Spatial Distribution Patterns Research Articles

Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region

Accurately estimating aboveground biomass (AGB) in forest ecosystems facilitates efficient resource management, carbon accounting, and conservation efforts. This study examines the relationship between predictors from Landsat-9 remote sensing data and several topographical features. While Landsat-9 provides reliable data crucial for long-term monitoring, it is part of a broader suite of available remote sensing technologies. We employ machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Random Forest (RF), alongside linear regression techniques like Multiple Linear Regression (MLR). The primary objectives of this study encompass two key aspects. Firstly, the research methodically selects optimal predictor combinations from four distinct variable groups: Landsat-9 (L1) data, a fusion of Landsat-9 data and Vegetation-based indices (L2), and the integration of Landsat-9 data with the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) (L3) and the combination of best predictors (L4) derived from L1, L2, and L3. Secondly, the research systematically assesses the effectiveness of different algorithms to identify the most precise method for establishing any potential relationship between field-measured AGB and predictor variables. Our study revealed that the Random Forest (RF) model was the most efficient method utilizing Landsat-9 OLI and SRTM DEM (L3) predictors, achieving remarkable accuracy. This conclusion was reached by assessing its outstanding performance when compared to an independent validation dataset. The RF model exhibited remarkable accuracy, presenting relative mean absolute error (RMAE), relative root mean square error (RRMSE), and R2 values of 14.33%, 22.23%, and 0.81, respectively. The XGBoost model is the subsequent choice with RMAE, RRMSE, and R2 values of 15.54%, 23.85%, and 0.77, respectively. The study further highlights the significance of specific spectral bands, notably B4 and B5 from Landsat 9 OLI data, in capturing spatial AGB distribution patterns. Integration of vegetation-based indices, including TNDVI, NDVI, RVI, and GNDVI, further refines AGB mapping precision. Elevation, slope, and the Topographic Wetness Index (TWI) are crucial proxies for representing biophysical and biological mechanisms impacting AGB. Through the utilization of openly accessible fine-resolution data and employing the RF algorithm, the research demonstrated promising outcomes in the identification of optimal predictor-algorithm combinations for forest AGB mapping. This comprehensive approach offers a valuable avenue for informed decision-making in forest management, carbon assessment, and ecological monitoring initiatives.

Spatio-Temporal Evolution of Ecological Environment Quality Based on High-Quality Time-Series Data Reconstruction: A Case Study in the Sanjiangyuan Nature Reserve of China

The Sanjiangyuan Nature Reserve of China (SNRC) is recognized as one of the most fragile and sensitive terrestrial ecosystems in China, posing challenges for obtaining reliable and complete Moderate Resolution Imaging Spectro Radiometer (MODIS) data for ecological environment quality (EEQ) monitoring due to adverse factors like clouds and snow. In this study, a complete high-quality framework for MODIS time-series data reconstruction was constructed utilizing the Google Earth Engine (GEE) cloud platform. The reconstructed images were used to compute the Remote Sensing based Ecological Index (RSEI) on a monthly scale in the SNRC from 2001 to 2020. The results were as follows: The EEQ of the study area exhibited a “first fluctuating decline, then significant improvement” trend, with the RSEI values increasing at a rate of 0.84%/a. The spatial pattern of the EEQ displayed significant spatial heterogeneity, characterized by a “low in the west and high in the east” distribution. The spatial distribution pattern of the RSEI exhibited significant clustering characteristics. From 2001 to 2020, the proportion of “high–high” clustering areas exceeded 35%, and the proportion of “low–low” clustering areas exceeded 30%. Poor ecological conditions are mainly associated with population agglomerations, cultivated land, unutilized land, and bare ground, while grasslands and forests have higher RSEI values. The result of the trend analysis revealed a significant trend in RSEI change, with 62.96% of the area significantly improved and 6.31% significantly degraded. The Hurst Index (HI) results indicated that the future trend of the RSEI is predominantly anti-persistence. The proportion of areas where the EEQ is expected to continue improving in the future is 33.74%, whereas 21.21% of the area is forecasted to transition from improvement to degradation. The results showed that the high-quality framework for MODIS time-series data reconstruction enables the effective continuous monitoring of EEQ over long periods and large areas, providing robust scientific support for long time-series data reconstruction research.

Spatial Distribution and Hierarchical Behaviour of Cattle Using a Virtual Fence System.

Interest in virtual fencing has increased due to its flexibility for agriculture and rewilding. However, systems like Nofence© require large financial investments, and the need for individual collars complicates large-scale use. If cattle herds maintain cohesive groups around leading individuals, fewer collars could be used, thereby enhancing cost efficiency. This study investigates the pattern in spatial distribution in a herd of 17 Angus cows on Fanø in Denmark with GPS locations, using a Nofence© system. The aim of this paper is to determine how individuals position themselves in a herd, spatially, and identify a pattern in ranks. The method used in this study examines the distances between an individual to the rest of the herdmates using nearest neighbour and descriptive statistics. Contrary to expectations, this study did not reveal a distinct pattern in herd distribution. While some tendencies in spatial distribution patterns were observed, only a low concordance could be found (W=0.15,p<0.001), indicating great variability in the cattle's ranks. A cumulative curve of the ranks estimated over the entire periods, however, allowed a rough estimation of the hierarchy and allowed identification of the highest-ranked cows, making the use of a cumulative curve a possible solution to finding the high-ranked cows. This research underscores the complexity of cattle social structures and highlights the need for extended observation periods and alternative methodologies to enhance the cost-effectiveness and scalability of virtual fencing in agricultural and rewilding contexts.

Spatial Distribution Pattern and Influencing Factors of Bed-and-breakfasts (B&amp;Bs) from the Perspective of Urban-rural Differences: A Case Study of Jiaodong Peninsula, China

Spatial Distribution Pattern and Influencing Factors of Bed-and-breakfasts (B&amp;Bs) from the Perspective of Urban-rural Differences: A Case Study of Jiaodong Peninsula, China

Ensemble learning landslide susceptibility assessment with optimized non-landslide samples selection

Non-landslide samples influence the outcomes of landslide susceptibility assessment. Existing studies did not fully consider the equilibrium between landslide and non-landslide samples in similar environments, resulting in poor reliability of landslide susceptibility assessment. This study proposed a non-landslide samples optimization method with a constraint of disaster-pregnant environment similarity to construct the balanced landslide and non-landslide samples. We employed the heterogeneous stacking and blending ensemble learning models to generate the landslide susceptibility assessment. This study focused on the Bailong River Basin with complex environment and frequent landslides as the study area. First, we extracted 12 landslide influencing factors based on multiple sources and analyzed the spatial distribution patterns of landslides. Second, we constructed similar environments based on the assessment units obtained from the curvature watershed method and selected an equal amount of both landslide and non-landslide samples in every different environment. Finally, three classic neural network models, namely multilayer perceptron, convolutional neural network, and gated recurrent unit were used as base models for stacking and blending ensemble learning models to assess landslide susceptibility. The findings suggested that the landslide susceptibility assessment results with optimized non-landslide samples were more reliable, especially the proposed method improved the prediction results in sample-sparse regions. The stacking ensemble constructed in this study demonstrated the highest area under the curve of 0.88 for the testing dataset, outperforming the blending ensemble and three base models. The issue of unreliable landslide susceptibility assessment results in sample-sparse regions within complex environments can be effectively addressed by considering the balanced sampling of non-landslide samples under the constraint of similar environments.

Spatial Distribution Patterns and Influencing Factors of Dominant Species in Plain Valley Forests of the Irtysh River Basin

The Irtysh River, which stretches for 633 km, is the second longest river in Xinjiang. The valley forests within its basin are unique forest resources that exhibit crucial ecological functions and form an integral part of China’s “Three North” Shelterbelt Forest Project. However, previous studies mainly focused on individual tributaries or main streams, lacking comprehensive research on the overall river and valley forest resources and their ecological functions. To address this research gap based on comprehensive investigations, this study analyzed the dominant species composition, spatial distribution patterns, and influencing factors of valley forests across various branches of the Irtysh River basin plain. The results revealed the presence of 10 local tree species in the area, with Populus laurifolia, Populus alba, Salix alba, and Betula pendula as the dominant species. However, seedling regeneration was relatively weak. P. laurifolia, P. alba, and S. alba were widely distributed across tributaries and main streams, whereas B. pendula was primarily found in the tributaries. The four dominant species exhibited distinct clustering patterns. The concentration intensity of these dominant species in the main stream of the Irtysh River basin was significantly higher than those in other tributaries, with P. laurifolia showing a lower concentration intensity across the entire basin than the other dominant species. Negative density dependence was the primary biological factor influencing species aggregation intensity, with significant positive effects on P. alba and S. alba and significant negative effects on B. pendula. Among the abiotic factors, elevation had a significant positive effect on the aggregation intensities of P. alba, S. alba, and B. pendula, indicating that these species tend to aggregate more densely at higher elevations. Conversely, slope had a significant negative impact on the aggregation intensities of P. laurifolia, P. alba, and S. alba, suggesting that increasing slope steepness leads to a decrease in the clustering of these species. Similarly, the distance from the river channel had a significant negative effect on the aggregation intensities of S. alba and B. pendula, implying that as the distance from the river increases, the clustering patterns of these species become less pronounced. This study aimed to detail the current state of valley forest resources and their ecological functions, thereby laying a foundation for their effective protection.

Contamination Characteristics and Source Apportionment of Heavy Metal in the Topsoil of a Small Watershed in South Taihang

With the rapid development of industrialization and urbanization, the issue of soil environmental pollution is becoming more and more prominent, especially concerning heavy metal contamination, which has garnered significant scholarly attention. The surface watershed formed by waterline is influenced by various factors such as topography, industrial emissions, and agricultural runoff, resulting in a complex process of migration and accumulation of heavy metal elements from multiple sources. In this study, the pollution characteristics and sources of heavy metal elements Hg, As, Pb, Ni, Cd, Cr, Cu and Zn in 165 surface soil samples from the Manghe River watershed in Jiyuan City were comprehensively analyzed using a variety of methods, including statistics, geostatistics, enriched factor analysis and the Positive Matrix Factorization Model (PMF). The results showed that the concentrations of Hg, Cd, As, Cu, Pb and Zn exceeded their corresponding background values with varying degrees of enrichment. Notably, the average contents of Cd, Hg and Pb were 26.70 times, 3.69 times and 4.49 times higher than those in Chinese soils on average, respectively, showing obvious enrichment characteristics. Moreover, there were distinct spatial distribution patterns for each heavy metal element; Ni and Cr exhibited similar trends mainly controlled by the parent material, while human activities significantly affect the other six elements forming high-value areas around mining and related industries. It is noteworthy that Cu, Hg and Zn were influenced by dominant wind direction in autumn and winter, forming sub-high-value zones in southern forested areas; meanwhile, Cu and Zn were also influenced by agricultural fertilizer application as well as surface runoff, leading to secondary high-value areas in the dryland areas. Further analysis revealed a significant positive correlation among these heavy metal elements, suggesting that they may share common sources. Through the PMF Model, four main factors were identified, with factor 2 (36.25%), factor 1 (23.00%), factor 3 (21.20%) and factor 4 (19.55%) ranked in descending order of contribution rate. The heavy metal pollution in the study area was attributed to anthropogenic activities and natural factors, accounting for 63.75% and 36.25%, respectively. Coal mining, chemical industry smelting, vehicle emissions and excessive use of agrochemicals were identified as the main sources of heavy metal pollution. These pollutants entered the soil through direct emissions, atmospheric deposition, transportation and agricultural activities, exerting a significant impact on the soil environment. Therefore, delving into the spatial distribution pattern of soil heavy metal pollution and precise analysis of its sources are of great importance for effective treatment and remediation of soil heavy metal pollution in small watersheds, maintaining healthy soil ecology and safeguarding human health.

Spatial patterns of Hyalomma marginatum-borne pathogens in the Occitanie region (France), a focus on the intriguing dynamics of Rickettsia aeschlimannii.

Hyalomma marginatum is an invasive tick species recently established in mainland southern France. This tick is known to host a diverse range of human and animal pathogens. While information about the dynamics of these pathogens is crucial to assess disease risk and develop effective monitoring strategies, few data on the spatial dynamics of these pathogens are currently available. We collected ticks in 27 sites in the Occitanie region to characterize spatial patterns of H. marginatum-borne pathogens. Several pathogens have been detected: Theileria equi (9.2%), Theileria orientalis (0.2%), Anaplasma phagocytophilum (1.6%), Anaplasma marginale (0.8%), and Rickettsia aeschlimannii (87.3%). Interestingly, we found a spatial clustered distribution for the pathogen R. aeschlimannii between two geographically isolated areas with infection rates and bacterial loads significantly lower in Hérault/Gard departments (infection rate 78.6% in average) compared to Aude/Pyrénées-Orientales departments (infection rate 92.3% in average). At a smaller scale, R. aeschlimannii infection rates varied from one site to another, ranging from 29% to 100%. Overall, such high infection rates (87.3% on average) and the effective maternal transmission of R. aeschlimannii might suggest a role as a tick symbiont in H. marginatum. Further studies are thus needed to understand both the status and the role of R. aeschlimannii in H. marginatum ticks.IMPORTANCETicks are obligatory hematophagous arthropods that transmit pathogens of medical and veterinary importance. Pathogen infections cause serious health issues in humans and considerable economic loss in domestic animals. Information about the presence of pathogens in ticks and their dynamics is crucial to assess disease risk for public and animal health. Analyzing tick-borne pathogens in ticks collected in 27 sites in the Occitanie region, our results highlight clear spatial patterns in the Hyalomma marginatum-borne pathogen distribution and strengthen the postulate that it is essential to develop effective monitoring strategies and consider the spatial scale to better characterize the circulation of tick-borne pathogens.

A comprehensive study of source apportionment, spatial distribution, and health risks assessment of heavy metal(loid)s in the surface soils of a semi-arid mining region in Matehuala, Mexico

BackgroundThis study investigates the contamination level, spatial distribution, pollution sources, potential ecological risks, and human health risks associated with heavy metal(loid)s (i.e., arsenic (As), copper (Cu), iron (Fe), manganese (Mn), lead (Pb), and zinc (Zn)) in surface soils within the mining region of Matehuala, located in central Mexico. ObjectivesThe primary objectives are to estimate the contamination level of heavy metal(loid)s, identify pollution sources, assess potential ecological risks, and evaluate human health risks associated with heavy metal(loid) contamination. MethodsSoil samples from the study area were analysed using various indices including Igeo, Cf, PLI, mCd, EF, and PERI to evaluate contamination levels. Source apportionment of heavy metal(loid)s was conducted using the APCS-MLR and PMF receptor models. Spatial distribution patterns were determined using the most efficient interpolation technique among five different approaches. The total carcinogenic risk index (TCR) and total non-carcinogenic index (THI) were used in this study to assess the potential carcinogenic and non-carcinogenic hazards posed by heavy metal(loid)s in surface soil to human health. ResultsThe study reveals a high contamination level of heavy metal(loid)s in the surface soil, posing considerable ecological risks. As was identified as a priority metal for regulatory control measures. Mining and smelting activities were identified as the primary factors influencing heavy metal(loid) distributions. Based on spatial distribution mapping, concentrations were higher in the northern, western, and central regions of the study area. As and Fe were found to pose considerable and moderate ecological risks, respectively. Health risk evaluation indicated significant levels of carcinogenic risks for both adults and children, with higher risks for children. ConclusionThis study highlights the urgent need for monitoring heavy metal(loid) contamination in Matehuala's soils, particularly in regions experiencing strong economic growth, to mitigate potential human health and ecological risks associated with heavy metal(loid) pollution.

Habitat degradation changes and disturbance factors in the Tibetan plateau in the 21st century

Land use changes driven by human activities significantly impact biodiversity in plateau regions. However, current research is largely confined to identifying correlations between various factors and both habitat quality and degradation, overlooking the nonlinear relationships between them. To address this gap, we applied the PLUS-INVEST model to investigate the spatial effects of land-use changes on habitat quality and degradation patterns across the Tibetan Plateau during the 21st century. By employing a geographic detector, we determined the contribution rates of disturbance factors to habitat quality and degradation, and established constraint lines and threshold ranges between these factors. The findings reveal that: (1) The PLUS model demonstrates an exceptional performance in land-use simulation, with an overall accuracy of 0.8465. (2) The high-quality habitat area exhibits a declining trend, while the habitat degradation index steadily rises from 2000 to 2100, indicating a significant loss of biodiversity within the region. Habitat quality displays a spatial distribution pattern characterized by higher values in the south and lower values in the north, with areas in proximity to road threat sources experiencing more pronounced habitat degradation. (3) NDVI emerges as the most influential factor in promoting habitat quality, while the interaction of NDVI_Temperature exerts the greatest influence on spatial heterogeneity. The distance to resident emerges as the primary disturbance factor contributing to habitat degradation, with the interaction strength of GI_Resident being the most significant contributor. (4) Threshold intervals for ANPP, NDVI, precipitation, temperature, and distance to resident of optimal habitat quality and most severe degradation. This provides a novel scientific approach for designating areas for targeted conservation and intensive management restoration.

The power of patent transfer: The impact of green technology acquisition on non-residential CO2 emissions under the intervention of government actions

Green technology acquisition solves the mismatch of innovation elements through transactions and realizes the rapid and reasonable allocation of green patents among enterprises with different technology levels. However, whether green technology acquisition can effectively reduce carbon emissions has not yet received the attention it deserves. Based on data of 153 Chinese cities from 2011 to 2019, this paper explores the impact direction and mechanism of green technology acquisition on non-residential CO2 emissions, and analyzes the moderating role of government actions in this process. The study finds that: (1) Green technology acquisition in China has formed a multi-core spatial distribution pattern based on urban agglomerations, among which the Yangtze River Delta, Beijing-Tianjin-Hebei and Pearl River Delta urban agglomerations constitute the main agglomeration areas for green technology acquisition. (2) Green technology acquisition can significantly reduce non-residential CO2 emissions, which is mainly achieved through the cleaner production effect and R&D innovation effect. (3) In terms of government actions, low-carbon advocacy can effectively moderate the mediating role of the cleaner production effect between green technology acquisition and non-residential CO2 emissions, and environmental incentives and environmental penalties can effectively moderate the mediating role of the R&D innovation effect between green technology acquisition and non-residential CO2 emissions, but the moderating paths are different. (4) There is a threshold effect of intellectual property protection in the impact of green technology acquisition on non-residential CO2 emissions. Only when the degree of intellectual property protection exceeds a certain threshold, green technology acquisition can effectively reduce non-residential CO2 emissions. This paper not only explains the key value of green technology for low-carbon development from the perspective of knowledge flow, but also provides a theoretical reference for the rational matching of government actions under the carbon neutrality target.

The relationship between structure and ecosystem services of forest and grassland based on pattern analysis method: A case study of the Mongolian Plateau

Global warming has led to severe land desertification on the Mongolian plateau. It puts great environmental pressure on vegetation communities. This pressure leads to fragmentation of land use and landscape patterns, thus triggering changes in the spatial distribution patterns of vegetation. The spatial distribution pattern of vegetation is crucial for the performance of its ecosystem services. However, there is not enough research on the relationship between large-scale spatial distribution patterns of vegetation and ecosystem services. Therefore, this study is to construct an ecological spatial network on the Mongolian Plateau based on landscape ecology and complex network theory. Combining pattern analysis methods to analyze the network, we obtained the spatial and temporal trends of forest and grass spatial distribution patterns from 2000 to 2100, and explored the relationship between the topological properties of source patches and ecosystem services in different patterns. It was found that there are four basic patterns of spatial distribution of forest and grass in the Mongolian Plateau. The Core-Linked Ring pattern accounts for 40.74 % and exhibits the highest stability. Under the SSP5-RCP8.5 scenario, source patches are reduced by 22.76 % in 2100. Topological indicators of source patches showed significant correlations with ecosystem services. For example, the CUE of grassland patches in the Centralized Star pattern was positively correlated with betweeness centrality. The most significant improvement in WUE after optimization is 19.90 % compared to pre-optimization. The conclusion of the study shows that the spatial distribution pattern of vegetation can be used to enhance the stability of ecological spatial network and improve ecosystem services at a larger scale. It can provide a certain reference for the study of spatial patterns of vegetation distribution in arid and semi-arid areas.

Diversity, distribution and knowledge gaps of Polychaeta on the continental shelf of southern Namibia

This study investigated the diversity, composition and distribution patterns of polychaete macrofauna inhabiting unconsolidated sediments on the continental shelf of southern Namibia. During the austral summer of 2021, 910 Van Veen grab samples were collected from 91 sites in water depths ranging between 43 and 146 m. All benthic macrofauna (> 1 mm) were extracted, identified, enumerated and weighed. Polychaetes were the most abundant taxon, equating to 66% of total abundance and 37% of total biomass. A total of 83 712 polychaete specimens comprising 112 species, 80 genera and 33 families were collected. Several taxa identified were listed as putative cosmopolitans (19 taxa) or have known wide local distributions (10 taxa). Voucher specimens were collected for DNA barcoding to improve reference sequence libraries for the region. Hierarchical cluster analyses using both abundance and biomass data were undertaken to determine spatial distribution patterns in polychaete communities. Both datasets yielded similar results with polychaete assemblages divided into inshore and offshore communities, that were further subdivided at a local scale. Investigation of physical and chemical drivers suggested that polychaete communities in southern Namibia are structured to varying degrees by water depth, latitude, sediment composition, redox potential and organic content. Deeper stations comprised the highest diversity of polychaeta fauna. Species adapted to hypoxic conditions (e.g. Sigambra parva, Pararionospio pinnata, Diopatra cf. monroi and Nepthys cf. hombergii) dominated an area known as the mudbelt, where organically enriched silts and clays originating from the Orange River are deposited on the mid-shelf between the 40 and 120 m isobaths.

Spatiotemporal dynamics and similarity in soil moisture in shallow soils on karst slopes

Understanding the spatiotemporal dynamics and similarity of soil moisture on shallow slopes is important for inferring soil hydrological processes and for vegetation restoration. In karst areas, the spatial distribution patterns and dynamic changes in shallow soil moisture are particularly complex; however, whether there is spatiotemporal similarity in shallow soil moisture on karst slopes has not been fully determined. This work investigated the spatiotemporal dynamic variations in shallow soil moisture in three types of karst slope plots (abandoned land, peanut and Zanthoxylum bungeanum). Wavelet coherence analysis was used to evaluate the spatiotemporal similarity and factors influencing shallow soil moisture under different vegetation types and slope positions. The results indicate that the alternation between dry and wet conditions and spatiotemporal heterogeneity were significant in the shallow soils of slopes. Comparatively, the dry-wet cycle in the shallow soil of abandoned land occurred faster than that in the shallow soil, and the shallow soil for planting vegetation had a better water holding capacity. The terrain and slope position controlled the distribution and dynamic variations in soil moisture on shallow slopes. Compared with those in the middle and upper slopes, the soil moisture in the bottom slopes was greater, and the coefficient of variation was lower. The average coherence between the soil moisture in the different plots was greater than 0.90, and the significant area was greater than 93.0%. The coherence and significant area in the rainy season were significantly greater than those in the dry season. Due to the influence of vegetation types and meteorological conditions, the drought interval after rainfall events led to a decrease in the spatiotemporal similarity of soil moisture. This study indicates that shallow soil moisture in karst slopes exhibits temporal stability and dynamic similarity. The alternation of dry-wet conditions controls the similarity of shallow soil moisture on the slope.

Subsurface urban heat island in the city of Ekaterinburg

Research subject. The subsurface thermal field in the city of Ekaterinburg (subsurface urban heat island). Aim. To determine criteria for the anomaly of mean annual subsurface temperatures in Ekaterinburg; to identify patterns of spatial distribution of underground temperatures; to quantify the main factors forming an urban heat island and changes in the heat content of rocks using mathematical modeling. Materials and methods. The main experimental data were obtained during the annual cycle of geothermal studies in observational boreholes of Ekaterinburg (22 boreholes) and surrounding areas (10 boreholes in Degtyarskiy, Verkh-Sysertskiy, Gagarskiy districts). Statistical analysis and mathematical modeling describing the impact of climate, local temperature anomalies of ground surface, and groundwater filtration to the underground thermal field were used when interpreting the obtained data. Results. At a depth of 20 m, the mean annual temperatures being less than 5°C and more than 6°C should be considered as anomalous. The maximum intensity of the urban heat island in Ekaterinburg is confined to densely built-up central areas of the city. The highest temperatures (&gt;10°C) at a depth of 20 m are observed in boreholes located near buildings or directly therein. Here, a rapid decrease in temperature with depth is typical. Moderate anomalies from 6°C to 10°C are observed far from buildings. Remoteness from the central regions apparently plays a more important role in the formation of temperature anomalies than the type of urban surfaces (asphalt, concrete, lawns). Background temperatures (less than 6°C) were recorded in boreholes located outside the Ring Road. An analysis of patterns in the attenuation of annual temperature variations with depth allowed an area with intense vertical filtration (up to 24 m/year) to be identified near the City Pond. The most significant changes in heat content in the range of 10–50 m are associated with heat leakage from the basements of buildings, equaling to (23–46) × 107 J/m2. However, this heat is only hundredths of a percent of the total energy consumption spent on heating. Conclusions. The subsurface urban heat island of a large Russian city has been characterized for the first time. The results obtained can be used when developing a strategy for megacities in changing climate conditions.

Ecological Function Zoning Framework for Small Watershed Ecosystem Services Based on Multivariate Analysis from a Scale Perspective

A thorough comprehension of distribution features of ecosystem services (ESs) and the influencing mechanisms can offer scientific guidance for the ecosystem management of small watersheds. We analyzed the spatial distribution patterns, interrelationships, and service hotspots of ESs across pixel and administrative scales using a multi-level statistical analysis approach in the Ashi River Basin. Through the quantitative calculation of the InVEST model, the findings revealed a decrease in soil retention, water yield, total nitrogen and phosphorus export, carbon storage, and habitat quality, while an increase in food production was observed during the period from 1995 to 2015. Through the geographical detector, the spatial heterogeneity of most individual ESs was influenced by land use patterns. Through redundancy analysis, terrain factors had the highest contribution rate to the integral ESs. Socio-economic factors and climate factors also drove the ESs’ amount and spatial distribution. At the pixel scale and administrative scale, there were distinctions in the correlations between all ESs, reflected by the fact that the relationships between ESs at the administrative scale were generally weaker and no more significant than at the pixel scale. Based on the number and distribution of hotspots in ESs and the clustering results of influencing factors, the framework of ecosystem zoning was constructed. This basin was divided into three ecological zones, and the management policies were formulated according to the ecological environment. This study clarifies the internal and mutual connection between ESs and influencing factors across two scales, thus contributing to the advancement of management strategies for ecological conservation and socio-economic development within the context of small watersheds.

Extensive inequality of residential greenspace exposure within urban areas in China

Ensuring residents' equal access to high quality urban greenspace is vital for urban environmental justice and sustainable urban development. However, most previous studies have mainly focused on greenspace quantity, overlooking its quality. Moreover, the national-level spatial distribution pattern of residential greenspace exposure (RGE) within urban areas remains unclear. Here, we have improved the existing RGE assessment framework by integrating both the quality and quantity of urban greenspace to evaluate RGE and its associated inequality across 119,692 blocks in 334 Chinese cities in 2020. We find that the spatial distribution pattern of RGE varies with urban size. Large cities exhibit a distinct clustering of low RGE in their central areas, whereas small cities tend to show a pronounced clustering of high RGE in the central areas. RGE in Chinese cities indicates extensive inequality, as the average RGE of high-exposed people is nearly four times greater than that of low-exposed people. Moreover, residents in larger cities are more prone to experiencing greater inequalities compared to those in smaller cities. We also find that the landscape metrics (i.e., connectance index and mean Euclidean nearest-neighbor distance) of greenspace possess a strong explanatory power (R2 = 0.431) for the observed inequality. Our study underscores the importance of optimizing the landscape structure of urban greenspace and enhancing equality in the quality of greenspace. These findings provide novel insights for urban greenspace planning and promoting urban environmental justice.

Added value of EURO-CORDEX downscaling over the complex orography region of the Pyrenees

This study presents an assessment of the added value of downscaling utilizing Regional Climate Models (RCMs) compared to Global Climate Models (GCMs) in the high mountain region of the Pyrenees, characterized by complex topography. The EURO-CORDEX ensemble was investigated, employing a gridded high-resolution observational database as a reference. A recently proposed method is applied to quantify the performance gains or losses associated with dynamic downscaling. Our analysis focuses on calculating the added value by exploring the extremes of the Probability Density Function (PDF), spatial distribution patterns, and its relationship with elevation. Overall, our findings reveal significant improvements in the representation and general characterization of precipitation, minimum temperature, and maximum temperature in the Pyrenean region. Furthermore, RCMs demonstrate enhanced performance in capturing maximum precipitation events; however, they struggle to represent low precipitation rates, particularly in the Mediterranean area of the mountain range. Regarding temperature extremes, dynamical downscaling exhibits improvements in capturing maximum events. Nevertheless, deficiencies are observed in the RCMs’ representation of minimum temperature events for both minimum and maximum temperature variables, as well as in representing near-freezing temperatures.

Co‐working office spaces in Sydney: Spatiotemporal dynamics and industry patterns

AbstractCo‐working spaces have emerged as cost‐effective office space management solutions for workers from diverse industries who share space, which generates cost savings and facilitates networking opportunities. Past studies have mainly focused on needs for and benefits of co‐working spaces and range in emphasis from design preferences to potential social and economic impacts. However, limited attention has been given to the temporal clustering and geographical distribution patterns of co‐working spaces. This study examines the spatial distribution patterns of co‐working spaces establishments in terms of building floor plans. We discern the subcategory of ‘co‐working office space’ from the floor space and employment survey data, spanning 2007, 2012, and 2017 as collected by the City of Sydney, a municipal government in Australia. Both co‐working offices offered by conventional enterprises and co‐working providers are identified, and the spatial associations between co‐working spaces and their relationships with industry composition are analysed. Using establishment analysis, three co‐working spaces are identified: (1) dedicated co‐working enterprises providing office service and well‐design spaces; (2) conventional office rental enterprises diversifying into co‐working style office; and (3) standard enterprises converting fixed office spaces to co‐working office configurations. The spatial analysis reveals an outward diffusion of co‐working spaces from the centre of Sydney CBD, which is of importance in geographical research focused on urban, social, and economic change.

Evaluation of Rice–Crayfish Field Fragmentation Based on Landscape Indices: A Case Study of Qianjiang City, China

Since the 21st century, rice–crayfish fields have been widely distributed in the Yangtze River Basin in China. However, the spontaneous construction of these fields by farmers has given rise to the issue of rice–crayfish field fragmentation (RCFF) in certain areas. This study introduced a novel method for evaluating RCFF using township-level administrative regions as the evaluation units. Three key evaluation elements, including five landscape indices, were employed: area and edge metrics (rice–crayfish area ratio), shape metrics (perimeter–area ratio distribution), and aggregation metrics (rice–crayfish patch density, percentage of like adjacencies, and rice–crayfish contagion index). The RCFF was quantified and its spatial distribution pattern was analyzed through the entropy method and GIS spatial analysis. Empirical studies conducted in Qianjiang city yielded insightful results: (1) The contribution of evaluation elements to the RCFF was ranked in descending order as follows: aggregation metrics &gt; shape metrics &gt; area and edge metrics. (2) The RCFF of Yunlianghu farm was the lowest at 0.06, while the RCFF of Yangshi subdistrict 2 was the highest at 0.94. The spatial distribution of the RCFF exhibited a distinct trend, showing a gradual decrease from the northeast to the southwest in Qianjiang, and a low-RCFF area in the southwest. This evaluation system enables local government decisionmakers to comprehend the current status of rice–crayfish field management and construction. It facilitates the scientific planning of rice–crayfish field layouts and provides guidance for farmers in their expansion strategies. This method can be promoted in counties (cities) where rice–crayfish fields are primarily distributed in the Yangtze River Basin, promoting the transition of traditional agriculture to environmentally friendly agriculture in China.