Large detail scale mapping from aerial orthophotos for understanding local-scale urban land use dynamics. The case of Madrid, Spain

The Greater Mekong Subregion (GMS) has experienced rapid economic growth and urbanization. However, few studies have paid attention to urban land use dynamics, especially spatiotemporal patterns of urban expansion and land use change, in this region. This research aimed to conduct a comprehensive study of urban land use change in Xishuangbanna, Yangon, Vientiane, Phnom Penh, Bangkok, and Ho Chi Minh City, from 1990 to 2015. The analysis was based on land use maps derived from Landsat satellite products and employed urban expansion intensity, sector analysis, gradient-direction analysis, and landscape metrics. The results show Xishuangbanna, Yangon, Vientiane, Phnom Penh, Bangkok, and Ho Chi Minh City all experienced dramatic urban expansion and land use change since 1990, with urban expansion intensities of 15.01, 5.26, 9.15, 1.56, 11.88 and 11.91, respectively. The landscape metrics analysis indicated that urban areas were always aggregated and self-connected, while other land use types showed trends of disaggregation and fragmentation. In the process of urban expansion, paddy and natural land use types were commonly transformed to built up area. The results further reveal several common issues in urban land use, e.g., land fragmentation and loss of natural land use types. Finally, the discussion on the relationship between government policy and land use change for these cities shows land reform and attitude toward foreign direct investments played important roles in urban land use change in GMS.

The extent of anthropogenic land use in watersheds determines the amount of pollutants discharged to streams. This indirectly and directly affects stream water quality and biological health. Most studies have therefore focused on ways to reduce non-point pollution sources to streams from the surrounding land use in watersheds. However, the mechanistic pathways between land use and the deterioration of stream water quality and biological assemblages remain unclear. This study estimated a structural equation model (SEM) representing the impact of agricultural and urban land use on water quality and the benthic macroinvertebrate index (BMI) using IBM AMOS in the Nam-Han river systems, South Korea. The estimated SEM showed that the percent of urban and agricultural land in the watersheds significantly affected both the water quality and the BMI of the streams. Specifically, a higher percent of urban land use had directly increased the biochemical oxygen demand (BOD) and total phosphorus (TP), and deteriorated the BMI of streams. Similarly, higher proportions of agricultural land use had also directly increased the BOD, total nitrogen (TN), and total phosphorus (TP) concentrations, and lowered the BMI of streams. In addition, it was observed that the percent of urban and agricultural land use had indirectly deteriorated the BMI through increased BOD. However, we were not able to observe any significant indirect effect of the percent of urban and agricultural land use through increased nutrients including TN and TP. These results indicate that increased urban and agricultural land use in the watersheds had directly and indirectly affected the physicochemical characteristics and benthic macroinvertebrate communities in streams. Our findings emphasize the need to develop more elaborate environmental management and restoration strategies to improve the water quality and biological status of streams.

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

Change in urban construction land use is an important factor when studying urban expansion. Many scholars have combined cellular automata (CA) with data mining algorithms to perform relevant simulation studies. However, the parameters for rule extraction are difficult to determine and the rules are simplex, and together, these factors tend to introduce excessive fitting problems and low modeling accuracy. In this paper, we propose a method to extract the transformation rules for a CA model based on the Classification and Regression Tree (CART). In this method, CART is used to extract the transformation rules for the CA. This method first adopts the CART decision tree using the bootstrap algorithm to mine the rules from the urban land use while considering the factors that impact the geographic spatial variables in the CART regression procedure. The weights of individual impact factors are calculated to generate a logistic regression function that reflects the change in urban construction land use. Finally, a CA model is constructed to simulate and predict urban construction land expansion. The urban area of Xinyang City in China is used as an example for this experimental research. After removing the spatial invariant region, the overall simulation accuracy is 81.38% and the kappa coefficient is 0.73. The results indicate that by using the CART decision tree to train the impact factor weights and extract the rules, it can effectively increase the simulation accuracy of the CA model. From convenience and accuracy perspectives for rule extraction, the structure of the CART decision tree is clear, and it is very suitable for obtaining the cellular rules. The CART-CA model has a relatively high simulation accuracy in modeling urban construction land use expansion, it provides reliable results, and is suitable for use as a scientific reference for urban construction land use expansion.

Intensive land use assessment is a key research topic in urban land use, and most of the existing studies focus on macro-level assessment. There is a lack of research on the micro-level assessment of intensive urban land use, especially at the parcel level. The objective of this research is to propose a method for the parcel-based evaluation of urban commercial land intensification. The study uses a multidimensional evaluation framework and index system, comprehensive evaluation, and spatially exploratory analysis of urban commercial intensive land use based on “building intensity, use efficiency, compatibility, and diversity”. The study finds that (1) the average value of intensive use of urban commercial land is 13.01, the standard deviation is 5.11, and the median value is 13, which generally indicate a medium level. (2) The degree of intensive use of commercial land has obvious characteristics of a high, medium, and low level. The study shows that when evaluating the degree of land use intensification at the parcel level, it is also necessary to consider the influence of the compatibility and diversity of external land use. The research results can provide a basis for spatial planning and the optimal design of urban land resources to improve urban vitality.

China’s urban land use has shifted from incremental expansion to inventory eradication. The traditional extensive management mode is difficult to maintain, and the fundamental solution is to improve land use efficiency. Xi’an, the largest central city in Western China, was selected as the research area. The super-efficiency data envelopment analysis (DEA) model and Malmquist index method were used to measure the land use efficiency of each district and county in the city from the micro perspective, and the spatial-temporal change characteristics and main influencing factors of land use efficiency were analyzed, which not only made up for the research content of urban land use efficiency in China’s underdeveloped areas, but also pointed out the emphasis and direction for the improvement of urban land use efficiency. The results showed that: (1) The land use efficiency of Xi’an reflected the land use intensive level of the underdeveloped areas in Western China, that is, the overall intensive level was not high, the gap between the urban internal land use efficiency was large, the land use efficiency of the old urban area and the mature built-up area was relatively high, and the land use efficiency of the emerging expansion area and the edge area was relatively low. (2) Like the eastern economically developed areas, the land use efficiency of western economically underdeveloped areas was generally on the rise, while Xi’an showed the U-shaped upward evolution characteristics, and there were four types of changes in the city, that is, highly intensive, medium intensive, high–medium–low-intensive, and intensive–extensive. (3) Various cities should configure resources and optimize mechanism to improve their land use efficiency based on economic and social development. During the study period, Xi’an showed the law of evolution from the south edge area and the emerging expansion area to the main urban area. (4) The improvement of technological progress was the main contribution factor of the land use efficiency in underdeveloped areas of China, and the low-scale efficiency was the main influence factor that caused low land use efficiency. In future urban land use, efforts should be made to optimize and upgrade technology and strictly control the extensive use of land.

Analysis on coupling relationship of urban scale and intensive use of land in China

Detailed and up-to-date information on urban land use plays a key role in understanding urban environment, evaluating urban planning and promoting the development of sustainable cities and communities. Recent years have witnessed many efforts dedicated to developing effective land use classification methods and generating products at different scales. Nevertheless, an accurate and fine-grained delineation of parcel-level urban land use for the entire China is still lacking. In this study, we developed a novel urban land use mapping framework to identify accurate land use categories by integrating multimodal deep learning model and multisource geospatial data. With complete and precise land parcels generated by road networks from two public source as minimum classification units, we produced a nationwide Urban Essential Land Use Categories (EULUC) map covering all cities in China for 2022, named as EULUC 2.0. The mapping results show that residential, industrial and park and greenspaces are the dominant land use categories across China, collectively accounting for nearly 80% of the urban area. The spatially explicit information provided by EULUC 2.0 can reveal distinct spatial patterns of the heterogeneous land use landscape in each city. The evaluation results found the overall accuracies of Level-I and Level-II classification could be as high as 72% and 79%, with substantial improvements across all categories over previous product. The advancements can be mainly attributed to the effectiveness of deep learning for multi-modal input, especially the graph modeling of Point-of-interest (POI) data. The free-access product and insights in this study can potentially help researcher and practitioners to investigate and address the pressing urban challenges in the process of urbanization.

The dynamic distribution of urban population density and the interaction with land use elements involve mutual constraints and guidance. However, in the existing research on the relationship between urban population density and land use, the discussion on the distribution patterns of urban population density typically spans long time periods and uses large spatial units, lacking analysis of the dynamic changes in population density within high granularity land parcels over a day. In studies related to the urban built environment, the complex relationships between different-dimensional land use elements and the dynamic distribution of population density also need further exploration. To address these bottlenecks, this study takes Shanghai’s central urban area as an example. Based on 24 h mobile signaling data on weekdays, weekends, and typical holidays, as well as urban land use data, clustering algorithms are used to summarize patterns of dynamic population density distribution. Pearson correlation analysis is then employed to study the correlation between dynamic population density distribution patterns and different land use elements. The results indicate that various urban land use factors such as locational centrality, functional diversity, transportation accessibility, compactness, and landscape quality have different impacts on the dynamic distribution of population density in spatial units, and the dynamic distribution patterns of population density in different land use types also vary. This research contributes to guiding the optimization of spatial quality and formulating planning and management measures that more effectively match construction intensity with population activity density.

Analyzing the spatiotemporal characteristics and driving mechanisms of the coupling coordination between the Green Transition of Urban Land Use and urban land use efficiency can help explore the future development direction of sustainable land use in cities. This paper constructs a theoretical framework for the coupling coordination between Green Transition of Urban Land Use and urban land use efficiency. We use several models, including the super-efficiency slack-based model, the coupling coordination degree model, the non-parametric kernel density estimation method, exploratory spatial data analysis, and the geographically and temporally weighted regression model to examine the real level of Green Transition of Urban Land Use and urban land use efficiency in the Yangtze River Delta region from 2003 to 2020. Based on this, we investigate the spatiotemporal evolution characteristics and driving mechanisms of the two coupling coordination processes. The study found that (1) from 2003 to 2020, the overall trend of the coupling coordination between Green Transition of Urban Land Use and urban land use efficiency in the Yangtze River Delta region tended to be coordinated and developed, but still at a primary coordination level, with sufficient room for improvement in the future. (2) The coupling coordination level of each city in the Yangtze River Delta region from 2003 to 2020 showed obvious spatial non-equilibrium and correlation characteristics, and overall dynamic polarization effects were exhibited during the study period; the spatial pattern of high-value areas showed a regularity of prioritizing Shanghai and Zhejiang Province, gradually penetrating into Jiangsu Province and Anhui Province. (3) Economic and social factors have a positive influence on the degree of coupling coordination; natural factors and policy factors have a predominantly negative influence on the degree of coupling coordination. Research conclusions include establishing a regional collaborative development mechanism, utilizing the spatial spillover effect of leading cities; emphasizing science, education, and culture, strengthening the introduction of scientific and technological talents, increasing fiscal inputs, raising the level of economic development, and further expanding the driving effect of economic and social factors; and optimizing the layout of urban and rural construction land, developing urban land in an orderly manner, appropriately strengthening environmental regulation, thereby suppressing the negative effects caused by natural and policy factors.

Empirical assessment of effects of urbanization on event flow hydrology in watersheds of Canada’s Great Lakes-St Lawrence basin

It is common to see urban land expansion worldwide, and its characteristics, mechanisms, and effects are widely known. As socio-economic transition and the change of land use policies may reverse the trend of urban expansion, in-depth research on the process of urban land use change is required. Through a process perspective, this paper reveals the change paths, development stages, and spatial patterns of urban residential land use with data from 323 cities in China from 2009 to 2016. The results show that: (1) theoretically, urban residential land use change can be divided into four development stages: an initial stage (Ⅰ), a rapid development stage (Ⅱ), a transition stage (Ⅲ), and a later stage of transition (Ⅳ). The rate of land use change is low—increase—decrease—approaching zero. (2) In about 68.7% of China’s cities, urban residential land is experiencing a transition, shifting from accelerating growth to decelerating growth. Given the distinctive transition process, it has been suggested that urban planning and land use policies should give timely responses to the new trends and spatial differences.

We explored land use, fish assemblage structure, and stream habitat associations in 20 catchments in Opequon Creek watershed, West Virginia. The purpose was to determine the relative importance of urban and agriculture land use on stream biotic integrity, and to evaluate the spatial scale (i.e., whole-catchment vs riparian buffer) at which land use effects were most pronounced. We found that index of biological integrity (IBI) scores were strongly associated with extent of urban land use in individual catchments. Sites that received ratings of poor or very poor based on IBI scores had > 7% of urban land use in their respective catchments. Habitat correlations suggested that urban land use disrupted flow regime, reduced water quality, and altered stream channels. In contrast, we found no meaningful relationship between agricultural land use and IBI at either whole-catchment or riparian scales despite strong correlations between percent agriculture and several important stream habitat measures, including nitrate concentrations, proportion of fine sediments in riffles, and the abundance of fish cover. We also found that variation in gradient (channel slope) influenced responses of fish assemblages to land use. Urban land use was more disruptive to biological integrity in catchments with steeper channel slopes. Based on comparisons of our results in the topographically diverse Opequon Creek watershed with results from watersheds in flatter terrains, we hypothesize that the potential for riparian forests to mitigate effects of deleterious land uses in upland portions of the watershed is inversely related to gradient.

Urban sector land use metabolism reveals inequalities across cities and inverse virtual land flows

The accessibility provided by the transportation system plays an essential role in driving urban growth and urban functional land use changes. Conventional studies on land use simulation usually simplified the accessibility as proximities and adopted the grid-based simulation strategy, leading to the insufficiencies of characterizing spatial geometry of land parcels and simulating subtle land use changes among urban functional types. To overcome these limitations, an Accessibility-interacted Vector-based Cellular Automata (A-VCA) model was proposed for the better simulation of realistic land use change among different urban functional types. The accessibility at both local and zonal scales derived from actual travel time data was considered as a key driver of fine-scale urban land use changes and was integrated into the vector-based CA simulation process. The proposed A-VCA model was tested through the simulation of urban land use changes in the City of Toronto, Canada, during 2012–2016. A vector-based CA without considering the driving factor of accessibility (VCA) and a popular grid-based CA model (Future Land Use Simulation, FLUS) were also implemented for comparisons. The simulation results reveal that the proposed A-VCA model is capable of simulating fine-scale urban land use changes with satisfactory accuracy and good morphological feature (kappa = 0.907, figure of merit = 0.283, and cumulative producer’s accuracy = 72.83% ± 1.535%). The comparison also shows significant outperformance of the A-VCA model against the VCA and FLUS models, suggesting the effectiveness of the accessibility-interactive mechanism and vector-based simulation strategy. The proposed model provides new tools for a better simulation of fine-scale land use changes and can be used in assisting the formulation of urban and transportation planning.