Structural properties of mobile armors formed at different flow strengths in gravel-bed rivers

Differences in the structure of mobile armors formed at three different flow strengths have been investigated in a laboratory flume. The temporal evolution of the bed surfaces and the properties of the final beds were compared using metrics of surface grain size, microtopography, and bed organization at both grain and mesoscales. Measurements of the bed condition were obtained on nine occasions during each experiment to describe the temporal evolution of the beds. Structured mobile armors formed quickly in each experiment. At the grain scale (1–45 mm; 9 ≤ Ds50 ≤ 17 mm where Ds50 is the median surface particle size), surface complexity decreased and bed roughness increased in response to surface coarsening and the development of the mobile armor. Particles comprising the armor also became flow aligned and developed imbrication. At a larger scale (100–200 mm), the surface developed a mesoscale topography through the development of bed patches with lower and higher elevations. Metrics of mobile armor structure showed remarkable consistency over prolonged periods of near-constant transport, demonstrating for the first time that actively transporting surfaces maintain an equilibrium bed structure. Bed structuring was least developed in the experiments conducted at the lowest flow strength. However, little difference was observed in the structural metrics of the mobile armors generated at higher flows. Although the range of transport rates studied was limited, the results suggest that the structure of mobile armors is insensitive to the formative transport rate except when rates are low (τ* ≈ 0.03 where τ* is the dimensionless shear stress)

Himalayan glaciers: combining remote sensing, field techniques and indigenous knowledge to understand spatio-temporal patterns of glacier changes and their impact on water resources

With contradictory statements about \u22disappearing Himalayan glaciers\u22 in the last few years, increasing concerns have been raised about the impact of snow and glacier changes on regional water supplies. Concomitantly, local communities in the western Himalaya report changes in glacier extents, snow cover and weather patterns. In response to perceived water scarcity, indigenous Himalayan cultures have begun a number of adaptive responses such as meltwater harvesting to construct \u22artificial\u22 glaciers. This research addresses the need for a detailed assessment of glacier and climate parameters in the Himalaya, with the goal of identifying \u22at risk\u22 glacierized areas and helping these local communities plan future water resources. The objectives of the research are threefold: 1) to review existing knowledge about glacier fluctuations and remote sensing methods for glacier mapping in the Himalaya; 3) to quantify spatio-temporal patterns of glacier changes in the eastern Himalaya in the last decades using remote sensing techniques and field measurements and 3) to quantify the role of glacier melt to streamflow using a combination of remote sensing and isotopic techniques. This thesis focuses on the monsoon-influenced eastern Himalaya (the Langtang and Khumbu regions in the Nepal Himalaya, and Sikkim in the Indian Himalaya). The research is grounded in extensive field surveys conducted from 2006 to 2010 across the Himalaya, including glacier mass balance expeditions, water sampling, ground-control point (GCP) acquisition and GPS-enabled photos. The goal of this research is to understand how topographic and climatic factors influence the rates of glacier change at various spatial scales, and how these changes re likely to affect future water resources. Multi-temporal (decadal) glacier datasets were derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor, Landsat ETM+, older topographic maps, declassified Corona imagery and very high-resolution QuickBird and Ikonos imagery. I used a combination of visible, near infrared and thermal multispectral data combined with texture analysis and topography for glacier mapping. The results of this research help fill a gap in the understanding of glacier patterns in the data-scarce eastern Himalaya. The results of this research are useful for assessing vulnerability of the Himalaya to water scarcity due to future glacier or climate changes

Complex land cover classifications and physical properties retrieval of tropical forests using multi-source remote sensing

The work presented in this thesis mainly focuses on two subjects related to the application of remote sensing data: (1) for land cover classification combining optical sensor, texture features generated from spectral information and synthetic aperture radar (SAR) features, and (2) to develop a non-destructive approach for above ground biomass (AGB) and forest attributes estimation employing multi-source remote sensing data (i.e. optical data, SAR backscatter) combined with in-situ data. Information provided by reliable land cover map is useful for management of forest resources to support sustainable forest management, whereas the generation of the non-destructive approach to model forest biophysical properties (e.g. AGB and stem volume) is required to assess the forest resources more efficiently and cost-effective, and coupled with remote sensing data the model can be applied over large forest areas. This work considers study sites over tropical rain forest landscape in Indonesia characterized by different successional stages and complex vegetation structure including tropical peatland forests. The thesis begins with a brief introduction and the state of the art explaining recent trends on monitoring and modeling of forest resources using remote sensing data and approach. The research works on the integration of spectral information and texture features for forest cover mapping is presented subsequently, followed by development of a non-destructive approach for AGB and forest parameters predictions and modeling. Ultimately, this work evaluates the potential of mosaic SAR data for AGB modeling and the fusion of optical and SAR data for peatlands discrimination. The results show that the inclusion of geostatistics texture features improved the classification accuracy of optical Landsat ETM data. Moreover, the fusion of SAR and optical data enhanced the peatlands discrimination over tropical peat swamp forest. For forest stand parameters modeling, neural networks method resulted in lower error estimate than standard multi-linear regression technique, and the combination of non-destructive measurement (i.e. stem number) and remote sensing data improved the model accuracy. The up scaling of stem volume and biomass estimates using Kriging method and bi-temporal ETM image also provide favorable estimate results upon comparison with the land cover map.Die in dieser Dissertation präsentierten Ergebnisse konzentrieren sich hauptsächlich auf zwei Themen mit Bezug zur angewandten Fernerkundung: 1) Der Klassifizierung von Oberflächenbedeckung basierend auf der Verknüpfung von optischen Sensoren, Textureigenschaften erzeugt durch Spektraldaten und Synthetic-Aperture-Radar (SAR) features und 2) die Entwicklung eines nichtdestruktiven Verfahrens zur Bestimmung oberirdischer Biomasse (AGB) und weiterer Waldeigenschaften mittels multi-source Fernerkundungsdaten (optische Daten, SAR Rückstreuung) sowie in-situ Daten. Eine zuverlässige Karte der Landbedeckung dient der Unterstützung von nachhaltigem Waldmanagement, während eine nichtdestruktive Herangehensweise zur Modellierung von biophysikalischen Waldeigenschaften (z.B. AGB und Stammvolumen) für eine effiziente und kostengünstige Beurteilung der Waldressourcen notwendig ist. Durch die Kopplung mit Fernerkundungsdaten kann das Modell auf große Waldflächen übertragen werden. Die vorliegende Arbeit berücksichtigt Untersuchungsgebiete im tropischen Regenwald Indonesiens, welche durch verschiedene Regenerations- und Sukzessionsstadien sowie komplexe Vegetationsstrukturen, inklusive tropischer Torfwälder, gekennzeichnet sind. Am Anfang der Arbeit werden in einer kurzen Einleitung der Stand der Forschung und die neuesten Forschungstrends in der Überwachung und Modellierung von Waldressourcen mithilfe von Fernerkundungsdaten dargestellt. Anschließend werden die Forschungsergebnisse der Kombination von Spektraleigenschaften und Textureigenschaften zur Waldbedeckungskartierung erläutert. Desweiteren folgen Ergebnisse zur Entwicklung eines nichtdestruktiven Ansatzes zur Vorhersage und Modellierung von AGB und Waldeigenschaften, zur Auswertung von Mosaik- SAR Daten für die Modellierung von AGB, sowie zur Fusion optischer mit SAR Daten für die Identifizierung von Torfwäldern. Die Ergebnisse zeigen, dass die Einbeziehung von geostatistischen Textureigenschaften die Genauigkeit der Klassifikation von optischen Landsat ETM Daten gesteigert hat. Desweiteren führte die Fusion von SAR und optischen Daten zu einer Verbesserung der Unterscheidung zwischen Torfwäldern und tropischen Sumpfwäldern. Bei der Modellierung der Waldparameter führte die Neural-Network-Methode zu niedrigeren Fehlerschätzungen als die multiple Regressions. Die Kombination von nichtdestruktiven Messungen (z.B. Stammzahl) und Fernerkundungsdaten führte zu einer Steigerung der Modellgenauigkeit. Die Hochskalierung des Stammvolumens und Schätzungen der Biomasse mithilfe von Kriging und bi-temporalen ETM Daten lieferten positive Schätzergebnisse im Vergleich zur Landbedeckungskarte

Water Resources Management and Modeling

Hydrology is the science that deals with the processes governing the depletion and replenishment of water resources of the earth's land areas. The purpose of this book is to put together recent developments on hydrology and water resources engineering. First section covers surface water modeling and second section deals with groundwater modeling. The aim of this book is to focus attention on the management of surface water and groundwater resources. Meeting the challenges and the impact of climate change on water resources is also discussed in the book. Most chapters give insights into the interpretation of field information, development of models, the use of computational models based on analytical and numerical techniques, assessment of model performance and the use of these models for predictive purposes. It is written for the practicing professionals and students, mathematical modelers, hydrogeologists and water resources specialists

Developing land management units using Geospatial technologies: An agricultural application

This research develops a methodology for determining farm scale land managementunits (LMUs) using soil sampling data, high resolution digital multi-spectral imagery (DMSI) and a digital elevation model (DEM). The LMUs are zones within a paddock suitable for precision agriculture which are managed according to their productive capabilities. Soil sampling and analysis are crucial in depicting landscape characteristics, but costly. Data based on DMSI and DEM is available cheaply and at high resolution.The design and implementation of a two-stage methodology using a spatiallyweighted multivariate classification, for delineating LMUs is described. Utilising data on physical and chemical soil properties collected at 250 sampling locations within a 1780ha farm in Western Australia, the methodology initially classifies sampling points into LMUs based on a spatially weighted similarity matrix. The second stage delineates higher resolution LMU boundaries using DMSI and topographic variables derived from a DEM on a 10m grid across the study area. The method groups sample points and pixels with respect to their characteristics and their spatial relationships, thus forming contiguous, homogenous LMUs that can be adopted in precision agricultural applications. The methodology combines readily available and relatively cheap high resolution data sets with soil properties sampled at low resolution. This minimises cost while still forming LMUs at high resolution.The allocation of pixels to LMUs based on their DMSI and topographic variables has been verified. Yield differences between the LMUs have also been analysed. The results indicate the potential of the approach for precision agriculture and the importance of continued research in this area

A multi-scale mapping approach based on a deep learning CNN model for reconstructing high-resolution urban DEMs

The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions

Using Spectral Analysis Techniques to Identify Characteristic Scales in Digital Elevation Models

Meandering river floodplains exhibit periodic structures which can be seen in features such as meander bends, point bars, and oxbow lakes. To improve our understanding and better analyze floodplain landscapes created by the dynamics of meandering rivers, characteristic scales need to be identified. Although methods that involve manual measurements of certain floodplain features are of utility, they are limited in their application and are typically very time intensive. Spectral analysis techniques represent an improved approach. For this research, two separate 2D spectral analysis techniques were used: the Fourier transform and the continuous wavelet transform. By using an appropriate theoretical red-noise background spectrum for the landscape, the spectral analysis techniques could provide a power spectrum which is then used to clearly identify the global and local characteristic scales. The results from the analysis of synthetic test images demonstrated such capability of both methodologies, and indicated that both performed similarly although the wavelet transform provides spatial information in addition to scale. The methodologies were then applied to simulated meandering river floodplain of meandering river where two ranges of characteristic scales were identified that corresponded to bend-scale and meander-train scale features. The characteristic meander-scale features also correlated with the surface metrics focal mean, the average elevation within a given area, and rugosity, the ratio between surface area of a given area and the surface area of a completely flat surface. The results show that the spectral analysis techniques can identify characteristic scale of a meander-river floodplain and that the relationship to the surface metrics indicate that it provides information to the topographic structure of the floodplain