Metrics analysis and evaluation of landscape mosaic changes to monitor the identity of forest monastery green space, Northeast Thailand

Achieving carbon neutrality has become one of the core objectives in contemporary urban development and sustainable growth, underscoring the importance of clarifying the relationship between urban green space landscape metrics and plant carbon sequestration. While existing research confirms the significant role of the structure and pattern of green spaces in carbon sequestration, systematic understanding of their relationship at the local scale within diverse built environments remains limited. To address this, this study objectively categorises five types of built environments using K-means clustering and conducts in-depth analysis on four representative areas. Employing the CatBoost machine learning model and the Shapley Additive Propensity (SHAP) method, we highlighted the influence of green space pattern characteristics on net prmary productivity (NPP) across different built environments. The findings are as follows: (1) Green Coverage Ratio (GCR) exhibits the highest contribution among all explanatory variables across different built environments. In low-intensity built environments, it contributes 74% to the overall explanation, showing a stable association between higher green space proportion and higher carbon sink levels. (2) In high-intensity built environments, limited green spaces exhibit a pronounced “spatial compensation effect” through morphological optimisation and enhanced spatial connectivity. In medium-intensity built environments, they demonstrate a “moderate positive effect,” with peak carbon sequestration efficiency occurring when GCR ranges from 0.25 to 0.75, aggregation index (AI) from 94 to 98, and splitting index (SI) from 1.2 to 1.4. (3) Significant interactions exist among green space landscape metrics, with moderately connected and moderately complex spatial structures enhancing carbon sink efficiency. This study reveals the differentiated impact by which green space landscape metrics influence carbon sink effects under varying urban built environments, providing scientific basis for optimising urban green space systems and low-carbon spatial planning.

Urban green spaces provide a host of ecosystem services, the quantity and structure of which play an important role in human well-being. Rapid urbanization may modify urban green spaces, having various effects on plant diversity. Tropical coastal cities have urbanized rapidly in recent decades, but few studies have been conducted with a focus on their green spaces. We studied the responses of cultivated and spontaneous plants, both key components of urban flora, to the landscape structure of urban green spaces and possible social drivers. We analyzed existing relationships between plant diversity indices, urban green space landscape metrics (using Systeme Probatoire d’Observation de la Terre (SPOT) data,), and social factors, including the type, population density, construction age, and GPS coordinates of each Urban Functional Unit, or UFU. We found that UFUs with more green space patches had higher cultivated and spontaneous species richness than those with fewer green space patches. Spontaneous species richness decreased when green space patches became fragmented, and it increased when green space patches were more connected (e.g., via land bridges). Conversely, cultivated species richness increased with green space patch fragmentation. The phylogenetic diversity of both cultivated and spontaneous plants were weakly associated with green space structure, which was strongly driven by land use. Old UFUs and those with larger populations had more green space patches overall, although they tended to be small and fragmented. Green space patch density was found to increase as the UFU age increased. From the viewpoint of knowledge transfer, understanding the effects and drivers of landscape patterns of urban green spaces could inform the development of improved policies and management of urban green space areas.

The impact of landscape patterns on urban plant diversity has received significant attention; however, previous studies have primarily focused on two-dimensional (2D) patterns. Limited investigations have been conducted to explore the effects of three-dimensional (3D) landscape patterns on urban plant diversity. Spontaneous plants are ideal objects for studying the response of plant diversity to urban environments due to their independence from intentional human inputs. Buildings, as a crucial symbol of urbanization, exhibit strong 3D characteristics in urban landscapes. Green spaces, the most important source of plant propagules in cities, are closely related to spontaneous plant diversity in urban areas. Therefore, we examined how 2D/3D building and green space patterns influence urban spontaneous plant species richness and compared the responses between different categories (native and non-native). We surveyed spontaneous plants in the built-up areas of Shenzhen, a highly urbanized region situated along China’s southern coast. Our survey recorded 278 species belonging to 208 genera and 77 families. Results from boosted regression tree models (BRT) indicated significant correlations between spontaneous plant species richness and various metrics, including building coverage ratio (BCR), mean building height (MAH), green space coverage ratio (GCR), edge density of green spaces (ED_G), spatial congestion degree (SCD), and floor area ratio (FAR). These metrics exhibited complex nonlinear correlations with the species richness of spontaneous plants. Furthermore, native and non-native species richness responded differently to building and green space patterns; while native species richness was equally affected by green space and building patterns, non-native species richness was more strongly impacted by building patterns than green space ones. Our study quantified the nonlinear relationships between urban spontaneous plant species richness and 2D/3D green space and building patterns, demonstrating that optimizing green space and building patterns can be a practical approach for managing and promoting spontaneous plant diversity in highly urbanized areas.

As an important part of the city, urban green space (UGS) plays an essential role in enhancing human well-being by virtue of multiple environmental, social and economic benefits. Study on landscape pattern of UGS is a focal point and hotspot in landscape ecology. The latest studies demonstrated that landscape metrics provides an effective method in quantifying UGS pattern. However, the study of the scale effect of landscape metrics should be strengthened. The objective of scale related research in UGS is to determine the appropriate scale in the measurement and evaluation of UGS and to find the underlying mechanisms by use of the selected scales. This study aims to identify the scale characteristics and scale domain of UGS pattern, and provide basic information for pattern analysis and scaling in UGS research. In this paper, taking the central urban area of Székesfehérvár in Hungary as an example, we firstly extracted UGS from WordView-2 multi-spectral image (2m), then obtained a series of grain sizes by upscaling, and finally calculated and analyzed the characteristics of different landscape metrics with varying grain sizes. In this study, both the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Green Index (NDGI) were used to ensure the accuracy of the green space extraction in high spatial resolution image. On the basis of green space extraction, the green space patterns at different grain sizes were obtain by the assembly of grid cells. A total of 20 grain sizes were selected in this paper, ranging from 2 m to 40 m with a step size of 2 m. Landscape metrics both under class and landscape levels, including Patch Density (PD), Percentage of Landscape (PLAND), Mean Perimeter-Area Fractal Dimension (FRAC_MN), Division Index (DIVISION), Cohesion Index (COHESION), and Shannon's Evenness Index (SHEI) were calculated. The results demonstrated that with the increase of grain size, the landscape metrics under class level and landscape level were significantly affected by the grain size, and there was obvious critical grain size. On the whole, 16 m is the critical grain size of the green space pattern, and the suitable grain size for landscape metrics calculation of UGS ranges from 2 m to 16 m. The responding curves were varied by landscape metrics. Some metrics had clear changing trend and obvious turning grain size, while the others also had obvious turning grain size, but without clear changing trend. According to scale inflexions and responding curves discussed in the paper, scale domains of landscape metrics were confirmed. Generally, from 2 m to 16 m was the scale domain of UGS pattern, which means that related ecological model of UGS can be scaled across this scale extent by ordinary transformation. The study of impacts of changing scale on UGS can provide a reference for understanding the ecological benefits of UGS and optimizing the green space pattern.

Structure of an urban green space indirectly affects the distribution of airborne particulate matter: A study based on structural equation modelling

Urban green and blue spaces (UGBS) provide key ecosystem services, and growing research has sought to examine their synergistic effects using landscape metrics. However, inconsistent choices of indicators for characterizing the coupled UGBS patterns hinder comparability across studies. To address this, we developed a systematic framework that integrates key spatial relationships between green and blue spaces—such as blue-green distances and waterfront green areas—into UGBS landscape characterization. Using Nanjing as a case study, we quantified the integrated UGBS patterns at 500 m and 1 km scales and assessed their distributive equity. At the 500 m scale, the average distance from green space to the nearest blue space was 334 ± 292 m, and mixed blue–green areas accounted for 43% of the total UGBS landscape. Composition metrics of UGBS showed weak positive associations with the proportion of elderly residents and negative associations with socioeconomic indicators. Newly developed urban areas contained larger, less fragmented green spaces, shorter blue–green distances, and more extensive waterfront green zones. Our findings highlight the frequent co-occurrence of green and blue spaces in subtropical cities. The proposed framework offers methodological support for advancing the understanding of UGBS synergies.

Promoting sustainable landscape planning has become an important policy goal in China. However, how and to what extent can the spatial patterns and changes of landscape in the urban fringe areas be systematically identified and managed is a critical research issue which requires in-depth research investigation. Using the pilot study region of the proposed Shenzhen low carbon eco-city as a case setting, this study explicitly examines the changes of landscape patterns by using Remote Sensing (RS) and selected landscape metrics. The result shows that there is a trend of decrease in green space parches and an increase in landscape fragmentation of the study region. The overall structure of green space in the region is also affected by urbanization and industrial development of the region. Considering the fact that the structure and diversity of landscape and green space are important for building an eco-city, the results reveal that more comprehensive green space policies and urban policies that preserve important ecological patches and corridors should be developed to enhance the overall ecological function of the region.

Green space in intra-urban regions plays a significant role in improving the human habitat environment and regulating the ecosystem service in the Inner Mongolian Plateau of China, the environmental barrier region of North China. However, a lack of multi-scale studies on intra-urban green space limits our knowledge of human settlement environments in this region. In this study, a synergistic methodology, including the main process of linear spectral decomposition, vegetation-soil-impervious surface area model, and artificial digital technology, was established to generate a multi-scale of green space (i.e., 15-m resolution intra-urban green components and 0.5-m resolution park region) and investigate multi-scale green space characteristics as well as its ecological service in 12 central cities of the Inner Mongolian Plateau. Results showed that: (1) Total urban areas and urban green space across the studied cities were 1249.87 km2 and 295.40 km2, indicating that the average proportion of green space to urban areas was 24.03%. (2) The proportion of green space to urban areas ranged from 17.09% to 32.17%, and the proportion of parks’ green space to green space ranged from 5.55% to 50.20%, indicating a wide range of quantitative discrepancies. (3) In different climate regions, there were higher proportions of urban/park green space in arid/semi-arid areas to reduce the impacts of dry climate on human settlements; by contrast, lower green space in humid areas mainly displayed a scattered pattern because of the relatively lower influence of climate pressure. (4) Green coverage was an essential indicator of the “Beautiful China” project, and its ratio within 500-m ecological service zones from parks across all cities was 46.14%, which indicated that the ratio of residential land and green space was close to 1:1. Overall, urban/park green space patterns in urban areas adapted to the different climate features in the Inner Mongolian Plateau. For better human settlement sustainability across all studied cities, more greening patches and ecological corridors should be designed in the lower green space regions of the Inner Mongolian Plateau.

Spatial-temporal gradient analysis of urban green spaces in Jinan, China

Background and Objectives: Urbanization refers to the change of both physical and human landscape structures within an area in response to socio-economic development. This transformation leads to a reduction in urban open spaces, alongside the expansion of diverse land uses into peri-urban areas, contributing to the decline of green spaces in both urban and rural environments. This research aims to assess the changes in landscape ecological structures during the period from 2011 to 2022 by monitoring the diversity of land cover types using the Landscape Mosaic (LM) model and the LM-Anthropic model to describe the main structures and continuity of landscape components, including developed areas, agricultural areas, natural areas, and mixed-use areas, in order to evaluate the condition of green spaces in Mueang District, Amnat Charoen Province. Methodology: This study applies geo-information technology to classify green and non-green areas based on Sentinel-2A satellite imagery, in conjunction with various indices, including the Normalized Difference Vegetation Index (NDVI), Bare Soil Index (NDBSI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI), using a hybrid classification method. The accuracy of the classification results was validated against ground truth points using real-time data collection via a Global Navigation Satellite System (GNSS). A confusion matrix was used to calculate overall classification accuracy and the Kappa coefficient, with the confidence level set at 80% and a minimum acceptance threshold of substantial agreement.The resulting data were further analyzed to examine landscape structure, patterns, and changes in order to assess spatial distribution, configuration, and component changes in relation to the intensity levels of human activities, following the principles of landscape ecology. Results: The land cover classification results for Mueang Amnat Charoen District in 2022 revealed an overall accuracy of 80.21% and a Kappa coefficient of 0.73, indicating substantial agreement. Agricultural land was the most dominant category, accounting for 60.46% of the area, followed by forest, barren land, perennial crops, community and built-up areas, and water bodies, at 8.59%, 6.85%, 4.72%, 1.85%, and 0.66%, respectively. These results characterize Mueang District’s core landscape structure as an agricultural matrix. Between 2011 and 2022, significant landscape changes were observed. The proportions of agricultural and natural landscape mosaics declined from 72.93% and 16.56% to 66.72% and 12.63%, respectively. In contrast, developed, mixed-use, and water landscape mosaics increased from 5.43%, 3.23%, and 1.84% to 8.93%, 9.83%, and 1.89%, respectively. Net changes in mosaic types revealed a transformation from uniqueness toward areas of dominance and presence. Specifically, dominant agricultural, natural, and developed mosaic types declined by 30.13%, 5.76%, and 1.48%, respectively, anrd were replaced by mixed-use mosaics influenced by the convergence of all three components.This pattern corresponds with the intensity levels of human activities. Areas of extreme activity intensity were concentrated in dense urban cores, covering 4.17% of the district. Moving outward from the urban center, the spatial pattern took on linear and dispersed forms, with decreasing levels of intensity and an increase in agricultural landscapes. Areas with very high and high levels of activity intensity accounted for 6.10% and 61.15%, respectively. Sparsely developed agricultural zones were categorized as moderate-intensity areas, comprising 13.59%. Low and very low-intensity areas —primarily undisturbed natural areas such as small and large forest patches and riparian woodlands—were scattered across urban and peri-urban areas, comprising 13.59% and 8.03% of the total area, respectively. Results: This study demonstrates the effective application of geo-information technology for quantitatively assessing green space conditions through Sentinel satellite imagery classification, integrated with multiple indices. The approach is further enhanced by incorporating landscape mosaic modeling and human activity intensity analysis to evaluate landscape structure. These models support spatial interpretation of interactions among developed urban areas, natural green spaces, agricultural land, and mixed-use areas—revealing patterns of uniqueness, dominance, and presence. The results highlight the directions and trends of landscape structural change, which potentially affect urban and community environments, particularly the loss of natural green space and open areas, and the ongoing expansion of urban zones characterized by increasingly complex land use. It offers essential spatial information to support planners in conserving and managing target areas for long-term sustainable environmental development.

Globally, rapid urban expansion has caused green spaces in urban areas to decline considerably. In this study, the rapid expansion of three Southeast Asia cities were considered, namely, Kuala Lumpur City, Malaysia; Jakarta, Indonesia; and Metro Manila, Philippines. This study evaluates the changes in spatial and temporal patterns of urban areas and green space structure in the three cities over the last two decades. Land use land cover (LULC) maps of the cities (1988/1989, 1999 and 2014) were developed based on 30-m resolution satellite images. The changes in the landscape and spatial structure were analysed using change detection, landscape metrics and statistical analysis. The percentage of green space in the three cities reduced in size from 45% to 20% with the rapid expansion of urban areas over the 25-year period. In Metro Manila and Jakarta, the proportion of green space converted to urban areas was higher in the initial 1989 to 1999 period than over the latter 1999 to 2014 period. Significant changes in green space structure were observed in Jakarta and Metro Manila. Green space gradually fragmented and became less connected and more unevenly distributed. These changes were not seen in Kuala Lumpur City. Overall, the impact of spatial structure of urban areas and population density on green space is higher in Jakarta and Metro Manila when this is compared to Kuala Lumpur. Thus, the results have the potential to clarify the relative contribution of green space structure especially for cities in Southeast Asia where only a few studies in urban areas have taken place.

China has experienced both rapid urbanization and major increases in myopia prevalence. Previous studies suggest that green space exposure reduces the risk of myopia, but the association between myopia risk and specific geometry and distribution characteristics of green space has yet to be explored. These must be understood to craft effective interventions to reduce myopia. To evaluate the associations between myopia and specific green space morphology using novel quantitative data from high-resolution satellite imaging. This prospective cohort study included students grades 1 to 4 (aged 6 to 9 years) in Shenzhen, China. Baseline data were collected in 2016-2017, and students were followed up in 2018-2019. Data were analyzed from September 2020 to January 2022. Eight landscape metrics were calculated using land cover data from high-resolution Gaofen-2 satellite images to measure area, aggregation, and shape of green space. The 2-year cumulative change in myopia prevalence at each school and incidence of myopia at the student level after 2 years were calculated as main outcomes. The associations between landscape metrics and school myopia were assessed, controlling for geographical, demographic, and socioeconomic factors. Principal component analyses were performed to further assess the joint effect of landscape metrics at the school and individual level. A total of 138 735 students were assessed at baseline. Higher proportion, aggregation, and better connectivity of green space were correlated with slower increases in myopia prevalence. In the principal component regression, a 1-unit increase in the myopia-related green space morphology index (the first principal component) was negatively associated with a 1.7% (95% CI, -2.7 to -0.6) decrease in myopia prevalence change at the school level (P = .002). At the individual level, a 1-unit increase in myopia-related green space morphology index was associated with a 9.8% (95% CI, 4.1 to 15.1) reduction in the risk of incident myopia (P < .001), and the association remained after further adjustment for outdoor time, screen time, reading time, and parental myopia (adjusted odds ratio, 0.88; 95% CI, 0.80 to 0.97; P = .009). Structure of green space was associated with a decreased relative risk of myopia, which may provide guidance for construction and renovation of schools. Since risk estimates only indicate correlations rather than causation, further interventional studies are needed to assess the effect on school myopia of urban planning and environmental designs, especially size and aggregation metrics of green space, on school myopia.

Impacts of population density and wealth on the quantity and structure of urban green space in tropical Southeast Asia

This study attempted to classify blocks in the second ring road of Changsha, a central city of urban agglomeration in central China, according to their green space patterns, and to explore the influence of green spaces in different blocks on the surrounding microclimate. Researchers divided the blocks into five types: green space enclosed by buildings type, green space parallel with buildings type, green space centralized in buildings type, green space interspersed in the block type, and green space dispersed in the block type. Thermal comfort conditions in the different blocks were studied by ENVI-met simulations and using the thermal comfort indicators physiological equivalent temperature (PET), predicted mean vote (PMV), and standard effective temperature (SET). Because the green space was more evenly distributed in the block of green space parallel type and green space interspersed type, the overall fluctuation of the thermal comfort value of all areas of the whole block was small, with more areas having a value close to the median value of the thermal comfort value of the block. In the green enclosed blocks, thermal comfort was better within the green space in the area enclosed in the middle when the surrounding buildings were lower. The green areas in the green space enclosure type significantly improved the thermal comfort around the buildings, and the thermal comfort in the areas decreased rapidly as the distance between the green areas and the buildings increased. The green space dispersion type was found more in older blocks that were not well planned and had poor thermal comfort in the areas. On the premise that the green space area in the different high-rise blocks was equal, if only the thermal comfort of the green space coverage area was considered, in the summer, the green space parallel type was the best (|ΔPET| = 7.96, |ΔPMV| = 1.22). In the winter, the green space centralized type was the best (|ΔPET| = 11.26, |ΔSET| = 10.88). On the premise of equal green space area in the different multilayer blocks, if only the thermal comfort of green space coverage area was considered, in the summer, the green space parallel type was the best (|ΔPET| = 8.89, |ΔPMV| = 1.49). In the winter, the green space centralized type (|ΔPET| = 11.04, |ΔSET| = 10.64) was the best. This shows that different greening patterns have different advantages and disadvantages in different seasons and different situations.

As a result of environmental degradation, urban green space has become a key issue for urban sustainable development. This paper takes Liaoyang City in Northeast China as an example to develop green space planning using the computational fluid dynamics (CFD) model, landscape ecological principles and Geographical Information System (GIS). Based on the influencing factors of topography, building density and orientation, Shou Mountain, Longding Mountain and the Taizi River were selected as the urban ventilation paths to promote wind and oxygen circulation. Oxygen concentration around the green spaces gradually decreased with wind speed increase and wind direction change. There were obvious negative correlation relationships between the oxygen dispersion concentration and urban layout factors such as the building plot ratio and building density. Comparison with the field measurements found that there was significant correlation relationship between simulated oxygen concentration and field measurements (R 2 = 0.6415, p < 0.001), moreover, simulation precision was higher than 92%, which indicated CFD model was effective for urban oxygen concentration simulation. Only less than 10% areas in Liaoyang City proper needed more green space urgently to improve oxygen concentration, mainly concentrated in Baitai and west Wensheng districts. Based on land- scape ecology principle, green space planning at different spatial scales were proposed to create a green space network system for Liaoyang City, including features such as green wedges, green belts and parks. Totally, about 2012 ha of green space need to be constructed as oxygen sources and ventilation paths. Compared with the current green space pattern, proposed green space planning could improve oxygen concentration obviously. The CFD model and research results in this paper could provide an effective way and theory support for sustainable development of urban green