Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring

Remote Sensing

Pauline Dusseux¹  Thomas Corpetti²  Laurence Hubert-Moy² 

[1] LETG Rennes COSTEL laboratory, UMR 6554 CNRS OSU, University of Rennes 2, Place du recteur Henri Le Moal, 35 043 Rennes Cedex, France;

关键词: imaging data;    land use and land cover monitoring;    biophysical parameters;    polarimetric parameters;    time series;   

DOI  :  10.3390/rs6076163

来源: mdpi

【 摘 要 】

The aim of this study was to assess the ability of optical images, SAR (Synthetic Aperture Radar) images and the combination of both types of data to discriminate between grasslands and crops in agricultural areas where cloud cover is very high most of the time, which restricts the use of visible and near-infrared satellite data. We compared the performances of variables extracted from four optical and five SAR satellite images with high/very high spatial resolutions acquired during the growing season. A vegetation index, namely the NDVI (Normalized Difference Vegetation Index), and two biophysical variables, the LAI (Leaf Area Index) and the fCOVER (fraction of Vegetation Cover) were computed using optical time series and polarization (HH, VV, HV, VH). The polarization ratio and polarimetric decomposition (Freeman–Durden and Cloude–Pottier) were calculated using SAR time series. Then, variables derived from optical, SAR and both types of remotely-sensed data were successively classified using the Support Vector Machine (SVM) technique. The results show that the classification accuracy of SAR variables is higher than those using optical data (0.98 compared to 0.81). They also highlight that the combination of optical and SAR time series data is of prime interest to discriminate grasslands from crops, allowing an improved classification accuracy.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland