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This study examined the influence of lifestyle habits on health self-assessments among elderly rural men and the effect of age on the strength of the impact to provide a basis for targeted health improvement in this population. Data from the 2018 Chinese longitudinal healthy longevity survey were analyzed using one-way analysis of variance, regression analysis, and Shapley value decomposition tests. The model analysis results showed that elderly rural men who did not drink alcohol or were moderate drinkers, maintained regular sleep patterns, exercised regularly, and had active social interactions had better health self-assessment results (p < 0.01). Notably, the impact of lifestyle habits on health perceptions varied with age. Specifically, the influence of alcohol consumption and sleep duration decreased with age, while the benefits of physical activity and social engagement became more pronounced. These results suggest that lifestyle habits significantly impact the health self-assessments of elderly rural men, with notable variations observed in the strength of these effects across different ages. To improve the overall health of elderly rural men, alcohol cessation interventions should be implemented as early as possible, attention should be paid to the sleep habits of young male seniors, and low-intensity group exercise and social activities should be actively organized for older male seniors.

The high-purity germanium gamma-ray spectrometer was used to measure the radioisotope in surface water of lakes in a Chinee petroleum industrial area. 92 samples were collected from surface water of three lakes. Activity concentrations of 232Th, 226Ra and 40K in three lakes were measured, distributed in the range of 101.8–209.4, 192.1–224.9 and 335.0–548.9 mBq/L, respectively. Results were all within the limits of WHO and China. Potential environmental and health risks were assessed by calculating some radiation hazard indicators, radium equivalent index, annual effective dose, excess lifetime cancer risk, absorbed dose rate, external hazard index, internal hazard index, annual gonadal dose equivalent, activity utilization index and representative gamma index, which ranged 0.38–0.54 Bq/L, 0.06–0.08 mSv/y, 0.23 × 10−3-0.31 × 10−3, 0.17–0.24 nGy/h, 1.01 × 10−3-1.46 × 10−3, 1.55 × 10−3-2.02 × 10−3, 1.16–1.66 μSv/y, 3.13 × 10−3-4.45 × 10−3 and 2.60 × 10−3-3.77 × 10−3. The results were all at acceptable levels, meaning no impact on human health. The relationship between the electrical conductivity of surface water and the activity concentration of 232Th, 226Ra and 40K was evaluated. The electrical conductivity value was 0.241–0.369 mS/cm, showing a significant correlation coefficient between 226Ra and 40K and electrical conductivity. Multivariate statistical methods were used to determine the relationship between the activity concentrations of 232Th, 226Ra, and 40K, radiation hazard indicators and electrical conductivity.

ABSTRACTIt is crucial to mangrove management by understanding its conservation efforts. This study proposed a spatiotemporal analysis model to evaluate mangrove changes in the Guangxi Beibu Gulf (GBG) and the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), focusing on past, present, and future dynamics. The analysis covered mangrove loss, stability, and gain from 1990 to 2020 and provided projections for 2020 to 2035 across multiple scenarios. Then further assessed the efficacy of past mangrove conservation with the Sustainable Development Goals (SDGs) and the prospective conservation potential with the Global Mangrove Alliance Goal (GMAG) indicators. Findings indicate that by 2020, the GBG and GBA had 81.65 km² and 40.92 km² of mangroves, respectively, with increases of 31.51% and 164.09% since 1990. Projected expansions are anticipated under all three scenarios during 2020- 2035, with the highest growth projected under the trend continuation scenario, followed by ecological restoration and protection, and economic development. While mangrove protection in these regions met SDG targets 6.6.1 and 14.5.1 from 2015 to 2020, the 80% conservation milestone set by GMAG for 2030 was not achieved, necessitating the establishment of nature reserves. The study offers novel insights for sustainable governance of mangrove forests at local, regional, and global levels.

ABSTRACTMapping detailed wetland types can offer useful information for wetland management and protection, which can strongly support the Global Biodiversity Framework. Many studies have conducted wetland classification at regional, national, and global scale, whereas fine-resolution wetland mapping with detailed wetland types is still challenging. To address this issue, we developed an integration of pixel- and object-based algorithms with knowledge (POK) by combining pixel-based random forest and an object-based hierarchical decision tree. Taking the Guangxi Beibu Gulf Economic Zone (GBGEZ) and Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as our study areas, we produced wetland maps with 10 wetland types and 6 non-wetland types using Landsat-8 time series. In addition, to comprehensively evaluate the accuracy of our wetland classification, we implemented accuracy validation based on test samples and data inter-comparison based on existing datasets, respectively. The results indicate that the overall accuracy of our wetland map was 91.6% ±1.2%. For wetland types, agricultural pond, coastal shallow water, floodplain, mangrove, reservoir, river, and tidal flat achieved good accuracies, with both user accuracy and producer accuracy exceeding 88.0%. For non-wetland types, most accuracies were greater than 72.0%. By comparison with existing datasets, it was found that our wetland map had good consistencies with the China Ecosystem-type Classification Dataset (CECD) land use dataset, MC_LASAC mangrove dataset, and Tidal Wetlands in East Asia (TWEA) tidal flat dataset. In 2020, the wetland area was 4,198.8 km2 in the GBGEZ and 10,932.2 km2 in the GBA. The main wetland types in the two coastal urban agglomerations were agricultural ponds, coastal shallow waters, mangroves, reservoirs, rivers, and tidal flats. Our study successfully mapped detailed wetland types in the GBGEZ and GBA, serving the Global Biodiversity Framework of Convention on Biological Diversity.

Abstract The sensitivity of tree growth to precipitation regulates their responses to drought, and is a crucial metric for predicting ecosystem dynamics and vulnerability. Sensitivity may be changing with continuing climate change, yet a comprehensive assessment of its change is still lacking. We utilized tree ring measurements from 3,044 sites, climate data and CO2 concentrations obtained from monitoring stations, combined with dynamic global vegetation models to investigate spatiotemporal changes in the sensitivity over the past century. We observed an increasing sensitivity since around 1950. This increased sensitivity was particularly pronounced in arid biomes due to the combined effect of increased precipitation and elevated CO2. While elevated CO2 reduced the sensitivity of the humid regions, the intensified water pressure caused by decreased precipitation still increased the sensitivity. Our findings suggest an escalating vulnerability of tree growth to precipitation change, which may increase the risk of tree mortality under future intensified drought.

The Guangxi Beibu Gulf Economic Zone (GBGEZ) and Guangdong–Hong Kong–Macao Greater Bay Area (GBA) have rich wetlands. However, long-term maps with detailed wetland types in these two regions are scarce, and studies on the driving forces of wetland changes are insufficient. To this end, our study produced long-term wetland maps to explore wetland dynamics and their driving forces. From 1990 to 2020, the mangrove, tidal flat, coastal shallow water and reservoir had similar change trends in two metropolitan areas. For the GBGEZ between 1990 and 2020, serious wetland degradation, slight wetland degradation, obvious wetland restoration and slight wetland restoration had areas of 569.23 km2, 426.89 km2, 274.83 km2 and 375.78 km2, respectively; for the GBA, the four change types had areas of 986.80 km2, 1854.63 km2, 547.34 km2 and 2183.57 km2, respectively. Overall, wetland degradation in the GBA were significantly larger than that in the GBGEZ. Sample-based analysis indicated that wetland changes in the GBGEZ and GBA mainly attributed to direct drivers, with the GBA suffering more from the influence of these direct drivers. Specifically, over 90 % of slight wetland degradations and 89 % of slight wetland restorations were affected by direct drivers (e.g. aquaculture). Serious wetland degradations were most affected by direct drivers (e.g. land reclamation) from 1990 to 2010 and by indirect drivers from 2010 to 2020 (e.g. water shrinkage). For obvious wetland restorations, most of them in GBGEZ were affected by indirect factors (e.g. water expansion) while in GBA were affected by direct factors (e.g. new reservoir). Overall, our study could offer valuable information to support wetland protection and urban sustainable development.

Accurate and reliable mangrove datasets are essential for the protection and management of mangrove ecosystems. Therefore, the evaluation of the current mangrove datasets and understanding the differences among them are critical. This study takes the Guangxi Beibu Gulf (GBG) and Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the study areas and analyzes the agreement and accuracy of eight mangrove datasets from 2000 to 2020 using area comparison, spatial agreement analysis, and absolute accuracy evaluation. The results show that; 1) significant differences exist in mangrove area and spatial distribution among the different mangrove datasets, with the percentage of high agreement areas ranging from 10% to 42%. 2) The overall accuracy of the evaluated mangrove datasets ranges from 56.2% to 95.6%, and the classification accuracy of mangrove datasets in inland areas is lower than the overall level. 3) There are regional differences in the quality of mangrove datasets, with the agreement and accuracy of mangrove datasets in the GBG being greater than those in the GBA. 4) Fine-scale mangrove mapping based on high-resolution remote sensing images, such as Sentinel, and global mangrove mapping based on the Google Earth Engine (GEE) cloud platform should be emphasized in the future. The findings of this study can provide guidance for data users to select appropriate mangrove datasets and a reference for future mangrove mapping research.

Wetlands provide vital ecological services for both humans and environment, necessitating continuous, refined and up-to-date mapping of wetlands for conservation and management. In this study, we developed an automated and refined wetland mapping framework integrating training sample migration method, supervised machine learning and knowledge-driven rules using Google Earth Engine (GEE) platform and open-source geospatial tools. We applied the framework to temporally dense Sentinel-1/2 imagery to produce annual refined wetland maps of the Dongting Lake Wetland (DLW) during 2015–2021. First, the continuous change detection (CCD) algorithm was utilized to migrate stable training samples. Then, annual 10 m preliminary land cover maps with 9 classes were produced using random forest algorithm and migrated samples. Ultimately, annual 10 m refined wetland maps were generated based on preliminary land cover maps via knowledge-driven rules from geometric features and available water-related inventories, with Overall Accuracy (OA) ranging from 81.82% (2015) to 93.84% (2020) and Kappa Coefficient (KC) between 0.73 (2015) and 0.91 (2020), demonstrating satisfactory performance and substantial potential for accurate, timely and type-refined wetland mapping. Our methodological framework allows rapid and accurate monitoring of wetland dynamics and could provide valuable information and methodological support for monitoring, conservation and sustainable development of wetland ecosystem.

Mangroves play a crucial role in maintaining coastal ecological balance. This study focused on the impact of branch-breaking behavior on the mortality of Rhizophora stylosa in the Guangxi Shankou Mangrove Reserve. However, we found mangrove mortality in areas devoid of egret habitation, prompting a reevaluation of our research hypothesis. Further investigation suggested that nesting behavior was the primary cause of mangrove mortality. A comparison of the data from areas with egrets (Egretta garzetta, Ardea intermedia) and lesser rice-field rats (Rattus losea) activity indicated significant mechanical damage caused by rats to mangroves as the main cause of mortality. Additionally, we found that the biological characteristics of R. stylosa, particularly its stunted growth and recovery abilities after branch breaking, were key factors affecting its survival. These findings imply that rat-induced mortality may not occur in other less susceptible mangrove species. The results contradict assumptions regarding the impact of egret behavior and highlight the importance of the biological characteristics of R. stylosa. This offers fresh insights into mangrove conservation and management, emphasizing the need for ongoing observation and hypotheses verification. Future studies should explore the influence of lesser rice-field rats’ activity and the intrinsic characteristics of R. stylosa on the ecosystem’s long-term stability.

Abstract Fire carbon emissions over Equatorial Asia (EQAS) play a critical role in the global carbon cycle. Most regional fire emissions (89.0%) occur in the dry season, but how changes in the dry-season length affect the fire emissions remains poorly understood. Here we show that, the length of the EQAS dry season has decreased significantly during 1979–2021, and the delayed dry season onset (5.4 ± 1.6 (± one standard error) days decade−1) due to increased precipitation (36.4 ± 9.1 mm decade−1) in the early dry season is the main reason. The dry season length is strongly correlated with the length of the fire season. Increased precipitation during the early dry season led to a significant reduction (May: −0.7 ± 0.4 Tg C decade−1; August: −12.9 ± 6.7 Tg C decade−1) in fire carbon emissions during the early and peak fire season. Climate models from the Coupled Model Intercomparison Project Phase 6 project a continued decline in future dry season length in EQAS under medium and high-emission scenarios, implying further reductions in fire carbon emissions.

Urban water bodies constitute a vital element within the urban ecosystem. Knowledge of the spatiotemporal changes in urban water bodies and their relationship with economic development in China is critical for sustainable development. However, previous studies usually investigated the spatiotemporal changes of urban water bodies at discrete time points and ignored the relationship between urban water bodies and economic development. Here, this study first corrected freely available water body products based on spatiotemporal majority filter, thereby obtaining an improved yearly urban water body dataset. Then, the research adopted Moran’s I and Anselin Local Moran’s I to explore the spatial distribution patterns of urban water bodies and employed linear regression and segmented linear regression to investigate the temporal abrupt change and long-term trend of urban water bodies. Finally, we used geodetector to evaluate the relative contributions of GDP to the spatial heterogeneity of trends in urban water bodies. The study revealed a significant spatial autocorrelation in the distribution of urban water bodies, particularly notable in the “High-High” cluster within the Yangtze and Pearl River Deltas. It was observed that approximately 40 % and 60 % of total cities experienced an abrupt change in permanent and seasonal urban water bodies, respectively. Specifically, the majority of these abrupt changes occurred during 2010–2013. When considering both long-term trends and abrupt changes, permanent urban water bodies in Chinese cities exhibited primarily a steadily increasing trend, while seasonal water bodies in Chinese cities displayed two distinct temporal patterns: a steadily increasing trend and a decreasing trend followed by a subsequent increasing trend. Furthermore, it was noted that GDP exhibited a significantly positive correlation with the temporal changes of permanent urban water bodies from 2000 to 2020 at the national scale while it first showed a significantly negative correlation with the temporal changes of seasonal urban water bodies from 2000 to 2012 and then showed a significantly positive correlation from 2012 to 2020 on a national scale. In conclusion, we found that GDP played a more important role in driving the spatial heterogeneity of trends in urban water bodies during 2012–2020 compared to 2000–2012, with the relative contribution of ∼ 32.5 %.

Sonneratia apetala Buch., an alien species with strong growth and adaptability, has been introduced and cultivated in Southeastern China. Meanwhile, Derris trifoliata Lour., native to coastal and riparian areas in Guangdong, Guangxi, and Fujian provinces, has experienced a rapid surge in population, impacting the health of mangrove ecosystems. Our research focuses on understanding the interactions between Oriental mangroves and D. trifoliata, particularly their proliferation and long-term symbiotic relationships. We investigated how Oriental mangrove proliferation promotes excessive D. trifoliata growth and explored the underlying mechanisms. In Leizhou Bay, Guangxi, the annual growth rate surged from 12.03% (2005–2015) to 55.36% (2015–2019), indicating a significant acceleration post-2015 and a concerning trend towards overgrowth. D. trifoliata failed to produce seeds on sea rockets or bulrushes, instead yielding 10.5 and 97.43 seeds/m2 on native red mangroves and Oriental mangroves, respectively. Along riverbanks, 68% of Oriental mangroves hosted D. trifoliata, and the suitable regions for these species overlapped significantly. Oriental mangroves reach 15 m tall with 10 × 10 m crown diameters, providing ample vine space, optimal photosynthesis conditions, sturdy support, and convenient dispersal routes. This study offers insights into introduced–native species interactions in mangrove ecosystems, with significance for management and preservation.

Mangrove forests are significant blue carbon pools on the Earth with strong carbon sequestration capacity and play an important role in combating climate change. To improve the capacity of regional carbon sinks, China has implemented a Special Action Plan for Mangrove Protection and Restoration (2020–2025). In this context, based on the MaxEnt model, this study analyzed the important environmental factors affecting the distribution of mangrove forests, combined with the planning objectives and carbon density parameters of different regions; assessed the habitat suitability areas of China’s mangrove forests; and predicted their future carbon stock potential. The results showed the following: (1) Elevation was the most important factor affecting the overall distribution of mangrove forests in China, and the optimal elevation of mangrove distribution was 0.52 m. (2) The most suitable areas of mangrove forests in China were mainly distributed in Hainan, Guangxi, and Guangdong, which had great potential for carbon stock. Danzhou Bay and Hongpai Harbor in Hainan, Lianzhou Bay in Guangxi, and the Huangmao Sea in Guangdong are potential areas for habitat suitability but are not yet under high levels of protection. (3) Achieving the goals of this action plan was expected to increase carbon stocks by 4.13 Tg C. Other suitable areas not included in this plan could still increase carbon stocks by 7.99 Tg C in the long term. The study could provide a scientific basis for siting mangrove restoration areas and developing efficient management policies.

Ecological carrying capacity is key to evaluating sustainable development capacity, and land use change has varying degrees of impact on ecosystems. This study explores the ecological carrying capacity based on land use change, which is important for the conservation and scientific and rational use of ecosystems. Therefore, in this study, three machine learning classification 3algorithms, namely support vector machine, random forest and artificial neural network, were selected for accuracy comparison and analysis based on multi-source data, using Southwest Guangxi Karst-Beibu Gulf as the study area. The Patch-generating Land Use Simulation Model is used to dissect the spatial and temporal changes in ecological carrying capaciyt from 1990 to 2040 and future trends. The results showed that (1) Coupling multiple sources of data and machine learning classification algorithms can ultimately improve land use classification accuracy. The RF is compared and found to be the optimal classification algorithm for this study. (2) The area of arable and grassland gradually decreases between 1990 and 2040, mainly encroached upon by build land; the area of forested land gradually recovers, the watershed fluctuates up and down, and the area of build land gradually increases. (3) The ecological carrying capacity from 1990 to 2040 shows a trend of decreasing and then increasing, and the area of low and medium–low decreases from 2020 to 2040, with arable land and forest land having a stronger potential to increase ecological carrying capacity. Nanning City, Qinzhou City and Chongzuo City are severely damaged. The framework provides an effective tool for exploring future ecological carrying capacity to support the ecological conservation and future sustainable development of the region.

As the second batch of international wetland cities, Wuhan and Nanchang, both provincial capital cities in China, have abundant wetland resources. An important sign of achievement of protecting the urban wetland areas is the international wetland city designation. Understanding the growth and changes of wetlands in international wetland cities is necessary for wetland protection and management. Thus, it is crucial to conduct proper wetland mapping in these international wetland cities. By studying wetland cities, sustainable planning can be provided to promote the coordinated development of wetlands and cities. In this research, Google Earth Engine and random forest machine learning were used. According to the “Data-Information-Knowledge-Wisdom” research framework, we carried out mapping of diverse wetlands in Wuhan and Nanchang with a resolution of 10 m in 2015 and 2020. Then, using the findings of wetland mapping, we examined changes in the wetland landscapes of the two cities. Finally, this study examined changes in international wetland city indicators over this time frame. The research results are as follows. 1) Our wetland mapping results in 2015 and 2020 achieved good accuracy, with an overall accuracy of over 0.90 and a kappa coefficient of over 0.85. 2) The total wetland area in both cities increased. Nanchang grew by 91.11 km2, whereas Wuhan grew by 290.68 km2. Most restored wetland areas were far from urban construction areas. In the two cities, the fragmentation of wetlands decreased, the diversity of wetlands increased, and the growth rate of wetlands was high. 3) Wetland rates rose from 17.79% to 19.07% in Nanchang and from 19.74% to 23.12% in Wuhan, according to mapping results between 2015 and 2020. The wetland protection rate in Nanchang remained unchanged, but the wetland protection rate in Wuhan decreased. Wuhan needs to strengthen the protection of increased wetlands. In addition, the study found that most of the increased areas of wetlands were previously cultivated land. The international wetland mapping framework of this study can be easily implemented in other regions of the world.

Urban wetlands play an important role in sustainable urban development. A wetland city recognized by the Ramsar Convention is a city with a remarkable ecological performance of urban wetlands and can provide an example for urban wetland protection. Therefore identifying the detailed characteristics of wetlands is of great value to the conservation of urban wetlands. However, there is no detailed wetland type extraction for wetland cities using multisource remote sensing data. This paper proposes a framework for fine- urban wetland extraction with multisource and multiperiod remote sensing data, which mainly includes Sentinel 1/2 and Landsat 8 data, and obtains the detailed wetland type data containing 14 wetland categories at a 10-m resolution. The classification methods of random forests were used to extract land cover types, and knowledge rules were used to classify detailed wetland types. The wetland cities, Yinchuan, and Haikou, located in southern and northern China, respectively, were chosen as case studies. The results indicate that the overall accuracy and wetland accuracy of the two cities in 2015 and 2020 were greater than 88% and 87%, respectively. The detailed wetland mapping results were more accurate and had a high resolution, and wetland categories were more diverse compared with other wetland-related products. According to the classification results, the wetland area in Haikou was 326.86 km2 (13.43%) in 2015 and 308.58 km2 (12.66%) in 2020. The wetland area in Yinchuan was 80.39 km2 (4.46%) in 2015 and 74.15 km2 (4.12%) in 2020. The fine urban wetland classification framework we propose and the detailed wetland results could serve for the creation of wetland cities and support the protection and rational use of urban wetlands.

Wetlands have suffered from considerable degradation due to anthropogenic and natural disturbances in recent decades. Although some advancements have been made, effective methods that can produce large-scale wetland maps with detailed categories are still lacking due to the diversity and complexity of wetland ecosystems. To address this issue, we developed a novel algorithm for detailed wetland types classification integrating k-fold random forest and hierarchical decision tree, and so named two-step classification algorithm. Firstly, the phenology-based features were composited based on time series Sentinel-1/2 images, and the k-fold random forest was used to extract five rough wetland types in Google Earth Engine platform. Secondly, the hierarchical decision tree designed based on geometric features was used to separate the rough wetland types into fourteen detailed types. Application of the two-step classification method in Northern, Central and Southern Asia (NCSA) resulted in a continental-scale wetland map with an overall accuracy of 90.0 ± 0.5%. Wetland types, including inland marsh, lake, river, coastal swamp, estuarine water, lagoon, shallow marine water, reservoir, canal/channel and agricultural pond, had good accuracy with both UA and PA over 77%. The remaining wetland types had moderate accuracy, with both UA and PA over 58%. As we calculated, total wetland areas of NCSA were 1,375,489.27 km2. Among the fourteen wetland categories, the inland marsh had the largest area (544,584.38 km2) and was primarily distributed in subarctic and humid continental climates, while the canal/channel had the smallest area (1,651.57 km2) and was primarily scattered in desert, semiarid and humid subtropical climates. The lake and floodplain shared generally large areas with value of 392,413.55 km2 and 173,255.71 km2 respectively, which were typically distributed across desert and semiarid climates. This study successfully mapped continental-scale wetlands with detailed categories at a 10-m spatial resolution, which can provide valuable information for the management of wetland ecosystems and facilitate the implementation of wetland-related sustainable development goals.

Mangrove wetlands are hotspots of global biodiversity and blue carbon reserves in coastal wetlands, with unique ecological functions and significant socioeconomic value. Annual fine-scale monitoring of mangroves is crucial for evaluating national conservation programs and implementing sustainable mangrove management strategies. However, annual fine-scale mapping of mangroves over large areas using remote sensing remains a challenge due to spectral similarities with coastal vegetation, tidal periodic fluctuations, and the need for consistent and dependable samples across different years. In previous research, there has been a lack of strategies that simultaneously consider spatial, temporal, and methodological aspects of mangrove extraction. Therefore, based on an approach that considers mangrove habitat, tides, and a semantic segmentation approach, we propose a method for fine-scale mangrove mapping suitable for long time-series data. This is an optimized hybrid model that integrates spatial, temporal, and methodological considerations. The model uses five sensors (GF-1, GF-2, GF-6, ZY-301, ZY-302) to combine deep learning U-Net models with mangrove habitat information and algorithms during low-tide periods. This method produces a mangrove map with a spatial resolution of 2 m. We applied this algorithm to three typical mangrove regions in the Beibu Gulf of Guangxi Province. The results showed the following: (1) The model scored above 0.9 in terms of its F1-score in all three study areas at the time of training, with an average accuracy of 92.54% for mangrove extraction. (2) The average overall accuracy (OA) for the extraction of mangrove distribution in three typical areas in the Beibu Gulf was 93.29%. When comparing the validation of different regions and years, the overall OA accuracy exceeded 89.84% and the Kappa coefficient exceeded 0.74. (3) The model results are reliable for extracting sparse and slow-growing young mangroves and narrow mangrove belts along roadsides. In some areas where tidal flooding occurs, the existing dataset underestimates mangrove extraction to a certain extent. The fine-scale mangrove extraction method provides a foundation for the implementation of fine-scale management of mangrove ecosystems, support for species diversity conservation, blue carbon recovery, and sustainable development goals related to coastal development.

The increasing frequency of global drought events poses a significant threat to the stability of grassland ecosystems’ functionality. The Inner Mongolian grasslands stand out as one of the world’s most drought-prone regions, facing elevated drought risks compared to other biomes. An in-depth comprehension of the impact of drought on grassland ecosystems is paramount for their long-term sustainability. Using the Standardized Precipitation Evapotranspiration Index (SPEI) from 1982 to 2018, this study identified various drought events within the Inner Mongolian grasslands, encompassing moderate drought, severe drought, and extreme drought. The resistance of the vegetation to the different drought conditions, assessed through net primary productivity (NPP) as a metric (reflecting its capacity to maintain its original level during drought periods), was examined. The research findings indicated that the period from 2001 to 2018 witnessed a substantial increase in both the frequency and the extent of drought events compared to the period from 1982 to 2000, particularly concerning severe and extreme droughts. The areas most severely impacted by extreme drought were the Xilingol League and the Alxa League. From 1982–2000 to 2001–2018, under moderate drought conditions, vegetation resistance exhibited a minor decrease in the central and eastern regions but experienced a slight increase in the western region. In contrast, under severe drought conditions, the western region saw a significant decrease in vegetation resistance. Remarkably, under extreme drought conditions, the western region showed a substantial increase in vegetation resistance, while the central and eastern regions experienced a slight decrease. Across all three drought conditions, as precipitation levels declined, the resistance of the meadow–steppe–desert ecosystems demonstrated a high–low–high distribution pattern. The temperate desert steppe exhibited a minimal vulnerability to drought, boasting resistance levels exceeding 0.9. Notably, extreme drought had the most pronounced impact on the temperate meadow steppe, temperate steppe, and temperate desert steppe, particularly within the temperate meadow steppe category. Given these findings, the authorities responsible for grassland management should prioritize regions characterized by frequent drought occurrences and low drought resistance, such as Ulanqab City, the Xilingol League, and the western part of Hulun Buir City. Safeguarding steppe ecosystems is of paramount importance for stabilizing vegetation productivity and land carbon sinks, especially under the anticipated exacerbation of climate conditions in the future.

Wetland carbon storage plays an essential role in the global carbon cycle. However, in recent decades, intensive human activities and rapid urbanization have reduced wetland C stocks in the Guangdong–Hong Kong–Macau Greater Bay Area (GBA). Long-term assessment of carbon storage in the wetland ecosystems of the GBA is needed for promoting regional sustainable development. Therefore, we proposed a framework integrating the integrated valuation of ecosystem services and tradeoffs (InVEST) model and water inundation frequency, which was retrieved via multiple index water detection rule based on Google Earth Engine platform, to estimate yearly carbon storage in wetlands across the GBA at a 30-m spatial resolution during 1995–2020. The incorporation of water inundation frequency in estimating carbon storage in wetlands presents features of long time series and higher temporal frequency. The results showed that: first, the carbon storage of wetlands estimated by the InVEST model decreased from 33.38 to 16.64 Tg·C between 1995 and 2020 in the GBA, mainly owning to the loss of paddy fields. Second, there is a significant exponentially decreasing relationship between water inundation frequency and carbon storage density within the potential wetland extent, with the R2 reaching 0.998, which denotes huge potential for estimating wetland carbon storage; third, annual wetland carbon storage estimated were divided into three main stages: a rapidly increasing period (1995–2004), a falling period (2005–2013), and a slightly fluctuating period (2015–2020). This research may provide references for supporting decision-making in implementing wetland-related sustainable development and carbon-neutrality policies in the GBA.

Understanding the variation regularity of water extent can provide insights into lake conservation and management. In this study, inter- and inner-annual variations of water extent during the period of 1987–2020 were analyzed to understand the temporal and spatial distribution characteristics of Dongting Lake. We applied the Multiple Index Water Detection Rule to extract the Dongting Lake water extent quickly and accurately based on Google Earth Engine platform, and then assessed the extraction accuracy. The water surface analysis results showed that (1) based on sustainable development goals (SDG) 6.6.1, the trend of water extent showed the downward fluctuating trend from 1987 to 2020, with the overall average water extent being 1894.48 km². (2) Among the monthly average water area, the largest extent was 2477.14km² (July) and the smallest was 848.14 km² (January). Among the seasonal mean water area, summer was the largest, with an area of 2438.06 km², and winter was the smallest at 967.34 km². (3) For the water inundation frequency, seasonal water bodies accounted for the largest proportion, with 1577.85 km²; the nonwater area was the smallest, with the area of 573.02 km²; and the permanent water area was 1086.21 km². Through the analysis of the historical water body extent of the long time series of Dongting Lake, this study reflected support for SDG, for which the research idea and design can help us understand the importance and feasibility of the SDG 6.6.1 indicator.

Wetland cities were proposed by the International Wetland Convention Organization for the protection of urban wetlands. Few studies have performed land cover classifications for internationally recognized wetland cities or explored what contribution the classification results can make to the establishment of additional wetland cities to date. Based on Sentinel-1 and 2 data, this study used a 10-fold random forest method to classify the land cover of the first six wetland cities recognized in China. A land cover dataset, which had a resolution of 10 m and included four wetland types, was obtained and the wetland area and protected wetland areas of the six cities were calculated. The results showed that (1) the classification accuracy of six cities was good, the overall accuracy was above 90%, and the Kappa coefficient was above 0.88. (2) Cropland or forested areas were the most common non-wetland land coverage type in wetland cities and accounted for more than 20% or 40% of the land coverages, while water was the most common wetland type and accounted for more than 2% of the land coverages. From 2015 to 2020, the built area in most cities increased, while cropland and forest decreased significantly. (3) The wetland rate was 6.68–37.56% and the wetland protection rate was 49.48–73.74% in the six wetland cities. From 2015 to 2020, the wetland rate of the six cities were relatively stable, and the wetland protection rate of inland cities (Yinchuan, Changde, Harbin and Changshu) increased significantly, while those of coastal cities (Haikou and Dongying) decreased, which might be related to the change in coastline. Therefore, we found that the wetlands in these cities were well protected. Land cover classification for wetland cities can provide a reference for using remote sensing techniques used to monitor internationally wetland cities while also supporting the creation of additional wetland cities.

Enhancing the spatio-temporal dynamic monitoring of the construction land of emerging major cities provides the basis for understanding the effects of human activities on the earth’s ecological environment changes, which is regarded as an important way to improve human wellbeing and to achieve the UN SDGs. This paper, which uses Nanning as an example, adopts 30 m annual remote sensing land cover datasets from 1990–2021 to elaborately analyze the temporal evolution and spatial expansion of construction land and to evaluate the sustainable development level based on SDG 11.3.1. The conclusions of this paper are as follows: (1) For Nanning City, during 1990–2021, construction land area increased from 54.77 km2 to 326.33 km2—a nearly five-fold increase. Spreading expansion and finger-like expansion along roads played a leading role during the development of the construction land in Nanning. Liangqing and Yongning are the future directions of urban development. The LCRPGR of Nanning declined with one peak period around 1995, and the LCRPGR was relatively stable with a high intensity of land use. (2) For the other emerging major cities, continuous long duration data are more suitable for urban construction land monitoring compared with traditional sparse time-series monitoring. The rich information derived from continuous long duration data can help decision makers to formulate and implement more comprehensive measures. The research paradigm adopted in this study can be applied in other cities. The newly developed urban districts will have foreseeable high sustainable development risks, and urban development strategies at the international, national, and urban levels could reduce those risks and promote the realization of UN SDGs from different angles and with different intensities.

Wetlands are an important transitional ecosystem, and they play an important role in maintaining ecological balance. However, human activities and climate change have led to a decrease in wetlands. Therefore, to explore the degree of damage and assess the future trends of Guangxi wetlands, this study used the Google Earth Engine (GEE) cloud platform, and the support vector machine algorithm was selected for comparison and analysis to simulate the accuracy of land cover. GIS was used to analyse the evolution and degree of damage of nearly 30 Guangxi wetlands. The geographic detector model was used to explore the driving mechanism, and finally, the CA-Markov model and multi-scenario simulation were used to predict the wetland evolution from 2018 to 2035 to reveal the future direction of development. The results showed that (1) From 1990 to 2018, paddy fields accounted for the largest proportion of wetlands in Guangxi. In the past 30 years, the total area of wetlands in Guangxi has been degraded, with a total decrease of 983.33 km2. (2) The degree of wetland damage results showed that the total damaged area was greater than the total restored area, The wetland damage in Nanning city was the most serious, with an area difference of 503.22 km2 between the damaged and restored areas. (3) The analysis of the driving mechanism of wetland damage showed that distance from cities and towns, average precipitation and population density were the main driving factors. (4) The spatial distribution of natural development and economic construction in 2035 will be slightly damaged; additionally, the spatial distribution of ecological protection will expand as a whole. From 2025 to 2035, wetlands will be basically stable under Natural Development Scenario (NDS), degraded each year under the Economic Construction Scenario (ECS), and steadily increased each year under Ecological Protection Scenario (EPS).

Poyang Lake and Dongting Lake are the two largest freshwater lakes in China. They play a critical ecological role in regional sustainable development, which is closely related to water occurrence (WO). This study aimed to characterize the spatiotemporal changes, ecological impacts, and influencing factors of WO in the two lakes. First, this study utilized the Google Earth Engine (GEE) platform and all available Landsat images to retrieve the time series of WO in Poyang Lake and Dongting Lake on a two-year scale from 1989 to 2020. Then, the study explored the landscape variation intensity, landscape structure, and their ecological impacts on net primary production (NPP) and total nitrogen (TN) based on the landscape transfer rate (LTR), landscape indices, and geostatistical connectivity analysis. Finally, the study investigated the natural and human factors that influence the changes in WO. The major conclusions include the following: (1) the mean WO showed a significantly decreasing trend in the two lakes from 1989 to 2020. According to the LTR, the landscape variation in Dongting Lake was more intense before 2002, while after 2002, the landscape variation in Poyang Lake was more intense. (2) The landscape structure of WO gradually fragmented and diversified, along with decreasing hydrological connectivity in the two lakes. (3) The increase in NPP in Poyang Lake and the increase in TN in Dongting Lake were closely correlated with the changes in WO. (4) The upstream runoff showed a significantly positive correlation with the mean WO in Poyang Lake and the annual precipitation showed a significantly positive correlation with the mean WO in Dongting Lake; the influences of natural factors on WO changes became weaker. This study reinforces the understanding of long-term changes in the water body in Poyang Lake and Dongting Lake.

Sustainable Development Goals (SDGs) target 11.a is a good vision for the coordinated development of the economy, society and environment in urban agglomerations. However, there was an extreme lack of indicators, data or case studies for SDG target 11.a, since it is a vague “process target”, which is not conducive to the implementation of SDG target 11.a. It is important to propose a quantitative, convenient, and local policies relevant method to promote the realization or to test the implementation effects of SDG target 11.a. Combined with socio-economic data and land use data, this study uses the methods of comprehensive evaluation model, coupling and coordination degree, and comparative advantage degree methods to study the pattern evolution, coordination characteristics and advantageous areas of production–living–ecological (PLE) functions in the Guangxi Beibu Gulf Urban Agglomeration (GBG_UA) from 1995 to 2019. The results showed that, (1) considering the spatiotemporal distribution of PLE functions, the study area has a relatively stable ecological function as well as fluctuating production and living functions. Considering the coordination characteristics of PLE functions, high–high and low–low clustering effects were observed, and primary coordination maintained the highest proportion, accounting from 55.26% in 1995 to 71.05% in 2019, indicating the SDG target 11.a level in the GBG_UA was poor. Considering the advantageous areas for PLE functions, the region mostly comprises single-function advantageous areas and a few multifunction advantageous areas, including 20 single-function advantage counties (accounting for 52%), 15 dual-function advantage counties (accounting for 39%), and three multi-function advantage counties (accounting for 7.8%), which indicates the lack of diversified land use structures in this region. (2) Optimization suggestions for the coordinated development and realization of SDG target 11.a for the GBG_UA were provided. Suggestions were made based on the radiation and driving role of Nanning city to guide the coordinated development of surrounding counties (districts). Suggestions were also made to improve the design of the integrated transportation network as well as to optimize allocation according to the resource endowment of land and to realize an upgraded ecology as well as agricultural products and services. (3) The evaluation of PLE functions is a quantitative and convenient method that can optimize national and regional development planning and test the implementation effects of SDG target 11.a. This study offers foundational knowledge for the realization of SDG target 11.a in the GBG_UA and provides a reference for the research and implementation of SDG target 11.a in other regions around the world.

As one of the most open and dynamic regions in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has been urbanizing rapidly in recent decades. The surface water in the GBA also has been suffering from urbanization and intensified human activities. The study aimed to characterize the spatiotemporal patterns and assess the losses and gains of surface water caused by urbanization in the GBA via long time-series remote sensing data, which could support the progress towards sustainable development goals (SDGs) set by the United Nations, especially for measuring SDG 6.6.1 indicator. Firstly, utilizing 4750 continuous Landsat TM/ETM+/OLI images during 1986–2020 and the Google Earth Engine cloud platform, the multiple index water detection rule (MIWDR) was performed to extract surface water extent in the GBA. Secondly, we achieved surface water dynamic type classification based on annual water inundation frequency time-series in the GBA. Finally, the spatial distribution and temporal variation of urbanization-induced water losses and gains were analyzed through a land cover transfer matrix. Results showed that (1) the average minimal and maximal surface water extents of the GBA during 1986–2020 were 2017.62 km2 and 6129.55 km2, respectively. The maximal surface water extent fell rapidly from 7897.96 km2 in 2001 to 5087.46 km2 in 2020, with a loss speed of 155.41 km2 per year (R2 = 0.86). (2) The surface water areas of permanent and dynamic types were 1529.02 km2 and 2064.99 km2 during 2000–2020, accounting for 42.54% and 57.46% of all water-related areas, respectively. (3) The surface water extent occupied by impervious land surfaces showed a significant linear downward trend (R2 = 0.98, slope = 36.41 km2 per year), while the surface water restored from impervious land surfaces denoted a slight growing trend (R2 = 0.86, slope = 0.99 km2 per year). Our study monitored the long-term changes in the surface water of the GBA, which can provide valuable information for the sustainable development of the GBA urban agglomeration. In addition, the proposed framework can easily be implemented in other similar regions worldwide.

Temperature and precipitation are considered to be the most important indicators affecting the green-up date. Sensitivity of the green-up date to temperature and precipitation is considered to be one of the key indicators to characterize the response of terrestrial ecosystems to climate change. We selected the main grassland types for analysis, including temperate steppe, temperate meadow steppe, upland meadow, and lowland meadow. This study investigates the variation in key meteorological indicators (daily maximum temperature (Tmax), daily minimum temperature (Tmin), and precipitation) between 2001 and 2018. We then examined the partial correlation and sensitivity of green-up date (GUD) to Tmax, Tmin, and precipitation. Our analysis indicated that the average GUD across the whole area was DOY 113. The mean GUD trend was −3.1 days/decade and the 25% region advanced significantly. Tmax and Tmin mainly showed a decreasing trend in winter (p > 0.05). In spring, Tmax mainly showed an increasing trend (p > 0.05) and Tmin a decreasing trend (p > 0.05). Precipitation showed no significant (p > 0.05) change trend and the trend range was ±10 mm/decade. For temperate steppe, the increase in Tmin in March promotes green-up (27.3%, the proportion of significant pixels), with a sensitivity of −0.17 days/°C. In addition, precipitation in April also promotes green-up (21.7%), with a sensitivity of −0.32 days/mm. The GUDs of temperate meadow steppe (73.9%), lowland meadow (65.9%), and upland meadow (22.1%) were mainly affected by Tmin in March, with sensitivities of −0.15 days/°C, −0.13 days/°C, and −0.14 days/°C, respectively. The results of this study reveal the response of vegetation to climate warming and contribute to improving the prediction of ecological changes as temperatures increase in the future.

In order to improve the chemical leachability, the alumina-based ceramic core material with the silica fiber was injected and sintered at 1100 °C/4 h, 1200 °C/4 h, 1300 °C/4 h and 1400 °C/4 h, respectively. The micrographs of ceramic core materials at sintered and leached state were characterized by scanning electron microscopy (SEM). The phase composition of ceramic core material after sintering and the leaching product after leaching were detected by X-ray diffraction (XRD). The porosity, room temperature bend strength, creep property at elevated temperature and the leaching rate in aqueous caustic solution were studied. The experimental results show that the ceramic core material with silica fiber obtain a fair balance between mechanical and chemical properties at sintering of 1300 °C/4 h. Specifically, the leach rate of ceramic core material with silica fiber is increased apparently. High leaching surface and weak adhesive strength between agglomerated alumina particles are the reasons that responsible for the ceramic core material with silica fiber be leaching fast than that of the ceramic core material without fiber.

Abstract Terrestrial water storage (TWS) variation is crucial for global hydrological cycles and water resources management under climatic changes. In the previous studies, changes in water storage of some part of China have been studied with GRACE data in recent ten years. However, the spatial pattern of changes in water storage over China may be different in a long period. Here, we aimed to present long-term spatial patterns of TWS over China between 1948 to 2015 by unique Global Land Data Assimilation System Version 2 data and identify possible factors to water storage changes. The results revealed that the inner-annual variations in TWS of China exhibited remarkable downward trends with decreased rate of 0.1 cm/yr. Meanwhile, we found that spatial patterns of TWS in China can be divided into three distinct sub-regions of TWS region with increased, TWS region with decreased, TWS region with insignificant variation. The Northeast had decreased trends (−0.05 cm/yr) due to climate change and anthropogenic activities. Urban expansion is a non-ignorable factor to TWS reduction in Jing-Jin-Ji region (r = 0.61); the west had increased from 1948 to 2015 (0.03 cm/yr) due to precipitation increased and recharge by glacier melt; the south had insignificant trends and TWS varied with precipitation (r = 0.78).

Irrigation is an important factor affecting the change of terrestrial water storage (TWS), especially in grain-producing areas. The Northeast China Plain (NECP), the Huang-Huai-Hai Plain (HHH) and the middle and lower reaches of the Yangtze River Basin Plain (YRB) are major grain-producing regions of China, with particular climate conditions, crops and irrigation schemes. However, there are few papers focusing on the different variation pattern of water storage between NECP, HHH and YRB. In this paper, the characteristics of terrestrial water storage anomaly (TWSA) and groundwater storage in the three regions mentioned above from 2003 to 2014 were analyzed, and the main reasons for water storage variations in the three regions were also discussed. The result shows that although effective irrigated areas increased in all three regions, TWSA only decreased in HHH and TWSA in the other two regions have shown an increasing trend. Spatially, the water storage deficit was more serious in middle and south NECP and HHH. In the three regions, water storage variations were impacted by meteorological condition and anthropogenic stress (e.g., irrigation). However, irrigation water consumption has a greater impact on water storage deficit in HHH than the other two regions, and water storage variation in YRB was mainly impacted by meteorological conditions. In this case, we suggest that the structure of agricultural planting in HHH should be adjusted to reduce the water consumption for irrigation.

Surface water is an essential element that supports natural ecosystem health and human life, and its losses or gains are closely related to national or local sustainable development. Monitoring the spatial-temporal changes in surface water can directly support the reporting of progress towards the sustainable development goals (SDGs) outlined by the government, especially for measuring SDG 6.6.1 indicators. In our study, we focused on Baiyangdian Lake, an important lake in North China, and explored its spatiotemporal extent changes from 2014 to 2020. Using long-term Sentinel-1 SAR images and the OTSU algorithm, our study developed an automatic water extraction framework to monitor surface water changes in Baiyangdian Lake at a 10 m resolution from 2014 to 2020 on the Google Earth Engine cloud platform. The results showed that (1) the water extraction accuracy in our study was considered good, showing high consistency with the existing dataset. In addition, it was found that the classification accuracy in spring, summer, and fall was better than that in winter. (2) From 2014 to 2020, the surface water area of Baiyangdian Lake exhibited a slowly rising trend, with an average water area of 97.03 km2. In terms of seasonal variation, the seasonal water area changed significantly. The water areas in spring and winter were larger than those in summer and fall. (3) Spatially, most of the water was distributed in the eastern part of Baiyangdian Lake, which accounted for roughly 57% of the total water area. The permanent water area, temporary water area, and non-water area covered 49.69 km2, 97.77 km2, and 171.55 km2, respectively. Our study monitored changes in the spatial extent of the surface water of Baiyangdian Lake, provides useful information for the sustainable development of the Xiong’an New Area and directly reports the status of SDG 6.6.1 indicators over time.

The eco-environment is dynamic and shows a continuous process of long-term change. It is helpful for policymakers to know the status of the regional eco-environment through accurate evaluations of the history and current situation of the regional eco-environment. The remote sensing ecological index (RSEI) model of China was established in this study by using four indexes: wetness, greenness, dryness, and heat. Knowledge granulation of the RSEIs were carried out, and a method to determine the weights of the knowledge granulation entropy of the indexes based on their characteristics was proposed. This study used Moderate Resolution Image Spectroradiometer (MODIS) data from the Google Cloud Computing Platform to study and calculate the eco-environmental quality of China from 2000–2017. The overall eco-environmental quality in China tended to improve from 2000–2017, although there were large areas of ecological degradation from 2009–2014. The eco-environment of eastern China was better than that of western China. Most of the national ecological areas were third-level ecological areas, which had moderate environmental quality. Dryness was the most important factor affecting the quality of the eco-environment, followed by greenness, which reflected the increasing environmental damage caused by human activities in China in recent years.

A relative lag in research methods, technical means and research paradigms has restricted the rapid development of geography and urban computing. Hence, there is a certain gap between urban data and industry applications. In this paper, a spatial association discovery framework for the urban service industry based on a concept lattice is proposed. First, location data are used to form the formal context expressed by 0 and 1. Frequent closed itemsets and a concept lattice are computed on the basis of the formal context of the urban service industry. Frequent closed itemsets can filter out redundant information in frequent itemsets, uniquely determine the complete set of all frequent itemsets, and be orders of magnitude smaller than the latter. Second, spatial frequent closed itemsets and association rules discovery algorithms are designed and built based on the formal context. The inputs of the frequent closed itemsets discovery algorithms include the given formal context and frequent threshold value, while the outputs are all frequent closed itemsets and the partial order relationship between them. Newly added attributes create new concepts to guarantee the uniqueness of the new spatial association concept. The inputs of spatial association rules discovery algorithms include frequent closed itemsets and confidence threshold values, and a rule is confident when and only if its confidence degree is not less than the confidence threshold value. Third, the spatial association of the urban service industry in Nanning, China is taken as a case to verify the method. The results are basically consistent with the spatial distribution of the urban service industry in Nanning City. This study enriches the theories and methods of geography as well as urban computing, and these findings can provide guidance for location-based service planning and management of urban services.

Due to the widespread presence of noise, such as clouds and cloud shadows, continuous, high spatiotemporal-resolution dynamic monitoring of lake water extents is still limited using remote sensing data. This study aims to take an approach to mapping continuous time series of highly-accurate lake water extents. Four lakes from diverse regions of China were selected as cases. In order to reduce the impact of noise and ensure high spatial and temporal resolution of the final results, two sets of MODIS products (including MOD09A1 and MOD13Q1) are used to extract water bodies. This approach mainly comprises preliminary classification, post processing and data fusion. The preliminary classification used the Random Forest (RF) classifier to efficiently and automatically obtain the initial classification results. Post-processing is implemented to repair the classification results affected by noise as much as possible. The processed results of the two sets of products are fused by using the Homologous Data-Based Spatial and Temporal Adaptive Fusion Method (HDSTAFM), which reduces the effect of noise and also improve the temporal and spatial resolution for the final water results. We determined the accuracy using Landsat-based water results, and the values of overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficients (KC) are mostly greater than 0.9. Good correlation was achieved for a time series of water area and altimetry data, obtained by multiple satellites, and also for water-level data selected from hydrological stations.

The spatiotemporal changes of open-surface water bodies in the Yangtze River Basin (YRB) have profound influences on sustainable economic development, and are also closely relevant to water scarcity in China. However, long-term changes of open-surface water bodies in the YRB have remained poorly characterized. Taking advantage of the Google Earth Engine (GEE) cloud platform, this study processed 75,593 scenes of Landsat images to investigate the long-term changes of open-surface water bodies in the YRB from 1984 to 2018. In this study, we adopted the percentile-based image composite method to collect training samples and proposed a multiple index water detection rule (MIWDR) to quickly extract the open-surface water bodies. The results indicated that (1) the MIWDR is suitable for the long-term and large-scale Landsat water bodies mapping, especially in the urban regions. (2) The areas of permanent water bodies and seasonal water bodies were 29,076.70 km2 and 21,526.24 km2, accounting for 57.46% and 42.54% of the total open-surface water bodies in the YRB, respectively. (3) The permanent water bodies in the YRB increased along with the decreases in the seasonal water bodies from 1984 to 2018. In general, the total open-surface surface water bodies in the YRB experienced an increasing trend, with an obvious spatial heterogeneity. (4) The changes of open-surface water bodies were associated with the climate changes and intense human activities in the YRB, however, the influences varied among different regions and need to be further investigated in the future.

This paper proposes a spatial data attribute similarity measure method based on granular computing closeness. This method uses the distance and membership degree of different index levels of spatial entities to measure the similarity of attributes. It not only reflects the degree of similarity of spatial entity types at different index levels but also reflects the integration similarity between spatial entity types under a comprehensive index. This method embodies the layered idea of granular computing and can provide a basis for spatial problem decision making and for spatial entity classification. Finally, the feasibility and applicability of the method are verified by taking the similarity measure of the land-use type attribute in Guangxi as an example.

Lakes have an important role in human life and the ecological environment, but they are easily affected by human activity and climate change, especially around urban areas. Hence, it is critical to extract water with a high precision method and monitor long-term sequence dynamic changes in lakes. As the greatest natural lake of the Beijing-Tianjin-Hebei region, Baiyangdian Lake has a significant function in human life, socio-economic development, and regional ecological balance. This lake area has shown large changes due to human activity and climate change. The change monitoring process of the water surface is of great significance in providing support for the management and protection of the lake. The Spectrum Matching based on Discrete Particle Swarm Optimization (SMDPSO) method is a new, robust, and low-cost method for water extraction, that has obvious advantages in extracting complex water surfaces. In this paper, the SMDPSO method was used to extract the water surface of Baiyangdian Lake by Landsat images from 1984 to 2018. This method has a good effect on complex water surface extraction with vegetation, shadows, and so forth, and the Landsat images have higher resolution and longer time series. The main contents and results of this paper are as follows: (1) We verified the applicability of the SMDPSO method in the Baiyangdian Lake using visual interpretation and correlation analysis. The relative errors between observed and extracted results were all less than 5% in spring, summer, and fall, and the correlation coefficient between the water area and water level was 0.96. (2) According to seasonal verification and comparison of the extraction results, the SMDPSO method was used to extract the water surface area of Baiyangdian Lake during spring of the years 1984–2018. Water area changes of Baiyangdian Lake can be divided into four periods: Dry period (1984–1988), degraded period (1989–2000), stable period (2000–2008), and recovery period (2008–2018). The water area reached a maximum of 280 km2 in 1989 and a minimum of 44 km2 in 2002. (3) The possible causes of the changes in the water area of Baiyangdian Lake were also analyzed. The changes were caused by climate and human activities during the first and second periods, but mainly human activities during the third and fourth periods. In fact, effective policies combined with water conservancy projects were directly conducive to improving or even recovering the water and ecological environment of Baiyangdian Lake. Considering its importance for the benign development of the Beijing-Tianjin-Hebei Region and the construction of the Xiong’an New Area, a policy is necessary to ensure that the lake’s ecological environment will not be destroyed under the premise of economic development.

The use of remote sensing to monitor surface water bodies has gradually matured. Long-term serial water change analysis and floods monitoring are currently research hotspots of remote sensing hydrology. However, these studies are also faced with some problems, such as coarse temporal or spatial resolution of some remote sensing data. In general, flood monitoring requires high temporal resolution, and small-scale surface water extraction requires high spatial resolution. The machine learning method has been proven to be effective against long-term serial surface water extraction, such as random forests (RFs). MODIS data are well suited for large-scale surface water dynamic analysis and flood monitoring because of its short return cycle and medium spatial resolution. In this paper, the Yangtze River Basin (YRB) in China was selected as the study area, and two MODIS products (MOD09A1 and MOD13Q1) and RF method were used to extract the surface water from 2000 to 2016. Considering the disadvantages of temporal or spatial resolution of these two MODIS products, this study also presents a data fusion method to combine them and get higher spatiotemporal resolution water results. Finally, 762 surface water maps from 2000 to 2016 are obtained, whose temporal and spatial resolution is every eight days and 250 m, respectively. In addition, water extent variation is analyzed and compared to observed precipitation data. The main conclusions are as follows: (1) this constructed approach for long-term serial surface water extraction based on the RF classifier is feasible, and a good fusion method is used to obtain the surface water body with higher spatiotemporal resolution; (2) the maximum area of the surface water extent is 48.53 × 103 km2, and seasonal and permanent water areas are 20.51 × 103 km2 and 28.01 × 103 km2, respectively; (3) surface water area is increasing in the YRB, such that seasonal water area decreased by 3450 km2, and the permanent water area increased by 3565 km2 in 2001–2015; (4) precipitation is the main factor causing variation in the surface water bodies, and they both show an increasing trend in 2000–2016. As such, the approach is worth referring to other remote sensing applications, and these products are very both valuable for water resource management and flood monitoring in the study area.

Depletion of water resources has threatened water security in the Beijing-Tianjin-Hebei urban agglomeration, China. However, the relative importance of precipitation and urbanization to water storage change has not been sufficiently studied. In this study, both terrestrial water storage (TWS) and groundwater storage (GWS) change in Jing-Jin-Ji from 1979 to the 2010s were investigated, based on the global land data assimilation system (GLDAS) and the EartH2Observe (E2O) outputs, and we used a night light index as an index of urbanization. The results showed that TWS anomaly varied in three stages: significant increase from 1981 to 1996, rapid decrease from 1996 to 2002 and increase from 2002 to the 2010s. Simultaneously, GWS has decreased with about 41.5 cm (500% of GWS in 1979). Both urbanization and precipitation change influenced urban water resource variability. Urbanization was a relatively important factor to the depletion of TWS (explains 83%) and GWS (explains 94%) since the 1980s and the precipitation deficit explains 72% and 64% of TWS and GWS variabilities. It indicates that urbanization coupled with precipitation deficit has been a more important factor that impacted depletion of both TWS and GWS than climate change only, in the Jing-Jin-Ji region. Moreover, we suggested that the cumulative effect should be considered when discussing the relationship between influence factors and water storage change.

Background and objective Multiple drug resisting (MDR) phenotype is the sign of intrinsic or acquired resistance and is the key factor which leads to chemotherapy failure. It has been proven that PTEN, p53, P-gp expressions were related to drug resistance and prognosis of the patients with lung cancer. The aim of this study is to analyze the association of the expression of PTEN, p53, P-gp with postoperative survival in patients with non-small cell lung cancer (NSCLC), and to explore the relationship between the characteristics and drug resistance of NSCLC patients. Methods A total of 61 patients with NSCLC were followed up. Immunohistochemical staining using polyclonal PTEN, p53 and P-gp antibody were performed on paraffinimbedded specimens from 61 patients with NSCLC and 20 specimens of tumor-surrounding normal lung tissue were used as control. Results The PTEN expression was significantly lower in NSCLC than in tumor-surrounding normal lung tissue and the expression of p53 and P-gp were in the opposite aspects (P

Urban lakes play an important role in urban development and environmental protection for the Wuhan urban agglomeration. Under the impacts of urbanization and climate change, understanding urban lake-water extent dynamics is significant. However, few studies on the lake-water extent changes for the Wuhan urban agglomeration exist. This research employed 1375 seasonally continuous Landsat TM/ETM+/OLI data scenes to evaluate the lake-water extent changes from 1987 to 2015. The random forest model was used to extract water bodies based on eleven feature variables, including six remote-sensing spectral bands and five spectral indices. An accuracy assessment yielded a mean classification accuracy of 93.11%, with a standard deviation of 2.26%. The calculated results revealed the following: (1) The average maximum lake-water area of the Wuhan urban agglomeration was 2262.17 km2 from 1987 to 2002, and it decreased to 2020.78 km2 from 2005 to 2015, with a loss of 241.39 km2 (10.67%). (2) The lake-water areas of loss of Wuhan, Huanggang, Xianning, and Xiaogan cities, were 114.83 km2, 44.40 km2, 45.39 km2, and 31.18 km2, respectively, with percentages of loss of 14.30%, 11.83%, 13.16%, and 23.05%, respectively. (3) The lake-water areas in the Wuhan urban agglomeration were 226.29 km2, 322.71 km2, 460.35 km2, 400.79 km2, 535.51 km2, and 635.42 km2 under water inundation frequencies of 5%–10%, 10%–20%, 20%–40%, 40%–60%, 60%–80%, and 80%–100%, respectively. The Wuhan urban agglomeration was approved as the pilot area for national comprehensive reform, for promoting resource-saving and environmentally friendly developments. This study could be used as guidance for lake protection and water resource management.

Taking the Quickbird optical satellite imagery of the small watershed of Beiyanzigou valley of Qixia city, Shandong province, as the study data, we proposed a new method by using a fused image of topography with remote sensing imagery (RSI) to achieve a high precision interpretation of gully edge lines. The technique first transformed remote sensing imagery into HSV color space from RGB color space. Then the slope threshold values of gully edge line and gully thalweg were gained through field survey and the slope data were segmented using thresholding, respectively. Based on the fused image in combination with gully thalweg thresholding vectors, the gully thalweg thresholding vectors were amended. Lastly, the gully edge line might be interpreted based on the amended gully thalweg vectors, fused image, gully edge line thresholding vectors, and slope data. A testing region was selected in the study area to assess the accuracy. Then accuracy assessment of the gully information interpreted by both interpreting remote sensing imagery only and the fused image was performed using the deviation, kappa coefficient, and overall accuracy of error matrix. Compared with interpreting remote sensing imagery only, the overall accuracy and kappa coefficient are increased by 24.080% and 264.364%, respectively. The average deviations of gully head and gully edge line are reduced by 60.448% and 67.406%, respectively. The test results show the thematic and the positional accuracy of gully interpreted by new method are significantly higher. Finally, the error sources for interpretation accuracy by the two methods were analyzed.

This paper took a subregion in a small watershed gully system at Beiyanzikou catchment of Qixia, China, as a study and, using object-orientated image analysis (OBIA), extracted shoulder line of gullies from high spatial resolution digital orthophoto map (DOM) aerial photographs. Next, it proposed an accuracy assessment method based on the adjacent distance between the boundary classified by remote sensing and points measured by RTK-GPS along the shoulder line of gullies. Finally, the original surface was fitted using linear regression in accordance with the elevation of two extracted edges of experimental gullies, named Gully 1 and Gully 2, and the erosion volume was calculated. The results indicate that OBIA can effectively extract information of gullies; average range difference between points field measured along the edge of gullies and classified boundary is 0.3166 m, with variance of 0.2116 m. The erosion area and volume of two gullies are 2141.6250 m2, 5074.1790 m3 and 1316.1250 m2, 1591.5784 m3, respectively. The results of the study provide a new method for the quantitative study of small gully erosion.