Evolution of the Southern Kenya Rift from Miocene to present with a focus on the Magadi area

The Kenya (a.k.a., Gregory) Rift is a geologically active area located within the eastern branch of the larger East African Rift System (EARS). The study area is located in the southern Kenya Rift between 1° South and the Kenya-Tanzania border (covering approximately 1.5 square degrees, semi-centered on Lake Magadi) and is predominantly filled with extrusive igneous rocks (mostly basalts, phonolites and trachytes) of Miocene age or younger. Sediments are thin, less than 1.5Ma, and are confined to small grabens. The EARS can serve both as an analogue for ancient continental rifting and as a modern laboratory to observe the geologic processes responsible for rifting. This study demonstrates that vintage (as in older, quality maps published by the Kenya Geological Survey, that may be outdated based on newer findings) quarter-degree maps can be successfully combined with recently published data, and used to interpret satellite (mainly Landsat 7) images to produce versatile, updated digital maps. The study area has been remapped using this procedure and although it covers a large area, the mapping retains a quadrangle level of detail. Additionally, all geologic mapping elements (formations, faults, etc.) have been correlated across older map boundaries so that geologic units don't end artificially at degree boundaries within the study area. These elements have also been saved as individual digital files to facilitate future analysis. A series of maps showing the evolution of the southern Kenya rift from the Miocene to the present was created by combining the updated geologic map with age dates for geologic formations and fault displacements. Over 200 age dates covering the entire length of the Kenya Rift have been compiled for this study, and 6 paleo-maps were constructed to demonstrate the evolution of the area, starting with the eruption of the Kishalduga and Lisudwa melanephelinites onto the metamorphic basement around 15Ma. These eruptions occurred before the initial rift faulting and were followed by a massive eruption of phonolites between 13-10 Ma that covered most of the Kenya dome. This was followed by a period of relative quiescence, until the initial faulting defined the western boundary of the rift around 7Ma. The resulting graben was asymmetrical until corresponding faults to the east developed around 3Ma. The rift valley was flooded by basalts and trachytes between 3Ma and 700ka, after which the volcanic activity slowed to a near halt. Since 700ka most of the deposition has been comprised of sediments, mainly from lakes occupying the various basins in the area. The main results of this study are, in addition to a detailed interpretation of the rift development, a new geologic map that correlates dozens of formations across old map boundaries and a compilation of over 300 age dates. Specific products include paleomaps, tables of fault timing and displacement, and volume estimates of volcanic formations. The study concludes with a generalization of the present environment at Magadi including discussions of lagoon chemistry, mantle gases in relation to the trona deposit, and biology of the hot springs. Several biologic samples were collected during the 2006 field season in an attempt to characterize the organisms that are commonly seen in the present Lake Magadi environment. Samples were selected to represent the different, distinctive forms that are found in the hotsprings. Each sample had it own distinctive growth habit, and analysis showed that each was formed by a different cyanobacterial. Actual algae was rare in the collected samples, and represented by a few scattered diatoms.

APPLICATION OF AN IMPUTATION METHOD FOR GEOSPATIAL INVENTORY OF FOREST STRUCTURAL ATTRIBUTES ACROSS MULTIPLE SPATIAL SCALES IN THE LAKE STATES, U.S.A.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

Computer Aided Design Algorithmic Technique for Very Large Scale Integrated Circuit Design and Smart Home Embedded System Design

To tackle the challenges at circuit level and system level VLSI and embedded system design, this dissertation proposes various novel algorithms to explore the efficient solutions. At the circuit level, a new reliability-driven minimum cost Steiner routing and layer assignment scheme is proposed, and the first transceiver insertion algorithmic framework for the optical interconnect is proposed. At the system level, a reliability-driven task scheduling scheme for multiprocessor real-time embedded systems, which optimizes system energy consumption under stochastic fault occurrences, is proposed. The embedded system design is also widely used in the smart home area for improving health, wellbeing and quality of life. The proposed scheduling scheme for multiprocessor embedded systems is hence extended to handle the energy consumption scheduling issues for smart homes. The extended scheme can arrange the household appliances for operation to minimize monetary expense of a customer based on the time-varying pricing model.