Multitemporal JERS-1 Research Articles

Multi temporal JERS-1 SAR investigation of Mt. Baekdu stratovolcano using differential interferometry

Mt. Baekdu, a Cenozoic stratovolcano, was studied using twenty-three JERS-1 SAR and two ERS-2 SAR images. A 70-day ERS-2 interferogram produces a very poor interferogram because of the temporal decorrelation probably due to vegetation and the layover effects. However, JERS-1 SAR data pairs produce coherent interferograms even when some of the time intervals between the data take was as long as 6 years. The measurement of surface changes was numerically implemented from 1992 to 1998 using three different SAR interferometry approaches: i) interferometry with large altitude of ambiguity, ii) 2-pass, and iii) 3-pass differential SAR interferometry (DInSAR). Analysis of 11 differential interferograms indicates that the large surface area surrounding Mt. Baekdu (several tens of kilometers) subsides, particularly the southwestern part of it. When the computed displacement vectors are projected onto the vertical components, the maximum subsidence rate is estimated at 9 cm/year. Although the estimated ubsidence rate is not confirmed in the field due to the lack of ground-truth data and accurate DEMs as well as logistic problems, the results provide important volcano-tectonic implications on the background information for future long-term monitoring of Mt. Baekdu.

Land use classification with textural analysis and the aggregation technique using multi-temporal JERS-1 L-band SAR images

This paper describes a method of improving classification accuracy when using Synthetic Aperture Radar (SAR) images. The classifier used is a maximum likelihood classifier. Texture and textural feature images were made and used for classification. The accuracy of various classification methods was compared. As a result, it was found that the best classification was produced by the aggregation of the classified image when using texture images as additional inputs to the classifier. It is also shown that textural analysis and the aggregation technique are useful in the classification of SAR images.

Monitoring of new plantation development in tropical rain forests using JERS-1 SAR data

Abstract The authors studied the applicability of multi temporal SAR data obtained from JERS-1 SAR for monitoring the change in tropical rain forest conditions due to plantation development. The test site was the southern part of Sumatra Island, Indonesia. A total of seven JERS-1 SAR and one ERS-2 SAR data from 1992 to 1997 were analyzed together with two optical data, MOS-1 MESSR in 1990 and SPOT HRV in 1997. The result of this study verified that the backscatter change in multi temporal JERS-1 SAR data can be used effectively to monitor the process of plantation development in the test site, which accompanies the burning of felled tree trunks after deforestation, one of the causes for forest fire and serious smoke.

Texture statistics for classification of land use with multitemporal JERS-1 SAR single-look imagery

Texture statistics are studied using multitemporal JERS-1 synthetic aperture radar (SAR) single-look imagery. A correct autocorrelation function of texture is derived and utilized to calculate textural features. It is demonstrated that a significant improvement in the classification accuracy results if the textural features are used as additional inputs to the classifier.

Temporal and spatial characteristics of irrigated rice in JERS-1 L-band SAR data

Multi-temporal JERS-1 SAR (Japanese Earth Resource Satellite 1 Synthetic Aperture Radar) data have been used in this study to investigate the temporal and spatial characteristics of irrigated rice in LHH-band SAR data. The two test sites chosen represent areas that are characterized by manual and mechanical planting practices that turn out to have a significant effect on the L-band backscatter. The results show a clear relationship between L-band backscatter and plant growth in manually planted areas. A dynamic range of about 8dB has been measured with no apparent saturation of the signal. Areas in which mechanical planting is practiced, however, display a more complex relationship where the radar response is strongly dependent on parameters related to the spatial distribution of the plants. The backscatter displays a strong dependency on planting orientation and plant spacing, where fields planted in the radar illumination direction with a certain plant spacing display extreme dynamic ranges of up to more than 20dB due to resonant scattering. Fields planted in other directions generally experience attenuation of the signal and dynamic ranges do not change by more than a few decibels. Despite these artifacts, however, a correlation between plant growth and backscatter has been observed for all fields, independent of plant spacing and field orientation.

Estimation of stem volume in boreal forests using ERS-1 C- and JERS-1 L-band SAR data

Multi-temporal ERS-1 C- and JERS-1 L-band Synthetic Aperture Radar (SAR) data were analysed to determine the relationship between radar backscattering and forest stem volume. The test site is located within the boreal conifer belt in northern Sweden with stem volume in the range of 0-300m3 ha-1. The statistical analysis was carried out using the water cloud model and various linear regression models. It was confirmed that the L-band of JERS-1 shows a consistently higher sensitivity to stem volumes in boreal forests with a larger backscatter contrast between nonforested areas and areas having dense tree cover in comparison with the C-band of ERS-1. The saturation levels for the ERS-1 and JERS-1 SAR sensors were estimated at 64m3 ha-1 and 143m3 ha-1, respectively. Furthermore, the temporal analysis shows that the backscattering coefficient can differ by approximately three decibels, atmost, between acquisitions for a given stem volume. However, a major finding is that the temporal variation could be modelled as an additive effect that is constant throughout the range of stem volume. For operational forestry purposes this would imply that stem volume can be predicted after calibration of the radar response using clear felled areas as reference targets. The multi-temporal JERS-1 dataset was used to develop a radarbased model of stem volume. The model was tested on a validation dataset resulting in a correlation coefficient of 0.78 between SAR estimated stem volume and ground data.

多時期二周波ＳＡＲデータによる湿原植生分類

Wetlands is one most valuable ecosystems on the earth. Also today the importance of wetland is pointed out as a major emission source of methane which is one of the green house gases.The development of remote sensing method to monitor wetlands vegetation distribution is urgent necessity. The purpose of this study is to investigate remote sensing methodology for monitoring wetland vegetation distribution by utilizing Synthetic Aperture Radar data. Vegetation classification was performed with multi-temporal JERS-1/SAR (L band) and ERS-1/AMI (C band) images. As initial step for classification, the speckle noise reduction was performed by MAP filter. As the result of supervised classification by Maximum likelihood method, an accurate wetland vegetation classification map was produced.