Landslides are widespread natural hazards that generate considerable damage and economic losses worldwide. Detecting terrain movements caused by these phenomena and characterizing affected urban areas is critical to reduce their impact. Here we present a fast and simple methodology to create maps of vulnerable buildings affected by slow-moving landslides, based on two parameters: (1) the deformation rate associated to each building, measured from Sentinel-1 SAR data, and (2) the building damage generated by the landslide movement and recorded during a field campaign. We apply this method to Arcos de la Frontera, a monumental town in South Spain affected by a slow-moving landslide that has caused severe damage to buildings, forcing the evacuation of some of them. Our results show that maximum deformation rates of 4 cm/year in the line-of-sight (LOS) of the satellite, affects La Verbena, a newly-developed area, and displacements are mostly horizontal, as expected for a planar-landslide. Our building damage assessment reveals that most of the building blocks in La Verbena present moderate to severe damages. According to our vulnerability scale, 93% of the building blocks analysed present high vulnerability and, thus, should be the focus of more in-depth local studies to evaluate the serviceability of buildings, prior to adopting the necessary mitigation measures to reduce or cope with the negative consequences of this landslide. This methodology can be applied to slow-moving landslides worldwide thanks to the global availability of Sentinel-1 SAR data.
Radar altimetry provides valuable measurements to characterize the state and the evolution of the ice sheet cover of Antartica and Greenland. Global Navigation Satellite System Reflectometry (GNSS-R) has the potential to complement the dedicated radar altimeters, increasing the temporal and spatial resolution of the measurements. Here we perform a study of the Greenland ice sheet using data obtained by the GNSS-R instrument aboard the British TechDemoSat-1 (TDS-1) satellite mission. TDS-1 was primarily designed to provide sea state information such as sea surface roughness or wind, but not altimetric products. The data have been analyzed with altimetric methodologies, already tested in aircraft based experiments, to extract signal delay observables to be used to infer properties of the Greenland ice sheet cover. The penetration depth of the GNSS signals into ice has also been considered. The large scale topographic signal obtained is consistent with the one obtained with ICEsat GLAS sensor, with differences likely to be related to L-band signal penetration into the ice and the along-track variations in structure and morphology of the firn and ice volumes The main conclusion derived from this work is that GNSS-R also provides potentially valuable measurements of the ice sheet cover. Thus, this methodology has the potential to complement our understanding of the ice firn and its evolution.
Knowledge of the exact statistical properties of the signal plays an important role in the applications of Polarimetric Synthetic Aperture Radar (PolSAR) data. In the last three decades, a considerable research effort has been devoted to finding accurate statistical models for PolSAR data, and a number of distributions have been proposed. In order to see the differences of various models and to make a comparison among them, a survey is provided in this paper. Texture models, which could capture the non-Gaussian behavior observed in high resolution data, and yet keep a compact mathematical form, are mainly explained. Probability density functions for the single look data and the multilook data are reviewed, as well as the advantages and applicable context of those models. As a summary, challenges in the area of statistical analysis of PolSAR data are also discussed.
Pablos, M.; Martínez-Fernández, J.; Piles, M.; Sánchez, N.; Vall-llossera, M.; Camps, A. Remote sensing Vol. 8(7), num. 587, p. 1-20 DOI: 10.3390/rs8070587 Data de publicació: 2016-07-11 Article en revista
Soil moisture (SM) is an important component of the Earth’s surface water balance and by extension the energy balance, regulating the land surface temperature (LST) and evapotranspiration (ET). Nowadays, there are two missions dedicated to monitoring the Earth’s surface SM using L-band radiometers: ESA’s Soil Moisture and Ocean Salinity (SMOS) and NASA’s Soil Moisture Active Passive (SMAP). LST is remotely sensed using thermal infrared (TIR) sensors on-board satellites,
such as NASA’s Terra/Aqua MODIS or ESA & EUMETSAT’s MSG SEVIRI. This study provides an assessment of SM and LST dynamics at daily and seasonal scales, using 4 years (2011–2014) of in situ and satellite observations over the central part of the river Duero basin in Spain. Specifically, the agreement of instantaneous SM with a variety of LST-derived parameters is analyzed to better understand the fundamental link of the SM–LST relationship through ET and thermal inertia.
Ground-based SM and LST measurements from the REMEDHUS network are compared to SMOS SM and MODIS LST spaceborne observations. ET is obtained from the HidroMORE regional hydrological model. At the daily scale, a strong anticorrelation is observed between in situ SM and maximum LST (R ˜ -0.6 to -0.8), and between SMOS SM and MODIS LST Terra/Aqua day (R ˜ - 0.7). At
the seasonal scale, results show a stronger anticorrelation in autumn, spring and summer (in situ R ˜ -0.5 to -0.7; satellite R ˜ -0.4 to -0.7) indicating SM–LST coupling, than in winter (in situ R ˜ +0.3; satellite R ˜ -0.3) indicating SM–LST decoupling. These different behaviors evidence changes from water-limited to energy-limited moisture flux across seasons, which are confirmed by
the observed ET evolution. In water-limited periods, SM is extracted from the soil through ET until critical SM is reached. A method to estimate the soil critical SM is proposed. For REMEDHUS, the critical SM is estimated to be ~0.12 m3/m3
, stable over the study period and consistent between in situ and satellite observations. A better understanding of the SM–LST link could not only help improving the representation of LST in current hydrological and climate prediction models, but also refining SM retrieval or microwave-optical disaggregation algorithms, related to ET and vegetation status.
Global Navigation Satellite Systems-Reflectometry (GNSS-R) is an emerging remote sensing technique that uses navigation signals reflected on the Earth's surface as sources of opportunity for scatterometry and altimetry. The time-domain statistics of the electromagnetic bias in GNSS-R altimetry are investigated to assess the residual electromagnetic bias after averaging during the dwell time (as long as 100 s). A three-dimensional time-evolving sea surface is generated using Elfouhaily's ocean surface height spectrum and spreading function. This surface is illuminated by a right hand circular polarization electromagnetic wave at L-band. Then, the scattered waves are computed using the Physical Optics method under the Kirchhoff Approximation. The electromagnetic bias is estimated using a numerical technique previously validated at C- and Ku-bands, and then extrapolated at L-band. Montecarlo simulations for different sea surface realizations consecutive in time are performed so as to analyze the electromagnetic bias statistics up to the 4(th) order moments. Histograms and distribution of the time domain electromagnetic bias are also used for statistical interpretation. All statistical descriptors confirmed that the electromagnetic bias has a non-Gaussian behavior. This study is important to assess the residual electromagnetic bias in future GNSS-R altimetry missions, such as the GNSS Reflectometry, Radio Occultation and Scatterometry on board the International Space Station experiment onboard the International Space Station.
Sánchez, N.; Alonso-Arroyo, A.; Martinez, J.; Piles, M.; González, Á.; Camps, A.; Vall-llossera, M. Remote sensing Vol. 7, num. 8, p. 9954-9974 DOI: 10.3390/rs70809954 Data de publicació: 2015-08-01 Article en revista
While the synergy between thermal, optical, and passive microwave observations is well known for the estimation of soil moisture and vegetation parameters, the use of remote sensing sources based on the Global Navigation Satellite Systems (GNSS) remains unexplored. During an airborne campaign performed in August 2014, over an agricultural area in the Duero basin (Spain), an innovative sensor developed by the Universitat Politecnica de Catalunya-Barcelona Tech based on GNSS Reflectometry (GNSS-R) was tested for soil moisture estimation. The objective was to evaluate the combined use of GNSS-R observations with a time-collocated Landsat 8 image for soil moisture retrieval under semi-arid climate conditions. As a ground reference dataset, an intensive field campaign was carried out. The Light Airborne Reflectometer for GNSS-R Observations (LARGO) observations, together with optical, infrared, and thermal bands from Landsat 8, were linked through a semi-empirical model to field soil moisture. Different combinations of vegetation and water indices with LARGO subsets were tested and compared to the in situ measurements. Results showed that the joint use of GNSS-R reflectivity, water/vegetation indices and thermal maps from Landsat 8 not only allows capturing soil moisture spatial gradients under very dry soil conditions, but also holds great promise for accurate soil moisture estimation (correlation coefficients greater than 0.5 were obtained from comparison with in situ data).
The scattering of GNSS signals over a water surface is studied when the receiver is at a low height, as in GNSS-R coastal altimetry. The precise determination of the local sea level and wave state from the coast will provide useful altimetry and wave information as "dry" tide and wave gauges. An experiment has been conducted at the Canal d'Investigacio i Experimentacio Maritima (CIEM) wave channel for two simulated "sea" states. The GNSS-reflectometer used is the P(Y) and C/A ReflectOmeter (PYCARO) instrument, a closed-loop receiver with delay and Doppler tracking loops that uses the conventional GNSS-R technique for the GPS C/A code. After retracking of the scattered GPS signals, the coherent and incoherent components have been studied. To reproduce the transmitted GPS signals indoors, a Rohde and Schwarz signal generator is used. It is found that, despite the ratio of the coherent and incoherent components being ~1, the coherent component is strong enough that it can be tracked. The coherent component comes from clusters of points on the surface that approximately satisfy the specular reflection conditions ("roughed facet"). The Pearson's linear correlation coefficients of the derived "sea" surface height with the wave gauge data are: 0.78, 0.85 and 0.81 for a SWH = 36 cm and 0.34, 0.74, and 0.72 for a SWH = 64 cm, respectively, for transmitter elevation angles of; [GRAPHICS]; = 60 degrees, 75 degrees and 86 degrees, respectively. Finally, the rms phase of the received signal before the retracking processing is used to estimate the effective rms surface height of the 'facets', where the waves get scattered. It is found to be between 2.5- and 4.1-times smaller than the theoretical values corresponding to the half of the coherent reflectivity decaying factor.
The scattering of GNSS signals over a water surface is studied when the receiver is at a low height, as in GNSS-R coastal altimetry. The precise determination of the local sea level and wave state from the coast will provide useful altimetry and wave information as “dry” tide and wave gauges. An experiment has been conducted at the Canal d'Investigació i Experimentació Marítima (CIEM) wave channel for two simulated “sea” states. The GNSS-reflectometer used is the P(Y) and C/A ReflectOmeter (PYCARO) instrument, a closed-loop receiver with delay and Doppler tracking loops that uses the conventional GNSS-R technique for the GPS C/A code. After retracking of the scattered GPS signals, the coherent and incoherent components have been studied. To reproduce the transmitted GPS signals indoors, a Rohde and Schwarz signal generator is used. It is found that, despite the ratio of the coherent and incoherent components being ~1, the coherent component is strong enough that it can be tracked. The coherent component comes from clusters of points on the surface that approximately satisfy the specular reflection conditions (“roughed facet”). The Pearson’s linear correlation coefficients of the derived “sea” surface height with the wave gauge data are: 0.78, 0.85 and 0.81 for a SWH = 36 cm and 0.34, 0.74, and 0.72 for a SWH = 64 cm, respectively, for transmitter elevation angles of = 60°, 75° and 86°, respectively. Finally, the rms phase of the received signal before the retracking processing is used to estimate the effective rms surface height of the ‘facets’, where the waves get scattered. It is found to be between 2.5- and 4.1-times smaller than the theoretical values corresponding to the half of the coherent reflectivity decaying factor.
The first-ever dual-frequency multi-constellation Global Navigation Satellite Systems Reflectometry (GNSS-R) polarimetric measurements over boreal forests and lakes from the stratosphere are presented. Data were collected during the Swedish National Space Board (SNSB)/European Space Agency (ESA) sponsored Balloon Experiments for University Students (BEXUS) 19 stratospheric balloon experiment using the P(Y) and C/A ReflectOmeter (PYCARO) instrument operated in closed-loop mode. Maps of the polarimetric ratio for L1 and L2 Global Positioning System (GPS) and GLObal Navigation Satellite System (GLONASS), and for E1 Galileo signals are derived from the float phase at 27,000 m height, and the specular points are geolocalized on the Earth's surface. Polarimetric ratio (Grl/Grr) maps over boreal forests are shown to be in the range 2-16 dB for the different GNSS codes. This result suggests that the scattering is taking place not only over the soil, but over the different forests elements as well. Additionally to the interpretation of the experimental results a theoretical investigation of the different contributions to the total reflectivity over boreal forests is performed using a bistatic scattering model. The simulated cross- (reflected Left Hand Circular Polarization LHCP) and co-polar (reflected Right Hand Circular Polarization RHCP) reflectivities are evaluated for the soil, the canopy, and the canopy-soil interactions for three different biomass densities: 725 trees/ha, 150 trees/ha and 72 trees/ha. For elevation angles larger than the Brewster angle, it is found that the cross-polar signal is dominant when just single reflections over the forests are evaluated, while in the case of multiple reflections the co-polar signal becomes the largest one.
Prior to the application of any persistent scatterer interferometry (PSI) technique for the monitoring of terrain displacement phenomena, an adequate pixel selection must be carried out in order to prevent the inclusion of noisy pixels in the processing. The rationale is to detect the so-called persistent scatterers, which are characterized by preserving their phase quality along the multi-temporal set of synthetic aperture radar (SAR) images available. Two criteria are mainly available for the estimation of pixels' phase quality, i.e., the coherence stability and the amplitude dispersion or permanent scatterers (PS) approach. The coherence stability method allows an accurate estimation of the phase statistics, even when a reduced number of SAR acquisitions is available. Unfortunately, it requires the multi-looking of data during the coherence estimation, leading to a spatial resolution loss in the final results. In contrast, the PS approach works at full-resolution, but it demands a larger number of SAR images to be reliable, typically more than 20. There is hence a clear limitation when a full-resolution PSI processing is to be carried out and the number of acquisitions available is small. In this context, a novel pixel selection method based on exploiting the spectral properties of point-like scatterers, referred to as temporal sublook coherence (TSC), has been recently proposed. This paper seeks to demonstrate the advantages of employing PSI techniques by means of TSC on both orbital and ground-based SAR (GB-SAR) data when the number of images available is small (10 images in the work presented). The displacement maps retrieved through the proposed technique are compared, in terms of pixel density and phase quality, with traditional criteria. Two X-band datasets composed of 10 sliding spotlight TerraSAR-X images and 10 GB-SAR images, respectively, over the landslide of El Forn de Canillo (Andorran Pyrenees), are employed for this study. For both datasets, the TSC technique has showed an excellent performance compared with traditional techniques, achieving up to a four-fold increase in the number of persistent scatters detected, compared with the coherence stability approach, and a similar density compared with the PS approach, but free of outliers.
The miniaturization of electronics, computers and sensors has created new opportunities for remote sensing applications. Despite the current restrictions on regulation, the use of unmanned aerial vehicles equipped with small thermal, laser or spectral sensors has emerged as a promising alternative for assisting modeling, mapping and monitoring applications in rangelands, forests and agricultural environments. This review provides an overview of recent research that has reported UAV flight experiments on the remote sensing of vegetated areas. To provide a differential trend to other reviews, this paper is not limited to crops and precision agriculture applications, but also includes forest and rangeland applications. This work follows a top-down categorization strategy and attempts to fill the gap between application requirements and the characteristics of selected tools, payloads and platforms. Furthermore, correlations between common requirements and the most frequently used solutions are highlighted.
Columns are one of the most usual supporting structures in a large number of cultural heritage buildings. However, it is difficult to obtain accurate information about their inner structure. Non-destructive testing (NDT) methodologies are usually applied, but results depend on the complexity of the column. Non-flat external surfaces and unknown and irregular internal materials complicate the interpretation of data. This work presents the study of one column by using ground-penetrating radar (GPR) combined with seismic tomography, under laboratory conditions, in order to obtain the maximum information about the structure. This column belongs to a “Modernista” building from Barcelona
(Spain). These columns are built with irregular and fragmented clay bricks and mortar. The
internal irregular and complex structure causes complicated 2D images, evidencing the
existence of many different targets. However, 3D images provide valuable information about the presence and the state of an internal tube and show, in addition, that the column is made of uneven and broken bricks. GPR images present high correlation with seismic data and endoscopy observation carried out in situ. In conclusion, the final result of the study provides information and 3D images of damaged areas and inner structures. Comparing the different methods to the real structure of the column, the potential and
limitations of GPR were evaluated.
Donana National Park wetlands are located in southwest Spain, on the right bank of the Guadalquivir River, near the Atlantic Ocean coast. The wetlands dry out completely every summer and progressively flood again throughout the fall and winter seasons. Given the flatness of Donana's topography, the wind drag action can induce the flooding or emergence of extensive areas, detectable in remote sensing images. Envisat/ASAR scenes acquired before and during strong and persistent wind episodes enabled the spatial delineation of the wind-induced water displacement. A two-dimensional hydrodynamic model of Donana wetlands was built in 2006 with the aim to predict the effect of proposed hydrologic restoration actions within Donana's basin. In this work, on-site wind records and concurrent ASAR scenes are used for the calibration of the wind-drag modeling by assessing different formulations. Results show a good adjustment between the modeled and observed wind drag effect. Displacements of up to 2 km in the wind direction are satisfactorily reproduced by the hydrodynamic model, while including an atmospheric stability parameter led to no significant improvement of the results. Such evidence will contribute to a more accurate simulation of hypothetic or design scenarios, when no information is available for the atmospheric stability assessment.
Doñana National Park wetlands are located in southwest Spain, on the right bank of the Guadalquivir River, near the Atlantic Ocean coast. The wetlands dry out completely every summer and progressively flood again throughout the fall and winter seasons. Given the flatness of Doñana’s topography, the wind drag action can induce the flooding or emergence of extensive areas, detectable in remote sensing images. Envisat/ASAR scenes acquired before and during strong and persistent wind episodes enabled the spatial delineation of the wind-induced water displacement. A two-dimensional hydrodynamic model of Doñana wetlands was built in 2006 with the aim to predict the effect of proposed hydrologic restoration actions within Doñana’s basin. In this work, on-site wind records and concurrent ASAR scenes are used for the calibration of the wind-drag modeling by assessing different formulations. Results show a good adjustment between the modeled and observed wind drag effect. Displacements of up to 2 km in the wind direction are satisfactorily reproduced by the hydrodynamic model, while including an atmospheric stability parameter led to no significant improvement of the results. Such evidence will contribute to a more accurate simulation of hypothetic or design scenarios, when no information is available for the atmospheric stability assessment.
This paper introduces a non-linear Polarimetric SAR data filtering approach able to preserve the edges and small details of the data. It is based on exploiting the data locality in both, the spatial and the polarimetric domains, in order to avoid mixing heterogeneous samples of the data. A weighted average is performed over a given window favoring pixel values that are close on both domains. The filtering technique is based on a modified bilateral filtering, which is defined in terms of spatial and polarimetric distances. These distances encapsulate all the knowledge in both domains for an adaptation to the data structure. Finally, the proposed technique is employed to process a real RADARSAT-2 dataset.
This paper proposes the optimization relaxation approach based on the analogue
Hopfield Neural Network (HNN) for cluster refinement of pre-classified Polarimetric
Synthetic Aperture Radar (PolSAR) image data. We consider the initial classification
provided by the maximum-likelihood classifier based on the complex Wishart distribution,
which is then supplied to the HNN optimization approach. The goal is to improve the
classification results obtained by the Wishart approach. The classification improvement is
verified by computing a cluster separability coefficient and a measure of homogeneity
within the clusters. During the HNN optimization process, for each iteration and for each
pixel, two consistency coefficients are computed, taking into account two types of relations
between the pixel under consideration and its corresponding neighbors. Based on these
coefficients and on the information coming from the pixel itself, the pixel under study is
re-classified. Different experiments are carried out to verify that the proposed approach
outperforms other strategies, achieving the best results in terms of separability and a
trade-off with the homogeneity preserving relevant structures in the image. The performance
is also measured in terms of computational central processing unit (CPU) times.
UAV (unmanned Aerial Vehicle) platforms represent a challenging opportunity for the deployment of a number of remote sensors. These vehicles are a cost-effective option in front of manned aerial vehicles (planes and helicopters), are easy to deploy due to the short runways needed, and they allow users to meet the critical requirements of the spatial and temporal resolutions imposed by the instruments. L-band radiometers are an interesting option for obtaining soil moisture maps over local areas with relatively high spatial resolution for precision agriculture, coastal monitoring, estimation of the risk of fires, flood prevention, etc. This paper presents the design of a light-weight, airborne L-band radiometer for deployment in a small UAV, including the hardware and specific software developed for calibration, geo-referencing, and soil moisture retrieval. First results and soil moisture retrievals from different field experiments are presented.
Surface soil moisture is a key variable needed to understand and predict the
climate. L-band microwave radiometry seems to be the best technique to remotely measure
the soil moisture content, since the influence of other effects such as surface roughness and vegetation is comparatively small. This work describes a numerical model developed to efficiently compute the four elements of the Stokes emission vector (Th, Tv, TU and TV) of vegetation-covered soils at low microwave frequencies, as well as the single-scattering albedo and the extinction coefficient of the vegetation layer over a wide range of incidence angles. A comparison with L-band (1.400–1.427 MHz) experimental radiometric data gathered during the SMOS REFLEX 2003 field experiment over vines is presented and discussed. The measured and simulated emissivities at vertical polarization agree very well.
However, at horizontal polarization there is some disagreement introduced by the soil
emission model. Important radiometric parameters, such as the albedo, the attenuation and the transmissivity are computed and analyzed in terms of their values and trends, as well as
their dependence on the observation and scene parameters. It is found that the vegetation
attenuation is mainly driven by the presence of branches and leaves, while the albedo is mainly driven by the branches. The comparison of the simulated parameters with the values obtained by fitting the experimental data with the τ-ω model is very satisfactory.
Accurate models are used today for infrared and microwave satellite radiance simulations of the first two Stokes elements in the physical retrieval, data assimilation etc. of surface and atmospheric parameters. Although in the past a number of theoretical and experimental works have studied the polarimetric emission of some natural surfaces, specially the sea surface roughened by the wind (Windsat mission), very limited studies
have been conducted on the polarimetric emission of rain cells or other natural surfaces. In this work, the polarimetric emission (four Stokes elements) of a rain cell is computed using the polarimetric radiative transfer equation assuming that raindrops are described by
Pruppacher-Pitter shapes and that their size distribution follows the Laws-Parsons law. The Boundary Element Method (BEM) is used to compute the exact bistatic scattering
coefficients for each raindrop shape and different canting angles. Numerical results are compared to the Rayleigh or Mie scattering coefficients, and to Oguchi’s ones, showing that above 1-2 mm raindrop size the exact formulation is required to model properly the
scattering. Simulation results using BEM are then compared to the experimental data
gathered with a X-band polarimetric radiometer. It is found that the depolarization of the radiation caused by the scattering of non-spherical raindrops induces a non-zero third Stokes parameter, and the differential phase of the scattering coefficients induces a non-zero fourth Stokes parameter.