seveseo: eo-based services in support to industrial and technological ...

SEVESEO: EO-BASED SERVICES IN SUPPORT TO INDUSTRIAL AND TECHNOLOGICAL RISK MANAGEMENT Koen Meuleman(1), Filip Lefebre(1), Eric Gontier(1), Els Knaeps(1), Sindy Sterckx(1), and Marc Paganini(2) (1)

VITO (Flemish Institute for Technological Research), Boeretang 200, 2400 Mol, Belgium, Email: [email protected] (2) European Space Agency (ESA), EO Science and Applications Department D/EOP-SEP, Via Galileo Galilei, Casella Postale 64, 00044 Frascati (RM, Italy, Email: [email protected]

ABSTRACT The ESA-funded SEVESEO project aims at developing a decision-making tool that integrates environmental parameters derived from satellite imagery with pollutant transport models in order to support the risk management at Seveso-II industries. The development of the SEVESEO Information System (SEVESEO IS) implies the integration of contaminant transport models that aim at determining the environmental impact of a Seveso type accident, by modeling transport of pollutants around the site. By integrating this information with local geographic information an operational GIS tool will be provided. This GIS tool will be able to support decision making during technological accidents, to assess the impact of the accident on the local environment and to indicate the areas at risk.

information on potential adverse impacts of accidents based on both pre-planned scenarios and pollutant transport models. This information must be available to responders in real time in order to facilitate rapid response. The SEVESEO project intends to apply space-based observations together with contaminant transport models to the management of industrial risks (including industrial exploitation) and technological accidents, in relation to the SEVESO II directive of the European Union. A complete assessment of a simulation of an accident involving hazardous chemicals or an actual emergency response to an accident requires a system that rapidly and accurately models the source term and the subsequent transport of the chemical trough different media (Fig. 1).

long distance transport

4. INTRODUCTION In 1976, an important industrial accident happened at a chemical plant in Seveso, Italy, manufacturing pesticides and herbicides. A dense vapor cloud containing tetrachlorodibenzo-paradioxin (TCDD) was released from a reactor. More than 600 people had to be evacuated from their homes and as many as 2000 were treated for dioxin poisoning. In Europe, the Seveso accident lead to the adoption of legislation aimed at the prevention and control of such accidents. In 1982, the first EU Directive 82/501/EEC was adopted. Following the first so called Seveso Directive in 1982, the Seveso II Directive is intended to prevent major accidents involving hazardous substances and to limit their consequences for man and the environment, with a view to ensure high levels of protection throughout the community. The directive particularly envisions industrial plants. Procedures that are imposed by the directive include amongst others notification of substances, prevention policy and safety reporting, emergency planning and the provision of information following a major accident. The establishment of a legal framework urged operators of industries as well as public emergency services to use models in both emergency planning and the response phase in order to support decisions. Clearly, there is a need for _____________________________________________________ Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007)

source term

human exposure and risk

vapour

run off soil

surface water

leaching

ecosystem exposure and risk

Fi sh in ge sti on

transport

vadose zone

leaching groundwater

Figure 1. Schematic view of the pathway of chemicals following an accidental release of substance stored in a tank (referred to as the source term). This information enables operators to define risk zones and perimeters. By integrating it with local geographic information derived amongst other form remote sensing data a valuable GIS tool will be provided. The SEVESEO decision-making tool - also called SEVESEO Information System (SEVESEO IS) will be able to support decision making during technological accidents, to assess the impact of the accident on the local environment (fauna and flora) and to indicate the areas at risk. The logistics available for the protection of

people and property will be better managed thanks to the cartographic elements that situate the danger zones. The SEVESEO IS will be demonstrated on a number of Seveso-II sites located in five countries: Belgium, Luxembourg, France, Germany and the Netherlands (Fig. 2).

Figure 2. Sites where The SEVESEO IS will be demonstrated. The SEVESEO consortium consists of 6 partners from 4 different European countries. The project also includes five public organizations that participate to the SEVESEO project as end-users. These core user organizations represent 5 countries of the European Union: Belgium, the Netherlands, France, Luxembourg and Germany (Fig. 3). SEVESEO project consortium

User partnerships

Figure 3. SEVESEO consortium and core user organizations.

5. RS AND GIS FOR RISK MANAGEMENT Remote sensing (RS) and geographic data in general are useful in all phases of the Disaster Management Cycle i.e. Preventive phase, Response phase and Post-disaster. Two distinct activities with the same twofold goal i.e. the prevention of accidents and the minimization of the consequences, can be distinguished during the preventive phase. In order to react rapidly during a crisis, training as well as emergency exercises are organized preventively in collaboration between the authorities, the field actors and the industry. These exercises are based on simulated accidents. In fact, preventive calculations based on pre-defined scenario’s provide very useful information. Based on these simulations authorities are able to set up emergency plans. Regarding the use of satellite information, preventive simulations are designed to approach as accurately as possible a real accident. Therefore, correctly prescribed surface conditions are desirable. For atmospheric dispersion modeling, if field data of roughness lengths are available, these should be used. In case these are not available, they can be derived from a land cover processed map. For the organization of the evacuation, high-resolution satellite imagery on which unknown access roads and obstacles can be identified, may be useful. During the crisis phase data on population distribution are required for assessing health risks associated with the industrial accident and to estimate the population at risk. The integration of this demographic data with detailed road network data can facilitate the determination of the optimal evacuation plan. In addition, land cover maps and maps of important resources such as rivers, water resources will help to specify sensitive areas and environmental areas really at risk. A major advantage of remote sensing, according to the involved users, would be its ability to provide a realtime image of the impact of any disaster.. For instance, explosions may destroy some key roads for evacuation and remotely sensed real-time info would be very helpful in such situations [1]. Amongst the end-users participating in the SEVESEO project, there’s a clear demand towards a very high temporal resolution/revisit time (preferably a few hours), and combined with this, a high spatial resolution (preferably less than one meter). Although today still some obstacles exist, both technological and institutional, which may hinder the optimal use of remote sensing data for disaster management during the response phase, we expect to see future initiatives aiming at providing satellitederived information more rapidly. Furthermore a number of new developments which could play an important role in quick response systems in general and

more specifically within the SEVESEO IS are expected for the near future. o PLEIADES HR is the optical high resolution component of a larger optical and radar multisensors system: ORFEO. PLEIADES HR will offer a spatial resolution at nadir of 0.7 m with a field of view of 20 km. The great agility enables a daily access all over the world, essentially for defense, civil security applications and to support risk management. o Miniature geostationary satellites of the future, designed to hover over a city or the deployment of a micro-satellite array are currently under investigation with some of them already almost being ready for operation. Such spaceborne systems could to a large extend satisfy the demand of the users towards a higher temporal but to a lesser extent the demand of high spatial resolution. o The concept of using UAV’s for (real-time) remote sensing purposes gradually gained interest during recent years. Mainly pushed by military industry, one has now UAV’s available varying in size from handheld micro-UAVs to High-Altitude LongEndurance (HALE) craft with 35 m wingspan. In many respects, the UAV technology offers the perfect carrier for state-of-the-art remote sensing with respect to real-time remote sensing. A recent initiative in this sense is the Pegasus Hale-UAV project (http://www.pegasus4europe.com/) aiming at almost real time observations with very high spatial resolution. o Imaging spectroscopy as remote observation methodology is becoming increasingly important in recent years. Imaging spectroscopy makes it possible to measure spectral information through the reflected solar spectrum and is thus sensitive to many processes. It’s because of this sensitivity that imaging spectroscopy has great potential as a diagnostic tool, thus enabling the support of several application fields. A promising application with special interest towards implementation with a SEVESEO IS is the development of (mainly) long wave infrared hyperspectral imaging which allows for the detection and identification of toxic clouds in the atmosphere. The spectra captured by those systems can be analyzed by a real-time algorithm, and result in the identification of the gaseous components. Currently test are being performed to integrate those systems in an airborne system setup. 3. REMOTE SENSING WITHIN SEVESEO The use of remote sensing within the SEVESEO information system will in the first step be limited to (1) the use of very high resolution Quickbird satellite images for site viewing to support decision making in the response phase and (2) SPOT4 HRVIR data to

produce land use maps. These maps will also be used for the determination of the surface roughness parameter of any site. This parameter plays an important role in the atmospheric modeling of pollutants around the accident. The SPOT4 satellite data from the different SEVESEO test site have been acquired through the ESA earth Observation P.I. portal by the submission of a Category-1 proposal. 3.1.

Methodology of land use classification

The supervised classification procedure that is followed to map the land use in the neighborhood (+/- 40 km radius) of all the selected test sites can be described in general as follows:
Definition of the classes.
Selection of the training data and signature extraction. The accuracy of the classification of satellite images depends highly on the quality of the training data. These training pixels belong to a class of land cover/use that is known, and to which a name or label can be attached. It is important that the training pixels are representative for the whole class, but at the same time include a range of variability for the class. Training pixels are selected from visual image inspection, existing maps and if required field visits. For Flanders, for example, reference data is deduced from the manure database and a detailed land use map. 50 % of the data is used as training samples for classification, the other 50 % is used to assess the accuracy of the classified land use map.
Image classification: First, the multiclass classification is reduced to a series of binary classifications. As binary classifier we will adopt a simple linear discriminant classifier (LDA). To combine the output of several binary classifiers a one-against-one approach will be used. In this approach all possible pairs of classes are compared using the maximum voting approach. In the maximum voting approach a vote is given to the winning class for each binary classification. The class of the maximum number of votes is assigned to the test sample.
Accuracy assessment : To compute the probability of error for the land cover/use map an error matrix or confusion matrix is produced based on the random selection of reference pixels in each class. The matrix contains several statistical measures of thematic accuracy including overall classification accuracy, user’s and producer’s accuracy by class, and the Kappa coefficient (KHAT). Furthermore it is tested if texture information can increase the classification accuracy. This is done by calculating texture measures as entropy and homogeneity. These texture features are then treated as additional bands in the classification process.

3.2.

Results

The derived land use map for Antwerp and environs is given in Fig. 4. Ten land use classes are distinguished with an total weighted accuracy of 94 %. Including texture information during the classification process had a clear impact on the classification accuracy of winter crops (i.e. bare soils at the moment of data acquisition) and infrastructure (roads, houses). For the latter land use class the producer’s classification accuracy increased from 75 % to 92 % by incorporating texture information. Although both classes are spectrally quite similar, they differ in texture. Fields with winter crops are quite homogenous at the image resolution of 20 m, while a 20 m pixel is often a mixture of houses, gardens and small roads which results in higher heterogeneity for the infrastructure class.

down a little. Water surfaces are even smoother than concrete runways, and will have even less influence on the wind, while long grass and shrubs and bushes will slow the wind down considerably. With the wind speed, also the transport velocity and deposition rates of atmospheric pollutants depend largely on the roughness length. Therefore it is expected that the incorporation of the roughness length in the atmospheric dispersion models may increase their performance. Based on the land use maps, roughness length maps will be derived. On the basis of the assumption that each land use category forms a roughness class, roughness length values can be assigned to each land use category. These roughness lengths will be used as input to the atmospheric deposition models. Choosing roughness length values is not an easy job. There are large differences between roughness length lists and most of them rely on a small set of experimental studies. We intend to use roughness length values from [2] which are based on some four dozen quality-selected experimental projects. 5. SEVESEO IS Based on user consultations, user requirements have been detailed. The major user requirements are: - Easy and fast to use; - Integration of 1) in-situ data, 2) geo-data and 3) multiple dispersion models inside one system; - Use in both the preventive and the response phase of a crisis. From the user requirements, system requirements have been developed. Fig 5. shows the base architecture along which the SEVESEO IS will be developed.

Figure 4. Land use map for Antwerp and surroundings derived from SPOT4 HRVIR data (Data provided by the European Space Agency). 4. ROUGHNESS LENGTH Atmospheric dispersion models are very sensitive to the input parameter “surface roughness length”. This parameter is a measure of the surface roughness and influences the near-ground wind speed. Roughness length maps are not a standard input in the prediction or dispersion models although it is expected that the incorporation of the roughness length in these models may increase their performance. This roughness length can be mapped from land use data derived from optical satellite data. In general, the more pronounced the roughness of the Earth's surface (larger roughness length), the more the wind will be slowed down. Forests and large cities obviously decrease the wind speed considerably, while concrete runways in airports will only slow the wind

Figure 5. Base architecture of the SEVESEO IS (in case of 1 available dispersion model). The proposed architecture uses a classical client-server approach. The data are centrally stored in a relational database management system. An integration level on the server side passes queries coming from the client software to the database. The client software communicates through HTTP (TCP) with the integration layer. For this, the integration level is

embedded in an Application Server. The functionality of the integration layer includes checking access rights of the client users and rendering maps for the client applications. It also performs system tasks such as optimizing retrieval of data from the database and caching map data. Furthermore, it can integrate map data from remote Web Map Services (WMS). One server installation can serve multiple client applications simultaneously. Administration of the system is performed with the help of a dedicated administration tool. Administration includes managing users and user rights, setting up map layers from data in the database or from WMS, and configuring map styles, e.g., colour, size, and symbology of the spatial data. The administration tool is complemented by tools for integration of data. The component visible to the user is the SEVESEO IS Viewer. After authentification, the users are presented a user interface including the map view as well as the means to access the functionality they are authorized to use. The viewer component is able to load data stored in the local filesystem. Also, map data generated by WMS can be integrated. Communication between the SEVESEO IS and the modeling software is established through a Web Service Interface. The user operates the model software not directly but through the SEVESEO IS Viewer. The model is available as Web Service that is known to SEVESEO IS by its URL. SEVESEO IS registers this URL and sends requests to the service. Communication is based on a well-defined Web Service protocol. When a model available through a particular Web Service has been selected for computation of dispersion or other consequences of an incident, the SEVESEO IS requests the list of parameters that have to be specified for the this model. The Modeling Web Service sends the parameter list, and the SEVESEO IS Viewer uses the list to generate a user interface allowing the user to enter all parameters. The user then confirms the entry, and a request is sent to the Web Service to run the model. The output is sent back through the Web Service Interface to the SEVESEO IS where the results are stored in the database and displayed on the map (Fig 6.)

Figure 6. Screenshot of the SEVESEO IS for a test area in Luxembourg (industrial site Bertrange). 6. ACKNOWLEDGEMENTS The authors want to acknowledge ESA for funding this project under the Data User Element programme (http://www.esa.int/DUE). More information about the project is available on www.seveseo.eu. Furthermore we would like to thank the different partners and endusers involved for their contribution to the project. 7. REFERENCES 1.

Kafatos, M., Yang, R., Yang, C., Gomez, R. & Boybeyi, Z. (2002). Utilizing Remote Sensed Data in A Quick Response System. In Proceedings of the ISPRS Commission I Symposium ‘Integrated Remote Sensing at the Global, Regional and Local Scale’ pp. 101-104, Denver, Colorado, November 11-14, 2002.

2.

Wieringa, J. (1993). Representative roughness parameters for homogeneous terrain, Boundary Layer Meteorology, 63, 323-363.