Yashon O. Ouma

A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes: an empirical analysis using Landsat TM and ETM+ data

Because of the complicated shorelines, inaccessibility and vast spread of some lakes, information on changing shorelines is difficult to acquire. A new water index (WI) has been applied to quantify changes in five saline and non‐saline Rift Valley lakes in Kenya using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) data. The WI is based on a logical combination of the Tasseled Cap Wetness (TCW) index and the Normalized Difference Water Index (NDWI). Using regression analysis with estimated shoreline coordinates, the WI detected the shorelines with an accuracy of 98.4%, which was 22.3% higher than the TCW, and 43.2% more accurate than the NDWI. Change detection was derived using image differencing followed by density slicing and unsupervised classification. The saline lakes (Bogoria, Nakuru and Elementaita) changed more with respect to the ratio of the change in the original surface areas than the non‐saline lakes (Baringo and Naivasha).

Analysis of co-occurrence and discrete wavelet transform textures for differentiation of forest and non-forest vegetation in very-high-resolution optical-sensor imagery

The extraction of texture features from high‐resolution remote sensing imagery provides a complementary source of data for those applications in which the spectral information is not sufficient for identification or classification of spectrally similar landscape features. This study presents the results of grey‐level co‐occurrence matrix (GLCM) and wavelet transform (WT) texture analysis for forest and non‐forest vegetation types differentiation in QuickBird imagery. Using semivariogram fitting, the optimal GLCM windows for the land cover classes within the scene were determined. These optimal window sizes were then applied to eight GLCM texture measures (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation) for the scene classification. Using wavelet transformation, up to five levels of macro‐texture were computed and tested in the classification process. Comparing the classification results, (1) the spectral‐only bands classification gave an overall accuracy of 58.69%; (2) the statistically derived 21×21 optimal mean texture combined with spectral information gave the best results among the GLCM optimal windows with an accuracy of 73.70%; and (3) the combined optimal WT‐texture levels 4 and 5 gave an accuracy of 63.56%. The combined classification of these three optimal results gave an overall accuracy of 77.93%. The results indicate that even though vegetation texture was generally measured better by the GLCM‐mean texture (micro‐textures) than by WT‐derived texture (macro‐textures), the results show that the micro–macro texture combination would improve the differentiation and classification of the overall vegetation types. Overall, the results suggests that computer‐assisted classification of high‐spatial‐resolution remotely sensed imagery has a good potential to augment the present ground‐based forest inventory methods.

On the optimization and selection of wavelet texture for feature extraction from high‐resolution satellite imagery with application towards urban‐tree delineation

Integration of spectral and multi‐scale texture is proposed in order to improve the detection and classification of urban‐trees from QuickBird imagery. Arguing that spatial‐structure semantic information exits at a hierarchy of scales and that texture is a consequence of objects in the hierarchy, multi‐scale wavelets decomposition is proposed for the extraction of vertical, horizontal and diagonal texture components. Pre‐selection of texture sub‐bands is achieved via mean, entropy, variance and second angular moment. The resulting sub‐bands are analysed for separability between trees and similarly reflecting features, such as rice‐paddy, grass/lawns, open ground and playground, based on Kullback–Leibler (KL) divergence and Battacharyya distance. The results are ranked and classified with k‐means. In comparison with the field data, KL gave the best results with omission and commission error of 4.4%. The proposed methodology has the ability to capture the increased natural variability in reflectance and improved the accuracy by 23.6%, in comparison with spectral‐only.

Multiscale remote sensing data segmentation and post-segmentation change detection based on logical modeling: Theoretical exposition and experimental results for forestland cover change analysis

Quantification of forestland cover extents, changes and causes thereof are currently of regional and global research priority. Remote sensing data (RSD) play a significant role in this exercise. However, supervised classification-based forest mapping from RSD are limited by lack of ground-truth- and spectral-only-based methods. In this paper, first results of a methodology to detect change/no change based on unsupervised multiresolution image transformation are presented. The technique combines directional wavelet transformation texture and multispectral imagery in an anisotropic diffusion aggregation or segmentation algorithm. The segmentation algorithm was implemented in unsupervised self-organizing feature map neural network. Using Landsat TM (1986) and ETM+ (2001), logical-operations-based change detection results for part of Mau forest in Kenya are presented. An overall accuracy for change detection of 88.4%, corresponding to kappa of 0.8265, was obtained. The methodology is able to predict the change information a-posteriori as opposed to the conventional methods that require land cover classes a priori for change detection. Most importantly, the approach can be used to predict the existence, location and extent of disturbances within natural environmental systems.

Multitemporal comparative analysis of TRMM-3B42 satellite-estimated rainfall with surface gauge data at basin scales: daily, decadal and monthly evaluations

In order to examine the reliability and applicability of Tropical Rainfall Measuring Mission (TRMM) and Other Satellites Precipitation Product (3B42) Version 6 (TRMM-3B42) at basin scales, satellite rainfall estimates were compared with geostatistically interpolated reference data from 12 rain gauge stations for three consecutive years: 2005, 2006 and 2007. Gauge–TRMM-3B42 statistical properties for daily, decadal and monthly multitemporal precipitations were compared using the following cross-validation continuous statistical measures: mean bias error (MBE), root mean square difference (RMSD), mean absolute difference (MAD) and coefficient of determination (r 2) metrics. The averaged spatial–temporal comparisons showed that the TRMM-3B42 rainfall estimates were much closer to the geostatistically interpolated gauge data, with minimal biases of −0.40 mm day−1, −1.78 mm decad−1 and −6.72 mm month−1 being observed in 2006. In the same year, the gauge and TRMM-3B42 rainfall estimates marginally correlated better than in 2005 and 2007, with the daily, decadal and monthly coefficients of determination being 82.2%, 93.9% and 96.5%, respectively. The results showed that the correlations between the gauge-derived precipitation and the TRMM-3B42-derived precipitation increased with increasing temporal intervals for all three considered years. Quantitatively, the TRMM-3B42 observations slightly overestimated the precipitations during the wet seasons and underestimated the observed rainfall during the dry seasons. The results of the study show that the estimates from TRMM-3B42 precipitation retrievals can effectively be applied in the interpolation of missing gauge data, and in the verification of precipitation uncertainties at the basin scales with minor adjustments, depending on the timescales considered.

Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular Radon transform

Display Omitted Mobile Mapping Systems using RGB camera for asphalt pavement distress imaging.Angular-geometric orientation for classification of linear distresses in asphalt road pavements.Linear crack detection, extraction and classification.Triple-transform algorithm: discrete wavelet transform, successive morphology transform and circular Radon transform.Similarity coefficient measures for crack detection and extraction accuracy assessment. The combined detection, extraction and identification of incipient or micro-linear distresses in asphalt pavements are important steps in the quantification and analyses of the occurrences of linear distresses for early pavement management and repair (MR (ii) successive morphologic transformation filtering (SMF) as an adaptive filter for the extraction of linear distress shape and continuity, and (iii) circular Radon Transform (CRT) for angular-geometric orientation analysis for the identification and classification of the distress types. Using mobile RGB camera imaging, 72 pavement distress images, at a spatial resolution of about 1mm were selected for evaluating the proposed approach. The results of the DWT-SMF were validated using the Dice coefficient of similarity between the manually segmented distresses and the study results. The validation results show that the linear distresses are satisfactorily extracted with an average detection rate of 83.2%. The average processing time for implementing the DWT-SMF phase of the algorithm was approximately 125s. To validate the classifications of the distress types, the CRT results were matched with the reference classifications from synthetic cracks, with all showing positively corresponding results. In overall, the results of the study illustrate that the proposed triple-transform approach provides a reliable approach for the detection, isolation and characterization of linear distresses in flexible asphalt pavements.

MCDA-GIS integrated approach for optimized landfill site selection for growing urban regions: an application of neighborhood-proximity analysis

The exponential rise in urban population and the resulting urban waste generation in developing countries over the past few decades, and the resulting accelerated urbanization phenomenon has brought to the fore the necessity to engineer environmentally sustainable and efficient urban waste disposal and management systems. Intelligent and integrated landfill siting is a difficult, complex, tedious, and protracted process requiring evaluation of many different criteria. Optimized siting decisions have gained considerable importance in ensuring minimum damage to the various environmental sub-components as well as reducing the stigma associated with the residents living in its vicinity. This article addresses the siting of a new landfill using a multi-criteria decision analysis integrated with overlay analysis within a geographical information system. The integrated multi-criteria decision analysis–geographical information system employs a two-stage analysis, synergistically, to form a spatial decision support system for landfill siting in fast-growing urban centers. Several correlated factors are considered in the siting process including transportation systems, water resources, land use, sensitive sites, and air quality. Weightings were assigned to each criterion depending upon their relative significance and ratings in accordance with the relative magnitude of impact. The results, analyzed using neighborhood-proximity analysis, show the effectiveness of the system in the site-selection process for Eldoret Municipality (Kenya), in the short- and long-term solid waste disposal siting options.

Comparison of Fuzzy AHP and Fuzzy TOPSIS for Road Pavement Maintenance Prioritization: Methodological Exposition and Case Study

For road pavement maintenance and repairs prioritization, a multiattribute approach that compares fuzzy Analytical Hierarchy Process (AHP) and fuzzy Technique for Order Preference by Ideal Situation (TOPSIS) is evaluated. The pavement distress data was collected through empirical condition surveys and rating by pavement experts. In comparison to the crisp AHP, the fuzzy AHP and fuzzy TOPSIS pairwise comparison techniques are considered to be more suitable for the subjective analysis of the pavement conditions for automated maintenance prioritization. From the case study results, four pavement maintenance objectives were determined as road safety, pavement surface preservation, road operational status and standards, and road aesthetics, with corresponding depreciating significance weights of . The top three maintenance functions were identified as Thin Hot Mix Asphalt (HMA) overlays, resurfacing and slurry seals, which were a result of pavement cracking, potholes, raveling, and patching, while the bottom three were cape seal, micro surfacing, and fog seal. The two methods gave nearly the same prioritization ranking. In general, the fuzzy AHP approach tended to overestimate the maintenance prioritization ranking as compared to the fuzzy TOPSIS.

Use of GRACE time-variable data and GLDAS-LSM for estimating groundwater storage variability at small basin scales: a case study of the Nzoia River Basin

This study integrates time-variable Gravity Recovery and Climate Experiment (GRACE) gravimetric measurements and Global Land Data Assimilation System (GLDAS) land surface models (LSM) in order to understand the inter-annual variations and groundwater storage changes (GWSC) in the Nzoia River Basin in Kenya, using the water balance equation and parameters. From averaged GRACE and GWSC data, the results showed that over the 10-year period, the basin experienced a groundwater depth gain of 6.38 mm year−1, which is equivalent to aquifer recharge of 298 million cubic metres (mcm) year−1. The deseasonalized groundwater variation analysis gave a net gain in groundwater storage of 6.21 mm year−1 that is equal to a groundwater recharge gain of 290 mcm year−1. The observed results are comparable to the groundwater safe yield of 330 mcm year−1 as estimated by the Water Resource Management Authority in Kenya. Through cross-plotting and analysis with averaged satellite altimetry data and in situ measurements from rainfall and streamflow discharge, the total water storage change (TWSC) and GWSC in the basin were consistent and closely correlated in variation trends. The inter-annual standard deviation of groundwater change was determined as ±0.24 mm year−1, which is equivalent to 85% degree of confidence in the obtained results. The results in this study show that GRACE gravity-variable solutions and GLDAS-LSM provide reliable data sets suitable for the study of small to large basin groundwater storage variations, especially in areas with scarce and sparsely available in situ data.

Estimation of Reservoir Bio-Optical Water Quality Parameters Using Smartphone Sensor Apps and Landsat ETM+: Review and Comparative Experimental Results

This study presents a comparative evaluation of three real-time imaging-based approaches for the prediction of optically active water constituents as chlorophyll-a (Chl-a), turbidity, suspended particulate matter (SPM), and reservoir water colour. The imaging models comprise of Landsat ETM+-visible and NIR (VNIR) data and EyeOnWater and HydroColor Smartphone sensor apps. To estimate the selected water quality parameters (WQP) from Landsat ETM+-VNIR, predictive models based on empirical relationships were developed. From the in situ measurements and the Landsat regression models, the results from the remote reflectances of ETM+ green, blue, and NIR independently yielded the best fits for the respective predictions of Chl-a, turbidity, and SPM. The concentration of Chl-a was derived from the Landsat ETM+ and HydroColor with respective Pearson correlation coefficients of 0.8977 and 0.8310. The degree of turbidity was determined from Landsat, EyeOnWater, and HydroColor with respective values of 0.9628, 0.819, and 0.8405. From the same models, the retrieved SPM was regressed with the laboratory measurements with value results of 0.6808, 0.7315, and 0.8637, respectively, from Landsat ETM+, EyeOnWater, and HydroColor. The empirical study results showed that the imaging models can be effectively applied in the estimation of the physical WQP.