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A sustainable development of forests and their ecosystem services requires the monitoring of the forests" state and changes as well as the prediction of their future development. To achieve the latter, eco-physiological forest growth models are usually applied. These models require calibration and validation with forestry reference data. This data includes forest structural parameters such as tree height or stem diameter which are easy to measure and can be used to estimate the core model parameters, i.e. the tree- biomass pools. The methods traditionally applied to derive the structural parameters are mainly manual and time-consuming. Hence, the in situ data acquisition is inefficient and limited in its ability to capture the vertical and horizontal variability in stand structure. Ground-based remote sensing bears the potential to overcome the limitations of the traditional methods. As they can be automated, ground-based remote sensing methods allow a much more efficient data acquisition and a larger spatial coverage. They are also able to capture forest structure in its three dimensions. Nevertheless, at present further research is required, in particular with respect to the practical integration of ground-based remote sensing data into forest growth models as well as regarding factors influencing the structural parameter retrieval from this data. Therefore, the goal of this PhD thesis was to investigate the influencing factors of two ground-based remote sensing methods (terrestrial laser scanning and hemispherical photography), which have not or only scarcely been studied to date. In addition, the use of forest structural parameters derived from these methods for the calibration of a forest growth model was assessed. Both goals were achieved. The results of this thesis could contribute significantly to a comprehensive assessment of ground-based remote sensing and its potential to derive the forest structural parameters. However, the use of these methods to calibrate forest growth models proved to be limited. An optimized data sampling design is expected to eliminate the major limitations, though. Furthermore, the combination of ground-based, airborne, and satellite remote sensing sensors was suggested to provide an optimized framework for the general integration of remotely sensed data into forest growth models. This combination of remote sensing observations at different scales will contribute greatly to a modern forest management with the purpose of warranting a sustainable forest development even under growing economic and ecological pressures.
Time series archives of remotely sensed data offer many possibilities to observe and analyse dynamic environmental processes at the Earth- surface. Based on these hypertemporal archives, which offer continuous observations of vegetation indices, typically at repetition rates from one to two weeks, sets of phenological parameters or metrics can be derived. Examples of such parameters are the beginning and end of the annual growing period, as well as its length. Even though these parameters do not correspond exactly to conventional observations of phenological events, they nevertheless provide indications of the dynamic processes occurring in the biosphere. The development of robust algorithms for the derivation of phenological metrics can be challenging. Currently, such algorithms are most commonly based on digital filters or the Fourier analysis of time series. Polynomial spline models offer a useful alternative to existing methods. The possibilities of using spline models in the analytical description of time series are numerous, and their specific mathematical properties may help to avoid known problems occurring with the more common methods for deriving phenological metrics. Based on a selection of different polynomial spline models suitable for the analysis of remotely sensed time series of vegetation indices, a method to derive various phenological parameters from such time series was developed and implemented in this work. Using an example data set from an intensively used agricultural area showing highly dynamic variations in vegetation phenology, the newly developed method was verified by a comparison of the results of the spline based approach to the results of two alternative, well established methods.
Arctic and Antarctic polynya systems are of high research interest since extensive new ice formation takes place in these regions. The monitoring of polynyas and the ice production is crucial with respect to the changing sea-ice regime. The thin-ice thickness (TIT) distribution within polynyas controls the amount of heat that is released to the atmosphere and has therefore an impact on the ice-production rates. This thesis presents an improved method to retrieve thermal-infrared thin-ice thickness distributions within polynyas. TIT with a spatial resolution of 1 km × 1 km is calculated using the MODIS ice-surface temperature and atmospheric model variables within the Laptev Sea polynya for the winter periods 2007/08 and 2008/09. The improvement of the algorithm is focused on the surface-energy flux parameterizations. Furthermore, a thorough sensitivity analysis is applied to quantify the uncertainty in the thin-ice thickness results. An absolute mean uncertainty of -±4.7 cm for ice below 20 cm of thickness is calculated. Furthermore, advantages and drawbacks using different atmospheric data sets are investigated. Daily MODIS TIT composites are computed to fill the data gaps arising from clouds and shortwave radiation. The resulting maps cover on average 70 % of the Laptev Sea polynya. An intercomparison of MODIS and AMSR-E polynya data indicates that the spatial resolution issue is essential for accurately deriving polynya characteristics. Monthly fast-ice masks are generated using the daily TIT composites. These fast-ice masks are implemented into the coupled sea-ice/ocean model FESOM. An evaluation of FESOM sea-ice concentrations is performed with the result that a prescribed high-resolution fast-ice mask is necessary regarding the accurate polynya location. However, for a more realistic simulation of other small-scale sea-ice features further model improvements are required. The retrieval of daily high-resolution MODIS TIT composites is an important step towards a more precise monitoring of thin sea ice and sea-ice production. Future work will address a combined remote sensing " model assimilation method to simulate fully-covered thin-ice thickness maps that enable the retrieval of accurate ice production values.
The main research question of this thesis was to set up a framework to allow for the identification of land use changes in drylands and reveal their underlying drivers. The concept of describing land cover change processes in a framework of global change syndrome was introduced by Schellnhuber et al. (1997). In a first step the syndrome approach was implemented for semi-natural areas of the Iberian Peninsula based on time series analysis of the MEDOKADS archive. In the subsequent study the approach was expanded and adapted to other land cover strata. Furthermore, results of an analysis of the relationship of annual NDVI and rainfall data were incorporated to designate areas that show a significant relationship indicating that at least a part of the variability found in NDVI time series was caused by precipitation. Additionally, a first step was taken towards the integration of socio-economic data into the analysis; population density changes between 1961 and 2008 were utilized to support the identification of processes related to land abandonment accompanied by cessation of agricultural practices on the one hand and urbanization on the other. The main findings of the studies comprise three major land cover change processes caused by human interaction: (i) shrub and woody vegetation encroachment in the wake of land abandonment of marginal areas, (ii) intensification of non-irrigated and irrigated, intensively used fertile regions and (iii) urbanization trends along the coastline caused by migration and the increase of mass tourism. Land abandonment of cultivated fields and the give-up of grazing areas in marginal mountainous areas often lead to the encroachment of shrubs and woody vegetation in the course of succession or reforestation. Whereas this cover change has positive effects concerning soil stabilization and carbon sequestration the increase of biomass involves also negative consequences for ecosystem goods and services; these include decreased water yield as a result of increased evapotranspiration, increasing fire risk, decreasing biodiversity due to landscape homogenization and loss of aesthetic value. Arable land in intensively used fertile zones of Spain was further intensified including the expansion of irrigated arable land. The intensification of agriculture has also generated land abandonment in these areas because less people are needed in the agricultural labour sector due to mechanization. Urbanization effects due to migration and the growth of the tourism sector were mapped along the eastern Mediterranean coast. Urban sprawl was only partly detectable by means of the MEDOKADS archive as the changes of urbanization are often too subtle to be detected by data with a spatial resolution of 1 km-². This is in line with a comparison of a Landsat TM time series and the NOAA AVHRR archive for a study area in the Greece that showed that small scale changes cannot be detected based on this approach, even though they might be of high relevance for local management of resources. This underlines the fact that land degradation processes are multi-scale problems and that data of several spatial and temporal scales are mandatory to build a comprehensive dryland observation system. Further land cover processes related to a decrease of greenness did not play an important role in the observation period. Thus, only few patches were identified, suggesting that no large-scale land degradation processes are taking place in the sense of decline of primary productivity after disturbances. Nevertheless, the land cover processes detected impact ecosystem functioning and using the example of shrub encroachment, bear risks for the provision of goods and services which can be valued as land degradation in the sense of a decline of important ecosystem goods and services. This risk is not only confined to the affected ecosystem itself but can also impact adjacent ecosystems due to inter-linkages. In drylands water availability is of major importance and the management of water resources is an important political issue. In view of climate change this topic will become even more important because aridity in Spain did increase within the last decades and is likely to further do so. In addition, the land cover changes detected by the syndrome approach could even augment water scarcity problems. Whereas the water yield of marginal areas, which often serve as headwaters of rivers, decreases with increasing biomass, water demand of agriculture and tourism is not expected to decline. In this context it will be of major importance to evaluate the trade-offs between different land uses and to take decisions that maintain the future functioning of the ecosystems for human well-being.
Die Beobachtung und Bewertung von Wäldern ist eins der zentralen Themen der Fernerkundung. Wälder sind auf der Erde die größten Speicher von Biomasse und damit, neben den Ozeanen, die größte Senke für Kohlendioxid. Eine genaue Kenntnis über Zusammensetzung, Zustand und Entwicklung der Wälder ist wegen ihrer vielfältigen Funktionen und ihres großen Anteils an der Landesfläche von großem wissenschaftlichem und gesellschaftlichem Wert. Eine flächen-deckende detaillierte Beobachtung ist nur mit fernerkundlichen Mitteln möglich. Eine vielversprechende moderne Technik für hochauflösende Waldfernerkundung ist luftgestütztes Laser-¬scanning. Für die Arbeit stand ein Laserscanner-Datensatz aus dem Idarwald bei Morbach in Einzelpunkten und als Wellenformdatensatz zur Verfügung, der zur Ableitung von strukturellen Waldparametern genutzt wurde. Als wichtigster Bestandsstrukturparameter wurde die Baumhöhe sowohl aus Einzelpunktdaten als auch aus gerasterten Bilddaten flächendeckend mit hoher Genauigkeit abgeleitet. Die Kronenuntergrenzen konnten anhand der Wellenformdaten identifiziert werden und stimmten ebenfalls in hoher Genauigkeit mit Geländemessungen überein. Aus Baumhöhen und Höhe der Kronenuntergrenzen konnte die jeweilige Kronenlänge bestimmt werden. Eine größere Herausforderung ist die Bestimmung der Anzahl der Bäume pro Hektar. Während die einzelnen Kronen älterer Nadelbäume gut erkennbar sind, lassen sich Laubbäume und jüngere Nadelbäume nur schwer identifizieren. Trotzdem konnte mit Hilfe eines adaptiven Moving-Window-Ansatzes eine hohe Übereinstimmung mit im Gelände bestimmten Stammzahlen erzielt werden. Aus dem Anteil der Laserstrahlen, die im Bestand den Boden erreichen, können der Kronenschlussgrad und der Blattflächenindex bestimmt werden. Beide Größen sind für den Strahlungstransfer im Bestand und für ökologische Fragestellungen von Bedeutung und konnten ebenfalls flächendeckend und mit hoher Genauigkeit gemessen werden. Eng verknüpft mit dem Blattflächenindex sind die Biomasse und der Holzvorrat. Der Holzvorrat kann zwar nicht direkt aus den Laser-¬scannerdaten abgeleitet werden, da aber enge Beziehungen zu Baumhöhe und Stammzahl bestehen, kann er aus diesen statistisch abgeleitet werden. Auch die Biomasse wurde indirekt bestimmt: aus den Baumhöhen und dem Bedeckungsgrad. Die detaillierteste Charakterisierung von Waldbeständen kann durch Kombination unterschiedlicher Datensätze erreicht werden. Neben dem Laserscanningdatensatz stand auch ein hyperspektrales Bild des Untersuchungsgebiets zur Verfügung. Um diese zu kombinieren, wurde aus den Wellenformen die jeweils über der Fläche eines Hyperspektralpixels zurückgestreute Laserenergie in Höhenschritten von 0.5 m berechnet. Diese Höhenprofile zeigen die Position und Dichte der Baumkronen. Der kombinierte Datensatz wurde für eine Klassifikation zwischen Fichten und Douglasien in jeweils mehreren Altersstufen verwendet und konnte gegenüber dem Hyperspektralbild alleine eine deutliche Verbesserung der Klassifikationsgenauigkeit erzielen. Als weitere Methode, die Vorteile von hyperspektraler Fernerkundung mit denen von Laser-scanning zu verbinden, wurden Methoden zur Verwendung von Laserscanning für die Invertierung von zwei Reflexionsmodellen entwickelt und getestet. Da mit Laserscanning Größen bestimmt werden können, die aus einem Reflexionsspektrum nicht eindeutig ableitbar sind, können die Daten verwendet werden, um den Parameterraum bei der Invertierung zu verkleinern und damit die Invertierung zuverlässiger zu machen.
Das Ziel dieser Forschungsarbeit liegt in der Entwicklung einer innovativen Klassifikationsstrategie zur satellitengestützten Forstinventur in einem europäischen Mittelgebirgsraum. Über die Ableitung von thematischen Karten der flächenscharfen Verbreitung von fünf Baumartengruppen (Eiche, Buche, Fichte, Douglasie und Kiefer) sowie drei Entwicklungsphasen (Qualifizierung, Dimensionierung und Reife) werden wichtige für eine nachhaltige Bewirtschaftung von Wäldern erforderliche Grundlagendaten bereitgestellt. rnDie nachhaltige Bewirtschaftung der Vielfachfunktionen von Wäldern (Nutz-, Schutz- und Erholungsfunktionen) sowie der steigende Informationsbedarf in Folge nationaler und internationaler Monitoring- und Berichtspflichten (u.a. Montréal Prozess und Kyoto Protokoll) erfordern aktuelle und flächendeckende Informationen über den Zustand der Wälder. In diesem Kontext können fernerkundliche Daten und Methoden zur Unterstützung konventioneller terrestrischer Verfahren zum Einsatz kommen.rnDas Untersuchungsgebiet dieser Studie umfasst den südlichen und östlichen Teil der rheinland-pfälzischen Eifel mit einer Fläche von mehr als 5200 km-², davon rund 2080 km-² bewaldet. Die naturräumliche Heterogenität, die wuchsklimatischen Unterschiede, die Variabilität von Relief und Topographie, die große Zahl vorkommender Baumarten sowie die kulturhistorische Waldentwicklung in der Eifel stellen eine besondere Herausforderung für satellitengestützte Inventurmethoden dar.rnDurch die bevorzugte Verwendung von Referenzdaten aus der unmittelbaren räumlichen Umgebung eines zu klassifizierenden Bereichs wird bei der Parametrisierung des Klassifikationsansatzes die jeweilige naturräumliche und wuchsklimatische Charakteristik berücksichtigt. Der Vergleich dieses räumlich adaptiven Klassifikationsansatzes mit einer konventionellen Maximum-Likelihood Klassifikation zeigt, dass eine Verbesserung der Klassifikationsgenauigkeit um 12 Prozentpunkte erreicht werden konnte. Die Adaptierung der Klassifikationsstrategie an die naturräumlichen und wuchsklimatischen Bedingungen sowie die Anpassung an bestehende Erhebungsmethoden und Datenorganisation bilden die Grundlage für eine erfolgreiche Anwendung des Verfahrens in einem heterogenen Mittelgebirgsraum. Die hohe erreichte Gesamtgenauigkeit des Klassifikationsergebnisses von rund 74% (über 87% für die fünf Hauptbaumarten) erlaubt die Einbindung der Methode in operationelle Erhebungsverfahren zur Unterstützung der terrestrischen Forstinventur.
Die polare Kryosphäre stellt einen Schlüsselfaktor für die Erforschung des Klimawandels dar. Insbesondere das Meereis und seine Schneebedeckung, die sich durch eine äußerst hohe und Zeitskalen-übergreifende Sensitivität gegenüber atmosphärischen Einflüssen auszeichnen, können als diagnostische Parameter für die Abschätzung von Veränderungen im Klimasystem herangezogen werden. Die komplexen Rückkopplungsmechanismen, durch die das Meereis mit der globalen Zirkulation der Atmosphäre und des Ozeans in Wechselwirkung steht, werden durch eine zusätzliche Schneeauflage deutlich verstärkt. Insofern tragen die saisonalen Veränderungen der physikalischen Eigenschaften des Schnees, und insbesondere der Beginn der Schneeschmelze, massgeblich zur lokalen und regionalen Energiebilanz sowie zur Meereismassenbilanz bei. In dieser Arbeit wird nun erstmals auf der Basis langjähriger Daten der satellitengestützten Mikrowellenfernerkundung, in Kombination mit Feldmessungen aus dem Weddellmeer während des Sommers 2004/2005, die Charakteristik der sommerlichen Schmelzperiode auf antarktischem Meereis untersucht. Die sommertypischen Prozesse zeichnen sich hier durch deutliche Unterschiede im Vergleich zu arktischem Meereis aus. Wie die Messungen vor Ort zeigen, kommt es während des antarktischen Sommers nicht zu einem kompletten Abschmelzen des Schnees. Vielmehr dominieren ausgeprägte Schmelz-Gefrier-Zyklen im Tagesgang, die eine Abrundung und Vergrösserung der Schneekristalle sowie die Bildung interner Eisschichten verursachen. Dies führt radiometrisch zu Mikrowellensignalen, deren Erfassung im Vergleich zu bestehenden Schmelzerkennungs-Methoden neue Ansätze erfordert. Durch den Vergleich von zeitlich hoch aufgelösten in-situ Messungen der physikalischen Schneeeigenschaften mit parallel dazu erfassten Satellitendaten, sowie durch eine Modellierung der mikrowellenradiometrischen Eigenschaften der Schneeauflage, konnte ein neuer Indikator entwickelt werden, über den das Einsetzen der typischen sommerlichen Schmelzperiode auf antarktischem Meereis identifiziert werden kann. Der DTBA-Indikator beschreibt die Tagesschwankung der radiometrischen Eigenschaften des Schnees und zeichnet sich durch ein Werteverhalten aus, das eine eindeutige Hervorhebung der Sommerphase innerhalb eines saisonalen Zyklus erkennen lässt. Der Indikator wurde verwendet, um mittels des neu entwickelten Schwellwertalgorithmus MeDeA das Einsetzen der sommerlichen Schmelzperiode für das gesamte antarktische Meereisgebiet zu bestimmen. Durch die Anwendung der neuen Methode auf die langjährigen Reihen der Satellitenmessungen konnte ein umfassender Datensatz erstellt werden, der für den Zeitraum von 1988 bis 2006 die räumliche und zeitliche Variabilität des Einsetzens der sommerlichen Schmelzperiode auf antarktischem Meereis beinhaltet. Die Ergebnisse zeigen, dass im Untersuchungszeitraum keine signifikanten Trends im Beginn des Schmelzens der Schneeauflage festzustellen sind, und dass das Schmelzen im Vergleich zur Arktis deutlich schwächer ausgeprägt ist. Eine Untersuchung der atmosphärischen Antriebe durch die Auswertung meteorologischer Reanalysen zeigt den grundlegenden Einfluss der zirkumpolaren Strömungsmuster auf die interannualen Schwankungen des Einsetzens und der Stärke der sommerlichen Schneeschmelze.
It has been the overall aim of this research work to assess the potential of hyperspectral remote sensing data for the determination of forest attributes relevant to forest ecosystem simulation modeling and forest inventory purposes. A number of approaches for the determination of structural and chemical attributes from hyperspectral remote sensing have been applied to the collected data sets. Many of the methods to be found in the literature were up to now just applied to broadband multispectral data, applied to vegetation canopies other than forests, reported to work on the leaf level or with modelled data, not validated with ground truth data, or not systematically compared to other methods. Attributes that describe the properties of the forest canopy and that are potentially open to remote sensing were identified, appropriate methods for their retrieval were implemented and field, laboratory and image data (HyMap sensor) were acquired over a number of forest plots. The study on structural attributes compared statistical and physical approaches. In the statistical section, linear predictive models between vegetation indices derived from HyMap data and field measurements of structural forest stand attributes were systematically evaluated. The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify leaf area index and crown volume with good accuracy. For broadband multispectral data, the accuracy was generally lower. The physically-based approach used the invertible forest reflectance model (INFORM), a combination of well established sub-models FLIM, SAIL and LIBERTY. The model was inverted with HyMap data using a neural network approach. In comparison to the statistical approach, it could be shown that the reflectance model inversion works equally well. In opposition to empirically derived prediction functions that are generally limited to the local conditions at a certain point in time and to a specified sensor type, the calibrated reflectance model can be applied more easily to different optical remote sensing data acquired over central European forests. The study on chemical forest attributes evaluated the information content of HyMap data for the estimation of nitrogen, chlorophyll and water concentration. A number of needle samples of Norway spruce were analysed for their total chlorophyll, nitrogen and water concentrations. The chemical data was linked to needle spectra measured in the laboratory and canopy spectra measured by the HyMap sensor. Wavebands selected in statistical models were often located in spectral regions that are known to be important for chlorophyll detection (red edge, green peak). Predictive models were applied on the HyMap image to compute maps of chlorophyll concentration and nitrogen concentration. Results of map overlay operations revealed coherence between total chlorophyll and zones of stand development stage and between total chlorophyll and zones of soil type. Finally, it can be stated that the hyperspectral remote sensing data generally contains more information relevant to the estimation of the forest attributes compared to multispectral data. Structural forest attributes, except biomass, can be determined with good accuracy from a hyperspectral sensor type like HyMap. Among the chemical attributes, chlorophyll concentration can be determined with good accuracy and nitrogen concentration with moderate accuracy. For future research, additional dimensions have to be taken into account, for instance through exploitation of multi-view angle data. Additionally, existing forest canopy reflectance models should be further improved.
In past years, desertification and land degradation have been acknowledged as a major threat to human welfare world-wide, and their environmental and societal implications have sparked the formulation of the UN Convention to Combat Desertification (UNCCD). Any measure taken against desertification, or the design of dedicated early warning systems, must take into account both the spatial and temporal dimensions of process driving factors. Equally important, past and present reactions of ecosystems to physical and socio-economical disturbances or management interventions need to be understood. In this context, remote sensing and geoinformation processing support the required assessment, monitoring and modelling approaches, and hence provide an essential contribution to the scientific component of the struggle against desertification. Supported by DG Research of the European Commission, the Remote Sensing Department of the University of Trier convened RGLDD to promote scientific exchange between specialists working on the interface of remote sensing, geoinformation processing, desertification/land degradation research and its socio-economic implications. Although targeted at the scientific community, contributions with application perspectives were of crucial importance and both an overview of the current state of the art as well as operational opportunities were presented. Hosted at the Robert-Schuman Haus in Trier, the conference gained widespread attention and attracted an international audience from all parts of the world, which underlines the global dimension of land degradation and desertification processes. Based on a rigorous review of submitted abstracts, more than 100 contributions were accepted for oral and poster presentation, which are found in these proceedings edition in full paper form. Please note: This document is optimised for screen resolution, to receive a high-resolution version please contact the editors.
Two areas were selected to represent major process regimes of Mediterranean rangelands. In the County of Lagads (Greece), situated east of the city of Thessaloniki, livestock grazing with sheep and goats is a major factor of the rural economy. In suitable areas, it is complemented by agricultural use. The region of Ayora (Spain) is located west of the city of Valencia. It is one of regions most affected by fires in Spain. First of all, long time series of satellite data were compiled for both regions on the basis of Landsat sensors, which cover the time until 1976 (Ayora) and 1984 (Lagadas) with one image per year. Using a rigorous processing scheme, the data were geometrically and radiometrically corrected Specific attention was given to an exact sensor calibration, the radiometric intercalibration of Landsat-TM and "MSS. Proportional cover of photosynthetically active vegetation was identified as a suitable quantitative indicator for assessing the state of rangelands. Using Spectral Mixture Analysis (SMA) it was inferred for all data sets. The extensive data base procured this way enabled to map fire events in the Ayora area based on sequential diachronic sets and provide fire dates, perimeter as well as fire recurrence for each pixel. The increasing fire frequency in the past decades is in large parts attributed to the accelerated abandonment of the area that leads to an encroachment of shrublands and the accumulation of combustible biomass. On the basis of the fire mapping results, a spatial and temporal stratification of the data set allowed to asses plant recovery dynamics on the landscape level through linear trend analysis. The long history of fire events in the Mediterranean frequently leads to processes of auto-succession. Following an initial dominance of herbaceous vegetation this commonly leads to similar plant communities as the ones present before the fire. On a temporal axis, this results in typical exponential post-fire trajectories which could also be shown in this study. The analysis of driving factors for post-fire dynamics confirmed the importance of aspect and slope. Locations with lower amounts of solar irradiation and favourable water supply yielded faster recovery rates and higher post-fire vegetation cover levels. In most cases, the vegetation cover levels observed before the fire were not reached within the post-fire observation period. In the area of Lagadas, linear trend analysis and additional statistical parameters were used to infer a degradation index. This could be used to illustrate a complex pattern of stability, regeneration and degradation of vegetation cover. These different processes and states are found in close proximity and are clearly determined by topography and elevation. Following a sequence of analyses, it was found that in particular steep, narrow valleys show positive trends, while negative trends are more abundant on plain or gently undulating areas. Considering the local grazing regime, this spatial differentiation was related to the accessibility of specific locations. Subsequently, animal numbers on community level were used to calculate efficient stocking rates and assess the temporal development of their relation with vegetation cover. This calculation of temporal trajectories illustrated that only some communities show the expected negative relation. To the contrary, a positive relation or even changing relation patterns are observed. This signifies recent concentration and intensification processes in the grazing scheme, as a result of which animals are kept in sheds, where additional feedstuffs are provided. In these cases, free roaming of livestock animals is often confined to some hours every day, which explains the spatial preference of easily accessible areas by the shepherds. Beyond these temporal trends, it was analysed whether the grazing pattern is equally reflected in a spatial trend. Making use of available geospatial information layers, the efforts required to reach each location was expressed as a cost. Then, cost zones could be defined and woody vegetation cover as a grazing indicator could be inferred for the different zones. Animal sheds were employed as starting features for this piospheric analysis, which could be mapped from very high spatial resolution Quickbird image data. The result was a clearly structured gradient showing increasing woody vegetation cover with increasing cost distance. On the basis of these two pilot studies, the elements of a monitoring and interpretation framework identified at the beginning of the work were evaluated and a formal interpretation scheme was presented.
