Marco Chini

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.

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.

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.

Comparing Statistical and Neural Network Methods Applied to Very High Resolution Satellite Images Showing Changes in Man-Made Structures at Rocky Flats

Parametric and nonparametric approaches to evaluate land-cover change detection using very high resolution (VHR) satellite imagery are applied to the analysis of the demolition of the Rocky Flats nuclear weapons facility located near Denver, CO. Both maximum-likelihood and neural network classifiers are used to validate a new parallel architecture which improves the accuracy when applied to VHR satellite imagery for the study of land-cover change between sequential satellite acquisitions. An enhancement of about 14% was found between the single-step classification and the new parallel architecture, confirming the advantage and the robust improvement obtained with this architecture regardless of the classification algorithm used. In this paper, we demonstrate and document the demolition and removal of hundreds of buildings taken down to bare soil between 2003 and 2005 at the Rocky Flats site.

Uplift and subsidence due to the 26 December 2004 Indonesian earthquake detected by SAR data

The Indonesian earthquake took place on 26 December 2004 at 00:58 GMT (moment magnitude 9.3) in the Indian Ocean, offshore the west coast of Sumatra, at a depth of about 30 km. This earthquake is one of the largest of the past 100 years, comparable only with those in Chile (1960) and Alaska (1964). The earthquake originated in the subduction zone of the Indian and Burma plates, moving at a relative velocity of 6 cm/year. The aftershocks were distributed along a plate boundary of about 1000–1300 km between Sumatra and the Andaman Islands. Some hours after the earthquake a destructive tsunami followed and hit the coastlines of the surrounding regions, causing widespread destruction in Indonesia, India, Thailand and Sri Lanka. The European Space Agency (ESA) made available a data package composed of European Remote Sensing Satellite Synthetic Aperture Radar (ERS‐SAR) and Environment Satellite Advanced SAR (ENVISAT‐ASAR) data covering the affected area, acquired before (four acquisition dates) and after (five acquisition dates) the earthquake. A total of 26 frames were analysed. We used this dataset to evaluate the effects of the earthquake and tsunami on the human settlements and the physiographic conditions along the coast. The proposed method is based on a visual comparison between pre‐ and post‐seismic SAR intensity images, and on an analysis of their correlation coefficients. No complex data were made available by the ESA to exploit phase coherence. Analysis of pre‐ and post‐earthquake SAR backscattering showed wide uplift areas between the Andaman Islands and Simeulue Island, and large modifications of the coastline of Sumatra. Subsiding areas were detected along the southeast coast of Andaman up to the west coast of Nicobar Island. Tidal effects were filtered out of the SAR images to identify the consequences of the earthquake. Global Positioning System (GPS) stations in the Andaman provided results confirming the surface displacement pattern detected by SAR. The analysis enabled us to draw a boundary line separating the uplift and subsidence.

The May 12, 2008, (Mw 7.9) Sichuan Earthquake (China): Multiframe ALOS-PALSAR DInSAR Analysis of Coseismic Deformation

A destructive (Mw 7.9) earthquake affected the Sichuan province (China) on May 12, 2008. The seismic event ruptured approximately 270 km of the Yingxiu-Beichuan fault and about 70 km of the Guanxian-Anxian fault. Surface effects were suffered over a wide epicentral area (about 300 km E-W and 250 km N-S). We apply the differential synthetic aperture radar interferometry (DInSAR) technique to detect and measure the surface displacement field, using a set of ALOS-PALSAR L-band SAR images. We combine an unprecedented high number of data (25 frames from six adjacent tracks) to encompass the entire area which has coseismically displaced. The resulting mosaic of differential interferograms covers an overall area of about 340 km E-W and 240 km N-S. We investigate the source of the Sichuan earthquake by modeling the DInSAR data. The geometry and position of the fault parameters are inferred by a nonlinear inversion, followed by a linear inversion to retrieve the relative slip distribution. Our results show two different source mechanisms for the 145-long Yingxiu-Beichuan fault and for the 105-long Beichuan-Qingchuan fault. Both faults are characterized by slip concentrations of up to 8 m.

X-, C-, and L-Band DInSAR Investigation of the April 6, 2009, Abruzzi Earthquake

This letter compares the coseismic deformation maps obtained from different synthetic aperture radar (SAR) sensors using the well-known differential SAR interferometry technique. In particular, four deformation maps have been obtained from X-, C-, and L-band SAR sensors onboard COSMO-SkyMed, Envisat, and ALOS satellite missions correspondingly. The test case is the April 6,2009, earthquake (Mw = 6.3). This seismic event struck a densely populated region of the Apennines and was felt all over Central Italy. The SAR data set is rather inhomogeneous, since it includes interferograms with three different wavelengths, four acquisition geometries, different spatial resolutions, variable temporal and spatial baselines, and differently emphasized signal noise. However, we find that the detected displacements are highly comparable. The outcome of this work is that, even though such differences have an impact on the properties of the interferograms, the displacements can be measured with an overall discrepancy of about half the value of the shortest wavelength (COSMO-SkyMed) data set.

The 2011 Tohoku (Japan) Tsunami Inundation and Liquefaction Investigated Through Optical, Thermal, and SAR Data

We studied the disastrous effects of the tsunami triggered by the Mw 9.0 earthquake that occurred on March 11, 2011, offshore the Honshu island (Japan). The tsunami caused a huge amount of casualties and severe damage along most of the eastern coastline of the island. The data set used is composed of images from ASTER, visible and thermal, and ENVISAT SAR sensors. The processing and the analysis of data from different sources were performed in order to obtain the tsunami inundation map of the Sendai coastal area, to analyze inland factors driving the tsunami inundation, and to detect the liquefaction effects in the Chiba bay area as well. The obtained inundation line, with a maximum value of about 6 km, has been jointly analyzed with digital elevation model providing the run-up values, which are generally below 21 m in the ca. 60-km-long study area of Sendai. Moreover, from SAR coherence and intensity correlation, a wide area of subsidence is mapped at Chiba bay, which is reasonably related to strong ground shaking and pervasive liquefaction.

Probabilistic Flood Mapping Using Synthetic Aperture Radar Data

Probabilistic flood mapping offers flood managers, decision makers, insurance agencies, and humanitarian relief organizations a useful characterization of uncertainty in flood mapping delineation. Probabilistic flood maps are also of high interest for data assimilation into numerical models. The direct assimilation of probabilistic flood maps into hydrodynamic models would be beneficial because it would eliminate the intermediate step of having to extract water levels first. This paper introduces a probabilistic flood mapping procedure based on synthetic aperture radar (SAR) data. Given a SAR image of backscatter values, we construct a total histogram of backscatter values and decompose this histogram into probability distribution functions of backscatter values associated with flooded (open water) and non-flooded pixels, respectively. These distributions are then used to estimate, for each pixel, its probability of being flooded. The new approach improves on binary SAR-based flood mapping procedures, which do not inform on the uncertainty in the pixel state. The proposed approach is tested using four SAR images from two floodplains, i.e., the Severn River (U.K.) and the Red River (U.S.). In all four test cases, reliability diagrams, with error values ranging from 0.04 to 0.23, indicate a good agreement between the SAR-derived probabilistic flood map and an independently available validation map, which is obtained from aerial photography.