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Social interaction has become one of the key factors affecting the spatial reconstruction of rural settlements (SRRS). However, most studies ignored the multi-scale impact of social networks on the identification of restructuring types of rural settlements. This paper, taking Ezhou City of Hubei Province, China as the case study area, developed a potential inter-settlement network through considering settlements as nodes, and inter-settlement interactions induced by the spatial disparity of public facilities as edges, divided towns in Ezhou City into three zones based on community structure at the town level, and then identified four types of rural settlements in light of the characteristics of cluster patterns and centrality at the patch level. The results show that the inter-settlement network in Ezhou City presents apparent disparities in terms of community structure, cluster patterns and centrality. In community analysis, high inter-community and intra-community interactions are concentrated in well-developed areas in the north and east, while weak interactions between communities occur in the southern areas dominated by traditional agricultural production. Accordingly, three zones are divided such as the urban-leading zone, urban-rural integration zone and rural-leading zone. For the network centrality and cluster patterns, high-level rural settlements are mainly distributed in the urban-leading zone, followed by the urban-rural integration zone and the rural-leading zone. Moreover, the lump cluster pattern is observed in each zone, but the chain pattern and dispersed pattern largely occur in the rural-leading zone. At same time, four types of rural settlements are identified, namely urbanized settlements, central settlements, grassroots settlements and relocated settlements. The corresponding plans are discussed in different zones regarding urbanization, integration and characteristics to provide meaningful insights for policymakers to guide SRRS. This study would contribute to our understanding of the impact of social network involved in daily life on rural settlement reconstruction, and expect to provide theoretical and methodological support for rural sustainable development in practice.

One of the key factors affecting rural settlements distribution is intervillage social connections. However, most of the current studies ignored the effects of social connections on the spatial distribution of rural settlements. This study proposed a multi-objective spatial reconstruction model for rural settlements based on the particle swarm optimization algorithm. The model considers intervillage social connections as one of the reconstruction objectives together with land-use quantity, spatial compactness, land-use suitability and relocation distance. Based on a questionnaire survey, the social connection strength between villages was measured from spatial and nonspatial aspects. The model was calibrated for the Chengui Township of Hubei Province in Central China. Five modeling scenarios for rural settlement reconstruction were designed according to potential development modes used in practice. A comparison of the reconstruction results of the different scenarios was performed to verify the applicability of the model and to analyze the joint interaction of the spatial objectives and social connections during the restructuring of rural settlements. The results indicated that five scenarios were effectively and robustly implemented using the proposed model. Social connections had a direct effect by guiding the restructuring direction of rural settlements. This study advances our understanding of the influence of social connections on rural settlement reconstruction and provides a useful exploratory tool for policymakers and rural planners in formulating reasonable reconstruction plans.

Severe tropospheric O3 pollution has swept across China in recent years. Consequently, investigation of tropospheric O3 concentration influencing mechanism is of significance for O3 pollution control in China. Previous studies have rarely detected combined impacts of natural factors and anthropogenic activities behind tropospheric O3 concentration in China at a national scale. Moreover, there is significant spatiotemporal heterogeneity of O3 pollution distribution in China due to the temporal and regional differences of socioeconomic and natural environmental condition in the vast territory. The targeted O3 control recommendations for different regions and seasons should be put forward in terms of the spatiotemporal heterogeneity of O3 concentration determinants. In this context, a three-level regression model integrating multi-scale biophysical and socioeconomic variables was proposed to explore the determinants of O3 pollution in China. The results showed that the tropospheric O3 concentration in the eastern and southeastern regions of China was strongly affected by meteorological conditions. In contrast, tropospheric O3 pollution concentrated in inland areas mainly depended on the emission intensity from anthropogenic sources. Supplementary Information The online version contains supplementary material available at 10.1007/s10653-020-00797-8.

A better understanding of regional differences in ecosystem health and its driving factors is conducive to ecosystem management and restoration. Although various studies on ecosystem health have been carried out in different regions, few studies have been devoted to the insightful exploration of the spatial heterogeneity of ecosystem health and its driving forces at a national scale. In this study, we used an evaluation framework in terms of vigor, organization, resilience, and ecosystem service functions to assess the ecosystem health level in China from 2000 to 2015. Then, spatial agglomeration and regional differences in ecosystem health were examined using the spatial autocorrelation method and K-means clustering analysis, and the factors driving the regional differences of ecosystem health were explored based on the geographical detector model. Our results showed the following: (1) the ecosystem health level in China spatially increases from the northwest to the southeast, exhibiting significant global spatial autocorrelation and local spatial agglomeration; (2) eleven zones with three types were identified to indicate the regional differences of ecosystem health; (3) In terms of the driving factors, the moisture index and land use intensity contributed 24.5% and 20.7% to the variation in ecosystem health at the national scale. The ecosystem health changes were influenced by the interaction of meteorological and socio-economic factors in most regions with high ecosystem health types. Socio-economic factors act as a bridge that linked and reinforced the other factors in most regions with low and medium ecosystem health types. Ecologically protected factors were found to exert a remarkable impact in the southwestern region and the Loess Plateau region. Our findings can provide more effective and detailed decision-making support for ecosystem conservation and management in China.

Habitat fragmentation has largely reduced biodiversity in the urban fringe. The prioritization of individual habitat patches can provide spatially explicit guidance for ecological conservation and restoration. Numerous studies discussed the effectiveness of purely spatial connectivity metrics to rank the conservation priority of habitats, but hardly resolved this issue in a dynamic landscape context. Meanwhile, spatio-temporal connectivity metrics were usually applied at the landscape scale, but are rarely extended to determine habitat priority at the patch scale. As such, we extend a generalized betweenness centrality metric into spatio-temporal connectivity networks to evaluate conservation priorities of individual habitat patches from a dynamic landscape perspective. The contribution rates of spatio-temporal connectivity metrics are examined at varying dispersal distances of the target species. Caidian District, the fringe of Wuhan City in central China, is taken as a case study. The results illustrate that a net decrease in habitat amounts and quality from 2005 to 2015 was mainly caused by urban expansion and agricultural production. The positive contribution rates of spatio-temporal connectivity metrics are observed at distance thresholds exceeding 200 m at the landscape scale, which allows us to capture the response of habitat priority to landscape dynamics. Accordingly, we develop a balanced conservation strategy for multiple species and the targeted conservation measures based on the coupled analysis of quality and conservation priority of habitat patches. Our findings are expected to provide solid support for biodiversity conservation in urban growth.

Maintaining biodiversity is vital for ecological processes. Yet, present static protected areas seem to be inefficient in biodiversity conservation. In this study, we use a set of indicators to evaluate topological features, landscape connectivity and resilience of the current habitat conservation institution, and apply the node addition method to prioritize habitats that play roles in improving connectivity and resilience outside the protected areas. Wanzhou District, Chongqing (SW China) is selected as the study area as it is a biodiversity hotspot in Three Gorges Reservoir Area. The results show that, in current conservation institution, average clustering coefficient and average weighted degree rise with the increasing dispersal distance of species; besides, landscape connectivity is rather low, while long dispersers have larger connectivity degree and resilience compared with short ones. Furthermore, how those patches could function as connectivity providers in different aspects (e.g. stepping-stone effect) is also presented by partitioning the connectivity indicator into three parts. However, it is interesting to find that, patches with good performance in improving connectivity may not have the equal efficiency in enhancing resilience, and the possible reasons are elaborately discussed. Prioritized patches are suggested to be placed under protection, and to update the habitat conservation institution. Our indicators and method can extend the knowledge of the importance of linking protected areas with surrounding habitats, and it can serve as a supporting tool for conservation planning or land use planning.

Cropland conversion is a significant theme of the influence of human activities on ecological systems and global food security. Conventionally, cropland conversion in China has been deemed to match the dynamic balance of cropland resources well and to ensure food security for the whole country. However, local studies have presented some conflicting signs that call for an integrated analysis from a national viewpoint. In this paper, by considering the significance of slope in cropland-related natural and socioeconomic issues, we explore the slope features of China’s cropland conversion during 2000–2015 and endeavor to identify some potential risks hidden in the process. High-resolution remotely sensed images, digital elevation model and spatial analytical methods are adopted to conduct the analysis. The results show that, the features of cropland conversion in diverse slope division areas are all distinct. Some risks indicated by these conversion features can be identified as follows: (1) unsuitable reclamation of cropland in flat areas causes ecological damage, (2) the invasion of sparsely populated construction land on cropland in steep areas has a dramatic impact on the local area, (3) excessive greening in Northwest and Northeast China, and insufficient greening in Southwest China are not healthy for regional sustainability, (4) cropland’s upslope reclamation and downslope loss pose potential risks for food security. In the context of dynamic balance management and other related national policies, cropland conversion in China is not as optimistic as former surveys have indicated, and promoted measures should be elaborately adjusted based on local conditions.

This article examines the driving forces of food security in the areas of the Nam Theun2 Hydropower Project (NT2) in Khamuan, Laos. A questionnaire survey was conducted to collect data from 100 NT2 resettlement households based on the random sampling technique. A linear regression technique was used to identify the influence of household food insecurity. The result showed that household size, food price, drought, shock, household income per month, number of laborers, gender of the household head, and farmland areas are important factors for household food insecurity. Policies should focus on irrigation that will permit yearlong cultivation. This will in turn become the stimulus for a concatenation of events in the process of development. People will resettle to practice agriculture while also expanding non-agricultural employment. Businesses in skills training, fish processing, textile, services, and crafts will be created, boosting household income. With inevitable population expansion, education in family planning will also be necessary to control population in relation to available resources.

Climate and land use change (CLUC) have a far-reaching influence on the ecosystem. Regional ecosystem health, which is the direct and comprehensive reflection of evaluating regional ecosystem quality, has been significantly affected by these changes. However, existing studies mainly focused on the influences of climate or land use change on ecosystem structure, functions, or services, few studies have explored and predicted their joint impacts on regional ecosystem health. In this study, to fill this gap, we proposed a simulation framework to predict the joint impact of future climate and land use change on ecosystem health under the different representative concentration pathways (RCP) scenarios based on the integration of FLUS (Future Land Use Simulation) model and ecosystem health assessment model. The Middle Reaches of the Yangtze River Economic Belt was taken as the study area to examine the prediction framework and identify relationships between future climate and land use change and ecosystem health index (EHI) during the period of 2015–2030. The prediction results show that the EHI in the north of the study area and urban agglomeration areas would suffer marked decrease under the joint influence of future climate and land use changes, indicating that these areas will likely to become the key conservation areas for future ecological protection and management. Additionally, predicted the future CLUC will result in a significant increase in EHI in more than 58% regions under the RCP 2.6 and RCP 4.5 scenarios from 2015 to 2030, whereas 74.87% areas would suffer the decrease of EHI under the RCP 8.5. From the perspective of regional development, the RCP 4.5 scenario can provide a compromise solution for tackling future climate and land use change to ensure the ecological conservation and economic development. Moreover, the direct impact of future climate change on ecosystem health will not be significant in the short term, while the indirect impact of future land use change will play a leading role in the joint influence. Furthermore, the flexible multi-model coupled prediction framework of this study provides a promising method for better predicting the joint impact of future climate and land use changes on regional ecosystem health, it can also be applied to explore the joint impact of these change on other ecological environment. Most importantly, the prediction results can provide scientific suggestions for ecological conservation, management and regional development.

Rapid land use/cover changes (LUCC) have triggered severe habitat degradation over recent decades. Existing studies mainly focus on how LUCC per se affects habitat quality, while less attention is paid to the integration of LUCC, socio-economic development and climate change as a land use system, and to investigate their synergistic impacts on habitat quality. This study addresses this issue by incorporating a top-down system dynamic model, a bottom-up cellular automata and a habitat quality assessment model. The potential impacts of urban growth, socio-economic development and climate change on habitat quality of Hubei Province, China, were predicted under the four IPCC RCP scenarios. Overall, construction land was predicted to expand fast whereas forested land and cultivated land to shrink significantly from 2015 to 2030, but RCP 2.6 and 4.5 scenarios may maintain higher habitat quality compared with RCP 6.0 and 8.5 scenarios. Severe habitat degradation is likely to occur in the megacity’s surrounding ecosystems under RCP 2.6 and 8.5 scenarios, while the degradation is more likely to occur in middle-sized and small cities under RCP 4.5 and 6.0 scenarios. Further, our findings suggest that fixed asset investments and technology innovation play important roles in mitigating the negative impacts of urban and socio-economic development on habitat quality. This study should provide a useful approach for decision makers to design alternative development scenarios and to strike a balance between regional development and habitat conservation.

Ecosystem services (ESs) are facing challenges from urbanization processes globally. Exploring how ESs respond to urbanization provides valuable information for ecological protection and urban landscape planning. Previous studies mainly focused on the global and single-scaled responses of ESs but ignored the spatially heterogenous and scale-dependent characteristics of these responses. This study chose Wuhan City in China as the study area to explore the spatially varying and scale-dependent responses of ESs, i.e., grain productivity, carbon sequestration, biodiversity potential and erosion prevention, to urbanization using geographically weighted regression (GWR). The results showed that the responses of ESs were spatially nonstationary evidenced by a set of local parameter estimates in GWR models, and scale-dependent indicated by two kinds of scale effects: effect of different bandwidths and effect of grid scales. The stationary index of GWR declined rapidly as the bandwidth increased until reaching to a distance threshold. Moreover, GWR outperformed ordinary least square at both grid scales (i.e., 5 km and 10 km scales) and behaved better at finer scale. The spatially non-stationary and scale-dependent responses of ESs to urbanization are expected to provide beneficial guidance for ecologically friendly urban planning.

Mycophenolate mofetil (MMF) is an alternative immunosuppressive agent that has been reported to be effective and well tolerated for the treatment of refractory inflammatory bowel disease (IBD). The aim of this study was to investigate the therapeutic effect of MMF on intestinal injury and tissue inflammation, which were caused by Crohn’s disease (CD). Here, trinitrobenzene sulfonic acid-relapsing (TNBS) colitis was induced in mice, then, we measured the differentiation of Th1/Th2 cells in mouse splenocytes by flow cytometry and the secretion of cytokines in mice with TNBS-induced colitis by real-time polymerase chain reaction and/or enzyme-linked immunosorbent assay (RT-PCR/ELISA). The results show that MMF significantly inhibited mRNA expression of pro-inflammatory cytokines IFN-γ, TNF-α, IL-12, IL-6, and IL-1β in mice with TNBS-induced colitis, however, MMF did not inhibit the expression of IL-10 mRNA. Additionally, ELISA showed that the serum levels of IFN-γ, TNF-α, IL-12, IL-6, and IL-1β were down-regulated in a TNBS model of colitis. Flow cytometric analysis showed MMF markedly reduced the percentages of Th1 and Th2 splenocytes in the CD mouse model. Mycophenolic acid (MPA) also significantly decreased the percentages of splenic Th1 and Th2 cells in vitro. Furthermore, MMF treatment not only significantly ameliorated diarrhea, and loss of body weight but also abrogated the histopathologic severity and inflammatory response of inflammatory colitis, and increased the survival rate of TNBS-induced colitic mice. These results suggest that treatment with MMF may improve experimental colitis and induce inflammatory response remission of CD by down-regulation of pro-inflammatory cytokines via modulation of the differentiation of Th1/Th2 cells.

A great challenge facing geocomputation and spatial analysis is spatial optimization, given that it involves various high-dimensional, nonlinear, and complicated relationships. Many efforts have been made with regard to this specific issue, and the strong ability of artificial immune system algorithms has been proven in previous studies. However, user-friendly professional software is still unavailable, which is a great impediment to the popularity of artificial immune systems. This paper describes a free, universal tool, named AITSO, which is capable of solving various optimization problems. It provides a series of standard application programming interfaces (APIs) which can (1) assist researchers in the development of their own problem-specific application plugins to solve practical problems and (2) allow the implementation of some advanced immune operators into the platform to improve the performance of an algorithm. As an integrated, flexible, and convenient tool, AITSO contributes to knowledge sharing and practical problem solving. It is therefore believed that it will advance the development and popularity of spatial optimization in geocomputation and spatial analysis.

Many studies have been conducted to evaluate the effects of different urban structures on landscape connectivity, and most of them rely on the comparison approach or ex-ante scenario analysis. However, we still lack an ex-post method to capture the consequences of accomplished urban structure shift (from monocentric to polycentric), which is guided by the land use planning. To fill this gap, we develop an ex-post evaluation approach which integrates counterfactual analysis and landscape graphs. Counterfactual analysis is combined with cellular automata simulation model, to uncover what the city might look like, if it had continued to expand in a monocentric structure; and the landscape graphs enable us to reveal the possible landscape connectivity in actual and counterfactual scenarios. We select Nanjing city as the study area and 4 target species, to delve into the varying impacts of the urban structure shift on different taxonomic groups. Our case study demonstrates that: (1) the impact of urban structure shift is more relevant to the long disperser; (2) the actual landscape (polycentric) would facilitate the bird's dispersal, while (3) forest mammals have higher connectivity in the counterfactual scenario (monocentric), and the possible reasons are discussed. Finally, we demonstrate that the urban structure shift might not necessarily cause the connectivity decline, on condition that the key connectivity providers are identified by integrating ecological network analysis into the land use planning, and well preserved in the shift.

Land use/cover change (LUCC) in the context of rapid urbanization process has exerted profound influences on carbon storage and ecosystem functions. Exploring the relationships between various urbanization patterns and carbon storage is conductive to developing scientific carbon storage polices. This study incorporated climate change and socioeconomic development into the urbanization process, and designed six future urbanization scenarios based on the combination of the Representative Concentration Pathways (RCPs) and the Shared Socio-economic Pathways (SSPs). A hierarchical framework that integrates system dynamic, land-use simulation and carbon storage evaluation models was proposed to predict urban land use change under six concurrent scenarios of climate and socioeconomic conditions, and examine their synergic effects on carbon storage. Hubei Province, a rapidly urbanized area in central China, was selected as a case study. The results show that the decline of carbon storage from 2015 to 2030 ranges from 16.40 Tg to 24.22 Tg under different scenarios, and the scenario featuring steady climate conditions, low population growth, moderate economic growth and high-quality urbanization (e.g., technology innovation) will better maintain carbon storage. Our findings also demonstrate the spatially heterogeneous patterns of carbon storage change at regional scale, and more severe carbon storage loss in the medium-sized cities than the metropolis. This study suggests that targeted ecological conservation strategies should be developed for different cities.

Alternative land use zoning scenarios provide guidance for sustainable land use controls. This study focused on an ecologically vulnerable catchment on the Loess Plateau in China, proposed a novel land use zoning model, and generated alternative zoning solutions to satisfy the various requirements of land use stakeholders and managers. This model combined multiple zoning objectives, i.e., maximum zoning suitability, maximum planning compatibility and maximum spatial compactness, with land use constraints by using goal programming technique, and employed a modified simulated annealing algorithm to search for the optimal zoning solutions. The land use zoning knowledge was incorporated into the initialisation operator and neighbourhood selection strategy of the simulated annealing algorithm to improve its efficiency. The case study indicates that the model is both effective and robust. Five optimal zoning scenarios of the study area were helpful for satisfying the requirements of land use controls in loess hilly regions, e.g., land use intensification, agricultural protection and environmental conservation.

Paeonol (2'-hydroxy-4'-methoxyacetophenone) is the main phenolic compound of the radix of Paeonia suffruticosa which has been used as traditional Chinese medicine. In this study, we primarily investigated the anti-inflammatory effects and the underlying mechanisms of paeonol in RAW macrophage cells; and based on these effects, we assessed the protective effects of paeonol on lipopolysaccharide-induced endotoxemia in mice. The in vitro study showed that paeonol regulated the production of TNF-α, IL-1β, IL-6, and IL-10 via inactivation of IκBα, ERK1/2, JNK, and p38 MAPK. In mouse model of lipopolysaccharide-induced endotoxemia, pro- and anti-inflammatory cytokines are significantly regulated, and thus the survival rates of lipolysaccharide-challenged mice are improved by paeonol (150, 200, or 250 mg/kg). Therefore, paeonol has a beneficial activity against lipopolysaccharide-induced inflammation in RAW 264.7 cell and mouse models.

A variety of studies have been conducted to assess the impact of farmland preservation policies in China. Most of these studies focus on the use of pure statistical approaches to assess the policy impact. A spatially explicit modeling framework is, however, often required to better assess the policy impact and to help understand the consequence of these policies. In this article, we developed such a spatially explicit modeling framework to assess the impact of policies on arable land loss and urban sprawl through the combination of counterfactual analysis with a scenario simulation approach. Counterfactual analysis provides support to assess the impact of farmland preservation policies, while scenario simulation enables us to generate counterfactual outcomes to capture possible land use patterns without policy intervention. With support from cellular automata, farmland preservation policies are integrated into the decision-making processes of land use conversion to link policy with scenario simulation. Our case study demonstrates the potential of the proposed modeling framework for the assessment of farmland preservation policies. Experimental results indicate that farmland preservation policies play an important role in terms of reducing the rate of arable land loss and governing spontaneous urban sprawl.

Land consolidation is the most effective land management approach to optimize land-use structure and improve the natural environment. This study aims to shed light on land consolidation planning processes in China, and to highlight the characteristics and contents of thematic maps in provincial-level planning. The study area, Hubei Province, has an area of 1,85,900 km2 and is located in Central China. The land consolidation potential, that is, the net increase area of arable land was calculated according to the land consolidation types in each county. The spatial characteristics of land consolidation potential, key areas, and key engineering and projects were presented on a scale of 1: 5,00,000 utilizing counties as evaluation units. The thematic maps can provide important information for policymakers and planning workers, and guide any subsequent land consolidation planning at the county level in China.

Support vector machines (SVMs), which are based on statistical learning theory, have recently got considerable potential in data mining by their good ability of generalization. Land grade evaluation, which provides information for land planning and decision-making, is a process of evaluating land quality for a particular use. It can be referred to as multiclass classification problem. As a result of SVMs' binary nature, they can not be directly applied to land grading process. By integrating SVMs with a binary tree, this paper applied a binary tree based SVMs (BTSVMs) approach into land grade evaluation. Arable land in Heping County, Guangdong, was chosen as study area and BTSVMs model was then applied to the data. In addition to BTSVMs, the same data were classified using decision tree (DT) and artificial neural network (ANN). Compared with DT and ANN, results showed that BTSVMs had better classification accuracy. While decreasing the size of training data, the accuracy of each approach dropped down positively with BTSVMs relatively more accurate than others. In general, BTSVMs is potentially feasible in the application of land grade evaluation with its good performance.

With the development of "3S" technologies, a large quantity of spatial-temporal data related to land use has been accessed. Being scattered across different departments and lacking of relevant analysis tools made them utilize insufficiently. Although some experts have applied data mining to solve this problem, most of them have only provided one method for single task to build the mining systems. However, it is undesirable to use just one method to mine. In addition, the single function systems can not be used widely and conveniently. Hence, under full investigation on operations of land use, a multi-source data mining prototype system for land use is proposed by integrating of technologies of GIS and spatial data mining. According to the general data mining process, aiming at the multi-demands of land evaluation and land planning and so on, the system is developed by using ArcEngine 9.0 and VB.net. The system integrates basic geospatial data, land use/cover data, and thematic data as data sources, excavates different knowledge of land Quality, land use zoning rules, land use patterns and change rules and so on. Based on the types of knowledge, the system accordingly provides several different mining methods, including decision tree, support vector machine, artificial neural network, time series, spatial association rules, etc. Wide adaptability of the system is demonstrated by using some cases. The results of the system can meet multipurpose needs and be used to support decision-making of the land management department.

At present, no feasible and effective methods meet the requirements of constructing a comprehensive and representative indicator system for degraded land evaluation, which orients spatial multi-scale and diversity of evaluation objects as well as integrates experts' judgments and objective information. This paper, tying to solve the problem, firstly proposes three propositions on evaluation indicator knowledge base (EIKB), universe evaluation indicator set (UEIS), evaluation indicator subset (EIS) and Mapping Rule of EIS (MREIS), and then constructs an heuristic indicator mining model (HIMM) based on above theories, variable precision rough set and information entropy. Finally, we applied HIMM to practical degraded land evaluation and examined the effectiveness of HIMM. The result shows that HIMM is applicable, especially in the aspect of solving the comprehensive and representative problem in the process of indicator system construction.

Recently land use/cover change detection (LUCC) has become an important aspect in nature resource and environment monitoring and protection. A data fusion method for LUCC is presented concerning the situation that there are only the remote sensing (RS) images of updated period and the land use/cover maps of original period. Firstly, multi-spectral and panchromatic images of SPOT-5 are fused by using principle component analysis (PCA) algorithm on Erdas Imagine platform. Then, after the co-registration of the land use/cover map and RS image, the RS image of the updated period is classified by K-means algorithm and the precise classification chart of the land use/cover is obtained. The land use/cover map is transformed from vector to raster format and the land class code is used as each pixel's value of the transformed raster image. Finally, land use/cover changes are found by comparing the corresponding land class codes. The study area is located in Huangpi District of Wuhan City, and experiments demonstrate the proposed data fusion method for land use/cover change detection is a feasible resolution.