In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were c...In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.展开更多
This paper introduces some definitions and defines a set of calculating indexes to facilitate the research, and then presents an algorithm to complete the spatial clustering result comparison between different cluster...This paper introduces some definitions and defines a set of calculating indexes to facilitate the research, and then presents an algorithm to complete the spatial clustering result comparison between different clustering themes. The research shows that some valuable spatial correlation patterns can be further found from the clustering result comparison with multi-themes, based on traditional spatial clustering as the first step. Those patterns can tell us what relations those themes have, and thus will help us have a deeper understanding of the studied spatial entities. An example is also given to demonstrate the principle and process of the method.展开更多
As innovation and technological change have become increasingly important for the competitiveness and sustainable growth of firms,cooperative innovation is now crucial for traditional industries in the context of glob...As innovation and technological change have become increasingly important for the competitiveness and sustainable growth of firms,cooperative innovation is now crucial for traditional industries in the context of globalization.This paper proposes a framework for analyzing the spatial pattern of cooperative innovation for traditional industries in developing countries.Based on in-depth interviews with 35 firms in the oil equipment manufacturing industry in Dongying City,China,this study argues that different firms in the innovation pyramid have various innovation activity preferences and spatial patterns.Firms with high innovation abilities tend to cooperate with various partners that are geographically dispersed and continuously expanding,while firms with inferior abilities usually cooperate with nearby fixed partners.Due to the differences in innovation environment and actor locations,firms tend to make different choices regarding innovation types and models,which highlight the importance of personnel training and basic scientific research at the global scale and practical product research and development at the national scale.Additionally,talent flow is the most important way to realize relationships for firm innovation activity.展开更多
This paper discusses the spatial knowledge related to a line ,and the characteristic points of lines is detected.According to the requirements of line generalization,new algorithms for identifying characteristic line ...This paper discusses the spatial knowledge related to a line ,and the characteristic points of lines is detected.According to the requirements of line generalization,new algorithms for identifying characteristic line points are presented.These characteristic points are used to improve the algorithms of line generalization.An algorithm for identifying bends is shown.In this paper,improved algorithms based on those by Douglas_Peucker,Visvalingam and Whyatt are shown.In this test,the progressive process of line generalization is emphasized.展开更多
基金Under the auspices of Major State Basic Research Development Program of China(No.2007CB714407)National Natural Science Foundation of China(No.40801070)Action Plan for West Development Program of Chinese Academy of Sciences(No.KZCX2-XB2-09)
文摘In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.
文摘This paper introduces some definitions and defines a set of calculating indexes to facilitate the research, and then presents an algorithm to complete the spatial clustering result comparison between different clustering themes. The research shows that some valuable spatial correlation patterns can be further found from the clustering result comparison with multi-themes, based on traditional spatial clustering as the first step. Those patterns can tell us what relations those themes have, and thus will help us have a deeper understanding of the studied spatial entities. An example is also given to demonstrate the principle and process of the method.
基金Under the auspices of National Natural Science Foundation of China(No.41901158)China Postdoctoral Science Foundation(No.2019M651428)+1 种基金Humanities and Social Sciences Research Planning Fund from Ministry of Education of China(No.19YJC790138)The Soft Science Research Program of Shanghai Science and Technology development Commission(No.19692102400).
文摘As innovation and technological change have become increasingly important for the competitiveness and sustainable growth of firms,cooperative innovation is now crucial for traditional industries in the context of globalization.This paper proposes a framework for analyzing the spatial pattern of cooperative innovation for traditional industries in developing countries.Based on in-depth interviews with 35 firms in the oil equipment manufacturing industry in Dongying City,China,this study argues that different firms in the innovation pyramid have various innovation activity preferences and spatial patterns.Firms with high innovation abilities tend to cooperate with various partners that are geographically dispersed and continuously expanding,while firms with inferior abilities usually cooperate with nearby fixed partners.Due to the differences in innovation environment and actor locations,firms tend to make different choices regarding innovation types and models,which highlight the importance of personnel training and basic scientific research at the global scale and practical product research and development at the national scale.Additionally,talent flow is the most important way to realize relationships for firm innovation activity.
文摘This paper discusses the spatial knowledge related to a line ,and the characteristic points of lines is detected.According to the requirements of line generalization,new algorithms for identifying characteristic line points are presented.These characteristic points are used to improve the algorithms of line generalization.An algorithm for identifying bends is shown.In this paper,improved algorithms based on those by Douglas_Peucker,Visvalingam and Whyatt are shown.In this test,the progressive process of line generalization is emphasized.
