In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be describ...In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be described by an input-output nonlinear discrete-time mathematical model, with unknown, but constant or slowly time-varying parameters. Then, two recursive estimation methods are used to solve the parametric estimation problem for the considered class of the interconnected nonlinear systems. These methods are based on the recursive least squares techniques and the prediction error method. Convergence analysis is provided using the hyper-stability and positivity method and the differential equation approach. A numerical simulation example of the parametric estimation of a stochastic interconnected nonlinear hydraulic system is treated.展开更多
Remote sensing-based methods of aboveground biomass(AGB)estimation in forest ecosystems have gained increased attention,and substantial research has been conducted in the past three decades.This paper provides a surve...Remote sensing-based methods of aboveground biomass(AGB)estimation in forest ecosystems have gained increased attention,and substantial research has been conducted in the past three decades.This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues–collection of field-based biomass reference data,extraction and selection of suitable variables from remote sensing data,identification of proper algorithms to develop biomass estimation models,and uncertainty analysis to refine the estimation procedure.Additionally,we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure.Although optical sensor and radar data have been primary sources for AGB estimation,data saturation is an important factor resulting in estimation uncertainty.LIght Detection and Ranging(lidar)can remove data saturation,but limited availability of lidar data prevents its extensive application.This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates over large areas.More research is needed to extract a vertical vegetation structure(e.g.canopy height)from interferometry synthetic aperture radar(InSAR)or optical stereo images to incorporate it into horizontal structures(e.g.canopy cover)in biomass estimation modeling.展开更多
基金supported by the Ministry of Higher Education and Scientific Research of Tunisia
文摘In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be described by an input-output nonlinear discrete-time mathematical model, with unknown, but constant or slowly time-varying parameters. Then, two recursive estimation methods are used to solve the parametric estimation problem for the considered class of the interconnected nonlinear systems. These methods are based on the recursive least squares techniques and the prediction error method. Convergence analysis is provided using the hyper-stability and positivity method and the differential equation approach. A numerical simulation example of the parametric estimation of a stochastic interconnected nonlinear hydraulic system is treated.
基金a grant from Research Center of Agricultural and Forestry Carbon Sinks and Ecological Environmental Remediation,Zhejiang A&F University.
文摘Remote sensing-based methods of aboveground biomass(AGB)estimation in forest ecosystems have gained increased attention,and substantial research has been conducted in the past three decades.This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues–collection of field-based biomass reference data,extraction and selection of suitable variables from remote sensing data,identification of proper algorithms to develop biomass estimation models,and uncertainty analysis to refine the estimation procedure.Additionally,we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure.Although optical sensor and radar data have been primary sources for AGB estimation,data saturation is an important factor resulting in estimation uncertainty.LIght Detection and Ranging(lidar)can remove data saturation,but limited availability of lidar data prevents its extensive application.This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates over large areas.More research is needed to extract a vertical vegetation structure(e.g.canopy height)from interferometry synthetic aperture radar(InSAR)or optical stereo images to incorporate it into horizontal structures(e.g.canopy cover)in biomass estimation modeling.
