Univ., Diss.--Göttingen, 2003. / Zsfassung in dt. Sprache.

Thesis (M.A.)--University of Oslo, 1996. / Includes bibliographical references (p. [291]-304) and index.

Risks and uncertainties are unavoidable in agriculture in Sudan, due to its dependence on climatic factors and to the imperfect nature of the agricultural decisions and policies attributed to land cover and land use changes that occur. The current study was conducted in the Gash Agricultural Scheme (GAS) - Kassala State, as a semi-arid land in eastern Sudan. The scheme has been established to contribute to the rural development, to help stability of the nomadic population in eastern Sudan, particularly the local population around the Gash river areas, and to facilitate utilizing the river flood in growing cotton and other cash crops. In the last decade, the scheme production has declined, because of drought periods, which hit the region, sand invasion and the spread of invasive mesquite trees, in addition to administrative negligence. These have resulted also in poor agricultural productivity and the displacement of farmers away from the scheme area. Recently, the scheme is heavily disturbed by human intervention in many aspects. Consequently, resources of cultivated land have shrunk and declined during the period of the study, which in turn have led to dissatisfaction and increasing failure of satisfying increasing farmer’s income and demand for local consumption. Remote sensing applications and geospatial techniques have played a key role in studying different types of hazards whether they are natural or manmade. Multi-temporal satellite data combined with ancillary data were used to monitor, analyze and to assess land use and land cover (LULC) changes and the impact of land degradation on the scheme production, which provides the managers and decision makers with current and improved data for the purposes of proper administration of natural resources in the GAS. Information about patterns of LULC changes through time in the GAS is not only important for the management and planning, but also for a better understanding of human dimensions of environmental changes at regional scale. This study attempts to map and assess the impacts of LULC change and land degradation in GAS during a period of 38 years from 1972-2010. Dry season multi-temporal satellite imagery collected by different sensor systems was selected such as three cloud-free Landsat (MSS 1972, TM 1987 and ETM+ 1999) and ASTER (2010) satellite imagery. This imagery was geo-referenced and radiometrically and atmospherically calibrated using dark object subtraction (DOS). Two approaches of classification (object-oriented and pixel-based) were applied for classification and comparison of LULC. In addition, the study compares between the two approaches to determine which one is more compatible for classification of LULC of the GAS. The pixel-based approach performed slightly better than the object-oriented approach in the classification of LULC in the study area. Application of multi-temporal remote sensing data proved to be successful for the identification and mapping of LULC into five main classes as follows: woodland dominated by dense mesquite trees, grass and shrubs dominated by less dense mesquite trees, bare and cultivated land, stabilized fine sand and mobile sand. After image enhancement successful classification of imagery was achieved using pixel and object based approaches as well as subsequent change detection (image differencing and change matrix), supported by classification accuracy assessments and post-classification. Comparison of LULC changes shows that the land cover of GAS has changed dramatically during the investigated period. It has been discovered that more significant of LULC change processes occurred during the second studied period (1987 to 1999) than during the first period (1972-1987). In the second period nearly half of bare and cultivated lands was changed from 41372.74 ha (20.22 %) in 1987 to 28020.80 ha (13.60 %) in 1999, which was mainly due to the drought that hit the region during the mentioned period. However, the results revealed a drastic loss of bare and cultivated land, equivalent to more than 40% during the entire period (1972-2010). Throughout the whole period of study, drought and invasion of both mesquite trees and sand were responsible for the loss of more than 40% of the total productive lands. Change vector analysis (CVA) as a useful approach was applied for estimating change detection in both magnitude and direction of change. The promising approach of multivariate alteration detection (MAD) and subsequent maximum autocorrelation factor (MAD/MAF) transformation was used to support change detection via assessment of maximum correlation between the transformed variates and the specific original image bands related to specific land cover classes. However, both CVA and MAD/MAD strongly prove the fact that bare and cultivated land have dramatically changed and decreased continuously during the studied period. Both CVA and MAD/MAD demonstrate adequate potentials for monitoring, detecting, identifying and mapping the changes. Moreover, this research demonstrated that CVA and MAD/MAF are superior in providing qualitative details about the nature of all kinds of change. Vegetation indices (VI) such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified adjusted vegetation index (MSAVI) and grain soil index (GSI) were applied to measure the quantitative characterization of temporal and spatial vegetation cover patterns and change. All indices remain very sensitive to structure variation of LULC. The results reveal that the NDVI is more effective for detecting the amount and status of the vegetation cover in the study area than SAVI, MSAVI and GSI. Therefore, it can be stated that NDVI can be used as a response variable to identify drought disturbance and land degradation in semi-arid land such as the GAS area. Results of detecting vegetation cover observed by using SAVI were found to be more reasonable than using MSAVI, although MSAVI reduces the background of bare soil better than SAVI. GSI proves high efficiency in determining the different types of surface soils, and producing a change map of top soil grain size, which is useful in assessment of land degradation in the study area. The linkage between socio-economic data and remotely sensed data was applied to determine the relationships between the different factors derived and to analyze the reasons for change in LULC and land degradation and its effects in the study area. The results indicate a strong relationship between LULC derived from remotely sensed data and the influencing socioeconomic variables. The results obtained from analyzing socioeconomic data confirm the findings of remote sensing data analysis, which assure that the decline and degradation of agricultural land is a result of further spread of mesquite trees and of increased invasion of sand during the study period. High livestock density and overgrazing, drought, invasion of sand, spread of invasive mesquite trees, overexploitation of land, improper management, and population growth were considered as the main direct factors responsible for degradation in the study area.

Climate change and land use transition are the main drivers of watershed hydrological processes. The main objective of this study was to assess the hydrometeorological responses to climate and land use changes in the Jhelum River Basin (JRB), Pakistan. The development of proper climate information is a challenging task. To date, Global Climate Models (GCMs) are used for climate projections. However, these models have a coarse spatial resolution, which is not suitable for regional/local impact studies such as water resources management in the JRB. Therefore, different downscaling methods and techniques have been developed as means of bridging the gap between the coarse resolution global models projection and the spatial resolution required for hydrological impact studies. Statistical Downscaling Model (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG) are selected in this study for downscaling of temperature and precipitation. Both downscaling approaches consider three climate models and two emission scenarios (i.e., RCP4.5 and RCP8.5) in order to sample the widest range of uncertainties in climate projections. Current land use and land cover (LULC) maps are generated from Landsat imagery to analyze the pattern and dynamic of land use change. Both climate projections and LULC are fed into SWAT (Soil and Water Assessment Tool) hydrological model to investigate the streamflow dynamics. The results indicate good applicability of SDSM and LARS-WG for downscaling of temperature and precipitation in three future periods (2020s, 2050s and 2080s). Both models show an increase mean annual max temperature, min temperature and precipitation as 0.4-4.2°C, 0.3-4.2°C and 4.4-32.2% under both RCPs scenarios. Similarly, results of SWAT model suggest an increase in mean annual discharge about 3.6 to 28.8% under RCP4.5 and RCP8.5. The study also revealed that water yield and evapotranspiration in the eastern part of the basin (sub-basins at high elevation) would be most affected by climate change. The results of LULC change detection show that forest exhibited maximum positive change while agriculture showed maximum negative change during 2001-2018. SWAT model simulations suggested that implementation of afforestation in the watershed would reduce surface runoff and water yield while enhancing the evapotranspiration. It is recommended that authorities should pay attention to both climate change and land use transition for proper water resources management.

Das betrachtete Einzugsgebiet der Neiße weist sowohl morphologisch als auch bezüglich der Landnutzung eine Zweiteilung auf, die sich in den hydrologischen Prozessen und auch in der Gewässergüte widerspiegelt. Morphologie, Bodeneigenschaften und Landnutzung steuern die hydrologischen Prozesse, besonders die Abflussbildung und damit den Stofftransport und die Gewässergüte. Das Projekt zeigt u.a. die Bedeutung der kombinierten Anwendung chemischer und isotopischer Verfahren, um die Unterschiede in der Abflussbildung und den Einfluss der Schneedecke auf den Abfluss zu erfassen. Die Wasserqualität in der Neiße und ihrer untersuchten Nebenflüsse ist bezüglich der betrachteten Parameter gut. Es gab nur wenige Überschreitungen von Grenzwerten, entweder in Trockenperioden bei Niedrigwasserabfluss oder nach starken Niederschlagsereignissen. Im Vergleich mit den langjährigen Daten des Standard-Monitorings ist für die letzte Periode eine Abnahme in der Konzentration für einige Stoffe festzustellen. Geringe Stoffkonzentrationen wurden im Bodenwasser unter unterschiedlich bewirtschaftetem Grünland unabhängig vom Management gemessen. Für Grünland wurde ein hohes Rückhaltevermögen konstatiert. Die Einleitungen aus Punktquellen (Kläranlagen) ändert die Gewässergüte besonders während der Niedrigwasserperioden. Während Hochwasserereignissen traten dagegen hohe Schwebstofffrachten auf, die auf den Eintrag aus diffusen Quellen (hauptsächlich von Ackerflächen) zurückzuführen sind. Die Herkunft der Schwermetalle in den Fließgewässern lässt sich nicht eindeutig zuordnen. Dafür müssten detailliertere Untersuchungen entlang der Fließgewässer unter Einbeziehung aller potenziellen Einleiter durchgeführt werden. Zink wird beispielsweise auch in wenig besiedelten Gebieten in erheblichem Maß über die Atmosphäre eingetragen, wie die Messungen zeigen. Werden die während der Projektlaufzeit erhobenen Daten für die 30 Probenahmestellen analysiert, so stellen sich entsprechend ihrer physiko-chemischen Signatur sechs Gruppen heraus. Jeder dieser Probenahmestellen kann ein Einzugsgebiet zugeordnet werden mit bestimmten Anteilen an den verschiedenen Landnutzungskategorien. Diese Gruppen weisen eine weitgehend ähnliche Landnutzungsstruktur auf. Bei der Analyse der Abflussbildungsprozesse im Gesamtgebiet werden auch hierfür die gleichen Teileinzugsgebiete statistisch zusammengefasst. Für das Einzugsgebiet wurden kritische Punkte bestimmt, an denen die Gefahr besteht, dass Stoffablagerung (Sedimente) bebaute Gebiete und Infrastruktur bedrohen. Es wurden vulnerable Flächen hinsichtlich der Bedrohung der Gewässergüte ausgewiesen.:Abbildungsverzeichnis VI Tabellenverzeichnis XI 1 Einleitung 1 2 Das Projekt 2 2.1 Projektziele 2 2.2 Projektpartner 2 3 Das Einzugsgebiet 3 3.1 Klima 4 3.2 Geologie und Boden 4 3.3 Hydrologie 6 3.4 Landnutzung 6 3.5 Die Teileinzugsgebiete 8 4 Methoden 10 4.1 Probenahme 10 4.2 Beschreibung der experimentellen Standorte 11 4.2.1 Zittauer Ökologische Forschungsstation 11 4.2.2 Experimentalgebiete des VURV 12 4.2.3 Experimentalflächen der ČVUT in Prag 16 4.3 chemische Analysen 17 4.4 Auswertung langjähriger Messreihen 17 4.4.1 Auswahl der Kennwerte für die Auswertung 22 4.4.2 Auswertemethoden 23 4.5 Berechnung des Wasserqualitätsindexes (WQI) 24 4.6 Analyse räumlich verteilter Daten 25 4.6.1 Landnutzung 25 4.6.2 Digitales Geländemodell 28 4.6.3 Hydrologische Bodengruppen 28 4.6.4 Linienartige Layer 30 4.7 Statistische Analysen 32 4.8 Modellierung 33 4.8.1 Das CN-Verfahren 34 4.8.1.1 Ermittlung der Abflussmengen mit Hilfe der CN-Kurven 34 4.8.1.2 Berechnung des Spitzendurchflusses aus dem Bemessungsniederschlag 36 4.8.2 WBS-FLAB 37 4.8.3 Erosion und Sedimenttransport 39 4.9 Vulnerabilität des Untersuchungsgebietes 43 4.9.1 Grundlagen 43 4.9.2 Auswertung der Ökologischen Stabilität des Gebietes 43 4.9.3 Bewertung der Vulnerabilität des Untersuchungsgebietes 46 5 Ergebnisse 48 5.1 Landnutzungsanalyse 48 5.2 Hydrologische Prozesse und Isotope 49 5.3 Gewässergüte 54 5.3.1 Bewertung der Qualität der Oberflächengewässer und Identifikation vonsignifikanten anthropogenen Belastungen 54 5.3.1.1 Ergebnisse der Auswertung des Standard-Monitorings des Oberflächenwassers 54 5.3.1.2 Ergebnisse der Auswertung des projektspezifischen Monitorings 62 5.3.2 Zusammenfassung - Fazit 66 5.3.3 Vergleich der aktuellen mit historischen Messwerten 68 5.3.4 Chemische Zusammensetzung des Niederschlags 69 5.3.5 Ausgewählte Stoffkonzentrationen in unterschiedlichen Kompartimenten 71 5.3.5.1 Mittlere zeitliche Verläufe von Bor und Calcium 71 5.3.5.2 Gelöster organischer Kohlenstoff (DOC) 73 5.3.6 Einfluss von Grünland-Managementsystemen 76 5.3.6.1 Historische Daten 76 5.3.6.2 Im Rahmen des Projektes AquaNisa erfasste Daten 79 5.3.6.3 Fazit 82 5.3.7 Einfluss der Landnutzung auf die physiko-chemische Güte der Fließgewässer 83 5.3.8 Längsprofile 90 5.4 Gewässergüteindex 93 5.5 Frachten 94 5.6 Erosion und Sedimenttransport 97 5.6.1 Ergebnisse der Modellierung 97 5.6.2 Identifikation von gefährdeten Standorten und mögliche Maßnahmen 100 5.6.3 Zusammenfassung und Schlussfolgerungen 103 5.7 Ergebnisse des CN-Verfahrens 104 5.7.1 Produktion von Oberflächenabfluss 104 5.7.2 Direktabfluss und Spitzendurchflüsse an kritischen Stellen 111 5.7.2.3 Kombination der Abflussakkumulierung und der CN-Kurven 117 5.8 Abflusskomponenten im Einzugsgebiet 119 5.9 Analyse der Vulnerabilität der Landschaft in dem Einzugsgebiet der oberen Neiße 122 5.9.1 Indikatoren 122 5.9.2 Synthese der Indikatoren 126 6 Vorschläge und Empfehlungen hinsichtlich der Landnutzungsänderung im Untersuchungsgebiet 127 6.1 Maßnahmen zur Förderung des Wasserrückhaltes 127 6.1.1 Reduzierung des Hochwasserrisikos 127 6.1.2 Erhöhung des Wasserrückhaltes in der Landschaft 129 6.2 Maßnahmen zur Einschränkung der Erosion und der Sedimentation 130 6.3 Maßnahmen zum Hochwasserschutz 131 6.4 Maßnahmen zur Reduzierung der Wasserverunreinigung aus diffusen Quellen 132 6.5 Maßnahmen zur Erhöhung der ökologischen Stabilität 132 7 Zusammenfassung 133 8 Literatur 135 9 Steckbriefe der Probenahmestellen

Following the hierarchical nature of forest resource management, the present work focuses on the natural forest cover at various abstraction levels of details, i.e. categorical land use/land cover (LU/LC) level and a continuous empirical estimation of local operational level. As no single sensor presently covers absolutely all the requirements of the entire levels of forest resource assessment, multisource imagery (i.e. RapidEye, TERRA ASTER and LANDSAT TM), in addition to other data and knowledge have been examined. To deal with this structure, an object-based image analysis (OBIA) approach has been assessed in the destabilized Blue Nile region of Sudan as a potential solution to gather the required information for future forest planning and decision making. Moreover, the spatial heterogeneity as well as the rapid changes observed in the region motivates the inspection for more efficient, flexible and accurate methods to update the desired information. An OBIA approach has been proposed as an alternative analysis framework that can mitigate the deficiency associated with the pixel-based approach. In this sense, the study examines the most popular pixel-based maximum likelihood classifier, as an example of the behavior of spectral classifier toward respective data and regional specifics. In contrast, the OBIA approach analyzes remotely sensed data by incorporating expert analyst knowledge and complimentary ancillary data in a way that somehow simulates human intelligence for image interpretation based on the real-world representation of the features. As the segment is the basic processing unit, various combinations of segmentation criteria were tested to separate similar spectral values into groups of relatively homogeneous pixels. At the categorical subtraction level, rules were developed and optimum features were extracted for each particular class. Two methods were allocated (i.e. Rule Based (RB) and Nearest Neighbour (NN) Classifier) to assign segmented objects to their corresponding classes. Moreover, the study attempts to answer the questions whether OBIA is inherently more precise at fine spatial resolution than at coarser resolution, and how both pixel-based and OBIA approaches can be compared regarding relative accuracy in function of spatial resolution. As anticipated, this work emphasizes that the OBIA approach is can be proposed as an advanced solution particulary for high resolution imagery, since the accuracies were improved at the different scales applied compare with those of pixel-based approach. Meanwhile, the results achieved by the two approaches are consistently high at a finer RapidEye spatial resolution, and much significantly enhanced with OBIA. Since the change in LU/LC is rapid and the region is heterogeneous as well as the data vary regarding the date of acquisition and data source, this motivated the implementation of post-classification change detection rather than radiometric transformation methods. Based on thematic LU/LC maps, series of optimized algorithms have been developed to depict the dynamics in LU/LC entities. Therefore, detailed change “from-to” information classes as well as changes statistics were produced. Furthermore, the produced change maps were assessed, which reveals that the accuracy of the change maps is consistently high. Aggregated to the community-level, social survey of household data provides a comprehensive perspective additionally to EO data. The predetermined hot spots of degraded and successfully recovered areas were investigated. Thus, the study utilized a well-designed questionnaire to address the factors affecting land-cover dynamics and the possible solutions based on local community's perception. At the operational structural forest stand level, the rationale for incorporating these analyses are to offer a semi-automatic OBIA metrics estimates from which forest attribute is acquired through automated segmentation algorithms at the level of delineated tree crowns or clusters of crowns. Correlation and regression analyses were applied to identify the relations between a wide range of spectral and textural metrics and the field derived forest attributes. The acquired results from the OBIA framework reveal strong relationships and precise estimates. Furthermore, the best fitted models were cross-validated with an independent set of field samples, which revealed a high degree of precision. An important question is how the spatial resolution and spectral range used affect the quality of the developed model this was also discussed based on the different sensors examined. To conclude, the study reveals that the OBIA has proven capability as an efficient and accurate approach for gaining knowledge about the land features, whether at the operational forest structural attributes or categorical LU/LC level. Moreover, the methodological framework exhibits a potential solution to attain precise facts and figures about the change dynamics and its driving forces. / Da das Waldressourcenmanagement hierarchisch strukturiert ist, beschäftigt sich die vorliegende Arbeit mit der natürlichen Waldbedeckung auf verschiedenen Abstraktionsebenen, das heißt insbesondere mit der Ebene der kategorischen Landnutzung / Landbedeckung (LU/LC) sowie mit der kontinuierlichen empirischen Abschätzung auf lokaler operativer Ebene. Da zurzeit kein Sensor die Anforderungen aller Ebenen der Bewertung von Waldressourcen und von Multisource-Bildmaterialien (d.h. RapidEye, TERRA ASTER und LANDSAT TM) erfüllen kann, wurden zusätzlich andere Formen von Daten und Wissen untersucht und in die Arbeit mit eingebracht. Es wurde eine objekt-basierte Bildanalyse (OBIA) in einer destabilisierten Region des Blauen Nils im Sudan eingesetzt, um nach möglichen Lösungen zu suchen, erforderliche Informationen für die zukünftigen Waldplanung und die Entscheidungsfindung zu sammeln. Außerdem wurden die räumliche Heterogenität, sowie die sehr schnellen Änderungen in der Region untersucht. Dies motiviert nach effizienteren, flexibleren und genaueren Methoden zu suchen, um die gewünschten aktuellen Informationen zu erhalten. Das Konzept von OBIA wurde als Substitution-Analyse-Rahmen vorgeschlagen, um die Mängel vom früheren pixel-basierten Konzept abzumildern. In diesem Sinne untersucht die Studie die beliebtesten Maximum-Likelihood-Klassifikatoren des pixel-basierten Konzeptes als Beispiel für das Verhalten der spektralen Klassifikatoren in dem jeweiligen Datenbereich und der Region. Im Gegensatz dazu analysiert OBIA Fernerkundungsdaten durch den Einbau von Wissen des Analytikers sowie kostenlose Zusatzdaten in einer Art und Weise, die menschliche Intelligenz für die Bildinterpretation als eine reale Darstellung der Funktion simuliert. Als ein Segment einer Basisverarbeitungseinheit wurden verschiedene Kombinationen von Segmentierungskriterien getestet um ähnliche spektrale Werte in Gruppen von relativ homogenen Pixeln zu trennen. An der kategorische Subtraktionsebene wurden Regeln entwickelt und optimale Eigenschaften für jede besondere Klasse extrahiert. Zwei Verfahren (Rule Based (RB) und Nearest Neighbour (NN) Classifier) wurden zugeteilt um die segmentierten Objekte der entsprechenden Klasse zuzuweisen. Außerdem versucht die Studie die Fragen zu beantworten, ob OBIA in feiner räumlicher Auflösung grundsätzlich genauer ist als eine gröbere Auflösung, und wie beide, das pixel-basierte und das OBIA Konzept sich in einer relativen Genauigkeit als eine Funktion der räumlichen Auflösung vergleichen lassen. Diese Arbeit zeigt insbesondere, dass das OBIA Konzept eine fortschrittliche Lösung für die Bildanalyse ist, da die Genauigkeiten - an den verschiedenen Skalen angewandt - im Vergleich mit denen der Pixel-basierten Konzept verbessert wurden. Unterdessen waren die berichteten Ergebnisse der feineren räumlichen Auflösung nicht nur für die beiden Ansätze konsequent hoch, sondern durch das OBIA Konzept deutlich verbessert. Die schnellen Veränderungen und die Heterogenität der Region sowie die unterschiedliche Datenherkunft haben dazu geführt, dass die Umsetzung von Post-Klassifizierungs- Änderungserkennung besser geeignet ist als radiometrische Transformationsmethoden. Basierend auf thematische LU/LC Karten wurden Serien von optimierten Algorithmen entwickelt, um die Dynamik in LU/LC Einheiten darzustellen. Deshalb wurden für Detailänderung "von-bis"-Informationsklassen sowie Veränderungsstatistiken erstellt. Ferner wurden die erzeugten Änderungskarten bewertet, was zeigte, dass die Genauigkeit der Änderungskarten konstant hoch ist. Aggregiert auf die Gemeinde-Ebene bieten Sozialerhebungen der Haushaltsdaten eine umfassende zusätzliche Sichtweise auf die Fernerkundungsdaten. Die vorher festgelegten degradierten und erfolgreich wiederhergestellten Hot Spots wurden untersucht. Die Studie verwendet einen gut gestalteten Fragebogen um Faktoren die die Dynamik der Änderung der Landbedeckung und mögliche Lösungen, die auf der Wahrnehmung der Gemeinden basieren, anzusprechen. Auf der Ebene des operativen strukturellen Waldbestandes wird die Begründung für die Einbeziehung dieser Analysen angegeben um semi-automatische OBIA Metriken zu schätzen, die aus dem Wald-Attribut durch automatisierte Segmentierungsalgorithmen in den Baumkronen abgegrenzt oder Cluster von Kronen Ebenen erworben wird. Korrelations- und Regressionsanalysen wurden angewandt, um die Beziehungen zwischen einer Vielzahl von spektralen und strukturellen Metriken und den aus den Untersuchungsgebieten abgeleiteten Waldattributen zu identifizieren. Die Ergebnisse des OBIA Rahmens zeigen starke Beziehungen und präzise Schätzungen. Die besten Modelle waren mit einem unabhängigen Satz von kreuz-validierten Feldproben ausgestattet, welche hohe Genauigkeiten ergaben. Eine wichtige Frage ist, wie die räumliche Auflösung und die verwendete Bandbreite die Qualität der entwickelten Modelle auch auf der Grundlage der verschiedenen untersuchten Sensoren beeinflussen. Schließlich zeigt die Studie, dass OBIA in der Lage ist, als ein effizienter und genauer Ansatz Kenntnisse über die Landfunktionen zu erlangen, sei es bei operativen Attributen der Waldstruktur oder auch auf der kategorischen LU/LC Ebene. Außerdem zeigt der methodischen Rahmen eine mögliche Lösung um präzise Fakten und Zahlen über die Veränderungsdynamik und ihre Antriebskräfte zu ermitteln.

Land use / land cover (LULC) change assessment is getting more consideration by global environmental change studies as land use change is exposing dryland environments for transitions and higher rates of resource depletion. The semiarid regions of northwestern Ethiopia are not different as land use transition is the major problem of the region. However, there is no satisfactory study to quantify the change process of the region up to now. Hence, spatiotemporal change analysis is vital for understanding and identification of major threats and solicit solutions for sustainable management of the ecosystem. LULC change studies focus on understanding the patterns, processes and dynamics of land use transitions and driving forces of change. The change processes in dryland ecosystems can be either seasonal, gradual or abrupt changes of random or systematic change processes that result in a pattern or permanent transition in land use. Identification of these processes of change and their type supports adoption of monitoring options and indicate possible measures to be taken to safeguard this dynamic ecosystem. This study examines the spatiotemporal patterns of LULC change, temporal trends in climate variables and the insights of the communities on change patterns of ecosystems. Landsat imagery, MODIS NDVI, CRU temperature, TAMSAT rainfall and socio-ecological field data were used in order to identify change processes. LULC transformation was monitored using support vector machine (SVM) algorithm. A cross-tabulation matrix assessment was implemented in order to assess the total change of land use categories based on net change and swap change. In addition, the pattern of change was identified based on expected gain and loss under a random process of gain and loss, respectively. Breaks For Additive Seasonal and Trend (BFAST) analysis was employed for determining the time, direction and magnitude of seasonal, abrupt and trend changes within the time series datasets. In addition, Man Kendall test statistic and Sen’s slope estimator were used for assessing long term trends on detrended time series data components. Distributed lag (DL) model was also adopted in order to determine the time lag response of vegetation to the current and past rainfall distribution. Over the study period of 1972- 2014, there is a significant change in LULC as evidenced by a significant increase in size of cropland of about 53% and a net loss of over 61% of woodland area. The period 2000-2014 has shown a sharp increase of cropland and a sharp decline of woodland areas. Proximate causes include agricultural expansion and excessive wood harvesting; and underlying causes of demographic factor, economic factors and policy contributed the most to an overuse of existing natural resources. In both the observed and expected proportion of random process of change and of systematic changes, woodland has shown the highest loss compared to other land use types. The observed transition and expected transition under random process of gain of woodland to cropland is 1.7%, implies that cropland systematically gains to replace woodland. The comparison of the difference between observed and expected loss under random process of loss also showed that when woodland loses cropland systematically replaces it. The assessment of magnitude and time of breakpoints on climate data and NDVI showed different results. Accordingly, NDVI analysis demonstrated the existence of breakpoints that are statistically significant on the seasonal and long term trends. There is a positive trend, but no breakpoints on the long term precipitation data during the study period. The maximum temperature also showed a positive trend with two breakpoints which are not statistically significant. On the other hand, there is no seasonal and trend breakpoints in minimum temperature, though there is an overall positive trend along the study period. The Man-Kendall test statistic for long term average Tmin and Tmax showed significant variation where as there is no significant trend within the long term rainfall distribution. The lag regression between NDVI and precipitation indicated a lag of up to forty days. This proves that the vegetation growth in this area is not primarily determined by the current precipitation rather with the previous forty days rainfall. The combined analysis showed declining vegetation productivity and a loss of vegetation cover that contributed for an easy movement of dust clouds during the dry period of the year. This affects the land condition of the region, resulting in long term degradation of the environment

Changes in climate and land use interact in a complex system with various feedbacks including water, carbon (C), and nitrogen (N) fluxes. In this dissertation, firstly measurements of surface fluxes were conducted via two different measurement systems, a Bowen Ratio (BR) and an Eddy Covariance (EC) system. Over two succeeding years, fluxes and gradients of heat, water vapour, and CO2 over winter barley and rapeseed were simultaneously measured at Klingenberg, a long-term cropland site in eastern Germany. The two independent systems (EC/BR) are compared with respect to energy and CO2 fluxes. Inspection as well as a neutral regression analysis show that differences between the systems were largest for latent heat LE. EC detects apparently lower LE due to the lack of closure of the energy balance of approximately 30%, whereas the fluxes of CO2 show only smaller differences up to 10%. Therefore, Bowen Ratio setups remain an alternative to EC systems when gradients are large and analysers with high measurement frequency are not available. Encouraged by this analysis, the Modified Bowen Ratio system was used to measure the vertical gradients of mixing ratios of nitrogen oxides (NOx) and ammonia (NH3). Fluxes of these nitrogen species are analysed and associated to the corresponding growth status of two crops within two growing periods. Integration of these nitrogen fluxes results in a net emission into the atmosphere of 1.25 kg N ha-1 for the total measurement period of 77 days, differing in the proportion of NOx and NH3. However, this net emission does not largely reduce the fertilization of the crop site. In a second step, the atmospheric boundary layer model HIRVAC (HIgh Resolution Vegetation Atmosphere Coupler) was improved and applied to three different land uses within the TU-Dresden-cluster for selected time periods in 2009 and 2010. Simulated fluxes of H2O and CO2 with the improved model HIRVAC show good agreement with measurements. Realistic fluxes were obtained with respect to the diurnal cycle as well as the order of magnitude. Modelling of energy and trace gas fluxes also gives the opportunity to assess effects of changing climate conditions on surface fluxes. Since in the improved HIRVAC version a coupled model for stomatal conductance is used, an increase in CO2 concentration is linked with a decrease of stomatal conductance in the simulation. Therefore, simulations of changes in climate condition along with elevated CO2 concentrations and their effect on latent heat fluxes are analysed. The grassland and agricultural site revealed increased evapotranspiration with elevated temperatures and CO2 concentrations, whereas the forest site came up with reduced evapotranspiration rates. Concerning the flux of CO2, all land uses considered here increased the amount of assimilated carbon, whereby the forest site increased the most. Finally, the scenario calculations revealed that regarding evapotranspiration and CO2, differences of land use dominate over differences of climate change. / Veränderungen des Klimas und von Landnutzungen wirken sich in einem komplexen System mit diversen Rückkopplungen auf die Wasser-, Kohlenstoff- und Stickstoffflüsse aus. In dieser Dissertation wurden zuerst Flüsse mit zwei unterschiedlichen Methoden erfasst, einem Bowen-Ratio (BR) und einem Eddy-Kovarianz (EC) System. Dafür wurden für zwei aufeinanderfolgende Jahre Gradienten bzw. Flüsse von Wärme, Wasserdampf und CO2 über Wintergerste und Raps an einem langjährigen Agrarstandort im Osten Deutschlands (Station Klingenberg) gemessen. Die zwei unabhängigen Messmethoden (EC/BR) werden in dieser Arbeit in Bezug auf die Energie- und CO2-Flüsse miteinander verglichen. Die genaue Analyse dieser Flüsse ergibt, dass die größten Unterschiede zwischen den Messmethoden im latenten Wärmefluss (LE) vorzufinden sind. Bedingt durch die Schließungslücke von ungefähr 30 % ergibt die EC-Methode einen geringeren latenten Wärmefluss, wohingegen die Flüsse von CO2 nur Unterschiede um 10 % aufweisen. Wie der Vergleich zeigt, ist die Bowen-Ratio-Messmethode besonders dann eine wertvolle Alternative zu EC-Systemen, wenn die Gradienten der gemessenen Komponenten groß sind oder wenn Analysatoren mit hoher Messfrequenz nicht verfügbar sind. Bestärkt durch diese Ergebnisse, wurde das modifizierte Bowen-Ratio-System (MBR) verwendet, um vertikale Gradienten der Mischungsverhältnisse von Stickoxiden (NOx) und Ammoniak (NH3) zu messen. Die ermittelten Flüsse dieser Stickstoffkomponenten werden mit den Entwicklungsstadien der jeweiligen Feldfrüchte innerhalb zweier Anbauperioden in Verbindung gebracht. Die Summe der gemessenen Stickstoffflüsse ergibt eine Nettoemission in die Atmosphäre von 1,25 kg N ha-1 über die gesamte Messperiode von 77 Tagen (mit unterschiedlichen Anteilen von NOx und NH3), wobei diese Emission die Düngung der Agrarfläche nur geringfügig reduziert. Diese Ergebnisse stehen im Einklang mit Messergebnissen an anderen Agrarstandorten. Im zweiten Teil wurde das atmosphärische Grenzschichtmodell HIRVAC (HIgh Resolution Vegetation Atmosphere Coupler) überarbeitet und für drei unterschiedliche Landnutzungen innerhalb des TU-Dresden-Clusters für ausgewählte Zeitscheiben der Jahre 2009 und 2010 angewandt. Die mit dem Modell HIRVAC simulierten Flüsse von Wasser und CO2 zeigen eine gute Übereinstimmung mit den Messungen. Bezüglich des Tagesganges sowie auch in ihrer jeweiligen Größenordung wurden realistische Flüsse berechnet. Die Modellierung der Energie- und Spurengasflüsse bietet außerdem die Möglichkeit, Auswirkungen von veränderlichen klimatischen Bedingungen auf die turbulenten Flüsse zu bewerten. Da in der verbesserten HIRVAC-Version ein gekoppeltes Modell für die stomatäre Leitfähigkeit verwendet wird, ist nun innerhalb der Simulation ein Anstieg der CO2-Konzentration mit einem Rückgang der stomatären Leitfähigkeit verknüpft. Somit können Szenariosimulationen von veränderlichen Klimabedingungen zusammen mit erhöhten CO2-Konzentrationen und deren Auswirkungen auf die latenten Wärmeflüsse analysiert werden. Die Grünland- sowie auch die Agrarfläche zeigen verstärkte Evapotranspirationsraten unter erhöhten Temperatur- und CO2-Bedingungen, wohingegen der Waldstandort verminderte Evapotranspirationsraten zeigt. Hinsichtlich des CO2-Flusses reagieren alle drei berücksichtigten Landnutzungen mit erhöhten Aufnahmeraten von Kohlenstoff, wobei der Waldstandort den höchsten Anstieg aufweist. Schlussendlich ergaben die Szenariosimulationen bezüglich Evapotranspiration und CO2, dass die Unterschiede zwischen den Landnutzungen gegenüber denen des prognostizierten Klimawandels überwiegen.

Risks and uncertainties are unavoidable in agriculture in Sudan, due to its dependence on climatic factors and to the imperfect nature of the agricultural decisions and policies attributed to land cover and land use changes that occur. The current study was conducted in the Gash Agricultural Scheme (GAS) - Kassala State, as a semi-arid land in eastern Sudan. The scheme has been established to contribute to the rural development, to help stability of the nomadic population in eastern Sudan, particularly the local population around the Gash river areas, and to facilitate utilizing the river flood in growing cotton and other cash crops. In the last decade, the scheme production has declined, because of drought periods, which hit the region, sand invasion and the spread of invasive mesquite trees, in addition to administrative negligence. These have resulted also in poor agricultural productivity and the displacement of farmers away from the scheme area. Recently, the scheme is heavily disturbed by human intervention in many aspects. Consequently, resources of cultivated land have shrunk and declined during the period of the study, which in turn have led to dissatisfaction and increasing failure of satisfying increasing farmer’s income and demand for local consumption. Remote sensing applications and geospatial techniques have played a key role in studying different types of hazards whether they are natural or manmade. Multi-temporal satellite data combined with ancillary data were used to monitor, analyze and to assess land use and land cover (LULC) changes and the impact of land degradation on the scheme production, which provides the managers and decision makers with current and improved data for the purposes of proper administration of natural resources in the GAS. Information about patterns of LULC changes through time in the GAS is not only important for the management and planning, but also for a better understanding of human dimensions of environmental changes at regional scale. This study attempts to map and assess the impacts of LULC change and land degradation in GAS during a period of 38 years from 1972-2010. Dry season multi-temporal satellite imagery collected by different sensor systems was selected such as three cloud-free Landsat (MSS 1972, TM 1987 and ETM+ 1999) and ASTER (2010) satellite imagery. This imagery was geo-referenced and radiometrically and atmospherically calibrated using dark object subtraction (DOS). Two approaches of classification (object-oriented and pixel-based) were applied for classification and comparison of LULC. In addition, the study compares between the two approaches to determine which one is more compatible for classification of LULC of the GAS. The pixel-based approach performed slightly better than the object-oriented approach in the classification of LULC in the study area. Application of multi-temporal remote sensing data proved to be successful for the identification and mapping of LULC into five main classes as follows: woodland dominated by dense mesquite trees, grass and shrubs dominated by less dense mesquite trees, bare and cultivated land, stabilized fine sand and mobile sand. After image enhancement successful classification of imagery was achieved using pixel and object based approaches as well as subsequent change detection (image differencing and change matrix), supported by classification accuracy assessments and post-classification. Comparison of LULC changes shows that the land cover of GAS has changed dramatically during the investigated period. It has been discovered that more significant of LULC change processes occurred during the second studied period (1987 to 1999) than during the first period (1972-1987). In the second period nearly half of bare and cultivated lands was changed from 41372.74 ha (20.22 %) in 1987 to 28020.80 ha (13.60 %) in 1999, which was mainly due to the drought that hit the region during the mentioned period. However, the results revealed a drastic loss of bare and cultivated land, equivalent to more than 40% during the entire period (1972-2010). Throughout the whole period of study, drought and invasion of both mesquite trees and sand were responsible for the loss of more than 40% of the total productive lands. Change vector analysis (CVA) as a useful approach was applied for estimating change detection in both magnitude and direction of change. The promising approach of multivariate alteration detection (MAD) and subsequent maximum autocorrelation factor (MAD/MAF) transformation was used to support change detection via assessment of maximum correlation between the transformed variates and the specific original image bands related to specific land cover classes. However, both CVA and MAD/MAD strongly prove the fact that bare and cultivated land have dramatically changed and decreased continuously during the studied period. Both CVA and MAD/MAD demonstrate adequate potentials for monitoring, detecting, identifying and mapping the changes. Moreover, this research demonstrated that CVA and MAD/MAF are superior in providing qualitative details about the nature of all kinds of change. Vegetation indices (VI) such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified adjusted vegetation index (MSAVI) and grain soil index (GSI) were applied to measure the quantitative characterization of temporal and spatial vegetation cover patterns and change. All indices remain very sensitive to structure variation of LULC. The results reveal that the NDVI is more effective for detecting the amount and status of the vegetation cover in the study area than SAVI, MSAVI and GSI. Therefore, it can be stated that NDVI can be used as a response variable to identify drought disturbance and land degradation in semi-arid land such as the GAS area. Results of detecting vegetation cover observed by using SAVI were found to be more reasonable than using MSAVI, although MSAVI reduces the background of bare soil better than SAVI. GSI proves high efficiency in determining the different types of surface soils, and producing a change map of top soil grain size, which is useful in assessment of land degradation in the study area. The linkage between socio-economic data and remotely sensed data was applied to determine the relationships between the different factors derived and to analyze the reasons for change in LULC and land degradation and its effects in the study area. The results indicate a strong relationship between LULC derived from remotely sensed data and the influencing socioeconomic variables. The results obtained from analyzing socioeconomic data confirm the findings of remote sensing data analysis, which assure that the decline and degradation of agricultural land is a result of further spread of mesquite trees and of increased invasion of sand during the study period. High livestock density and overgrazing, drought, invasion of sand, spread of invasive mesquite trees, overexploitation of land, improper management, and population growth were considered as the main direct factors responsible for degradation in the study area.

Für das Verbundprojekt EMTAL (Einzugsgebietsmanagement von Talsperren in Mittelgebirgslandschaften) wurden Methoden zur Klärung hydrologischer Fragen entwickelt. Das dafür gewählte Modell WaSiM-ETH kann den Abfluss im Untersuchungsgebiet gut reproduzieren und ist unter Verwendung physikalisch basierter Teilmodule auf ähnliche Einzugsgebiete übertragbar. Es kann in einer hohen Bandbreite zeitlicher und räumlicher Diskretisierung verwendet werden. Bei der Modellierung verschiedener Landnutzungsszenarien zeigen sich Grenzen im Prozessverständnis, der Parametrisierung bekannter oder vermuteter Prozessse und in der Darstellung verschiedener Prozesse durch das Modell. Innerhalb streng festgelegter Randbedingungen können aber plausible Ergebnisse erlangt werden. Zusätzlich wurden meteorologische Zeitreihen für die Niederschlag-Abfluss-Modellierung bis 2050 erstellt. Die Effekte von Klimaänderungen auf den Abfluss werden gut abgebildet. Die Grenzen der Modellierung liegen hier in erster Linie bei der Güte der Eingangsdaten aus den Klimaprognosen.