Papers by Joseph L Awange
ACA 2014, July 9 - 12, 2014. Fordham University, New York City.
140 people attended the twentiet... more ACA 2014, July 9 - 12, 2014. Fordham University, New York City.
140 people attended the twentieth conference in this series.
The study highlights the salient agricultural production practices that impact on the environment... more The study highlights the salient agricultural production practices that impact on the environment in the savannas of northern Nigeria. Due to population pressure on land and the need to maintain household food supplies farmers have increased their land-use intensity and natural resources extraction practices that degrade the environment. Some agricultural production practices were, however, found to be environmental friendly. The study recommends remedial measures that have to be taken to avert agricultural production practices that predispose farmers to practices and extractive activities that undermine the environment.
Earth Science Informatics, 2013
Reconstruction of architectural structures from photographs has recently experienced intensive ef... more Reconstruction of architectural structures from photographs has recently experienced intensive efforts in computer vision research. This is achieved through the solution of nonlinear least squares (NLS) problems to obtain accurate structure and motion estimates. In Photogrammetry, NLS contribute to the determination of the 3-dimensional (3D) terrain models from the images taken from photographs. The traditional NLS approach for solving the resection-intersection problem based on implicit formulation on the one hand suffers from the lack of provision by which the involved variables can be weighted. On the other hand, incorporation of explicit formulation expresses the objectives to be minimized in different forms, thus resulting in different parametric values for the estimated parameters at non-zero residuals. Sometimes, these objectives may conflict in a Pareto sense, namely, a small change in the parameters results in the increase of one objective and a decrease of the other, as is often the case in multi-objective problems. Such is often the case with error-in-all-variable (EIV) models, e.g., in the resection-intersection problem where Communicated by: such change in the parameters could be caused by errors in both image and reference coordinates. This study proposes the Pareto optimal approach as a possible improvement to the solution of the resection-intersection problem, where it provides simultaneous estimation of the coordinates and orientation parameters of the cameras in a two or multistation camera system on the basis of a properly weighted multiobjective function. This objective represents the weighted sum of the square of the direct explicit differences of the measured and computed ground as well as the image coordinates. The effectiveness of the proposed method is demonstrated by two camera calibration problems, where the internal and external orientation parameters are estimated on the basis of the collinearity equations, employing the data of a Manhattan-type test field as well as the data of an outdoor, real case experiment. In addition, an architectural structural reconstruction of the Merton college court in Oxford (UK) via estimation of camera matrices is also presented. Although these two problems are different, where the first case considers the error reduction of the image and spatial coordinates, while the second case considers the precision of the space coordinates, the Pareto optimality can handle both problems in a general and flexible way.
Disaster Management
Environmental Monitoring using GNSS, 2012
Climate change and weather related impacts
Environmental Monitoring using GNSS, 2012
GNSS Remote Sensing of the Environment
Environmental Monitoring using GNSS, 2012
Coastal Resources
Environmental Monitoring using GNSS, 2012
Ocean & Coastal …, 2012
Monitoring and management of shorelines along populated coastal areas is a very important task, b... more Monitoring and management of shorelines along populated coastal areas is a very important task, but remains a difficult endeavor. The historical information used for short-term analysis and prediction are always underpinned by uncertainties associated with old data. Predictions of shoreline positions normally depend on the accuracy of the input data as well as the validity of the mathematical models used. With the requirement to study shoreline changes along the Parana (PR) coast in Brazil, it was necessary to obtain related cartographic information, which included temporal shoreline data obtained from orthophotos. In this contribution, photogrammetric together with GPS data are used to compare the capability of three shoreline prediction models; linear regression, robust parameter estimation, and neural network to predict the 2008 Parana shoreline position, which is then validated using the GPS measured position of 2008. The results indicate a MAPE (Mean Absolute Percentage Error) of 0.61% for the linear regression, 0.14% for the robust estimation, and 0.33% for the artificial neural network method. Although the coefficient of determinant (R 2 ) value for the neural network was the best, i.e., 0.997 compared to 0.994 for the robust model and 0.984 for the linear regression, its maximum deviation from the control values (i.e., 16.46) was almost twice that of robust model (7.63). On the one hand, the robust estimation model provides a more suitable approach for managing outliers in shoreline prediction, and also validating traditional methods such as linear regression. On the other hand, the neural network method offers an alternative approach to the robust prediction model. The results of the study highlightthe importance of a model choice for predicting the shoreline position.
Water resources management, 2012
Normalized Difference Vegetation Index (NDVI), which is a measure of vegetation vigour, and lake ... more Normalized Difference Vegetation Index (NDVI), which is a measure of vegetation vigour, and lake water levels respond variably to precipitation and its deficiency. For a given lake catchment, NDVI may have the ability to depict localized natural variability in water levels in response to weather patterns. This information may be used to decipher natural from unnatural variations of a given lake's surface. This study evaluates the potential of using NDVI and its associated derivatives (VCI (vegetation condition index), SVI (standardised vegetation index), AINDVI (annually integrated NDVI), green vegetation function (F g ), and NDVIA (NDVI anomaly)) to depict Lake Victoria's water levels. Thirty years of monthly mean water levels and a portion of the Global Inventory Modelling and Mapping Studies (GIMMS) AVHRR (Advanced Very High Resolution Radiometer) NDVI datasets were used. Their aggregate data structures and temporal co-variabilities were analysed using GIS/spatial analysis tools. Locally, NDVI was found to be more sensitive to drought (i.e., responded more strongly to reduced precipitation) than to water levels. It showed a good ability to depict water levels one-month in advance, especially in moderate to low precipitation years. SVI and SWL (standardized water levels) used in association with AINDVI and AMWLA (annual mean water levels
Water Resources
Environmental Monitoring using GNSS, 2012
Advances in Space …, 2012
The influence of Low Frequency Sea Surface Temperature modes on Delineated Decadal Rainfall zones in Eastern Africa region
Advances in Water …, 2013
ABSTRACT Influence of low frequency global Sea Surface Temperatures (SSTs) modes on decadal rainf... more ABSTRACT Influence of low frequency global Sea Surface Temperatures (SSTs) modes on decadal rainfall modes over Eastern Africa region is investigated. Fore-knowledge of rainfall distribution at decadal time scale in specific zones is critical for planning purposes. Both rainfall and SST data that covers a period of 1950 to 2008 were subjected to a ‘low-pass filter’ in order to suppress the high frequency oscillations. VARIMAX-Rotated Principal Component Analysis (RPCA) was employed to delineate the region into decadal rainfall zones while Singular Value Decomposition (SVD) techniques was used to examine potential linkages of these zones to various areas of the tropical global oceans. Ten-year distinct decadal signals, significant at 95% confidence level, are dominant when observed in-situ rainfall time series are subjected to spectral analysis. The presence of variability at El Niño Southern Oscillation (ENSO)-related timescales, combined with influences in the 10-12 year and 16-20 year bands were also prevalent. Nine and seven homogeneous decadal rainfall zones for long rainfall season i.e. March-May (MAM) and the short rainfall season i.e. October-December (OND), respectively, are delineated. The third season of June – August (JJA), which is mainly experienced in western and Coastal sub-regions had eight homogenous zones delineated. The forcing of decadal rainfall in the region is linked to the equatorial central Pacific Ocean, the tropical and South Atlantic Oceans, and the Southwest Indian Ocean. The high variability of these modes highlighted the significant roles of all the global oceans in forcing decadal rainfall variability over the region.
Positioning by intersection methods
Algebraic Geodesy and …, 2010
Positioning by ranging
Algebraic Geodesy and …, 2010
LPS-GNSS orientations and vertical deflections
Algebraic Geodesy and …, 2010
ABSTRACT
Solutions of Overdetermined Systems
Algebraic Geodesy and …, 2010
Procrustes solution
Algebraic Geodesy and …, 2010
Linear homotpy
Algebraic Geodesy and …, 2010
Groebner basis
Algebraic Geodesy and …, 2010
Computational Study of the 3D Affine Transformation
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Papers by Joseph L Awange
140 people attended the twentieth conference in this series.
140 people attended the twentieth conference in this series.