Urban sprawl significantly impacts ecosystem services and functions. The exact impacts, however are difficult to quantify and are often neglected in policy making. The evaluation of ecosystem ...services is conducive to clarifying the ecological and environmental changes caused by urbanization. The objective of this study is to investigate variations in ecosystem services in response to land use changes during urbanization. The aim is to provide useful information and advice for policy makers concerned with sustainable development. Shenzhen, one of the fastest growing metropolitan areas in China, is selected as the study area. A fast evaluation method for ecological service values based on land use change was proposed and applied to the city for 1996, 2000 and 2004. The total value of ecosystem services in Shenzhen was 2776.0 million Yuan in 1996, 2911.4 million Yuan in 2000 and 2544.7 million Yuan in 2004 respectively, with a decrease of 231.3 million Yuan from 1996 to 2004 mainly due to the decreasing areas of woodland, wetland and water body. The combined ecosystem service value of woodland, wetland, water body and orchard was over 90% of the total value. Water supply and waste treatment were the top two service functions with high service value, contributing about 40% of the total service value. The results suggest that a reasonable land use plan should be made with emphasis on protecting wetland, water body and woodland, which have the highest ecosystem service value.
In remote-sensing classification, there are situations when users are only interested in classifying one specific land-cover type, without considering other classes. These situations are referred to ...as one-class classification. Traditional supervised learning is inefficient for one-class classification because it requires all classes that occur in the image to be exhaustively assigned labels. In this paper, we investigate a new positive and unlabeled learning (PUL) algorithm, applying it to one-class classifications of two scenes of a high-spatial-resolution aerial photograph. The PUL algorithm trains a classifier on positive and unlabeled data, estimates the probability that a positive training sample has been labeled, and generates binary predictions for test samples using an adjusted threshold. Experimental results indicate that the new algorithm provides high classification accuracy, outperforming the biased support-vector machine (SVM), one-class SVM, and Gaussian domain descriptor methods. The advantages of the new algorithm are that it can use unlabeled data to help build classifiers, and it requires only a small set of positive data to be labeled by hand. Therefore, it can significantly reduce the effort of assigning labels to training data without losing predictive accuracy.
COVID-19 has become a serious global pandemic. This study investigates the clinical characteristics and the risk factors for COVID-19 mortality and establishes a novel scoring system to predict ...mortality risk in patients with COVID-19.
A cohort of 1,663 hospitalized patients with COVID-19 in Wuhan, China, of whom 212 died and 1,252 recovered, were included in this study. Demographic, clinical, and laboratory data on admission were collected from electronic medical records between January 14, 2020 and February 28, 2020. Clinical outcomes were collected until March 26, 2020. Multivariable logistic regression was used to explore the association between potential risk factors and COVID-19 mortality. The receiver operating characteristic curve was used to predict COVID-19 mortality risk. All analyses were conducted in April 2020.
Multivariable regression showed that increased odds of COVID-19 mortality was associated with older age (OR=2.15, 95% CI=1.35, 3.43), male sex (OR=1.97, 95% CI=1.29, 2.99), history of diabetes (OR=2.34, 95% CI=1.45, 3.76), lymphopenia (OR=1.59, 95% CI=1.03, 2.46), and increased procalcitonin (OR=3.91, 95% CI=2.22, 6.91, per SD increase) on admission. Spline regression analysis indicated that the correlation between procalcitonin levels and COVID-19 mortality was nonlinear (p=0.0004 for nonlinearity). The area under the receiver operating curve of the COVID-19 mortality risk was 0.765 (95% CI=0.725, 0.805).
The independent risk factors for COVID-19 mortality included older age, male sex, history of diabetes, lymphopenia, and increased procalcitonin, which could help clinicians to identify patients with poor prognosis at an earlier stage. The COVID-19 mortality risk score model may assist clinicians in reducing COVID-19–related mortality by implementing better strategies for more effective use of limited medical resources.
Summary There is growing recognition that the ultimate success of China's ambitious health reform (enacted in 2009) and higher education reform (1998) depends on well educated health professionals ...who have the clinical, ethical, and human competencies necessary for the provision of quality services. In this Review, we describe and analyse graduate education of doctors in China by discussing the country's health workforce and their clinical residency education. China has launched a new system called the 5 + 3 (5 year undergraduate and 3 year residency standardised residency training), which aims to set national quality standards. To improve understanding for the Chinese model, we present a comparative perspective with systems from the UK and USA. To succeed, the 5 + 3 model will need to overcome major challenges of accreditation and certification, alternative education pathways, and China's unique degree and credentialing system. We conclude by reviewing the challenges of clinical competencies in China, especially the complementarity of specialist training and general practitioner training, which are essential for the quality and equity of China's health-care system.
Light Detection and Ranging (Lidar) can generate three-dimensional (3D) point cloud which can be used to characterize horizontal and vertical forest structure, so it has become a popular tool for ...forest research. Recently, various methods based on top-down scheme have been developed to segment individual tree from lidar data. Some of these methods, such as the one developed by Li et al. (2012), can obtain the accuracy up to 90% when applied in coniferous forests. However, the accuracy will decrease when they are applied in deciduous forest because the interlacing tree branches can increase the difficulty to determine the tree top. In order to solve challenges of the tree segmentation in deciduous forests, we develop a new bottom-up method based on the intensity and 3D structure of leaf-off lidar point cloud data in this study. We applied our algorithm to segment trees in a forest at the Shavers Creek Watershed in Pennsylvania. Three indices were used to assess the accuracy of our method: recall, precision and F-score. The results show that the algorithm can detect 84% of the tree (recall), 97% of the segmented trees are correct (precision) and the overall F-score is 90%. The result implies that our method has good potential for segmenting individual trees in deciduous broadleaf forest.
In one-class remote sensing classification, users are only interested in classifying one specific land type (positive class), without considering other classes (negative class). Previous researchers ...have proposed different one-class classification methods without requiring negative data. An appropriate accuracy measure is usually needed to tune free parameters/threshold and to evaluate the classification result. However, traditional accuracy measures, such as the kappa coefficient and F-measure (F), require both positive and negative data, and hence, they are not applicable for positive-only data. In this paper, we investigate a new accuracy assessment method that does not require negative data. Two new statistics Fpb (proxy of F-measure based on positive-background data) and Fcpb (prevalence-calibrated proxy of F-measure based on positive-background data) can be calculated from a modified confusion matrix, where the observed negative data are replaced by background data. To investigate the effectiveness of the new method, we produced different one-class classification results using two scenes of aerial photograph, and the accuracy values were evaluated by Fpb, Fcpb, kappa coefficient, and F. The effectiveness of F pb in model and threshold selection was investigated as well. Experimental results show that the behaviors of Fpb, Fcpb, F, and kappa coefficient are similar, and they all rank the models by accuracy similarly. In model and threshold selection, the classification accuracy values produced by maximizing Fpb and F are similar, and they are higher than those produced by setting an arbitrary rejection fraction. Therefore, we conclude that the new method is effective in model selection, threshold selection, and accuracy assessment, and it will have important applications in one-class remote sensing classification since negative data are not needed.
Recently, the stable light products and radiance calibrated products from Defense Meteorological Satellite Program's (DMSP) Operational Linescan System (OLS) have been useful for mapping global ...fossil fuel carbon dioxide (CO2) emissions at fine spatial resolution. However, few studies on this subject were conducted with the new-generation nighttime light data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite, which has a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. Therefore, this study performed the first evaluation of the potential of NPP-VIIRS data in estimating the spatial distributions of global CO2 emissions (excluding power plant emissions). Through a disaggregating model, three global emission maps were then derived from population counts and three different types of nighttime lights data (NPP-VIIRS, the stable light data and radiance calibrated data of DMSP-OLS) for a comparative analysis. The results compared with the reference data of land cover in Beijing, Shanghai and Guangzhou show that the emission areas of map from NPP-VIIRS data have higher spatial consistency of the artificial surfaces and exhibit a more reasonable distribution of CO2 emission than those of other two maps from DMSP-OLS data. Besides, in contrast to two maps from DMSP-OLS data, the emission map from NPP-VIIRS data is closer to the Vulcan inventory and exhibits a better agreement with the actual statistical data of CO2 emissions at the level of sub-administrative units of the United States. This study demonstrates that the NPP-VIIRS data can be a powerful tool for studying the spatial distributions of CO2 emissions, as well as the socioeconomic indicators at multiple scales.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
It is very common that only presence data are available in ecological niche modeling. However, most existing methods for evaluating the accuracy of presence–absence (binary) predictions of species ...require presence–absence data.
The aim of this study is to present a new method for accuracy assessment that does not rely on absence data. Two new statistics F
pb and F
cpb were derived based on presence–background data. With generated six virtual species, we used DOMAIN, generalized linear modeling (GLM), and maximum entropy (MAXENT) to produce different species presence–absence predictions. To investigate the effectiveness of the new statistics in accuracy assessment, we used F
pb, F
cpb, the traditional F-measure (F), kappa coefficient, true skill statistic (TSS), area under the receiver operating characteristic curve (AUC), and the contrast validation index (CVI) to evaluate the accuracy of predictions, and the behaviors of these accuracy measures were compared. The effectiveness of F
pb for threshold selection and estimation of species prevalence was also investigated.
Experimental results show that F
cpb is an estimate of F. The Pearson's correlation coefficient (COR) between F
cpb and F is 0.9882, with a root-mean-square error (RMSE) of 0.0171. In general, F
pb, F
cpb, F, kappa coefficient, TSS, and CVI can sort models by the accuracy of binary prediction, but AUC is not appropriate to evaluate the accuracy of binary prediction. For DOMAIN, GLM, and MAXENT, finding the threshold by maximizing F
pb and by maximizing F result in similar accuracies. In addition, the estimation of species prevalence based on binary output with maximizing F
pb as the thresholding method is significantly more accurate than simply averaging the original continuous output. The best estimate of prevalence is provided by the binary output of MAXENT, with an RMSE of 0.0116.
Finally, we conclude that the new method is promising in accuracy assessment, threshold selection, and estimation of species prevalence, all of which are important but challenging problems with presence-only data. Because it does not require absence data, the new method will have important applications in ecological niche modeling.
The responses of atmospheric variability to Tibetan Plateau (TP) snow cover (TPSC) at seasonal, interannual and decadal time scales have been extensively investigated. However, the atmospheric ...response to faster subseasonal variability of TPSC has been largely ignored. Here, we show that the subseasonal variability of TPSC, as revealed by daily data, is closely related to the subsequent East Asian atmospheric circulation at medium-range time scales (approximately 3-8 days later) during wintertime. TPSC acts as an elevated cooling source in the middle troposphere during wintertime and rapidly modulates the land surface thermal conditions over the TP. When TPSC is high, the upper-level geopotential height is lower, and the East Asia upper-level westerly jet stream is stronger. This finding improves our understanding of the influence of TPSC at multiple time scales. Furthermore, our work highlights the need to understand how atmospheric variability is rapidly modulated by fast snow cover changes.
This study aims to quantify the effects of topographic variability (measured by coefficient variation of elevation, CV) and lidar (Light Detection and Ranging) sampling density on the DEM (Digital ...Elevation Model) accuracy derived from several interpolation methods at different spatial
resolutions. Interpolation methods include natural neighbor (NN), inverse distance weighted (IDW), triangulated irregular network (TIN), spline, ordinary kriging (OK), and universal kriging (UK). This study is unique in that a comprehensive evaluation of the combined effects of three influencing
factors (CV, sampling density, and spatial resolution) on lidar-derived DEM accuracy is carried out using different interpolation methods. Results indicate that simple interpolation methods, such as IDW, NN, and TIN, are more efficient at generating DEMs from lidar data, but kriging-based
methods, such as OK and UK, are more reliable if accuracy is the most important consideration. Moreover, spatial resolution also plays an important role when generating DEMs from lidar data. Our results could be used to guide the choice of appropriate lidar interpolationmethods for DEM generation
given the resolution, sampling density, and topographic variability.
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