A high-resolution dataset of water bodies distribution over the Tibetan Plateau.

Water body (WB) extraction is the basic work of water resources management. Tibetan Plateau is one of the largest alpine lake systems in the world. However, research on the characteristics of water bodies (WBs) is mainly focused on large and medium WBs due to spatial resolution. This research presents a dataset containing a 2-m resolution map of WBs in 2020 based on Gaofen-1 data, and morphometric and landscape indices of WBs across the Tibetan Plateau. The Swin-UNet model is well performed with overall accuracy at 98%. The total area of WBs is 56354.6 km2 across Tibetan Plateau in 2020. The abundance compared with that from size-abundance relationship indicate WBs in the Tibetan Plateau conformed to the classic power scaling law. We evaluate the influence of spatial-resolution in WB extraction, which shows the dataset could be valuable to fill the gap of existing WBs map, especially for small waters. The dataset is valuable for revealing the spatial patterns of WBs, and understanding the impacts of climate change on water resources in Plateau.

The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe.

Trees are an integral part in European landscapes, but only forest resources are systematically assessed by national inventories. The contribution of urban and agricultural trees to national-level carbon stocks remains largely unknown. Here we produced canopy cover, height and above-ground biomass maps from 3-meter resolution nanosatellite imagery across Europe. Our biomass estimates have a systematic bias of 7.6% (overestimation; R = 0.98) compared to national inventories of 30 countries, and our dataset is sufficiently highly resolved spatially to support the inclusion of tree biomass outside forests, which we quantify to 0.8 petagrams. Although this represents only 2% of the total tree biomass, large variations between countries are found (10% for UK) and trees in urban areas contribute substantially to national carbon stocks (8% for the Netherlands). The agreement with national inventory data, the scalability, and spatial details across landscapes, including trees outside forests, make our approach attractive for operational implementation to support national carbon stock inventory schemes.

Factors affecting the frequency and contents of routine antenatal care in remote rural China in 2009-2016: An observational study.

OBJECTIVE: We assessed factors associated with the frequency and contents of antenatal care (ANC) in remote rural China, including the province of residence and individual-level factors. DESIGN: Survey-based cross-sectional study. SETTING: Five provinces in remote rural China: Guizhou, Hunan, Jilin, Ningxia and Shaanxi. SAMPLE: A cohort of 3918 women with a live birth in 2009-2016. METHODS: Poisson regression. MAIN OUTCOME MEASURES: ANC frequency: five or more visits, starting in the first trimester. ANC contents: coverage of six care components and overuse of ultrasound. RESULTS: Three-quarters (72.9%) of women had five or more ANC visits, starting in the first trimester; 68.8% received all six care components and 94.5% had three or more ultrasounds. Only 30.9% of women sought ANC from township hospitals, paying between $3.80 and $25.80 per visit. ANC frequency and contents were associated with the socio-economic characteristics of the women, but provincial effects were much greater, even after adjusting for individual factors. Women living in Guizhou and Ningxia, the two poorest provinces, with high proportions of ethnic minorities, were particularly underserved. Compared with women in Shaanxi, women in Guizhou were 33% (adjusted RR 0.67, 95% CI 0.61-0.74) less likely to receive five or more ANC visits, starting in the first trimester; women in Ningxia were 17% less likely (adjusted RR 0.83, 95% CI 0.76-0.90) to receive all six care components. CONCLUSIONS: The province of residence was a stronger predictor of ANC frequency and contents than the individual characteristics of women in China, suggesting that strengthening the decentralised system of the financing and organisation of ANC at the province level is crucial for achieving success. Future efforts are warranted to engage subregional administrations. TWEETABLE ABSTRACT: The province of residence was a stronger predictor of ANC frequency and contents than the individual characteristics of women.

A Multi-Level Feature Fusion Network for Remote Sensing Image Segmentation.

High-resolution remote sensing image segmentation is a mature application in many industrial-level image applications and it also has military and civil applications. The scene analysis needs to be automated as much as possible with high-resolution remote sensing images. This plays a significant role in environmental disaster monitoring, forestry industry, agricultural farming, urban planning, and road analysis. This study proposes a multi-level feature fusion network (MFNet) that can integrate the multi-level features in the backbone to obtain different types of image information. Finally, the experiments in this study demonstrate that the proposed network can achieve good segmentation results in the Vaihingen and Potsdam datasets. By aiming to achieve a large difference in the scale of the target objects in remote sensing images and achieving a poor recognition result for small objects, a multi-level feature fusion solution is proposed in this study. This investigation improves the recognition results of the remote sensing image segmentation to a certain extent.

Estimation of the time-varying reproduction number of COVID-19 outbreak in China.

BACKGROUND: The 2019 novel coronavirus (COVID-19) outbreak in Wuhan, China has attracted world-wide attention. As of March 31, 2020, a total of 82,631 cases of COVID-19 in China were confirmed by the National Health Commission (NHC) of China. METHODS: Three approaches, namely Poisson likelihood-based method (ML), exponential growth rate-based method (EGR) and stochastic Susceptible-Infected-Removed dynamic model-based method (SIR), were implemented to estimate the basic and controlled reproduction numbers. RESULTS: A total of 198 chains of transmission together with dates of symptoms onset and 139 dates of infections were identified among 14,829 confirmed cases outside Hubei Province as reported as of March 31, 2020. Based on this information, we found that the serial interval had an average of 4.60 days with a standard deviation of 5.55 days, the incubation period had an average of 8.00 days with a standard deviation of 4.75 days and the infectious period had an average of 13.96 days with a standard deviation of 5.20 days. The estimated controlled reproduction numbers, Rc, produced by all three methods in all analyzed regions of China are significantly smaller compared with the basic reproduction numbers R0. CONCLUSIONS: The controlled reproduction number in China is much lower than one in all regions of China by now. It fell below one within 30 days from the implementations of unprecedent containment measures, which indicates that the strong measures taken by China government was effective to contain the epidemic. Nonetheless, efforts are still needed in order to end the current epidemic as imported cases from overseas pose a high risk of a second outbreak.

Forest management in southern China generates short term extensive carbon sequestration.

Land use policies have turned southern China into one of the most intensively managed forest regions in the world, with actions maximizing forest cover on soils with marginal agricultural potential while concurrently increasing livelihoods and mitigating climate change. Based on satellite observations, here we show that diverse land use changes in southern China have increased standing aboveground carbon stocks by 0.11 ± 0.05 Pg C y-1 during 2002-2017. Most of this regional carbon sink was contributed by newly established forests (32%), while forests already existing contributed 24%. Forest growth in harvested forest areas contributed 16% and non-forest areas contributed 28% to the carbon sink, while timber harvest was tripled. Soil moisture declined significantly in 8% of the area. We demonstrate that land management in southern China has been removing an amount of carbon equivalent to 33% of regional fossil CO2 emissions during the last 6 years, but forest growth saturation, land competition for food production and soil-water depletion challenge the longevity of this carbon sink service.

Multi-GPU Based Parallel Design of the Ant Colony Optimization Algorithm for Endmember Extraction from Hyperspectral Images.

Spectral unmixing is a vital procedure in hyperspectral remote sensing image exploitation. The linear mixture model has been widely utilized to unmix hyperspectral images by extracting a set of pure spectral signatures, called endmembers in hyperspectral jargon, and estimating their respective fractional abundances in each pixel of the scene. Many algorithms have been proposed to extract endmembers automatically, which is a critical step in the spectral unmixing chain. In recent years, the ant colony optimization (ACO) algorithm has been developed for endmember extraction from hyperspectral data, which was regarded as a combinatorial optimization problem. Although the ACO for endmember extraction (ACOEE) can acquire accurate endmember results, its high computational complexity has limited its application in the hyperspectral data analysis. The GPUs parallel computing technique can be utilized to improve the computational performance of ACOEE, but the architecture of GPUs determines that the ACOEE should be redesigned to take full advantage of computing resources on GPUs. In this paper, a multiple sub-ant-colony-based parallel design of ACOEE was proposed, in which an innovative mechanism of local pheromone for sub-ant-colonies is utilized to enable ACOEE to be preferably executed on the multi-GPU system. The proposed method can avoid much synchronization among different GPUs to affect the computational performance improvement. The experiments on two real hyperspectral datasets demonstrated that the computational performance of ACOEE significantly benefited from the proposed methods.

Influencing Factors of Health-related Quality of Life:An Analysis Based on the National Health Services Survey in Jilin Province,2013.

Objective To understand the health-related quality of life (HRQoL) and its influencing factors in Jilin province and compare two life quality measurement methods. Methods The data of 13 700 people aged above 15 years in Jilin province were extracted from the National Health Services Survey 2013. Self-rated health scores and EuroQol 5-dimension utility values were used to evaluate the HRQoL. Multivariable linear regression model was used to analyze the influencing factors. Results The self-rated health score was 81.26±15.73 in Jilin province,while utility values of health status were 0.959±0.124. The influencing factors of HRQoL included age,residency,education,income,type of health insurance,having non-communicable chronic disease and having disease in last two weeks. The age under 45,income,having non-communicable chronic disease,and having disease in last two weeks had stronger effect on self-rated health,whereas residency and type of health insurance had more effect on utility values of health status. Conclusion People in Jilin province have good quality of life. Both methods have their advantages and weakness. It is important to choose the right evaluation method depended on the objective of a specific study.

Exploring the relationship between vegetation spectra and eco-geo-environmental conditions in karst region, Southwest China.

Remote sensing of local environmental conditions is not accessible if substrates are covered with vegetation. This study explored the relationship between vegetation spectra and karst eco-geo-environmental conditions. Hyperspectral remote sensing techniques showed that there were significant differences between spectral features of vegetation mainly distributed in karst and non-karst regions, and combination of 1,300- to 2,500-nm reflectance and 400- to 680-nm first-derivative spectra could delineate karst and non-karst vegetation groups. Canonical correspondence analysis (CCA) successfully assessed to what extent the variation of vegetation spectral features can be explained by associated eco-geo-environmental variables, and it was found that soil moisture and calcium carbonate contents had the most significant effects on vegetation spectral features in karst region. Our study indicates that vegetation spectra is tightly linked to eco-geo-environmental conditions and CCA is an effective means of studying the relationship between vegetation spectral features and eco-geo-environmental variables. Employing a combination of spectral and spatial analysis, it is anticipated that hyperspectral imagery can be used in interpreting or mapping eco-geo-environmental conditions covered with vegetation in karst region.