Patch Shape Index Research Articles

Unlocking Urban Breathability: Investigating the Synergistic Mitigation of PM2.5 and CO2 by Community Park Green Space in the Built Environment Using Simulation

Reducing carbon emissions and controlling air pollution is a dual challenge for China in addressing climate change. Analyzing the synergistic relationship between PM2.5 and CO2 in urban green spaces has become an important part of promoting pollution control. The study investigated the influence and synergistic relationship between the spatial pattern of community parks on PM2.5 and CO2 in Xi’an City, Shaanxi Province, through practical measurement and ENVI-met/Open Studio simulation calculations. The results showed that: (1) Within the sphere of influence, community parks exhibit a positive synergy varying with distance, peaking at 400 m and declining as 300 m > 500 m > 200 m > 100 m. (2) The green space rate, total edge (TE), and mean patch shape index (SHAPE_MN) positively influence the synergistic mitigation of PM2.5 and CO2, with a defined maximum impact boundary. The strongest synergistic reduction of PM2.5 and CO2 occurs at a green space rate of 85%, TE1200, and SHAPE1.2, with optimal influence boundaries of 300 m, 200 m, and 100 m, respectively. This conclusion demonstrates the key role of green space in community parks in the synergistic abatement and provides a scientific basis and practical guidance for the planning and design of urban green space under the goal of “dual-carbon”.

Exploring the relationship between preferred routes for walking exercise and green space layout in a dense urban area: a series of poisson regression models

ABSTRACT The literature considers urban green spaces as a key to promoting physical activity and health. Despite the increasingly recognized walking exercise promotion benefits of urban green spaces, the relationship between walking exercise preferences and green space layout remains unclear. We extracted preferred routes for walking exercise and measured the route heat using a one-year shared data of walking exercise from a mobile outdoor assistant app in the old city of Fuzhou, China. We then evaluated green space layout features within a 500-m buffer zone along the preferred routes in dimensions of scale, form, and function. The relationship between the route heat and green space layout features was analyzed using a series of poisson regression models, with the influence of urban morphology and the built environment allocation being controlled. Results showed that there existed a significant association between preferred routes for walking exercise and the combination of green space layout, urban morphology, and the built environment allocation; the route heat had positive associations with NDVI and comprehensive park area density, and showed negative associations with patch shape index and patch density. This study illustrates the necessity for targeted intervention strategies to promote the development of walkable and healthy urban areas.

Assessing the effects of urban green spaces metrics and spatial structure on LST and carbon sinks in Harbin, a cold region city in China

In the face of accelerated urbanization, urban green spaces (UGSs) are crucial in alleviating urban heat island effects. This study investigates the link between UGS landscape patterns, plant community characteristics, Land surface temperature (LST), and Annual carbon sequestration of plant community (ACSPC) across different seasons and scales in Harbin by remote sensing and field measurements. The results show that (1) The LST in urban built-up areas shows an increasing trend, and its correlation with UGS exhibits seasonal fluctuations. (2) Landscape pattern metrics significantly relate to LST, particularly at 1500 and 1260 m window scales. (3) In summer and winter, Aggregation index (AI) , Mean patch area (AREA_MN), Edge density (ED), Percentage of landscape (PLAND), Mean patch shape index (SHAPE_MN), Mean diameter at breast height (MDBH), Mean canopy diameter (MCD), Average carbon sequestration capacity(ACSC), and Leaf area index (LAI) are positively correlated, whereas Sky view factor (SVF) is negatively correlated with ACSPC. (4) LAI, SVF and MCD (winter) significantly affect LST. MDBH, MCD, and SVF in summer significantly influence ACSPC. This research elucidates the effect of UGS landscape patterns and plant community characteristics on urban heat islands and carbon sinks, providing vital insights for urban ecological improvement.

Evaluating the impact of landscape configuration, patterns and composition on land surface temperature: an urban heat island study in the Megacity Lahore, Pakistan.

The urban heat island (UHI) phenomenon is negatively impacted by rapid urbanization, which significantly affects people's everyday lives, socioeconomic activities, and the urban thermal environment. This study focuses on the impact of composition, configuration, and landscape patterns on land surface temperature (LST) in Lahore, Pakistan. The study uses Landsat 5-TM and Landsat 8-OLI/TIRS data acquired over the years 2000, 2010 and 2020 to derive detailed information on land use, normalized difference vegetation index, LST, urban cooling islands (UCI), green cooling islands (GCI) and landscape metrics at the class and landscape level such as percentage of the landscape (PLAND), patch density (PD), class area (CA), largest patch index (LPI), number of patches (NP), aggregation index (AI), Landscape Shape Index (LSI), patch richness (PR), and mean patch shape index (SHAPE_MN). The study's results show that from the years 2000 to 2020, the built-up area increased by 17.57%, whereas vacant land, vegetation, and water bodies declined by 03.79%, 13.32% and 0.4% respectively. Furthermore, landscape metrics at the class level (PLAND, LSI, LPI, PD, AI, and NP) show that the landscape of Lahore is becoming increasingly heterogeneous and fragmented over time. The mean LST in the study area exhibited an increasing trend i.e. 18.87°C in 2000, 20.93°C in 2010, and 22.54°C in 2020. The significant contribution of green spaces is vital for reducing the effects of UHI and is highlighted by the fact that the mean LST of impervious surfaces is, on average, roughly 3°C higher than that of urban green spaces. The findings also demonstrate that there is a strong correlation between mean LST and both the amount of green space (which is negative) and impermeable surface (which is positive). The increasing trend of fragmentation and shape complexity highlighted a positive correlation with LST, while all area-related matrices including PLAND, CA and LPI displayed a negative correlation with LST. The mean LST was significantly correlated with the size, complexity of the shape, and aggregation of the patches of impervious surface and green space, although aggregation demonstrated the most constant and robust correlation. The results indicate that to create healthier and more comfortable environments in cities, the configuration and composition of urban impermeable surfaces and green spaces should be important considerations during the landscape planning and urban design processes.

Multitemporal Analysis of Land Use/Cover Changes and Landscape Fragmentation in Murshidabad District of West Bengal, India

Rapid population growth, human migration, and commercial activities are changing land use and Land cover at a faster rate. The human being's need and greed to sustain themselves alter the earth's natural environment, and that change affected us. However, poor and unmanageable land conversion led to severe environmental effects. For planning and management purposes, precise information regarding land use and its characteristics is required to ensure the sustainability of the area. The current study uses multi-temporal satellite images to analyze the decadal change from 1991 to 2021. Supervised image classification is performed using the Maximum likelihood classifier. The main goal of this study is to compare post-classification results using change vector analysis and analyze human impact on the environment using FRAGSTAT. Fragstat is a widely used software program designed for analyzing spatial patterns in categorical maps. It is commonly employed in landscape ecology, conservation biology, and land management studies. The primary purpose of Fragstats is to quantify and assess the composition and configuration of patches or landscape elements within a given area. The built-up area increased from 2.57% to 8.41% over the past 30 years, while the agricultural land decreased from 83.51% to 70.05%. It was observed that the density of patches and percentage of landscape reduction over time, the rise in the number of patches for agricultural class from 3570 in 1991 to 10173 in 2021 indicates that spatial diversity is increasing in the class with higher levels of anthropogenic disturbances. Moreover, in landscape-level indices, the number of patch and landscape shape index increases, and a fall in the largest patch index indicate that the landscape is becoming more complicated and fragmented. To achieve the sustainable land-use planning and safeguard natural ecosystems and biodiversity from anthropogenic activities, land-use change maps are utilized as an early warning system.

How spatial patterns affect urban green space equity at different equity levels: A Bayesian quantile regression approach

In many cities, urban green space (UGS) is not evenly distributed, and marginalized communities often lack UGS due to legacies of disinvestment. Existing research mainly focused on the effect of socioeconomic factors to understand the disparity in access to UGS. However, little is known on how spatial patterns affect UGS equality at the regional and subregional levels. Moreover, the potential quantile effects in the UGS pattern and equality relationship remain unclear. Here, we explored how UGS equality varied among UGS spatial patterns and socioeconomic gradients in seven counties (1,227 census tracts) in Southeast Michigan. First, we quantified UGS equality of each tract with a spatially explicit Gini coefficient and UGS spatial patterns via landscape metrics. Then, we applied a Bayesian quantile regression model to investigate the nonlinear relationship between spatial pattern and UGS equality in the whole study area and its three subregions of different population density. Our results showed that spatial patterns had quantile effects on UGS equality. At the regional scale, patch density and the largest patch index had significant positive effects on UGS equality at all equality levels. The mean patch shape index positively correlated with UGS equality in areas with higher equality level (Gini coefficient: 0.52–0.92). At the subregional level, patch density most efficiently predicted UGS equality in densely populated areas, while the largest patch index also affected UGS equality in areas with low population density. We suggest policymakers should focus on areas with extremely unequal access to UGS to increase the number rather than the cumulative area of UGS. Besides, more linear and irregular UGS patches with longer perimeters have potential to serve more communities.

Linking urban park cool island effects to the landscape patterns inside and outside the park: A simultaneous equation modeling approach

Urban parks have significant cooling effects that alleviate the urban heat island (UHI). The park cool island intensity (PCII), the temperature difference between outside and inside a park, is a widely used indicator of a park’s cooling ability. Understanding the driving factors of the PCII is crucial for better urban park planning and management. Taking the subtropical city of Changsha, China, as a case study, factors such as the landscape patterns inside and outside a park and the geometric morphology of a park were investigated, based on 153 urban parks. A novel simultaneous equation modeling (SEM) approach was used to account for the mutual interactions between the temperatures of a park and its buffer. The results show that: (1) Park geometric morphology and landscape patterns inside and outside the park all significantly impact the PCII. Specifically, doubling the park size can increase the PCII by 0.8 K. Increasing by ten percent the tree cover and water cover in the park can enhance the PCII by 0.12 K and 0.41 K, respectively. Increasing the mean tree patch size by 1 ha can increase the PCII by 0.43 K. Decreasing by one unit the mean tree patch shape index can increase the PCII by 0.31 K. (2) An elasticity analysis, which measures the PCII percent change resulting from one percent change in a driving factor, indicates the relative importance ranking of these driving factors: percent cover of impervious surface outside the park (0.77), park size (0.72), percent cover of tree within the park (0.51), percent cover of water within the park (0.12), and mean patch size of tree cover within the park (0.086). To enhance the PCII, we recommend building parks in the hot areas of the city, increasing their size, increasing the percentages of tree cover and water cover in the park, and optimizing the spatial configuration of the tree cover in the park (i.e., aggregating trees in a smaller number of large patches and reducing the irregularity of tree patches).

Does the Spatial Pattern of Plants and Green Space Affect Air Pollutant Concentrations? Evidence from 37 Garden Cities in China.

Relevant studies have demonstrated that urban green spaces composed of various types of plants are able to alleviate the morbidity and mortality of respiratory diseases, by reducing air pollution levels. In order to explore the relationship between the spatial pattern of urban green spaces and air pollutant concentrations, this study takes 37 garden cities with subtropical monsoon climate in China as the research object and selects the urban air quality monitoring data and land use type data in 2019 to analyze the relationship between the spatial pattern and the air pollutant concentration through the landscape metrics model and spatial regression model. Moreover, the threshold effect of the impact of green space on air pollutant concentrations is estimated, as well. The results showed that the spatial pattern of urban green space was significantly correlated with the concentrations of PM2.5 (PM with aerodynamic diameters of 2.5 mmor less), NO2 (Nitrogen Dioxide), and SO2 (Sulfur dioxide) pollutants in the air, while the concentrations of PM10 (PM with aerodynamic diameters of 10 mmor less) pollutants were not significantly affected by the green space pattern. Among them, the patch shape index (LSI), patch density (PD) and patch proportion in landscape area (PLAND) of forest land can affect the concentration of PM2.5, NO2, and SO2, respectively. The PLAND, PD, and LSI of grassland and farmland can also have an additional impact on the concentration of SO2 pollutants. The study also found that there was a significant threshold effect within the impact mechanism of urban green space landscape pattern indicators (LSI, PD, PLAND) on the concentrations of PM2.5, NO2, and SO2 air pollutants. The results of this study not only clarified the impact mechanism of the spatial pattern of urban green space on air pollutant concentrations but also provided quantitative reference and scientific basis for the optimization and updating of urban green space to promote public health.

Landscape Design of Rural Characteristic Towns Based on Big Data Technology

This study explores an effective method for constructing a landscape model of characteristic rural towns. In this study, the landscape pattern index of characteristic rural towns is obtained by calculating the total area, patch density, patch shape index, and average patch fractal dimension of the landscape area of characteristic rural towns. At the same time, this study calculates the minimum function of the three-dimensional rural characteristic town landscape cloud fusion transformation. At the same time, this study uses this function to calculate the 3D translation transformation, the rotation matrix of the 3D model, and the scaling factor transformation of the 3D model to construct the 3D model of the rural characteristic town landscape area. The simulation results show that the above method can reduce the error, reduce the registration time, improve the convergence, and reduce redundancy. This method can enhance the overall effect of constructing a three-dimensional model of a rural characteristic town landscape area.

Temporal dynamics of satellite-derived vegetation pattern and growth in an arid inland river basin, Tibetan Plateau

Exploring alpine vegetation dynamics and its driving factors help to predict changes in hydrothermal circulations at high elevations in the future. Currently, there is a lack of systematic research on this topic in the permafrost regions at a basin scale. Spatiotemporal variations of vegetation pattern and growth estimated from satellite remote sensing images at a 30 m spatial resolution on the Google Earth Engine cloud platform were investigated in the Shule River headwater region from 2000 to 2021. The results showed shrinking trends in alpine grassland (AG) and wetland (AW) area, while an expanding trend in alpine desert (AD) area. AD moved to lower altitudes and occupied AG and AW area, possibly owing to mountain permafrost degradation and extreme precipitation. Vegetation patches became fragmented and complex, reflected by increases in patch number and shape index. Vegetation growth, as approximated by the normalized difference vegetation index (NDVI), increased significantly in 42 % of the vegetated area ( p < 0.05), particularly in river valleys. In addition, NDVI was negatively correlated with solar radiation over 30 % of the vegetated area ( p < 0.05). At the regional scale, vegetation NDVI was positively correlated with root-zone soil moisture and grazing intensity ( p < 0.05). The findings suggested that AG and AW at their upper limit of distribution were vulnerable to soil erosion, and the inter-annual variation of vegetation growth was affected by soil moisture and grazing. Moreover, future climate warming may cause alpine vegetation decline by increasing active layer thickness in the absence of adequate precipitation supply. • Alpine desert mainly invades downward into alpine wetland. • Vegetation patches tend to be fragmented. • NDVI increases significantly in alpine desert. • A negative correlation between NDVI and solar radiation is observed. • Regional vegetation NDVI positively correlates with grazing intensity.

Association between residential greenspace structures and frailty in a cohort of older Chinese adults

BackgroundFrailty is a late-life clinical syndrome resulting from the accumulation of aging-induced decline. Greenspaces measured with normalized difference vegetation index (NDVI) are protective of frailty. However, NDVI is not as informative as structure indices in describing greenspaces’ constitution, shape, and connectivity measured by the largest patch index (LPI), shape index, and cohesion index representing larger, more complex, and more dense greenspaces through higher values. We aim to study the association between greenness structures and frailty in a cohort of Chinese older adults.MethodsWe included older adults from 2008–2014 China Longitudinal Healthy Longevity Survey (CLHLS). We used greenspace indices from satellite to quantify structures (area-edge, shape, proximity) at county-level, and calculated frailty index (FI) as an outcome. We did cross-sectional analyses using linear and logistical regression, and longitudinal analyses using the generalized estimating equations (GEE).ResultsAmong 8776 baseline participants, mean LPI, shape, cohesion, and FI are 7.93, 8.11, 97.6, and 0.17. In cross-sectional analyses, we find negative dose-response relationships for greenspace structures and frailty, especially in females, centenarians, illiterate people, city residents, unmarried people, and individuals with increased frailty. Participants living in the highest quartile of LPI, shape, and cohesion have 32% (95%CI: 21–42%), 35% (95%CI: 24–44%), and 37% (95%CI: 26%–46%) lower odds of frailty than the lowest quartile. However, we do not find a significant association in longitudinal analyses.ConclusionsHigher levels of greenness structures (area-edge, shape, and proximity) might be related to lower frailty, while a clear longitudinal benefit cannot be identified in this analysis.

北京市景观格局演变及其对多种生态系统服务的影响

PDF HTML阅读 XML下载 导出引用 引用提醒 北京市景观格局演变及其对多种生态系统服务的影响 DOI: 10.5846/stxb202107272042 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 城市与区域生态国家重点实验室开放基金项目（SKLURE2020-2-1）；国家自然科学基金青年科学基金项目（41901227） Changes of landscape pattern and its impacts on multiple ecosystem services in Beijing Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:景观格局是人与自然共同作用的结果，深刻影响着区域的生态系统服务及可持续发展。以高度城市化地区——北京市为案例，选取了粮食生产、水质净化、空气净化、气体调节、生境质量以及休闲娱乐6项重要生态系统服务，采用InVEST （Integrated Valuation of Ecosystem Services and Tradeoffs）、ROS （Recreation Opportunity Spectrum）等模型定量评估了1980-2018年生态系统服务时空分布。通过对北京市景观组成与景观配置指数进行定量分析，进一步探索了景观格局演变对生态系统服务的影响。结果表明：（1）北京市1980-2018年建设用地扩张了120%，建设用地扩张是耕地面积减少的主要原因。景观格局总体上趋于破碎化，斑块形状复杂化，斑块异质性增加；（2）时间上，北京市1980-2018年粮食生产、水质净化、气体调节、生境质量、休闲娱乐5项生态系统服务均呈现下降趋势；空间上，粮食生产呈现东南部较高，西北部较低的分布特征，其余生态系统服务均呈现西北部较高，东南部较低，市中心最低的分布特征；（3）景观水平上，林地、建设用地等景观组成变化对生态系统服务影响最为显著。破碎化指数、多样性指数等景观配置指数对生态系统服务影响最为显著；类型水平上，平均斑块面积、斑块聚集度对生态系统服务影响相对最为显著。总体来讲，通过分析景观格局演变如何影响生态系统服务，为如何通过设计和优化景观格局来提升生态系统服务提供了定量依据，从而为促进区域的景观可持续规划提供理论依据和案例参考。 Abstract:Landscape pattern is the result of the interaction between human and nature. During the urbanization process, the landscape pattern had changed significantly, which affected the ecological process and thus threatened ecosystem services. The assessment of the impacts of landscape pattern on ecosystem services is crucial for regional sustainable development. Taking Beijing as a case study, we quantified the spatiotemporal changes of six selected ecosystem services, including food production, water purification, air quality regulation, carbon storage, habitat quality, and recreation opportunity from 1980 to 2018 by using the InVEST and Recreation Opportunity Spectrum (ROS) models. In addition, we analyzed changes of landscape patterns in Beijing, and explored the impacts of landscape patterns on ecosystem services. The results showed that:1) the landscape pattern in Beijing experienced dramatically changes from 1980 to 2018. The build-up land expanded by 120%, which mainly resulted in the reduction of cropland. The overall landscape pattern tended to be fragmented, the patch shape index became more complicated and the heterogeneity of the patches also increased. 2) Food production, water purification, carbon storage, habitat quality, and recreation opportunity showed a decreasing trend from 1980 to 2018. Spatially, food production was higher in the southeast and lower in the northwest. For the other ecosystem services, they were higher in the northwest, lower in the southeast, and the lowest in the central downtown area. During the past 38 years, the food production decreased the most in the central region of Beijing, while the air quality regulation, carbon storage, habitat quality, and recreational opportunity decreased the most in the eastern and southern regions. 3) At the landscape level, changes in land-use types, such as woodland, grassland, and build-up land had the most significant impacts on ecosystem services. Landscape metrics, such as the fragmentation index and diversity index influenced ecosystem services more obviously. At the class level, the mean patch area and the patch aggregation index had the most significant impacts on ecosystem services. Among them, the mean patch area and aggregation index of woodland showed positive effects on air quality and carbon storage. However, the aggregation index of built-up land showed a negative effect on habitat quality. In general, this study focused on analyzing the impacts of landscape pattern changes on ecosystem services, which can help decision makers to improve the ecosystem services by optimizing landscape patterns. The results of this study also can provide theoretical and practical basis for promoting regional landscape sustainable planning. 参考文献 相似文献 引证文献

Revisiting the cooling effects of urban greening: Planning implications of vegetation types and spatial configuration

While it is well recognised that increasing vegetation cover reduces the Urban Heat Island (UHI) effect in cities, less is understood about the spatial pattern of vegetation type required to maximise cooling benefits. This study examines how different urban vegetation spatial configuration and composition impact on the UHI phenomenon. We investigated this on a set of sites in Metropolitan Melbourne, Australia. Urban vegetation raster data at 20 cm resolution was used to define five height-based vegetation cover types (grass, shrub, small, medium and large trees); and to calculate eight landscape metrics: percentage of landscape (PLAND), mean patch area (AREA_MN), patch density (PD), edge density (ED), mean patch shape index (SHAPE_MN), mean Euclidean nearest-neighbour distance (ENN_MN), Shannon’s diversity index (SHDI) and Landscape shape index (LSI). These vegetation landscape metrics of vegetation types were established as independent variables and statistically analysed with the UHI intensity as the dependent variable using Classification and Regression Tree analysis (CRT). The CRT model was developed based on 6469 records and including depth of 5, 41 nodes and 21 terminal nodes. From the total 43 independent variables, 11 were identified as high impact factors on UHI intensity. Our findings revealed a consistent negative statistical relationship between UHI intensity and PLAND-landscape, PLAND-large tree and PLAND-medium tree across the study sites. The PLAND, ED and AREA_MN were the most prevalent metrics to explain UHI effect, which has also principally demonstrated the joint impact of the three metrics on UHI effect. This study presents a new framework of a fine-scale assessment and modelling for the impact of urban vegetation on UHI; and elaborates a practical approach of using CRT technique to design local-based UHI mitigating strategies taking advantage of different vegetation structure.

Characteristics of spatial pattern evolution of blowouts in the Hulunbuir sandy grassland

Blowout is the initial form of sand steppe, sand belts, or dunes; the continuous development of blowouts leads to desertification expansion and ecological degradation. The study area is a typical blowout area of 30762 hm2 near a sand belt north of the Hulunbuir Grassland. Seven images from Landsat TM (30 m) taken in 1984, 1990, 1995, 2000, 2005, 2010, and 2016 were analyzed using ArcGIS and FRAGSTATS software and the landscape pattern index method to investigate the spatial pattern change of blowouts. The results show that the distribution of blowouts is intensive, primarily comprising blowouts < 10 hm2, with the largest blowout in the study area being equal to 96.59 hm2. There was a significant correlation among blowout patch area, shape index, and fractal dimension. The long axis of the blowouts is consistent with the prevailing wind. As the blowout area increases, it forms a saucer-trough irregular shape. In summary, although there is a wide blowout distribution, there is a local aggregation distribution in certain areas resulting in significant landscape fragmentation. As a result, there is a risk of blowout fragments forming a large area of sandy land or partially broken sand dune landforms. To prevent desertification, corresponding measures for vegetation restoration should be implemented during early-stage blowouts.

Greenspace configuration impact on the urban heat island in the Olympic Area of Beijing.

As urban green spaces have significant cooling effects on the urban heat island (UHI), a precise understanding of these effects is necessary to devise precise greenspace strategies for abating the UHI. This paper explores the impacts of different greenspace (trees, grass, and water) patterns on the UHI in Beijing's Olympic Area, using different grid cell sizes and spatial statistical models. Greenspace pattern metrics include percent cover, mean patch size (MPS), mean patch shape index (MSI), edge density (ED), and largest percent index (LPI). The results show that different greenspace metrics have varying effects on surface temperature. The spatial error model (SEM) turns out to be a good choice for estimating the relationship between Land Surface Temperature (LST) and the greenspace metrics. The regression coefficients of these metrics vary with grid cell size. Tree and grass edge densities have opposite effects, which suggest that trees should be planted in smaller clusters, whereas grass should be planted in larger and continuous patches in order to reach maximum LST cooling. The optimal grid cell size is in the [120-240m] range. These findings can help urban planners mitigate the UHI in a city with limited green space availability.

Predicting the surface urban heat island intensity of future urban green space development using a multi-scenario simulation

The distribution and morphology of urban green space (UGS) have a significant impact on its ability to mitigate surface urban heat island intensity (SUHII). In this study, the Future Land Use Simulation model was used to predict land use in Xuchang City by 2030 under three different scenarios. Landsat Operational Land Imager images from 2014 were used to calculate SUHII. And, the relationships and regression models between summer SUHII and land use data were analyzed and developed. The SUHII in 2030 based on simulated land use was predicted using regression models in 2014. Results showed that reductions in SUHII were greater under present land use scenario with a greater proportion of UGS. Furthermore, the degree of aggregation, average patch area, maximum patch shape index, and complexity of the green space also impacted the effectiveness of SUHII mitigation. In future land use scenarios with different constraints, the ecological service function guided scenario was the most effective at mitigating SUHII. These findings provide a reference for the reasonable allocation of urban land and the optimization of UGS in terms of SUHII reduction in small- and medium-sized cities where there is a need for expansion.

Research on Key Technical Indexes of Coastal Reclamation Planning and Design

Large-scale coastal reclamation has become a significant land use issue worldwide for urban construction and economic development. The reclamation of coastal wetlands brings substantial economic benefits, however, the structure and function of coastal ecosystem are affected by drivers of human-caused landscape change. This research takes Hangzhou Bay and Zhoushan Islands as the case study to investigate the correlation between the coastal geomorphic complexity and the tidal range reduction rate, and to explore the control technical indexes of the design in reclamation area by a multidisciplinary approach that integrates the basic theories and quantitative methods of fractal geometry with the hydrodynamic mechanism of ocean dynamics. The results show that the coastal tidal range reduction rate is closely related to the coastline fractal dimension and patch shape index (D, S), and reveals the influence of the complexity of the coastal landscape on the tidal energy loss. In addition, based on model predictions, it can be found that the large-scale reclamation in Zhoushan will cause a serious decline in the complexity of the coastal landscape and the reduction of tidal energy, which is extremely detrimental to coastal disaster prevention. In the end, the scientific design theory and quantitative control indexes of reclamation are put forward to provide theoretical basis and design reference for future coastal reclamation and disaster prevention.

Quantifying landscape-metrics impacts on urban green-spaces and water-bodies cooling effect: The study of Nanjing, China

Urban green spaces and water bodies play major roles in effectively alleviating the urban heat island (UHI) effect by decreasing temperature. Here, statistical correlation and regression analyses were carried out to examine the relationships between metrics (vegetation coverage, water coverage, area, leaf area (LA), and patch shape index (PSI)) and land surface temperature (LST) or cooling effect intensity. Besides, the cooling ranges of green spaces and water bodies were explored by sampling and thresholds detection. Landsat satellite images were used to derive land cover map and LST map. Results show that: (1) average LST within given residential area decreases with the vegetation coverage and water coverage increasing; (2) area and LA are both significantly positively correlated with the cooling effect beneficial to people entering green spaces; (3) increasing the vegetation coverage in the range from approximately 0–28 % can effectively and economically alleviate the thermal environment of residential areas, and the economical optimal green space area and LA are both 0.26 ha; and (4) from 2010 to 2017, the cooling range of green spaces in the central districts of Nanjing is around between 180 and 810 m with the average value of 540 m, while the cooling range of water bodies ranges approximately from 510 to 1260 m with the average value of 840 m.

Consolidating the layout of rural settlements using system dynamics and the multi-agent system

The Chinese government has emphasized the consolidation of rural settlements because of their extensive use and hollowing-out in the hope that agricultural land losses can be mitigated nationally, the ecological environment can be protected, and improvements can be made to agricultural production and rural livelihoods. Rural settlement consolidation, however, necessitates a complex optimization approach that involves multiple aspects including rural development policies as well as the evaluation and classification of settlement layout, the land-use decision-making behaviors of villagers, and spatial layout optimization models. Current rural settlement consolidation methods lack these respective modules and have therefore led to unsatisfactory results. We therefore coupled a spatial layout evaluation module, system dynamics (SD) models, and the multi-agent system (MAS) to establish the rural settlement consolidation model (RSCM), an approach which can comprehensively consider all these aspects. This model was then applied in Jizhou District, Tianjin, using the three policy scenarios of ecology priority, coordinated ecology and economic development, and economy priority. The results of this analysis show that, in the first place, given an optimized rural settlement layout, the average suitability value and degree of average spatial compactness increased by 3.23% and 96.68% respectively. These outcomes indicate that the model can improve land-use suitability and solve the village hollowing-out issue. Data also show that landscape pattern indices including average plaque density, average patch shape index, and average fractal dimension all decreased by 54.17%, 26.20, and 4.60%, respectively, while average plaque area increased by 117.22%. These outcomes indicate that the use of this model can reduce rural settlement complexity and alleviate the pressure on the landscape matrix. Secondly, results show that the consolidation of rural settlements leads to clear socioeconomic and ecological benefits within the study area as well as improvements in agricultural production and rural livelihoods because of spatial layout adjustments. Third, ecological and economic policy priorities reduce the effectiveness of rural settlement consolidation, hinder economic development, and can even exacerbate the hollowing-out of villages. Rural development policies must therefore strike a balance between environmental protection and economic development.

Spatial structure of woody cover affects habitat use patterns of ocelots in Texas

About 80% of the known breeding population of ocelots (Leopardus pardalis) in the USA occurs exclusively on private ranches in northern Willacy and Kenedy counties in South Texas. These private ranches support several large contiguous undisturbed patches of thornscrub, which is preferred by ocelots. Past studies have indicated ocelots in South Texas select for woody patches that contain extremely dense thornscrub (i.e., 95% canopy cover and 85% vertical cover) and require large patches of woody cover to survive. Landscape metrics have been used to explain ocelot habitat use in fragmented areas, but their application in less-fragmented rangelands is lacking. From 2011 to 2018, we used camera traps on the East Foundation’s El Sauz Ranch to assess seasonal habitat use of ocelots relative to landscape structure, configuration, and complexity and other site-level factors in South Texas. Seasonal habitat use and detection were positively influenced by larger mean patch area and lower landscape shape index values. We also observed ocelots were less likely to be detected during periods of drought and exhibited a seasonal trend in detection. Ocelots used woody patches that were larger and more regularly shaped, indicating a preference for areas with a lower degree of fragmentation across the study area. As patches become larger, they will coalesce over time and form larger woody aggregates, which will promote ocelot habitat use. Brush management needs to be strategic as patch area and shape index are a limiting factor to promote ocelot habitat use on working rangelands in South Texas. These results demonstrate the ability to use landscape metrics to discern the effects of spatial structure of vegetation communities relative to ocelot occupancy parameters.