550 Geowissenschaften
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.
Dry tropical forests are facing massive conversion and degradation processes and they are the most endangered forest type worldwide. One of the largest dry forest types are Miombo forests that stretch across the Southern African subcontinent and the proportionally largest part of this type can be found in Angola. The study site of this thesis is located in south-central Angola. The country still suffers from the consequences of the 27 years of civil war (1975-2002) that provides a unique socio-economic setting. The natural characteristics are a representative cross section which proved ideal to study underlying drivers as well as current and retrospective land use change dynamics. The major land change dynamic of the study area is the conversion of Miombo forests to cultivation areas as well as modification of forest areas, i.e. degradation, due to the extraction of natural resources. With future predictions of population growth, climate change and large scale investments, land pressure is expected to further increase. To fully understand the impacts of these dynamics, both, conversion and modification of forest areas were assessed. By using the conceptual framework of ecosystem services, the predominant trade-off between food and timber in the study area was analyzed, including retrospective dynamics and impacts. This approach accounts for products that contribute directly or indirectly to human well-being. For this purpose, data from the Landsat archive since 1989 until 2013 was applied in different study area adapted approaches. The objectives of these approaches were (I) to detect underlying drivers and their temporal and spatial extent of impact, (II) to describe modification and conversion processes that reach from times of armed conflicts over the ceasefire and the post-war period and (III) to provide an assessment of drivers and impacts in a comparative setting. It could be shown that major underlying drivers for the conversion processes are resettlement dynamics as well as the location and quality of streets and settlements. Furthermore, forests that are selectively used for resource extraction have a higher chance of being converted to a field. Drivers of forest degradation are on one hand also strongly connected to settlement and infrastructural structures. But also to a large extent to fire dynamics that occur mostly in more remote and presumably undisturbed forest areas. The loss of woody biomass as well as its slow recovery after the abandonment of fields could be quantified and stands in large contrast to the amount of potentially cultivated food that is necessarily needed. The results of the thesis support the fundamental understanding of drivers and impacts in the study area and can thus contribute to a sustainable resource management.
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 EU-weite Naturschutznetz Natura 2000 (FFH) umfasst über 11% der terrestrischen Ökosystemfläche. Zur langfristigen Erhaltung dieser Gebiete fehlt ein funktionierendes Monitoringsystem mit geeigneten Indikatoren, Parametern und Datenprodukten, die eine regelmäßig wiederholbare, flächendeckende und vor allem kosteneffiziente Erhebung ermöglichen. Hierfür untersucht diese Dissertation moderne, höchstauflösende Satellitendaten und die Möglichkeiten ihrer Anwendung im Naturschutz, insbesondere als Grundlage zur Indikatorenableitung. Es wurden konkrete Anforderungen von Behörden und NGO bzgl. Daten und Indikatorwerten gesammelt und für zwei Untersuchungsgebiete im Naturpark "Hoher Fläming" in Brandenburg umgesetzt. Dazu wurden zwei Aufnahmen des QuickBird-Satelliten akquiriert und mit vorhandenen GIS-Daten kombiniert. Der praktische Teil der Arbeit beschreibt Eigenschaften und Vorverarbeitung aller Daten, ihre Auswertung nach einem objektbasierten Ansatz und die Ableitung spezifischer quantitativer Parameter. Diese beschreiben den Zustand der Ökosysteme und berücksichtigen die sozio-ökonomischen Belastungen, die auf die Flächen einwirken und Nutzungskonflikte verursachen. Auf der Basis dieser Parameter wurden räumliche Indikatoren erprobt. Zur Anwendung auf der lokalen Ebene in bewaldeten Gebieten und für das Monitoring von Offenland-Flächen werden je zwei Indikatoren vorgeschlagen. Für die regionale Ebene wird ein sozio-ökomischer Indikator empfohlen. Diese fünf Indikatoren sind dazu geeignet, ausgewählte Aspekte der (Bio)Diversität in Schutzgebieten zu beschreiben. Sie analysieren Komposition, Struktur und Funktion der Habitat-Typen sowohl auf der regionalen Landschafts-Ebene, als auch auf der lokalen Ökosystem- bzw. Schutzgebiets-Ebene. Alle Indikatoren besitzen einen Nutzen für das Management von Schutzgebieten und bieten zumindest indirekte Hilfe für die Berichterstattung im Sinne der FFH-Richtlinie. Die vorgeschlagenen Indikatoren sind zwar spezifisch auf die lokalen Untersuchungsgebiete zugeschnitten, doch sind die ökologischen Rahmenbedingungen allgemein gültig. Es ist möglich, diese Indikatoren auch in anderen europäischen Regionen mit den gleichen natürlichen Gegebenheiten und sozio-ökonomischen Strukturproblemen anzuwenden. Für die Anwendung verschiedener Fernerkundungsdaten zur Erfüllung von Monitoringaufgaben sprechen die positiven Ergebnisse der durchgeführten Kosten-Nutzen-Analyse. Vor- und Nachteile von Daten und Auswertungsmethoden werden ausführlich diskutiert.
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.
Earth observation (EO) is a prerequisite for sustainable land use management, and the open-data Landsat mission is at the forefront of this development. However, increasing data volumes have led to a "digital-divide", and consequently, it is key to develop methods that account for the most data-intensive processing steps, then used for the generation and provision of analysis-ready, standardized, higher-level (Level 2 and Level 3) baseline products for enhanced uptake in environmental monitoring systems. Accordingly, the overarching research task of this dissertation was to develop such a framework with a special emphasis on the yet under-researched drylands of Southern Africa. A fully automatic and memory-resident radiometric preprocessing streamline (Level 2) was implemented. The method was applied to the complete Angolan, Zambian, Zimbabwean, Botswanan, and Namibian Landsat record, amounting 58,731 images with a total data volume of nearly 15 TB. Cloud/shadow detection capabilities were improved for drylands. An integrated correction of atmospheric, topographic and bidirectional effects was implemented, based on radiative theory with corrections for multiple scatterings, and adjacency effects, as well as including a multilayered toolset for estimating aerosol optical depth over persistent dark targets or by falling back on a spatio-temporal climatology. Topographic and bidirectional effects were reduced with a semi-empirical C-correction and a global set of correction parameters, respectively. Gridding and reprojection were already included to facilitate easy and efficient further processing. The selection of phenologically similar observations is a key monitoring requirement for multi-temporal analyses, and hence, the generation of Level 3 products that realize phenological normalization on the pixel-level was pursued. As a prerequisite, coarse resolution Land Surface Phenology (LSP) was derived in a first step, then spatially refined by fusing it with a small number of Level 2 images. For this purpose, a novel data fusion technique was developed, wherein a focal filter based approach employs multi-scale and source prediction proxies. Phenologically normalized composites (Level 3) were generated by coupling the target day (i.e. the main compositing criterion) to the input LSP. The approach was demonstrated by generating peak, end and minimum of season composites, and by comparing these with static composites (fixed target day). It was shown that the phenological normalization accounts for terrain- and land cover class-induced LSP differences, and the use of Level 2 inputs enables a wide range of monitoring options, among them the detection of within state processes like forest degradation. In summary, the developed preprocessing framework is capable of generating several analysis-ready baseline EO satellite products. These datasets can be used for regional case studies, but may also be directly integrated into more operational monitoring systems " e.g. in support of the Reducing Emissions from Deforestation and Forest Degradation (REDD) incentive. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Trier University's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.
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.
Considering actual climatic and land use changes the problem of available water resources or the estimation of potential flood risks gain eco-political and economical relevance. Adequate assessments, thus, require precise process-based hydrological knowledge. Spatially distributed hydrological modelling enables a both abstractive and realistic description of hydrological processes, and therefore contributes to the understanding of the hydrological system- responses. Referring to the example of the mesoscale Ruwer basin (a tributary to the Mosel river), a modified version of the distributive modelling system PRMS/MMS (Precipitation Runoff Modeling System/Modular Modeling System) is applied to calculate spatially and temporally explicit water budgets. To achieve modelling results as precise as possible, integration of detailed land use information (spatial distribution of the existing land use classes, crop- and site-specific growth patterns) is necessary. This information is derived here by analysis of multitemporal, geometrically and radiometrically pre-processed Landsat TM-data. This enables separation of different land use classes and differentiated quantification of the leaf area index (LAI). The LAI is estimated by a spectral unmixing approach using statistically optimized endmember sets, referring to the example of winter grain and grassland plots. As a result, numerical inputs (coefficients for calculating evapotranspiration, interception storages) and extracted non-numerical (classified) information can be provided for hydrological modelling. The version of PRMS applied in this study allows important land use terms to be parameterized in high temporal resolution. Using model input derived from the available satellite data, simulation results are obtained that prove to be realistic compared to gauge data and with respect to their spatial differentiation. Results differ significantly from those obtained by using parameters from literature or by experience without distinguishing specific and site-dependent growth patterns. It can be concluded that the quality of modelling results notably improves by integration and quantitative analysis of remote sensing data; thus, these methods are a significant contribution to physically-based hydrological modelling.
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.