Built-up Land Change Research Articles

Multitemporal Analysis of the Relationship Between Vegetation Density and Built-Up Land to the Urban Heat Island Phenomenon in Cirebon City

Abstract The attractiveness of Cirebon City as a target for urbanization lies in its strategic location and significant development, particularly in tourism, the service industry, and culture. As a result, vegetation has been transformed into urban development, leading to a subsequent rise in land surface temperatures and causing the Urban Heat Island (UHI) phenomenon. UHI is prevalent in urban areas and diminishes in peripheral regions. This research aims to investigate the correlation between vegetation density and built-up areas to the UHI phenomenon in Cirebon City, utilizing Landsat-8 data for the years 2014 and 2023. Normalized Difference Built-Up Index (NDBI) is used to identify built-up areas, vegetation density is identified with the Normalized Difference Vegetation Index (NDVI) algorithm, and Land Surface Temperature (LST) data is obtained from thermal band extraction. The results of the LST analysis were used to describe the Urban UHI phenomenon. Findings throughout the study period revealed significant changes in built-up land and vegetation density, while dynamic changes occurred in LST and UHI. The results also show that there is a positive correlation between the built-up land index and UHI, which indicates a unidirectional relationship. Meanwhile, the vegetation density index to UHI is negatively correlated, which means it shows an inverse relationship.

Study on the Characteristics and Influencing Factors of Land Use Changes in the Metropolitan Fringe Area: The Case of Shenzhen Metropolitan Area in China

With the development of the regional economy, the metropolitan area has gradually shifted from the rapid development stage of concentrating on the central city to the stage of coordinated and integrated regional development. This trend has brought new development opportunities to the metropolitan fringe area; however, due to the differences in resource endowment and the complex relationship between different levels of government, the metropolitan fringe area inevitably has contradictions and imbalances in economic and social development. There has been extensive research on land use and urban governance in the metropolitan area, but less attention has been paid to the metropolitan fringe area, and it is difficult to quantitatively characterize the complex interactions between various forces in this area. This paper summarizes the spatial pattern and spatiotemporal characteristics of construction land use in the fringe area of Shenzhen metropolitan area based on the 30 m resolution land cover dataset from 2000 to 2020, and analyzes the driving factors affecting the changes of construction land use through logistic regression. The results show that the overall land use in the study area is shifting from cropland and forest land to urban and rural construction land. With different stages of development, the rate of land change varies greatly from one period to another. Three factors, population density, lighting index and distance to highway, have a significant correlation with the changes in built-up land across the region, while the boundary effects of administrative boundaries show some variation in the impact of urban land expansion at different economic levels. Finally, we put forward that reducing the negative impact of administrative divisions on the integration of resources in different cities is important for realizing the synergistic development of the Shenzhen metropolitan area.

Spatiotemporal patterns of urban expansion and trade-offs and synergies among ecosystem services in urban agglomerations of China

Urbanization is advancing rapidly across the globe, and urban agglomeration (UA) has become an important by-product of urbanization expansion. The rapid development of UAs has profound impacts on land use and directly affects various regional ecosystem services (ES). Understanding these impacts in UAs with developed economies and fragile ecosystems can help decision-makers formulate ecological protection and land use planning policies. Here, we selected 13 UAs in eastern, central, and western China and analyzed the characteristics of spatiotemporal changes in built-up land and the trade-off and synergy between ES in UAs from 2000 to 2020. We found that the built-up land increased rapidly to the outer edge of a city in the form of edge expansion, which was more prominent in UAs at national and regional scales. Carbon storage, biodiversity conservation, habitat quality, soil and water conservation, and water conservation tended to decrease, but the nutrition delivery ratio and food supply tended to increase. Various ES presented a negative synergistic relationship. The difference in ES in UAs was closely related to the level of urbanization and geographical location. Our findings demonstrate the trade-off and synergy of ES in UAs at different levels and can be used to sustain ES and develop high-quality UAs.

The inter-provincial trade inequality in China: An assessment of the impact of changes in built-up land and carbon storage

Inter-regional trade contributes to the expansion of built-up land and carbon storage loss, which results in inequality in unequal exchange. Revealing this inequality can help socio-environmental sustainability be better achieved. This paper uses a multi-regional input-output model to analyze the virtual built-up land flows embodied in the inter-provincial trade in China, and calculates the carbon storage loss caused by the expansion of built-up land. The inter-provincial trade inequality in China is discussed subsequently. The results show that regional unfair trade exists in China, and less developed regions are in an inferior position. Compared to less developed regions, developed regions obtain more benefits from inter-provincial trade, in both economic and environmental aspects. Regionally, the expansion of built-up land mainly occurs in the eastern and southern regions, which lost much carbon storage, especially Guangdong and Yunnan. Coastal regions had a high built-up land utilization efficiency, followed by the central regions. The built-up land interactions are mainly concentrated in the central and eastern regions, and the degree of trade participation in less developed regions is low.

The Effect of City Development on Temperature in The City of Semarang

Semarang City is one of the most densely populated cities in Central Java Province, which has experienced land conversion due to increased development. The purpose of this study is to analyze the relationship and influence of urban development on temperature in the urban area of Semarang from 1980 to 2020. Population data, land use changes, temperature change data from 1980 to 2020, and remote sensing images were used.The influence of city development and temperature used inferential statistics, which is a method related to data analysis for forecasting or drawing conclusions about the overall data. Regression analysis is one part of inferential statistics that is widely used in the decision-making process. Semarang is experiencing development, both in terms of physical appearance and population growth. The population continues to increase until it almost doubles over a period of 40 years, which is directly proportional to the increase in temperature in the city of Semarang. The results of the analysis show that the development of the city seen from changes in population and changes in built-up land has an effect on temperature changes in the city of Semarang by up to 69.9%. The spatial distribution of temperature changes shows the direction of the center of Semarang City activities in the Districts of Central Semarang, North Semarang, East Semarang, South Semarang, and Gajah Mungkur, which is also in accordance with the condition of the area, which is also a residential area and the city center.

Comprehensive assessment of land use carbon emissions of a coal resource-based city, China

Carbon emissions reduction is crucial for global sustainable development. While few studies pay attention to land use carbon emissions (LUCEs) in coal-based cities. We firstly used the emission factor method proposed by IPCC to estimate the carbon sources and carbon sinks of land use in Huainan, a typical city of China, from 1991 to 2020. Then we used Logarithmic mean Divisia index (LMDI) model and Tapio decoupling elasticity to identify the driving factors of land use carbon emission changes and analyze their intrinsic relationship with economic growth of Huainan. The results revealed that the total LUCEs in Huainan increased significantly, and the carbon sources from land use were always higher than the carbon sinks. It also showed that economic development and built-up land change were the main driving factors of LUCEs growth in Huainan. Moreover, LUCEs had a synergistic relationship with the decoupling elasticity of economic growth in different development stages of the coal-based city. Therefore, we predicted LUCEs in Huainan City from 2020 to 2035 under three scenarios. Finally, we proposed some recommendations to reduce the LUCEs and develop the low-carbon economy. We hope to not only extend the research of land use carbon emissions on coal-based city level, but also promote the low-carbon development of coal-based cities in China.

PENGARUH INFRASTRUKTUR JALAN TOL TERHADAP PENGGUNAAN LAHAN TERBANGUN DAN KONDISI SOSIAL EKONOMI DI KAWASAN METROPOLITAN PATUNGRAYA AGUNG

The development of the metropolitan area is in line with the development of toll road infrastructure. This has implications for changes in the use of built-up land, population and economy. The areas most affected by the construction are located around the entrances and exits of the toll gates. However, along with changes in the use of built-up land that continues to grow, the amount of land available is limited. So that it can cause a mismatch of land use designation with the designation plan. Based on these problems, it is necessary to study the effect of toll road infrastructure development on changes in built-up land use, population and economy. The results showed that there was an increase in built-up land covering an area of ​​3,148.31 Ha, population of 273,997 people and in several PDRB sectors. Meanwhile, based on the results of changes in built-up land against the suitability of the South Sumatra Province Spatial Planning there are only 75.69% of the area of ​​built-up land that is in accordance with its designation. It is necessary to evaluate changes in built up land in Patungraya Agung Metropolitan Area so that land changes are built in accordance with efficient spatial planning.

Characteristics of built-up land change in resource-based cities and their impact on land surface temperature—Taking Wu'an as an example

The effects of built-up land on land surface temperature (LST) and its spatial patterns remain poorly understood over resource-based cities. Using Landsat-5,8 satellite imagery of Wu'an, a resource-based city in China, from 2001, 2007, and 2015, this paper classified the land use types and calculated the percentage of built-up land (PLAND). Changes in built-up land and their effects on the distribution of LST were analyzed through statistics and regression analysis. Statistical results showed that the amount of built-up land in 2007 and 2015 increased significantly compared with 2001. The same PLAND level increased LST more in 2007 and 2015. Furthermore, PLAND = 35% split the heating effects of PLAND on LST. The LST in areas with high PLAND (PLAND >35%) was mainly impacted by the local built-up land. However, the LST in the areas with low PLAND (PLAND <35%) was mainly correlated with the built-up land of areas with high PLAND. The results of regression analysis showed that the k value (slope of the correlation analysis) in 2007 and 2015 was larger than that in 2001, indicating the local heating effects of built-up land on LST. The spatial regression analysis results revealed that there was a closer spatial correlation between PLAND and LST in 2015, and built-up land with high PLAND (PLAND >35%) would increase the LST of surrounding built-up land with low PLAND (PLAND <35%). These research results could provide more references for urban expansion in urban design and planning.

Analysis on Land-Use Change and Its Driving Mechanism in Xilingol, China, during 2000–2020 Using the Google Earth Engine

Large-scale, long time-series, and high-precision land-use mapping is the basis for assessing the evolution and sustainability of ecosystems in Xilingol, the Inner Mongolia Autonomous Region, China. Based on Google Earth Engine (GEE) and Landsat satellite remote-sensing images, the random forest (RF) classification algorithm was applied to create a yearly land-use/land-cover change (LULC) dataset in Xilingol during the past 20 years (2000–2020) and to examine the spatiotemporal characteristics, dynamic changes, and driving mechanisms of LULC using principal component analysis and multiple linear stepwise regression methods. The main findings are summarized as follows. (1) The RF classification algorithm supported by the GEE platform enables fast and accurate acquisition of the LULC dataset, and the overall accuracy is 0.88 ± 0.01. (2) The ecological condition across Xilingol has improved significantly in the last 20 years (2000–2020), and the area of vegetation (grassland and woodland) has increased. Specifically, the area of high-coverage grass and woodland increases (+13.26%, +1.19%), while the area of water and moderate- and low-coverage grass decreases (−15.96%, −7.23%, and −3.27%). Cropland increases first and then decreases (−34.85%) and is mainly distributed in the southeast. The area of deserted land decreases in the south and increases in the center and north, but the total area still decreases (−13.74%). The built-up land expands rapidly (+108.45%). (3) In addition, our results suggest that regional socioeconomic development factors are the primary causes of changes in built-up land, and climate-related factors are the primary causes of water changes, but the correlations between other land-use types and relevant factors are not significant (cropland and grassland). We conclude that the GEE+RF method is capable of automated, long time-series, and high-accuracy land-use mapping, and further changes in climatic, environmental, and socioeconomic development factors, i.e., climate warming and rotational grazing, might have significant implications on regional land surface morphology and landscape dynamics.

ANALISIS PERKEMBANGAN DAN FAKTOR-FAKTOR YANG MEMPENGARUHI LAHAN TERBANGUN DI KOTA SEMARANG

Development of a city is something that cannot be avoided and will continue to happen dynamically from time to time. Along with the development of various urban activities, where the population as a means of implementation has a direct impact on the increasing demand for land. Semarang City, which is the capital of Central Java Province, is also one of the centers for the growth of trade and services, education and service centers for other activities, which have developed very rapidly. This is what stimulates the flow of urbanization in the city of Semarang with the aim of getting a more decent life which will have an impact on the development of built-up land. The purpose of this study is to identify developments and factors that affect built-up land in the city of Semarang as an effort to monitor and anticipate to prevent unwanted negative impacts in the future. Through secondary data collection and continued with analysis using the Supervised Classification method, the results showed that there was a change in land use into built-up land in Semarang City from 1999-2019 covering an area of 11,403.46 Ha or an increase of 29.36%. Based on the results of the analysis of the level of development of built-up land using the quantitative method, it was found that three sub-districts with the category of very high development of built-up land in Semarang City were Ngaliyan District, Mijen District and Tembalang District. Based on multiple linear regression analysis, the equation Y = 20.453 + 0.007X1 + 38.425X2 - 6.775X3 + 3.170X4 and has an R² of 0.835. This means that the independent variables (independent) namely population (X1), distance to the city center (X2), land value (X3) and public facilities (X4) have an influence of 83.5% on changes in built-up land while the remaining 0.165 or 16.5% influenced by other variables not explained in this study.

Remote Sensing in Predicting Development Patterns of Built-up Land

Indonesia is a developing country whose construction and development are centered on the island of Java, especially in West Java Province. Sukabumi City is one of the areas in West Java. The development of urban areas is expanding due to various human needs to carry out the construction of buildings. Remote sensing that can be used to store developments with multi-temporal analysis with materials is Landsat imagery from 2001 to 2020. The method used is the Normalized Difference Built-up Index (NDBI). The purpose of this study is to map the development of the built-up land from year to year and predict the following years. The results of the research on the significant changes in built-up land occurred between 2013-2020, while from 2001 to 2013 there was not much change. Based on the research results, the total growth of built-up land was 1.539% per year with a population growth rate of 1.4% per year. The results of the analysis show that the area of ​​land built in Sukabumi City in 2028 is 186,7194 km2 or has increased by 21,2808 km2 since 2020.

Global trends and local variations in land take per person

Globally, urban areas are growing at a faster rate than their population, potentially reducing environmental sustainability due to undesirable land take in (semi)natural and agricultural lands. However, it is unclear to what extent this trend varies locally, which may hamper the formulation and implementation of local-scale policies in the context of the global competition for land. Here, we attribute built-up land change to population dynamics and changes in land take per person, for >75,000 administrative regions worldwide, typically representing municipalities or counties. Results show that changes in land take per person, expressed as the area of built-up land per capita, relate to 38.3%, 49.6%, and 37.5% of the total increase in built-up land during the periods 1975–1990, 1990–2000, and 2000–2015, respectively, but with large local variations. Interestingly, we find that centres of large cities densify in all three periods, while their rural areas show an opposite development, suggesting an urban polarization effect. We also find densification in many regions in the Global South that already have a high population density, leading to potential trade-offs in terms of human wellbeing. Therefore, our work provides novel insights into the debate on sustainable urban development at a global scale.

Measurement of Urban–Rural Integration Level in Suburbs and Exurbs of Big Cities Based on Land-Use Change in Inland China: Chengdu

Urban growth and development can be interpreted as a combined process of “urban spillover” and “local urban sprawl”, from overall urban–rural development to urban–rural integration (URI). The process of suburban development in western China is a complex system, which reflects the characteristics of industrialization and urbanization in western China. Chengdu is the most representative of the big cities for economic and social structure change in western China. To analyze the changes on URI degree based on the built-up land change, and to explore the practical URI paths in both the suburbs and exurbs of Chengdu, we use land-use remote-sensing monitoring data from 2000, 2005, 2010, and 2015 in this paper, whereafter URI indexes are built from space, economy, and society. The land-use change of the suburbs and exurbs of Chengdu from 2000 to 2015 are analyzed by ArcGIS. Results indicate that the biggest growth part of built-up land is other built-up land, followed by commercial/industrial land, and the last is residential land. The built-up land spreads quickly from 2000 to 2005, and shows distinct separation characteristics in the suburbs of Chengdu. It is relatively slow in the exurbs. Moreover, built-up land connects better in the suburbs than in the exurbs. Based on the change of built-up land in Chengdu from 2000 to 2015, spatial integration data are calculated, economic integration and social integration data are chosen from statistics, and the change of URI levels in the suburbs and exurbs of Chengdu is calculated. The results show that first, economic integration and social integration have great influence on URI, and their effects are increasing. The significance of spatial integration in URI has gradually reduced. Second, URI levels in counties of the suburbs and exurbs of Chengdu rose from 2000 to 2015, more highly in the suburbs than in the exurbs. URI in counties of the exurbs showed a marked difference. URI in the southeast counties of the exurbs is generally high. The foundation of URI is weaker in the counties and districts in the southwest counties of the exurbs in Chengdu, but it is growing steadily in URI, and the northern counties in the exurbs of Chengdu are in the process of rapid URI. The paths of URI in the suburbs and exurbs in Chengdu can be roughly divided into an industry-developing model in the suburbs, service-industry-developing model in the suburbs, agriculture-developing model in the exurbs, service-industry-developing model in the exurbs and infrastructure-developing model in the exurbs.

Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine

Island ecosystems are particularly susceptible to climate change and human activities. The change of land use and land cover (LULC) has considerable impacts on island ecosystems, and there is a critical need for a free and open-source tool for detecting land cover fluctuations and spatial distribution. This study used Google Earth Engine (GEE) to explore land cover classification and the spatial pattern of major land cover change from 1990 to 2019 on Haitan Island, China. The land cover classification was performed using multiple spectral bands (RGB, NIR, SWIR), vegetation indices (NDVI, NDBI, MNDWI), and tasseled cap transformation of Landsat images based on the random forest supervised algorithm. The major land cover conversion processes (transfer to and from) between 1990 and 2019 were analyzed in detail for the years of 1990, 2000, 2007, and 2019, and the overall accuracies ranged from 88.43% to 91.08%, while the Kappa coefficients varied from 0.86 to 0.90. During 1990–2019, other land, cultivated land, sandy land, and water area decreased by 30.70%, 13.63%, 3.76%, and 0.95%, respectively, while forest and built-up land increased by 30.94% and 16.20% of the study area, respectively. The predominant land cover was other land (34.49%) and cultivated land (26.80%) in 1990, which transitioned to forest land (53.57%) and built-up land (23.07%) in 2019. Reforestation, cultivated land reduction, and built-up land expansion were the major land cover change processes on Haitan Island. The spatial pattern of forest, cultivated land, and built-up land change is mainly explained by the implementation of a ‘Grain for Green Project’ and ‘Comprehensive Pilot Zone’ policy on Haitan Island. Policy and human activities are the major drivers for land use change, including reforestation, population growth, and economic development. This study is unique because it demonstrates the use of GEE for continuous monitoring of the impact of reforestation efforts and urbanization in an island environment.

Delineation of built-up land change from SAR stack by analysing the coefficient of variation

One main challenge in detecting built-up land cover changes using synthetic aperture radar (SAR) instruments is that complicated backscattering behaviours and the superimposition of speckles on rich textures cause a large number of false alarms. Using trajectory-based analyses from time-series SAR imagery can mitigate false alarms since the temporal variability in backscattering during construction improves discrimination capability. This paper presents an approach towards the detection of built-up land change based on a single-channel SAR stack. The proposed methodology includes the generation of a change indicator, the Markov modelling procedure and the delineation of changes over built-up areas. The generation of the change indicator aims to provide a feature with abundant contrast between changed and stable areas, a high signal-to-noise ratio and detail preservation. To this end, all temporal information is converted into a map of the coefficient of variation. After error removal, this change detector is combined with a Markov random field (MRF) criterion function. Rather than MRF modelling by iteration with very complex stochastic models, we propose using SAR temporal trajectory under a hypothesis test framework and interferometric coherence series to establish conditional density for each class. Then, the Graph-cuts theory is applied to delineate the boundary between changed and stable areas, followed by a binary classification procedure based on speckle divergence to exclude natural areas. The technique is tested on both synthetic data and two TerraSAR-X datasets covering representative areas with rich texture. We found that in a complex built environment that is challenging for classical change indicators and state-of-the-art techniques, the presented method can provide smaller overall error with better detail preservation.

Spatial Forecasting of the Landscape in Rapidly Urbanizing Hill Stations of South Asia: A Case Study of Nuwara Eliya, Sri Lanka (1996–2037)

Forecasting landscape changes is vital for developing and implementing sustainable urban planning. Presently, apart from lowland coastal cities, mountain cities (i.e., hill stations) are also facing the negative impacts of rapid urbanization due to their economic and social importance. However, few studies are addressing urban landscape changes in hill stations in Asia. This study aims to examine and forecast landscape changes in the rapidly urbanizing hill station of Nuwara Eliya, Sri Lanka. Landsat data and geospatial techniques including support vector machines, urban–rural gradient, and statistical analysis were used to map and examine the land use/land cover (LULC) change in Nuwara Eliya during the 1996–2006 and 2006–2017 periods. The multilayer perceptron neural network-Markov model was applied to simulate future LULC changes for 2027 and 2037. The results show that Nuwara Eliya has been directly affected by rapid urban development. During the past 21 years (1996–2017), built-up areas increased by 1791 ha while agricultural land declined by 1919 ha due to augmented urban development pressure. The pressure of urban development on forest land has been relatively low, mainly due to strict conservation government policies. The results further show that the observed landscape changes will continue in a similar pattern in the future, confirming a significant increase and decrease of built-up and agricultural land, respectively, from 2017 to 2037. The changes in agricultural land exhibit a strong negative relationship with the changes in built-up land along the urban–rural gradient (R2 were 0.86 in 1996–2006, and 0.93 in 2006–2017, respectively). The observed LULC changes could negatively affect the production of unique upcountry agricultural products such as exotic vegetables, fruits, cut flowers, and world-famous Ceylon tea. Further, unplanned development could cause several environmental issues. The study is important for understanding future LULC changes and suggesting necessary remedial measures to minimize possible undesirable environmental and socioeconomic impacts.

Mapping annual land use changes in China's poverty-stricken areas from 2013 to 2018

China aims to end absolute poverty by 2020. In pursuit of this goal, a series of poverty reduction policies and measures have been proposed. As a vital element of poverty reduction, land use in China's poverty-stricken areas also undergone great changes accordingly. However, the land use change patterns in these areas are not well understood. It's necessary to analyze the spatial-temporal land use change patterns to provide data that support poverty alleviation programs. In this study, we proposed a framework for mapping annual land use changes in China's poverty-stricken areas from 2013 to 2018. The Landsat 8 surface reflectance datasets from 2013 to 2018 (available on Google Earth Engine) were utilized to detect the changes in arable land, built-up land, water, vegetation, and un-used land. The land use transition matrix was computed to describe characteristics of the transition, and a Bayesian hierarchical model was employed to investigate the spatial-temporal land use change patterns. Our results demonstrated that the arable land continuously decreased over the study period, while built-up land and vegetation gradually expanded. The primary land use transition occurred between the arable land and vegetation. The local trends of each county indicated obvious regional differences of land use change. Moreover, significant differences existed between deep poverty-stricken counties and normal poverty-stricken counties on arable land and built-up land change, indicating that more intense human construction activities in normal poverty-stricken areas. The annual land use mapping results generated for poverty-stricken areas, along with further analysis of overall temporal change and local change trends, could provide a better understanding of land use changes and regional differences in China's poverty-stricken areas and promote the poverty reduction and sustainable development in those areas.

Towards a novel backdating strategy for creating built-up land time series data using contemporary spatial constraints

For retrospective land cover change analysis extending back in time earlier than approximately the year 2000, multispectral remote sensing data such as the Landsat archive are often the only available and systematically archived data source. However, the spatial resolution of such observations often impedes the change analysis of geographic phenomena that may occur at sub-pixel level, such as changes in built-up land. In such cases, the integration of these data products with increasingly available contemporary high resolution settlement data employed as spatial constraints can potentially mitigate this drawback. Little research has been done regarding the quantitative potential of such integrative approaches, often due to the lack of multi-temporal reference data about changes in built-up area at sufficient spatio-temporal granularity and extent. In this contribution, we present and evaluate a time-series based approach for built-up area change analysis using Landsat time series data, spatially constrained by contemporary building footprint data. We evaluate the potential of our approach using a highly accurate multi-temporal reference database of built-up areas created through integrating publicly available building footprint and cadastral data in selected regions of the United States of America.

The influence of landscape alterations on changes in ground beetle (Carabidae) and spider (Araneae) functional groups between 1995 and 2013 in an urban fringe of China

Urbanization is one of the main causes of land use change, especially from 1990 to now in China, but knowledge of its effect on different functional groups of carabids and spiders in the adjacent rural areas over time remains limited. We assessed whether landscape alterations (1993 versus 2013) drove changes in carabid and spider functional groups (1995 versus 2013) in an agricultural landscape located on the fringe of a rapidly growing city in China. Although built-up land increased from 6.3% to 32% across the whole landscape, the overall species richness of carabids and spiders did not decline. In contrast to the reduction in species richness of large carabids, the species richness of small carabids increased. Species richness of both large and small spiders increased. The species composition of carabids and spiders significantly changed between 1995 and 2013. Species compositions of large, predatory carabids and large or ground-hunting spiders were more sensitive to the changes in built-up land than those of small, omnivorous carabids and small or web-building spiders. The amount of grassland (abandoned land covered by wild grass) also increased as farmers began to work in the city. The increased grassland significantly contributed to the increased species richness of predatory and macropterous carabids. However, increased landscape diversity did not affect species richness of either carabids or spiders. High landscape diversity was related to reduction in field size, resulting in a decrease in the mean body size of carabids. This indicates that evaluating the effect of landscape change on carabid and spider diversity should be based on their functional traits. Different taxa, even different functional groups, have different responses to landscape change. The increase in built-up land did not immediately reduce species richness at the urban fringe. Increasing wild grasslands and combining smaller fields may benefit farmland biodiversity in this region.

Beyond the urban-rural dichotomy: Towards a more nuanced analysis of changes in built-up land

Urban land and rural land are typically represented as homogenous and mutually exclusive classes in land change analyses. As a result, differences in urban land use intensity, as well as mosaic landscapes combining urban and rural land uses are not represented. In this study we explore the distribution of urban land and urban land use intensity in Europe and the changes therein. Specifically, we analyze the distribution of built-up land within pixels of 1 km2. At that resolution we find that most built-up land is distributed over predominantly non-built-up pixels. Consistently, we find that most urban land use changes between 2000 and 2014 come in small incremental changes, rather than sudden large-scale conversions from rural to urban land. Using urban population densities, we find that urban land use intensity varies strongly across 1 km2 pixels in Europe, as illustrated by a coefficient of variation of 85%. We found a similarly high variation between urban population densities for most individual countries and within areas with the same share of built-up land. Population changes have led to different combinations of urban land expansion and urban intensity changes in different study periods (1975–1990, 1990–2000, and 2000–2015) and countries. These findings suggest that land use change models could be improved by more nuanced representations of urban land, including mosaic classes and different urban land use intensities.