The leaf area index(LAI) is an important vegetation parameter,which is used widely in many applications.Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop cano...The leaf area index(LAI) is an important vegetation parameter,which is used widely in many applications.Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies.During the last two decades,hyperspectral remote sensing has been employed increasingly for crop LAI estimation,which requires unique technical procedures compared with conventional multispectral data,such as denoising and dimension reduction.Thus,we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques.First,we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation.Second,we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types:approaches based on statistical models,physical models(i.e.,canopy reflectance models),and hybrid inversions.We summarize and evaluate the theoretical basis and different methods employed by these approaches(e.g.,the characteristic parameters of LAI,regression methods for constructing statistical predictive models,commonly applied physical models,and inversion strategies for physical models).Thus,numerous models and inversion strategies are organized in a clear conceptual framework.Moreover,we highlight the technical difficulties that may hinder crop LAI estimation,such as the "curse of dimensionality" and the ill-posed problem.Finally,we discuss the prospects for future research based on the previous studies described in this review.展开更多
Based on the absolute and relative gravity observations in North China from 2009 to 2014,spatial dynamic variations of the regional gravity field are obtained. We employed the Euler deconvolution method and the theore...Based on the absolute and relative gravity observations in North China from 2009 to 2014,spatial dynamic variations of the regional gravity field are obtained. We employed the Euler deconvolution method and the theoretical model to get the best estimates of parameters. Gravity field change caused by the depth and distribution in North China is calculated by back analysis. The results show the structural index that equals 1 is suitable for inversion of the gravity variation data. The inversion results indicate that the depths of anomaly field sources are spread over the Hetao fault. The research method of this paper can be used in the quantitative study on the field source and may shed new light on the interpretations of gravity change, and also provide quantitative basis for earthquake prediction index criterions based on the gravity change.展开更多
The first Zagreb index M1 (G) is equal to the sum of squares of the degrees of the vertices, and the second Zagreb index M2 (G) is equal to the sum of the products of the degrees of pairs of adjacent vertices of t...The first Zagreb index M1 (G) is equal to the sum of squares of the degrees of the vertices, and the second Zagreb index M2 (G) is equal to the sum of the products of the degrees of pairs of adjacent vertices of the underlying molecular graph G. In this paper, we obtain lower and upper bounds on the first Zagreb index MI(G) of G in terms of the number of vertices (n), number of edges (m), maximum vertex degree (△), and minimum vertex degree (δ). Using this result, we find lower and upper bounds on M2(G). Also, we present lower and upper bounds on M2(G) + M2(G) in terms of n, m, △, and δ, where denotes the complement of G. Moreover, we determine the bounds on first Zagreb coindex MI(G) and second Zagreb coindex M2(G). Finally, we give a relation between the first Zagreb index and the second Zagreb index of graph G.展开更多
基金financed by the National High-Tech R&D Program of China(2012AA12A304)the National Natural Science Foundation of China(41271112 and 41201089)
文摘The leaf area index(LAI) is an important vegetation parameter,which is used widely in many applications.Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies.During the last two decades,hyperspectral remote sensing has been employed increasingly for crop LAI estimation,which requires unique technical procedures compared with conventional multispectral data,such as denoising and dimension reduction.Thus,we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques.First,we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation.Second,we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types:approaches based on statistical models,physical models(i.e.,canopy reflectance models),and hybrid inversions.We summarize and evaluate the theoretical basis and different methods employed by these approaches(e.g.,the characteristic parameters of LAI,regression methods for constructing statistical predictive models,commonly applied physical models,and inversion strategies for physical models).Thus,numerous models and inversion strategies are organized in a clear conceptual framework.Moreover,we highlight the technical difficulties that may hinder crop LAI estimation,such as the "curse of dimensionality" and the ill-posed problem.Finally,we discuss the prospects for future research based on the previous studies described in this review.
基金funded by the Natural Science Foundation of China(61627824,41274083)the Youth Foundation of Earthquake Prediction(2017010227)
文摘Based on the absolute and relative gravity observations in North China from 2009 to 2014,spatial dynamic variations of the regional gravity field are obtained. We employed the Euler deconvolution method and the theoretical model to get the best estimates of parameters. Gravity field change caused by the depth and distribution in North China is calculated by back analysis. The results show the structural index that equals 1 is suitable for inversion of the gravity variation data. The inversion results indicate that the depths of anomaly field sources are spread over the Hetao fault. The research method of this paper can be used in the quantitative study on the field source and may shed new light on the interpretations of gravity change, and also provide quantitative basis for earthquake prediction index criterions based on the gravity change.
基金Acknowledgements The authors are grateful to two anonymous referees for their careful reading of this paper and strict criticisms, and valuable comments on this paper, which have considerably improved the presentation of this paper. The first author was supported by the National Research Foundation funded by the Korean government (Grant No. 2013R1A1A2009341) the second author was supported by the National Natural Science Foundation of China (Grant No. 11201227), the China Postdoctoral Science Foundation (2013M530253), and the Natural Science Foundation of Jiangsu Province (BK20131357).
文摘The first Zagreb index M1 (G) is equal to the sum of squares of the degrees of the vertices, and the second Zagreb index M2 (G) is equal to the sum of the products of the degrees of pairs of adjacent vertices of the underlying molecular graph G. In this paper, we obtain lower and upper bounds on the first Zagreb index MI(G) of G in terms of the number of vertices (n), number of edges (m), maximum vertex degree (△), and minimum vertex degree (δ). Using this result, we find lower and upper bounds on M2(G). Also, we present lower and upper bounds on M2(G) + M2(G) in terms of n, m, △, and δ, where denotes the complement of G. Moreover, we determine the bounds on first Zagreb coindex MI(G) and second Zagreb coindex M2(G). Finally, we give a relation between the first Zagreb index and the second Zagreb index of graph G.