Near real-time SAR based processing to support flood monitoring R. Cossu, E. Schoepfer, Ph. Bally, and L. Fusco ESA-ESRIN, Directorate of Earth Observation Programmes, via Galileo Galilei, 00044 Frascati, Italy +39 06 94180 607 +39 06 94180 532 e-mail:
[email protected]
Abstract Earth Observation has proven to be a synoptic and objective source of information to derive crisis and damage maps. In case of flood events, often characterized by weather conditions which prevent the possibility of exploiting data acquired by optical sensors, Synthetic Aperture Radar sensors become the only space born source of information due to their all-weather capability. In order to assure the delivery of damage maps as soon as possible after a disaster, the access and the exploitation of SAR data must be accelerated and simplified as respect to the current procedures. In this context, two issues needed to be addressed: fast access to large data archives, and provision of near real time on demand processing services. This paper presents a near-real time SAR processing service to support the mapping of flooded areas. The service exploits Grid technology to manage large volumes of data and to provide the computational resources to cope with SAR processing demanding tasks. The algorithm for the implemented orthorectification of the final products is presented. The validation of the derived products shows a reliable accuracy for co-registration of half a pixel. The geolocation accuracy resulted below 100 meters. The service makes a significant contribution in accelerating the access and exploitation of ESA SAR data.
Keywords Near-real time processing; fast data access; grid; SAR; Earth Observation for disaster management
1 Introduction Earth Science (ES) covers a large number of domains varying over topics like the solid earth, the oceans, the land, the cryosphere, the atmosphere and their interfaces up to space weather. ES data come from sensors on different platforms, satellite, plane, boat, balloon, buoy or mast, or located at ground on the land. Petabytes of data are presently underexploited, because there are not enough computing resources, tools and algorithms available to get the results in a 1
reasonable time. This is particularly the case for Earth Observation (EO) sciences where less than 10% of the total amount of data acquired mainly by satellites is currently exploited. However, even if the data were to be made easily available, an efficient infrastructure to handle and treat very large data sets is missing. In order to facilitate the access to data, their processing and visualization, the ES community has developed portals based on web services. In some cases these portals provide access to service-based Grid infrastructure and resources for dynamic processing of ES specific datasets and for high performance processing. There is a wide variety of applications in EO that involve very intensive processing. In case of results used for prediction or rapid damage assessment, the application has to be executed in near-real time. In this case, it also requires fast access to several datasets that are usually distributed in different centres and depends on the availability of computing resources. In the following, we focus on a special case of damage assessment which is flood mapping.
Each year natural and man-made disasters around the world cause loss of life, damage to the environment, infrastructure, and property. EO has proven to be a synoptic and objective source of information to derive crisis and damage maps, which help committed agencies to provide useful information to rescuers and emergency operators in the field within shortest time. In case of flood events, often characterized by weather conditions which prevent the possibility of exploiting data acquired by optical sensors, Synthetic Aperture Radar (SAR) [1] sensors become the only space born source of information due to their all-weather capability. In order to assure the delivery of damage maps as soon as possible after a disaster, the access and the exploitation of SAR data must be accelerated and simplified with respect to the current procedures. One widely used approach to flood mapping is based on change detection techniques. This requires comparing an image acquired after the crisis event with some reference image(s) acquired under normal conditions. To this end, two challenges have to be considered: i) fast access to both recent and historical data; ii) near-real time processing of the products to generate a change detection map (or intermediate products as in the study reported in this paper). To give an idea of the first issue, we mention as an example the ESA SAR archive, which contains several TeraBytes of SAR data useful for this kind of application. With respect to 2
the second issue, it must be stated that SAR products are usually complex to be treated and understood for non specialists. Before comparing two SAR products it is necessary to calibrate them, using auxiliary data and taking into account the geometry of the acquisition. Furthermore given the nature of the sensor, SAR images are in fact affected by serious geometric distortions. Additional data such as digital elevation model are needed to partially correct such distortions. This correction is usually a time consuming task. It is indeed envisaged to provide users with an easy to use environment where emerging technology allows them deriving processed products ready to be used. A description of the complexity of SAR processing is out of the scope of this paper and the reader is kindly referred to [1] for further details on the ENVISAT ASAR sensor and processing algorithms. The importance of a service able to deal with the previous challenges becomes clear with the analysis of the requirements of users of Global Monitoring for Environment and Security (GMES) Service Element (GSE) RESPOND [2] and “International Charter Space and Major Disasters” [3], who come from both the humanitarian aid and the disaster management communities. EO based crisis mapping services are generally delivered via projects such as GSE RISK EOS [8] and GSE RESPOND alongside the International Charter “Space and Major Disasters”, which aims at providing a unified system of space data acquisition and delivery to those affected by natural or man-made disasters through Authorized Users [2],[3]. The previous paragraphs point out the need of considering Research and Technological Developments (RTD) to improve the present system capabilities. Among the many RTD initiatives this paper investigates, in particular, the impact of Grid concepts and technology [9] on the provision of accurate, rapid and large SAR based coverage observations of flooded areas. More specifically, a near-real time SAR processing service to support mapping of flooded area is presented and discussed. This service has become an operational service, called Fast Access to Imagery for Rapid Exploitation (FAIRE) [5],[6],[7]. This service is integrated in the EO Grid Processing on Demand (G-POD) [9]-[11], operated at ESA-ESRIN. G-POD, which is based on the “grid-ify” product (developed by Terradue srl [13]), exploits Grid technology not only to facilitate data access and processing, but also to provide suitable resources to face computational demanding tasks. 3
However, all the Grid related operations are fully managed by the system itself, so completely hiding the complexity of Grid technology to the user. The user can access FAIRE via a dedicated Web Interface from which he/she can browse for and select (A)SAR data archived at ESRIN in the Grid storage elements (i.e., in the G-POD online archive) and can specify a number of processing parameters. The results obtained, as well as the discussion with damage mapping specialists, allow us saying that similar solutions can provide a significant contribution to develop and enhance flood monitoring capability. In Section 2 the ESA Grid Processing on Demand is presented. Section 3 focuses on the proposed near-real time service to support SAR based mapping. The validation and quality assessment of the products and examples of operational use of the service are reported in Section 4. Section 5 discusses a wider perspective for using Grid technology in Earth Science. The discussion and outlook is given in Section 6.
2 ESA Grid Processing on Demand Following the participation to the DATAGRID [15] project, the first large European Commission funded Grid project, the ESA Science and Application Department of Earth Observation Programs Directorate at ESRIN has focused on the development of a dedicated Earth Science Grid infrastructure, under the name EO Grid Processing on-Demand (G-POD). This generic Grid based environment ensures that specific EO data handling and processing applications can be seamlessly plugged into the system. Coupled with high-performance and sizeable computing resources managed by Grid technologies, G-POD provides the necessary flexibility for building a virtual environment that gives applications quick access to data, computing resources, and results. Using a dedicated Web interface, each application has access to a catalogue like the ESA Multi-mission User Interface System (MUIS) and storage elements. It furthermore communicates with the underlying Grid middleware, which coordinates all the necessary steps to retrieve, process, and display the requested products selected from the large database of ESA and third-party missions. This makes G-POD a sound environment for processing large amounts of data, developing services which require fast production and delivery of results, comparing approaches and fully validating algorithms. 4
At present, the ESRIN controlled infrastructure has a computing element (CE) of more than 200 PCs, mainly part of four clusters with storage elements of more than 120 Terabytes, all part of the same Grid LAN in ESRIN, partially interfaced to other Grid elements in other ESA facilities, partners research centers, and companion project’s infrastructures, such as EGEE [16]. The PCs use different Intel processors ranging from XEON to WoodCrest. Different versions of Scientific Linux are installed on different PCs. The key feature of this Grid environment is the layered approach based on the Grid-ENGINE, which interconnects the application layer with different Grid middleware (at present interfaced with three different brand/releases of middleware, Globus Toolkit 4 (GT4) [17], and gLite [18]. This characteristic enables the clear separation and development path between the EO applications and the middleware being used. Thanks to this separation, EO scientists can focus on their applications without dealing with Grid technology issues and can take full advantage of the underlying Grid infrastructure. Many EO applications are fully operational and available through the ESA EO Grid portal. It is important to note the wide diversity of EO application themes, such as: meteorology, chemistry of the atmosphere, oceanography, simulations, operational generation of Level 3 products1, generation of different products relevant to Essential Climate Variables (ECVs) [20] defined by Global Climate Observing System (GCOS), and production of maps for fast damage assessment. Methodologies for the analysis of multi-source data, time series, and data assimilation are being considered. Many universities, institutions, research centers, international organizations, and private companies are involved.
3 A near-real time SAR processing service to support flood mapping A near-real time SAR processing service to support flooded area mapping is presented, which has become a fully automatic and operational Grid-based service, called Fast Access to Imagery for Rapid Exploitation (FAIRE). New
1
According to CEOS definition, Level 3 products are data or retrieved geophysical parameters which have been spatially and/or temporally re-sampled (i.e. derived from Level 1 or 2 products), usually with some completeness and consistency. Such re-sampling may include averaging and compositing. 5
acquisition from Envisat ASAR and ERS-2 SAR are available to the service within half an hour until their processing into the ESA ground segment. The computational power and fast data access capability of G-POD allows the user deriving SAR based products, using both recent and historical products, within few hours (approximately 3 hours) from the most recent acquisition. The output includes a stack of co-registered (A)SAR images (geocoded and optionally orthorectified) in standard GeoTIFF format. The service is based on different SAR toolboxes, which have been integrated in G-POD, such as BEST [21] and in-house developed software, which implements the required processing steps, backscattering computation, image co-registration, temporal filtering, and different levels of geocoding [12]. Following the user-friendly approach of G-POD, the access to FAIRE is provided via a web interface (see Fig.1). The user can define the geographical area of interest and acquisition time to obtain the desired products. In addition the service provides the capability to restrict the search process to ascending orbits, descending orbits or to acquisitions characterized by a given incidence angle. All Envisat ASAR medium resolution products acquired after June 2005 and all ERS2 SAR acquired over Europe after June 2007 are available in the system. The user is asked to select one product acquired after the crisis event and one or more products acquired before the crisis event. The user can afterwards specify different levels of geocoding, e.g., flat ellipsoid projection or geoterrain correction. Miller and Universal Transverse Mercator (UTM) projections are supported in output. Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) v3 (which has a resolution of 3 arcsec, comparable with the pixel scale of ASAR medium resolution products, i.e., approximately 75 meters) and GTOPO30 DEM are also stored in the system [22][23]. This allow, when required, product orthorectification at every geographical coordinates [24]. The main service functions of FAIRE are: •
Automatic co-registration of (A)SAR data products;
•
Absolute calibration of the data in order to obtain data comparable for flood monitoring;
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Background mapping (temporal averaging of pre-event data);
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Geocoding (ellipsoid correction, optional orthorectification with SRTM, and projection to Miller or UTM coordinate systems); 6
•
Difference image between background reference and crisis data;
•
Multitemporal RGB composition using background reference and crisis data to support flood monitoring analysis.
On account of the G-POD environment, after the task submission, the system automatically retrieves the data stored on different storage elements (e.g. distributed archive), identifies the jobs needed for accomplishing the task and distributes them on different computing nodes of the Grid. All those steps are completely transparent to the user. FAIRE provides as output the stack of co-registered images (optionally orthorectified), the background reference image and a multitemporal RGB product (generated using archive and crisis data). This last product codes in reddish and bluish positive and negative backscatter changes between reference and crisis data, respectively; the saturation is related to the backscatter difference, i.e., no threshold is used, e.g., a bigger negative backscatter difference corresponds to a more saturated blue pixel. It has to be stated that such a product is not yet the final flood map and needs to be further interpreted by disaster mapping specialists, in fact: •
No threshold is applied, i.e., pixels are not labelled as changed or unchanged;
•
Changes can be related to other than flood effects, i.e., changes in agricultural practices.
To support such an interpretation, cloud free Landsat imagery mosaic over the investigated area is also provided as output. The mosaic is derived from the WMS Global Mosaic [25] and, despite being generated with images collected during 1999-2003, it provides a useful mean to help in interpreting the SAR based products generated by FAIRE. Topographic information such as elevation and slope is also provided. It has to be stated that Landsat imagery is not used in the generation of the multitemporal RGB product. Differences displayed in such a product are related only to the information contained in the SAR products. Landsat imagery is only used to support human operators in the interpretation of the RGB product. Other (more recent) optical images should be used when available, but it must be considered that in the case of rapid mapping following flood events, weather conditions very often prevent the use of optical imagery. As opposite, the considered Landsat mosaic, despite being not updated, has a major advantage: it is cloud free. 7
Recent developments of the service aim to include further products to aid the interpretation. These products include recent land cover maps (e.g., GlobCover [26]). The information contained in Landsat imagery as well as in land cover maps may be not updated but nonetheless can provide useful information.
The full processing of SAR images requires different steps to derive the aforementioned products. Some of these are particularly demanding from the computational point of view. An example of the workflow needed by the services is shown in Fig. 2. When “N” is present inside a block, it means that the processing can be performed in parallel over the inputs. A detailed description of the different blocks is given in the following: •
“Ingestion”: SAR products in native format are ingested, calibrated, and converted to logarithmic (dB) scale. The outputs are floating point tiff images. This operation is split into as many jobs as the input products.
•
“Ortho”: ingested products are orthorectified and projected in the reference system selected by the user (geographical coordinate system or UTM). The outputs are floating point geotiff images. This task is performed in parallel; it is split into as many jobs as the input products; in some cases a bigger parallelism can be achieved by processing independently each granule of the products (please refer to [1] for the definition of granule entity within Envisat ASAR products).
•
“Coreg”: the orthorectification procedure (see following subsection) is suboptimal. For this reason, an additional coregistration task is performed. This procedure is based on image matching and aims at reducing misregistration (that can be still present after orthorectification) up to a fraction of a pixel. The outputs are floating point geotiff images. This task is performed in parallel; it is split into as many jobs as the pairs of products, i.e., in N-1 jobs if N products are considered.
•
“Multitemp”: This task performs different operations: o all images related to products acquired before the crisis event are averaged pixel by pixel to produce a further image, called mean_backscatter image. If only one product acquired before the 8
crisis event has been selected, the mean_backscatter image is the product itself. o each image (including the mean_backscatter image) is converted to a byte image performing a linear histogram stretching between prefixed values (stretched_images) o a byte image is generated as the minimum value (pixel by pixel) between the stretched_image related to the crisis product and the the mean_backscatter stretched image. o an RGB image is generated as follow: the red channel contains the stretched image related to the crisis product; the blue channel contains the mean_backscatter stretched image; the green channel contains the minimum image. This task is performed sequentially, however is not computationally demanding. •
“Images”: this block converts all the previously generated images in jpeg format, at full resolution and at lower resolution for thumbnails generation. This task is performed in parallel; it is split into as many jobs as the image to be converted.
In addition to the previously described steps, blocks related to Landsat imagery retrieval are performed when needed: •
“Landsat prepare”: this task collects landsat tiles (channel 0, 1, and 2) over the selected area of interest and projects them to the reference system selected be the user. The task is split into as many jobs as the tiles to be processed.
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“Landsat singleBand” performs a mosaic over the all the tiles for a specified channel. This task is performed sequentially.
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“Landsat RGB” composes the mosaics generated in the previous steps to form a single RGB geotiff file. This task is performed sequentially.
Calibration and coregistration are performed by means of standard algorithms. A detailed description can be found in [21]. The orthorectification is usually performed in a semi-automatic approach (see sub-section). Since ASAR products have medium-resolution and precise 9
geolocation accuracy, it was possible to develop a fully automatic approach to perform this critical task. The details of the algorithm and method are described in the following sub-section. It has to be stated that this is just a template of the workflow used by the service and some modifications may occur. For instance, when projection on flat ellipsoid is required rather than orthorectification, the DEM related tasks are not present, and the orthorectification block is substituted by a “geocoding” block. In addition, the user is suggested to select acquired products related to the same track. In fact, comparing acquired SAR products related to different tracks is not trivial. However, if this is not possible (e.g., lack of products related to the same track in the archive) the user can select acquired products related to different tracks. In this case, the coregistration step is skipped as it is not possible to compare (by an image matching procedure) such kind of products (especially when ascending and descending products are considered).
3.1 Orthorectification Given the nature of the sensor, SAR images are affected by well-known geometric distortions, which includes foreshortening, layover and shadow effects [27]. Being the ASAR products focalised at zero Doppler, topography leads to a displacement of points of target along the range direction. As we are dealing with plain flood events, the major distortion of interest for our application is foreshortening. Fig.3 represents the geometry of such a special case of topographical distortions. As an example, a point B at elevation h (topographic height) above the reference ellipsoid is imaged at position B ′ (please note that the range distances R of the two points are the same), though its real position is B ′′ . The offset ∆r between B ′ and B ′′ exhibits the effect of topographic distortions. When a Digital Elevation Model (DEM) of the observed geographical area is available, a procedure called orthorectification can be applied allowing the (partial) correction of the above distortions [28][29][30][34]. SAR orthorectification usually requires four steps: i) a SAR image is simulated based on available DEM and satellite orbital parameters; ii) simulated image and original product are coregistered (in a manual or automatic way); iii) each pixel of the (coregistered) original image is associated to height information; iv) height 10
information associated to each pixel is used to locally correct the distortion (i.e., the offset ∆r is compensated). Thanks to precise geolocation information contained in the header of ASAR Image Mode Medium resolution (IMM) and Wide Swath Mode (WSM) products [1], we herein proposed a simple fully automatic algorithm composed of few steps, which does not require SAR image simulation or simulated image to coregister the original product. Let X and X ortho be a ground range image corresponding to a medium resolution ASAR product and its orthorectified version, respectively. Let x(i, j ) ( xortho (i, j ) ) represent a generic pixel at row i, column j in X ( X ortho ). Let x (i, j ) ( x ortho (i, j ) ) be the value (e.g., intensity) associated with the pixel x(i, j ) ( xortho (i, j ) ). Let lat i, j , long i , j , R(i, j ) , and η (i, j ) be the latitude, the longitude, the slant-range distance and the local incidence angle corresponding to the pixel x(i, j ) , respectively. Let hi, j = h(lat i, j , long i, j ) represent the elevation corresponding to the geographical coordinates lat i, j , long i , j . For each pixel position coordinate (i, j ) in X ortho , the orthorectification algorithm is composed of the following steps: 1. The values of lat i, j , long i , j R(i, j ) , and η (i, j ) are computed. Product header contains accurate information related to the above values for a reduced (but significant) number of pixels in the image, called tie points. Values corresponding to other pixels are estimated via Delaunay triangulation [31]; 2. given the geographical coordinates lat i, j and long i , j , elevation information hi , j is derived from the available DEM with a bi-linear interpolation;
3. compute x ortho (i, j ) as follow: xortho (i, j ) = x (i, j − ∆ri , j )
(1)
where ∆ri , j is a slant range displacement estimated taking into account the geometry of topographical distortions in SAR imagery (see Fig. 3): ∆ri , j ≅
1 s
[(Qi, j − hi, j ) tan η (i, j ) −
11
2
Qi , j − hi , j cosη (i, j ) − Qi , j
( )
2
(2)
where Qi , j is the sensor’s distance from the plane tangent to the ellipsoid at position lat i , j , long i , j and is given by: Qi , j = R(i, j ) cosη (i, j )
(3)
with s being the pixel size in range direction. Eq. 2 is obtained with simple geometric considerations under the reasonable assumption that the incidence angle η (i, j ) (as respect to a flat ellipsoid) and the angle between the line connecting the sensor to the observed point and the perpendicular to the ellipsoid at position lat i , j , long i , j are similar (see Fig.3). An equivalent assumption is made in [29]. Since ∆ri , j usually does not assume an integer value, linear or spline interpolation is used in eq. 1. The developed tool also produces shadow maps and layover maps [33]. However, as we are dealing with plain flood events, these maps do not have particular importance for our application. Fig.4 shows a detail of an ASAR IMM product acquired on January, 11th 2006 over Mt Etna, Italy, before and after the proposed automatic orthorectification. Concerning DEM dataset, the Global Shuttle Radar Topographic Mission (SRTM) DEM v3 (which has a resolution of 3 arcsec, comparable with the one of ASAR medium resolution products) was downloaded from the U.S. Geological Service web site [22]. For latitudes above 60 degrees north and under latitudes 56 south, GTOPO30 DEM [23] was downloaded. Both DEMs were stored in different storage elements of ESRIN Grid infrastructure (the total size of the DEMs is approximately 120Gbytes). Necessary DEM tiles are automatically identified and retrieved (in a fully transparent way) from the storage elements. As discussed in the previous section, in the proposed procedure, each pixel of the product is automatically associated to height information in the DEM and the geometric distortion is corrected. As SRTM DEM contains some holes (area for which height information is not available) and EGM-96 geoid is used as vertical datum, a preprocessing phase is performed in which holes are filled using Delaunay triangulation and vertical datum is converted to WGS-84.
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It has to be stated that internal organization of ASAR products (which are structured in units called granules) allows a distributed orthorectification of Envisat ASAR data. The tool is developed in such a way that different granules of the same product can be processed in parallel on different working nodes of the Grid in order to reduce the computational time. It has to be stated the following: •
the used DEM is quite old. More recent and more accurate DEM exists. Nonetheless, as we are addressing plain flood events, this fact should not significantly influence the accuracy of the result;
•
the proposed orthorectification approach assumes that precise geolocation information is contained in the header of ASAR IMM and WSM products. However, as geolocation error can be present, this may lead to suboptimal orthorectification. For this reason the coregistration step is required. After this, problems may still be present in mountainous areas, but images should be properly aligned in flat areas, i.e., the areas that can be affected by plain flood events.
4 Experimental Results 4.1 Validation and quality assessment An extensive study on validation and quality assessment was performed. The objective of the validation procedure is to ensure the high quality of the results derived from FAIRE and to provide a measure for the end users of the respective data products. As the output of the system is a series of coregistered and geocoded images the quality assessment was performed with respect to coregistration accuracy, and geolocation accuracy. More in details, the validation is referred to the accurate geolocation and co-registration of the images with respect to another one and to a global coordinate system. The study included the validation of co-registration and geolocation of: 1. the observations based on control points (CP) between SAR and ASAR products; 2. the observations based on ground control points (GCP) from Landsat imagery;
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3. the observations based on ground control points (GCP) from map products. The validation was performed in five different test sites: India, Italy, Netherlands, UK and Vietnam. Co-registration accuracy is estimated as a measure of pixel misalignment, geolocation accuracy is estimated as a measure of meter displacement. No thematic assessment is performed as we are not producing any change detection map, but an RGB composition still to be interpreted by a human operator. For both the estimations, a manual selection of control points was performed. It has to be stated that this task was performed only on selected datasets for validation purpose. These accuracies are now assessed and provided to the user as an estimate for newly generated outputs. FAIRE service is completely automatic and all the previously described output products are generated without manual interaction.
4.1.1 Co-registration accuracy estimation For the image-to-image co-registration accuracy a manual selection of control points was performed. However, since the visual comparison of radar images is not trivial, the collection of suitable reference points was limited. In total 20 points were uniformly distributed around the image. The results of the assessment of the precision in co-registration of ASAR and SAR images are summarized in Table 1. Overall, the co-registration accuracy was less than half a pixel size. It has to be stated the following. One could argue that, since we register two (A)SAR images orthorectified using the same DEM and algorithm, as a consequence it may be expected that i) the results are self-coherent, ii) the use of CPs results in evaluating the agreement between the two CP sets rather than the image registration accuracy. However, as discussed in Section 3.1 the adopted orthorectification strategy is suboptimal. The orthorectification is based on the tie point information contained in the header of the products, no other fine matching between DEM and product is performed (neither manual nor automatic). Thus
14
error in geolocation information contained in the headers can lead to residual misregistration between products despite of orthorectification.
4.1.2 Geolocation accuracy estimation The geometric location accuracy of the G-Pod SAR products was determined by using control points. Coordinate measurements for the ground coordinates of such points are retrieved from a reference map. In our study we used Landsat imagery (Landsat WMS mosaic) as well as topographic maps to gather the respective GCPs. The image coordinates in either case had to be measured interactively (20 GCPs/image). All measurements were done in the geographic reference system (Lat/Lon WGS84). The results from the geolocation accuracy estimation vary on the basis of the choice of reference input. The comparison of ASAR/SAR imagery to the Landsat results in a total Root Mean Square (RMS) error below one pixel, whereas the comparison of ASAR imagery to map coordinates peaked at nearly two pixels (see Table 2). In addition further experiments have been performed on manually registered images by identifying ground control points. The comparisons between the ASAR/SAR imagery and manually registered images with 20 points led to a total RMS error of 0.378316 and 0.232608 pixel, ie., approximately between 28 and 17 meters. For illustration Fig. 5 shows a comparison between the spatial resolutions of ASAR, Landsat as well as QuickBird images (derived from GoogleEarth). The confrontation shows that a distinction of the same points is not trivial. However, not only the spatial resolution but also the spectral resolution has a major influence in the correct determination of reference points. The location of GCPs presented some difficulties when comparing the radar images with topographic maps. Fig. 6 illustrates this selection process with one sample point.
4.1.3 Quality assessment conclusions Accurate control points are essential for an accurate co-registration. Since the selection of ground control points was performed manually, the results may 15
diverge to a certain extent when different interpreters are involved in this work task. However, the co-registration accuracy appears to be very good with results less than half a pixel. The RMS error of the geolocation showed less than 1 pixel (i.e., less than approximately 75 meters) in the case of Landsat comparison, and less than 2 pixel (i.e., less than approximately 150 meters) using map coordinates. It has to be mentioned that this last measure is not of significant relevance as it is not trivial to identify matching point in SAR and topographic maps.
4.2 Examples in which the service has been operationally used The International Charter “Space and Major Disasters” [3] aims at providing a unified system of space data acquisition and delivery to those affected by natural or man-made disasters through Authorized Users. As soon as a natural disaster occurs, an authorized user can request the mobilization of the space and associated ground resources of the member agencies to obtain data and information on a disaster occurrence. On November 1, 2000 it was declared formally operational. At the moment the Charter consists of the following members: ESA, CNES (France), CSA (Canada), NOAA (USA), ISRO (India), CONAE (Argentina), JAXA (Japan), USGS (U.S.), BNSC/DMCii (UK) and CNSA (China). One of the first tests run on FAIRE has been conducted during Charter Call 169 related to the severe flood, which hit India in August 2007. FAIRE was run in parallel to the Charter Call to test its efficiency without disrupting the work of operational partners. The Charter was triggered at 10:25 UTC on August 6th, 2007 and the FAIRE processing was started at 12:20 UTC. At 15:42 UTC G-POD FAIRE delivered to the International Charter the ASAR WSM crisis data acquired over the region on August 7th (native format), the ASAR WSM archive data acquired on July 3rd, 2007 (native format), the GeoTIFF files of the calibrated, orthorectified and co-registered previous products and the RGB multitemporal composition of the previous products. The FAIRE processing has been also run for the acquisitions of the subsequent days (on August 10th, 12nd, 15th, and 17th) and the RGB multitemporal compositions for each acquisition are reported in Fig. 7. In those figures it is possible to observe in blue, red and grey the areas where the signal has experienced - between the crisis and the archive images - a 16
decrease, an increase or no change respectively. A more recent example of the use of FAIRE in support to rapid flood mapping is the one shown in Fig.8 and referred to Charter Call 199, activated on March 20th, 2008 for a severe flood in the states of Arkansas, Indiana, Kentucky, Missouri, Ohio and Texas. The ASAR WSM crisis image (orbit 31736, track 119) covers the states of Minnesota, Iowa, Missouri, Arkansas, Mississippi, Louisiana and small parts of Wisconsin and Tennessee. The RGB composition highlights the flooded areas along Mississippi, White and Black rivers. Other activations include events that affected Myanmar, Texas, Nepal, Ecuador, Honduras, etc. On May 2008, the presented service was used to to determine the hydrological situation after the passage of Typhoon Nargis over the Irrawady delta, Myanmar. In this case, a time series of about twenty products, i.e., 3 products acquired after during the crisis, i.e., on May 15th, May 17th, and May 18th, and 16 dry season images acquired in 2007-2008, were used. In this case, processed products were also used to estimate potential draw-off tendencies in flooded areas. In all the cases, FAIRE has been run in parallel to the standard data provision procedure. Details on the image sets used are reported in table 3. Related final flood maps derived by interpretation our results can be viewed at [4]. In the operational chain, FAIRE is usually used with one archive product, and without splitting the products to be orthorectified into granules. This means that the system usually halves the processing time as respect to running the same processing (with the same software) on a single machine. For examples, the India August 7th 2007 processing required a total of 2h 21min, as compared to 4h 45min estimated on a single machine. Activation for USA on March 20th 2008 required 4h 6min, as compared to 7h 15min estimated in a single machine. Obviously processing time depends on many factors including the load of G-POD (despite a priority mechanism is adopted), and the dimension of the area to be processed. However, we believe that one of the main advantages of the system is not only the reduction in the processing time, but the near real time access to both historical and recent data. This provides the possibility to significantly reduce the time from Envisat acquisition to the delivery of processed and ready to be used products. This statement is confirmed by the comments of the beta-testers involved so far 17
At the present, a new version of FAIRE is in the final stage of development. The use of more advanced, and optimised SW, results in a significant reduction of the overall processing time (in some first trials, the reprocessing of the previously discussed India data set was accomplished in approx. 40 minutes).
5 A wider perspective for using Grid technology in Earth Science As previously mentioned the Grid technology might play an important role in the ES community to provide fast access to data and product generation. Within this context the main needs of ES community have been studied and are summarized as follows: (1) The increased complexity of ES applications leads to more intensive computing and access to many large databases and spatially distributed physical models. The existing computing resources available in one institute or organization are not sufficient. (2) Datasets are too large to be copied by each end-user or even stored in a single location. (3) For data policy reasons, (as well as the large volume of data), some datasets cannot be copied by the end-users. In this case, the data provider also hosts computing resources able to process the data. (4) The ability to install some specific ES environment on different available computing systems; many web services have been developed. Data play a central role in the Earth Science needs. Grid is a possible solution for building up a data infrastructure, where data management, e-collaboration, services, and community building are possible. In this perspective, ES community poses itself as Grid user more than Grid developer. Grid is a commodity ES users can (and should) benefit from to pursue their challenging objectives. Earth Science has explored the Grid technology since 2000 via European projects such as DataGrid and EGEE in different domains to test the possibility to deploy their applications on a larger scale. The experience acquired via several academic and R&D applications during DataGrid [15], CrossGrid [35], EGEE [16], Int.eu.Grid [36], etc., has demonstrated that Grid infrastructure could respond to the complexity and constraints imposed by ES applications. 18
So far however, the adoption and exploitation of Grid technology throughout the ES community has been slower than expected. The EU-IST DEGREE SSA project [37] has been proposed in order to assist and accelerate this process in a number of different ways, including the generation of a roadmap, outlining the key steps towards establishing a sustainable, dedicated Grid infrastructure that is suitable for the ES community [38]. At the same time it is important that the DEGREE team responded to the anticipated ES community role in accessing the Scientific Data Repositories infrastructures being proposed within the recent FP7 calls on Research Infrastructures, such as the GENESI-DR (Ground European Network for Earth Science Interoperations – Digital Repositories) project [39]. GENESI-DR has the challenge of establishing open Earth Science Digital Repository access for European and world-wide science users. The project shall operate, validate and optimize the integrated access and use available digital data repositories to demonstrate how Europe can best respond to the emerging global needs relating to the state of the Earth. In GENESI-DR, a big effort will be devoted in integrating space-borne, ground and in-situ measurements in homogeneous systems. Not only dedicated Grid projects should be considered in further research, but also projects with more specific foci. The Network of Excellence (NoE) GMOSS (Global Monitoring for Security and Stability) [40] just finished its joint work on integrating European human security research to develop and maintain an effective capacity for global monitoring using EO satellites. Grid-based solutions could provide fast access to heterogeneous data and processing resources for the methods and algorithms shared within this network. A recent collaboration between the Open Geospatial Consortium and the Open Grid Forum aims to develop open standards that address the distributed computing needs of geospatial applications while accommodating the inevitability of diverse formats, schemas, and processing algorithms. These standards will provide the necessary infrastructure for developing tools, software, and services that work together and can be used by multiple communities. They will support interoperability and the development and wide adoption of shared best practices, as well as help technology providers and users innovate and leverage one another's strengths and accomplishments [41].
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6 Discussion and outlook Today the presented near-real time SAR processing service is used during real activations of the International Charter Space and Major Disasters (temporary access to the system has been provided to Value Adding providers like UNOSAT, DLR ZKI and SERTIT). It has to be remarked here that FAIRE is never run instead of standard procedures, but in parallel to them. Obtained products are accurate enough for the addressed applications. Based on the positive feedbacks received so far it is planned to further develop FAIRE, providing access to additional EO data, e.g., ASAR IMP data, and using more advanced tools. As the interpretation of radar images is not trivial, it is also foreseen to provide in the next future detailed land cover maps to support the interpretation of the radar images. It has to be stated that the advantage of using Grid technology for such an application is not only the reduction of the processing time but rather the capabilities to have the whole archive on line so providing fast access to both crisis and archive data. At the present, the main effort is aimed at replacing the processing software with a more optimised version so as to reduce the overall processing time (first results on India testbed resulted in less than one hour). G-POD is operationally used for many different applications where fast data access, or bulk processing is needed. Currently, the authors are investigating the performances of the system in the context of subsidence mapping, which is in fact much more resource-consuming than the described flood application.
Acknowledgements We are grateful to Giovanna Trianni for her guidance in gathering and understanding the requirements of users of GSE RESPOND and “International Charter Space and Major Disasters”. We thank Olivier Colin and his team, as responsible for the operations of ESA G-POD.
References [1] http://envisat.esa.int/handbooks/asar/CNTR.htm
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[2] http://www.respond-int.org/respondlive/ [3] http://www.disasterscharter.org [4] http://www.disasterscharter.org/disasters [5] R. Cossu, Ph. Bally, O. Colin, L. Fusco, “Rapid mapping of flood events through the use of FAIRE”, Proceedings of “6th Workshop on Remote Sensing for Disaster Management Applications”, Pavia, Italy, September 11-12, 2008. [6] R. Cossu, Ph. Bally, O. Colin, L. Fusco, “ESA Grid Processing on Demand for fast access to Earth Observation data and rapid mapping of flood events”, European Geosciences Union General Assembly 2008, Vienna, Austria, 13 – 18 April 2008. [7] R. Cossu, P. Bally, F. Brito, K. Fellah, P. Goncalves and L. Fusco “ESRIN GPOD: ASAR products handling and analysis for a quasi systematic flood monitoring service”, 2007 ESA ENVISAT Symposium, Montreux Switzerland, 23-27 April 2007. [8] http://www.riskeos.com [9] http://eogrid.esrin.esa.int [10] L. Fusco, R. Cossu, C. Retscher: “Open Grid services for Envisat and earth observation applications” in High Performance Computing in Remote sensing, Ed: Antonio Plaza, Taylor and Francis Group, Chapter 13, published by Chapman & Hall/CRC, 2007 [11] L. Fusco, P. Goncalves, F. Brito, R. Cossu, C. Retscher: “A new Grid-based system to assist user in ASAR handling and analysis”, European Geoscience Union General Assembly, Vienna, 02 – 07 April 2006 [12] M. Paces, P. Bares, R. Cossu, L. Fusco, “PECS-GRID project – SAR image processing on Grid”, Proceedings of “58th International Astronautical Congress”, Hyderabad, India, September 24-29, 2007. [13] http://www.terradue.com [14] http://earth.esa.int/gmes [15] http://eu-datagrid.web.cern.ch [16] http://www.eu-egee.org/ [17] http://www.globus.org/ [18] gLite http://glite.web.cern.ch/glite/ [19] EO-Grid Processing-on-Demand Call for CAT-1 Proposals. http://eopi.esa.int/G-POD [20] Essential Climate Variables (ECVs). http://ioc3.unesco.org/oopc/obs/ecv.php [21] http://earth.esa.int/services/best/ [22] http://srtm.usgs.gov [23] http://edc.usgs.gov/products/elevation/gtopo30/gtopo30.html [24] R. Cossu, F. Brito, L. Fusco, P. Goncalves, M. Lavalle: “Global automatic orthorectification of ASAR products in ESRIN G-POD”, Proc. of ESA ENVISAT 2007 Symposium. [25] http://onearth.jpl.nasa.gov/
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[26] http://asimov.esrin.esa.it/esaEO/SEMGSY2IU7E_index_0.html [27] F. M. Henderson, A. J. Lewis, “Manual of Remote Sensing, Principles and Applications of Imaging Radar”, Wiley, 1998, ISBN: 0-471-294063. [28] J. C. Curlander, R. N. McDonough, “Synthetic Aperture Radar: Systems and Signal Processing”, Wiley Series in Remote Sensing, 1991, ISBN: 0-471-85770X. [29] Y.Sheng and D.E.Alsdorf, ”Automated geo-referencing and orthorectification of Amazon Basin-wide SAR Mosaics using SRTM DEM Data”, IEEE Trans. Geoscience and Remote Sensing, vol43, 8, 2005, pp1929-1940 [30] G.M. Huang, J.K. Guo, J.G. Lv, Z. Xiao, Z. Zhao, C.P. Qiu, “Algorithms And Experiment On Sar Image Orthorectification Based On Polynomial Rectification And Height Displacement Correction”, Proc. of Isprs, 12 -23 July 2004, Istanbul, Turkey. [31] Leland Pierce, Josef Kellndorfer, Fawwaz Orthorectification”, IGARSS, 4, pp. 2329 –2331.
Ulaby,
1996.
“Practical
SAR
[32] Mark de. Berg, “Computational Geometry: Algorithms and Applications”, Springer, 2000, ISBN 3-540-656200. [33] W. G. Kropatsch and D. Strobl, “The Generation of SAR Layover and Shadow Maps From Digital Elevation Models”, IEEE Transactions On Geoscience and Remote Sensing, Vol. 28, No. I . January 1990. [34] L. Ulander, "Radiometric slope correction of synthetic-aperture radar images," IEEE Transactions on Geoscience and Remote Sensing, vol. 34, no. 5, pp. 1115–1122, Sep. 1996. [35] http://www.crossgrid.org/ [36] http://www.interactive-grid.eu/ [37] http://www.eu-degree.eu/ [38] M. Petitdidier, L. Fusco, J.P. Vilotte, C. Retscher, R. Cossu, “Grid computing for new Earth Science paradigms”, invited paper to 2007 AAAS Annual Meeting, "Science and Technology for Sustainable Well-Being," San Francisco, 15-19 February, 2007. [39] http://www.genesi-dr.eu/ [40] http://gmoss.jrc.it [41] C. Lee, G. Percivall, “Standards-Based Computing Capabilities for Distributed Geospatial Applications”, Computer, Vol. 41, No. 11. (November 2008), pp. 50-57.
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Fig.1 FAIRE Web Interface
23
Fig.2 Workflow used by the FAIRE service.
24
Fig.3 Geometry of topographical distortions in SAR imagery. A point B at elevation h above the ellipsoid is imaged at position B ′ , though its real position is B ′′ . The offset ∆r between B ′ and
B ′′ exhibits the effect of topographic distortions. Eq. 2 is derived under the assumption that the two angles η and θ are similar.
25
a)
b)
Fig 4 Detail of an ASAR IMM product acquired on January, 11th 2006 over Mt Etna, Italy, ascending pass. a) before orthorectification, b) after the proposed automatic orthorectification.
26
Fig.5 - Comparison of spatial resolution.
27
Fig.6 - Detection of GCPs in ASAR image and topographic map.
28
(a)
(b)
(c)
(d)
(e)
(f )
Fig. 7 - (a) Composition of the ASAR WSM images acquired before the crisis event on July, 6th 2007, June 18th, 2006, June 11st, 2007 and January 19th,2007; (b) crisis event image acquired on August 7th; (c) crisis event image acquired on August 10th; (d) crisis event image acquired on August 12nd; (e) crisis event image acquired on August 15th; (f) crisis event image acquired on August 17th. The mosaic of the different results has been generated externally to FAIRE. Nonetheless, being the products geolocated, the mosaic generation is a trivial task.
29
(a)
(b)
(c)
Fig.8 - (a) ASAR WSM archive image acquired on July 25th, 2007 over Eastern United States; (b) ASAR WSM crisis image acquired on March 26th, 2008 over the same area; (c) RGB composition with R: crisis image; G: min(crisis, archive); B: archive image.
30
Table 1. Co-registration accuracy results.
Study area
Image # 1 (Date,
Image # 2 (Date,
Sensor)
Sensor)
Total RMS Error
India
20070807 (ASAR) 20070703 (ASAR) 0.469407
Netherlands
20070328(ASAR)
20060308(ASAR)
0.381492
Netherlands
20070328(ASAR)
20061004(ASAR)
0.461095
Netherlands
20070328(ASAR)
20051123(ASAR)
0.639613
UK
20070726(ASAR)
20060706(ASAR)
0.480110
Vietnam
20070807(ASAR)
20070703(ASAR)
0.436197
Italy
20070917(SAR)
20070709(SAR)
0.570818
Italy
20071003(SAR)
20070829(ASAR)
0.315839
31
Table 2. Geolocation accuracy results.
Study area
Image # 1 (Date,
Image # 2 (Date,
Total RMS Error
Sensor)
Sensor)
[meters]
Netherlands
20070711(ASAR)
Landsat
50.6265
UK
20060706(ASAR)
Landsat
46.6284
UK
20070726(ASAR)
Map www.streetmap.co.uk 132
Italy
20071003(SAR)
Landsat
69.00983
32
Table 3. Image sets used in some of the cases where FAIRE has been operationally used.
Region
Crisis Product
Archive Product
Ecuador
ASAR_WSM 01/03/2008
ASAR_WSM 20/12/2007
India
ASAR_WSM 07/08/2007
ASAR_WSM 03/07/2007
India
ASAR_WSM 10/08/2007
ASAR_WSM 25/08/2006 ASAR_WSM 03/11/2006 ASAR_WSM 01/06/2006 ASAR_WSM 06/07/2006
India
ASAR_WSM 12/08/2007
ASAR_WSM 23/07/2006 ASAR_WSM 18/06/2006
India
ASAR_WSM 15/08/2007
ASAR_WSM 17/01/2007 ASAR_WSM 02/05/2007 ASAR_WSM 11/07/2007
India
ASAR_WSM 17/08/2007
ASAR_WSM 03/02/2006 ASAR_WSM 19/01/2007
Honduras
ASAR_WSM 25/10/2008
ASAR_WSM 20/09/2008
Myanmar
ASAR_WSM 15/05/2008
16 products acquired on 2007-2008
Myanmar
ASAR_WSM 16/05/2008
16 products acquired on 2007-2008
Myanmar
ASAR_WSM 18/05/2008
16 products acquired on 2007-2008
Nepal
ASAR_IMM 31/08/2008
ASAR_IMM 27/07/2008 ASAR_IMM 22/06/2008
U.S.
ASAR_WSM 26/03/2008
ASAR_WSM 25/07/2007
U.S.
ASAR_WSM 28/07/2008
ASAR_WSM 23/06/2008
33
