Estimating FPAR of maize crop using airborne laser scanning
The fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter in ecosystem,crop growth monitoring and yield prediction models,and therefore it is very essential to estimate FPAR accurately,rapidly and objectively.However,direct measurements of FPAR are difficult,tedious and time consuming,although ground-based FPAR measurement methods are the classical and accurate techniques.In addition,they are also not feasible to apply for spatial pattern studies across landscape.Passive optical remote sensing can indirectly estimate FPAR and provides a unique way to obtain the spatial distribution and variability of FPAR over large areas.However,optical remotely sensed data do not take into account the three-dimensional structure information of vegetation canopy and the estimation accuracy of FPAR will be affected.LiDAR (Light Detection and Ranging) is an active remote sensing technology and can provide accurate vertical vegetation structure parameters.The purpose of this study is to explore the potential of airborne discrete-return LiDAR in estimating FPAR of maize crop.In this study,the LiDAR raw point clouds were processed to separate ground returns from vegetation returns using a filter method over maize in Zhangye City of northwest China.And then,the fractional cover (fcover) of maize canopy from LiDAR was computed using the ratio of canopy return counts or intensity sums to the total of returns or intensities.We established empirical regressions between fCover LiDAR-derived and field-measured FPAR and the regression relationships were significant and the largest value of the coefficient of determination (R2) is 0.90 (RMSE = 0.032,p<0.001).Overall,our study substantiates that airborne discrete-return LiDAR data can accurately estimate FPAR of maize canopy and may be an attractive data source for characterizing FPAR across large spatial extents.
LiDAR FPAR fcover LAI
Shezhou Luo Cheng Wang Xiaohuan Xi
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
国际会议
北京
英文
263-270
2013-10-09（万方平台首次上网日期，不代表论文的发表时间）