Nazzareno Pierdicca

Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies

In case of a seismic event, a fast and draft damage map of the hit urban areas can be very useful, in particular when the epicentre of the earthquake is located in remote regions, or the main communication systems are damaged. Our aim is to analyse the capability of remote sensing techniques for damage detection in urban areas and to explore the combined use of radar (SAR) and optical satellite data. Two case studies have been proposed: Izmit (1999; Turkey) and Bam (2003; Iran). Both areas have been affected by strong earthquakes causing heavy and extended damage in the urban settlements close to the epicentre. Different procedures for damage assessment have been successfully tested, either to perform a pixel by pixel classification or to assess damage within homogeneous extended areas. We have compared change detection capabilities of different features extracted from optical and radar data, and analysed the potential of combining measurements at different frequency ranges. Regarding the Izmit case, SAR features alone have reached 70% of correct classification of damaged areas and 5 m panchromatic optical images have given 82%; the fusion of SAR and optical data raised up to 89% of correct pixel‐to‐pixel classification. The same procedures applied to the Bam test case achieved about 61% of correct classification from SAR alone, 70% from optical data, while data fusion reached 76%. The results of the correlation between satellite remote sensing and ground surveys data have been presented by comparing remotely change detection features averaged within homogeneous blocks of buildings with ground survey data.

Bayesian estimation of precipitating cloud parameters from combined measurements of spaceborne microwave radiometer and radar

The objective of this paper is to evaluate the potential of a Bayesian inversion algorithm using microwave multisensor data for the retrieval of surface rainfall rate and cloud parameters. The retrieval scheme is based on the maximum a posteriori probability (MAP) method, extended for the use of both spaceborne passive and active microwave data. The MAP technique for precipitation profiling is also proposed to approach the problem of the radar-swath synthetic broadening; that is, the capability to exploit the combined information also where only radiometric data are available. In order to show an application to airborne data, two case studies are selected within the Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE). They refer to a stratiform storm region and an intense squall line of two mesoscale convective systems, which occurred over the ocean on February 20 and 22, 1993, respectively. The estimated rainfall rates and columnar hydrometeor contents derived from the proposed algorithms are compared to each other and to radar estimates based on reflectivity-rainrate (Z-R) relationships. Results in terms of reflectivity profiles and upwelling brightness temperatures, reconstructed from the estimated cloud structures, are also discussed. A database of combined measurements acquired at nadir during various TOGA-COARE flights, is used for applying the radar-swath synthetic broadening technique in the case of an along-track radar-failure countermeasure. A simulated test of the latter technique is performed using the case studies of February 20 and 22, 1993.

Exploiting SAR and VHR Optical Images to Quantify Damage Caused by the 2003 Bam Earthquake

Using satellite sensors to detect urban damage and other surface changes due to earthquakes is gaining increasing interest. Optical images at different resolutions and radar images represent useful tools for this application, particularly when more frequent revisit times will be available with the implementation of new missions and future possible constellations of satellites. Very high resolution (VHR) images (on the order of 1 m or less) may provide information at the scale of a single building, whereas images at resolutions on the order of tens of meters may give indications of damage levels at a district scale. Both types of information may be extremely important if provided with sufficient timeliness to rescue teams. The earthquake that hit the city of Bam, Iran, has been taken as a test case, where QuickBird VHR optical images and advanced synthetic aperture radar data were available both before and after the event. Methods to process these data in order to detect damage and to extract features used to estimate damage levels are investigated in this paper, pointing out the significant potential of these satellite data and their possible synergy.

Airborne GNSS-R Polarimetric Measurements for Soil Moisture and Above-Ground Biomass Estimation

Soil moisture content (SMC) and above-ground biomass (AGB) are key parameters for the understanding of both the hydrological and carbon cycles. From an economical perspective, both SMC and AGB play a significant role in the agricultural sector, one of the most relevant markets worldwide. This paper assesses the sensitivity of Global Navigation Satellite System (GNSS) reflected signals to soil moisture and vegetation biomass from an experimental point of view. For that, three scientific flights were performed in order to acquire GNSS reflectometry (GNSS-R) polarimetric observations over a wide range of terrain conditions. The GNSS-R data were used to obtain the right-left and right-right reflectivity components, which were then georeferenced according to the transmitting GNSS satellite and receiver positions. It was determined that for low-altitude GNSS-R airborne platforms, the reflectivity polarization ratio provides a highly reliable observable for SMC due to its high stability with respect to surface roughness. A correlation coefficient of 0.93 and a sensitivity of 0.2 dB/SMC (%) were obtained for moderately vegetated fields with a surface roughness standard deviation below 3 cm. Similarly, the copolarized reflection coefficient shows a stable sensitivity to forest AGB with equal to 0.9 with a stable sensitivity of 1.5 dB/(100 t/ha) up to AGB values not detectable by other remote sensing systems.

Global Navigation Satellite Systems Reflectometry as a Remote Sensing Tool for Agriculture

The use of Global Navigation Satellite Systems (GNSS) signals for remote sensing applications, generally referred to as GNSS-Reflectometry (GNSS-R), is gaining increasing interest among the scientific community as a remote sensing tool for land applications. This paper describes a long term experimental campaign in which an extensive dataset of GNSS-R polarimetric measurements was acquired over a crop field from a ground-based stationary platform. Ground truth ancillary data were also continuously recorded during the whole experimental campaign. The duration of the campaign allowed to cover a full crop growing season, and as a consequence of seasonal rains on the experimental area, data could be recorded over a wide variety of soil conditions. This enabled a study on the effects of different land bio-geophysical parameters on GNSS scattered signals. It is shown that significant power variations in the measured GNSS reflected signals can be detected for different soil moisture and vegetation development conditions. In this work we also propose a technique based on the combination of the reflected signal’s polarizations in order to improve the integrity of the observables with respect to nuisance parameters such as soil roughness.

Analysis and Interpretation of the COSMO-SkyMed Observations of the 2011 Japan Tsunami

The major outcomes of the analysis of the COSMO-SkyMed (CSK) synthetic aperture radar (SAR) observations of the area hit by the 2011 Japan tsunami are presented. The height of the tsunami waves was such as to cause a widespread inundation of the coastal area. The SAR acquisitions have been performed on March 12 (i.e., one day after the tsunami occurred) and March 13, 2011 in interferometric mode, so that not only the information on the intensity of the radar signals, but also the complex coherence has been used. The interpretation of the available data has allowed us to detect the flooded areas, as well as the receding of the floodwater from March 12 to March 13, 2011 and the presence of the debris floating above the water surface. Moreover, thanks to the high spatial resolution of the CSK images, the presence of floodwater in some urban areas in the Sendai harbor has been revealed by exploiting the information on the coherence. Our interpretations have been confirmed by a couple of optical images used as benchmarks.

Integrating Physical and Topographic Information Into a Fuzzy Scheme to Map Flooded Area by SAR

A flood mapping procedure based on a fuzzy sets theory has been developed. The method is based on the integration of Synthetic Aperture Radar (SAR) measurements with additional data on the inundated area, such as a land cover map and a digital elevation model (DEM). The information on land cover has allowed us to account for both specular reflection, typical of open water, and double bounce backscattering, typical of forested and urban areas. DEM has been exploited to include simple hydraulic considerations on the dependence of inundation probability on surface characteristics. Contextual information has been taken into account too. The proposed algorithm has been tested on a flood occurred in Italy on November 1994. A pair of ERS-1 images, collected before and after (three days later) the flood, has been used. The results have been compared with the data provided by a ground survey carried out when the flood reached its maximum extension. Despite the temporal mismatch between the survey and the post-inundation SAR image, the comparison has yielded encouraging results, with the 87% of the pixels correctly classified as inundated.

GEROS-ISS: GNSS REflectometry, Radio Occultation, and Scatterometry Onboard the International Space Station

GEROS-ISS stands for GNSS REflectometry, radio occultation, and scatterometry onboard the International Space Station (ISS). It is a scientific experiment, successfully proposed to the European Space Agency in 2011. The experiment as the name indicates will be conducted on the ISS. The main focus of GEROS-ISS is the dedicated use of signals from the currently available Global Navigation Satellite Systems (GNSS) in L-band for remote sensing of the Earth with a focus to study climate change. Prime mission objectives are the determination of the altimetric sea surface height of the oceans and of the ocean surface mean square slope, which is related to sea roughness and wind speed. These geophysical parameters are derived using reflected GNSS signals (GNSS reflectometry, GNSS-R). Secondary mission goals include atmosphere/ionosphere sounding using refracted GNSS signals (radio occultation, GNSS-RO) and remote sensing of land surfaces using GNSS-R. The GEROS-ISS mission objectives and its design, the current status, and ongoing activities are reviewed and selected scientific and technical results of the GEROS-ISS preparation phase are described.

Monitoring Flood Evolution in Vegetated Areas Using COSMO-SkyMed Data: The Tuscany 2009 Case Study

Synthetic Aperture Radar (SAR) systems represent a powerful tool to monitor floods because of their all-weather capability, the very high spatial resolution of the new generation of instruments and the short revisit time of the present and future satellite constellations. To exploit these technological advances, an accurate interpretation of the multitemporal radar signature of the flooded areas is required. Mapping flooded vegetation is a task in which the interpretation of SAR data is not straightforward and should rely on the knowledge about the radar scattering phenomena in the volume between canopy, trunks and floodwater. This paper presents a methodology aiming at mapping flooded areas with a focus on flooded vegetation; the algorithm is based on an image segmentation technique and a fuzzy logic classifier. The tuning of the parameters of the fuzzy algorithm, based on the outputs of a theoretical backscattering model, is described in detail. Ancillary data giving accurate information on land cover are also used to set the input parameters of the model. The methodology is tested on a case study regarding a flood occurred in Tuscany (Central Italy) on December 2009 monitored using COSMO-SkyMed data. The multitemporal radar signatures observed during the event are discussed; it is shown that the simulated radar measurements produced by the selected electromagnetic model agree well with actual data and help their interpretation. Furthermore, a qualitative evaluation of the produced flood maps carried out with the aid of a couple of aerial photos indicates that the proposed methodology is reliable.

Discrimination of Water Surfaces, Heavy Rainfall, and Wet Snow Using COSMO-SkyMed Observations of Severe Weather Events

An automatic method to distinguish water surfaces (either flooded or permanent water bodies) from artifacts caused by heavy precipitation and wet snow is designed to improve flood detection accuracy in X-band synthetic aperture radar (SAR) images. The algorithm implementing the proposed method, mainly based on image segmentation techniques and on the fuzzy logic, consists of two principal steps: 1) detection of regions (or segments) of low-radar backscatter that appear dark in a SAR image, and 2) classification of each detected segment. Ancillary data, such as a local incidence angle map, a land cover map, and an optical image (helpful to detect wet snow), are also used. Through the fuzzy logic, the algorithm integrates different rules for the detection of dark areas, as well as for their classification based on radiometric, geometrical and shape features extracted from the segmented SAR image and on the ancillary data. The algorithm is tested on the COSMO-SkyMed imagery of the severe weather event that hit Northwest Italy on November 2011. A comparison with measured data, provided by the weather radars belonging to the Italian radar national network, and with the ground precipitation, forecasted by a numerical weather prediction model routinely used within the framework of the EUMETSAT Hydrology Satellite Application Facility project, indicates that the algorithm produces reliable classification maps, being able to distinguish the rainfall signature on X-band SAR images from that of flooded areas.