Use of GIS and Statistics for an Environmental Monitoring System in Germany

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1 EnviroInfo 2002 (Wien) Environmental Communication in the Information Society - Proceedings of the 16th Conference Use of GIS and Statistics for an Environmental Monitoring System in Germany Gerlinde Knetsch 1, Winfried Schröder 2 Abstract Umweltbeobachtung umfasst neben der Probenentnahme, dem Messen und Modellieren auch die Integration von Einzelinformationen zur Beschreibung und Bewertung des Zustandes der Umwelt. Ein Ziel der Umweltbeobachtung besteht darin, eine Verknüpfung der Beobachtungsprogramme und Messnetze je nach fachlicher Fragestellung und umweltpolitischem Bedarf unter dem Blickwinkel einer medienübergreifenden Betrachtung zu erreichen. Unverzichtbare Werkzeuge dazu sind Methoden und Modelle, die es ermöglichen, unter Nutzung eines Geografischen Informationssystems und dem Einsatz von geostatistischen Verfahren Daten aus der Umweltbeobachtung räumlich und inhaltlich zu vernetzen. Eine Grundlage für die Analyse und Bewertung ist die Einordnung der Programme in landschaftsökologische Raumklassen und die Bestimmung der raumstrukturellen Landschaftsrepräsentanz. Aufbauend auf dieser geostatistischen Analyse der Messdatenrepräsentanz und der mit der Raumgliederung bestimmten landschaftsstrukturellen Messstellenrepräsentanz können Vorschläge für die Optimierung von Beobachtungs- und Messprogrammen abgeleitet werden. 1. Introduction The purpose of environmental monitoring is to process the data generated by monitoring programmes and measuring networks according to thematic and methodological aspects, and to combine them in a cross-cutting way and assess them in the light of the problem under investigation, in order to provide the information needed for environmental policies. Statistical methods and simulation models are indispensable tools for such processing and integration, enabling environmental monitoring data to be linked in space and time. 1 Umweltbundesamt, Postfach , Berlin gerlinde.knetsch@uba.de, Internet: 2 Hochschule Vechta, Institut für Umweltwissenschaften, Postfach 53 53, Vechta winfried.schroeder@ispa.uni-vechta.de, Internet :

2 554 For this purpose, a multivariate statistical procedure is used to aggregate data according to the similarity of selected characteristics like climate, soil, orography and potentially natural vegetation to ecoregions. They play an important role in verifying if the spatial distribution of environmental monitoring sites represent the ecological structure. This ecoregionalization is used as a basis for the cartographic description, combined analysis and assessment of different environmental monitoring networks. It can be used to describe representativity in terms of landscape units, and as a spatial reference base for measuring data following geostatistical verification of their extrapolability from the measuring point to surrounding areas. Based on this geostatistical analysis of the representativity of measuring data, and the determination of monitoring sites representativity of landscape structures by means of ecoregionalization, proposals can be formulated for the optimisation of monitoring networks. 2. Methods and Instruments The multivariate statistical method CART (Classification and Regression Trees), implemented in the statistics software SPLUS, was used. The results of ecoregionalization, visualized in the geographical information system ArcView, are used to divide Germany into different ecoregions (landscapes units), based on two criteria: homogeneity, the elements within each class should be as similar as possible, and discrimination, the classes (groups) should be as distinct as possible. The following ecologically significant site characteristics have been selected for the multivariate classification of areas using CART: target (dependent) variable: potentially natural vegetation descriptive (independent) variables: soil texture, elevation, and climate data as monthly averages (air temperature, precipitation, evaporation, radiation). CART allows spatial units of a higher level to be broken down into lower-level ones, based on the criteria homogeneity and discrimination. CART does not require the input data to be available on a particular level. It can process categorical variables, like soil types together with metrical variables, like climate data without transformation and the associated losses of information. This makes CART particularly suitable for processing data on ecological characteristics, which are often available on different levels and whose distribution patterns are usually unknown. The derivation of a soil map via the regionalization of soil profiles using CART deserves particular mention in this context (Mertens et al. 2002).

3 555 Raumgliederung Deutschlands - 21 Klassen Raumklassenbeschreibung Teterow Schwerin Hamburg Bremen Berlin Hannover Osnabrück Magdeburg Leipzig Köln Dresden Siegen Erfurt Fulda Koblenz Mainz Würzburg Nürnberg Regensburg Stuttgart Ulm München Copyright: Umweltbundesamt Bundesamt für Naturschutz Statistisches Bundesamt Bearbeitung: IUW, HS Vechta Hochschule Vechta Datengrundlagen: BfN (PNV), DWD (Klima) Institut für Umweltwissenschaften BGR (Bodenart) Abt. Landschaftökologie UNEP (Topographie) Stand: Januar Altmark, Prignitz und Uckermark Hochlagen der kristallinen und paläozoischen Mittelgebirge Brandenburger Jungmoränenlandschaft 19 Leipziger Tieflandsbucht Mecklenburger Seenplatte Ost- und Nordfriesische Marsch 26 Schwäbische Alb 30 Sächsische Börden 46 Niedersächsische Geest, Westfälische Tieflandsbucht Schl.-Holst. Geest und Lüneburger Heide Schl.-Holst. und Mecklenb. Jungmoränenlandschaft Allgäu und Hochalpen 55 Alpenvorland 56 Bayrisches Hügelland und Fränkisches Keuper-Lias- Land 57 Kristalline Mittelgebirge 58 Fränkische Alb Nieders. Börden, Rheinland und Oberrheintal Rheinisches Schiefergebirge und südwestdeutsche Schichtstufenlandschaft Schwäbisches Schichtstufenland Lausitz und Thüringer Schiefergebirge Schotterterrassen des Alpenvorlands und Keuper-Bergland Admin. Grenzen Moorflächen (Corine Landcover) Naturräume (n. Meynen et al. 1962) Fig. 1: Ecoregionalization of Germany, based on 21 ecoregions/landscape units The log-likelihood partition algorithm of CART s Splus version detects the impurities of resultant sub-groups for all descriptive variables at all levels. The algorithm selects the partition with the most favourable misclassification value. It describes the relationships between regions in terms of instances of the target variable and the descriptive variables. CART first produces a tree with the maximum number of classes obtainable with the available data. The maximum number of classes ultimately obtained in the ecoregionalization presented here was 73. The maximal tree thus produced can then be pruned by the user manually according to criteria which must be justified in detail. This allows him or her to modify the classification in the light to substantive considerations. Each class is described statistically and verbally, and assigned a name.

4 556 Parameter-Vergleich, Raumgliederung Dt. Hoehe, Bodenart, PNV Oekoklassen 54, 55 Allgäu und Hochalpen Knoten 54 V ora lpin es H üg elland Knoten Höhe [m] Schluff und Lehm aus Kalk-/Mergelsteinen Geschiebemergel Geschiebemergel Sandiger bis toniger Lehm aus Ton-/Mergelsteinen Lehmige Flußschotter Molasse mit Lößüberlagerung Bodenart Tannen-Buchenwälder m./o. Fichte Traubeneichen- Buchenwälder Tannen-Buchenwälder mit Eiche Tannen-Buchenwälder m it Fichte PNV Fig. 2: Ecological elements of ecoregions 54 and Results The method described in Section 2 above produces an ecoregionalization on a comprehensible statistical basis while affording the largest possible degree of userindependence, i.e. objectivity. The target variable potentially natural vegetation is defined to be the ecological potential of an area which could be expected under the present climatic, orographical and pedological conditions if all human impacts are excluded. It thus describes a reference state that is important in precaution-oriented environmental protection. When ecoregionalization is used in combination with environmental monitoring data which have been generalised, where possible, to cover larger areas, these monitoring data can be interpreted via the ecological elements which have been aggregated to ecoregions. The method and the ecoregionalization presented here validate the expert-knowledge-based delineation of landscape units developed by Meynen and Schmidthüsen in 1962.

5 Applications The result of the ecoregionalization is entered into a GIS and overlaid with maps showing the distribution of measuring stations of different environmental monitoring programmes. The measuring-network density can be calculated for each ecoregion. Based on this, the representativity of the monitoring sites in relation to the spatial landscape structure can be verified. This can be done in two ways: First, by determining whether the distribution of the measuring stations is proportional to the ecoregion area. Second, a neighbourhood analysis method developed by Vetter and Maass (1994) allows the extended surroundings of the monitoring sites to be included in the representativity considerations. Finally, by combining metadata and network geometries, conclusions can be drawn with respect to geographical, temporal and thematic characteristics of the measuring stations (identification of geographical redundancies and gaps, possible need for harmonisation). The ecoregionalization of Germany offers a basis for planning and development measures (optimisation, harmonisation, linkage) in the fields of nature and landscape protection, forestry, environmental monitoring, and for the identification of representative monitoring sites. In addition to monitoring data analysis and the metadata, ecoregionalization constitutes a further module of an environmental monitoring system for Germany. It is currently being used for the designation of areas for monitoring the environmental impacts of genetically modified organisms. 5. Outlook It is planned to apply the ecoregionalization methodology described here to the target variable water catchments. For environmental monitoring, the recording of substance flows within ecosystems and beyond ecosystem boundaries is an essential basis for the identification of input and output pathways. Bibliography Breimann et al. (1984): Classification and regression trees (CART). - Monterey, Wadsworth, Inc. Knetsch, G. (2000): Raumbezug in der Umweltbeobachtung des Bundes und der Länder, UWSF 12 (4), p. 235 Mertens et al. (2002): GIS-based regionalization of soil profiles with Classification and Regression Trees (CART). In: Journal of Plant Nutrition and Soil Science, Vol. 165 (1), pp Meynen, E. et al. (1962): Handbuch der naturräumlichen Gliederung Deutschland. Bad Godesberg

6 558 Schröder, W. et al. (2001): Konkretisierung des Umweltbeobachtungsprogramms im Rahmen eines Stufenkonzeptes der Umweltbeobachtung des Bundes und der Länder. - Berlin (UFOPLAN 2000, FKZ /01 und 02) Schröder, W. et al. (2002): Harmonisierung der Umweltbeobachtung. Instrumente zur Prüfung methodischer Vergleichbarkeit und räumlicher Repräsentanz. In: Fränzle, O. et al. (Hrsg.): Handbuch der Umweltwissenschaften. Grundlagen und Anwendungen der Ökosystemforschung. - Landsberg am Lech, Chapter V-1.3 (8. Erg.Lfg.) Vetter, L., Maass, R. (1994): Nachbarschaftsanalytische Verfahren. In: Schröder, W. et al. (eds.) Neuere statistische Verfahren und Modellbildung in der Geoökologie. - Braunschweig, Wiesbaden, pp