Urban Landscape Pattern Research Articles

Correlation analysis of landscape patterns and urban thermal environment in Kunming based on a panel data model

With the development of urbanization, the spatial and temporal distribution characteristics of the urban landscape pattern play a decisive role in the intensity of the urban heat island (UHI) effect. A panel data model was constructed to study the relationship between the UHI effect and landscape pattern in Kunming from 1995 to 2020 at four different scales. The results indicate: (1) The landscape pattern of Kunming changed obviously with time, the artificial patch increased, the natural patch decreased, and the UHI effect became more and more obvious; (2) With an increase in scale, the number of influencing variables continues to grow, and the impact of artificial patches gradually intensifies; (3) The normalized difference water index (NDWI) has the greatest influence on the UHI, and the cooling effect is more obvious with the increase in scale. Unlike previous studies, the spatial configuration of the landscape in Kunming City had a stronger effect on the UHI effect than the landscape grouping and vegetation index; (4) This paper introduces the panel data model into the discussion of the UHI for the first time, providing a new method for better understanding the changing patterns of the urban thermal environment.

Scale-specific effects of urban landscape pattern on the COVID-19 epidemic in Hangzhou, China

ContextUnderstanding the scale-specific effects of different landscape variables on the COVID-19 epidemics is critical for developing the precise and effective prevention and control strategies within urban areas.ObjectiveBased on the landscape epidemiology framework, we analyzed the scale-specific effects of urban landscape pattern on COVID-19 epidemics in Hangzhou, China.MethodsWe collected COVID-19 cases in Hangzhou from 2020‒2022 and combined the datasets of land use and land cover (LULC) and social gathering point (SGP) to quantify the urban landscape pattern. Optimal general linear model with stepwise regression was applied to explore the dominant landscape factors driving the COVID-19 transmission in the city. Furthermore, multi-scale geographically weighted regression illustrated the spatial heterogeneity and scale specificity of these landscape variables’ effects to COVID-19 epidemic.ResultsEight landscape variables of LULC and SGP patterns were identified which explained 68.5% of the variance in spatial risk of COVID-19. Different optimal bandwidths across these variables in MGWR indicated their scale-specific effects. LSI of green space enhanced the spatial risk across the entire region. The effects of landscape contagion, the number of water bodies, LSI of cropland and built-up areas, and the density of commercial houses were detected to vary between urban and suburban areas. The effects of LSI of water bodies and the density of shopping malls were found to vary among different districts.ConclusionsIn this study, we firstly discriminated the scale-specific effects of different landscape variables on the COVID-19 epidemic in the urban region. These findings can help to optimize the differentiated zoning prevention and control strategies for COVID-19 in cities and guide policy-making and urban planning at a multi-scale hierarchical perspective to improve public health and urban sustainability.

Impact of urban landscape patterns on butterfly diversity in Fuzhou City parks

Butterflies are crucial bioindicators of urban environmental changes. Utilizing urban green spaces to create ecological corridors and habitats is essential for maintaining biodiversity. Therefore, understanding the factors that influence landscape patterns in urban green spaces and their impact on butterfly diversity is crucial. This study investigated how landscape patterns in urban green spaces affect butterfly diversity through a year-long survey across 24 urban parks in Fuzhou. The main findings were as follows: (1) A total of 7786 butterflies from 59 species were recorded, with the highest species diversity and richness observed in Fushan Country Park. Seasonal abundance peaked in summer, while richness was highest in autumn. Species composition varied significantly across park types. (2) Monophagous butterfly diversity was positively correlated with woodland proportion and negatively to waterbody proportion. Polyphagous and oligophagous butterfly diversity showed positive correlations with woodland aggregation index and grassland proportion, but negative with construction land proportion. Factors such as the woodland proportion and grassland proportion influenced butterfly diversity in different activity spaces. (3) Butterfly communities with different feeding types and vertical activity space ranges responded differently to landscape pattern indices. Seasonal variations indicated that polyphagous butterflies and those occupying the middle activity space exhibited similar responses to environmental factors.

Carbon Sequestration and Landscape Influences in Urban Greenspace Coverage Variability: A High-Resolution Remote Sensing Study in Luohe, China

Urbanization has significantly altered urban landscape patterns, leading to a continuous reduction in the proportion of green spaces. As critical carbon sinks in urban carbon cycles, urban green spaces play an indispensable role in mitigating climate change. This study aims to evaluate the carbon capture and storage potential of urban green spaces in Luohe, China, and identify the landscape factors influencing carbon sequestration. The research combines on-site data collection with high-resolution remote sensing, utilizing the i-Tree Eco model to estimate carbon sequestration rates across areas with varying levels of greenery. The study reveals that the carbon sequestration capacity of urban green spaces in Luohe City is 1.30 t·C·ha−1·yr−1. Among various vegetation indices, the Enhanced Vegetation Index (EVI) explains urban green space carbon sequestration most effectively through an exponential model (R2 = 0.65, AIC = 136.5). At the city-wide scale, areas with higher greening rates, better connectivity, and more complex edge morphology exhibit superior carbon sequestration efficiency. The explanatory power of key landscape indices on carbon sequestration is 78% across the study area, with variations of 71.5%, 62%, and 84.9% for low, medium, and high greening rate areas, respectively. Moreover, when greening rates reach a certain threshold, maintaining and optimizing the quality of existing green spaces becomes more critical than simply expanding the green area. These insights provide valuable guidance for urban planners and policymakers on enhancing the ecological functions of urban green spaces during urban development.

Analysing Spatio-temporal Dynamics of Urban Sprawl, Evolving Pattern of Urban Landscape and Driving Forces of Urban Growth: A Case of Varanasi Planning Region, India

Urban sprawl is at the centre of contemporary urban debates and is very often criticised for its negative social and environmental externalities. Building on geo-spatial methodology and field observations, this study sets out to develop a holistic approach to understanding urban sprawl through a particular focus on a rapidly growing metropolitan urban agglomeration of northern India, that is, Varanasi. Following an analysis of principal urban growth modes through landscape expansion index and changing patterns of urban landscape through landscape metrics, this research refers to a geo-spatially grounded logistic regression model in order to shed light on the dynamic impact of multiple growth factors on the trajectory of urban sprawl in the study area. Moreover, in order to address the evolving nature of urban sprawl in Varanasi and its immediate surroundings this study focuses on a temporal span of 20 years, encompassing two consecutive decades of the 21st century (2001–2011 and 2011–2021). The research concludes by paving a platform for an amalgamation of the findings from different geo-spatial metrics as well as by corroborating the principal findings with the propositions of a number of contemporary hypotheses concerning the spatial growth of urbanised territories.

Scale effect on the relationship between urban landscape patterns and land surface temperature

Great efforts have been made to examine the linkages between land surface temperature (LST) and urban landscape patterns (ULPs), which, however, focus on a single spatial scale and linear relationships. This study aims to examine the influence range of four key ULP indicators on LST, namely sky view factor (SVF) and three indices of built-up area, greens, and blue spaces. The analyses are conducted at street block level in 38 big cities in China, and leverage a multiscalar approach to investigate change patterns of indicators within multiple buffer zones and identify the buffer distance that yields the greatest influence. Results reveal that (1) the greatest local impacts are produced within 0−150 m buffer zones in most cities for all key ULP metrics; (2) the maximum impacts of most indicators in summer are greater than other seasons; and (3) the influence magnitude of key ULP indicators increase after considering scale effect, and the influence of SVF varies significantly across cities. Results suggest that the consideration of maximum influence ranges of ULP indicators can better explain LST spatial variations. Our findings offer evidence on the local impact of ULPs on LST and contribute to urban design in urban heat mitigation.

Scale matters: How spatial resolution impacts remote sensing based urban green space mapping?

Urban green spaces (UGS) provide ecological and habitat benefits such as carbon sequestration, oxygen production, humidity increase, noise reduction, and pollution absorption. UGS maps derived from remote sensing images serve as the fundamental data for urban planning and carbon sequestration assessments. However, the spatial resolution of remote sensing image and the pattern of urban structures significantly influence UGS mapping, making it challenging to obtain accurate UGS maps. To investigate the impact of spatial resolution on UGS mapping, this study utilized five different spatial resolution datasets: Gaofen2 (1 m, 4 m), Sentinel2 (10 m), and Landsat8 (15 m, 30 m). Random forest, LightGBM, and support vector machine were employed to map UGS, and the accuracies of UGS maps at different spatial resolutions were compared. Subsequently, the spatial distribution patterns of uncertainties in UGS maps were analyzed from both overall and urban functional zone perspectives. Furthermore, the uncertainty analysis of UGS mapping was conducted considering different landscape patterns in urban functional zones. The results indicate: (1) UGS map varies at different spatial resolution. Higher uncertainties associated with coarser spatial resolutions. Medium and coarse spatial resolution images inadequately capture the fine-grained distribution of urban green spaces. (2) Uncertainty in UGS mapping at different spatial resolutions is generally consistent in spatial distribution. From a functional zoning perspective, the accuracy of green space mapping over non-natural zones is sensitive to spatial resolution. (3) The distribution pattern of UGS patches affects the accuracy of UGS mapping. Uncertainty can be reduced in UGS mapping at medium and coarse spatial resolutions based on UGS landscape pattern indices by multiple linear regression, random forest and LightGBM model. This study comprehensively reveals that uncertainties in mapping UGS from multi-spatial resolution remote sensing images vary across urban functional zones and landscape pattern indices, and it is the first attempt to propose methods for UGS area correction based on landscape pattern indices. The results of this study will facilitate the application of remote sensing data at different spatial resolutions in urban areas.

Mapping fine-scale carbon sequestration benefits and landscape spatial drivers of urban parks using high-resolution UAV data

Urban parks offer dual ecological benefits by increasing carbon sinks and reducing carbon emissions and are considered an important natural strategy for cities to achieve “double carbon” goals. However, rapid, efficient, and accurate quantification of the carbon sequestration benefits of urban parks poses a significant challenge. To address this, we utilized unmanned aerial vehicle (UAV) multispectral data to construct large-area, high-resolution models of urban park carbon sequestration benefits, replacing traditional, time-consuming, and laborious large-scale field surveys. Additionally, we explored the relationship between urban park landscape patterns and the benefits of carbon sequestration. First, we used data from 12 tree species to calculate the carbon storage based on tree species and compared these results with those calculated based on forest type. Second, three prediction models were constructed using multispectral vegetation index only, texture features only, and a combination of both, in conjunction with gradient boosting decision Trees (GBDT), random forest (RF), and backpropagation (BP) neural network to generate carbon sequestration benefit maps for the entire park. These maps allowed us to determine how variations in urban park landscape structures affect carbon stocks. The results show that UAV multispectral imagery provides a fast and accurate method for measuring the carbon sequestration benefits of urban parks and offers an alternative method for generating carbon sequestration benefit maps. This research reveals the benefits of urban park carbon sequestration and explores the spatial patterns within landscapes. The findings are of great significance for guiding the estimation of urban carbon sequestration benefits and achieving carbon neutrality.

Analyzing Thermal Environment Contributions and Driving Factors of LST Heterogeneity in Different Urban Development Zones

Analyzing the impacts of urban landscape patterns on the thermal environment has become one of the key research areas in addressing urban heat islands (UHIs) and improving the living environment. A case study was carried out in Fuzhou, Fujian Province of China, and bi-temporal Landsat imagery was selected to calculate land surface temperature (LST), percent impervious surface area (ISA), and fractional vegetation cover (FVC). The urban area was further divided into three concentric urban zones, ranging from the city center to the urban periphery, based on urban development densities. The spatial pattern of LST and its variance were analyzed and compared between different zones and different dates. The thermal environment contribution of different zones was also quantified to indicate the change in urban landscape patterns resulting from urban expansion in different zones. Furthermore, Geodetector was used to explore the single factors and interaction factors controlling the spatial patterns of LST in each zone. The results showed that (i) urban expansion primarily increased in Zone 2 and Zone 3, and the areal proportion of high and sub-high LST areas increased from 56.11% and 21.08% to 62.03% and 32.49% in Zone 2 and Zone 3, respectively, from 2004 to 2021; (ii) the heat effect contribution of Zones 2 and 3 reached from 75.16% in 2004 to 89.40% in 2021, indicating that the increase in ISA with >LSTmean was more pronounced in Zone 3 and Zone 2 during the period; (iii) the driving factors of LST spatial distribution were regionally different because of the different landscape patterns, and the explanatory power for the heterogeneity of LST in Zone 1 was weaker than in Zone 2 and Zone 3 in the study area; (iv) the interaction of different factors had a higher explanatory power in the spatial distribution of LST than a single factor in each zone because the distributions of land cover types are heterogeneous in urban areas. The results of this study can be used to improve urban planning for urban ecology and UHI mitigation.

Urban landscape affects scaling of transportation carbon emissions across geographic scales

Understanding the carbon dynamics of the transportation sector is necessary to mitigate global climate change. While urban scaling laws have been used to understand the impact of urban population size on carbon efficiency, the instability of these scaling relationships raises additional questions. Here, we examined the scaling of on-road transportation carbon emissions across 378 US metropolitan statistical areas (MSAs) using diverse urban landscape patterns and spatial units, from the MSA level down to 1 km grid cells. Beginning with a baseline scaling model that uses only population size, we expanded the model to include landscape metrics at each spatial scale based on correlation results. We found that: (1) urban landscape characteristics provide insights into carbon mechanisms not fully captured by population size alone, (2) the impact of population size on on-road carbon emissions transitions from linear to sub-linear scaling relationships as the geographic scale of analysis decreases, and (3) clustered urban developments can form carbon-efficient landscapes, while fragmented urban areas tend to be carbon-inefficient. Based on empirical evidence, this research advocates for hierarchical spatial planning and supports the implementation of policy measures aligned with smart growth principles to mitigate carbon pollution.

Indirect non-linear effects of landscape patterns on vegetation growth in Kunming City

Urban greening is becoming an important strategy in improving urban ecosystem services and sustainability. Identifying the response of vegetation to urbanization and urban landscape patterns can help in planning for urban greening. Urbanization may lead to both direct and indirect effects on vegetation, and the indirect effects of urbanization on vegetation growth (UIE-VG) have been paid much attention recently in large scale. In this study, we investigated the spatiotemporal evolution of UIE-VG and the effects of landscape patterns on UIE-VG using the boosted regression tree model and remotely sensed data. An increase in average UIE-VG from 4 to 56% was found during urbanization of Kunming, the case study area in southwest China. However, UIE-VG exhibited high variations due to landscape pattern changes at the local scale. Overall, area-related and aggregation-related landscape metrics had greater effects on UIE-VG than the other metrics. The increase and aggregation of built-up land enhanced UIE-VG by 3.1–81.3% while the increase and aggregation of unused land and waterbodies reduced UIE-VG by 0.7–20.6%. Moreover, we found that the large and aggregated vegetation areas may mitigate the negative UIE-VG in low urbanization areas. Our findings have important implications for integrating urban landscape planning into sustainable urban greening strategies.

How do landscape patterns in urban parks affect multiple cultural ecosystem services perceived by residents?

Cultural ecosystem services (CES) provided by urban parks are crucial for encouraging residents to engage with nature and enhance their physical and mental well-being. Measuring these services from the residents' perception perspective is essential. Previous studies often focus on a specific type of CES, lacking explicit links between the landscape composition and configuration of urban parks and residents' perceptions of various CES. The main objective of this study, therefore, was to explore the effects of urban park landscape patterns on residents' CES perceptions. We took 12 urban parks in Chengdu, China, and assessed residents' CES perceptions through content analysis of social media texts. Spatial patterns of the parks were analyzed using remote sensing interpretation and field surveys. Correlation analysis examined the relationship between landscape patterns and residents' perceptions, with further verification through questionnaires and face-to-face interviews. Findings revealed that at the landscape level, landscape aggregation of parks was negatively correlated with aesthetic perceptions but positively correlated with recreational perceptions. Landscape diversity negatively impacted perceptions of sports and health (S&H). At the class level, natural elements significantly influenced residents' perceptions of aesthetic and S&H. Specifically, aesthetic perceptions were minimized when the landscape shape index of water bodies reached 6.36 or when the proportion of green space was 56.5 %. Road edge density negatively affected perceptions of S&H and influenced the distribution of water bodies and green spaces. These findings are crucial for optimizing park structures to deliver efficient CES and provide strategies for integrating ecosystem services into environmental management from a public perception perspective.

Urban landscape pattern mainly drives the early epidemic distribution of dengue fever in Hangzhou, China

ContextA new dengue fever (DF) epidemic in Hangzhou, China has placed a serious burden on the urban sustainable development. However, the key drivers of DF epidemic are still unclear, especially the effects of urban landscape patterns.ObjectivesIn the present study, the spatial relationships between DF epidemic and urban landscape attributes in Hangzhou were investigated based on the framework and approach of landscape epidemiology.MethodsThe landscape indices of green space, waterbody, and built-up area were calculated based on land cover and land use (LULC) data. Population density, road density, GDP, and property price were used to represent socioeconomic conditions. The densities of social gathering places (SGPs) such as restaurants, malls, services, entertainment, traditional markets, and parks were recorded. A geographically weighted zero-inflated poisson regression (GWZIPR) model was applied to analyze the effects of LULC patterns, socioeconomic conditions, and SGPs on the risk of DF epidemics. An additional ZIPR-LASSO algorithm was used to explore the dominant drivers of spatial patterns of DF risk to construct the optimal model.ResultsDF cases were mainly clustered in central Hangzhou. Landscape patterns involving LULC and SGPs played a more important role in DF epidemic than the socioeconomic conditions. Among these factors, the total area of built-up area had the greatest effect to DF risk. Furthermore, property price, density of entertainment and services could better explain the spatial variation of DF risk in Hangzhou.ConclusionsOverall, the contribution of landscape patterns to the DF epidemic was more important than socioeconomic conditions within the urban region, especially in the new epidemic region. These findings revealed the key role of landscape epidemiology in public health management and landscape governance to improve the urban sustainable development and human well-being.

A Study on Spatial Differentiation of Landscape Pattern Based on Three-Dimensional Morphology of Urban Buildings

Abstract. Urban buildings are an important part of urban morphology, and building height has an important impact on the three-dimensional spatial morphology of cities. At present, research on the two-dimensional morphology of cities is relatively abundant, but there are fewer studies on the characteristics of the three-dimensional undulating morphology of cities and their spatial distribution patterns, and thus knowledge of the degree of utilization of the airspace above the city and its developmental pattern is still relatively lacking.Based on the multi-scale urban agglomeration, the landscape pattern is converted from two-dimensional to three-dimensional, and the overall spatial differentiation can be analyzed more intuitively. The purpose of this paper is to explore the three-dimensional spatial differentiation law of urban architectural landscape pattern. Through in-depth spatial analysis of the architectural landscape in urban areas, it is found that the landscape patterns in different areas show significant differences, and the interrelationship between them and the urban landscape pattern is revealed through the study of three-dimensional characteristics of urban buildings, such as height, density, form and layout. It is found that the different characteristics of urban buildings have a significant impact on the landscape pattern, which leads to the differentiation of urban space. This study provides more comprehensive spatial information for urban planning, contributes to a better understanding of the complexity of urban architectural landscapes, and provides a scientific basis for future urban design and planning.

Study on the Evaluation of Urban Park Landscape Pattern Index and Its Driving Mechanisms in Nanchang City

Urban planning is often influenced by industrial and construction activities, leading to a lack of attention to the planning and construction of urban parks, which results in prominent spatial layout problems. Urban parks, as an important part of the urban green space system, play a significant role in improving the ecological environment, promoting urban economic development, and enhancing the daily living standards of the people. As a typical representative of China’s second and third-tier cities, Nanchang’s analysis of the evolution process of urban landscape patterns has reference significance for other similar cities in China. This paper is based on the theoretical foundations of landscape ecology, human geography, and urban planning, and analyzes the evolution of the park landscape pattern in the central urban area of Nanchang from 1999 to 2019 from the perspective of urban context, revealing the driving mechanisms. It provides important references and bases for the further optimization and construction development of the park landscape pattern in Nanchang. The results show that the park area and number in the central urban area of Nanchang have significantly increased, with the overall layout evolving from “central aggregation” to “core aggregation in each area”, and from “central scarcity, more on the periphery” to “inward concentration, outward diffusion”. However, the distribution of various types of parks is uneven, and there is a lack of green corridor links between parks. The park landscape pattern is driven by multiple factors such as natural factors of urban context, socio-economic factors, urban construction factors, historical policies, and related planning, which can have positive or negative effects. Incorporating relevant urban factors into the park planning system analysis can promote the benign development of urban context and park landscape layout, thereby achieving the “parkification” of the city.

Investigating the multiscale associations between urban landscape patterns and PM1 pollution in China using a new combined framework

Urban landscape patterns serve as the spatial carriers of emission sources and influencing factors of air contaminants, which inevitably impact submicron particle (PM1) pollution. However, most studies ignore the spatial nonstationary and nonlinear effects of urban landscape patterns on the PM1 concentration. In this study, a novel framework was developed that integrates a multiscale geographically weighted regression (MGWR) model and Shapley Additive exPlanations (SHAP) machine learning method to explore the spatial heterogeneity effect, relative contribution and nonlinear impact of landscape patterns on the PM1 concentration at the national and urban agglomeration scales. The results indicated that urban landscape patterns were closely related to PM1 pollution and exhibited obvious spatial differences. The disorderly expansion of urban built-up areas and an irregular urban morphology could aggravate PM1 pollution, while urban fragmentation and connectivity could impose reducing effects. Dispersed urban landscape patterns could reduce the PM1 concentration in the Yangtze River Delta (YRD), Pearl River Delta (PRD), and Chengdu-Chongqing (CDCQ) regions, whereas compact and continuous landscape patterns positively affected PM1 pollution mitigation in the Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZH) urban agglomerations. The impact of urban landscape patterns on PM1 pollution was greater in urban agglomerations than elsewhere. The ENN_MN landscape index exhibited the highest feature importance and interpretability. The threshold effects between the urban shape indicators and PM1 concentration were more complex than those with the other landscape indices. This critical knowledge provides a scientific basis for further understanding the correlation mechanism between PM1 pollution and the landscape pattern, urban sustainable planning and air pollution control.

Impact of territorial spatial landscape pattern on PM2.5 and O3 concentrations in the Yangtze River delta urban agglomeration: Exploration and planning strategies

PM2.5-O3 composite pollution has become a pressing atmospheric challenge in China. Assessing the impact of territorial spatial landscape pattern on PM2.5 and O3 concentrations within the context of territorial spatial planning will provide ideas for atmospheric governance and spatial planning. Based on the agricultural-urban-ecological space system, this study first reclassifies territorial space within the Yangtze River Delta urban agglomeration (YRDUA) and calculates multilevel and multidimensional landscape metrics. Then, variables are screened by the random forest model and collinearity test. Furthermore, the spatiotemporally heterogeneous impact of selected landscape metrics on PM2.5 and O3 concentrations is explored using the geographically and temporally weighted regression (GTWR) model, with a tradeoff-synergy analysis being conducted to propose planning strategies. The conclusions show: (1) Two pollutant concentrations have both numerical and spatial correlations. The temporal stability of concentrations' responses to metrics is increasing, while the spatial heterogeneity continues to be significant. (2) Aggregation index of agricultural production space (AP_AI) and percentage of forest area (FO_PLAND) are trade-off and synergy factors of coordinated control, respectively. Largest patch index of grassland (GR_LPI) and patch density of other ecological spaces (OT_PD) are individual factors of PM2.5 reduction. (3) Spatial zoning supported by intra-zonal and inter-zonal cooperation policies, and coordinated control guidelines centered around the utilization of agricultural production space, have been advocated. This study aims to provide theoretical supplement and practical support for urban agglomeration's atmospheric governance and territorial spatial landscape pattern optimization.

Exploring the relationships between 3D urban landscape patterns and PM2.5 pollution using the multiscale geographic weighted regression model

Fine particulate matter (PM2.5) is a major source of air pollution and exerts serious impacts on human health. The 3D urban landscape patterns can significantly affect the diffusion and emissions of PM2.5. However, studies on the relationships between 3D urban landscape patterns and PM2.5 pollution across different seasons remain understudied. With the ground-level air pollutants estimated by the remote sensing and fine-scale building information, this study applied the multiscale geographically weighted regression model to explore such relationships. Wuhan, the largest metropolis in Central China, was selected as the study area for the application of our methodology. The results showed that the direction, degree, and scale of the effect of 3D urban landscape patterns on PM2.5 pollution varied across seasons. For building height, the standard deviation of building height had a significant positive correlation with PM2.5 all year round. For building density, the building count density showed a significant positive correlation with PM2.5 in general, with the bandwidth in winter and autumn smaller than in spring and summer. The building plan area fraction exerted both positive and negative influences on PM2.5, dependent on season and location. The bandwidth of it gradually increased from spring to winter, with the effect changing from local to regional scale. For building volume, the floor area ratio showed a significant negative correlation with PM2.5 in winter and autumn, and a localized effect was found, especially in winter. The findings of this study provide practical implications for urban planning and policy making to mitigate PM2.5 pollution in the rapidly urbanizing regions.

Landscape Analysis and Ecological Risk Assessment during 1995–2020 Based on Land Utilization/Land Coverage (LULC) and Random Forest: A Case Study of the Fushun Open-Pit Coal Area in Liaoning, China

China’s Fushun open-pit mine is the largest century-old coal mine in Asia. Large-scale mining and the use of coal has caused dramatic changes in the regional urban landscape pattern, seriously affecting the ecological function and ecosystem stability of its surrounding landscape. Evaluating the ecological risks of the landscape in the urban areas of open-pit mines contributes to the risk management of regional ecosystems and the sustainable development of society. This study selected six-phase Landsat ETM/OLI remote sensing images from 1995 to 2020 and combined them with the random forest model to carry out an LULC classification of the open-pit mine and its surrounding areas and, on this basis, discusses the evolution of its landscape pattern and evaluates the ecological risks. It fills the gap in the research on the evolution of regional landscape patterns and ecological risks in the study area and improves the automatic classification efficiency of LULC for use in open-pit mines. The results show that the classification accuracy of LULC regarding open-pit mines based on image pixels and the random forest model can reach 30 m, and the rate of accuracy can reach 92–97%. From 1995 to 2020, the coverage area of forest land and building land in the study area has increased and is mainly composed of grassland and undeveloped land. The use of land was transferred to the mining area, the water body area maintained a relative dynamic balance, and the overall vegetation coverage of the mining area was greatly improved; the forest land began to expand from the surrounding area to the mining area in 2010, and the construction land began to move from the areas surrounding the mining area to the surrounding valleys in 2015. The landforms have extended radially, and the landscape sprawl index has increased, indicating the optimization of the ecological environment; the high- and medium-risk areas decreased by 75.51 km2, the low-risk areas expanded by 461.48 km2, and the overall ecological risk index decreased. From this, it is possible to conclude that the landscape restoration project adopted in the study area has achieved great results, and the improvement of the ecological environment also directly affects the increase of construction land. These research results can provide scientific guidance for the rational utilization and sustainable development of land resources in urban areas of open-pit mines.

Multiscale effects and drivers of landscape heterogeneity for water‐related ecosystem services in urban agglomerations

AbstractUnderstanding the multiscale impacts and drivers of urban agglomeration landscape patterns for ecosystem services (ESs), especially water‐related ecosystem services (WESs), is essential for the development of regional ecological management. However, the multiscale impacts and driving mechanisms of urban agglomeration landscape patterns for ESs have not been adequately explained. In this study, multivariate data were employed, and the InVEST model, trend test method, coupled GeoDetector and geographically and temporally weighted regression (GTWR) method were utilized to comprehensively explore the spatial and temporal changes in landscape patterns and WESs in the Pearl River Delta urban agglomeration (PRDUA) at various grid and administrative scales from 1990 to 2020 and to determine the driving mechanisms affecting WESs. The results indicated that the variation characteristics of landscape patterns and WESs in the PRDUA were consistent, forming a binary spatial structure of core and peripheral areas in an inverted “U” shape around the estuary of the Pearl River. The relationship between landscape patterns and WESs weakened with the increase of scale, and the correlation coefficient decreased by approximately 0.10 from 5 km to 10 km grid scale. Additionally, precipitation (PRE) was the main factor controlling WESs changes in the PRDUA, explaining more than 50% of the changes in WESs, and the regression coefficients ranged from 0.0825 to 0.1584. Changes in WESs were the result of the combined effects of natural factors, including PRE, landscape pattern, elevation, slope, and socioeconomic factors, such as population and gross domestic product (GDP). Overall, these findings could contribute to optimizing regional landscape patterns and fostering sustainable development of the ecological environment in urban agglomerations.