Urban Land Use Change Research Articles

Integrating Nature-Based Solutions for urban stormwater management into existing urban fabrics

Abstract Nature-Based Solutions (NBSs) have been proven to address urban stormwater challenges effectively while providing multiple benefits to the economy, society, and environment. Urban fabrics profoundly influence flood risk as the changes in urban form, land use, and building layout will affect the urban hydrological cycle, thereby altering precipitation and runoff. However, few studies look at the connection between NBSs and urban fabrics, which can contribute to urban stormwater management. This paper aims to categorize the general types of urban fabrics and consider NBSs as strategic interventions that can improve water-related issues and help to create a resilient neighborhood. This paper elaborates on some strategic approaches to integrate NBSs in existing urban fabrics at the neighborhood scale to optimize their response capacity according to the local conditions. The novelty lies in adopting a systemic vision to deliver a design toolkit that can be adapted to different conditions and with different typologies across the places and countries facing similar water-related challenges. In conclusion, this paper emphasizes the importance of understanding how different NBSs fit various urban fabric types, and future work is needed to provide assessment tools to evaluate the efficiency of different design scenarios.

Urban land use simulation and carbon-related driving factors analysis based on RF-CA in Shanghai, China

As global climate change intensifies, climate protection is important for the sustainable development of human society. In the process of urbanization and industrialization, carbon dioxide emissions are an important factor contributing to global warming. Therefore, modelling projections of future urban land use under low-carbon scenarios are essential for sustainability policy development. Few studies have focused on the impact of carbon emissions on urban land use change in Shanghai, as current research has primarily concentrated on developing methods for urban land use modelling. This paper utilizes a cellular automata (CA) simulation model based on the Random Forest (RF) algorithm to select various spatial variables of carbon emissions as the driving factors that affect urban land use changes. These variables include traffic location factors, economic development factors, electricity consumption, and population density. In this study, remote sensing imagery of urban nighttime lighting is also used to construct a simulation model of land use types in Shanghai. The model is then used to analyze the contribution of carbon emission constraints to urban land use changes. Actual historical land use data from 2013 and 2019 are used for validation, and the prediction model is used to predict land use outcomes under different low-carbon scenarios in 2025. The model is validated by simulating multiple intra-city land use maps for 2019 (kappa = 0.88, OA=92.71 %). The method of out-of-bag error from the random forest is used to evaluate the significance of carbon emission constraints. Using the validated model, the constraints in the CA model are changed to predict the land use simulation results of Shanghai in 2025 under different low-carbon scenarios. In terms of significance, factors such as distance to power plants, distance to major roads, real GDP, and population density can all have a significant impact on changes in urban land use. By selecting the low-carbon scenario with the most appropriate thresholds for each driver, it is possible to obtain the land use simulation results of Shanghai in 2025 under the optimal low-carbon scenario, while ensuring the high accuracy of the RF-CA model and simultaneously reducing the impact of factors on the city’s overall carbon emissions. This paper provides a scientific base for urban planners and scholars to thoughtfully design urban land use while cutting down on carbon emissions. Furthermore, it can aid government agencies in establishing associated planning approaches.

Rethinking urban spatial planning and industrial development approaches in Ethiopia: bridging gaps for better outcomes

Urbanization is occurring at an unprecedented rate in developing countries like Ethiopia, especially with the rapid growth of industrialization. However, this urbanization and industrial development surge puts immense pressure on preparing and implementing city spatial plans. Urban spatial planning plays a crucial role in shaping the development of cities, aiming to create livable and sustainable urban development. The plan-making process and its subsequent implementation often encounter challenges that hinder the realization of planned objectives. Galan and Dukem, cities near Addis Ababa’s capital, have seen substantial industrial investment in recent decades. This article explores the challenges associated with urban spatial planning and industrial development while proposing alternative approaches. The paper relies on information gathered from primary and secondary sources, including expert surveys and key informants’ interviews. The research highlights that industrial developments have led to significant and uncontrolled changes in urban land use and urbanization in the study areas. Due to the lack of clear planning guidelines and institutional capacity, the study cities are experiencing haphazard development without effective urban spatial plans. Proximity to Addis Ababa has driven the urbanization process through increased investments. The article contends that effective management of urbanization and industrialization can create enjoyable living conditions and foster job growth. Macroeconomic policies, including industrial policies, should pay attention to spatial elements and prevent policy-making processes that are “space-blind”. This, however, requires clairvoyance and a high level of expertise, integrating inputs from advocacy planning and community participation to bridge gaps and ensure effective urban spatial plans and industrialization processes.

Uniqueness of tree stand composition and soil microbial communities are related across urban spruce-dominated forests

ContextUrban forest soils represent significant reservoirs of biodiversity in cities. Retaining this diversity under urban land-use change requires understanding on how species richness, community assembly and uniqueness of species assemblages are related to local forest characteristics and surrounding landscape structure.ObjectivesOur aim was to assess the significance and relative importance of logging history, tree species composition and urbanization in shaping soil microbial communities across urban spruce-dominated forest landscapes. We investigated responses of microbial diversity from three complementary viewpoints: local diversity, community assembly patterns and community uniqueness.MethodsWe collected soil bacterial and fungal metabarcoding data from 73 spruce-dominated forest sites distributed in three urban centers across southern Finland. We related these data to measurements of logging intensity, tree species composition and degree of urbanization.ResultsLogging intensity, tree species composition and urbanization affected site-scale microbial diversity, but the effects varied between microbial groups. Only logging intensity had a significant imprint on microbial assembly, and this effect was restricted to bacteria. Relative uniqueness of microbial assemblages at the landscape-scale was coupled with the uniqueness of tree species composition in all microbial groups, and further affected by tree diversity in saprotrophic fungi and urbanization in ectomycorrhizal fungi.ConclusionsIn the context of urban spruce-dominated forests, locally diverse tree stands are not necessarily the same as those that contribute the most to landscape-scale diversity. Identifying and preserving contrasting tree stand structures, which support distinctive soil microbial assemblages, may be the winning strategy in maintaining a wide range of soil microbial diversity.

Examining the effect of urbanization on various land cover classes and environmental quality using remote sensing and GIS methods

It is vital for public awareness and decision-makers to investigate the impact caused by changes in urban land use and land cover on ecosystem service values (ESVs) in order to promote sustainable urban development, protect the environment, protect public health, and ensure communities’ long-term well-being. Changes in ecosystem services brought on by changes in land use and cover (LULC) are a crucial early indicator and warning sign of ecological shifts. Using remote sensing and GIS tools, this study examined the shifting dynamics of ESVs in the Haridwar district in Uttarakhand state, India. The evaluation of land use land cover (LULC) changes was carried out by analyzing satellite images of the research region from 2001 to 2021. In addition, we examined changes in ecosystem services according to the study area’s LULC classifications. Using a random forest machine learning classifier, satellite images were classified into five categories of land cover: water bodies, forests, built-up areas, agricultural land, and bare land. The findings showed that from 2001 to 2021, forest cover and water bodies fell by 15.35% and 41.50%, respectively, while urban area and agricultural area rose by 69.24% and 12.31%, respectively. Due to increased urbanization and degradation of water bodies and forest areas, ESV fell from 103.19 USD ha −1 year −1 in 2001 to 70.53 USD ha −1 year −1 in 2021. The combination of LULC with the value of ecosystem services may improve our knowledge of several difficulties in an urban ecosystem. As a result, we advocate for the incorporation of LULC and ESVs as a tool that might be useful to urban planners and land managers in the future for sustainable development of natural resources.

HashGAT-VCA: A vector cellular automata model with hash function and graph attention network for urban land-use change simulation

Vector cellular automata (VCA) models, which excel at representing spatiotemporal dynamics of irregularly shaped land parcels, have been widely employed in land use change simulations. However, current research faces the following issues: (1) most VCA models neglect the spatial heterogeneity of driving factors within each land parcel when evaluating the environmental driving effects; (2) when calculating the neighborhood effects, simple statistics of land use types in neighboring parcels are often used, overlooking the influence of driving factors within neighboring parcels; (3) the ability to explore the interactions between land parcels is often limited. To address the aforementioned issues, this study proposes a HashGAT-VCA model for investigating urban land use changes. The model utilizes a Hash function to encode the non-uniform distribution of each driving factor within each irregularly shaped land parcel into a fixed-length vector, and constructs a graph structure between land parcels based on their spatial topological relationships. By employing a Graph Attention Network (GAT), the model explores the mechanisms of environmental driving effects and inter-parcel interactions to calculate the probability of land use change for each parcel. The proposed HashGAT-VCA model was applied to simulate urban land use changes in Shenzhen, China, from 2009 to 2012. Compared to other VCA models, the HashGAT-VCA demonstrated higher simulation accuracy. The results indicated that HashGAT-VCA can effectively capture the impacts of the heterogeneously distributed driving factors and the interactions between land parcels on land use changes. Additionally, this study simulated land use patterns for the years 2025 and 2030 under ecological control strategies, providing decision support for urban planning.

ANALISIS DAMPAK PERUBAHAN PENGGUNAN LAHAN PERKOTAAN TERHADAP URBAN HEAT ISLAND DI KECAMATAN KEDATON

The Urban Heat Island phenomenon is defined as a form of local climate change. In the formation of the Urban Heat Island phenomenon, there are several factors, namely changes in land use, vegetation density, building density, and vehicle emissions. Dense development areas that have caused changes in land use, settlements, and transportation activities have begun to accumulate in Kedaton District. Kedaton District is included in the activity center area so that changes in land use from 2017 to 2022 occur significantly, which can cause an increase in urban temperatures. This study aims to analyze the impact of changes in urban land use on Urban Heat Island in Kedaton District. The variables used are land use, surface temperature, vegetation density, building density, motor vehicle intensity, and motor vehicle emissions. The analytical methods used are LST, NDVI, NDBI, road service level analysis, vehicle emission analysis, and correlation analysis. The results of the analysis show that the impact of changes in land use will result in a 76% decrease in vegetation density in 2022, a 95% increase in building density, and a 99% increase in vehicle emissions. The impact of changes in land use has contributed to the distribution of urban heat islands in Kedaton District. To minimize the impact of changes in use on the Urban Heat Island in Kedaton District, mitigation efforts based on the resilient city concept are needed, namely the addition of private green open spaces, reflective roof paint in densely populated areas, and optimizing public transportation.

Identifying drivers of urban landuse changes in the Wenchuan earthquake-affected area by using night-time light data

Identifying drivers of urban landuse changes in the Wenchuan earthquake-affected area by using night-time light data

An Evaluation of the Impact of Urban Growth on Runoff in Abeokuta, Southwestern Nigeria

Abeokuta is one of the urban areas in Nigeria with high cases of runoff fatalities in recent times, indicating the need for a proper understanding of prominent runoff-generating mechanisms, as well as the causative factors. Consequently, this paper, which is focused on flood-prone settlements, is aimed at providing information on the impact of change in urban land use on runoff in the study area. The specific objectives were to determine the change in average runoff in terms of the Soil Conservation Service Curve Number (SCN-CN), and to assess the impact of urban growth on runoff in the area. The SCN-CN was derived from the 30m spatial Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). At the same time, land cover change was estimated using Landsat TM+ from 2000 and Landsat 8 OLI from 2018. Data were analysed using the Hydrologic Engineering Centre’s Hydrologic Modelling System (HEC-HMS) and ArcGIS (version 10.1). The results showed a 14% increase (from 39% in 2000 to 53% in 2018) in urban areas of selected catchments; and a relative increase in average CN from 76.9 units in 2000 to 79.9 units in 2018, suggesting an increase in runoff potential relative to the increase in urban/impermeable space in the catchment. Annual discharge depth increased from 891.84mm to 956.9mm, while peak discharge increased from 161.9m3 /s to 196.2m3 /s. Runoff in the study area tends to exhibit spatial variability that is similar to the pattern exhibited by built-up areas across the study area, suggesting that the development of built-up areas can explain runoff exacerbation in part of the area. The use of SCN-CN and satellite images makes the approach reproducible, and the mixed methods of geographic information system and hydrological model revealed the spatial variability typically hidden in stand-alone hydrological models. The paper recommends further studies with the use of less coarse datasets, as well as the implementation of policies that focus on sustainable urban growth in the region, and other cities.

A cellular automata model coupled with partitioning CNN-LSTM and PLUS models for urban land change simulation

Urbanisation is a key aspect of land use change (LUC), and accurately modelling of urban LUC is crucial for sustainable development. Cellular automata (CA) are widely used in LUC research. However, previous studies have overlooked the significant temporal dependence and spatial heterogeneity associated with LUC. To address these gaps, this study proposes a novel model called KCLP-CA, which integrates k-means, a convolutional neural network (CNN), a long and short-term memory neural network (LSTM), and the popular patch-generation land use model (PLUS). Initially, k-means and CNN are utilised to address spatial heterogeneity, while LSTM tackles temporal dependence. The LSTM and land expansion analysis strategy (LEAS) models of PLUS are employed to obtain land use conversion probability maps. Finally, a simulation of land use dynamic change was conducted using a linear weighted fusion conversion probability map that accounts for random factors. To validate the KCLP-CA model, land use data collected from Hangzhou between 1995 and 2000 were employed. The results showed that the KCLP-CA model outperformed traditional methods, including artificial neural networks and random forest model, with the figure of merit (FoM) index increasing from 2.12% to 4.19%. Random forest analysis of drivers impacting LUC revealed that distance to water and road network density exerted the greatest influence on urban land development in Hangzhou. Incorporation of various policy planning factors affecting urban development yielded simulation results aligning more closely with reality, resulting in a FoM index increase of 1.64–1.76%. In summary, the model developed in this study combines the strengths of two sub models to deliver an accurate and effective simulation of future land use.

Spatiotemporal Changes of Terrestrial Carbon Storage in Rapidly Urbanizing Areas and Their Influencing Factors: A Case Study of Wuhan, China

Terrestrial carbon storage plays a vital role in limiting global climate change and achieving regional carbon neutrality. However, intensive human activities and rapid urbanization have led to a rapid decline in carbon storage. Understanding what causes carbon storage to decline and how this happens is important for the scientific regulation of urbanization and safeguarding of urban ecological security. This study takes Wuhan as an example and analyzes the quantity, structure, and spatial patterns of urban land-use changes in the context of human activities and natural conditions, and applies correlation methods to identify general relationships between influencing factors and carbon storage. The results of the study are as follows: over the 30-year period studied, the area devoted to construction land increased by 757 km2 and the carbon storage decreased by 7.68 × 106 t. Outside Wuhan’s Third Ring Road, there was a significant increase in the carbon storage, but in the areas where construction increased, there was a reduction in carbon storage. Carbon storage in the remote suburbs was significantly higher than in the city center, and the distribution pattern was characterized by significant spatial heterogeneity. Our analysis revealed that human economic and social activities have affected Wuhan’s ecosystem carbon storage to a significant extent. Policymakers should focus on industrial optimization, strictly control the red line of ecological protection, and ultimately achieve high-quality urban development.

Density, Division and Distance: Understanding China’s Urban Land-Use Change from an Economic Geography Perspective

Density, Division and Distance: Understanding China’s Urban Land-Use Change from an Economic Geography Perspective

EMPOWERING WOMEN THROUGH TRAINING ON HYDROPONIC AGRICULTURE METHODS IN INCREASING FOOD INDEPENDENCE IN MERUYA UTARA,WEST JAKARTA

Changes in urban land use are increasing along with the growth and development of urban areas. Economic and population growth as well as community preferences as drivers of land use change are reflected in land use patterns in urban areas. Urban areas need policies to control changes in land use to realize family food independence and sustainable urban development. Urban agricultural practices that are currently developing follow zone characteristics and are in accordance with land availability. Urban agricultural activities are limited to vegetables, horticulture, ornamental plants and medicinal plants. This paper aims to describe the use of limited urban land for agriculture, women's empowerment and family food independence. The results of this activity are direct practice of the hydroponic method which can be carried out on limited land as well as ongoing guidance to women and housewives in North Meruya.

Incorporation of Spatially Heterogeneous Area Partitioning into Vector-Based Cellular Automata for Simulating Urban Land-Use Changes

In cellular automata (CA) modeling, spatial heterogeneity can be delineated by geographical area partitioning. The dual constrained space clustering method is a prevalent approach for providing an objective and effective representation of differences within urban regions. However, previous studies faced issues by ignoring spatial heterogeneity, which could lead to an over- or under-estimation of the simulation results. Accordingly, this study attempts to incorporate spatially heterogeneous area partitioning into vector-based cellular automata (VCA), producing more accurate and reliable simulations of urban land-use change. First, an area partition strategy with DSC algorithm was employed to generate multiple relatively homogeneous sub-regions, which can effectively capture the spatial heterogeneity in the distribution of land-use change factors. Second, UrbanVCA, a brand-new VCA-based framework, was utilized for simulating land-use changes in distinct urban partitions. Finally, the constructed partitioned VCA model was applied to simulate rapid urban development in Jiangyin city from 2012 to 2017. The results indicated that the combination of DSC clustering and UrbanVCA model could obtain satisfying results as the average FoM values for the partitions and the entire study area exceeded 0.22. Furthermore, a comparative analysis of results from traditional area-partitioned CA models revealed that the proposed area partitioning approach had the potential to yield more accurate simulation outcomes as the FoM values were higher and SHDI and LSI metrics were closer to real-world observations, indicating its good performance in simulating fragmented urban landscapes.

Data-driven approach to spatiotemporal dynamic risk assessment of urban flooding based on shared socio-economic pathways

Driven by the change in intense land use and land cover (LULC) due to fast urbanization, urban flooding events have become the most frequent and influential hazards over the last few decades. Accurately predicting possible flood-prone locations under the dynamic fluctuations of LULC is crucial for sustainable urban development. However, there has been sparse studies on systematic integration of LULC changes into anticipate urban development scenarios coupled with flooding vulnerability assessment. Therefore, this study proposed a robust and powerful cascade modeling chain consisting of Maximum Entropy, System Dynamics and Patch-generating Land Use Simulation in combination with shared socio-economicpathwaysfor projecting temporal and spatial dynamic changes associated with urban flooding vulnerability. Taking Guangdong Hong Kong Macao Greater Bay Area (GBA) as case study, the results showed that the increase in urban flooding was largely caused by the expansion of construction land. Overall, a substantial distinction within the scenarioswas observed and the flooding vulnerability was ranked in the order of SSP126 < SSP245 < SSP585. Under SSP585, the areas with high flooding risk will be expected to increase significantly,accounting for26% of the total areas,and nearly half of the built-upareas are exposed to flooding risk by 2050.Under SSP245, the built-up areas exposed tomedium and high flooding riskwere anticipated to cover nearly a fifth of the total areas.Under SSP126 scenario, nearly no change was predicted in the area with high flooding risk, with only increase of 1%,future urbanization hotspots associated with serious flooding risks will likely to be found on thefringeof the GBA’s construction areas,in line with the extent of construction land expansion.The finding of this study will shed a comprehensive insight into the identification of spatial and temporal distribution of urban flooding vulnerability to facilitate the exploration of flood mitigation measures associated with dynamic changes in urban land use.

Increasing contribution of urban greenery to residential real estate valuation over time

Cities can experience losing greenery to urban development, while also pursuing greening efforts. However, whether and how long-term changes in land-use and policies affect ecosystem services and greenery preferences is not well-known, due to the paucity of longitudinal data. We created a novel land cover fraction map in Singapore from 1990 to 2020 using high-resolution satellite imagery, finding an overall decline in greenery area within public housing towns and evidence of greenery intensification in the remaining greenery area. Hedonic pricing analysis suggested positive and increasing marginal effects of greenery area on real estate prices; for each apartment unit this was S$39,130 ± S$15,951 (9.2% ± 3.8% of the average real estate price) during the 1990s; S$46,049 ± S$14,124 (10.9% ± 3.3%) during the 2000s; and S$50,048 ± S$17,079 (11.7% ± 4.0%) during the 2010s. An increased preference for tree cover and decreased preference for grass cover was observed, reflecting long-term changes in urban land-use and greening efforts. Our results suggest that urban greenery has become increasingly valuable to home owners and society in Singapore, occurring alongside decreasing quantity and increasing quality of urban greenery, illustrating how combining careful development with greening efforts can lead to an increase in urban ecosystem services.

Modeling urban land use dynamics using Markov-chain and cellular automata in Gondar City, Northwest Ethiopia

Modeling urban land-use dynamics is critical for urban experts’ and infrastructure managers’ planning. This study attempts to explore the land-use/land-cover (LULC) dynamics of Gondar using satellite images from 1984 to 2020. Markov-Chain and Cellular Automata (MC-CA) models have been recognized as performing well in predicting urban land-use change. However, only a few models work in Ethiopia in general, and no study in Gondar has applied this approach to study urban land-use patterns. Therefore, Gondar land-use/land-cover changes of Gondar were predicted using the MC-CA model in IDRISI. The built-up area in Gondar city covered 1 413 ha (3% of the total area) in 1984 and increased to 2 380 ha (5%) in 1994; 21 153 ha (45.5%) in 2004; 22 622 ha (48.7%) in 2014; and 23 427 ha (50.5%) in 2020. The area has been predicted to reach 57.5% in the 2050s, showing a faster increase that will cause a very vast loss of farmland. This will increase urban sprawl challenges as well as overall environmental disequilibrium in the preceding decade. Thus, innovative and careful structures and systems in urban planning are required to secure a sustainable urban future and to make our cities livable and competitive in the paradigm of sustainable cities.

A novel spatio-temporal cellular automata model coupling partitioning with CNN-LSTM to urban land change simulation

Land use change (LUC) has gained attention as a core topic of global ecological environment change research. The cellular automata (CA) model affects the global layout through local changes, and is widely used in LUC. However, most previous studies are based on the assumption of the Markov model which ignores the temporal dependency of LUC. In addition, most researchers have used the identical transition rules when simulating LUC variation across a region, ignoring the spatial heterogeneity in LUC studies.Accordingly, we propose a novel CA model integrating K-means, convolutional neural networks (CNN), and long-short-term memory neural networks (LSTM) to solve temporal dependency and spatial heterogeneity, named K-means-CNN-LSTM-CA (KCL-CA). First, in order to resolve spatial heterogeneity, we divided the study area into homogeneous sub-regions using K-means clustering algorithm. We then extracted multi-year spatial neighbourhood features and assigned weights with Gaussian functions according to the time sequence order to realise the fusion of multi-year features. LSTM was used to extract the spatio-temporal dependency features of historical land use data and to calculate the transition probability maps for sub-regions. Finally, CA generated the dynamic simulation results for the whole region.The KCL-CA model was validated based on data collected in Hangzhou from 1995 to 2020 Traditional logistic regression (LR)-CA and artificial neural network (ANN)-CA were used for comparison. Comparing the traditional model with the results shows that the proposed KCL-CA model improves the FoM index by 9.86%–19.43%. Considering the temporal dependency, the FoM index increased by 0.98%–3.51%; when considering spatial heterogeneity, the FoM index increased by 1.08%–5.15%. KCL-CA can deal with the temporal dependency of spatial heterogeneity in urban land expansion simulations and can effectively predict future urban expansion. The simulation results can effectively monitor the future trend of urban LUC and help to provide policy support for urban planning and management for decision makers.

Spatio-Temporal Analysis of Land Use Change in Uyo Urban, Akwa Ibom State, Nigeria

The increasing population size of cities and the physical expansion of the built-up area beyond the city limits as well as rising demand for more land for various purposes induce changes in urban land-use. This study therefore evaluates the spatio-temporal changes of land uses in Uyo urban. The study utilizes Geographic Information System (GIS); where land use maps of Uyo were produced from Landsat ETM imagery of 2003, 2012 and 2021 using Erdas imagine and ArcGIS softwares. Post classification and change detection analysis for the three years was used to depict changes in identified land uses. The results indicate that in 2003 agricultural land use occupied an area of 37.307 sq.km while residential land use occupied an area of 50.494 sq.km. In 2012, agricultural land use reduced to 25.347 sq.km while residential land use increased to 62.879 sq.km. Furthermore, by 2021, agricultural lands use further indicated drastic reduction occupying an area of 6.207 sq.km while residential land use indicated remarkable increase to 73.469 sq.km. Between 2003 and 2012, residential land uses increased by 8.88 sq.km translating into 15.4 per cent within 10 years and 1.5 per cent per annum; whereas agricultural land use decreased annually by -2.5 per cent. Other land uses such as institutional, commercial and transportation land uses also increased, while forest and water bodies also decreased within the years. It is recommended that periodic monitoring of the land use should be encouraged to ascertain the pattern of changes in order to curtail negative impacts on the environment.

Impact of land use compactness on the habitat services from green infrastructure in Wuhan, China

As an important carrier of biodiversity, green infrastructure (GI) is significantly affected by changes in urban land use in the process of urbanization. In this study, habitat services from GI were used to represent biodiversity support services, and geographic information system (GIS), remote sensing and statistical analysis were combined to analyze the changes in urban land use compactness and GI habitat services in Wuhan in 2005, 2013 and 2018. Seven indices for land use compactness mainly covering three aspects (land use morphology, land use intensity and land function layout) were selected to explore the correlation and regression relationships between urban land use compactness and habitat services on a grid scale. The results show that: i) The urban land use in Wuhan presents a compact development trend except for the degree of integration; and habitat services decrease along with increasing land use compactness. ii) Relative to land use morphology and land function layout, land use intensity is a more prominent factor affecting habitat services, with construction land density and residential land density always being significant indicators for the habitat pattern. iii) Urban areas with different degrees of land use compactness tend to have different indices that affect the habitat services. Therefore, differential urban development strategies should be formulated based on the regional characteristics of land use compactness levels, so as to coordinate urban compact land use and biodiversity conservation.