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Abstract Understanding the exercise behaviors of urban residents in green open spaces is crucial for optimizing future urban spatial planning and improving residents’ quality of life. This study provides an in-depth analysis of residents’ running behaviors across 87 green open spaces in Nanchang City. Running data collected from the exercise platform Keep, coupled with urban geographic data and field research, reveals the spatial distribution and temporal variations of running activities, including running flow, distance, time, and pace. A set of indicator systems, such as total area, landscape form index, surrounding residential land area, and road conditions, is used to analyze its relationship with the environmental elements of green open spaces. The research results show that the running flow, running distance, and running time in green open spaces in Nanchang City are significantly influenced by different objective elements, while running pace shows little correlation with these environmental elements. Specifically, the study highlights the significant impact of elements such as total area and road length, as well as the number of nearby bus stops and population density, on residents’ running behaviors in Nanchang City’s green open spaces.

Chlorophyll-a (Chl a) is an important indicator of algal biomass frequently used to evaluate the severity of cultural eutrophication. Identifying the key covariates of Chl a concentrations is essential to understand the mechanisms that drive eutrophication and to develop forecasting tools that guide the restoration process. In this study, we present a novel ensemble modeling framework founded upon the complementary features of Random Forest (RF) and Generalized Additive modeling (GAMs). A series of RF models are first developed to forecast Chl a concentrations based on the antecedent values of a multitude of environmental predictors. GAMs are then used to explore the presence of non-linearities in the seasonal relationships between Chl a and the identified predictors. The optimal RF models using a 0–8 day time lag displayed high predictive skills with adjusted R2 values consistently above 0.80. Analyses of the RF models revealed that the modulating factors of Chl a display significant seasonality. Dissolved oxygen (DO) and turbidity were the key covariates of Chl a in the spring, while the water level fluctuations predominantly regulated phytoplankton biomass in the summer and winter. The occurrence and severity of algal blooms in the summer and autumn were associated with threshold levels of 0.06 and 1.50 mg/L for total phosphorus (TP) and total nitrogen (TN) concentrations, respectively. These results reveal the potential of the introduced modeling framework to shed light on the regulatory factors of algal biomass as well as to establish real-time predictions in the Nanji wetland waters of Poyang Lake.

Rich observation data generated by ubiquitous sensors are vital for wetland monitoring, spanning from the prediction of natural disasters to emergency response. Such sensors use different data acquisition and description methods and, if combined, could provide a comprehensive description of the wetland. Unfortunately, these data remain hidden in isolated silos, and their variety makes integration and interoperability a significant challenge. In this work, we develop a semantic model for wetland monitoring data using an agile and modular approach, namely, wetland monitoring ontology (WMO), which contains five modules: wetland ecosystem, monitoring indicator, monitoring context, geospatial context, and temporal context. The proposed ontology supports the semantic interoperability and integration of wetland monitoring data from multiple sources, domains, modes, and spatiotemporal scales. We also provide two real-world use cases to validate the WMO and demonstrate the WMO’s usability and reusability.

Multiscale virtual environments (MSVEs) allow the integration of elements and environments at different scale levels into a unified space, which facilitates researchers’ perception, understanding, and experimental research of complex geospatial spaces. Although there have been several methods for achieving multiscale effects in virtual environments (VEs), they cannot assist users in constructing more complete spatial cognitive maps and presenting multiscale information efficiently. This study proposes a hierarchical-structure-based MSVE construction method, which can effectively integrate multiscale information and ensure that the richness of details of information is gradually enhanced with the progression of the hierarchical structure. In addition, a spatial navigation study is conducted, considering the relationship between users’ perspective changes and spatial cognition, and the effects of users’ perspective changes on their spatial cognition in an MSVE are explored. A multiscale virtual wetland environment covering four levels is constructed to conduct a case study of a virtual environment of a wetland of Poyang Lake. The research results show that the proposed method is feasible. Moreover, the spatial navigation based on the change in the hierarchical perspective is in line with the spatial cognitive habits of users, which can satisfy the cognitive needs of users from the macro-region to specific wetland landscapes.

As a World Cultural Heritage site with sacred landscape featuring an exceptional range of Buddhist art and architecture, much attention has been focused on the sustainable development of Bagan (Myanmar). Particularly, the monitoring of landscape surface subsidence and monument instability is of great importance to the protection and development planning of the Bagan heritage site. In this study, we applied high resolution TerraSAR-X imagery acquired from 2019 to 2020 for deformation monitoring based on the small baseline subset (SBAS) and persistent scatterer synthetic aperture radar interferometry (PSInSAR) approaches. We identified several hotspots and pagodas with displacement anomalies linked to land cover change and previous earthquakes. The cross comparison between SBAS and PSInSAR and the precision of height estimates derived by PSInSAR indicated a millimetric precision of the derived deformation products. The combination of the two multi-temporal SAR interferometry approaches satisfies the two-scale monitoring requirements from landscapes to monuments, particularly for large-scale World Heritage sites. The non-contact monitoring method has potential when traditional methods using field accessibility and surveillance are constrained.

Blue space is an important place for physical activity and provides physical and mental health benefits to residents. However, little is known about the mechanism of association between blue space and physical activity. Based on China National Knowledge Infrastructure and Web of Science Core Collection, the Chinese and English literature on the correlation between blue space and physical activity were selected and analyzed. The research field has the following characteristics so far: there are few studies on blue space in inland cities, developing countries, and vulnerable populations, and it is an emerging research field. Most included studies demonstrate positive effects of blue space exposure on physical activity, while studies conducted in different populations demonstrate population differences. The combination of multi-source data and multidisciplinary approaches should be an important foundation of this research field. Based on this, a research framework was proposed in this paper to analyze the complex association between them from a perspective of multi-dimensional characteristics, and blue physical activity was modeled as ecosystem services to explore the relationship between blue space and physical activity from a perspective of ecosystem service supply-demand and flow.

The quantification of the extent and dynamics of land-use changes is a key metric employed to assess the progress toward several Sustainable Development Goals (SDGs) that form part of the United Nations 2030 Sustainable Development Agenda. In terms of anthropogenic factors threatening the conservation of heritage properties, such a metric aids in the assessment of achievements toward heritage sustainability solving the problem of insufficient data availability. Therefore, in this study, 589 cultural World Heritage List (WHL) properties from 115 countries were analyzed, encompassing globally distributed and statistically significant samples of “monuments and groups of buildings” (73.2%), “sites” (19.3%), and “cultural landscapes” (7.5%). Land-cover changes in the WHL properties between 2015 and 2020 were automatically extracted from big data collections of high-resolution satellite imagery accessed via Google Earth Engine using intelligent remote sensing classification. Sustainability indexes (SIs) were estimated for the protection zones of each property, and the results were employed, for the first time, to assess the progress of each country toward SDG Target 11.4. Despite the apparent advances in SIs (10.4%), most countries either exhibited steady (20.0%) or declining (69.6%) SIs due to limited cultural investigations and enhanced negative anthropogenic disturbances. This study confirms that land-cover changes are among serious threats for heritage conservation, with heritage in some countries wherein the need to address this threat is most crucial, and the proposed spatiotemporal monitoring approach is recommended.

Abstract In the Internet age, emotions exist in cyberspace and geospatial space, and social media is the mapping from geospatial space to cyberspace. However, most previous studies pay less attention to the multidimensional and spatiotemporal characteristics of emotion. We obtained 211,526 Sina Weibo data with geographic locations and trained an emotion classification model by combining the Bidirectional Encoder Representation from Transformers (BERT) model and a convolutional neural network to calculate the emotional tendency of each Weibo. Then, the topic of the hot spots in Nanchang City was detected through a word shift graph, and the temporal and spatial change characteristics of the Weibo emotions were analyzed at the grid-scale. The results of our research show that Weibo’s overall emotion tendencies are mainly positive. The spatial distribution of the urban emotions is extremely uneven, and the hot spots of a single emotion are mainly distributed around the city. In general, the intensity of the temporal and spatial changes in emotions in the cities is relatively high. Specifically, from day to night, the city exhibits a pattern of high in the east and low in the west. From working days to weekends, the model exhibits a low center and a four-week high. These results reveal the temporal and spatial distribution characteristics of the Weibo emotions in the city and provide auxiliary support for analyzing the happiness of residents in the city and guiding urban management and planning.

The Poyang Lake wetland is home to many unique and threatened species. However, it has been severely degraded in recent decades due to the joint effects of human influence and climate change. Here we establish a wetland health index (WHI) for Poyang Lake, which considers five types of attributes (biological, water quality, sediment, land use and remote sensing, and socio-economic attributes) of the wetland to evaluate wetland conditions. Forty-nine variables across five categories were assembled as candidate metrics for the WHI through field surveys conducted in 2019 at 30 sample sites. Principal component analyses were performed to identify the most important variables in each of the five categories as the primary metrics of each index category (e.g., biological index). Eighteen variables were finally selected from the five categories to construct the WHI. The WHI scores varied from 0.34 to 0.80 at the 30 sample sites, with a mean of 0.55. The Poyang Lake wetland is generally in fair condition according to our WHI scores. Sample sites where connected rivers flow into the lake were assessed to be in a poor condition, highlighting the importance of reducing pollution input from rivers for wetland conservation. Scores of individual indices of the five categories were not highly correlated (0.29 ≤ pairwise Spearman’s r ≤ 0.69), suggesting that information provided by each index is different and might be complementary. The composite WHI as well as the individual category indices can provide comprehensive information on wetland conditions that would facilitate the development of more targeted and effective strategies for wetland management.

In this study, an integrated monitoring and evaluation approach was proposed to fix the systematic safeguarding gap of the Great Wall corridor using space technologies. Two representative sections of the Great Wall located in Qingtongxia County and Zhangjiakou City in China were selected for a preliminary comparative investigation to ascertain the coupling mechanism and spatiotemporal characteristics of the driving forces for the heritage damage. The surface deformation rates were estimated by synthetic aperture radar interferometry, and the interaction between the deformation rates and the normalized difference vegetation index (NDVI) and meteorological and topographic data was calculated using the correlation matrix. The results showed the following: 1) the surface motions along the observed landscape corridor of the Great Wall were decreased in 2015-2018; 2) the correlation coefficients between the deformation rate and the elevation, slope, annual wind speed, and NDVI in Qingtongxia were 0.524, 0.115, 0.582, and 0.522, respectively, indicating the dominant influence of surface runoff and high winds on the degradation of the rammed-earth wall in the western arid regions; vice versa, 3) the correlation coefficients between the deformation rate and the aforementioned factors in Zhangjiakou were 0.065, 0.027, 0.025, and 0.052, respectively, indicating negligible effects of natural processes for the decline of Great Wall in the eastern section. This study not only provides new insights into preventive monitoring and risk assessment of the entire Great Wall but also highlights the potential of space technologies and a geographical perspective for the sustainable conservation of large-scale heritage sites.

An urban green space network provides safe and green exercise routes for residents. This study selected Nanchang as the study area. Using fitness application data, we explored the mobility of people exercising in the network, i.e., the flow of green exercise. Spatial analysis based on social networks, GIS, and the gravity model was used to analyze the nodes, network characteristics, and mechanism of the flow of green exercise. The results show that there were differences in the hierarchy and attractiveness of nodes. Distance had an important influence on green exercise. It was found that walkers moved between adjacent parks, runners visited more parks and corridors, while cyclists covered longer distances and preferred to explore suburban green spaces. The length of the exercise route in green spaces had a positive effect on mobility. Because of the many combinations of patches and corridors, three flow subnetworks were formed. In addition, the green space network expanded the scope of exercise services in the central node. The management of green spaces should pay attention to the social value of urban green space networks and create a hierarchical and interconnected green space for exercisers.

Access to real-time environmental sensing data is key to representing real-time environmental changes in the digital twin of lakes. The visualization of environmental sensing data is an important part of establishing a digital twin of lakes. In the past, environmental sensing data display methods were either charts or two-dimensional map-based visualization methods. Breaking through the traditional visualization methods of environmental sensing data and realizing a multi-dimensional and multi-view display of environmental sensing data in a digital twin of lakes is something that this particular paper tries to resolve. This study proposes a visualization framework to integrate, manage, analyze, and visualize the environmental sensing data in the digital twin of lakes. In addition, this study also seeks to realize the coupling expression of geospatial data and long-term monitoring sequence data. Different visualization methods are used to realize the visualization of environmental sensing data in the digital twin of lakes. Using vector and scalar visualization methods to display ambient wireless sensor monitoring data in a digital twin of lakes provides researchers with richer visualization methods and means for deeper analysis. Using video fusion technology to display environmental sensing video surveillance data strengthens the integration of the virtual environment and real space and saves time for position identification using video surveillance. These findings may also help realize the integration and management of real-time environmental sensing data in a digital twin of lakes. The visualization framework uses various visualization methods to express the monitoring data of environmental wireless sensors and increases the means of visualizing environmental sensing data in the world of a lake digital twin. This visualization framework is also a general approach that can be applied to all similar lakes, or other geographical scenarios where environmental sensing devices are deployed. The establishment of a digital twin of Poyang Lake has certain practical significance for improving the digital management level of Poyang Lake and monitoring its ecological changes. Poyang Lake is used as an example to verify the proposed framework and method, which shows that the framework can be applied to the construction of a lake-oriented digital twin.

Due to the complexity of wetland ecosystems, wetlands have a wide area of alternating land and water zones and complex vegetation composition, making it challenging to achieve dynamic displays of virtual wetland scenes using three-dimensional modeling. This study proposes a workflow of game engine-based virtual wetland scene construction for the rapid modeling of virtual wetland scenes. The virtual wetland scene construction work utilized Poyang Lake as the primary research area. It integrated unmanned aerial vehicle data collection technology and geographic information technology with 3D (three-dimensional) modeling of wetland elements and scene program modeling of the game engine to complete the construction and dynamic development of virtual wetland scenes. In addition, it used various virtual reality technologies to display the virtual wetland scene. The virtual scene of Poyang Lake combined with actual data was more realistic and had higher simulation. In reality, the digital wetland scene of Poyang Lake realizes multiple forms of virtual experience and provides users with a profoundly immersive virtual experience. This comprehensive virtual scene workflow in the study can serve as a technical resource for building 3D scenes. It can also provide a technical reference for the digital twin watershed project of Poyang Lake, which has practical application value.

The preventive monitoring and sustainable conservation of large-scale cultural heritage sites require satellite-based Earth observations. In this study, we present the first monitoring and thematic mapping results of the Badaling Great Wall (Beijing, China) using deformation indicators calculated from very high-resolution (VHR) multi-temporal spotlight TerraSAR-X data. The proposed coarse–fine search algorithm achieved high computational efficiency for calculating the unknown parameters of the two-tier network persistent scatterer synthetic aperture radar (SAR) interferometry (PSInSAR) approach. The spatiotemporal deformation anomalies, characterized by the absolute velocity, deformation deviation and acceleration, are informative to identify suspected hotspots for prioritizing monitoring activities. We provide an understandable method for thematic mapping and subsequent sustainable conservation of heritage sites by synergistically exploiting impacts from natural degradation and the tourism industry. We determine that the optimum tourist capacity of the site could be 1.0 million per month based on comparing pre- and post-COVID data (2019–2020). This study demonstrates the potential and performance of spaceborne PSInSAR tools for the intelligent management of large-scale architectural heritage sites by integrating InSAR deformation products with environmental and social data.

Abstract The land surface, which interacts with lower atmosphere, is subject to substantial amounts of human activities. It results in regional climate fluctuations and must be assessed with a combination of future socioeconomic and emission policies to improve regional resilience and responsive efficiency on global mitigation and adaptation. Spatial heterogeneity in regional climate induced by land use changes of future global socioeconomic and emission scenarios is explored. Comparisons are carried out among both historical and future land use‐induced regional meteorological changes to investigate various climatic roles of land use conversions in China. The underlying changes in the surface albedo, leaf area index, energy exchange, and water balances are also examined. It is found that the increase of the summer temperature and the diurnal temperature range are the largest under the fifth Shared Socioeconomic Pathway characterized by more intensive urbanization. While temperature increase from urbanization and deforestation can be offset by cooling from vegetation evaporation increase under the extreme Representative Concentration Pathway (RCP) 8.5, which has no climate mitigation policies and high greenhouse gas concentration. In addition, RCP4.5 scenario would cause an average temperature increase of 1.41°C by 2050 at an annual rate of 0.028°C in China. The global climatic forcing warms the entire region and enhances precipitation intensity, and these effects are more substantial than those by regional land use changes, showing a strong background influence. Our research reveals the future climatic responses to regional anthropogenic land use changes under various scenarios and policies and provides ways to downscale global mitigation impacts into regional insights.

With industrialization and urbanization, the competition among land production, living, and ecological (PLE) spaces has intensified. Particularly in ecological reserves, competition among various types of land use restricts the coordinated development of PLE space. To explore spatial sustainable development, this study starts from a PLE spatial perspective, based on Landsat long time series images. Object-based image analysis (OBIA) and landscape index analysis were selected to monitor the spatial and temporal land use and landscape pattern changes in the Poyang Lake region (PYL region) from 1989 to 2020. The RF-Markov-FLUS coupled model was used to simulate spatial changes in 2030 under four scenarios: production space priority (PSP), living space priority (LSP), ecological space priority (ESP), and an integrated development (ID). Finally, the goal-problem-principle was used to enhance PLE space. The results showed that: (1) production space and ecological spaces decreased in general from 1989 to 2020 by 3% and 7%, respectively; living space increased by 11%. (2) From 1989 to 2020, the overall landscape spread in the Poyang Lake (PYL) area decreased, connectivity decreased, fragmentation increased, landscape heterogeneity increased, and landscape geometry became more irregular. (3) Compared with the other three scenarios, the ID scenario maintained steady production space growth in 2030, the expansion rate of living space slowed, and the area of ecological space decreased the least. (4) Spatial pattern optimization should start with three aspects: the transformation of the agricultural industry, improving the efficiency of urban land use, and establishing communities of “mountains, water, forests, fields, lakes and grasses”. The results provide scientific planning and suggestions for the future ecological protection of Poyang Lake area with multiple scenarios and perspectives.

Goddard’s LiDAR (Light Detection And Ranging), hyperspectral and thermal (G-LiHT) airborne imager is a new system to advance concepts of data fusion for worldwide applications. A recent G-LiHT mission conducted in June 2016 over an urban area opens a new opportunity to assess the G-LiHT products for urban land-cover mapping. In this study, the G-LiHT hyperspectral and LiDAR-canopy height model (LiDAR-CHM) products were evaluated to map five broad land-cover types. A feature/decision-level fusion strategy was developed to integrate two products. Contemporary data processing techniques were applied, including object-based image analysis, machine-learning algorithms, and ensemble analysis. Evaluation focused on the capability of G-LiHT hyperspectral products compared with multispectral data with similar spatial resolution, the contribution of LiDAR-CHM, and the potential of ensemble analysis in land-cover mapping. The results showed that there was no significant difference between the application of the G-LiHT hyperspectral product and simulated Quickbird data in the classification. A synthesis of G-LiHT hyperspectral and LiDAR-CHM products achieved the best result with an overall accuracy of 96.3% and a Kappa value of 0.95 when ensemble analysis was applied. Ensemble analysis of the three classifiers not only increased the classification accuracy but also generated an uncertainty map to show regions with a robust classification as well as areas where classification errors were most likely to occur. Ensemble analysis is a promising tool for land-cover classification.

Hydrological connectivity is important for maintaining the stability and function of wetland ecosystems. Small-scale hydrological connectivity restricts large-scale hydrological cycle processes. However, long-term evolutionary studies and quantitative evaluation of the hydrological connectivity of wetlands in the Poyang Lake area have not been sufficiently conducted. In this study, we collected 21 Landsat remote sensing images and extracted land use data from 1989 to 2020, introducing a morphological spatial pattern analysis model to assess the wetland hydrological connectivity. A comprehensive method for evaluating the hydrological connectivity of wetlands was established and applied to the Poyang Lake area. The results showed that, over the course of 31 years, the wetland landscape in the Poyang Lake area changed dramatically, and the wetland area has generally shown a decreasing and then increasing trend, among which the core wetland plays a dominant role in the hydrological connectivity of the Poyang Lake area. In addition, the hydrological connectivity decreases as the core wetland area decreases. From 1989 to 2005, the landscape in the Poyang Lake area focused mainly on the transition from wetland to non-wetland. From 2005 to 2020, the conversion of wetland landscape types shows a clear reversal compared to the previous period, showing a predominant shift from non-wetland to wetland landscapes. The eco-hydrological connectivity of the wetlands in the Poyang Lake area from 1989 to 2020 first decreased, and then increased after 2005. In the early stage of the study (1989−2005), we found that the connectivity of 0.3444 in 2005 was the lowest value in the study period. A resolution of 30 m and an edge effect width of 60 m were optimal for studying the hydrological connectivity of wetlands in the Poyang Lake area. The main drivers of the changes in hydrological connectivity were precipitation and the construction of large-scale water conservation projects, as well as changes in land use. This study provides a good basis for assessing hydrological connectivity in a meaningful way, and is expected to provide new insights for maintaining and restoring biodiversity and related ecosystem services in the Poyang Lake area.

“A picture is worth a thousand words”. Analysis of the visual content of tourist photos is an effective way to explore the image of tourist destinations. With the development of computer deep learning and big data mining technology, identifying the content of massive numbers of tourist photos by convolutional neural network (CNN) approaches breaks through the limitations of manual approaches of identifying photos’ visual information, e.g., small sample size, complex identification process, and results deviation. In this study, 531,629 travel photos of Jiangxi were identified as 365 scenes through deep learning technology. Through the latent Dirichlet allocation (LDA) model, five major tourism topics are found and visualized by map. Then, we explored the spatial and temporal distribution characteristics of different tourism scenes based on hot spot analysis technology and the seasonal evaluation index. Our research shows that the visual content mining on travel photos makes it possible to understand the tourism destination image and to reveal the temporal and spatial heterogeneity of the image, thereby providing an important reference for tourism marketing.

Virtual Geographic Environment Cognition is the attempt to understand the human cognition of surface features, geographic processes, and human behaviour, as well as their relationships in the real world. From the perspective of human cognition behaviour analysis and simulation, previous work in Virtual Geographic Environments (VGEs) has focused mostly on representing and simulating the real world to create an ‘interpretive’ virtual world and improve an individual’s active cognition. In terms of reactive cognition, building a user ‘evaluative’ environment in a complex virtual experiment is a necessary yet challenging task. This paper discusses the outlook of VGEs and proposes a framework for virtual cognitive experiments. The framework not only employs immersive virtual environment technology to create a realistic virtual world but also involves a responsive mechanism to record the user’s cognitive activities during the experiment. Based on the framework, this paper presents two potential implementation methods: first, training a deep learning model with several hundred thousand street view images scored by online volunteers, with further analysis of which visual factors produce a sense of safety for the individual, and second, creating an immersive virtual environment and Electroencephalogram (EEG)-based experimental paradigm to both record and analyse the brain activity of a user and explore what type of virtual environment is more suitable and comfortable. Finally, we present some preliminary findings based on the first method.

The identification of species within an ecosystem plays a key role in formulating an inventory for use in the development of conservation management plans. The classification of mangrove species typically involves intensive field surveys, whereas remote sensing techniques represent a cost-efficient means of mapping and monitoring mangrove forests at large scales. However, the coarse spectral resolution of remote sensing technology has up until recently restricted the ability to identify individual species. The more recent development of very high-resolution spatial optical remote sensing sensors and techniques has thus provided new opportunities for the accurate mapping of species within mangrove forests over large areas. When dealing with the complex problems associated with discriminating among species, classifier performance could be enhanced through the adoption of more intrinsic features; such as textural and differential spectral features. This study explored the effectiveness of textural and differential spectral features in mapping mangrove inter-species obtained from WorldView-3 high-spatial-resolution imagery for mangrove species in Hong Kong. Due to the different arrangement of leaves, the branch density, and the average height and size of plants, we found that the differential spectral features could aid in reducing inner-species variability and increasing intra-species separation. Using a combination of textural and differential spectral features thus represents a promising tool for discriminating among mangrove species. Experimental results suggest that combining these features can greatly improve mapping accuracy, thereby providing more reliable mapping results.