633 research outputs found
DInSAR deformation time series for monitoring urban areas: The impact of the second generation SAR systems
We investigate the capability improvement of the DInSAR techniques to map deformation phenomena affecting urban areas, by performing a comparative analysis of the deformation time series retrieved by applying the full resolution Small BAseline Subset (SBAS) DInSAR technique to selected sequences of SAR data acquired by the ENVISAT, RADARSAT-1 and COSMO-SkyMed (CSK) SAR data. The presented study, focused on the city of Napoli (Italy), allows us to quantify the dramatic increase of the DInSAR coherent pixel density achieved by exploiting the high resolution X-Band CSK SAR images with respect to the RADARSAT-1 and ENVISAT products, respectively; this permits us to analyze nearly all the structures located within the investigated urbanized area and, in many cases, also portions of a same building. © 2012 IEEE
Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches
Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies â„ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF
Remote Sensing Retrieval Study of the Surface Kinetic Parameters in the Yangtze Estuary and Its Adjacent Waters
Wind and current are significant parameters in the hydrodynamic processes, making a significant effect on the expansion of the Yangtze (Changjiang River) Diluted Water, sediment transport, resuspension and shelf circulation in the Yangtze Estuary. They are indispensable as input parameters in the numerical simulation of these phenomena. Synthetic aperture radar (SAR) can acquire data with different resolutions (down to 1Â m) and coverage (up to 400Â km) over a site during day or night time under all weather conditions, being capable of providing ocean surface kinetic parameters with high resolution. SAR images were collected to verify and improve the validity of wind direction retrieval by 2D fast Fourier transformation (FFT) method, wind speed by CMOD4 model and current by Doppler frequency method. These SAR-retrieved wind and current results were analyzed and assessed against in situ data and corresponding numerically simulated surface wind and current fields. Comparisons to the in situ and simulations show that 1) SAR can measure sea surface wind fields with a high resolution at sub-km scales and provide a powerful complement to conventional wind measurement techniques. 2) The Doppler shift anomaly measurements from SAR images are able to capture quantitative surface currents, thus are helpful to reveal the multi-scale upper layer dynamics around the East China Sea
Offshore oil spill detection using synthetic aperture radar
Among the different types of marine pollution, oil spill has been considered as a major threat to the sea ecosystems. The source of the oil pollution can be located on the mainland or directly at sea. The sources of oil pollution at sea are discharges coming from ships, offshore platforms or natural seepage from sea bed. Oil pollution from sea-based sources can be accidental or deliberate. Different sensors to detect and monitor oil spills could be onboard vessels, aircraft, or satellites. Vessels equipped with specialised radars, can detect oil at sea but they can cover a very limited area. One of the established ways to monitor sea-based oil pollution is the use of satellites equipped with Synthetic Aperture Radar (SAR).The aim of the work presented in this thesis is to identify optimum set of feature extracted parameters and implement methods at various stages for oil spill detection from Synthetic Aperture Radar (SAR) imagery. More than 200 images of ERS-2, ENVSAT and RADARSAT 2 SAR sensor have been used to assess proposed feature vector for oil spill detection methodology, which involves three stages: segmentation for dark spot detection, feature extraction and classification of feature vector. Unfortunately oil spill is not only the phenomenon that can create a dark spot in SAR imagery. There are several others meteorological and oceanographic and wind induced phenomena which may lead to a dark spot in SAR imagery. Therefore, these dark objects also appear similar to the dark spot due to oil spill and are called as look-alikes. These look-alikes thus cause difficulty in detecting oil spill spots as their primary characteristic similar to oil spill spots. To get over this difficulty, feature extraction becomes important; a stage which may involve selection of appropriate feature extraction parameters. The main objective of this dissertation is to identify the optimum feature vector in order to segregate oil spill and âlook-alikeâ spots. A total of 44 Feature extracted parameters have been studied. For segmentation, four methods; based on edge detection, adaptive theresholding, artificial neural network (ANN) segmentation and the other on contrast split segmentation have been implemented. Spot features are extracted from both the dark spots themselves and their surroundings. Classification stage was performed using two different classification techniques, first one is based on ANN and the other based on a two-stage processing that combines classification tree analysis and fuzzy logic. A modified feature vector, including both new and improved features, is suggested for better description of different types of dark spots. An ANN classifier using full spectrum of feature parameters has also been developed and evaluated. The implemented methodology appears promising in detecting dark spots and discriminating oil spills from look-alikes and processing time is well below any operational service requirements
Quantitative Estimation of Surface Soil Moisture in Agricultural Landscapes using Spaceborne Synthetic Aperture Radar Imaging at Different Frequencies and Polarizations
Soil moisture and its distribution in space and time plays an important role in the surface energy balance at the soil-atmosphere interface. It is a key variable influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Due to their large spatial variability, estimation of spatial patterns of soil moisture from field measurements is difficult and not feasible for large scale analyses. In the past decades, Synthetic Aperture Radar (SAR) remote sensing has proven its potential to quantitatively estimate near surface soil moisture at high spatial resolutions. Since the knowledge of the basic SAR concepts is important to understand the impact of different natural terrain features on the quantitative estimation of soil moisture and other surface parameters, the fundamental principles of synthetic aperture radar imaging are discussed. Also the two spaceborne SAR missions whose data was used in this study, the ENVISAT of the European Space Agency (ESA) and the ALOS of the Japanese Aerospace Exploration Agency (JAXA), are introduced. Subsequently, the two essential surface properties in the field of radar remote sensing, surface soil moisture and surface roughness are defined, and the established methods of their measurement are described. The in situ data used in this study, as well as the research area, the River Rur catchment, with the individual test sites where the data was collected between 2007 and 2010, are specified. On this basis, the important scattering theories in radar polarimetry are discussed and their application is demonstrated using novel polarimetric ALOS/PALSAR data. A critical review of different classical approaches to invert soil moisture from SAR imaging is provided. Five prevalent models have been chosen with the aim to provide an overview of the evolution of ideas and techniques in the field of soil moisture estimation from active microwave data. As the core of this work, a new semi-empirical model for the inversion of surface soil moisture from dual polarimetric L-band SAR data is introduced. This novel approach utilizes advanced polarimetric decomposition techniques to correct for the disturbing effects from surface roughness and vegetation on the soil moisture retrieval without the use of a priori knowledge. The land use specific algorithms for bare soil, grassland, sugar beet, and winter wheat allow quantitative estimations with accuracies in the order of 4 Vol.-%. Application of remotely sensed soil moisture patterns is demonstrated on the basis of mesoscale SAR data by investigating the variability of soil moisture patterns at different spatial scales ranging from field scale to catchment scale. The results show that the variability of surface soil moisture decreases with increasing wetness states at all scales. Finally, the conclusions from this dissertational research are summarized and future perspectives on how to extend the proposed model by means of improved ground based measurements and upcoming advances in sensor technology are discussed. The results obtained in this thesis lead to the conclusion that state-of-the-art spaceborne dual polarimetric L-band SAR systems are not only suitable to accurately retrieve surface soil moisture contents of bare as well as of vegetated agricultural fields and grassland, but for the first time also allow investigating within-field spatial heterogeneities from space
Coupled modelling of land surface microwave interactions using ENVISAT ASAR data
In the last decades microwave remote sensing has proven its capability to provide
valuable information about the land surface. New sensor generations as e.g.
ENVISAT ASAR are capable to provide frequent imagery with an high information
content. To make use of these multiple imaging capabilities, sophisticated
parameter inversion and assimilation strategies have to be applied. A profound
understanding of the microwave interactions at the land surface is therefore
essential.
The objective of the presented work is the analysis and quantitative description of
the backscattering processes of vegetated areas by means of microwave
backscattering models. The effect of changing imaging geometries is investigated
and models for the description of bare soil and vegetation backscattering are
developed. Spatially distributed model parameterisation is realized by synergistic
coupling of the microwave scattering models with a physically based land surface
process model. This enables the simulation of realistic SAR images, based on bioand
geophysical parameters.
The adequate preprocessing of the datasets is crucial for quantitative image
analysis. A stringent preprocessing and sophisticated terrain geocoding and
correction procedure is therefore suggested. It corrects the geometric and
radiometric distortions of the image products and is taken as the basis for further
analysis steps.
A problem in recently available microwave backscattering models is the inadequate
parameterisation of the surface roughness. It is shown, that the use of classical
roughness descriptors, as the rms height and autocorrelation length, will lead to
ambiguous model parameterisations. A new two parameter bare soil backscattering
model is therefore recommended to overcome this drawback. It is derived from
theoretical electromagnetic model simulations. The new bare soil surface scattering
model allows for the accurate description of the bare soil backscattering coefficients.
A new surface roughness parameter is introduced in this context, capable to
describe the surface roughness components, affecting the backscattering
coefficient. It is shown, that this parameter can be directly related to the intrinsic
fractal properties of the surface.
Spatially distributed information about the surface roughness is needed to derive
land surface parameters from SAR imagery. An algorithm for the derivation of the
new surface roughness parameter is therefore suggested. It is shown, that it can be
derived directly from multitemporal SAR imagery.
Starting from that point, the bare soil backscattering model is used to assess the
vegetation influence on the signal. By comparison of the residuals between
measured backscattering coefficients and those predicted by the bare soil
backscattering model, the vegetation influence on the signal can be quantified.
Significant difference between cereals (wheat and triticale) and maize is observed in
this context.
It is shown, that the vegetation influence on the signal can be directly derived from
alternating polarisation data for cereal fields. It is dependant on plant biophysical
variables as vegetation biomass and water content.
The backscattering behaviour of a maize stand is significantly different from that of
other cereals, due to its completely different density and shape of the plants. A
dihedral corner reflection between the soil and the stalk is identified as the major
source of backscattering from the vegetation. A semiempirical maize backscattering
model is suggested to quantify the influences of the canopy over the vegetation
period.
Thus, the different scattering contributions of the soil and vegetation components
are successfully separated. The combination of the bare soil and vegetation
backscattering models allows for the accurate prediction of the backscattering
coefficient for a wide range of surface conditions and variable incidence angles.
To enable the spatially distributed simulation of the SAR backscattering coefficient,
an interface to a process oriented land surface model is established, which provides
the necessary input variables for the backscattering model. Using this synergistic,
coupled modelling approach, a realistic simulation of SAR images becomes possible
based on land surface model output variables. It is shown, that this coupled
modelling approach leads to promising and accurate estimates of the backscattering
coefficients. The remaining residuals between simulated and measured backscatter
values are analysed to identify the sources of uncertainty in the model. A detailed
field based analysis of the simulation results revealed that imprecise soil moisture
predictions by the land surface model are a major source of uncertainty, which can
be related to imprecise soil texture distribution and soil hydrological properties.
The sensitivity of the backscattering coefficient to the soil moisture content of the
upper soil layer can be used to generate soil moisture maps from SAR imagery. An
algorithm for the inversion of soil moisture from the upper soil layer is suggested
and validated. It makes use of initial soil moisture values, provided by the land
surface process model. Soil moisture values are inverted by means of the coupled
land surface backscattering model. The retrieved soil moisture results have an RMSE
of 3.5 Vol %, which is comparable to the measurement accuracy of the reference
field data.
The developed models allow for the accurate prediction of the SAR backscattering
coefficient. The various soil and vegetation scattering contributions can be
separated. The direct interface to a physically based land surface process model
allows for the spatially distributed modelling of the backscattering coefficient and
the direct assimilation of remote sensing data into a land surface process model.
The developed models allow for the derivation of static and dynamic landsurface
parameters, as e.g. surface roughness, soil texture, soil moisture and biomass from
remote sensing data and their assimilation in process models. They are therefore
reliable tools, which can be used for sophisticated practice oriented problem
solutions in manifold manner in the earth and environmental sciences
Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review
The coastal zone offers among the worldâs most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of landâ and waterârelated applications in coastal zones. Compared to optical satellites, cloudâcover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have allâweather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloudâprone tropical and subâtropical climates. The canopy penetration capability with long radar wavelength enables Lâband SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban
sprawl and climate changeâinduced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne Lâband SAR data for geoscientific
analyses that are relevant for coastal land applications
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