In August 2003, we investigated spatial pattern in soil carbon and nutrients in the Alpine tundra of Changbai Moun-tain, Jilin Province, China. The analytical results showed that the soil C concentrations at different...In August 2003, we investigated spatial pattern in soil carbon and nutrients in the Alpine tundra of Changbai Moun-tain, Jilin Province, China. The analytical results showed that the soil C concentrations at different depths were significantly (p<0.05) higher in Meadow alpine tundra vegetation than that in other vegetation types; the soil C (including inorganic carbon) concentrations at layer below 10 cm are significantly (p<0.05) higher than at layer of 1020 cm among the different vegetation types; the spatial distribution of soil N concentration at top surface of 0-10 cm depth was similar to that at 1020 cm; the soil P concentrations at different depths were significantly (p<0.05) lower at Lithic alpine tundra vegetation than that at other vegetation types; soil K concentration was significantly (p<0.05) higher in Felsenmeer alpine tundra vegetation and Lithic alpine tundra vegetation than that in Typical alpine tundra, Meadow alpine tundra, and Swamp alpine tundra vegetations.. However, the soil K had not significant change at different soil depths of each vegetation type. Soil S concentration was dramatically higher in Meadow alpine tundra vegetation than that in other vegetation types. For each vegetation type, the ratios of C: N, C: P, C: K and C: S generally decreased with soil depth. The ratio of C: N was significantly higher at 010 cm than that at 1020 cm for all vegetation types except at the top layer of the Swamp alpine tundra vegetation. Our study showed that soil C and nutrients storage were significantly spatial heterogeneity.展开更多
In order to analyze the heterogeneity in vehicular traffic speed, a new method that integrates cluster analysis and probability distribution function fitting is presented. First, for identifying the optimal number of ...In order to analyze the heterogeneity in vehicular traffic speed, a new method that integrates cluster analysis and probability distribution function fitting is presented. First, for identifying the optimal number of clusters, the two-step cluster method is applied to analyze actual speed data, which suggests that dividing speed data into two clusters can best reflect the intrinsic patterns of traffic flows. Such information is then taken as guidance in probability distribution function fitting. The normal, skew-normal and skew-t distribution functions are used to fit the probability distribution of each cluster respectively, which suggests that the skew-t distribution has the highest fitting accuracy; the second is skew-normal distribution; the worst is normal distribution. Model analysis results demonstrate that the proposed mixture model has a better fitting and generalization capability than the conventional single model. In addition, the new method is more flexible in terms of data fitting and can provide a more accurate model of speed distribution.展开更多
In this paper we, firstly, classify the complex networks in which the nodes are of the lifetime distribution. Secondly, in order to study complex networks in terms of queuing system and homogeneous Markov chain, we es...In this paper we, firstly, classify the complex networks in which the nodes are of the lifetime distribution. Secondly, in order to study complex networks in terms of queuing system and homogeneous Markov chain, we establish the relation between the complex networks and queuing system, providing a new way of studying complex networks. Thirdly, we prove that there exist stationary degree distributions of M-G-P network, and obtain the analytic expression of the distribution by means of Markov chain theory. We also obtain the average path length and clustering coefficient of the network. The results show that M-G-P network is not only scale-free but also of a small-world feature in proper conditions.展开更多
According to Chinese daylighting climate data,combined with the standard of Commission Inernationale de l'Eclairgae(CIE) general sky luminance distribution(SLD),the sky luminance distribution of typical daylightin...According to Chinese daylighting climate data,combined with the standard of Commission Inernationale de l'Eclairgae(CIE) general sky luminance distribution(SLD),the sky luminance distribution of typical daylighting climate zones in China was studied.Through the research on reference SLD of overcast,intermediate and clear types,the standards of clear and overcast SLD are consistent with CIE general sky,and the intermediate SLD has greater error with CIE general sky.The main types of Chinese sky luminance in different daylighting climate zones were obtained,which provide the basic data for simulation and forecast of China natural light,and provide reference for using the standard amendments of China future natural light.展开更多
Long-term analyses of vegetation succession after catastrophic events are of high interest for an improved understanding of succession dynamics. However, in many studies such analyses were restricted to plot-based mea...Long-term analyses of vegetation succession after catastrophic events are of high interest for an improved understanding of succession dynamics. However, in many studies such analyses were restricted to plot-based measurements. Contrarily, spatially continuous observations of succession dynamics over extended areas and timeperiods are sparse. Here, we applied a change vector analysis(CVA) to investigate vegetation succession dynamics at Mount St. Helens after the great volcanic eruption in 1980 using Landsat. We additionally applied a supervised random forest classification using Sentinel-2 data to map the currently prevailing vegetation types. Change vector analysis was performed with the normalized difference vegetation index(NDVI) and the urban index(UI) for three subsequent decades after the eruption as well as for the whole observation time between 1984 and 2016. The influence of topography on the current vegetation distribution was examined by comparing altitude, slope angles and aspect values of vegetation classes derived by the random forest classification. WilcoxRank-Sum test was applied to test for significant differences between topographic properties of the vegetation classes inside and outside of the areas affected by the eruption. For the full time period, a total area of 516 km2 was identified as re-vegetated, whereas the area and magnitude of re-growing vegetation decreased during the three decades and migrated closer to the volcanic crater. Vegetation losses were mainly observed in regions unaffected by the eruption and related mostly to timber harvesting. The vegetation type classification reached a high overall accuracy of approximately 90%. 36 years after the eruption, coniferous and deciduous trees have established at formerly devastated areas dominating with a proportion of 66%, whereas shrubs are more abundant in riparian zones. Sparse vegetation dominates at regions very close to the crater. Elevation was found to have a great influence on the reestablishment and distribution of the vegetation classes within the devastated areas showing in almost all cases significant differences in altitude distribution. Slope was less important for the different classes-only representing significantly higher values for meadows, whereas aspect seems to have no notable influence on the reestablishment of vegetation at Mount St. Helens. We conclude that major vegetation succession dynamics after catastrophic events can be assessed and characterized over large areas from freely available remote sensing data and hence contribute to an improved understanding of succession dynamics.展开更多
The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is conside...The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.展开更多
基金This research was supported by National Natural Science Foundation of China (40173033) and Important Direction Project of Knowl-edge Innovation of Chinese Academy of Sciences (KZCX3-SW-423).
文摘In August 2003, we investigated spatial pattern in soil carbon and nutrients in the Alpine tundra of Changbai Moun-tain, Jilin Province, China. The analytical results showed that the soil C concentrations at different depths were significantly (p<0.05) higher in Meadow alpine tundra vegetation than that in other vegetation types; the soil C (including inorganic carbon) concentrations at layer below 10 cm are significantly (p<0.05) higher than at layer of 1020 cm among the different vegetation types; the spatial distribution of soil N concentration at top surface of 0-10 cm depth was similar to that at 1020 cm; the soil P concentrations at different depths were significantly (p<0.05) lower at Lithic alpine tundra vegetation than that at other vegetation types; soil K concentration was significantly (p<0.05) higher in Felsenmeer alpine tundra vegetation and Lithic alpine tundra vegetation than that in Typical alpine tundra, Meadow alpine tundra, and Swamp alpine tundra vegetations.. However, the soil K had not significant change at different soil depths of each vegetation type. Soil S concentration was dramatically higher in Meadow alpine tundra vegetation than that in other vegetation types. For each vegetation type, the ratios of C: N, C: P, C: K and C: S generally decreased with soil depth. The ratio of C: N was significantly higher at 010 cm than that at 1020 cm for all vegetation types except at the top layer of the Swamp alpine tundra vegetation. Our study showed that soil C and nutrients storage were significantly spatial heterogeneity.
基金The National Science Foundation by Changjiang Scholarship of Ministry of Education of China(No.BCS-0527508)the Joint Research Fund for Overseas Natural Science of China(No.51250110075)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK200910046)the Postdoctoral Science Foundation of Jiangsu Province(No.0901005C)
文摘In order to analyze the heterogeneity in vehicular traffic speed, a new method that integrates cluster analysis and probability distribution function fitting is presented. First, for identifying the optimal number of clusters, the two-step cluster method is applied to analyze actual speed data, which suggests that dividing speed data into two clusters can best reflect the intrinsic patterns of traffic flows. Such information is then taken as guidance in probability distribution function fitting. The normal, skew-normal and skew-t distribution functions are used to fit the probability distribution of each cluster respectively, which suggests that the skew-t distribution has the highest fitting accuracy; the second is skew-normal distribution; the worst is normal distribution. Model analysis results demonstrate that the proposed mixture model has a better fitting and generalization capability than the conventional single model. In addition, the new method is more flexible in terms of data fitting and can provide a more accurate model of speed distribution.
基金Project supported by the Shanghai Leading Academic Discipline Project, China （Grant No T0502） and by the Shanghai Municipal Education Commission Natural Science Foundation, China （Grant No 05EZ35）.
文摘In this paper we, firstly, classify the complex networks in which the nodes are of the lifetime distribution. Secondly, in order to study complex networks in terms of queuing system and homogeneous Markov chain, we establish the relation between the complex networks and queuing system, providing a new way of studying complex networks. Thirdly, we prove that there exist stationary degree distributions of M-G-P network, and obtain the analytic expression of the distribution by means of Markov chain theory. We also obtain the average path length and clustering coefficient of the network. The results show that M-G-P network is not only scale-free but also of a small-world feature in proper conditions.
基金Project(50908239) supported by National Natural Science Foundation of China Project(2010KB14) supported by State Key Laboratory of Subtropical Building Science of South China University of Technology, China Project(CDJZR10190006) supported by the Fundamental Research Funds for the Central Universities, China
文摘According to Chinese daylighting climate data,combined with the standard of Commission Inernationale de l'Eclairgae(CIE) general sky luminance distribution(SLD),the sky luminance distribution of typical daylighting climate zones in China was studied.Through the research on reference SLD of overcast,intermediate and clear types,the standards of clear and overcast SLD are consistent with CIE general sky,and the intermediate SLD has greater error with CIE general sky.The main types of Chinese sky luminance in different daylighting climate zones were obtained,which provide the basic data for simulation and forecast of China natural light,and provide reference for using the standard amendments of China future natural light.
文摘Long-term analyses of vegetation succession after catastrophic events are of high interest for an improved understanding of succession dynamics. However, in many studies such analyses were restricted to plot-based measurements. Contrarily, spatially continuous observations of succession dynamics over extended areas and timeperiods are sparse. Here, we applied a change vector analysis(CVA) to investigate vegetation succession dynamics at Mount St. Helens after the great volcanic eruption in 1980 using Landsat. We additionally applied a supervised random forest classification using Sentinel-2 data to map the currently prevailing vegetation types. Change vector analysis was performed with the normalized difference vegetation index(NDVI) and the urban index(UI) for three subsequent decades after the eruption as well as for the whole observation time between 1984 and 2016. The influence of topography on the current vegetation distribution was examined by comparing altitude, slope angles and aspect values of vegetation classes derived by the random forest classification. WilcoxRank-Sum test was applied to test for significant differences between topographic properties of the vegetation classes inside and outside of the areas affected by the eruption. For the full time period, a total area of 516 km2 was identified as re-vegetated, whereas the area and magnitude of re-growing vegetation decreased during the three decades and migrated closer to the volcanic crater. Vegetation losses were mainly observed in regions unaffected by the eruption and related mostly to timber harvesting. The vegetation type classification reached a high overall accuracy of approximately 90%. 36 years after the eruption, coniferous and deciduous trees have established at formerly devastated areas dominating with a proportion of 66%, whereas shrubs are more abundant in riparian zones. Sparse vegetation dominates at regions very close to the crater. Elevation was found to have a great influence on the reestablishment and distribution of the vegetation classes within the devastated areas showing in almost all cases significant differences in altitude distribution. Slope was less important for the different classes-only representing significantly higher values for meadows, whereas aspect seems to have no notable influence on the reestablishment of vegetation at Mount St. Helens. We conclude that major vegetation succession dynamics after catastrophic events can be assessed and characterized over large areas from freely available remote sensing data and hence contribute to an improved understanding of succession dynamics.
基金supported by proposal No.OSD/BCUD/392/197 Board of Colleges and University Development,Savitribai Phule Pune University,Pune
文摘The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.
