Desertification in Europe: mitigation strategies, land use planning: Proceedings of the advanced study course held in Alghero, Sardinia, Italy from 31 May to 10 June 1999

The present volume is based on lectures given at the course held in Alghero, Sardinia, Italy, from 31 May to 10 June 1999 on ‘Desertification in Europe: Mitigation Strategies, Land Use Planning’. It also contains presentations, given by the participating students, on their own research activities and interests. With the adoption of the International Convention to Combat Desertification, which represents a follow up of the Rio recommendations, this publication is timely. It highlights the specific situation of the Southern European regions and provides a comprehensive and state-of-the-art review of this complex issue

The use of remote sensing to evaluate and detect desert regions

Die Fernerkundung spielt eine signifikante Rolle bei der Bereitstellung von aktuellen Daten zur Schätzung von empirischen Indizes bei Untersuchungen der Umwelt, insbesondere in Trockengebieten. Spektral- und thermische Kanäle in Satellitenbildern werden auch zur Berechnung von Indizes verwendet, um natürliche Phänomene in Trockengebieten – wie etwa Bodendegradation und Desertifikation – aufzuspüren, zu bestimmen und zu evaluieren. In dieser Arbeit wurden zur Identifikation von Desertifikation in der Kashan-Qom Region im Zentraliran fünf Desertifikationsindikatoren verwendet: Vegetation, Oberflächentemperatur, Erosion, Trockenheit und Überflutungen. Diese Indikatoren wurden dargestellt mit Hilfe von: Vegetationsindex (VCI), Temperaturindex (TCI), Revidierte Universelle Bodenverlustgleichung (RUSLE), standardisierter Niederschlagsindex (SPI) und Abfluss. Multispektrale Bilder des MODIS Satelliten wurden für die Berechnung von VCI und TCI herangezogen. Des Weiteren wurden RUSLE, SPI und Abfluss bestimmt. Schließlich wurden mehrere Desertifikationskarten anhand von zwei Modellen – einem konventionellen Modell und einem unscharfen Modell – erstellt. Die Ergebnisse der Modelle wurden mit Hilfe von Feldproben und der Erstellung einer Fehlermatrix analysiert. Im unscharfen Modell wurde ein regelbasiertes System aufgrund von Expertenwissen und einer induktiven datengetriebenen Methode erstellt. Obwohl das unscharfe Modell weniger genau als die konventionelle Methode ist, zeigt es die Unbestimmtheit in den Desertifikationsklassen der erstellten Karten.Remote sensing plays a significant role in providing up-to-date data for the estimating of empirical indices in studying the environment, especially in drylands. The spectral and thermal bands in satellite images are also applied to calculate the indices to detect, identify, and evaluate the natural phenomena in drylands such as land degradation and desertification. In this project, for the identification of desertification in the Kashan-Qom region in Central Iran, five main indicators of desertification are used as follows: vegetation, land surface temperature, erosion, drought, and flooding; therefore, these indices are selected as Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Revised Universal Soil Loss Equation (RUSLE), and Standardized Precipitation Index (SPI), and runoff (Q), respectively. The multi-spectral satellite images of MODIS are used for the calculation of remotely sensed indices such as Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). Furthermore, the ancillary data-based indices, Revised Universal Soil Loss Equation (RUSLE), and Standardized Precipitation Index (SPI), and runoff (Q), are also estimated. Then several desertification maps are produced in two models: conventional method and fuzzy model. The result of each model is also evaluated, that is, the results are assessed by the supplying of field sampling as ground truth references and the defining of error matrix. In the fuzzy modelling, a rule-based system is built by expert knowledge and data-induction method. According to the obtained results, even though the accuracy of the fuzzy model is lower than the conventional method, the fuzzy model represents the uncertainty in the classes of resulted desertification by providing a map for each class

A geo-information theoretical approach to inductive erosion modelling based on terrain mapping units

Three main aspects of the research, namely the concept of object orientation, the development of an Inductive Erosion Model (IEM) and the development of a framework for handling uncertainty in the data or information resulting from a GIS are interwoven in this thesis. The first and the second aspect of the thesis discuss simultaneously the application of a terrain mapping unit (TA" in hierarhical observational procedures and an IEM in a GIS environment. These aspects were aimed at providing an alternative solution to the traditional approach to data acquisition, data capture and producing aggregated information for a GIS.The third aspect discusses the application of standard deviation, probability of misclassification, membership degree and plausibility reasoning for handling error and uncertainty associated with data inputs and information outputs handled by a GIS in general and into and from the Indonesian Field Engineering Design Plan (FEDP) in particular. It is aimed mainly at establishing a framework for representing uncertainty in geographical data manipulation. GIS logical models, the characteristics of logical GIS models, types of uncertainty including error due to variability, imprecision, ambiguity and a proposed conceptual framework based on the concept of certainty factors are discussed.The research involved the establishment of stable basic mapping units that allow the definition of repeatable and hierarchical observational procedures. This solution was addressed especially to the situation when sophisticated software and good quality data are not available. In this research, TMUs are defined as areas with a particular combination of geology, geomorphology, morphometry and soil characteristics, usually obtained by interpretation of aerial photo or SPOT images. Terrain areas having similar relief characteristics are identified, delineated and verified in the field. The delineated TMUs represent natural divisions of the terrain often with distinct boundaries.Attributes associated with the established TMUs were selected and used to clasify TMUs. A classification hierarchy of TMU was established in the fight of object oriented modelling including abstraction, inheritance, aggregation and association of terrain objects. The hierarchy has three levels, namely level +1 (superclass level refered to as TMU), level 0 (class level refered to as sub TMU) and level -1 (elementary object refered to as subsub TMU). A lower level in the classification hierarchy represents more refined or specialised information.The well known deductive erosion model, the Universal Sod Loss Equation (USLE) is incomplete in predicting spatial erosion processes. More sophisticated models (i.e. CREAMS, ANSWERS, EPIC, WEPP, GAMES) have failed to account for the complexity of erosion processes and there are no means for validation of model predictions. An alternative to the problem is suggested through an inductive (bottom-up) approach. This approach involves an Inductive Erosion Model (IEM), which was built on observations including dynamic (resilience) and static (inertia) site specific erosion influencing factors in one or more sample areas, made on site at the farmer's field level which is the best functional unit to describe erosion class at local level. An IEM model therefore is region specific. Once an IEM is built and tested for each type of TMU then it can be incorporated within the GIS environment as an acceptable means to predict safely the severity of sod erosion for the entire study area. Erosion severity classes predicted by an IEM are considered as active or dynamic attributes of the established TMUs. By definition TMU provides inherently erosion influencing factors, so called terrain characteristics including morphometry, geology, soil and ground cover. An IEM is intended to predict homogeneous erosion severity classes, related to TMUs at different aggregation or hierarchical levels. The aggregation levels are related to point observations, farmers field level (FFL) and larger parts of the terrain. The discussion of this aspect is focused on the role of the TMU in the observational procedure providing input for an IEM.The established hierarchical mapping units served as a basis for inductive erosion modelling, incorporating expert knowledge-based inference rules. The inductive erosion modelling followed a multi-scale approach and was implemented in a GIS environment. Application of the concepts of regionalization, observed pattern, and decision rules in predicting and modelling purposes are discussed. At regional level patterns associated with the main erosive processes such as sheet, rill, gully and ravine features are generally still identifiable on the aerial photos at scale 1 : 50 000. However, more detailed information on these types of active process at local level can be obtained only by more detailed study, i.e., erosion study at the FFL. In this regard, the FFL is considered as a suitable basic functional unit to describe erosion at local level.Instead of using probability reasoning, which must follow statistical constraints, production rules allow the introduction of a Certainty Factor (CF) for handling both uncertainty in data, models and the resulting information. The C17 can be obtained as a subjective judgment made by experts and comes naturally to experts either in inferring underlying processes or estimating quality of data and models being used. With special reference to the situation when all procedures and techniques for determining probability and obtaining quantitative information particularly in data poor environment are unlikely to be performed, this study demonstrated sufficiently the application of the concept of CF.In the fight of evidence theory, an IEM for predicting erosion severity at a specific TMU was built as a function of various certainty factors of spatial erosion influencing factors. The certainty factor has a value between -1 and +1 and its value indicates the estimated change in belief of allocation of a TMU to a particular erosion class as evidence (from maps, air photos, field observations etc.) is gathered, for each contributing factor. The erosion severity class to which a TMU is finally allocated is the one with overall certainty factor closest to +1. It is proposed as a method of handling uncertain information caused by incompleteness such as inferences established and derived by experts from a set of observations including the effect of causal relationships among various uncertain evidences

Quantitative Techniques in Participatory Forest Management

Forest management has evolved from a mercantilist view to a multi-functional one that integrates economic, social, and ecological aspects. However, the issue of sustainability is not yet resolved. Quantitative Techniques in Participatory Forest Management brings together global research in three areas of application: inventory of the forest variables that determine the main environmental indices, description and design of new environmental indices, and the application of sustainability indices for regional implementations. All these quantitative techniques create the basis for the development of scientific methodologies of participatory sustainable forest management

Quantitative Techniques in Participatory Forest Management

Forest management has evolved from a mercantilist view to a multi-functional one that integrates economic, social, and ecological aspects. However, the issue of sustainability is not yet resolved. Quantitative Techniques in Participatory Forest Management brings together global research in three areas of application: inventory of the forest variables that determine the main environmental indices, description and design of new environmental indices, and the application of sustainability indices for regional implementations. All these quantitative techniques create the basis for the development of scientific methodologies of participatory sustainable forest management

Agronomic suitability studies in the Russian Altai using remote sensing and GIS

Diese Doktorarbeit beschreibt Methoden und geeignete Anpassungen bereits existierender Lösungen, um auf zwei verschiedenen Wegen die Landeignung für die Tal- und Beckenregionen der Südsibirischen Altaigebirges innerhalb eines Geoinformationssystems zu modellieren (GIS). Die Ausgangsmethoden sind: 1) die Bodeneignungsmodelle Almagra" and Cervatana (MicroLEIS System), entwickelt für die Mittelmeerregionen (De la Rosa et al. 1992 and 1998) und die Gewichtsmethode, welche Burlakova L. M. (1988) speziell für die Altairegion entwickelte. Letztgenannte Methode basiert auf den gewichteten Mitteln für eine gegebene Anzahl von Faktoren. 2) Zum Vergleich, die zweite, dritte und vierte Version des gleichen Modells mit drei unterschiedlichen Typen wurden mit Fuzzy-Logik-Methoden entwickelt. Sie werden benutzt, um darzustellen, wie unscharfe Mengen zum einen die Berechnung von Gauß-Mitgliedschaftsfunktionen bestimmter Klassen veranschaulichen können, welche zu anderen Klassen gehören, und wie die Variablen in einer mathematischen Handhabung angefasst werden können. Außerdem stellt diese Arbeit Ideen vor, wie die Fernerkundung das Geoinformationssystem (GIS) eingesetzt werden kann, wenn - wie im vorliegenden Fall - nur unzureichend Geodaten vorhanden sind, (i) um in die Modellierung der Boden- und Klimabedingungen einzugehen und (ii) um die Charakteristik des Landmanagements im Untersuchungsgebiet zu kennzeichnen. Drei landwirtschaftliche Agrarkulturen (Sommerweizen, Sonnenblumen und Kartoffeln) sind für die Altairegion auf regionaler Ebene von Bedeutung und wurden daher in die vorliegende Untersuchung einbezogen. Die Bewertung erfolgte nach fünf Eignungskategorien, entsprechend der FAO Klassifikation (1976). Das Uimon-Becken wurde als Untersuchungsgebiet ausgewählt. Soziale und ökonomische Faktoren wurden bisher ausgeschlossen, können aber innerhalb einer weiteren Entwicklungsphase hinzugenommen werden.thesi

Feasibility Study on the Valuation of Public Goods and Externalities in EU Agriculture

The present report develops and test an up-scaled non-market valuation framework to value changes in the provision level of the Public Goods and Externalities (PGaE) of EU agriculture from the demand-side (i.e. using valuation surveys). The selected PGaE included in the study are the following: cultural landscape, farmland biodiversity, water quality and availability, air quality, soil quality, climate stability, resilience to fire and resilience to flooding. The following achievements have been accomplished along the project development: 1) comprehensive description of the study selected PGaE, 2) quantification of the selected agricultural PGaE using agri-environmental indicators, 3) standardised description of PGaE disentangling the macro-regional agro-ecological infra-structures from its ecological and cultural services, 4) delimitation of wide areas with homogeneous agro-ecological infra-structures across EU (macro-regions), 5) delimitation of the macro-regions, independently from their supply of PGaE, 6) definition of “Macro-Regional Agri-Environmental Problems” (MRAEP), through the association of the macro-regions with the core PGaE supplied by them, delivering non-market demand-side valuation problems relevant to the agricultural and agri-environmental policy decision-makers, 7) design of a Choice Modelling (CM) survey able to gather multi-country value estimates of changes in the provision level of different PGaE supplied by different macro-regions, 8) successful testing of the valuation framework through a pilot survey and 9) delivering of alternative sampling plans for the EU level large-scale survey allowing for different options regarding the number of surveyed countries, the size and composition of respective samples, and the survey administration-mode, balanced with estimates for the corresponding budgetary cost.JRC.J.4-Agriculture and Life Sciences in the Econom