination relationship of population density in urban and rural built-up land and its influencing factors. The findings reveal the following: (1) Throughout the study period, population density in urban built-up areas (PUL) experienced a slow increase, whereas population density in rural built-up areas (PRL) declined rapidly. (2) Spatially, high levels of coupling coordination in population density between urban and rural built-up areas are primarily concentrated in the southwestern region, particularly in Sichuan, demonstrating a trend of gradual diffusion from the core to the periphery. Overall, a southwest-high to northeast-low pattern emerged. (3) The regression results indicate that economic development, agricultural structure, public services, and urbanization significantly affect the coupling coordination of population densities in urban and rural built-up areas. Among natural conditions, both elevation and temperature show significantly positive effects. This research provides theoretical foundations and policy recommendations for promoting urban-rural integrated development and achieving regional sustainable development.

Diverging trajectories of built-up land dynamics for country groups by income until 2100

Land surface temperature (LST) provides critical insight into the thermal behaviour of different land cover types, especially in sensitive coastal landscapes. In India, salt fields in the Thoothukudi district of Tamil Nadu State, covering about 101 km2, contribute a major share in the total salt output of the country. This study assessed the influence of coastal salt pans on LST dynamics in Thoothukudi district using multi-temporal Landsat 8 data of the 2019-2023 period within Google Earth Engine (GEE). Unlike the earlier studies dealing with LST dynamics in urban or agricultural landscapes, this study quantifies the distinct thermal behaviour of active salt-production land and the surrounding vegetation. Differences in the LST dynamics were comparatively evaluated in different land cover types, i.e., sand, vegetation zones near and away from salt pans, built-up land and salt pans. This study uniquely contributes by introducing a comparative LST evaluation in two vegetation covers, one in close proximity to salt pans and another away from them. The results revealed the effect of a localized microclimate, which is not documented in the earlier studies. The Normalized difference vegetation index (NDVI), surface emissivity, and thermal band (Band 10) of Landsat 8 were used to determine LST. The results showed that the mean LST values of 27.11°C in sand, 26.95°C in built-up areas, 25.78°C in salt pans and 24.54°C in distant vegetation cover in the order of sand > built-up areas > salt pans > vegetation cover (near salt pans) > vegetation cover (away from salt pans). Further, the vegetation covers close to salt pans and built-up areas had comparatively higher LST values due to the influence of surrounding salt pans and built-up areas. Also, the LST of sand was almost comparable to that of salt pans. Findings of this study are useful to policymakers in planning appropriate strategies for land management and to overcome the impact of LST in future.

Abstract Insufficient understanding of coastal natural resources hinders the efforts to reconcile economic development and ecological protection. The dynamics of ecosystem service value (ESV) needs to be assessed for opening opportunities in understanding the ecological consequences of human activities. This study identifies the spatiotemporal patterns of ESV from the “past-present-future” perspective, coupling hotspot analysis, geo-detector and patch-generating land simulation model into the framework of ESV assessment. Land use resources in Liaoning coastal zone over past two decades that are utilized for wetlands (-31.86%), croplands (-11.38%) and grasslands (-4.76%) decreased to different degrees, whereas built-up lands increased by 100.02%. The magnitude of coastal ESVs is 149.41 billion CNY over past two decades average, and has shown constant fluctuations as a consequence of land use changes. The distribution of ESVs evolved towards greater coverage of cold spots and smaller hot spot areas during 2000–2020 year. Watersheds and wetlands with 13.25% and 3.77% of total area contribute 70.37% and 8.29% of total ESVs, respectively, especially in water supply, hydrological regulation, and biodiversity. Compared to 2020 year, the value of ESVs in 2050 year increased to 212.72 and 311.48 billion CNY for economic and ecological (maximize ecosystem service/ecological capacity) scenarios, respectively, whereas decreased to 89.14 billion CNY for baseline scenario. Intense human activities are going to reshape the patterns of ESV generated from coastal natural resources. These findings provide timely and precisely evidence for balancing ecological protection and economic development in the context of Liaoning coastal construction from now on. Graphical Abstract

As urbanization accelerates, the land use/land cover (LULC) changes significantly impact land surface temperature (LST) and urban heat island effect (UHI). Urban green spaces play a crucial role in regulating the urban thermal environment, typically characterized by the normalized difference vegetation index (NDVI). This study utilizes remote sensing data from Shanghai spanning 2000-2024, combined with the CatBoost model and SHAP method to characterize the nonlinear marginal effects of NDVI on LST and its spatial heterogeneity across different LULC types. The research findings reveal that the regulatory effect of NDVI on LST exhibits significant variations across different land types, demonstrating nonlinear and threshold characteristics. Built-up land types show the strongest cooling sensitivity within the NDVI range of 0.15-0.35, while vegetation land types exhibit saturated regulatory effects with diminishing marginal returns. Water bodies maintain stable negative regulation characteristics, showing insensitivity to NDVI changes. Other land types demonstrate higher uncertainty. Additionally, this study simulates two scenarios to predict LST changes under different LULC-NDVI combinations. The simulation results further validate the significant benefits of enhancing urban green space in built-up areas for mitigating the urban heat island effect, emphasizing that future green infrastructure planning should focus on areas with low green coverage while optimizing the spatial structure of high-vegetation areas. This study provides quantitative evidence to support the achievement of SDG 13 climate action goals and offers guidance for urban green planning and climate adaptation policies.

Given China’s rapid urbanization in the 21st century, understanding the complexity of its spatial structure is crucial for appropriately regulating the country’s urban development. Entropy and fractal analysis are two important quantitative means for describing the complexity of geographical space. These two methods can be integrated to comprehensively analyze the characteristics of self-organization and evolution of the urban spatial structure, as well as the internal factors driving this process. As a typical single-center city in the Central Plain of the country, Kaifeng City is representative of the rapid urbanization of China in the past four decades. This study uses data on Kaifeng City from 1980 to 2020 to examine the spatial entropy of the structure and evolution of its built-up land, and to subject it to a multi-fractal analysis. The results show that the spatial entropy of built-up land in Kaifeng City has been increasing in the last four decades, and the degree to which its urban space is filling up has been increasing each year. The spatial entropy of the urban center is higher than that of the surrounding areas, while built-up areas in the region have become more orderly. The multi-fractal analysis further reveals that Kaifeng City has undergone prominent spatial expansion, and its built-up land has expanded particularly rapidly in the past two decades. Changes in the central and marginal areas of the city have been asynchronous and periodic, where the marginal areas have undergone a disorderly expansion. The heterogeneity of urban space has gradually decreased. The work here provides a comprehensive understanding of the patterns of urban spatial expansion as well as the structure of land use.