The Normalized Diff erence Vegetation Index(NDVI),one of the earliest remote sensing analytical products used to simplify the complexities of multi-spectral imagery,is now the most popular index used for vegetation as...The Normalized Diff erence Vegetation Index(NDVI),one of the earliest remote sensing analytical products used to simplify the complexities of multi-spectral imagery,is now the most popular index used for vegetation assessment.This popularity and widespread use relate to how an NDVI can be calculated with any multispectral sensor with a visible and a near-IR band.Increasingly low costs and weights of multispectral sensors mean they can be mounted on satellite,aerial,and increasingly—Unmanned Aerial Systems(UAS).While studies have found that the NDVI is effective for expressing vegetation status andquantified vegetation attributes,its widespread use and popularity,especially in UAS applications,carry inherent risks of misuse with end users who received little to no remote sensing education.This article summarizes the progress of NDVI acquisition,highlights the areas of NDVI application,and addresses the critical problems and considerations in using NDVI.Detailed discussion mainly covers three aspects:atmospheric eff ect,saturation phenomenon,and sensor factors.The use of NDVI can be highly eff ective as long as its limitations and capabilities are understood.This consideration is particularly important to the UAS user community.展开更多
A new numerical integration scheme incorporating a predict-correct algorithm for solving the nonlinear dynamic systems was proposed in this paper. A nonlinear dynamic system governed by the equation v = F（v, t） was ...A new numerical integration scheme incorporating a predict-correct algorithm for solving the nonlinear dynamic systems was proposed in this paper. A nonlinear dynamic system governed by the equation v = F（v, t） was transformed into the form as v = Hv ＋ f（v, t）. The nonlinear part f（v, t） was then expanded by Taylor series and only the first-order term retained in the polynomial. Utilizing the theory of linear differential equation and the precise time-integration method, an exact solution for linearizing equation was obtained. In order to find the solution of the original system, a third-order interpolation polynomial of v was used and an equivalent nonlinear ordinary differential equation was regenerated. With a predicted solution as an initial value and an iteration scheme, a corrected result was achieved. Since the error caused by linearization could be eliminated in the correction process, the accuracy of calculation was improved greatly. Three engineering scenarios were used to assess the accuracy and reliability of the proposed method and the results were satisfactory.展开更多
基金the USDA National Institute of Food and Agriculture McIntire Stennis project(IND011523MS).
文摘The Normalized Diff erence Vegetation Index(NDVI),one of the earliest remote sensing analytical products used to simplify the complexities of multi-spectral imagery,is now the most popular index used for vegetation assessment.This popularity and widespread use relate to how an NDVI can be calculated with any multispectral sensor with a visible and a near-IR band.Increasingly low costs and weights of multispectral sensors mean they can be mounted on satellite,aerial,and increasingly—Unmanned Aerial Systems(UAS).While studies have found that the NDVI is effective for expressing vegetation status andquantified vegetation attributes,its widespread use and popularity,especially in UAS applications,carry inherent risks of misuse with end users who received little to no remote sensing education.This article summarizes the progress of NDVI acquisition,highlights the areas of NDVI application,and addresses the critical problems and considerations in using NDVI.Detailed discussion mainly covers three aspects:atmospheric eff ect,saturation phenomenon,and sensor factors.The use of NDVI can be highly eff ective as long as its limitations and capabilities are understood.This consideration is particularly important to the UAS user community.
基金Project supported by the Department of Industrial and Systems Engineering,The Hong Kong Polytechnic University (No.1-45-56-0000).
文摘A new numerical integration scheme incorporating a predict-correct algorithm for solving the nonlinear dynamic systems was proposed in this paper. A nonlinear dynamic system governed by the equation v = F（v, t） was transformed into the form as v = Hv ＋ f（v, t）. The nonlinear part f（v, t） was then expanded by Taylor series and only the first-order term retained in the polynomial. Utilizing the theory of linear differential equation and the precise time-integration method, an exact solution for linearizing equation was obtained. In order to find the solution of the original system, a third-order interpolation polynomial of v was used and an equivalent nonlinear ordinary differential equation was regenerated. With a predicted solution as an initial value and an iteration scheme, a corrected result was achieved. Since the error caused by linearization could be eliminated in the correction process, the accuracy of calculation was improved greatly. Three engineering scenarios were used to assess the accuracy and reliability of the proposed method and the results were satisfactory.
