Recreational ecosystem services (RES), understood as the numerous benefits people obtain from landscapes and the natural environment, are a topical area of policy, research and society. This Editorial introduces the current state of RES research, provides an overview of the 21 contributions comprising this Special Issue of Ecosystem Services, and outlines opportunities for further research. This issue's publications employ diverse methods for assessing and valuing RES at different scales in Europe and beyond. The papers present advancements in mapping and valuation, provide evidence for the contributions of biodiversity and landscapes to the generation of RES and human well-being, and shed light on distributional effects across different beneficiaries. Taken together, contributions emphasize that RES may be a prime vehicle for reconnecting people with nature with positive effects on societal well-being. The diversity of approaches currently applied in RES research reflects much creativity and new insights, for example by harnessing georeferenced social media data. Future research should aim towards harmonizing datasets and methods to enhance comparability without compromising the need for context-specific adaptations. Finally, more research is needed on options for integrating RES information in decision making, planning and management in order to enhance actual uptake in public and private decisions.

In the process of rapid urbanization, the outdoor recreational resources are becoming scarcely. Although the construction of parks can promote recreational welfare and happiness for urban dwellers, few studies have focused on the recreational happiness of park users and its inherent differences, which carried great significance for urban governments to allocate the recreational facilities and services. Thus, this study, taking Hangzhou as a case, carried out a questionnaire survey on the dwellers in urban parks so as to explore the components, spatial disparities and social differences of recreational happiness for urban residents by using the methods of Principal Component Regression and Multivariate Analysis of Variance. The results show that: firstly, the park-based recreational happiness of urban residents exists three dimensions with emotional happiness as the first principal component which can be interpreted by the variables of enjoyment, relaxation and fullness, perceptional happiness as the second component which can be explained by the variables of ‘increasing efficiency at work’, ‘promoting family harmony’ and ‘enhancing personal health’, and social happiness as the third component which can be illustrated by the variables of ‘making new or old friends’, ‘developing interpersonal relationship’ and ‘improving life quality’; secondly, there exist spatial disparities for the recreational happiness of park users,with the large urban parks mainly undertaking the functions of improving the emotional and perceptional happiness and the community parks raising the social happiness for urban residents; thirdly, the four demographic variables, including income, age, education and occupation have significance influences on park-based recreational happiness, which can be further illustrated by the impact of income on the emotional happiness variable, education on social happiness, age on emotional and social happiness, and occupation on the whole components of recreational happiness. Hence the urban governances should allocate the recreational facilities in parks according to the components of recreational happiness and the characteristics of its social and spatial differences.

以杭州市为例,对公园游憩者进行问卷调查,运用主成分回归和方差分析法揭示游憩幸福感的因素构成、空间分异与社会差异。研究发现：城市居民的游憩幸福感主要由情感幸福感、认知幸福感和社会幸福感构成,是具有层次性的主观幸福感;公园游憩幸福感存在空间分异性,居民主要通过城市大公园游憩获取情感幸福感和认知幸福感、通过社区小公园游憩获取社会幸福感;公园游憩幸福感存在社会差异性,收入、年龄、文化程度、职业4个变量对游憩幸福感有显著影响。为此,城市政府应以游憩幸福感为核心,根据居民游憩幸福感的感知现状及其空间、社会差异特征进行公园游憩空间的建构与游憩设施配置。

Urban parks provide multiple non-material benefits to human health and well-being; measuring these “intangible” benefits mainly co-produced by the spatial interactivity between dwellers and urban parks is vital for urban green space management. This paper introduced “vitality” to measure the intangible benefits of urban parks and constructed a straightforward and spatially explicit approach to assess the park vitality based on visiting intensity and recreational satisfaction rate. Freely available data of check-in comments on parks, points-of-interest (POIs), and other multi-source data from Beijing were used to assess the urban park vitality and explore the factors influencing it from the perspectives of recreational service supply, demand, and spatial linking characteristics. We found that the urban park vitalities decreased along the urban–rural gradient. The presence of water and facility density in the parks have significant positive impacts on park vitality, and high population density nearby was a positive factor. Moreover, the external higher levels of the POI-based urban function mix and density, as well as developed public transportation, were strongly associated with greater park vitality. Our research proposed a feasible and effective method to assess the park vitality, and the findings from this study have significant implications for optimizing the spatial configuration of urban parks.

The scale effect and spatial heterogeneity determine that the effect of the same influencing factor on ecological processes varies in different geographic units. In this study, urban thermal environmental effect, a typical urban ecological process, was examined. In view of the current situation that the thermal environmental response pattern of different geographic units has rarely been studied, four geographic units—land use type, grid, city block, and local climate area, were selected. Normalized Difference Vegetation Index (NDVI), Impervious Surface Area (ISA), and Modified Normalized Difference Water Index (MNDWI)—the commonly used indices for landscape components, were used as the impact factors of thermal environment to explore the differences in the response pattern of urban thermal environment in different geographic units. The urban area of Beijing Municipality as redefined by point of interest (POI) data was chosen as the study area. The results show that: 1) The Pearson correlation analysis of single factor and land surface temperature shows that the block and grid are the division units, and the influence factors have a high correlation with land surface temperature. 2) The multi-factor regression method is more suitable for explaining the spatial variability of land surface temperature (LST). The combination of vegetation and impervious factors is in the 4-km grid unit, while the combination of vegetation and water and the combination of vegetation, water and impervious factors is the strongest in explaining the spatial variability of LST in the local climate zone unit. This paper provides a basis for the selection of the influencing factors of heat island in big cities in northern China, enriches the case study on the response law of thermal environmental effects at different scales, and provides a basis for the selection of appropriate research units for urban ecological environmental effects.

尺度效应及空间异质性决定了同一种影响因子在不同地理单元中对生态过程的作用程度存在差异。论文选择城市热环境效应这一典型城市生态过程为研究对象,针对地理单元划分对热环境效应响应规律研究较少的现状,选择土地利用分类、网格、街区、局地气候区4种地理单元,选取常用景观组分指标——归一化差值植被指数(normalized difference vegetation index, NDVI)、不透水面比例(impervious surface area, ISA)和改进的归一化差值水体指数(modified normalized difference water index, MNDWI)为热环境影响因子,探究不同地理单元中城市热环境对景观组分响应规律的差异。以POI数据重新定义的北京市城区为研究区,结果表明：① 单因子与地表温度Pearson相关性分析得出街区和网格作为划分单元,各影响因子与地表温度相关性较高;② 利用多因子回归方法更适合于地表温度空间分异的解释,植被和不透水面比例因子的组合在4 km网格单元,植被、水体组合及植被、水体、不透水面比例的因子组合在局地气候区单元对地表温度空间分异的解释力度最强。论文为北方大城市热岛影响因素选择提供依据,充实了不同尺度热环境效应响应规律的案例研究,并为城市生态环境效应选择适当研究单元提供依据。

Habitat quality determines the status of regional biodiversity which is an important reflection of ecosystem service function and health. Research on the habitat quality change is of great significance for regional ecological security. The reconstruction of regional spatial pattern of habitat quality can restore the background of ecological environment in historical periods and provide scientific support for revealing the evolution law of regional ecological environment quality. This study selected ten driving factors (elevation, slope, aspect, GDP, population, temperature, rainfall, river distance, urban distance and coastline distance) to build CA-Markov model parameters and simulate the historical land use data. The spatial pattern of habitat quality was rebuilt by InVEST model, and the classification and statistics of habitat degradation and habitat quality were examined. The results showed that: (1) The Kappa coefficient is 0.88, which proves that the land use spatial pattern reconstruction method is feasible. On this basis, the simulation of habitat quality is valid. (2) During 1975-2010, the degree of habitat degradation increased gradually and the spatial distribution of it showed a regular pattern of circle layer change. The habitat quality decreased steadily. The high-value areas were mainly distributed in mountainous areas, while the low-value areas were mostly distributed across built-up areas. In this period, the low-value areas gradually engulfed the high-value areas around, and the habitats of the high-value areas tended to be fragmented. (3) In 1975-2010, the spatio-temporal variations of habitat quality indicated that the region with the worst habitat quality were difficult to recover; the regions with the poor habitat quality accounted for 6.40% of the total area, which were prone to deteriorate and occur around the built-up areas; the areas with the best or better habitat quality grades accounted for 5.68% of the total area, which could be easily transformed into the poor and worst habitat grades, making the habitat more scattered. (4) There was a marked change with land use of study areas during 1975-2010, which had a huge impact on the quality of the habitat. The above results show that the habitat quality of the study area decreases gradually. More than 60% of the regions have poor or even worst habitat quality. Construction land is the biggest factor threatening the habitat quality.

区域生境质量决定了区域内生物多样性状况,是生态系统服务功能和生态系统健康程度的重要体现,生境质量变化研究对区域生态安全具有重要的意义,重建区域生境质量空间格局可以还原历史时期的生态环境本底,为揭示区域生态环境质量演变规律提供科学支撑。选取高程、坡度、坡向、GDP、人口、温度、降雨量、河流距离、城市距离和海岸线距离10个土地利用变化驱动因子,构建CA-Markov模型参数,模拟得到历史土地利用空间数据。在此基础上,运用InVEST模型重建泛长三角地区生境质量空间格局,并对计算得到的生境退化度指数和生境质量指数进行等级划分与统计分析。结果表明：① 土地利用模拟精度验证结果显示Kappa系数为0.88,土地利用空间格局重建方法可行,可以在此基础上开展生境质量模拟研究。② 泛长三角地区1975—2010年生境退化度逐期上升,生境退化等级在空间分布上呈圈层递变分布规律。生境质量则表现为逐期下降,高值区主要分布于山区,低值区大部分位于建成区;1975—2010年期间,低值区逐渐向周边高值区域演变,生境高值区域趋于破碎化。③ 1975—2010年生境质量时空变化特征表明：生境质量差等级区域,其生境难以修复,维持原状居多;生境质量较差等级区域易继续恶化,且易发生在建成区周边区域,占比为研究区总面积的6.40%;生境等级为良好和优等的区域,其生境等级易转换为差等和较差等级,使得该区域的生境破碎化,该类型的面积占比为研究区总面积的5.68%。④ 1975—2010年期间,研究区土地利用变化显著,对生境质量造成了巨大的影响;研究区生境质量逐期变差,其生境质量等级位于较差及以下区域面积占比均达到了60%以上,建设用地为生境质量最大的威胁因子。

Biodiversity is the material foundation and the environment guarantee that people are taken for their survival and development, and has four levels: molecular, species, ecosystem and landscape. The identification and understanding of the spatiotemporal variation of biodiversity in the landscape level are not just an important part of regional biodiversity monitoring and assessment, but also the first step in the formulation and implementation of the protection scheme. The Bailong River Watershed of the Gansu Province (BRWGP), one of the most abundant biodiversity regions in China, locating in the transitional ecotone along the Tibetan Plateau, Loess Plateau, and Qinba Mountains. The BRWGP served as a case to analyze the spatiotemporal heterogeneity of biodiversity. Based on the data integration analysis platforms of RS, GIS technology and InVEST 2.5.6, a comprehensive index system of biodiversity spatial patterns constructed with the incorporation of habitat quality, vegetation net primary productivity, and landscape state index by considering the difference of three assessment indicators intensity and influence degree. Meanwhile, AHP was used to determine the weights of index and GIS spatial technology apply to analyze the spatial distribution and differentiation of biodiversity under the landscape geography perspective. Our goal went to revise the biodiversity evaluation module of the InVEST model and demonstrate biodiversity spatial patterns on a grid cell. The results indicated that biodiversity was high and had obvious spatial pattern variations in the BRWGP. Areas with higher biodiversity mainly distribute in the national nature reserve and forest, while the relatively poor biodiversity areas distribute in the valleys of the BRWGP between Zhouqu-Wudu-Wenxian, the valley of Minjiang in Tanchang County, alpine mountain snow regions, and bare rock zones. Moderate biodiversity areas appeared in agroforestry ecosystem, shrub and grassland, which a vegetation cover, species richness and habitat quality were general. In 1990-2010, biodiversity was high and increasing, the area proportion of higher biodiversity (critical areas) increased from 16.74% to 26.02%. Meanwhile, the high growth area of biodiversity change was concentrated in the ecological engineering zone and forest districts (Such as these forest districts of Duoer, Axia, the Minshan Mountain, Baishuijiang Nature Reserve), while biodiversity reduction area locates in the region of human activities intensively and frequently, such as cultivated area, urban and rural areas.

以甘肃白龙江流域为例,结合遥感、GIS技术和InVEST模型,从景观地理学角度选取区域生境质量、植物净初级生产力和景观状态指数为评价指标,构建区域景观尺度上生物多样性空间格局综合评估方法,并在栅格像元尺度上开展白龙江流域景观生物多样性时空变化特征分析。结果表明：白龙江流域景观生物多样性空间格局差异显著,其生物多样性高值区主要集中在自然保护区和林业管护区,低值区主要分布在舟曲-武都-文县的白龙江两岸及其以北区域、宕昌县岷江沿岸、高寒稀疏植被区和高山积雪-裸岩区。1990~2010年,流域景观生物多样性较为丰富,整体呈现不断增长的趋势,个别局部区域减弱,其增长区主要体现在生态工程实施区和林业管护区,减少区多分布在人类活动频繁的城乡农耕区和灾害多发区。

The ability to predict spatial variation in biodiversity is a long-standing but elusive objective of landscape ecology. It depends on a detailed understanding of relationships between landscape and patch structure and taxonomic richness, and accurate spatial modelling. Complex heterogeneous environments such as cities pose particular challenges, as well as heightened relevance, given the increasing rate of urbanisation globally. Here we use a GIS-linked Bayesian Belief Network approach to test whether landscape and patch structural characteristics (including vegetation height, green-space patch size and their connectivity) drive measured taxonomic richness of numerous invertebrate, plant, and avian groups. We find that modelled richness is typically higher in larger and better-connected green-spaces with taller vegetation, indicative of more complex vegetation structure and consistent with the principle of 'bigger, better, and more joined up'. Assessing the relative importance of these variables indicates that vegetation height is the most influential in determining richness for a majority of taxa. There is variation, however, between taxonomic groups in the relationships between richness and landscape structural characteristics, and the sensitivity of these relationships to particular predictors. Consequently, despite some broad commonalities, there will be trade-offs between different taxonomic groups when designing urban landscapes to maximise biodiversity. This research demonstrates the feasibility of using a GIS-coupled Bayesian Belief Network approach to model biodiversity at fine spatial scales in complex landscapes where current data and appropriate modelling approaches are lacking, and our findings have important implications for ecologists, conservationists and planners.

Accurate quantification of aboveground biomass of grasslands in alpine regions plays an important role in accurate quantification of global carbon cycling. The monthly normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), mean air temperature (Ta), ≥5℃ accumulated air temperature (AccT), total precipitation (TP), and the ratio of TP to AccT (TP/AccT) were used to model aboveground biomass (AGB) in grasslands on the Tibetan Plateau. Three stepwise multiple regression methods, including stepwise multiple regression of AGB with NDVI and EVI, stepwise multiple regression of AGB with Ta, AccT, TP and TP/AccT, and stepwise multiple regression of AGB with NDVI, EVI, Ta, AccT, TP and TP/AccT were compared. The mean absolute error (MAE) and root mean squared error (RMSE) values between estimated AGB by the NDVI and measured AGB were 31.05 g m^-2 and 44.12 g m^-2, and 95.43 g m^-2 and 131.58 g m^-2 in the meadow and steppe, respectively. The MAE and RMSE values between estimated AGB by the AccT and measured AGB were 33.61g m^-2 and 48.04 g m^-2 in the steppe, respectively. The MAE and RMSE values between estimated AGB by the vegetation index and climatic data and measured AGB were 28.09 g m^-2 and 42.71 g m^-2, and 35.86 g m^-2 and 47.94 g m^-2, in the meadow and steppe, respectively. The study finds that a combination of vegetation index and climatic data can improve the accuracy of estimates of AGB that are arrived at using the vegetation index or climatic data. The accuracy of estimates varied depending on the type of grassland.

As the most important parts of urban systems, jobs and housing spaces and their balance directly affect the spatial structure of cities, the behavior and experience of the residents, and the harmony and livability of the society. This study used more than 100 million records of mobile phone signaling data, covering the whole city of Beijing and over a period of one month, to identify the jobs-housing spaces by targeting the origin-destination (OD) oriented connections applying the density-based spatial clustering of applications with noise (DBSCAN) method. Furthermore, this study explored the spatial distribution pattern and matching characteristics of Beijing's jobs and housing spaces from different spatial scales by using various measurement methods of jobs-housing balance. The spatial scales for analysis cover the whole city, ring roads, districts, and residential community and town and townships, and the methods applied include the spatial mismatch index, deviation degree, commuting flow rate, and so on. The results show that: 1) Mainly influenced by the spatial layout of the Beijing Master Plan, Beijing's housing space is characterized by dispersion at the large scale and agglomeration at the small scale, showing a pattern of scattered groupings. In contrast, its working space presents features of agglomeration at the large scale and dispersion at the small scale, retaining a significant single-centered layout. 2) Although working in local areas is the first choice for people at both the ring road scale and the district scale, there are still a great number of people works outside their residential areas. The degree of jobs-housing mismatch gradually decreases from the central city to the periphery no matter which method was adopted or at which scale. At the residential community and town and township scale, however, a more detailed feature of three-zones, with job agglomeration inside, housing agglomeration in between, and balanced distribution outside, was observed. 3) Both the general lack of jobs in certain areas caused by the high concentration of working space and the two-way commuting phenomenon in the majority of the areas caused by the high spatial concentration of jobs indicate the necessity of spatial reorganization of residential function and employment function. Specifically, for the regions with a high proportion of two-way commuting flow between them, such as Chaoyang-Changping, Tongzhou-Daxing, Haidian-Changping, Mentougou-Shijingshan, and Chaoyang-Tongzhou, further in-depth investigation should be conducted to find out the reasons for its formation and then possible industrial adjustment or functional reconstruction from the city level should be coordinated. Relying on big data, on the one hand, the job preference and demand of local residents can be identified, so the types and number of jobs in each region could be adjusted accordingly; on the other hand, the proper locations for new job and residential centers may be identified to help rearrange the land use of the whole city.

职住空间作为城市系统最重要的组成部分,直接影响了城市的形态结构、居民的行为体验以及社会的和谐宜居,长期以来受到城市研究者的关注和重视。论文利用覆盖北京全市域并持续1个月的1亿多条手机信令数据,基于DBSCAN的聚类方法,通过OD定向联系,识别出同时具备居住—就业关系特征的职住空间。在此基础上,针对北京市辖区、环路、街道乡镇等不同空间尺度,综合运用空间错位指数、职住偏离度、职住分离率、通勤流动率等计算方法,研究北京职住空间分布格局及匹配特征。研究发现：① 北京市居住空间呈现大分散、小集聚特征,就业空间呈现大集聚、小分散特征;② 基于各个空间尺度、不同测度方法的分析结果均表明,职住空间的不匹配程度呈现出由中心城区向外围逐渐降低的态势,但基于街道乡镇尺度呈现出由内向外更细化的就业集聚—居住集聚—二者均衡的三段式变化特征;③ 无论是就业空间高度集聚导致的非集聚区就业岗位数量不足,还是包括就业高集聚区在内大量区域出现的双向通勤现象,均说明居住功能和就业功能空间重组的必要性。

More than half of the world's population currently live in urban areas and are particularly at risk from the combined effects of the urban heat island phenomenon and heat increases due to climate change. Here, by using remotely sensed surface temperature data and social-ecological indicators, focusing on the hot dry season, and applying the risk framework of the Intergovernmental Panel on Climate Change, we assessed the current heat health risk in 139 Philippine cities, which account for about 40% of the country's total population. The cities at high or very high risk are found in Metro Manila, where levels of heat hazard and exposure are high. The most vulnerable cities are, however, found mainly outside the national capital region, where sensitivity is higher and capacity to cope and adapt is lower. Cities with high levels of heat vulnerability and exposure must be prioritized for adaptation. Our results will contribute to risk profiling in the Philippines and to the understanding of city-level heat health risks in developing regions of the Asia-Pacific.

This paper combines the one-week bus smart card data (SCD) and one-day household travel survey as well as the parcel-level land use map for identifying jobs-housing places and commuting trips in the Beijing Metropolitan Area with an area of 16,410 square kilometers. The identification result is aggregated in the bus stop and traffic analysis zone (TAZ) levels, respectively. In particular, commuting trips with commuting time and distance attached from three typical residence communities and those to five typical business zones are mapped and compared with each other to analyze commuting patterns of Beijing. The identified commuting trips are compared with those in the household travel survey in terms of commuting time and distance, indicating that our results are coincident with the survey significantly. Our approach is proved to have its potential in identifying more solid identification result based on rules extracted from existing surveys or censuses.

The COVID-19 epidemic in 2020 has a severe impact on China's national economic and social development. Evaluating the short-term impact of the COVID-19 epidemic and the recovery of China's economy and society, as well as revealing its spatiotemporal characteristics, can provide a strong support for the economic situation research and urban restoration of the normalized epidemic prevention and control stage. Based on Baidu migration big data from January 13 to April 8 in 2020 and that of the same period in history, this paper constructs the Relative Recovery Index (RRI) and Recovery Gap Index (RGI). Furthermore, it reveals the daily characteristics, stage characteristics, and spatiotemporal patterns of the short-term impact of the COVID-19 epidemic at multiple scales. The results are as follows: (1) The outbreak did not affect the travel rush before the Spring Festival. The process after the Spring Festival experienced a recovery stagnation period, a rapid recovery period, and a slow recovery period. The overall degree of recovery nationwide rose from less than 20% during the stagnation period to about 60% at the end of the rapid recovery period. The slow recovery period began on March 3, with a recovery index of over 70%. It will take a long time to fully recover to the historical level. (2) The intercity activities on weekends and in holidays were significantly weakened, especially in the central and northeastern regions. (3) The impact of the epidemic on each region is significantly different, in terms of the RRI, the western region > eastern region > central region > northeastern region. (4) The degree of recovery varies significantly between cities. From the Spring Festival to April 8th, the spatial pattern was high in the south and low in the north. According to the severity of the epidemic, Guangzhou, Shenzhen and Chongqing are in the cluster of High confirmed case-High recovery; Hebei, Tianjin, Heilongjiang, Henan, Anhui and Hunan are in the cluster of Low confirmed case-Low recovery. (5) With the effective control of the epidemic, the recovery gap has shifted from the large-scale insufficiency of labor force in the urban agglomerations such as Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta into the insufficiency in the central cities and some provincial capital cities. The results of this paper show that the use of spatiotemporal big data for real-time impact assessment of major public health emergencies have good application prospects.

新型冠状病毒肺炎（COVID-19）疫情对中国国民经济和社会发展产生剧烈冲击。科学评价中国受新冠疫情短期影响及恢复情况并揭示其时空特征,可为常态化疫情防控阶段的经济形势研判和城市恢复提供有力支撑。基于2020年1月13日—4月8日百度迁徙大数据,通过构建恢复指数（RRI）和恢复缺口（RGI）等指标,从多尺度揭示中国受COVID-19疫情短期影响的逐日特征、阶段特征以及时空格局。结果发现：① 疫情未影响春节前返乡迁徙,节后恢复经历恢复停滞期、快速恢复期、平稳恢复期,全国总体恢复程度由恢复停滞期不足20%上升至快速恢复期末60%左右,3月3日开始进入平稳恢复期,恢复指数达70%以上,完全恢复至历史同期水平仍需较长时间。② 疫情对周末和节假日城市间交往活动影响显著,中部和东北地区尤为明显。③ 疫情影响的区域差异性明显,相对恢复程度西部>东部>中部>东北地区。④ 城市间恢复程度差异显著,节后至4月8日呈现南高北低空间格局。结合疫情程度,广州、深圳、重庆处于高确诊高恢复聚类,河北、天津、黑龙江、河南、安徽、湖南处于低确诊低恢复聚类。⑤ 随着疫情有效控制,城市层面恢复缺口由京津冀、长三角、珠三角等城市群的大规模成片劳动力迁入缺口,转变为国家中心城市和部分省会城市的点状缺口。本文研究结果表明时空大数据在重大突发公共卫生事件实时影响评价方面具有较好应用前景。