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

Pauline Dusseux,Laurence Hubert-Moy,Thomas Corpetti,Samuel Corgne

doi:10.3390/rs6076163

Abstract

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.

Highlights

Land cover and land use changes, which are often associated with agriculture intensification, may have important impacts on environmental systems by increasing water and air pollution, soil degradation or biodiversity loss [1] and on socio-economic systems for stock and winter fodder [2]
It can be observed that transformed divergence (TD) values are very high (T D ≥ 1.9), indicating that the land cover classes have very good separability for any of the optical and Synthetic Aperture Radar (SAR) variables
We have evaluated the ability of optical and/or SAR time series images to discriminate between grasslands and crops in agricultural areas where cloud cover is very high most of the time

Summary

Introduction

Land cover and land use changes, which are often associated with agriculture intensification, may have important impacts on environmental systems by increasing water and air pollution, soil degradation or biodiversity loss [1] and on socio-economic systems for stock and winter fodder [2]. Grassland can be identified over large areas using optical remote sensing data through the calculation of parameters related to vegetation cover, such as vegetation density, crop height and biomass [7,8]. Vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), or biophysical variables, such as the Leaf Area Index (LAI) or the fraction of Vegetation Cover (fCOVER), can be used to monitor vegetation growth and assess land cover and land uses [9,10,11]. They describe the polarimetric response of features in the image and allow land cover classification

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Results

Conclusion