Ecological quality is a critical factor affecting the livability of urban areas. Remote sensing technology enables the rapid assessment of ecological quality (EQ), providing scientific theoretical support for the maintenance and management of urban ecology. This paper evaluates and analyzes the EQ and its driving factors in the city of Wuhan using remote sensing data from five periods: 2001, 2006, 2011, 2016, and 2021, supported by the Google Earth Engine (GEE) platform. By employing principal component analysis, a Remote Sensing Ecological Index (RSEI) was constructed to assess the spatiotemporal differences of EQ in Wuhan City. Furthermore, the study utilized the optimal parameter-based geographical detector model to analyze the influence of factors such as elevation, slope, aspect, population density, greenness, wetness, dryness, and heat on the RSEI value in 2021 and further explored the impact of changes in precipitation and temperature on the EQ in Wuhan. The results indicate that (1) principal component analysis shows that greenness and wetness positively affect Wuhan’s EQ, while dryness and heat have negative impacts; (2) spatiotemporal analysis reveals that from 2001 to 2021, the EQ in Wuhan showed a trend of initial decline followed by improvement, with the classification grades evolving from poor and average to good and better; (3) the analysis of driving factors shows that all nine indicators have a certain impact on the EQ in Wuhan, with the influence ranking as NDVI > NDBSI > LST > WET > elevation > population density > GDP > slope > aspect; (4) the annual average temperature and precipitation in Wuhan have a non-significant impact on the EQ. The EQ in Wuhan has improved in recent years, but comprehensive management still requires enhancement.

Accurate assessments of the historical and current status of eco-environmental quality (EEQ) are essential for governments to have a comprehensive understanding of regional ecological conditions, formulate scientific policies, and achieve the United Nations Sustainable Development Goals (SDGs). While various approaches to EEQ monitoring exist, they each have limitations and cannot be used universally. Moreover, previous studies lack detailed examinations of EEQ dynamics and its driving factors at national and local levels. Therefore, this study utilized a remote sensing ecological index (RSEI) to assess the EEQ of China from 2001 to 2021. Additionally, an emerging hot-spot analysis was conducted to study the spatial and temporal dynamics of the EEQ of China. The degree of influence of eight major drivers affecting EEQ was evaluated by a GeoDetector model. The results show that from 2001 to 2021, the mean RSEI values in China showed a fluctuating upward trend; the EEQ varied significantly in different regions of China, with a lower EEQ in the north and west and a higher EEQ in the northeast, east, and south in general. The spatio-temporal patterns of hot/cold spots in China were dominated by intensifying hot spots, persistent cold spots, and diminishing cold spots, with an area coverage of over 90%. The hot spots were concentrated to the east of the Hu Huanyong Line, while the cold spots were concentrated to its west. The oscillating hot/cold spots were located in the ecologically fragile agro-pastoral zone, next to the upper part of the Hu Huanyong Line. Natural forces have become the main driving force for changes in China’s EEQ, and precipitation and soil sand content were key variables affecting the EEQ. The interaction between these factors had a greater impact on the EEQ than individual factors.

Microorganisms play a crucial role in the cycling and transformation of nitrogen (N) within ecosystems. However, there is limited understanding regarding the impact of vegetation restoration on soil N cycling. A field study investigated the effects of different vegetation restoration strategies on soil microbial N cycling in sandy deserts of northern China including the use of metagenomic sequencing technology. The restoration strategies included the planting of Bromus inermis Leyss (SB), Medicago sativa L. (AF), and combined planting of Salix babylonica L. and Bromus inermis Leyss (FG). Compared with the natural restoration (CK), the abundance of genes related to soil N nitrification and denitrification processes was found to be higher in the AF and SB restoration strategies. On the other hand, the SB strategy specifically led to an enrichment of genes linked to dissimilatory nitrate reduction to ammonium (DNRA). Compared with the CK, the abundance of amoC/pmoC, hao and nxrA involved in soil N nitrification were higher in AF. The diversity of the fungal community was more strongly influenced by various vegetation restoration strategies compared to bacteria. Interestingly, FG had no significant effect on bacterial and fungal diversity compared to CK. However, alpha diversity of fungal communities was lower in AF and higher in SB compared to the CK. Soil pH was positively related to functional genes that drive nitrification and denitrification processes ( nirK , amoC/pmoC, and hao ). N fixation and DNRA exhibited a negative correlation with both microbial biomass carbon and microbial biomass nitrogen. Consequently, the planting of AF and SB hold significant importance in promoting soil N cycling within degraded lands. The study offered valuable insights into the microbial functional potentials associated with long-term vegetation restoration efforts, potentially bearing significant implications for soil N cycling in the degraded lands of northern China.

The East China Sea frequently experiences red tide outbreaks, significantly impacting marine carbon sink dynamics and ecosystem health. This study examines the temporal and spatial variations of red tide events in the East China Sea 2012 -2021, utilizing MODIS remote sensing imagery with a focus on summer outbreaks. Using the Two-Step algorithm, we assess surface particle organic carbon (POC) levels in red tide-affected areas, providing crucial insights into the carbon sink potential and distribution patterns of phytoplankton. Key findings include: (1) The largest red tide outbreak area occurred in 2015, with substantial fluctuations observed along the Zhejiang coast compared to the more stable Yangtze River estuary, especially in 2018 and 2020. (2) Surface POC concentrations reached their peak at 404.37 g m(-3) in the Yangtze River estuary in 2021 and 359.83 g m(-3) along the Zhejiang coast in 2017, with the lowest values recorded in 2013 and 2015. (3) Spatially, The distribution of severe algal bloom and higher concentrations of POC are observed along the coastal areas of the Yangtze River estuary and Zhejiang-Fujian, but with different causes. These findings highlight the importance of red tide events in regional carbon dynamics and their role in achieving sustainable development goals.

In rural areas, land use decisions are not only shaped by economic considerations but also deeply influenced by cultural and social factors. The objective of this research is to examine the complex and diverse aspects of making decisions about how land is used in rural communities, specifically by investigating the influence of cultural and social elements. Using empirical data and rigorous analysis, this research examine how traditional practices, social norms, and community dynamics influence land use patterns. The research topic focuses on the need to have a thorough understanding of the fundamental elements that affect land use choices in rural regions, going beyond only economic incentives. This research objective is to address a significant vacuum in the current literature by examining the cultural and social aspects of land usage. This research provides vital insights for policymakers and stakeholders engaged in land management and rural development projects. This research utilizes a mixed-methods approach, using qualitative interviews, participatory observations, and quantitative surveys to collect comprehensive data on the cultural and socioeconomic elements that impact land use choices. The research sample includes a wide range of rural areas, guaranteeing a thorough representation of various cultural settings and socioeconomic backgrounds. Our study reveals that cultural traditions, social networks, and power structures have a substantial impact on land use practices in rural regions. Traditional land tenure systems, community ownership arrangements, and customary land-use practices play a vital role in influencing land-use choices and resource distribution within communities. The significance of these results is substantial for policymakers, land managers, and rural development practitioners. Policymakers may create land use policies and actions that are more appropriate to the specific cultural and socioeconomic environment by understanding the complex relationship between these aspects. Furthermore, promoting community involvement and allowing local actors to participate in decision-making may result in land management results that are both more sustainable and fair.

A reasonable assessment of urban ecological resilience (UER), as well as quantitative identification of critical thresholds of UER, is an important theoretical basis for the formulation of scientific urban development planning. The existing UER assessment methods ignore the dynamic relationship between protection factors and disturbance factors in urban systems and do not address the question of where UER starts to become unstable. Therefore, based on the “source-sink” landscape theory, we constructed a UER assessment model and a method to quantitatively identify the UER’s critical distance belt (UER-CDB) using the transect gradient analysis. Additionally, we combined scenario simulation to analyze the change characteristics of UER and its critical distance belt in different urban development directions over past and future periods. The results show that: (1) Based on the “source-sink” theory and transect gradient method, the UER can be effectively assessed and the UER-CDB can be quantitatively identified. (2) The UER in Beijing shows a distribution pattern of high in the northwest and low in the southeast, and the High resilience area accounts for more than 40%. (3) The changes in UER-CDB in Beijing in different development directions have obvious variability, which is mainly influenced by topography and policy planning. (4) Compared with the natural development scenario (NDS), the ecological protection scenario (EPS) is more consistent with Beijing’s future urban development plan and more conducive to achieving sustainable development. The methodology of this paper provides a fresh perspective for the study of urban ecological resilience and the critical threshold of ecosystems.

Quantitative analysis of the influence of potential factors on regional geological hazards is of great significance for regional sustainable development.This paper detected the influence degree of potential impact factors of geological hazards in Beijing-Tianjin-Hebei Urban Agglomeration from four aspects: factor, risk, ecology and interaction based on the geodetector model.The results show that:（1） Regions with high density values of geological hazard points in Beijing-Tianjin-Hebei Urban Agglomeration mainly distributed in southwestern Beijing, western Xingtai, and the junction of Tangshan, Qinhuangdao, and Chengde, while regions with low density values mainly located in the southeast Hebei plain.（2） Elevation and slope had high influence degree, while the distance from river, NDVI,the distance from road and aspect had low influence degree.（3） Interaction between factors included nonlinear enhancement and bi-enhancement, and the interaction between elevation and precipitation had the highest explanatory power（48.89%）.

As an important ecological-economic development area in China, scientific understanding of the spatial and temporal changes in eco-environment quality (EEQ) and its drivers in the Yangtze River Basin (YRB) is crucial for the effective implementation of ecological protection projects in the YRB. To address the lack of large-scale EEQ assessment in the YRB, this paper uses the Google Earth Engine (GEE) platform and the Remote Sensing Ecological Index (RSEI) to investigate the spatial and temporal characteristics of EEQ in the YRB from 2000 to 2020, and to analyze the impact of various factors on the EEQ of the YRB. This study showed that: (1) The overall EEQ of YRB was at the ‘good’ grade over the past 20 years, showing an increasing trend, with the value changing from 0.70 to 0.77. (2) The YRB's EEQ has positive spatial aggregation characteristics, with the northern part of the Jialing River basin and the Han River basin exhibiting a high-high aggregation type and the upper reaches exhibiting a low-low aggregation type. (3) In the past 20 years, the human activities had a greater impact on the EEQ of the YRB; moreover, all factors had a greater impact on the EEQ than a single factor. The interaction between the biological abundance index and population density had the most effect, with a q-value of 0.737 in 2020.

The modern urban transportation service network could be split into unrestricted and restricted networks depending on whether travelers face limitations in route selection. Along with the continuous expansion of the city, it is difficult for travelers to find a more reasonable travel solution when confronted with such a complex transportation service network, which combines both unrestricted and restricted networks, especially for the park-and-ride (P&R) travel mode. This paper addresses the issue of route analysis in modern urban transportation service systems to provide travelers with reasonable travel solutions based on multiple types of transportation services. An improved A* algorithm is proposed to address the optimal path analysis for restricted networks to provide reasonable travel solutions for public transportation trips. Furthermore, by establishing the topological relationship between restricted and unrestricted networks, this paper presented an improved A* algorithm based on hybrid networks that solves the optimal path analysis problem for P&R trips, bringing convenience to many urban travelers.

Quantitative evaluation of the coupling coordination degree (CCD) between regional haze the disaster risk index (HRI) and urbanization development index level (UDI) is of great significance for the realization of regional sustainable development goals. Given the lack of the combination of remote sensing and statistical data to evaluate the CCD between two systems, the Chinese mainland’s 31 provinces and autonomous regions were taken to evaluate their HRI and UDI by building index systems. Then, an entropy method and one improved coupling coordination model were used to calculate and analyze the spatiotemporal characteristics of CCD between HRI and UDI during 2000–2020. The results showed that: (1) From 2000 to 2020, the value of HRI in China showed a “W” type change trend with its value increased from 0.7041 in 2000 to 0.8859 in 2020, indicating that haze pollution level showed a fluctuating downward trend; (2) From 2000 to 2020, China’s UDI values showed a gradual upward trend with its value increased from 0.1647 in 2000 to 0.4640 in 2020, with an average annual growth rate of 8.63%; (3) From 2000 to 2020, CCD values between HRI and UDI showed a fluctuating upward trend with its value increased from 0.5374 in 2000 to 0.7781 in 2020, with an average annual growth rate of 2.13%; the overall level of China’s CCD had raised from low coordination to moderate coordination, and eastern coastal provinces had higher CCD values, while those of central and western provinces had lower CCD values; (4) HRI, UDI and CCD could be well fitted with the R2 of 0.9869. Specifically, UDI had a higher contribution to improving the CCD than the HRI.

The main stream of the Tarim River in China is typical of ecologically sensitive areas that have been heavily disturbed by human activities; as such, the monitoring of the quality of its eco-environment constitutes an important task for researchers. By using GlobeLand30 data and applying the disturbance degree model and revised ecosystem service value (ESV) model, the study presented in this paper undertook a quantitative estimation of the effects of the disturbance impacts of human activities on the eco-environment of this area in the period of 2000 to 2020. The main conclusions are as follows: (1) disturbance index values, which reflect disturbance to the local ecosystem by human activities, increased over the study period. Further, cultivated land experienced the largest increase, which, in turn, brought about the most significant disturbance to the eco-environment. High disturbance index values presented a patchy distribution in the west of the main stream of the Tarim River and formed bands and dots in the east; the area of land characterized by high and moderate disturbance index values increased, with growth areas taking on a scattered distribution of patches, bands, and dots without significant spatial continuity. (2) The total ESV increased, indicating the quality of the eco-environment improved. The increase of cultivated land offset the increase in ESV, which counteracted the effects of ecological governance measures. Areas with high ESV values were mainly located in the western and central parts of the study area, while low values were found in the middle east and east. Areas with higher increases in ESV were mainly located in the western and the western part of the middle reaches and took on a zonal distribution, while areas of decrease followed a scattered distribution, presenting as dots or patches. Using the quantitative analysis methods and high-resolution remote sensing data to evaluate the changes in the eco-environment was considered as the innovation of this study, and the findings are useful in exploring the influence of human activities on ecosystems and evaluating the eco-environment in the minor watershed of an arid area. This piece of quantitative research contributes to the task of monitoring eco-environmental changes using remote sensing techniques in ecologically sensitive areas.

Risk assessment of human activities on landscape fragmentation in nature reserves can effectively balance the conflict between wildlife conservation and human development. However, previous studies had been unable to quantitatively assess the risk of human activities on landscape fragmentation. Thus, we constructed a risk assessment methodology to quantitatively assess the risk of different human activities on the Landscape Fragmentation Composite Index (LFCI) in the Northeast China Tiger and Leopard National Park (NCTLNP). First, we fitted the relationship curve between LFCI and different human activity factors based on the Generalized Additive Model (GAM) to determine the impact patterns of each factor on LFCI. Secondly, we identified impact risk areas of each human activity factor on LFCI by the location of threshold points in the curve and analyzed their spatiotemporal variation characteristics from 2015 to 2020. The results show that the relationship between LFCI and Land Use Intensity (LUI) showed an inverted "U" shape, the relationship with Population Density (POPD) showed a "rising-flat-rising" trend, and the relationship with Traffic Accessibility (TA) and Industrial and Mining Activity (IMA) showed a positive correlation after a flat interval. In addition, we found that the LUI and IMA impact risk areas were widely distributed and remained stable for five years. But the POPD impact risk area was mainly distributed around settlements and expanded by 6.6 % from 2015 to 2020. The TA impact risk area was distributed in strips and expanded by 16.38 % from 2015 to 2017 due to the construction of the G331 national road. And the joint impact risk area of these four factors expanded by 1.55 times in five years. Our research can provide a reference for ecological risk assessment under the impact of human activities on other nature reserves in the world.

Evaluating and exploring regional eco-environmental quality (EEQ), economic development equality (EDE) and the coupling coordination degree (CCD) at multiple scales is important for realizing regional sustainable development goals. The CCD can reflect both the development level and the interaction relationship of two or more systems. However, relevant previous studies have ignored non-statistical data, lacked multiscale analyses, misused the coupling coordination degree model or have not sufficiently considered economic development equality. In response to these problems, this study integrated multisource remote sensing datasets to calculate and analyse the remote sensing ecological index (RSEI) and then used nighttime light data and population density data to calculate the proposed nighttime difference index (NTDI). Next, a modified coupling coordination degree (MCCD) index was proposed to analyse the MCCD between EEQ and EDE. Then, spatiotemporal and multiscale analyses at the county, city, province, urban agglomeration and country levels were performed. Global and local spatial autocorrelation and trend analyses were performed to evaluate the spatial aggregation degree and change trends from 2001 to 2020. The main conclusions are as follows: (1) The EEQ of China displayed a fluctuating upwards trend (0.0048 a−1), with average RSEI values of 0.5950, 0.6277, 0.6164, 0.6311 and 0.6173; the EDE of China showed an upwards trend (0.0298 a−1), with average NTDI values of 0.1271, 0.1635, 0.1642, 0.2181 and 0.2490; and China’s MCCD indicated an upwards trend (0.0220 a−1), with values of 0.4614, 0.5027, 0.4978, 0.5401 and 0.5525. (2) The highest global Moran’s I of NTDI and MCCD was achieved at the city scale, while the highest RSEI was achieved at the county scale. From 2001 to 2020, the spatial agglomeration effect of the RSEI decreased, while that of the NTDI and MCCD increased. (3) A power function relationship occurred between NTDI and MCCD at different scales. Furthermore, the NTDI had a higher contribution to improving the MCCD than the RSEI and the R2 of the fitted curve at different scales ranged from 0.8183 to 0.9915.

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.

This paper presented the distribution of vegetation fraction and analyzed its spatial-temporal variation between 2000 and 2010 in Inner Mongolia Autonomous Region. The Moderate-resolution Imaging Spectrometer (MODIS) normalized difference vegetation index (NDVI) data in the growing season in 2000 and 2010 were used for analyzing. We also explored the relationship between vegetation change and the precipitation factor. It indicated that the vegetation fractions in the study area became higher from west to east. Generally, the vegetation in 2010 grew better than that in 2000. The increasing regions of vegetation fraction were mainly distributed in northeastern region of Da Hinggan Ling and plains and hills in central and southern of Inner Mongolia. The decreasing regions were mostly distributed in Hulun Beuir plateau, southwestern region of Da Hinggan Ling and hills in the northern-central Inner Mongolia.

Soil erosion has been one of the worldwide environmental disasters which severely threaten the sustainable development of socio-economic, natural resources, and the environment. The Universal Soil Loss Equation (USLE) is the most widely used model to quantify soil erosion. The cover and management factor C is perhaps the most important USLE factor because it represents conditions that can most easily be managed to reduce erosion. Satellite remote sensing can contribute through providing spatial data to assessment of C factor. Thus, many studies have been launched during the past 40 years. The paper mainly discusses the spatial data that is extracted from remote sensing images for estimating C factor: (1) land cover classification map, (2) image bands or ratios, (3) vegetation indices, (4) vegetation coverage. It is concluded that satellite remote sensing has been indispensible in C factor studies and its application need to penetrate deeply in future.