LAI is a crucial parameter and a basic quantity indicating crop growth situation. Empirical models comprising spectral indices (SIs) and LAI have widely been applied to the retrieval of LAI. SI method already has exhibited feasibility in the estimation of vegetation LAI. However, it is largely subject to the inconsistency from different remote sensors which have varied specifications, such as spectral response features and central wavelength. To address this issue, a new vegetation index (VIUPD) based on the universal pattern decomposition method was proposed. It is expressed as a linear sum of the pattern decomposition coefficients and features in sensor-independency. The aim of this study was to evaluate the prediction accuracy and stability of VIUPD for estimating LAI, compared with three other common-used SIs. In this study, the measured spectra were resampled to simulated TM multispectral data and Hyperion hyperspectral data respectively, using the Gaussian spectral response function. The three typical SIs chosen were including NDVI, TVI and MCARI, which were constructed with the sensitive bands to the LAI. Finally, the regression equations between four selected SIs and LAI were established. The best index evaluated using the simulated TM data was VIUPD which exhibits the best correlation with LAI (R2=0.92) followed by NDVI (R2=0.80). For the simulated Hyperion data, VIUPD again ranks first with R2=0.89, followed by TVI (R2=0.63). Meanwhile, the consistence of VIUPD also was studied based on simulated TM and Hyperion sensor data and the R2 reached to 0.95. It is demonstrated that VIUPD has the best accuracy and stability to estimate LAI of winter wheat whether using simulated TM data or Hyperion data, which reaffirms that VIUPD is comparatively sensor independent.
Remote sensing is an indispensable means for coastal band monitoring. Using satellite remote sensing data to monitor coastline variation and to analysis the eroding, depositing features and evolution process will be of great significance for the river mouth regulation, river course planning, coastal protective project program and trend prediction of coastal evolution. So, it is necessary to establish a coastline dynamic monitoring system. The system is mainly based on remote sensing and spatial information analysis techniques. In this paper, the system framework, design methodology and system functions are described in detail. The key techniques and methods involved in the system construction are particularly discussed, and they include the data preprocessing techniques, such as cloud identification and geometry fine correction, multi-scale coast edge extracting algorithm based on MRF model and coastline tide correction model with measured data or numerical simulation result as input, and coastline dynamic analysis method based on time series analysis and spatial topological analysis. Finally, an example to apply the system to the Yellow River mouth delta is given and the process flow diagram and procedures are described. The comparison of monitoring results with manually interpreted results has verified the favorable effect of the system.