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The explanation of spatial errors in geospatial modelling has long been a challenge. This study introduces an index that captures the complexity of local spatial distribution, which can partially provide insight into spatial errors. While previous studies have explored the complexity of geographical data from various perspectives, there is limited knowledge on assessing the complexity while taking spatial dependence into account. This study proposes a measure of geocomplexity, i.e. the spatial local complexity indicator, which characterizes the complexity of local spatial patterns while considering spatial neighbor dependence. We used both aspatial and spatial models to estimate the economic inequality in Australia, and applied the spatial local complexity indicator to explain spatial errors in these models. Results show that the developed geocomplexity indicator, using a binary spatial matrix, can effectively explain spatial errors arising from models, including 17%-47% of errors in aspatial models and 14% in a spatial model. The experiments in this study support our hypothesis that geocomplexity is an essential component in explaining spatial errors. The proposed geocomplexity indicator, along with our hypothesis, has the potential for advancing the understanding complex geospatial systems and enabling applications in various fields related to spatial data analysis. [ABSTRACT FROM AUTHOR]

Understanding geographical characteristics of distribution patterns and spatial association is essential for spatial statistical inference such as factor exploration and spatial prediction. The geographical similarity principle was recently developed to explain the association between geographical variables. It describes the comprehensive degree of approximation of a geographical structure instead of explicit relationships between variables. However, there are still challenges for geographical similarity-based methods. For instance, all samples are used for prediction, leading to increased calculation burden and reduced prediction accuracy due to the noise and unrelated data in large spatial data sets. This study develops a geographically optimal similarity (GOS) model for accurate and reliable spatial prediction based on the geographical similarity principle. In GOS, the geographical configurations are first characterized, and similarities between unknown and known observation locations are assessed. Next, an optimal threshold is determined to select a small number of observations with optimal similarities for the prediction at each unknown location. Finally, a reliable uncertainty assessment approach is developed to assess and map uncertainties of GOS predictions. A new R package "geosimilarity" is developed to conduct GOS models. In this study, GOS is implemented in predicting spatial distributions of trace elements in a mining region in Australia. Results show that GOS can use a small number of observations to derive more accurate and reliable spatial predictions than linear regression and basic configuration similarity models. In addition, pattern characteristics of predictions can be improved by GOS by eliminating the phenomenon wherein predictions are clustered near mean values and contain striped textures. Therefore, GOS demonstrates greater potential for implementing the geographical similarity principle in spatial predictions by bringing information from samples with relatively high similarities at any location across space for more accurate and effective predictions in broader fields and practice. [ABSTRACT FROM AUTHOR]

Spatial heterogeneity refers to uneven distributions of geographical variables. Spatial interpolation methods that utilize spatial heterogeneity are sensitive to the way in which spatial heterogeneity is characterized. This study developed a Generalized Heterogeneity Model (GHM) for characterizing local and stratified heterogeneity within variables and to improve interpolation accuracy. GHM first divides a study area into multiple spatial strata according to the sample values and locations of a variable. Then, GHM estimates simultaneously the spatial variations of the variable within and between the spatial strata. Finally, GHM interpolates unbiased estimates and uncertainty at unsampled locations. We demonstrated the GHM by predicting the spatial distributions of marine chlorophyll in Townsville, Queensland, Australia. Results show that GHM improved both the overall interpolation accuracy across the study area and along strata boundaries compared with previous interpolation models. GHM also avoided bull's eye patterns and abrupt changes along strata boundaries. In future studies, GHM has the potential to be integrated with machine learning and advanced algorithms to improve spatial prediction accuracy for studies in broader fields. [ABSTRACT FROM AUTHOR]

Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR) model for more accurate spatial predictions for small data samples. The LUQR is an integration of the LUR and quantile regression, which both have advantages in predictions with a small data set of samples. In this study, the LUQR model is applied in predicting spatial distributions of annual mean PM 2.5 concentrations across the Greater Sydney Region, New South Wales, Australia, with observations at 19 valid monitoring stations in 2020. Cross validation shows that the goodness-of-fit can be improved by 25.6–32.1% by LUQR models when compared with LUR, and prediction root mean squared error (RMSE) and mean absolute error (MAE) can be reduced by 10.6–13.4% and 19.4–24.7% by LUQR models, respectively. This study also indicates that LUQR is a more robust model for the spatial prediction with small data samples than LUR. Thus, LUQR has great potentials to be widely applied in spatial issues with a limited number of observations. [ABSTRACT FROM AUTHOR]

Soil moisture is a fundamental ecological component for climate and hydrological studies. However, the distribution patterns of soil moisture are spatially heterogenous and influenced by multiple environmental factors. The knowledge is still limited in assessing the large-scale spatial heterogeneity of soil moisture in in situ data modelling, in situ network design, spatial down-scaling, and remote sensing-based soil moisture retrieval. Heterogeneity models are effective in characterizing spatial disparities, but they are not capable of examining the maximum regional disparities. To address this bottleneck, the authors of this study developed a geographically optimal zones-based heterogeneity (GOZH) model. By progressively optimizing geographical zones of soil moisture and quantifying the heterogeneity among zones, GOZH may help identify individual and interactive determinants of soil moisture across a large study area. It was applied to identify spatial determinants of in situ soil moisture data collected at 653 monitoring stations in the Northern Hemisphere in unfrozen and frozen seasons from April 2015 to December 2017, with only thawed data considered in both seasons. Correspondingly, a series of variables were derived from Google Earth Engine (GEE) remote sensing data. The results demonstrated the significant regional disparities of soil moisture, and the combinations of determinants are critically different among geographical zones and between unfrozen and frozen seasons. At a global scale, the combinations of determinants can explain about 48% of the spatial pattern of soil moisture. Spatial heterogeneity of soil moisture in frozen seasons is much more complex than that in unfrozen seasons regarding geographical zones and explanatory variables. The variability of soil moisture during unfrozen seasons can be more explainable than that during frozen seasons, which was a convincing evidence for previous studies that soil moisture predictions were mostly performed during unfrozen seasons. Primary variables that determine spatial patterns of soil moisture are changed from climate variables during the unfrozen season to geographical variables during the frozen season. Results show that GOZH model can effectively explore spatial determinants of soil moisture through avoiding the underestimation of individual variables, overestimation of multiple variables, and finely divide zones. The research findings from this study provide an in-depth understanding of the spatial heterogeneity of soil moisture and can be implemented in more effective in situ sampling network design, spatial down-scaling of soil moisture, and accurate inversion of surface parameters from the satellite data of soil moisture. [ABSTRACT FROM AUTHOR]

Soil properties, such as organic carbon, pH and clay content, are critical indicators of ecosystem function. Visible–near infrared (vis–NIR) reflectance spectroscopy has been widely used to cost-efficiently estimate such soil properties. Multivariate modelling, such as partial least squares regression (PLSR), and machine learning are the most common methods for modelling soil properties with spectra. Often, such models do not account for the multiresolution information presented in the vis–NIR signal, or the spatial variation in the data. To address these potential shortcomings, we used wavelets to decompose the vis–NIR spectra of 226 soils from agricultural and forested regions in south-western Western Australia and developed a wavelet geographically weighted regression (WGWR) for estimating soil organic carbon content, clay content and pH. To evaluate the WGWR models, we compared them to linear models derived with multiresolution data from a wavelet decomposition (WLR) and PLSR without multiresolution information. Overall, validation of the WGWR models produced more accurate estimates of the soil properties than WLR and PLSR. Around 3.5–49.1% of the improvement in the estimates was due to the multiresolution analysis and 1.0–5.2% due to the integration of spatial information in the modelling. The WGWR improves the modelling of soil properties with spectra. [ABSTRACT FROM AUTHOR]

Geographical variables are usually not independent of each other. Hence, it is necessary to investigate the effect of interactions among explanatory variables on a response variable to characterize spatially enhanced or weakened relationships among all variables. The geographical detector (GD) model identifies zones for each explanatory variable, divides the study area into spatial units by overlapping these zones, and quantifies spatial associations as the power of interactive determinant (PID) between a response variable and explanatory variables. Consequently, the PID values depend upon the distributions of explanatory variables (i.e. spatial characteristics) and the subsequent division of spatial units out of these explanatory variables. This study has therefore proposed an Interactive Detector for Spatial Associations (IDSA) to optimize spatial division and improve PID. IDSA utilizes spatial autocorrelation of each explanatory variable and optimizes spatial units based on spatial fuzzy overlay to compute PID. We test the IDSA on both a simulation study and practical case that analyzes road deterioration in Australia. Results showed that the IDSA model could effectively assess the PID while existing GD overestimated PID. Hence, the IDSA improves the GD with refined spatial units based on explanatory variables to enhance their local spatial associations with a response variable. [ABSTRACT FROM AUTHOR]

Abstract China has been the largest contributor to global carbon emissions since 2008, with its building and construction industry considered as one of the most significant sources. However, few studies have been conducted on analyzing the influencing factors of the carbon emissions following a life cycle perspective. This study aims to evaluate the carbon emissions of this industry from a life cycle perspective, including the extraction, manufacturing, construction and construction-related transportation, and building operation, using Logarithmic Mean Divisia Index. The key findings are: 1) the extraction and manufacturing of raw materials, and building operation are the two biggest contributors to the life cycle carbon emissions of the building and construction industry, accounting for 58% and 40% respectively; 2) the most effective strategies to reduce carbon emissions in the construction stage from 2000 to 2015 are to improve energy efficiency and lower emission factor; and 3) the most effective strategies to reduce carbon emissions in the building operation stage from 2000 to 2015 are to increase development density, improve emission factor, energy structure and industry structure. This study provides useful scientific evidence for policy makers to establish appropriate emission targets and relevant reduction strategies that are relevant to China's building and construction industry. Highlights • The carbon emissions of China's building and construction industry is calculated. • Extraction and manufacturing of materials contributes 58% of the carbon emissions. • Building operation contributes 40% of the carbon emissions. • Better energy efficiency is effective in reducing construction emissions. • High development density is effective in reducing building operation emissions. [ABSTRACT FROM AUTHOR]

Urban forms reflect spatial structures of cities, which have been consciously and dramatically changing in China. Fast urbanisation may lead to similar urban forms due to similar habits and strategies of city planning. However, whether urban forms in China are identical or significantly different has not been empirically investigated. In this paper, urban forms are investigated based on two spatial units: city and block. The boundaries of natural cities in terms of the density of human settlements and activities are delineated with the concept of ‘redefined city’ using points of interests (POIs), and blocks are determined by road networks. Urban forms are characterised by city-block two-level spatial morphologies. Further, redefined cities are classified into four hierarchies to examine the effects of different city development stages on urban forms. The spatial morphology is explained by urbanisation variables to understand the effects. Results show that the urban forms are spatially clustered from the perspective of city-block two-level morphologies. Urban forms tend to be similar within the same hierarchies, but significantly varied among different hierarchies, which is closely related to the development stages. Additionally, the spatial dimensional indicators of urbanisation could explain 41% of the spatial morphology of redefined cities. [ABSTRACT FROM AUTHOR]

As the coming decades will witness a big trend in the decommissioning of offshore platforms, simultaneously disassembling topsides of multiple offshore platforms is getting increasingly common. Considering high risk and cost of offshore operations, module lift planning among multiple offshore platforms with transport vessels is required to be carefully conducted. The lift planning usually contains two main parts: module layout on vessels planning and vessel transport schedules arrangement. In contrast to the current experience-driven module lift planning, this paper formulates the lift planning optimization problem and develops a web system integrating building information modeling (BIM) and geographical information system (GIS) to efficiently disassemble topsides for multiple offshore platforms. BIM provides detailed information required for planning module layout on vessels and GIS contains the management and analysis of geospatial information for the vessel transport schedule arrangement. As for module layout optimization, three heuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and firefly algorithm (FA) are implemented and compared to obtain the module layout with the minimum total lift time. While for vessel transport schedule, graph search technique is integrated with a developed schedule clash detection function to obtain the transport schedule with the minimum sailing time. The proposed optimization algorithms and techniques are integrated into a developed BIM/GIS-based web system. An example of three offshore platforms with eighteen modules in total is used to illustrate the developed system. Results show that the developed system can significantly improve the efficiency of lift planning in multiple topsides disassembly. The developed BIM/GIS-based web system is also effective and practical in the resource allocation and task assignment among multiple locations, such as construction sites, buildings, and even cities. [ABSTRACT FROM AUTHOR]

Charitable foundations are a critical part of public services. However, there is a large gap between the locations and expenditures of charitable foundations and the real population needs for most nations. Three types of Chinese local charity foundations, i.e., those for poverty,education and medical assistance, are used as examples to explore the distinct gaps. The spatial distributions of local charity foundations are characterized by spatial scan statistics and spatial autocorrelation models. The local population needs of charitable assistance for poverty, education and medical services are quantified with their respective weighted proxy indexes of the current conditions. Thus, the nonlinear relationships between population needs and the expenditures of local charitable foundations are described with generalized additive models. The results show that both the participation rate and the charity expenditures of the foundations are highly clustered within a few cities where the population needs are relatively small and are furthermore rare among the other cities. The charity expenditures of local foundations are nonlinearly correlated with the current conditions of socioeconomic development, education and medical levels due to the diverse development stages of the cities. This study provides quantitative evidence for local authorities and charitable foundations to make targeted and constructive decisions to gradually reduce the distinct gaps. [ABSTRACT FROM AUTHOR]

Geographically weighted regression (GWR) is an important local method to explore spatial non-stationarity in data relationships. It has been repeatedly used to examine spatially varying relationships between epidemic diseases and predictors. Malaria, a serious parasitic disease around the world, shows spatial clustering in areas at risk. In this article, we used GWR to explore the local determinants of malaria incidences over a 7-year period in northern China, a typical mid-latitude, high-risk malaria area. Normalized difference vegetation index (NDVI), land surface temperature (LST), temperature difference, elevation, water density index (WDI) and gross domestic product (GDP) were selected as predictors. Results showed that both positively and negatively local effects on malaria incidences appeared for all predictors except for WDI and GDP. The GWR model calibrations successfully depicted spatial variations in the effect sizes and levels of parameters, and also showed substantially improvements in terms of goodness of fits in contrast to the corresponding non-spatial ordinary least squares (OLS) model fits. For example, the diagnostic information of the OLS fit for the 7-year average case is R2 = 0.243 and AICc = 837.99, while significant improvement has been made by the GWR calibration with R2 = 0.800 and AICc = 618.54. [ABSTRACT FROM AUTHOR]

[Display omitted] Sustainable development in impoverished areas is still a global challenge owing to trade-offs between development and conservation. There are large poverty-stricken areas (PSAs) in China, which overlap highly with ecologically sensitive areas. China has made great efforts to alleviate poverty over the years. The coordinated relationship between the social economy and the environment in PSAs, however, remains under-recognized. This study developed a county-level index system encompassing the socioeconomic and environmental sectors of China's PSAs. The integrated indexes of the two sectors were developed to reveal the spatial–temporal socioeconomic and environmental patterns and coupling coordination degree (CCD) levels were calculated to assess the coordinated relationships between them. The CCD indicated the increasingly coordinated development of socioeconomic and environmental conditions in China's PSAs from 2000 to 2020. Meanwhile, although the socioeconomic index achieved considerable growth with a growth rate of 58.4%, the environmental index was mildly improved with a growth rate of 19.6%, instead of a reduction. PSAs still have a large gap in socioeconomic development compared to non-poor areas; however, PSAs perform better in environmental index. Overall, the increased coordinated development between the social economy and the environment from 2000 to 2020 can be attributed to China's long-term, large-scale, and targeted interventions in poverty reduction and environmental conservation. Further, benefiting from the geodiversity of China, we identified four poverty reduction models which include advantageously, sustained, periodic, and limited effective models, on the basis of CCD change patterns. The four models can provide valuable experience for the rest of the world in tackling similar trade-offs of poverty reduction and environmental challenges. [ABSTRACT FROM AUTHOR]

Offshore oil and gas platforms (OOGPs) usually have a lifetime of 30–40 years. An increasing number of OOGPs across the world will be retired and decommissioned in the coming decade. Therefore, a safe and efficient approach in planning the disassembly of the topsides of OOGPs is required. One commonly applied disassembly method is reverse installation, which moves the OOGP modules from the platform deck to a heavy lift vessel (HLV) in reverse order of their installation. Considering the high risk and cost of working offshore, shortening the lift time is crucial. In contrast to the traditional experience-driven lift operations, this paper describes minimizing the lift path for each OOGP module during disassembly, leveraging building information modeling (BIM) technology and an improved A* algorithm. BIM models provide accurate component-based geometric and semantic information that can be used for planning and optimization. However, there has been no previous study on the use of BIM for offshore disassembly. Industry Foundation Classes (IFC), which is a neutral data model of BIM, is used in this study to represent OOGP models. In particular, the IfcBuildingElementProxy entity is used to represent the OOGP components, and the information in IfcBuildingElementProxy is automatically extracted to obtain the location and dimension information of each OOGP module. Then, for a given layout of modules on the removal vessel, the lift path and removal sequence of different modules, with the shortest lift path distance, are obtained. The lift path distance is calculated using the A* algorithm, which has been widely applied in 2D environments and is modified in this study to suit the 3D environment. Finally, the genetic algorithm (GA) technique is applied to optimize the layout plan on the removal vessel by minimizing the total lift path distance. The developed BIM-based framework is illustrated and evaluated through an illustrative example. The results show that the proposed framework can generate and visualize the shortest lift path for each OOGP module directly and automatically, and significantly improve the efficiency of OOGP disassembly. [ABSTRACT FROM AUTHOR]

The concept of green building has gained rapid recognition recently. Developed by the U.S. Green Building Council, the Leadership in Energy and Environmental Design (LEED) is one of the most successful green building rating systems. Understanding the performance of LEED certified buildings on various LEED credits is therefore important for practitioners and regulatory authorities. This paper analyses the use of LEED 2009 and investigates the credit allocation pattern of 3416 LEED 2009 certified projects. The results show that compared to its previous version LEED v2.2, the point chasing problem in innovation related credits is mitigated. However, energy-related and material-related credits remain difficult for developers to obtain. LEED 2009 certified projects perform differently in water efficiency at the country level. At the state level, the projects perform differently in water efficiency, energy and atmosphere, material and resources, as well as indoor environmental quality. Varied credit achievement patterns are also identified on cross-certification and cross-sector levels. The study offers a useful guidance for practitioners to achieve relevant certification levels and for regulatory authorities to continuously improve the rating system. [ABSTRACT FROM AUTHOR]

The spatial dependency discovered by Tobler in his first law of geography has been one of the most prevalent guiding principles for spatial prediction and a widely accepted variable in numerous spatial analysis algorithms. When conducting the spatial prediction based on finite samples, the estimated position is expected to be influenced more by nearby than by distant samples, although the influence strength may vary in different directions. For example, the semivariogram in kriging is a function of distance. It describes how the variance decreases with the increase of the distance between a pair of samples. The spatial weight matrix of geographically weighted regression is also constructed according to the principle of distance decay. [ABSTRACT FROM AUTHOR]

Floating islands in Lake Victoria, the world's second-largest fresh water lake, disrupt transportation, fisheries, irrigation, and water quality. Despite their impact, the dynamics of these islands remain unexplored. This study investigates island dynamics within the Nalubaale, Kiira, and Bujagali dams in Uganda, exploring the causes of their formation and the subsequent impact on hydropower production. The study collects data of Landsat imagery from 2000 to 2020, CHIRPS precipitation, and Lake Victoria's water level datasets from 2004, 2010, 2013, 2017, and 2020. The results reveal a strong correlation between precipitation, fluctuating water levels, and floating island formation, with nutrient-rich runoff from municipal waste and agriculture promoting island growth. In addition, rising water levels lead to the dislodging of rocks and soil, contributing to floating island formation, which may manifest with a lag time of up to one month. The analysis shows higher correlations between precipitation, water levels, and floating islands during the long (March–May) and short (September–November) rainy seasons as opposed to drier periods (June–August, December–February). The findings indicate that southeast monsoon winds, which transport floating vegetation, also are essential in influencing island dynamics. Consequently, the major drivers of floating islands in Lake Victoria are identified as precipitation, water level fluctuations and wind variations. Finally, a negative correlation between floating island eutrophication and power production at Kiira and Nalubaale stations suggests that the increased eutrophication caused by the presence of floating islands leads to reduced power output at both Kiira and Nalubaale power stations. [ABSTRACT FROM AUTHOR]

Identifying the factors influencing wetland variations is crucial for understanding the relationship of climate change with wetland conservation and management. The wetland distribution is associated with multiple variables, and the interactions among these variables are complex. In this study, we aim to explore an interpretable and quantitative analysis of factors related to wetland spatiotemporal variations on the Tibetan Plateau (TP). By combining SHapley Additive exPlanations with a spatially stratified heterogeneity model, we propose a locally explained stratified heterogeneity (LESH) model that well reveals the effects of multiple variable interactions on the spatiotemporal variations of wetlands. The results show that topographic variables are the most important variables related to the spatial distribution of wetlands on the TP, and climatic variables are the most relevant factors for the increase in the wetland area on the TP from 2015 to 2019. In addition, the interactions among multiple variables strongly influence wetlands on the TP. Among them, when other geographic variables interact with the evaporation variable, its explanatory power on wetland distribution is significantly increased. Knowledge of wetland distribution determinants can help us understand the evolution of wetlands and the impacts of climate change on wetland variations. [ABSTRACT FROM AUTHOR]

The use of fossil fuels has been a major contributor to global climate change. To mitigate the negative impact of fossil fuels, renewable energy (RE) has been increasingly promoted. This study aims to investigate factors impacting the adoption of RE in the residential sector, using the U.S. as a case study. Residential RE consumption rate data in 51 states/districts of the U.S. were collected from 2010 to 2018 to examine the adoption of RE. Correspondingly, four categories of explanatory variables affecting the consumption rate were investigated, including demographics, availability, consumers' affordability, and regulatory compliance motivation. An optimal parameters-based geographical detector (OPGD) model was implemented to investigate spatio-temporal impacts of variables on the consumption rate. Results show that factors that have consistently significant impacts on residential RE adoption include percentage of RE production of the availability category and residential monthly electricity bill of the consumers' affordability category. Population was also an important factor from 2012 to 2018. The impact of RE production rate can also be further enhanced when combined with other factors, such as residential monthly bill and number of regulatory policies. Based on these results, several practical recommendations are proposed as references for government to increase RE adoption. [ABSTRACT FROM AUTHOR]

Background: Soybean (Glycine max), a vital grain and oilseed crop, serves as a primary source of plant protein and oil. Soil salinization poses a significant threat to soybean planting, highlighting the urgency to improve soybean resilience and adaptability to saline stress. Melatonin, recently identified as a key plant growth regulator, plays crucial roles in plant growth, development, and responses to environmental stress. However, the potential of melatonin to mitigate alkali stress in soybeans and the underlying mechanisms remain unclear. Results: This study investigated the effects of exogenous melatonin on the soybean cultivar Zhonghuang 13 under alkaline stress. We employed physiological, biochemical, transcriptomic, and metabolomic analyses throughout both vegetative and pod-filling growth stages. Our findings demonstrate that melatonin significantly counteracts the detrimental effects of alkaline stress on soybean plants, promoting plant growth, photosynthesis, and antioxidant capacity. Transcriptomic analysis during both growth stages under alkaline stress, with and without melatonin treatment, identified 2,834 and 549 differentially expressed genes, respectively. These genes may play a vital role in regulating plant adaptation to abiotic stress. Notably, analysis of phytohormone biosynthesis pathways revealed altered expression of key genes, particularly in the ARF (auxin response factor), AUX/IAA (auxin/indole-3-acetic acid), and GH3 (Gretchen Hagen 3) families, during the early stress response. Metabolomic analysis during the pod-filling stage identified highly expressed metabolites responding to melatonin application, such as uteolin-7-O-(2''-O-rhamnosyl)rutinoside and Hederagenin-3-O-glucuronide-28-O-glucosyl(1,2)glucoside, which helped alleviate the damage caused by alkali stress. Furthermore, we identified 183 differentially expressed transcription factors, potentially playing a critical role in regulating plant adaptation to abiotic stress. Among these, the gene SoyZH13_04G073701 is particularly noteworthy as it regulates the key differentially expressed metabolite, the terpene metabolite Hederagenin-3-O-glucuronide-28-O-glucosyl(1,2)glucoside. WGCNA analysis identified this gene (SoyZH13_04G073701) as a hub gene, positively regulating the crucial differentially expressed metabolite of terpenoids, Hederagenin-3-O-glucuronide-28-O-glucosyl(1,2)glucoside. Our findings provide novel insights into how exogenous melatonin alleviates alkali stress in soybeans at different reproductive stages. Conclusions: Integrating transcriptomic and metabolomic approaches, our study elucidates the mechanisms by which exogenous melatonin ameliorates the inhibitory effects of alkaline stress on soybean growth and development. This occurs through modulation of biosynthesis pathways for key compounds, including terpenes, flavonoids, and phenolics. Our findings provide initial mechanistic insights into how melatonin mitigates alkaline stress in soybeans, offering a foundation for molecular breeding strategies to enhance salt-alkali tolerance in this crop. [ABSTRACT FROM AUTHOR]

Infrastructure is a fundamental sector for sustainable development and Earth observation has great potentials for sustainable infrastructure development (SID). However, implementations of the timely, large–scale and multi–source Earth observation are still limited in satisfying the huge global requirements of SID. This study presents a systematical literature review to identify trends of Earth observation for sustainable infrastructure (EOSI), investigate the relationship between EOSI and Sustainable Development Goals (SDGs), and explore challenges and future directions of EOSI. Results reveal the close associations of infrastructure, urban development, ecosystems, climate, Earth observation and GIS in EOSI, and indicate their relationships. In addition, from the perspective of EOSI–SDGs relationship, the huge potentials of EOSI are demonstrated from the 70% of the infrastructure influenced targets that can be directly or indirectly derived from Earth observation data, but have not been included in current SDG indicators. Finally, typical EOSI cases are presented to indicate challenges and future research directions. This review emphasizes the contributions and potentials of Earth observation to SID and EOSI is a powerful pathway to deliver on SDGs. [ABSTRACT FROM AUTHOR]

Climatic impact on re-established ecosystems at reclaimed mined lands may have changed. However, little knowledge is available about the difference in vegetation–climate relationships between reclaimed and unmined lands. In this study, ecological responses to climatic variability on reclaimed and neighbouring unmined lands were estimated using remote-sensing data at the Pingshuo Mega coal mine, one of the largest coal mines with long-term reclamation history in China. Time-series MODIS enhanced vegetation index (EVI) data and meteorological data from 1997 to 2017 were collected. Results show significantly different vegetation–climate relationships between reclaimed and unmined lands. First, the accumulation periods of all climatic variables were much longer on reclaimed mining lands. Second, vegetation on reclaimed lands responded to variabilities in temperature, rainfall, air humidity, and wind speed, while undisturbed vegetation only responded to variabilities of temperature and air humidity. Third, climatic variability made a much higher contribution to EVI variation on reclaimed land (20.0–46.5%) than on unmined land (0.7–1.7%). These differences were primarily caused by limited ecosystem resilience, and changed site hydrology and microclimate on reclaimed land. Thus, this study demonstrates that the legacy effects of surface mining can critically change on-site vegetation–climate relationships, which impacts the structure, functions, and stability of reclaimed ecosystems. Vegetation–climate relationships of reclaimed ecosystems deserve further research, and remote-sensing vegetation data are an effective source for relevant studies. [ABSTRACT FROM AUTHOR]

Road infrastructure performance is closely associated with passengers and freight transportation systems and socio-economic development. The performance of road infrastructure is commonly measured by sensor-monitored indicators, and the ability of monitored indicators in revealing actual performance is generally determined by decision makers and road users. However, it is usually unreliable to directly apply monitored indicators in road performance evaluation, due to the limited aspects of individual sensor-monitored indicators, and potential biases and uncertainties of human experience. To address the issues, this study proposes a model-driven fuzzy spatial multi-criteria decision making (MFSD) approach to derive a comprehensive and accurate indicator of sustainable road performance. In this study, the MFSD approach is applied in exploring the road network in the Wheatbelt region in Western Australia, Australia. Spatial variables of road properties, traffic vehicles and climate conditions are used as criteria in the decision making. Four sensor monitored indicators are collected for estimating contributions of criteria. Results show that the MFSD-based indicator can more comprehensively and accurately characterize sustainable road infrastructure performance. In the study area, the MFSD-based indicator can improve 30.46% of the correlation with road maintenance cost compared with roughness, which is the optimal sensor monitored indicator. At the local government areas, the MFSD-based indicator can explain 45.8% of practical road maintenance cost. Sensitivity analysis from multiple aspects indicates that MFSD is a reliable and accurate method in decision making. The proposed method and analysis have broad potentials in the network-level sustainable infrastructure management. Image 1 • Propose an MFSD method to generate sustainable road infrastructure performance indicators. • MFSD-based indicator can describe comprehensive performance of road infrastructure. • MFSD is a model-driven approach that reduces potential biases of human decisions. • Sensitivity analysis shows that the MFSD is a reliable decision-making method. • MFS-AHP is more capable of differentiating the relative importance of alternatives MFS-TOPSIS. [ABSTRACT FROM AUTHOR]

Background: Air pollution has been related to incidence of type 2 diabetes (T2D). We assessed the joint association of various air pollutants with the risk of T2D and examined potential modification by obesity status and genetic susceptibility on the relationship.Methods and Findings: A total of 449,006 participants from UK Biobank free of T2D at baseline were included. Of all the study population, 90.9% were white and 45.7% were male. The participants had a mean age of 56.6 (SD 8.1) years old and a mean body mass index (BMI) of 27.4 (SD 4.8) kg/m2. Ambient air pollutants, including particulate matter (PM) with diameters ≤2.5 μm (PM2.5), between 2.5 μm and 10 μm (PM2.5-10), nitrogen dioxide (NO2), and nitric oxide (NO) were measured. An air pollution score was created to assess the joint exposure to the 4 air pollutants. During a median of 11 years follow-up, we documented 18,239 incident T2D cases. The air pollution score was significantly associated with a higher risk of T2D. Compared to the lowest quintile of air pollution score, the hazard ratio (HR) (95% confidence interval [CI]) for T2D was 1.05 (0.99 to 1.10, p = 0.11), 1.06 (1.00 to 1.11, p = 0.051), 1.09 (1.03 to 1.15, p = 0.002), and 1.12 (1.06 to 1.19, p < 0.001) for the second to fifth quintile, respectively, after adjustment for sociodemographic characteristics, lifestyle factors, genetic factors, and other covariates. In addition, we found a significant interaction between the air pollution score and obesity status on the risk of T2D (p-interaction < 0.001). The observed association was more pronounced among overweight and obese participants than in the normal-weight people. Genetic risk score (GRS) for T2D or obesity did not modify the relationship between air pollution and risk of T2D. Key study limitations include unavailable data on other potential T2D-related air pollutants and single-time measurement on air pollutants.Conclusions: We found that various air pollutants PM2.5, PM2.5-10, NO2, and NO, individually or jointly, were associated with an increased risk of T2D in the population. The stratified analyses indicate that such associations were more strongly associated with T2D risk among those with higher adiposity. [ABSTRACT FROM AUTHOR]

Road infrastructure is important to the well-being and economic health of all nations. The performance of road pavement infrastructure is sophisticated and affected by numerous factors and varies greatly across different roads. Large scale spatial analysis for assessing road infrastructure performance is increasingly required for road management, therefore multi-source factors, including satellite remotely sensed climate and environmental data, and ground-monitored vehicles observations, are collected as explanatory variables. Different from the traditional point or area based geospatial attributes, the performance of pavement infrastructure is the line segment based spatial data. Thus, a segment-based spatial stratified heterogeneity method is utilized to explore the comprehensive impacts of vehicles, climate, properties of road and socioeconomic conditions on pavement infrastructure performance. Segment-based optimal discretization is applied on discretizing segment-based pavement data, and a segment-based geographical detector is utilized to assess the spatial impacts of variables and their interactions. Results show that the segment-based methods can more reasonably and accurately describe the characteristics of line segment based spatial data and assess the spatial associations. The two major categories of factors associated with pavement damage are the variables of traffic vehicles and heavy vehicles in particular, and climate and environmental conditions. Meanwhile, the interactions between the explanatory variables in these two categories have much more influence than the single explanatory variables, and the interactions can explain more than half of the pavement damage. This study highlights the great potential of remote sensing based large scale spatial analysis of road infrastructures. The approach in this study provides new ideas for spatial analysis for segmented geographical data. The findings indicate that the quantified comprehensive impacts of variables are practical for wise decision-making for road design, construction and maintenance. [ABSTRACT FROM AUTHOR]

Resilience has a critical role to promote human well-being in turbulent environments. However, extensive interventions and stimulus measures not only achieve resilience goals with numerous unacceptable consequences, but also highlight the narrow definition of resilience. The study proposes the concept of resilience effectiveness to constrain the unsustainability of resilience in concept and practice. The resilience effectiveness of Chinese cities in the early stages of the pandemic is first implemented for evaluation based on input-output relationship, and then patterns and determinants are investigated using the GIS methods and the geographically optimal zones-based heterogeneity (GOZH) model. The study finds that the average resilience effectiveness is not high, indicating that China's strong resilience is the loss of safety, greenness, inclusiveness, and efficiency in the early stage of the pandemic. Resilience effectiveness has significant spatial heterogeneity and is dominated by innovation ability. The optimal geographic zones of Chinese cities for resilience effectiveness are identified, revealing that it is a complex and multifactorial process. This paper broadens resilience research and provides implications for city planning in the post-pandemic era. [ABSTRACT FROM AUTHOR]

Due to the instantaneous field-of-view (IFOV) of the sensor and diversity of land cover types, some pixels, usually named mixed pixels, contain more than one land cover type. Soft classification can predict the portion of each land cover type in mixed pixels in the absence of spatial distribution. The spatial distribution information in mixed pixels can be solved by super resolution mapping (SRM). Typically, SRM involves two steps: soft class value estimation, which is similar to the image super resolution of image restoration, and land cover allocation. A new SRM approach utilizes a deep image prior (DIP) strategy combined with a super resolution convolutional neural network (SRCNN) to estimate fine resolution fraction images for each land cover type; then, a simple and efficient classifier is used to allocate subpixel land cover types under the constraint of the generated fine fraction images. The proposed approach can use prior information of input images to update network parameters and no longer require training data. Experiments on three different cases demonstrate that the subpixel classification accuracy of the proposed DIP-based SRM approach is significantly better than the three conventional SRM approaches and a transfer learning-based neural network SRM approach. In addition, the DIP-SRM approach performs very robustly about small-area objects within multiple land cover types and significantly reduces soft classification uncertainty. The results of this paper provide an extension for utilizing SRCNN to address SRM issues in hyperspectral images. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid advancement of China's urbanization, the contradiction between urban development and the ecological environment has become increasingly prominent, and the urban ecological system now faces severe challenges. In this study, we proposed an ecological index-based approach to monitor and evaluate the ecological environment using a Google Earth Engine cloud-based platform and Landsat time series. Firstly, a long-term series of Landsat images was obtained to construct and calculate the remote sensing-based ecological index (RSEI). Then, the Theil–Sen median estimation and the Mann–Kendall test were used to evaluate the trend and significance of the RSEI time series and combined with the Hurst index to predict the future development trend of the ecological environment in the study area. Finally, the coefficient of variation method was used to determine the temporal stability of the ecological environment. Taking Zhoushan Archipelago, China, as the study area, we mapped the distribution of the ecological environment using a spatial resolution of 30 m and evaluated the ecological environment from 1985 to 2020. The results show that (1) from 1985 to 2020, the average RSEI in the Zhoushan Archipelago decreased from 0.7719 to 0.5817, increasing at a rate of −24.64%. (2) The changes in the areas of each level of ecological environmental quality show that the ecological environment in the Zhoushan Archipelago generally exhibited a decreasing trend. During the study period, the proportion of the areas with excellent ecological environmental quality decreased by 38.83%, while the proportion of areas with poor and relatively poor ecological environmental quality increased by 20.03%. (3) Based on the overall change trend, the degradation in the ecological environment in the Zhoushan Archipelago was greater than the improvement, with the degradation area accounting for 84.35% of the total area, the improvement area accounting for 12.61% of the total area, and the stable area accounting for 3.05% of the total area. (4) From the perspective of the sustainability of the changes, in 86.61% of the study area, the RSEI exhibited positive sustainability, indicating that the sustainability of the RSEI was relatively strong. (5) The coefficient of variation in the RSEI was concentrated in the range of 0–0.40, having an average value of 0.1627 and a standard deviation of 0.1467, indicating that the RSEI values in the Zhoushan Archipelago during the study period were concentrated, the interannual fluctuations of the data were small, and the time series was relatively stable. The results of this study provide theoretical methods and a decision-making basis for the dynamic monitoring and regional governance of the ecological environment in island areas. [ABSTRACT FROM AUTHOR]

Accidents on offshore oil and gas platforms (OOGPs) usually cause serious fatalities and financial losses considering the demanding environment where such platforms are located and the complicated topsides structure that the platforms have. Conducting evacuation planning on OOGPs is challenging. Computational tools are considered as a good way to plan evacuation by emergency simulation. However, the complex structure of OOGPs and various evacuation behaviors can weaken the advantages of computational simulation. Therefore, this study develops a simulation model for OOGPs to evaluate different evacuation plans to improve evacuation performance by integrating building information modeling (BIM) technology and agent-based model (ABM). The developed model consists of four parts: evacuation model input, simulation environment modeling, agent definition, and simulation and comparison. Necessary platform information is extracted from BIM and then used to model the simulation environment by integrating matrix model and network model. In addition to essential attributes, environment sensing and dynamic escape path planning functions are developed and assigned to agents in order to improve simulation performance. Total evacuation time for all agents on an offshore platform is used to evaluate the evacuation performance of each simulation. An example OOGP BIM topsides with different emergency scenarios is used to illustrate the developed evacuation evaluation model. The results show that the developed model can accurately simulate evacuation and improve evacuation performance on OOGPs. The developed model is also applicable to other industries such as the architecture, engineering, and construction industry, where there is an increasing demand for evacuation planning and simulation. [ABSTRACT FROM AUTHOR]

The rational function model (RFM) is widely applied to orthorectification of aerial and satellite imagery. This article proposes a newmethod named Ortho-WTLS to solve the RFM in remote-sensing imagery orthorectification. Based on a weighted total least squares (WTLS) estimator, the proposed method allows one to handle coordinates of ground control points (GCPs) that contain errors and are of unequal accuracies. This situation occurs, e.g. if GCPs are automatically selected. In the proposed model, first, the relationship of two linearization methods for an RFM with errors contained in GCPs is investigated and results in a hybrid linearization. Next, based on WTLS, RFM coefficients are estimated with an iterative computation function. Finally, the performance of the Ortho-WTLS method thus obtained is investigated using simulated images and remotely sensed images by collecting GCPs with varying errors. Experimental results show that the Ortho-WTLS method achieves a more robust estimation of model parameters and a higher orthorectification accuracy when compared with standard LS-based RFM estimation. We conclude that the quality of GCPs has a large impact on the accuracy and that an increasing number of low-precision GCPsmay lead to a decrease in orthorectification quality. [ABSTRACT FROM AUTHOR]

• A novel spatio-temporally stratified heterogeneity (STSH) model is proposed. • The model can help identify and analyze the influencing factors of carbon emissions. • STSH can include more independent and interdependent intensity and quantity factors. • STSH can reveal the complex enhanced or weakened interaction of factors. • STSH has a higher capacity to investigate the spatio-temporally stratified heterogeneity of data. Understanding factors influencing carbon emissions is important for achieving carbon abatement goals. Traditionally, decomposition approaches, e.g. Logarithmic Mean Divisia Index (LMDI) method, are used to uncover factors influencing the change of carbon emissions. This study aims to develop a novel spatio-temporally stratified heterogeneity (STSH) model to better identify, analyze and understand influencing factors of carbon emissions. Compared with LMDI, this model can analyze more independent intensity and quantity factors and reveal the complex interactions between factors. Using China's construction industry as a case study, the model can successfully identify top influencing factors in the same order of importance similar to LMDI, although a significantly larger number of factors are considered. In the whole construction stage, cement usage, steel usage, completed floor area and construction output value have the highest contributions of 0.599, 0.528, 0.448, 0.446 respectively. In construction and construction-related transportation activities, the top influencing factors are fixed capital assets, construction output value, completed floor area, and electricity usage, with contributions of 0.632, 0.613, 0.599, and 0.597, respectively. The model can also reveal the complex interactions between factors, including bi-variate enhanced interaction and nonlinearly enhanced interaction. The results demonstrate that the model is more useful to evaluate the individual and aggregate impact of a large number of independent factors on carbon emissions when compared with previous models. [ABSTRACT FROM AUTHOR]

In China, backfill mining has been selected as an effective approach to realize the green mining of underground coal resources, where backfill materials are the major factor in the development of backfill mining. In order to provide a better reference for further research and development of backfill mining, the experience and research achievements are reviewed in this paper. Firstly, the backfill materials that have been successfully applied in Chinese underground backfill coal mining are divided into two categories according to whether water is used during the backfill mining operation. Then, the primary considerations of each backfill material during its preparation, transportation and activation stages are discussed and evaluated. Finally, suggestions for backfill material exploration are proposed in terms of the future backfill mining of coal resources in Western China, where there is serious surface desertification. [ABSTRACT FROM AUTHOR]

There is global interest in spectroscopy and the development of large and diverse soil spectral libraries (SSL) to model soil organic carbon (SOC) and monitor, report, and verify (MRV) its changes. The reason is that increasing SOC can improve food production and mitigate climate change. However, 'global' modelling of SOC with such diverse and hyperdimensional SSLs do not generalise well locally, e.g. at a field scale. To address this challenge, we propose deep transfer learning (DTL) to leverage useful information from large-scale SSLs to assist local modelling. We used one global, three country-specific SSLs and data from three local sites with DTL to improve the modelling and localise the SOC estimates in individual fields or farms in each country. With DTL, we transferred instances from the SSLs, representations from one-dimensional convolutional neural networks (1D-CNNs) trained on the SSLs, and both instances and representations to improve local modelling. Transferring instances effectively used information from the global SSL to most accurately estimate SOC in each site, reducing the root mean square error (RMSE) by 25.8% on average compared with local modelling. Our results highlight the effectiveness of DTL and the value of diverse, global SSLs for accurate local SOC predictions. Applying DTL with a global SSL one could estimate SOC anywhere in the world more accurately, rapidly, and cost-effectively, enabling MRV protocols to monitor SOC changes. [ABSTRACT FROM AUTHOR]

The carbon isotope composition (δ13C Leaf) of C 3 plant leaves provides valuable information on the carbon-water cycle of vegetation and their responses to climate change within terrestrial ecosystems. However, global applications of δ13C Leaf are hindered by a lack of global long-term spatial maps (isoscapes) that capture vegetation δ13C Leaf variations. The ways in which δ13C Leaf varies over time and across regions are still unknown. In this study, we collected leaf carbon isotope samples across the globe and selected the optimal predictive model from three machine learning algorithms to construct long-term annual global δ13C Leaf isoscapes at a spatial resolution of 0.05° for natural C 3 plants between 2001 and 2020. We also assessed the potential of remotely sensed spectral bands, atmospheric CO 2 characteristics, geographic, and physiological information to estimate the δ13C Leaf of the global C 3 plants. Our results show that the random forest (RF) algorithm can more accurately construct the δ13C Leaf isoscape (R2 = 0.61, Nash Sutcliffe = 0.61, RMSE = 1.21‰, MAE = 0.91‰) than the multilayer perceptron (MLP) and support vector machine (SVM). The inclusion of atmospheric CO 2 characteristics, geographical, and physiological information greatly improves prediction compared to relying only on spectral bands. Among the variables, elevation, band 3 spectral reflectance, and solar-induced chlorophyll fluorescence were the three most important variables for constructing the isoscape model, and their relative importance all exceeded 85%. The predicted isoscape revealed strong spatial heterogeneity of δ13C Leaf for C 3 plants at a global scale between different continental regions, with enriched values occurring in high-altitude cold and arid regions, and depleted values occurring in warm, humid, or tropical regions. For the first time, we estimated the global depleted rate of δ13C Leaf in C 3 plant (−0.0491 ± 0.07 ‰ year−1 from 2001 to 2020). Over the past two decades, 86.49% and 1.00% of global grid cells showed more depleted and more enriched trends in δ13C Leaf of C 3 plants, respectively. Our results demonstrated the potential for establishing isoscapes by combining remote sensing, atmospheric CO 2 characteristics, physiological, and geographic variables using the RF machine learning algorithm. The isoscape products generated in this study are valuable for assessing carbon-water coupling of terrestrial ecosystems and for improving land surface models. • Global mapping of δ13C of leaves of natural C 3 plants at 0.05° resolution. • Combining remote sensing and environmental information with machine learning. • Obtain global spatial and temporal patterns of δ13C in the leaves of C 3 plants. • Isoscapes such as these will benefit biogeochemical cycling studies. [ABSTRACT FROM AUTHOR]

Road infrastructure is an important source of carbon emissions. To estimate the carbon emissions of roads, life cycle assessment (LCA) methods are widely used. Due to budget constraints, road maintenance and rehabilitation (M&R) is preferred by road agencies over new construction. However, existing LCA studies on roads have limited considerations on the emissions from the use and M&R phases. This study aims to propose and illustrate a structured hybrid LCA approach that can be adopted by road agencies to evaluate carbon emissions of the use and M&R phases of roads. A path exchange LCA method and a tiered hybrid LCA method are integrated in the proposed approach. To illustrate the proposed approach, a case study of a road network in Western Australia is conducted, which includes 17,764 road segments. The results show that from 2017 to 2026, the GWP of use and M&R phases is increased from 467.8 t CO2-e/km to 589.5 t CO2-e/km·with an increasing trend. The use phase has much higher global warming potential than the M&R phase during the service life of roads. The results are the most sensitive to annual average daily traffic (AADT). In addition, heavy traffic roads in the metropolitan area and freeways with AADT higher than 20,000 are identified to be the most carbon intensive. The proposed approach is believed to have better accuracy when compared to a process-based approach and a tiered approach, which provide the results of 128.6–164.4 t CO2-e/km and 468.0–608.9 t CO2-e/km, respectively. The results can provide implications for road authorities to evaluate their priorities when allocating limited M&R funds to achieve sustainable objectives. • A hybrid life cycle assessment method is proposed for maintenance and use of roads. • Path exchange and Tiered hybrid methods are hybridized to improve accuracy. • The use phase has dominant global warming potential during the service life. • Road global warming potential is most sensitive to annual average daily traffic. • Heavy traffic roads in metropolitan area are the most carbon intensive. [ABSTRACT FROM AUTHOR]

Although efficient hyperspectral image (HSI) denoising relies on complete and accurate description and modeling the spatial–spectral signal in HSI, the current approaches do not fully account for key characteristics of HSI, i.e., the mixed spectra effect, the spatial nonstationarity effect, and noise variance heterogeneity effect. To address this issue, this article presents a linear spectral mixture model with nonlocal means constraint (LSMM-NLMC), with the following advantages. First, LSMM-NLMC can effectively learn the signal in mixed pixels in HSI by estimating clean endmembers and abundances for image restoration. Second, LSMM-NLMC can efficiently address nonstationary spatial correlation effect by imposing NLMC on the latent scene signal. Last, LSMM-NLMC provides accurate noise characterization by accounting for noise variance heterogeneity effect using a band-dependent noise model and a band-weighted Mahalanobis distance for similarity measurement. A novel optimization method based on the expectation–maximization (EM) algorithm and the purified means approach is used to efficiently solve the resulting maximum a posterior (MAP) problem. The experiments on both simulated and real HSI data sets demonstrate that the visual quality and denoising accuracy are significantly improved by the proposed LSMM-NLMC compared with previous methods. [ABSTRACT FROM AUTHOR]

• The carbon and water cycles of frozen soil ecosystems on the Qinghai-Tibet Plateau were analyzed. • Water use efficiency and gross primary productivity decreased progressively with stronger freezing of the frozen soil. • Evapotranspiration increased and then decreased with increasing freezing gradient. • Vegetation dominated the change in water-carbon coupling over the largest area in each permafrost type except predominantly continuous and island permafrost. The carbon and water cycle patterns of frozen soil ecosystems in regions, such as the Qinghai-Tibet Plateau (QTP), which have fragile and sensitive ecologies, are possibly affected by climate change. However, these patterns along the gradients of varying degrees of freezing remain poorly understood. In this study, we examine the spatio-temporal patterns of water use efficiency (WUE), gross primary productivity (GPP), and evapotranspiration (ET) in six frozen soil zones of the Qinghai-Tibet Plateau (QTP) with varying degrees of frozen soil. The analysis is based on the frozen soil classification criteria and remote sensing data from 2001 to 2020. The influences of six climatic, nine edaphic, and two botanic parameters on these patterns is also investigated. The results indicate that 48.05%, 24.07%, and 20% of the regions in QTP exhibit significantly upward trends of GPP, ET, and WUE, respectively. The highest increase trends were observed in GPP in the middle-thick seasonally frozen ground zone (2.36 g C m−2 yr−1), ET in the mountain permafrost zone (2.35 mm yr−1), and WUE in the predominantly continuous permafrost zone (0.003 g C kg−1 H 2 O yr−1). Meanwhile, GPP and WUE decrease progressively with the increase of freezing degrees, from the short-time frozen ground zone (GPP: 604.99 ± 51.99 g C m−2; WUE: 2.34 ± 0.15 g C kg−1 H 2 O) to the predominantly continuous permafrost zone (GPP: 114.07 ± 11.77 g C m−2; WUE: 0.33 ± 0.04 g C kg−1 H 2 O), while ET does not show significant differences across the three permafrost zones. The enhanced vegetation index is the primary factor affecting GPP and WUE in most frozen soil zones, whereas climate variables mainly impact ET. Our findings emphasize the importance of considering the effects of different freezing degrees of frozen soil when analyzing the response of the carbon and water cycles in the QTP in the context of climate change. [ABSTRACT FROM AUTHOR]

• Inequality in urban greenspace exposure is assessed for 303 cities in China. • Dynamic inequality is characterized using multi-source geospatial data. • Severe inequality in greenspace exposure is pervasive in Chinese cities. • Dry cold climate and urban densification contribute to high inequality. Given the important role of green environments playing in healthy cities, the inequality in urban greenspace exposure has aroused growing attentions. However, few comparative studies are available to quantify this phenomenon for cities with different population sizes across a country, especially for those in the developing world. Besides, commonly used inequality measures are always hindered by the conceptual simplification without accounting for human mobility in greenspace exposure assessments. To fill this knowledge gap, we leverage multi-source geospatial big data and a modified assessment framework to evaluate the inequality in urban greenspace exposure for 303 cities in China. Our findings reveal that the majority of Chinese cities are facing high inequality in greenspace exposure, with 207 cities having a Gini index larger than 0.6. Driven by the spatiotemporal variability of human distribution, the magnitude of inequality varies over different times of the day. We also find that exposure inequality is correlated with low greenspace provision with a statistical significance (p-value < 0.05). The inadequate provision may result from various factors, such as dry cold climate and urbanization patterns. Our study provides evidence and insights for central and local governments in China to implement more effective and sustainable greening programs adjusted to different local circumstances and incorporate the public participatory engagement to achieve a real balance between greenspace supply and demand for developing healthy cities. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a fundamental environmental variable for characterizing climate, land surface and atmosphere. In recent years, several SM products have been developed based on remote sensing (RS), land surface model (LSM) or land data assimilation system (LDAS). However, little knowledge is available in understanding spatial patterns of the uncertainty of different SM products and potential regional drivers over the Qinghai-Tibet Plateau (QTP), a complex environment for accurate SM estimation. This paper investigates the sensitivity of the SM uncertainties based on the three-cornered hat (TCH) method and a generalized additive model (GAM) in terms of underlying surface characteristics (sand fraction, soil organic matter, vegetation, land surface temperature, and topography) and near-ground meteorology (precipitation and air temperature) in the third pole environment over the 2015–2018 period. Eleven SM products are involved in this work, including Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity INRA-CESBIO (SMOS-IC), Japan Aerospace Exploration Agency (JAXA), Land Surface Parameter Model (LPRM), Climate Change Initiative - Active/Combined (CCI_A/CCI_C), Global Land Data Assimilation System (GLDAS), European Centre for Medium-Range Weather Forecasts Interim reanalysis (ERA-Interim), Global Land Evaporation Amsterdam Model product a/b (GLEAM_a/GLEAM_b), and Random Forest Soil Moisture (RFSM). Results show that most of the SM products perform well across QTP, while SMOS-IC is strongly affected by radio-frequency interference in this region, JAXA has a relatively higher noise level over QTP, and LPRM has larger relative uncertainties (RUs) in the southeast of QTP. Nonlinear regression analysis demonstrates that variations of RUs in SMOS-IC and JAXA are driven by topography, while LPRM's are mainly controlled by vegetation. In addition, two groups of opposite (positive/negative) effects from topography and vegetation, topography and precipitation, and precipitation and land surface temperature affect the spatial variations of RUs in CCI_A, RFSM, and ERA-Interim, respectively. Meanwhile, more complex relationships are found between multiple surface factors and RUs of different products. In general, the underlying surface factors explain on average 39.41% and 28.34% of the variations in RS and LSM/LDAS SM RUs, respectively. Comparatively, the near-ground meteorology factors have a slightly larger effect on LSM/LDAS products than that on RS products. • Three-cornered hat (TCH) method is used to estimate the uncertainties in multisource soil moisture products over the Qinghai-Tibet Plateau. • Field measurements from three in-situ networks are adopted to evaluate the estimated TCH results. • A two-stage data detection method is utilized to quantify the spatial variations in SM uncertainties. [ABSTRACT FROM AUTHOR]

High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms. [ABSTRACT FROM AUTHOR]

The integration of facility management and building information modelling (BIM) is an innovative and critical undertaking process to support facility maintenance and management. Even though recent research has proposed various methods and performed an increasing number of case studies, there are still issues of communication processes to be addressed. This paper presents a theoretical framework for digital systems integration of virtual models and smart technologies. Based on the comprehensive analysis of existing technologies for indoor localization, a new workflow is defined and designed, and it is utilized in a practical case study to test the model performance. In the new workflow, a facility management supporting platform is proposed and characterized, featuring indoor positioning systems to allow end users to send geo-referenced reports to central virtual models. In addition, system requirements, information technology (IT) architecture and application procedures are presented. Results show that the integration of end users in the maintenance processes through smart and easy tools can overcome the existing limits of barcode systems and building management systems for failure localization. The proposed framework offers several advantages. First, it allows the identification of every element of an asset including wide physical building elements (walls, floors, etc.) without requiring a prior mapping. Second, the entire cycle of maintenance activities is managed through a unique integrated system including the territorial dimension. Third, data are collected in a standard structure for future uses. Furthermore, the integration of the process in a centralized BIM-GIS (geographical information system) information management system admit a scalable representation of the information supporting facility management processes in terms of assets and supply chain management and monitoring from a spatial perspective. [ABSTRACT FROM AUTHOR]