Multi-temporal RADARSAT-2 Images Research Articles

SAR Backscatter and InSAR Coherence for Monitoring Wetland Extent, Flood Pulse and Vegetation: A Study of the Amazon Lowland

Synthetic aperture radar (SAR) data have been identified as a potential source of information for monitoring surface water, including open water and flooded vegetation, in frequent time intervals, which is very significant for flood mapping applications. The SAR specular reflectance separates open water and land surface, and its canopy penetration capability allows enhanced backscatter from flooded vegetation. Further, under certain conditions, the SAR signal from flooded vegetation may remain coherent between two acquisitions, which can be exploited using the InSAR technique. With these SAR capabilities in mind, this study examines the use of multi-temporal RADARSAT-2 C band SAR intensity and coherence components to monitor wetland extent, inundation and vegetation of a tropical wetland, such as Amazon lowland. For this study, 22 multi-temporal RADARSAT-2 images (21 pairs) were used for InSAR processing and the pairs in the low water stage (November, December) showed high coherence over the wetland areas. The three-year intensity stack was used for assessing wetland boundary, inundation extent, flood pulse, hydroperiod, and wetland vegetation. In addition to the intensity, derived coherence was used for classifying wetland vegetation. Wetland vegetation types were successfully classified with 86% accuracy using the statistical parameters derived from the multi-temporal intensity and coherence data stacks. We have found that in addition to SAR intensity, coherence provided information about wetland vegetation. In the next year, the Canadian RADARSAT Constellation Mission (RCM), will provide more data with frequent revisits, enhancing the application of SAR intensity and coherence for monitoring these types of wetlands at large scales.

Optimized Rule-Based Flood Mapping Technique Using Multitemporal RADARSAT-2 Images in the Tropical Region

Flood is one of the most common natural disasters in Malaysia. Preparing an accurate flood inventory map is the basic step in flood risk management. Flood detection is a complex process because of the limitation of methodological approaches and cloud coverage over tropical areas. An efficient approach is proposed to identify flooded areas using multitemporal RADARSAT-2 imageries. First, multispectral Landsat image was used to extract and subtract permanent water bodies, and this image was later utilized to extract the same information from multitemporal RADARSAT-2 imageries. Next, water bodies during a flood event were extracted from RADARSAT-2 images. Permanent water bodies, shadow, and paddy were detected from synthetic aperture radar (SAR) images by analyzing their temporal backscattering values. During feature extraction, rule-based object-oriented technique was applied to classify both SAR and Landsat images. Image segmentation during object-based analysis was performed to distinguish the boundaries of various dimensions and scales of objects. Moreover, a Taguchi-based method was employed to optimize the segmentation parameters. After segmentation, the rules were defined and images were classified to produce an accurate flood inventory map for the 2014 Kelantan flood. A confusion matrix was generated to evaluate the performance of the classification method. The overall accuracy of 86.16% was achieved for RADARSAT-2 using rule-based classification and optimization technique. The resulting flood inventory map using the proposed approach supported the efficiency of the proposed methodology.

Estimation of rice biophysical parameters using multitemporal RADARSAT-2 images

Compared with optical sensors, synthetic aperture radar (SAR) has the capability of acquiring images in all-weather conditions. Thus, SAR images are suitable for using in rice growth regions that are characterized by frequent cloud cover and rain. The objective of this paper was to evaluate the probability of rice biophysical parameters estimation using multitemporal RADARSAT-2 images, and to develop the estimation models. Three RADARSTA-2 images were acquired during the rice critical growth stages in 2014 near Meishan, Sichuan province, Southwest China. Leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), height, biomass and canopy water content (WC) were observed at 30 experimental plots over 5 periods. The relationship between RADARSAT-2 backscattering coefficients (σ0) or their ratios and rice biophysical parameters were analysed. These biophysical parameters were significantly and consistently correlated with the VV and VH σ0 ratio (σ0VV/ σ0VH) throughout all growth stages. The regression model were developed between biophysical parameters and σ0VV/ σ0VH. The results suggest that the RADARSAT-2 data has great potential capability for the rice biophysical parameters estimation and the timely rice growth monitoring.

Adaptive vectorial total variation models for multi‐channel synthetic aperture radar images despeckling with fast algorithms

This study proposes two adaptive vectorial total variation models for multi-channel synthetic aperture radar (SAR) images despeckling with the help of prior knowledge of the image amplitude. Besides despeckling the multi-channel SAR images efficiently, the proposed new models have advantages over other total variation methods in many aspects, such as preserving the radar reflectivity, the targets and edges contrast. The Bermudez-Moreno algorithm and the accelerated fast iterative shrinkage thresholding algorithm are employed to implement the new two models, respectively. Experimental results on multi-polarimetric, multi-temporal RADARSAT-2 images show that the visual quality and evaluation indexes of the proposed models and the corresponding algorithms outperform the other methods with edge preservation.

Vectorial total variation model for multi-channel SAR image denoising

The vectorial total variation model and algorithm are studied for multi-channel SAR image denoising.After introducing the vectorial total variation model,an accelerative fix-point iterative algorithm was proposed and its convergence was proved.By improving the filter coefficient of the fix-point iterative process,an adaptive vectorial total variation model was developed for multi-channel SAR image denoising,whose iterative algorithm and convergence theorem were present.The performance of denoising and resolution preservation of our models was tested by multi-polarimetric,multi-temporal RADARSAT-2 images,in addition to the validation of the convergence and the convergent speed of the proposed algorithms.