Marine pollution from oil spills destroys ecosystems. In order to minimize the damage, it is important to fast cleanup it after predicting how the oil will spread. In order to predict the spread of oil spill, remote sensing technique, especially radar satellite image is widely used. In previous studies, only the back-scattering value is generally used for the detection of oil spill. However, in this study, oil spill was detected by applying ANN (Artificial Neural Network) as input data from the back-scattering value of the radar image as well as the phase information extracted from the dual polarization. In order to maximize the efficiency of oil spill detection using a back-scattering value, the speckle noise acting as an error factor should be removed first. NL-means filter was applied to multi-look image to remove it without smoothing of spatial resolution. In the coherence image, the sea has a high value and the oil spill area has a low value due to the scattering characteristics of the pulse. In order to using the characteristics of radar image, training sample was set up from NL-means filtered images(HH, VV) and coherence image, and ANN was applied to produce probability map of oil spill. In general, the value was 0.4 or less in the case of the sea, and the value was mainly in the range of 0.7 to 0.9 in the oil spill area. Using coherence images generated from different polarizations showed better detection results for relatively thin oil spill areas such as oil slick or oil sheen than using back-scattering information alone. It is expected that if the information about the look-alike of oil spill such as algae, internal wave and rainfall area is provided, the probability map can be produced with higher accuracy.
The accuracy of a digital elevation model (DEM) generated from synthetic aperture radar (SAR) interferometry (InSAR) crucially depends on the length of the perpendicular baseline between SAR acquisitions. ERS-2 and Envisat cross-InSAR (CInSAR) are superior methods to create high precise DEM because the perpendicular baseline can be extended sufficiently long by compensating a slight difference in radar carrier frequency. We have assessed the accuracy of DEM generated by using ERS and Envisat satellite CInSAR techniques using the ice, cloud, and land elevation satellite global elevation data, which has an absolute vertical accuracy of about 2 cm. The study area is high flat land covered up with ice and snow in northern Alaska. Our result shows that the CInSAR-derived DEM can achieve an accuracy of about 0.50 m. This is much better than that of the National Elevation Dataset (DEM) (1.95 m) and is slightly lower than that of the airborne InSAR DEM (0.36 m).
The Southern Volcanic Zone (SVZ) of Chile consists of many volcanoes, and all of the volcanoes are covered with snow
at the top of mountain. Monitoring snow cover variations in these regions can give us a key parameter in order to
understand the mechanisms of volcanic activity. In this study, we investigate on the volcanic activity and snow cover
interaction from snow cover area mapping, snow-line extraction. The study areas cover Mt. Villarrica and Mt. Llaima,
Chile. Both of them are most active volcanos in SVZ. Sixty Landsat TM and Landsat ETM+ images are used for
observing snow cover variations of Mt. Villarrica and Mt. Llaima, spanning the 25 years from September 1986 to
February 2011. Results show that snow cover area between volcanic activity and non-activity are largely changed from
42.84 km2 to 13.41 km2, temporarily decreased 79% at the Mt. Villarrica and from 28.98 km2 to 3.82 km2, temporarily
decreased 87% at the Mt. Villarrica. The snow line elevation of snow cover retreated by approximately 260 m from
1,606m to 1,871 m at the Mt. Villarrica, approximately 266m from 1,741m to 2,007m at the Mt. Llaima. The results
show that there are definitely correlations between snow cover and volcanic activity.
The objectives of this study are to precisely observe time-series land surface temperature (LST) variations at Mt. Baekdu
using total of 23 Landsat TM and ETM+ thermal infrared (TIR) images spanning the 26 years from 1987 to 2012. For
this study, we focused on LST of vegetation area, because vegetation area has high surface emissivity. At the same time,
we used land surface temperature difference (LSTD) algorithm, which measures the LST difference between reference
and target area to minimize the atmospheric effect and the difficulty of surface emissivity determination. The results
show that most of the LSTD variations are distributed from -1 °C to 1 °C. However, the north of Mt. Baekdu has some
anomaly in June 2004, it represented about 3 °C.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.