As an S-shaped curve,the logistic curve has both high and low limit,which provides advantages in modelling the influences of environmental factors on biogeological processes.However,although the logistic curve and its...As an S-shaped curve,the logistic curve has both high and low limit,which provides advantages in modelling the influences of environmental factors on biogeological processes.However,although the logistic curve and its transformations have drawn much attention in theoretical modelling,it is often used as a classification method to determine a true or false condition,and is less often applied in simulating the real data set.Starting from the basic theory of the logistic curve,with observed data sets,this paper explored the new application scenarios such as modelling the time series of environmental factors,modelling the influence of environmental factors on biogeological processes and modelling the theoretical curve in ecology area.By comparing the performance of traditional model and the logistic model,the results indicated that logistic modelling worked as well as traditional equations.Under certain conditions,such as modelling the influence of temperature on ecosystem respiration,the logistic model is more realistic than the widely applied Lloyd-Taylor formulation under extreme conditions.These cases confirmed that the logistic curve was capable of simulating nonlinear influences of multiple factors on biogeological processes such as carbon dynamic.展开更多
Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on t...Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on time series spectral index data of the complete growth season.A recent development in maize phenology detection research is to use canopy height(CH)data instead of spectral indices,but its robustness in multiple treatments and stages has not been confirmed.Meanwhile,because data of a complete growth season are needed,the need for timely in-season TD identification remains unmet.This study proposed an approach to timely identify and forecast the maize TD.We obtained RGB and light detection and ranging(Li DAR)data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments.After CH estimation,the feature points(inflection point)from the Logistic curve of the CH time series were extracted as TD.We examined the impact of various independent variables(day of year vs.accumulated growing degree days(AGDD)),sensors(RGB and Li DAR),time series denoise methods,different feature points,and temporal resolution on TD identification.Lastly,we used early CH time series data to predict height growth and further forecast TD.The results showed that using the 99th percentile of plot scale digital surface model and the minimum digital terrain model from Li DAR to estimate maize CH was the most stable across treatments and stages(R~2:0.928 to0.943).For TD identification,the best performance was achieved by using Li DAR data with AGDD as the independent variable,combined with the knee point method,resulting in RMSE of 2.95 d.The high accuracy was maintained at temporal resolutions as coarse as 14 d.TD forecast got more accurate as the CH time series extended.The optimal timing for forecasting TD was when the CH exceeded half of its maximum.Using only Li DAR CH data below 1.6 m and empirical growth rate estimates,the forecasted TD showed an RMSE of 3.90 d.In conclusion,this study exploited the growth characteristics of maize height to provide a practical approach for the timely identification and forecast of maize TD.展开更多
In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 （Internet Protocol ...In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 （Internet Protocol version 4） addresses, this paper proposes a forecasting model by using S curve （logistic curve）. The growing trend of IPv4 addresses in China is forecasted. There are some reference values for optimizing the distribution of IPv4 address resource and the development of IPv6. Based on the laws of IPv4 growth, that is, the bulk growth and the finitely growing limit, it proposes a finite network model with a bulk growth. The model is said to be an S-curve network. Analysis demonstrates that the analytic method based on uniform distributions （i.e., Barabasi-Albert method） is not suitable for the network. It develops an approximate method to predict the growth dynamics of the individual nodes, and uses this to calculate analytically the degree distribution and the scaling exponents. The analytical result agrees with the simulation well, obeying an approximately power-law form. This method can overcome a shortcoming of Barabasi-Albert method commonly used in current network research.展开更多
The values of non-marketable forest products have largely been ignored, which made the conservation of the natural resources increasingly more economically difficult. Based on the previous studies, compensation subsid...The values of non-marketable forest products have largely been ignored, which made the conservation of the natural resources increasingly more economically difficult. Based on the previous studies, compensation subsidy for the values of non-marketable forest products was computed with a method of compensation coefficient that combines the Engel Coefficient and Logistic Curve. The method was applied in Changbai Mountain area. The total value of the compensation subsidy in 1999 was supposed to 637.93 Yuan·hm-2, of which 70% would be paid directly to the local stakeholders and is much higher than the compensation subsidy previously computed (75Yuan·hm-2·year-1). It is currently impossible for the central government to bear all the costs and investment of natural forest protection. A practical solution is that the local government should invest in forest and put the compensation subsidy into the current revenue.展开更多
基金This study was jointly funded by the National Key Research and Development Program of China(2016YFE0109600)China Geological Survey projects(1212010611402,DD20189503).
文摘As an S-shaped curve,the logistic curve has both high and low limit,which provides advantages in modelling the influences of environmental factors on biogeological processes.However,although the logistic curve and its transformations have drawn much attention in theoretical modelling,it is often used as a classification method to determine a true or false condition,and is less often applied in simulating the real data set.Starting from the basic theory of the logistic curve,with observed data sets,this paper explored the new application scenarios such as modelling the time series of environmental factors,modelling the influence of environmental factors on biogeological processes and modelling the theoretical curve in ecology area.By comparing the performance of traditional model and the logistic model,the results indicated that logistic modelling worked as well as traditional equations.Under certain conditions,such as modelling the influence of temperature on ecosystem respiration,the logistic model is more realistic than the widely applied Lloyd-Taylor formulation under extreme conditions.These cases confirmed that the logistic curve was capable of simulating nonlinear influences of multiple factors on biogeological processes such as carbon dynamic.
基金supported by National Science and Technology Major Project(2022ZD0115701)Nanfan Special Project,CAAS(YBXM2305,YBXM2401,YBXM2402,PTXM2402)+1 种基金National Natural Science Foundation of China(42071426,42301427)the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences。
文摘Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on time series spectral index data of the complete growth season.A recent development in maize phenology detection research is to use canopy height(CH)data instead of spectral indices,but its robustness in multiple treatments and stages has not been confirmed.Meanwhile,because data of a complete growth season are needed,the need for timely in-season TD identification remains unmet.This study proposed an approach to timely identify and forecast the maize TD.We obtained RGB and light detection and ranging(Li DAR)data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments.After CH estimation,the feature points(inflection point)from the Logistic curve of the CH time series were extracted as TD.We examined the impact of various independent variables(day of year vs.accumulated growing degree days(AGDD)),sensors(RGB and Li DAR),time series denoise methods,different feature points,and temporal resolution on TD identification.Lastly,we used early CH time series data to predict height growth and further forecast TD.The results showed that using the 99th percentile of plot scale digital surface model and the minimum digital terrain model from Li DAR to estimate maize CH was the most stable across treatments and stages(R~2:0.928 to0.943).For TD identification,the best performance was achieved by using Li DAR data with AGDD as the independent variable,combined with the knee point method,resulting in RMSE of 2.95 d.The high accuracy was maintained at temporal resolutions as coarse as 14 d.TD forecast got more accurate as the CH time series extended.The optimal timing for forecasting TD was when the CH exceeded half of its maximum.Using only Li DAR CH data below 1.6 m and empirical growth rate estimates,the forecasted TD showed an RMSE of 3.90 d.In conclusion,this study exploited the growth characteristics of maize height to provide a practical approach for the timely identification and forecast of maize TD.
基金Project supported by the National Natural Science Foundation of China (Grant No. 70871082)the Shanghai Leading Academic Discipline Project (Grant No. S30504)
文摘In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 （Internet Protocol version 4） addresses, this paper proposes a forecasting model by using S curve （logistic curve）. The growing trend of IPv4 addresses in China is forecasted. There are some reference values for optimizing the distribution of IPv4 address resource and the development of IPv6. Based on the laws of IPv4 growth, that is, the bulk growth and the finitely growing limit, it proposes a finite network model with a bulk growth. The model is said to be an S-curve network. Analysis demonstrates that the analytic method based on uniform distributions （i.e., Barabasi-Albert method） is not suitable for the network. It develops an approximate method to predict the growth dynamics of the individual nodes, and uses this to calculate analytically the degree distribution and the scaling exponents. The analytical result agrees with the simulation well, obeying an approximately power-law form. This method can overcome a shortcoming of Barabasi-Albert method commonly used in current network research.
文摘The values of non-marketable forest products have largely been ignored, which made the conservation of the natural resources increasingly more economically difficult. Based on the previous studies, compensation subsidy for the values of non-marketable forest products was computed with a method of compensation coefficient that combines the Engel Coefficient and Logistic Curve. The method was applied in Changbai Mountain area. The total value of the compensation subsidy in 1999 was supposed to 637.93 Yuan·hm-2, of which 70% would be paid directly to the local stakeholders and is much higher than the compensation subsidy previously computed (75Yuan·hm-2·year-1). It is currently impossible for the central government to bear all the costs and investment of natural forest protection. A practical solution is that the local government should invest in forest and put the compensation subsidy into the current revenue.
