Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations

Urban cellular automata (CA) models are broadly used in quantitative analyses and predictions of urban land-use dynamics. However, most urban CA developed with neighborhood rules consider only a small neighborhood scope under a specific spatial resolution. Here, we quantify neighborhood effects in a relatively large cellular space and analyze their role in the performance of an urban land use model. The extracted neighborhood rules were integrated into a commonly used logistic regression urban CA model (Logistic-CA), resulting in a large neighborhood urban land use model (Logistic-LNCA). Land-use simulations with both models were evaluated with urban expansion data in Xiamen City, China. Simulations with the Logistic-LNCA model raised the accuracies of built-up land by 3.0%–3.9% in two simulation periods compared with the Logistic-CA model with a 3 × 3 kernel. Parameter sensitivity analysis indicated that there was an optimal large window size in cellular space and a corresponding optimal parameter configuration.     

The influence of filtration and decomposition window size on the threshold value and accuracy of land-cover classification of polarimetric SAR images

In this study we use ALOS PALSAR satellite data to classify land cover using a decision tree algorithm. We apply polarimetric decomposition methods to coherence and covariance matrices obtained from the data and then use threshold values to classify terrain. We evaluate the influence of speckle filter and decomposition window sizes on the threshold value used in the decision algorithm and on the accuracy of the classification. We also study the sensitivity of the classification to the accuracy of the threshold value.

First, we processed a fully polarimetric Synthetic Aperture Radar (SAR) L-band image using different sizes of speckle filtration and decomposition window (3 × 3 pixels, 5 × 5, 7 × 7, 9 × 9), and the decomposition methods available in PolSARPro software. We evaluated these methods and chose the most efficient. Then we developed a simple hierarchical classification scheme based on threshold values. In the first step we divided the terrain into smooth and rough areas and then separated these into more detailed subclasses (water and agriculture, and forest and urban) which correspond to smooth and rough areas, respectively. A more detailed analysis separated continuous and discontinuous urban fabric and deciduous and coniferous forests. The maximum overall accuracy of the classification was 86.1% for the four main land cover classes, and 80.4% for the six more detailed classes. The accuracy of the classification dropped by about 10% when non-optimal window sizes were used in image filtration or decomposition.     

Modeling urban growth in Atlanta using logistic regression

This study applied logistic regression to model urban growth in the Atlanta Metropolitan Area of Georgia in a GIS environment and to discover the relationship between urban growth and the driving forces. Historical land use/cover data of Atlanta were extracted from the 1987 and 1997 Landsat TM images. Multi-resolution calibration of a series of logistic regression models was conducted from 50 m to 300 m at intervals of 25 m. A fractal analysis pointed to 225 m as the optimal resolution of modeling. The following two groups of factors were found to affect urban growth in different degrees as indicated by odd ratios: (1) population density, distances to nearest urban clusters, activity centers and roads, and high/low density urban uses (all with odds ratios < 1); and (2) distance to the CBD, number of urban cells within a 7 × 7 cell window, bare land, crop/grass land, forest, and UTM northing coordinate (all with odds ratios > 1). A map of urban growth probability was calculated and used to predict future urban patterns. Relative operating characteristic (ROC) value of 0.85 indicates that the probability map is valid. It was concluded that despite logistic regression’s lack of temporal dynamics, it was spatially explicit and suitable for multi-scale analysis, and most importantly, allowed much deeper understanding of the forces driving the growth and the formation of the urban spatial pattern.     

The dynamics of urban expansion and land use/land cover changes using remote sensing and spatial metrics: the case of Mekelle City of northern Ethiopia

Information on the rate and pattern of urban expansion is required by urban planners to devise proper urban planning and management policy directions. This study evaluated the dynamics and spatial pattern of Mekelle City’s expansion in the past three decades (1984–2014). Multi-temporal Landsat images and Maximum Likelihood Classifier were used to produce decadal land use/land cover (LULC) maps. Changes in LULC and spatial pattern of urban expansion were analysed by post-classification change detection and spatial metrics, respectively. The results showed that in the periods 1984–1994, 1994–2004, and 2004–2014, the built-up area increased annually by 10%, 9%, and 8%, respectively; with an average annual increment of 19% (100 ha year^?1), from 531 ha in 1984 to 3524 ha in 2014. Between 1984 and 2014, about 88% of the gain in built-up area was from conversion of agricultural lands, which decreased by 39%. Extension of existing urban areas was the dominant growth type, which accounted for 54%, 75%, and 81% of the total new development during 1984–1994, 1994–2004, and 2004–2014, respectively. The spatial metrics analyses revealed urban sprawl, with increased heterogeneity and gradual dispersion in the outskirts of the city. The per capita land consumption rate (ha per person) increased from 0.009 in 1984 to 0.014 in 2014, indicating low density urban growth. Based on the prediction result, the current (2014) built-up area will double by 2035, and this is likely to have multiple socioeconomic and environmental consequences unless sustainable urban planning and development policies are devised.     

A model to predict land susceptibility to wind erosion in western Queensland,Australia

This paper describes the development and validation of the Australian Land Erodibility Model (AUSLEM), designed to predict land susceptibility to wind erosion in western Queensland, Australia. The model operates at a 5 × 5 km spatial resolution on a daily time-step with inputs of grass and tree cover, soil moisture, soil texture and surficial stone cover. The system was implemented to predict land erodibility, i.e. susceptibility to wind erosion, for the period 1980–1990. Model performance was evaluated using cross-correlation analyses to compare trajectories of mean annual land erodibility at selected locations with trends in wind speed and observational records of dust events and a Dust Storm Index (DSI). The validation was conducted at four spatial length scales from 25 to 150 km using windows to represent potential dust source areas centered on and positioned around eight meteorological stations within the study area. The predicted land erodibility had strong correlations with dust-event frequencies at half of the stations. Poor correlations at the other stations were linked to the inability of the model to account for temporal changes in soil erodibility, and comparing trends in the land erodibility of regions with dust events whose source areas lie outside the regions of interest. The model agreement with dust-event frequency trends was found to vary across spatial scales and was highly dependent on land type characteristics around the stations and on the types of dust events used for validation.     

Study on the estimation of near-surface air temperature from MODIS data by statistical methods

Spatially distributed air temperature is desired for various scientific studies, including climatalogical, hydrological, agricultural, environmental and ecological studies. In this study, empirical models with regard to land cover and spatial scale were introduced and compared to estimate air temperature from satellite-derived land surface temperature and other environmental parameters. Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data obtained throughout 2005 in the Yangtze River Delta were adopted to develop statistical algorithms of air temperature. Four empirical regression models with different forms and different independent variables resulted in errors ranging from 2.20°C to 2.34°C. Considering the different relationships between air temperature and land surface temperature for different land types, these four models were evaluated and the most proper equation for each land-cover type was determined. The model containing these selected equations gave a slightly improved mean absolute error (MAE) of 2.18°C. Then the spatial scale effect of this empirical model was analysed with observed air temperature and spatially averaged land surface characteristics. The result shows that the estimation error of air temperature tends to be lower as spatial window size increases, suggesting that the model performances are improved by spatially averaging land surface characteristics. Comprehensively considering the accuracy and computational demand, 5 × 5 pixel size is the most favourable window size for estimating air temperature. The validation of the empirical model at 5 × 5 pixel size shows that it achieves an MAE of 1.98°C and an R ² of 0.9215. This satisfactory result indicates that this approach is proper for estimating air temperature, and spatial window size is an important factor that should be considered when calculating air temperature. It is expected that better accuracy will be achieved if the different weights of pixels at different distances can be set according to high-density micro-meteorological data.     

Modelling multi-regional urban growth with multilevel logistic cellular automata

Simulation models based on cellular automata (CA) are useful for revealing the complex mechanisms and processes involved in urban growth and have become supplementary tools for urban land use planning and management. Although the urban growth mechanism is characterized by multilevel and spatiotemporal heterogeneity, most existing studies focus only on simulating the urban growth of singular regions without considering the heterogeneity of the urban growth process and the multilevel factors driving urban growth within regions that consist of multiple subregions. Thus, urban growth models have limited performance when simulating the urban growth of multi-regional areas. To address this issue, we propose a multilevel logistic CA model (MLCA) by incorporating a multilevel logistic regression model into the traditional logistic CA model (LCA). In the MLCA, multilevel driving factors are considered, and the multilevel logistic model allows the transition rules to not only vary in space, but also change when the subregional level factors change. To verify the MLCA's validity, it was applied to simulate the urban growth of Tongshan County, located in China's Xuzhou Prefecture. The results were compared with three comparative models, LCA1, which only considered grid cell-level factors; LCA2, which considered both grid cell- and subregional-level factors; and artificial neural network CA. Urban growth data for the periods 2000–2009 and 2009–2017 were used. The results show that the MLCA performs better on both visual comparison and indicators for accuracy verification. The Kappa of the results increased by <5%, but the improvement was significant, while increases for the accuracy of urban land and figure of merit were much higher than 5%. In addition, the results of MLCA had the smallest mean absolute percentage error when allocating new urban land areas to the various subregions. The results reveal that higher-level (e.g., town level) factors either strengthened or weakened the effects of grid cell-level factors on urban growth, which indirectly affected the spatial allocation of new urban land. The MLCA model is an effective step towards simulating nonstationary urban growth of multi-regional areas, using the comprehensive effects of multilevel driving factors.     

基于卷积神经网络的青海湖区域遥感影像分类

科学准确的获取青海湖区域土地覆盖分类对于研究该区域生态环境变化有着重要的意义.本文使用30米分辨率的LandSat 8 OLI青海湖区域遥感影像数据展开相关研究,30米分辨率属于中等分辨率,当前中分遥感影像的分类方法尚存在特征提取困难、分类精度不高等问题.本文借鉴GoogLeNet Inception结构,设计并提出了一种卷积神经网络模型进行特征提取及分类,分析了用于样本生成的邻域窗口尺寸对分类结果的影响,并与最大似然分类和SVM分类方法进行比较.结果表明,在窗口尺寸为9×9时,CNN模型的总体分类效果最好,且CNN的分类结果明显优于最大似然分类方法和SVM.     

基于Landsat 8卫星影像的北京地区土地覆盖分类

目的 土地覆盖分类能为生态系统模型、水资源模型和气候模型等提供重要信息,遥感技术运用于土地覆盖分类具有诸多优势。作为区域性土地覆盖分类应用的重要数据源,Landsat 5/7的TM和ETM+等数据已逐渐失效,Landsat 8陆地成像仪(OLI)较TM和ETM+增加了新的特性,利用Landsat 8数据进行北京地区土地覆盖分类研究,探讨处理方法的适用性。方法 首先,确定研究区域内土地覆盖分类系统,并对Landsat 8多光谱数据进行预处理,包括大气校正、地形校正、影像拼接及裁剪;然后,利用灰度共生矩阵提取全色波段纹理信息,与多光谱数据进行融合;最后,使用支持向量机(SVM)进行分类,获得土地覆盖分类结果。结果 经过精度评价和分析发现,6S模型大气校正和C模型地形校正预处理提高了不同类别之间的可分性,多光谱数据结合全色波段纹理特征能有效提高部分地物的土地覆盖分类精度,总体精度提高2.8%。结论 相对于Landsat TM/ETM+数据,Landsat 8 OLI数据新增特性有利于土地覆盖分类精度的提高。本文方法适用于Landsat 8 OLI数据土地覆盖分类研究与应用,能够满足大区域土地覆盖分类应用需求。     

Modelling of urban growth using spatial analysis techniques: a case study of Ajmer city (India)

The concentration of people in densely populated urban areas, especially in developing countries like India and China, calls for the use of sophisticated monitoring systems, like remote sensing and Geographical Information Systems (GIS). Time series of land use/cover changes can easily be generated using sequential satellite images, which are required for the prediction of urban growth, verification of growth model outputs, estimation of impervious area, parameterization of various hydrological models, water resources planning and management and environmental studies. In the present work, urban growth of Ajmer city (India) in the last 29 years has been studied at mid‐scale level (5–25 m). Remote sensing and GIS have been used to extract the information related to urban growth, impervious area and its spatial and temporal variation. Statistical classification approaches have been used to derive the land use information from satellite images of eight years (1977–2005). The Shannon's entropy and landscape metrics (patchiness and map density) are computed in order to quantify the urban form (impervious area) in terms of spatial phenomena. Further, multivariate statistical techniques have been used to establish the relationship between the urban growth and its causative factors. Results reveal that land development (200%) in Ajmer is more than three times the population growth (59%). Shannon's entropy and landscape metrics has revealed the spatial distribution of the sprawl.     

Urban expansion simulation from the perspective of land acquisition-based on bargaining model and ant colony optimization

As a major method to serve the demand for land requirement in Chinese urban construction, land acquisition has been intensified since the speeding up of urbanization. However, clashes arise during the process of land acquisition, out of conflicts of interests, which to some extent have affected the development and direction of urban expansion, as well as social harmony and stability. Therefore, the urban expansion simulation should be based on smooth land acquisition technique. In this study, urban expansion was simulated from the perspective of land acquisition, based on coupled bargaining model and modified ant colony optimization (ACO) algorithm. First, the bargaining model with fairness preferences should be set up for the government and farmers, to search for the candidate areas for urban expansion, both sides of which could reach a consensus without conflicts of interest. Then, urban expansion simulation is applied to all the candidate areas, taking advantage of the modified ACO algorithm. The model considered the built-up area in Wuhan city as the demonstration area and simulated conditions of land use in 2016 and 2026. The result showed that the coupled model could simulate decision-making behavior of the government and farmers in land acquisition veritably, so as to protect farmers' economic interests, with an increase of over 50% on average, and ensure government's appropriate profit. Moreover, the simulation accuracies of the coupled model was found to be better than that of the traditional cellular automata model, and the Kappa coefficient was 0.65, which supports the effectiveness of the model in simulating urban expansion. Further, it was estimated that the urban land use of Wuhan will cover 516.22 km² in 2026, and the southeastern part of the city will be the hot spot area of urban expansion.     

The use of digital aerial photography and CORINE‐derived methodology for monitoring recent and historic changes in land cover near UK Natura 2000 sites for the BIOPRESS project

BIOPRESS – Linking pan‐European land cover change to pressures on biodiversity – is a European Community Framework 5 project, which aims to develop a standardised product that will link quantified historical (1950–2000) land cover change to pressures on biodiversity. It exploits archived historic and recent aerial photographs (a data source that has remained consistent over the last 60 years) to assess land cover change around Natura 2000 sites within 30×30 km windows and 15×2 km transects. The CORINE (Coordination of Information on the Environment) land cover mapping methodology has been adapted for use with aerial photographs. Sample sites are mapped to CORINE Land Cover (CLC) classes, and then backdated to assess change. Results from eight UK transects (and associated windows) are presented. Changes in land cover classes are interpreted as pressures: urbanisation, intensification, abandonment, afforestation, deforestation and drainage. Urbanisation was the major pressure in all but two transects (both in the uplands), and intensification was of similar importance in most transects. Afforestation was a significant pressure in two transects. In six out of the eight transects, annual change was greater in the 1990–2000 period than in the 1950–1990 period. The methodology has been demonstrated to provide quantitative results of long‐term land cover change in the UK rural landscape at a spatial scale that is relevant to management decisions. The methods are transferable and applicable to a wide range of landscape studies.     

The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product

The results of the first consecutive 12 months of the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) global burned area product are presented. Total annual and monthly area burned statistics and missing data statistics are reported at global and continental scale and with respect to different land cover classes. Globally the total area burned labeled by the MODIS burned area product is 3.66 × 10⁶ km² for July 2001 to June 2002 while the MODIS active fire product detected for the same period a total of 2.78 × 10⁶ km², i.e., 24% less than the area labeled by the burned area product. A spatio-temporal correlation analysis of the two MODIS fire products stratified globally for pre-fire leaf area index (LAI) and percent tree cover ranges indicate that for low percent tree cover and LAI, the MODIS burned area product defines a greater proportion of the landscape as burned than the active fire product; and with increasing tree cover (> 60%) and LAI (> 5) the MODIS active fire product defines a relatively greater proportion. This pattern is generally observed in product comparisons stratified with respect to land cover. Globally, the burned area product reports a smaller amount of area burned than the active fire product in croplands and evergreen forest and deciduous needleleaf forest classes, comparable areas for mixed and deciduous broadleaf forest classes, and a greater amount of area burned for the non-forest classes. The reasons for these product differences are discussed in terms of environmental spatio-temporal fire characteristics and remote sensing factors, and highlight the planning needs for MODIS burned area product validation.     

Characterizing multi-decadal,annual land cover change dynamics in Houston,TX based on automated classification of Landsat imagery

In 2017, Hurricane Harvey caused substantial loss of life and property in the swiftly urbanizing region of Houston, TX. Now in its wake, researchers are tasked with investigating how to plan for and mitigate the impact of similar events in the future, despite expectations of increased storm intensity and frequency as well as accelerating urbanization trends. Critical to this task is the development of automated workflows for producing accurate and consistent land cover maps of sufficiently fine spatio-temporal resolution over large areas and long timespans. In this study, we developed an innovative automated classification algorithm that overcomes some of the traditional trade-offs between fine spatio-temporal resolution and extent – to produce a multi-scene, 30m annual land cover time series characterizing 21 years of land cover dynamics in the 35,000 km2 Greater Houston area. The ensemble algorithm takes advantage of the synergistic value of employing all acceptable Landsat imagery in a given year, using aggregate votes from the posterior predictive distributions of multiple image composites to mitigate against misclassifications in any one image, and fill gaps due to missing and contaminated data, such as those from clouds and cloud shadows. The procedure is fully automated, combining adaptive signature generalization and spatio-temporal stabilization for consistency across sensors and scenes. The land cover time series is validated using independent, multi-temporal fine-resolution imagery, achieving crisp overall accuracies between 78–86% and fuzzy overall accuracies between 91–94%. Validated maps and corresponding areal cover estimates corroborate what census and economic data from the Greater Houston area likewise indicate: rapid growth from 1997–2017, demonstrated by the conversion of 2,040 km² (± 400 km²) to developed land cover, 14% of which resulted from the conversion of wetlands. Beyond its implications for urbanization trends in Greater Houston, this study demonstrates the potential for automated approaches to quantifying large extent, fine resolution land cover change, as well as the added value of temporally-dense time series for characterizing higher-order spatio-temporal dynamics of land cover, including periodicity, abrupt transitions, and time lags from underlying demographic and socio-economic trends.     

Integrating Corine Land Cover data and Landsat TM for surface emissivity definition: application to the urban area of Athens,Greece

This study examines the potential of the combined use of the land cover/land use information provided by the Corine Land Cover (CLC) database with Landsat satellite data for the definition and quantitative correlation of emissivity with various land covers and land uses that describe a certain territory. Surface emissivity in the 10.5–12.5 µm wavelength range is derived using Landsat data and the Normalized Difference Vegetation Index Thresholds method (NDVI^THM), whereas mean emissivity values for selected urban/non‐urban land cover types are estimated by integrating the emissivity image with the land cover vector data. The method is applied to the greater Athens area, Greece, in order to estimate the emissivity of various land cover types found within the urban setting. Analysis of variance (ANOVA) indicates statistically significant differences in emissivity associated with different land cover types. Furthermore, statistical results demonstrate that the method is very effective and can provide emissivity values of different land cover types with good accuracy and therefore can quantitatively link emissivity with surface type.     

Modelling net primary productivity of terrestrial ecosystems in East Asia based on an improved CASA ecosystem model

By using a land cover map, normalized difference vegetation index (NDVI) data sets, monthly meteorological data and observed net primary productivity (NPP) data, we have improved the method of estimating light use efficiency (LUE) for different biomes and soil moisture coefficients in the Carnegie–Ames–Stanford Approach (CASA) ecosystem model. Based on this improved model we produced an annual NPP map (in 1999) for the East Asia region located at 10–70° N, 70–170° E (about 19.66% of the terrestrial surface of the Earth). The results show that the mean NPP for the study area in 1999 was 374.12 g carbon (C) m^?2 year^?1 and the total NPP was 1.096 × 10¹⁴ kg C year^?1, making up 17.51–18.39% of the global NPP. Comparison between the estimated NPP obtained from this improved CASA ecosystem model and the observed NPP obtained from two NPP databases indicates that the estimated NPP is close to the observed NPP, with an average error of 5.15% for the study region. We used two different land cover maps of China to drive the improved CASA model by keeping other inputs unchanged to determine how the classification accuracy of the land cover map affects the estimated NPP, and the results indicate that an accurate land cover map is important for obtaining an accurate and reliable estimate of NPP for some regions, especially for a particular biome.     

Miniaturized 1D beam switching and 2D printed Yagi-Uda antenna arrays

This article presents a multi-board arrangement of printed Yagi-Uda antennas that can be configured into 1D and 2D arrays. First, a 1 × 4 collinear array is designed and fed with a metamaterial Butler matrix (BM) network to provide beam switching at four azimuthal directions. Slow-wave concept is used in designing the hybrid, crossover and delay sections of BM to achieve a footprint reduction of 67%. The 1 × 4 collinear array with the feed network achieves 8.42–11.7 dBi gain and 21.7–25.7 degrees half power beam width (HPBW) in horizontal plane for the four switched beam patterns at 5.8 GHz in simulations. Second, measurement results of the fabricated 1 × 4 collinear array with its miniaturized feed network confirm a range of 22–27 degrees in HPBW in the horizontal plane. Finally, parasitic structures are designed to reduce antenna coupling and a 3-shelf holder is proposed to stack the 1 × 2 printed Yagi antenna subarray boards in compact 2D planar array configurations. Simulations of the 2 × 4-array demonstrate achieving 13.09 dBi peak gain at 5.8 GHz along with reduction of the HPBW by 24.7 degrees in horizontal plane in comparison with the 1 × 4-array prototype.     

Aboveground forest biomass derived using multiple dates of WorldView-2 stereo-imagery: quantifying the improvement in estimation accuracy

ABSTRACT

The aim of this study was to investigate the capabilities of two date satellite-derived image-based point clouds (IPCs) to estimate forest aboveground biomass (AGB). The data sets used include panchromatic WorldView-2 stereo-imagery with 0.46 m spatial resolution representing 2014 and 2016 and a detailed digital elevation model derived from airborne laser scanning data. Altogether, 332 field sample plots with an area of 256 m² were used for model development and validation. Predictors describing forest height, density, and variation in height were extracted from the IPC 2014 and 2016 and used in k-nearest neighbour imputation models developed with sample plot data for predicting AGB. AGB predictions for 2014 (AGB₂₀₁₄) were projected to 2016 using growth models (AGB_{Projected_2016}) and combined with the AGB estimates derived from the 2016 data (AGB₂₀₁₆). AGB prediction model developed with 2014 data was also applied to 2016 data (AGB_{2016_pred2014}). Based on our results, the change in the 90^th percentile of height derived from the WorldView-2 IPC was able to characterize forest height growth between 2014 and 2016 with an average growth of 0.9 m. Features describing canopy cover and variation in height derived from the IPC were not as consistent. The AGB₂₀₁₆ had a bias of ?7.5% (?10.6 Mg ha^?1) and root mean square error (RMSE) of 26.0% (36.7 Mg ha^?1) as the respective values for AGB_{Projected_2016} were 7.0% (9.9 Mg ha^?1) and 21.5% (30.8 Mg ha^?1). AGB_{2016_pred2014} had a bias of ?19.6% (?27.7 Mg ha^?1) and RMSE of 33.2% (46.9 Mg ha^?1). By combining predictions of AGB₂₀₁₆ and AGB_{Projected_2016} at sample plot level as a weighted average, we were able to decrease the bias notably compared to estimates made on any single date. The lowest bias of ?0.25% (?0.4 Mg ha^?1) was obtained when equal weights of 0.5 were given to the AGB_{Projected_2016} and AGB₂₀₁₆ estimates. Respectively, RMSE of 20.9% (29.5 Mg ha^?1) was obtained using equal weights. Thus, we conclude that combination of two date WorldView-2 stereo-imagery improved the reliability of AGB estimates on sample plots where forest growth was the only change between the two dates.     

Integrating spatial nonstationarity into SLEUTH for urban growth modeling: A case study in the Wuhan metropolitan area

Accurate forecasting of future urban land expansion can provide useful information for policy makers and urban planners to better plan for the impacts of future land use and land cover change (LULCC) on the ecosystem. However, most current studies do not emphasize spatial variations in the influence intensities of the various driving forces, resulting in unreliable predictions of future urban development. This study aimed to enhance the capability of the SLEUTH model, a cellular automaton model that is commonly used to measure and forecast urban growth and LULCC, by embedding an urban suitability surface from geographically weighted logistic regression (GWLR). Moreover, to examine the performance of the loosely-coupled GWLR-SLEUTH model, a layer with only water bodies excluded and a layer combining the former with an urban suitability surface from logistic regression (LR) were also used in SLEUTH in separate model calibrations. This study was applied to the largest metropolitan area in central China, the Wuhan metropolitan area (WMA). Results show that the integrated GWLR-SLEUTH model performed better than either the traditional SLEUTH model or the LR-SLEUTH model. Findings demonstrate that spatial nonstationarity existed in the drivers' impacts on the urban expansion in the study area and that terrain, transportation and socioeconomic factors were the major drivers of urban expansion in the study area. Finally, with the optimal calibrated parameter sets from the GWLR-SLEUTH model, an urban land forecast from 2017 to 2035 was conducted under three scenarios: 1) business as usual; 2) under future planning policy; and 3) ecologically sustainable growth. Findings show that future planning policy may promise a more sustainable urban development if the plan is strictly obeyed. This study recommended that spatial heterogeneity should be taken into account in the process of land change modeling and the integrated model can be applied to other areas for further validation and forecasts.