Next Article in Journal
Open Access Data in Polar and Cryospheric Remote Sensing
Next Article in Special Issue
Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery
Previous Article in Journal
Analysis of the Relationship between Land Surface Temperature and Wildfire Severity in a Series of Landsat Images
Previous Article in Special Issue
Evaluating Remotely Sensed Phenological Metrics in a Dynamic Ecosystem Model
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2014, 6(7), 6163-6182; doi:10.3390/rs6076163

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

LETG Rennes COSTEL laboratory, UMR 6554 CNRS OSU, University of Rennes 2, Place du recteur Henri Le Moal, 35 043 Rennes Cedex, France
*
Author to whom correspondence should be addressed.
Received: 23 April 2014 / Revised: 30 May 2014 / Accepted: 23 June 2014 / Published: 30 June 2014
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
View Full-Text   |   Download PDF [8109 KB, uploaded 30 June 2014]   |  

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. View Full-Text
Keywords: imaging data; land use and land cover monitoring; biophysical parameters; polarimetric parameters; time series imaging data; land use and land cover monitoring; biophysical parameters; polarimetric parameters; time series
Figures

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Dusseux, P.; Corpetti, T.; Hubert-Moy, L.; Corgne, S. Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring. Remote Sens. 2014, 6, 6163-6182.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top