JERS-1 Synthetic Aperture Radar Image Research Articles

Sea ice monitoring by L-band SAR: an assessment based on literature and comparisons of JERS-1 and ERS-1 imagery

Spaceborne single-polarization C-band synthetic aperture radar (SAR) imagery is widely used to gather information about the state of the sea ice cover in the polar regions. C-band is regarded as a reasonable choice for all-season monitoring capabilities. For specific mapping tasks, however, other frequency bands can be more suitable. In the first part of this paper, the summary of a literature study dealing with the utilization of L-band SAR imagery for sea ice monitoring is presented. Investigations reveal that if deformation features such as ice ridges, rubble fields, and brash ice are to be mapped, L-band radar is superior in a number of cases. The second part of this paper addresses the comparison of JERS-1 and ERS-1 SAR images that were acquired over sea ice east of Svalbard and along the east coast of Greenland. The effects of the different frequencies, polarizations, and incidence angles of the two SAR systems are discussed. It is demonstrated that the images of both sensors complement one another in the analysis of ice conditions, resulting in a more detailed view of the sea ice cover state.

Sinergismo entre dados ópticos e de radar no estudo geológico da região de Bezerra-Cabeceiras, Goiás

Optical images provide spectral information related to chemical composition of targets, whereas the synthetic aperture radar (SAR) data present information related to geometry and dielectric properties of objects. Because it can combine complementary spectral data, the synergism between different sensors tends to improve the potential of remote sensing for geological mapping and other types of mapping. In this study, the synergy of the following aircraft and satellite data was evaluated for geological mapping of the Bezerra-Cabeceiras region, Goias State: a scanned GEMS-1 SAR image from Radambrasil Project (Xband); two C-band RADARSAT images, with different incident angles and look direction; a L-band JERS-1 SAR image; and an optical Landsat 5-TM dataset. The following digital image processing techniques were analyzed: principal components, decorrelation, linear contrast and IHS color composite. In this study we obtained a high quality IHS color composite for lithological and structural mapping and the reduction of land use interference in the relatively recent Landsat-5 TM data. Such quality was demonstrated through the analyses of comission and omission errors, in addition to the evaluation of accuracy.

A detail-preserving and flexible adaptive filter for speckle suppression in SAR imagery

Speckle filtering and detail preservation are two key issues in speckle suppression of Synthetic Aperture Radar (SAR) imagery. Different applications may require different balances between speckle reduction and detail retention. This paper presents a detail-preserving and flexible filter. Both quantitative and qualitative criteria, including speckle reduction, edge retention, texture preservation and visual assessment, were used to evaluate the performance of this adaptive filter. One JERS-1 SAR image and three SIR-C/X-SAR images (L-HH, L-HV and C-HV) were employed in the evaluation. The results show that the proposed filter is slightly better than, or comparable to commonly used filters based on the spatial domain, such as Lee, Frost, Lee-Sigma and Gamma-Map, in terms of detail preservation. Moreover, the proposed filter can achieve a wide range of balances between speckle reduction and detail preservation, and thus is applicable in different applications, including both broad-scale interpretation or mapping and applications in which fine details and high resolution are required. Furthermore, the proposed filter requires no knowledge of speckle standard deviation, which is required in most commonly used filters.

Estimation of tree growth in a conifer plantation over 19 years from multi-satellite L-band SAR

The general capability of synthetic aperture radar (SAR) for monitoring forest ecosystems is well documented. However, the majority of SAR studies of forest dynamics use only imagery acquired by one SAR system and are thus limited to the lifecycle of a particular satellite. The synergistic analysis of SAR data from one of the earliest spaceborne SAR missions, the SEASAT mission, with the Japanese JERS-1 satellite-borne SAR is presented. Biophysical parameters frequently retrieved from SAR are tree biomass using backscatter and tree height from the interferometric phase. One potential application that has not been thoroughly examined is mapping of incremental tree growth from SAR backscatter changes. Tree growth measures biomass changes over time, and is correlated to the amount of carbon sequestered by the trees. This paper examines the retrieval of tree growth from multitemporal spaceborne L-band SAR. A SEASAT SAR image from 1978 and a JERS-1 SAR image from 1997 over Thetford forest, UK are used to retrieve tree growth of Corsican Pine stands. Incremental growth was estimated from the changes in backscatter coefficient, and compared to the expected tree growth from general yield class models used by the UK Forestry Commission. The accuracy of the retrieval algorithm depends on the minimum forest stand size included in the analysis. For managed forest plantations, multitemporal L-band SAR has some potential for detecting incremental biomass to support sustainable forest management.

Textural information of multitemporal ERS-1 and JERS-1 SAR images with applications to land and forest type classification in boreal zone

The textural information of a multitemporal set of ERS-1 and JERS-1 synthetic aperture radar (SAR) images was studied with the first- and second-order statistical measures. These measures had a higher information value for the land-cover and forest type classification than the SAR image intensity. The multitemporal approach was beneficial for the application of the textural measures; the textural parameters significantly improved the classification of land-cover and forest types. Based on the SAR image texture, the overall classification accuracy for seven land-cover types was 65%, while with the SAR image intensity, the classification accuracy was 50%, respectively. In the forest type classification based on the SAR image texture and intensity, the overall classification accuracy for four forest types was 66%, while with the intensity, the accuracy was 40%, respectively. The weather and seasonal conditions had a significant effect on the textural information of SAR images. The best separability of the signatures and the best land-cover and forest type classification accuracy was achieved under summer conditions. The snow cover and arid conditions decreased the textural information of the SAR images.

Retrieval of biomass in boreal forests from multitemporal ERS-1 and JERS-1 SAR images

The response of JERS-1 and ERS-1 synthetic aperture radar (SAR) to the forest stem volume (biomass) was investigated by employing a digital stem volume map and weather information. The stem volume map was produced from the National Forest Inventory sample plot data together with a LANDSAT thematic mapper (TM) image. A new indirect inversion method was developed and tested to estimate the forest blockwise stem volume from JERS-1 and/or ERS-1 SAR images. The method is based on using a semiempirical backscatter model for inversion. The model presumes that backscatter from a forest canopy is determined by the stem volume, soil moisture, and vegetation moisture. The area of interest is divided into a training and test area. In this study, the training area was 10% of the test site, while the remaining 90% was used for testing the method. The inversion algorithm is carried out in the following three steps. 1) For the training area, the soil and vegetation moisture parameters are estimated from the backscattering coefficients and stem volume (must be known for training areas) with the semiempirical backscatter model. 2) For the area of interest, the stem volume is estimated from the moisture parameters and backscattering coefficients with the semiempirical backscattering model. 3) If several SAR images are used, the stem volume estimates are combined with a multiple linear regression. The regression equation is defined using the stem volume estimates for the training area. The results for the stem volume estimation using L-band and/or C-band SAR data showed promising accuracies: the relative retrieval rms error varied from 30 to 5% as the size of the forest area varied from 5 to 30000 ha.

Investigation of Tropical Rain Forest in Central Amazonia, Brazil based on JERS-1 SAR Images

Taking advantage of capability of SAR (Synthetic Aperture Radar) on board Japanese earth observation satellite, JERS-1, which can observe ground condition through clouds, an attempt is made to investigate of tropical rain forests in two sites in Central Amazonia, Brazil. The first site extends from 0.5 S to 5.5 S and from 59 W to 65 W and the second site is a smaller area of about 75 km×150km located to the south of the first site.Since the coverage of 1 scene of SAR image is approximately 75 km×80km, it is necessary to mosaic 90 scenes of SAR images for the first site and 2 scenes of SAR images for the second site respectively. A practical scheme of processing a large amount of SAR image data is developed.Through the analyses of the mosaic images following features are found. The tropical rain forest is well conserved in the first site while a fairly lage deforestation is found in the second site. The estimated deforested area from SAR images is at least 607.1 km2 on October 27 1992 and 661.5 km2 on June 4 1993 in Sena Madureira, while in Rondonia it is estimated that at least forest of 302.7 km2 was newly cut down between July 19 1992 and April 9 1993 and the deforested area is at least 4, 629.0 km2 on October 2 1993. It is also found that features of river system under the continuous coverage of tree layer can be detected.As the typical values of σ° (differential scattering coefficient, which is the average value of the scattering cross-section per unit area) following values are obtained. (1) Dense forest -10.88-5.96dB, (2) Forest in wet lands (or flooded land) -7.67-2.48 dB, (3) Deforestedarea -15.24-8.68 dB, (4) Shrubs/short trees in sterile soils called “campinarana” -17.87-8.16dB.