Peer-Reviewed Articles
245 InSAR Imaging of Volcanic Deformation over
Cloud-prone Areas - Aleutian Islands
Zhong Lu
Mapping ground surface deformation of volcanoes over the Aleutian Islands using satellite interferometric synthetic aperture radar (InSAR).
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Interferometric synthetic aperture radar (INSAR) is capable of
measuring ground-surface deformation with centimeter-to-subcentimeter precision and spatial resolution of tens-of-meters over a relatively large region. With its global coverage
and all-weather imaging capability, INSAR is an important
technique for measuring ground-surface deformation of
volcanoes over cloud-prone and rainy regions such as the
Aleutian Islands, where only less than 5 percent of optical
imagery is usable due to inclement weather conditions. The
spatial distribution of surface deformation data, derived
from INSAR images, enables the construction of detailed
mechanical models to enhance the study of magmatic
processes. This paper reviews the basics of INSAR for volcanic deformation mapping and the INSAR studies of ten
Aleutian volcanoes associated with both eruptive and non-eruptive activity. These studies demonstrate that all-weather
INSAR imaging can improve our understanding of how the
Aleutian volcanoes work and enhance our capability to
predict future eruptions and associated hazards.
259 Mine Subsidence Monitoring Using Multi-source
Satellite SAR Images
Linlin Ge, Hsing-Chung Chang, and Chris Rizos
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Ground subsidence due to underground mining has posed a
constant threat to the safety of surface infrastructure such as
motorways, railways, power lines, and telecommunications
cables. Traditional monitoring techniques like using levels,
total stations and GPS can only measure on a point-by-point
basis and hence are costly and time-consuming. Differential
interferometric synthetic aperture radar (DINSAR) together
with GPS and GIS have been studied as a complementary
alternative by exploiting multi-source satellite SAR images
over a mining site southwest of Sydney.
Digital elevation models (DEMs) derived from ERS-1 and ERS-2 tandem images, photogrammetry, airborne laser scanning, and the Shuttle Radar Topography Mission were assessed based on ground survey data using levelling as well as GPS-RTK. The identified high quality DEM was then used in the DINSAR analysis. Repeat-pass acquisitions by the ERS-1, ERS-2, JERS-1, RADARSAT-1 and ENVISAT satellites were used to monitor mine subsidence in the region with seven active mine collieries. Sub-centimeter accuracy has been demonstrated by comparing DINSAR results against ground survey profiles. The ERS tandem DINSAR results revealed mm-level resolution.
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267 Study of Rain Events over the South China Sea by
Synergistic Use of Multi-sensor Satellite and Groundbased
Meteorological Data
Werner Alpers, Cho Ming Cheng, Yun Shao, and Limin Yang
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Rain cells and rain bands over the South China Sea off the
coast of Hong Kong are studied by using multi-sensor
satellite and ground-based meteorological data. These
include synthetic aperture radar (SAR) images acquired by
the Advanced Synthetic Aperture Radar (ASAR) onboard the
European ENVISAT satellite, weather radar images from the
Hong Kong Observatory (HKO), rain rate data acquired by the
Special Sensor Microwave Imager (SSM/I) sensor onboard
the F15 satellite of the American Defense Meteorological
Satellite Program (DMSP) and the rain sensors onboard the
Tropical Rainfall Measurement Mission (TRMM) satellite,
cloud image of GOES-9 satellite, sea surface wind maps
acquired by the scatterometer onboard the QUIKSCAT satellite, and meteorological data from weather stations in Hong
Kong. Three rain events, typical of Hong Kong, are studied.
The first event consists of a cluster of rain cells associated
with the summer monsoon, the second one of rain cells
aligned in a rain band generated by an upper-air trough,
and the third one consists of small rain cells embedded in
a cold front. It is shown that ASAR images, which have
a resolution of 30 m in the Image Mode (IM) and 150 m
resolution in the Wide Swath Mode (WSM), yield much more
detailed information on the spatial structure of rain events
over the ocean than data obtained from SSSM/I and the rain
sensors onboard the TRMM satellite. The precipitation radar
(PR) onboard TRMM, which is the rain measuring instrument
flown in space with the next best resolution, has a resolution of only 4 km. However, the disadvantage of SAR is that
it is sometimes difficult to identify SAR signatures visible on
SAR images of the sea surface unambiguously as caused by
rain events. By comparing SAR images with simultaneously
acquired weather radar images of the Hong Kong Observatory, a better knowledge of radar signatures on SAR images
resulting from rain events over the ocean is obtained. This
knowledge then helps greatly in detecting rain events on SAR
images which are acquired over ocean areas, which are not
in the reach of weather radar stations. SAR images containing radar signature of rain events allow a much more
detailed study of fine-scale structures of rain events over the
World’s ocean, in particular of clusters of rain cells, than
any other sensor presently flown in space.
279 Quantitative Evaluation of Polarimetric Classification
for Agricultural Crop Mapping
Erxue Chen, Zengyuan Li, Yong Pang, and Xin Tian
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Agricultural crops classification capability of single band
full polarization SAR data with different classification
methods was evaluated using AIRSAR L-band polarimetric
SAR data. It has been found that if only maximum likelihood
(ML) classifiers, such as Wishart-maximum likelihood (WML)
and normal distribution probability density functions (PDF)-based Maximum Likelihood (NML) classifier can be utilized,
it is better to choose WML directly applied to complex
coherency or covariance matrix. NML cannot achieve acceptable classification result if intensity and phase images
derived from coherency matrix are directly used for training
the classifier. But if these images were supplied to the
spatial-spectral based classifier, Extraction and Classification of Homogenous Objects (ECHO), higher classification
accuracy can be obtained. Very low crop types discrimination accuracy has been observed when only H-Alpha
polarimetric decomposition resultant images, such as entropy,
alpha and anisotropy, were supplied to NML or a spatial-spectral based classifier such as ECHO.
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285 Efficient Water Area Classification Using Radarsat-1 SAR
Imagery in a High Relief Mountainous Environment
Yeong-Sun Song, Hong-Gyoo Sohn, and Choung-Hwan Park
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It is important to determine quickly the extent of flooding
during extreme cases. Even though SAR imagery with its own
energy sources is highly applicable to flood monitoring
owing to its sensitivity to the water area, topographic effects
caused by local terrain relief must be carefully considered
before the actual classification process. Since backscattering
coefficients of the shadow area in high relief regions are
very similar to those of the water area, it is essential to
regard these areas before and after the classification procedure, although the process is a difficult and time-consuming
task. In this study, efficient and economical methods for
water area classification during floods in mountainous area
are described. We tested five different cases using various
synthetic aperture radar (SAR) image processing techniques,
texture measures, and terrain shape information such as
elevation and slope. The case whereby the SAR image was
classified with the local slope information exhibited the best
result for water area classification, even in small streams of
different elevation categories. Consequently in mountainous
areas, the combination of a SAR image and local slope
information was the most appropriate method in estimating
flooded areas.
297 Comparisons of Compositing Period Length for
Vegetation Index Data from Polar-orbiting and
Geostationary Satellites for the Cloud-prone Region
of West Africa
Rasmus Fensholt, Assaf Anyamba, Simon Stisen,
Inge Sandholt, Ed Pak, and Jennifer Small
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Land surface data from MODIS and AVHRR have been extensively used for vegetation monitoring. In cloud-prone areas
like West Africa the use of Normalized Difference Vegetation
Index (NDVI) data for vegetation monitoring is hampered by
persistent cloud cover especially during the rainy season.
The new geostationary satellite Meteosat Second Generation
(SEVIRI MSG) is the first geostationary satellite suited for
vegetation monitoring allowing NDVI to be derived with a
15-minute temporal resolution. For West Africa, MODIS
(combined TERRA and AQUA) produce above 85 percent
cloud-free pixels in the scene during the entire rainy season
using 16-day composite periods. SEVIRI MSG data produces>98 percent cloud-free pixels during the entire season using
a 3-day composite period. Therefore, there is a much higher
probability for producing high quality cloud free data using
SEVIRI MSG data for a short time composite period compared
to Polar Orbiting Environmental Satellite (POES) data, which
is expected to substantially improve various applications of
satellite based natural resource management, including
vegetation monitoring, in West Africa.
311 Remote Sensing Change Detection Based on Canonical
Correlation Analysis and Contextual Bayes Decision
Lu Zhang, Mingsheng Liao, Limin Yang, and Hui Lin
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In this paper, we present a new approach combining
canonical correlation analysis and contextual Bayes decision
for change detection in bi-temporal multispectral remotely
sensed images. Canonical correlation analysis in the form of
Minimum Noise Fraction/Multivariate Alteration Detection
transformation was first applied to two multispectral images
to effectively yield a difference image, followed by a contextual Bayes decision procedure using automatic thresholding
and Markov random field modeling techniques to identify
areas where changes may have actually occurred from the
difference image. An experiment of monitoring land reclamations in Hong Kong from Landsat TM/ETM+ images was
conducted, and the results demonstrated effectiveness of our
approach.
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319 LUCC Impact on Sediment Loads in Sub-tropical
Rainy Areas
Xiaoling Chen, Shuming Bao, Hui Li, Xiaobin Cai, Peng Guo,
Zhongyi Wu, Weijuan Fu, and Hongmei Zhao
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In this paper, we evaluate the impacts of land-use/cover
changes (LUCC) on sediment loads at the outlets of five sub
watersheds of the Poyang Lake watershed by integrating
remote sensing and GIS with statistical analysis. The intensively farmed watershed is characterized by a mountainous
and hilly topography and a rainy climate. The primary goal
of this paper is to help a better understanding of land-use/cover change and its driving forces. We discuss spatio
temporal variations in rainfall and sediment loads and
identify factors contributing to those variations, analyze the
comprehensive impacts of land-use/cover change on changing climate and human activities, and conclude that the
changing rates of forest cover and climate regimes are
primary factors for sediment discharges in the Poyang Lake
watershed. Our results suggest that the eco-system still have
large capacities to support human activities in the area.
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